A new perspective on the development of motor variability during middle childhood Golenia, Laura
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A new perspective on the development of motor variability during middle childhood
Laura Golenia
Ph.D. training was facilitated by the research institute School of Health Research (SHARE), part of the Graduate School of Medical Science Groningen.
The printing of this thesis was financially supported by:
• University of Groningen
• University Medical Center Groningen
• Center for Human Movement Sciences
• Stichting Beatrixoord Noord-Nederland
Paranymphs: Anniek Heerschop
Inge Tuitert
Cover and layout: evelienjagtman.com
Printed by: Gildeprint
ISBN printed version: 978-94-034-0857-6 ISBN digital version: 978-94-034-0856-9
© Copyright 2018, Laura Golenia
All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic and mechanical, including photocopying, recording or any information storage or retrieval system, without written permission from the author.
of motor variability during middle childhood
PhD Thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnificus Prof. E. Sterken
and in accordance with the decision by the College of Deans.
This thesis will be defended in public on Wednesday 3 October 2018 at 12.45 hours
by
Laura Golenia born on 17 July 1989
in Munich, Germany
Dr. M.M. Schoemaker
Copromotor Dr. R.M. Bongers
Reading committee Prof. B.Vereijken Prof. M. Hadders-Algra Prof. B. Steenbergen
Prologue 6
Chapter 1 General introduction 9
Chapter 2 Development of reaching during mid-childhood from a developmental systems perspective
25
Chapter 3 The development of consistency and flexibility in manual pointing during middle childhood
47
Chapter 4 Variability in coordination patterns in children with Developmental Coordination Disorder (DCD)
65
Chapter 5 What the dynamic systems approach can offer for understanding devel- opment: an example of mid-childhood reaching
87
Chapter 6 General discussion 101
Epilogue 118
Appendices Summary 123
Samenvatting (short summary in Dutch) 127
Zusammenfassung (short summary in German) 131
Acknowledgments 135
Curriculum vitae 139
Scientific output 141
Conference contributions 143
“They’re made out of meat.”
“Meat?”
“Meat. They’re made out of meat.”
“Meat?”
“There’s no doubt about it. We picked up several from different parts of the planet, took them aboard our recon vessels, and probed them all the way through. They’re completely meat.”
“That’s impossible. What about the radio signals? The messages to the stars?”
“They use the radio waves to talk, but the signals don’t come from them.
The signals come from machines.”
“So who made the machines? That’s who we want to contact.”
“They made the machines. That’s what I’m trying to tell you. Meat made the machines.” […]
“No brain?”
“Oh, there’s a brain all right. It’s just that the brain is made out of meat!
That’s what I’ve been trying to tell you.”
“So ... what does the thinking?”
They’re made out of meat - Terry Bisson (1991)
discover the human race, a life form entirely made of “meat” - talking, moving, thinking meat.
For us, meat talking to meat, it is normal to behave like we behave. Reading Terry Bisson’s short story, I came to realize that it is astonishing that our physical bodies are somehow capable of our behavior. I was so astonished, in fact, that this short story sparked my interest in the study of thinking meat and its behavior. I also realized that neither cognitive ghosts nor neural machines are sufficient for describing the rich array of human behavior. Instead, I came to believe that we must study an integrated system, with each meaty component of the body contributing to the emerging behavior. During classes of my Master study, I learned about a theory that encompasses my thoughts about Terry Bisson’s short story; the Dynamic Systems theory. This theory states that behavior emerges from interactions of multiple components entailed by the person, environ- ment, and task. I was hooked.
To be continued on page 118
CHAPTER 1
General introduction
CHAPTER 1
Variability is inherent in motor development
Many researchers have argued in recent decades that describing and examining variability in motor behavior is essential in understanding motor development [1–8]. It has even been proposed that variability is a fundamental measure of change in human behavior [9,10]. In general terms, vari- ability refers to a series of observations that are non-constant [11]. This variability is an inherent characteristic of motor development. In early childhood development (0-5 years of age), several studies have shown how the focus on variability resulted in a more comprehensive understanding about the occurring developmental processes [e.g., 12–17]. For example, the description of vari- ability in infants’ natural walking revealed that omnidirectional steps and curved walking paths generate rich combinations of movements that allow infants to explore the environment [12].
