Move and Be Moved: The Effect of Moving Specific Movement Elements on the Experience of Happiness

Dynamic body feedback is used in dance movement therapy (DMT), with the aim to facilitate emotional expression and a change of emotional state through movement and dance for individuals with psychosocial or psychiatric complaints. It has been demonstrated that moving in a specific way can evoke and regulate related emotions. The current study aimed to investigate the effects of executing a unique set of kinetic movement elements on an individual mover's experience of happiness. A specific sequence consisting of movement elements that recent studies have related to the feeling of happiness was created and used in a series of conditions. To achieve a more realistic reflection of DMT practice, the study incorporated the interpersonal dimension between the dance movement therapist (DMTh) and the client, and the impact of this interbodily feedback on the emotional state of the client. This quantitative study was conducted in a within-subject design. Five male and 20 female participants (mean age = 20.72) participated in three conditions: a solo executed movement sequence, a movement sequence executed with a DMTh who attuned and mirrored the movements, and a solo executed movement sequence not associated with feelings of happiness. Participants were only informed about the movements and not the feelings that may be provoked by these movements. The effects on individuals were measured using the Positive and Negative Affect Schedule and visual analog scales. Results showed that a specific movement sequence based on movement elements associated with happiness executed with a DMTh can significantly enhance the corresponding affective state. An additional finding of this study indicated that facilitating expressed emotion through movement elements that are not associated with happiness can enhance feelings such as empowerment, pride, and determination, which are experienced as part of positive affect. The results show the impact of specific full-body movement elements on the emotional state and the support outcome of DMT on emotion regulation.

How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis

There is significant clinical evidence showing that creative and expressive movement processes involved in dance/movement therapy (DMT) enhance psycho-social well-being. Yet, because movement is a complex phenomenon, statistically validating which aspects of movement change during interventions or lead to significant positive therapeutic outcomes is challenging because movement has multiple, overlapping variables appearing in unique patterns in different individuals and situations. One factor contributing to the therapeutic effects of DMT is movement's effect on clients' emotional states. Our previous study identified sets of movement variables which, when executed, enhanced specific emotions. In this paper, we describe how we selected movement variables for statistical analysis in that study, using a multi-stage methodology to identify, reduce, code, and quantify the multitude of variables present in unscripted movement. We suggest a set of procedures for using Laban Movement Analysis (LMA)-described movement variables as research data. Our study used LMA, an internationally accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing movement. We began with Davis's (1970) three-stepped protocol for analyzing movement patterns and identifying the most important variables: (1) We repeatedly observed video samples of validated (Atkinson et al., 2004) emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. (2) We use the criteria repetition, frequency, duration and emphasis to eliminate additional variables. (3) For each emotion, we analyzed motor expression variations to discover how variables cluster: first, by observing ten movement samples of each emotion to identify variables common to all samples; second, by qualitative analysis of the two best-recognized samples to determine if phrasing, duration or relationship among variables was significant. We added three new steps to this protocol: (4) we created Motifs (LMA symbols) combining movement variables extracted in steps 1-3; (5) we asked participants in the pilot study to move these combinations and quantify their emotional experience. Based on the results of the pilot study, we eliminated more variables; (6) we quantified the remaining variables' prevalence in each Motif for statistical analysis that examined which variables enhanced each emotion. We posit that our method successfully quantified unscripted movement data for statistical analysis.

New Perspectives on Burnout: A Controlled Study on Movement Analysis of Burnout Patients

