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The State of the Art in Creative Arts Therapies

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Front. Psychol. | doi: 10.3389/fpsyg.2019.00572

How shall I count the ways? A method for quantifying the qualitative aspects of unscripted movement with Laban Movement Analysis

  • 1School of Theatre & Music, University of Illinois at Chicago, United States
  • 2Emili Sagol Creative Arts Therapies Research Center (CATRC), Israel
  • 3Department of Psychiatry, Michigan Medicine, University of Michigan, United States

Dance Movement Therapy (DMT) has significant clinical evidence showing that creative and expressive movement processes 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 the effect of movement on clients' emotional states. Our previous study identified sets of movement variables which, when executed, contribute to the enhancement of specific emotions. In this paper we describe how we selected movement variables used in the statistical analysis, using a multi-stage methodology for identifying, reducing, coding, and quantifying the multitude of variables present in unscripted movement.

In our study, we used Laban Movement Analysis (LMA), an internationally-accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing the movement analysis. We began with Martha Davis’s three-stepped protocol for analyzing movement patterns and identifying the most important variables: 1) We repeatedly observed samples of validated motor emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. 2) We used the criteria frequency, duration, and emphasis to eliminate additional variables. 3) We analyzed variations of motor expressions of the same emotion to discover how variables cluster: first, observing 10 movement samples from the same emotion to identify variables common to all samples; second, by qualitative analysis of two highly-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) of combinations of the movement variables extracted in steps 1-3; 5) We asked pilot participants 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 prevalence of the remaining variables in each Motif to enable us to use the results of our experiment in a statistical analysis that examined which variables enhanced each emotion.
Our method successfully quantified unscripted movement data for statistical analysis.

Keywords: Laban Movement Analysis, Dance-movement therapy, Movement, bodily emotional expressions, Motion analysis, Human motion analysis, Movement Quality, Non-verbal behavior

Received: 14 Sep 2018; Accepted: 28 Feb 2019.

Edited by:

Gianluca Castelnuovo, University Cattolica del Sacro Cuore, Italy

Reviewed by:

Kim F. Dunphy, The University of Melbourne, Australia
Sharon W. Goodill, Drexel University, United States
Karolina Ł. Bryl, Drexel University, United States  

Copyright: © 2019 Tsachor and Shafir. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ms. Rachelle P. Tsachor, University of Illinois at Chicago, School of Theatre & Music, Chicago, United States, rtsachor@uic.edu