In another study it was discovered that infants’ first goal-directed reaches emerge from variable non-reaching arm movements [14]. Unfortunately, motor variability has been a core theme only in the field of early childhood development. The exciting findings on variability did not yet spark similar enthusiasm in middle childhood (5-10 years of age) research. Middle childhood signifies the developmental phase between early childhood and adolescence. The years of middle childhood are sometimes even labeled as the ‘forgotten years’ of development [18], as this developmental period has often been overshadowed by the focus on the first years of life. Only few studies have described and characterized variations in movements of children aged 5-10 years in detail [19–23], which means that the role of variability in mid-childhood development has received only marginal attention compared to early childhood development.
Describing and understanding variability in movements does not only increase our understanding about typically developmental processes, but also that of atypical processes. Indeed, atypical motor development is often linked with extreme values of variability (either low or high) [e.g., 24–27].
One such disorder is Developmental Coordination Disorder (DCD) [28], a coordination disorder that emerges during middle childhood, which is characterized by high levels of motor variability [e.g., 27,29]. The cause of this disorder is as of yet still unknown. Recently, discussions have been launched in the DCD literature about how the variable motor behavior of children with DCD relates to possible underlying mechanisms of DCD [5,30–33]. It is, therefore, crucial to know more about developmental changes in motor variability during middle childhood in typical as well as atypical developing children.
In this thesis, a novel perspective on motor variability in middle childhood is presented to increase the understanding about age-related changes in motor variability occurring in this developmental period. Doing so, this thesis focuses on the variability that is inherent in reaching movements across repetitions of trials. Studying goal-directed reaching movements is important as they are involved in many everyday life activities. Also, a few studies have shown that reaching skills refine during middle childhood [e.g., 34–38], indicating the importance of understanding the develop-
mental changes taking place in reaching movements during middle childhood. Ideas of the Dynamic Systems (DS) theory guided this novel perspective on motor variability in middle childhood, which is introduced in the study of typical developing (TD) children, but also in children with DCD.
The development of reaching during middle childhood
The few studies that focused on the development of reaching in middle childhood have given important insight in the developmental changes occurring in reaching in general and in terms of variability [34,35,38–41]. The task of goal-directed manual reaching comprises movements of the joints of the arm (shoulder, elbow, wrist and finger) with the goal of bringing the endpoint (i.e., the index finger) to a desired location in the workspace, considering task and environmental constraints (e.g., gravity, speed and accuracy instructions, or the availability of visual feedback about the moving arm or target) [42].
Short synopsis of the results of the existing studies
Previous studies that examined goal-directed reaching in TD children focused on the performance level of the reach [34,35,39,43–45]. Performance is quantified by the characteristics of endpoint movements. This means that these studies analyzed spatio-temporal measures of movements of the index finger, such as movement time or accuracy at the target. In general, studies showed that reaching performance improves during middle childhood, indicated, for example, by a decrease of movement time from 5- to 10-year-old children [35,38,39,46]. Regarding variability in reach- ing movements, variability has mostly been quantified by means of the amount of performance variability about a mean or average value, such as the amount of the dispersion of errors around a target across repetitions of the same task [39,46–49]. It has been shown that performance variabil- ity decreases between 5 and 10-year-old children, revealed, for instance, by a decrease of errors at the target [35,39,46,47,49]. Variability measured in this particular way reflects inconsistency. The general view on variability in middle childhood until now has therefore been somewhat negative in connotation, taken to mean ‘‘less consistent” or error ridden.
Theoretical approaches used in previous studies
Most previous studies examining middle childhood reaching were conducted in the light of the computational neuroscience approach [34,35,39,40,44,45]. Early studies were published at a time in which the information processing approach was popular [35,38,41], whereas more recent studies followed internal model approaches [36,40] and representational explanations [34,37]. Following these approaches, researchers tacitly assumed that there is a single process or component in the system (probably in the brain) that controls motor behavior. It was proposed that developmental changes in motor behavior are caused by developmental changes in this single process or compo- nent. For instance, it was suggested that the ability to generate an internal model (i.e., predicting
CHAPTER 1 the outcome of movement commands) and to integrate online sensory feedback within this model
is reflected in the performance improvements of reaching errors in middle childhood [36,40].