Introduction: Despite extensive research on burnout, there has been to date no systematic movement analysis of burnout patients, although it is well known that psychiatric diseases express themselves through movements, such as psychomotor retardation or agitation. Since the movement expression of burnout patients has not been systematically investigated so far, the aim of this study is to close this knowledge gap in order to obtain a new perspective on burnout. Methods: Hospitalized burnout patients (n = 22; age 47.2 ± 9.1 years) and health controls (n = 20; age 41.5 ± 15.0 years) participated in a standardized movement sequence with verbal instructions. The objective Burnout Inventory Scale and diagnostics by psychiatrists were used for diagnosis. Two certified movement-analysts independently rated each participant via video by using the Effort System of Laban Movement Analysis as an instrument of dance therapy and behavior observation. Cohen's Kappa was used to test the inter-rater reliability of the movement analysts and non-parametric Mann-Whitney U tests were undertaken to assess the differences between the two groups. Results: The rater-agreement Kappa ranges from 0.66 to 0.92 (p < 0.001) with the Confidence Interval (95%) from 0.46 to 1.1. Results of the Mann-Whitney U tests indicate that burnout patients show significantly less frequent movements for the following Effort elements: Bound U(n1 = 22, n2 = 20) = 112.5, p = 0.001; Indirect U(n1 = 22, n2 = 20) = 114.5, p = 0.001; Light U(n1 = 22, n2 = 20) = 115, p = 0.001 and Sustained U(n1 = 22, n2 = 20) = 130, p = 0.01. Discussion: Burnout patients have significant deficits in all four Effort elements of the Laban Movement Analysis (Flow, Space, Time, Weight) and therefore have deficits regarding their body movement. The findings presented here provide an additional perspective on burnout.

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.

A Somatic Movement Approach to Fostering Emotional Resiliency through Laban Movement Analysis

Although movement has long been recognized as expressing emotion and as an agent of change for emotional state, there was a dearth of scientific evidence specifying which aspects of movement influence specific emotions. The recent identification of clusters of Laban movement components which elicit and enhance the basic emotions of anger, fear, sadness and happiness indicates which types of movements can affect these emotions (Shafir et al., 2016), but not how best to apply this knowledge. This perspective paper lays out a conceptual groundwork for how to effectively use these new findings to support emotional resiliency through voluntary choice of one's posture and movements. We suggest that three theoretical principles from Laban Movement Analysis (LMA) can guide the gradual change in movement components in one's daily movements to somatically support shift in affective state: (A) Introduce new movement components in developmental order; (B) Use LMA affinities-among-components to guide the expansion of expressive movement range and (C) Sequence change among components based on Laban's Space Harmony theory to support the gradual integration of that new range. The methods postulated in this article have potential to foster resiliency and provide resources for self-efficacy by expanding our capacity to adapt emotionally to challenges through modulating our movement responses.

Emotion Regulation through Movement: Unique Sets of Movement Characteristics are Associated with and Enhance Basic Emotions

We have recently demonstrated that motor execution, observation, and imagery of movements expressing certain emotions can enhance corresponding affective states and therefore could be used for emotion regulation. But which specific movement(s) should one use in order to enhance each emotion? This study aimed to identify, using Laban Movement Analysis (LMA), the Laban motor elements (motor characteristics) that characterize movements whose execution enhances each of the basic emotions: anger, fear, happiness, and sadness. LMA provides a system of symbols describing its motor elements, which gives a written instruction (motif) for the execution of a movement or movement-sequence over time. Six senior LMA experts analyzed a validated set of video clips showing whole body dynamic expressions of anger, fear, happiness and sadness, and identified the motor elements that were common to (appeared in) all clips expressing the same emotion. For each emotion, we created motifs of different combinations of the motor elements common to all clips of the same emotion. Eighty subjects from around the world read and moved those motifs, to identify the emotion evoked when moving each motif and to rate the intensity of the evoked emotion. All subjects together moved and rated 1241 motifs, which were produced from 29 different motor elements. Using logistic regression, we found a set of motor elements associated with each emotion which, when moved, predicted the feeling of that emotion. Each emotion was predicted by a unique set of motor elements and each motor element predicted only one emotion. Knowledge of which specific motor elements enhance specific emotions can enable emotional self-regulation through adding some desired motor qualities to one's personal everyday movements (rather than mimicking others' specific movements) and through decreasing motor behaviors which include elements that enhance negative emotions.

Neural decoding of expressive human movement from scalp electroencephalography (EEG)

Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.