In sum, over the last decades, useful and interesting explanations of developmental changes in reaching during mid-childhood have been put forward by focusing on the development of specific components or processes, fitting within the theoretical framework underlying these studies.
Regarding variability in behavior, computational neuroscience approaches assume that performance variability originates from intrinsic neuromotor noise arising from all levels of the sensorimotor system, which corrupts information transmission in the neuromotor system, resulting in random fluctuations, i.e., variability [5,11,47,49,50]. Noise is defined as unstructured variability, both in the temporal and spatial domain [11,50]. The internal model approach proposes, for example, that neuromotor noise produces deviations from the single optimal solution generated by the model, causing variability in the system. Changes in the magnitude of movement variability with age have been interpreted as evidence that the level of noise in the sensorimotor system decreases through middle childhood [40,47–49].
The view on motor variability in early childhood development
In early childhood developmental literature, it has been shown that the role of variability in development is not so simple [e.g., 1–3,8]. By studying variability in different situations and with different analysis techniques, the conception of variability has changed from its negative concep- tion, to a more positive conception. Thus, a shift has taken place in thinking about variability in early childhood motor development in recent decades [1,3,8]. From its original conception of being noise in computational neuroscience theories, recent approaches, such as the DS theory [7,51–54], have highlighted the adaptive value of motor variability for flexibility and the possibility to explore new solutions. As a result, there has been a renewed interest in the function of motor variability during early childhood development.
What the Dynamic Systems theory has to offer
The theoretical approach that has engendered the shift in thinking about variability in early child- hood development is the DS approach. This theory emphasizes that developmental changes emerge within a complex system involving many components acting on different levels that interact over multiple time scales [7,51–59]. Importantly, the system is not confined to the body, but includes the task and the environment. Automatically, this means that the environment and the task are equally important parts of the system [54]. The starting point of the DS approach is that over devel- opment each component is equally important and could potentially contribute to the emerging
behavior. Hence, if one or multiple components change, the behavior might change. In contrast with, for example, the internal model approach, the DS perspective states that there is not one single optimal solution for a task but that there are multiple equivalent solutions [4,7].
Motor variability is elevated in a DS approach as it views variability as signature component of development and motor behavior in general [4,7,51,52,54]. Variability is seen as a ubiquitous and informative biological feature that has inherent structure and meaning in itself [7]. That is, variability can provide information about the state of the system. It can, for example, provide understanding about exploratory processes. Special emphasis is put on the structure of variabil- ity. Deutsch & Newell [22,60,61] showed, for example, how decomposing the time and frequency structure of variability in a force task revealed that reductions in the amount of performance variability from middle childhood to adulthood are not reflections of different levels of noise in the sensorimotor system, but are primarily due to a more appropriate mapping of the sensorimotor system to task constraints (more effective use of visual information). The DS theory has increased in popularity in early childhood development in recent decades, but it has not been used a lot in middle childhood development.
A novel way to approach variability
in reaching movements during middle childhood
Research on variability in goal-directed reaching during middle childhood has mainly focused on performance variability, quantified in terms of the amount of variability about a mean [34,39].
Variability measured in this particular way reflects inconsistency, which resulted in a negative connotation on variability in middle childhood. Considering that in early childhood development variability has been shown to reflect more than inconsistency [12,21,23,60], it is time to approach variability in reaching movements in middle childhood in a novel way. This thesis follows the DS theory’s assumption that variability can also be useful for development and therefore something positive. This creates opportunities for novel ways of characterizing and describing variability in reaching movements during middle childhood, which might reveal a richer, more complex devel- opmental story. This thesis follows the DS perspective by examining variability at other levels of the system and by characterizing structure in variability.
Variability at the joint angle level
One aspect that is emphasized by the DS perspective is to focus on all levels of the system and not only on the performance level. As already described in the definition of reaching, endpoint movements emerge from joint rotations of the arm. Thus, an important level in reaching is the joint angle level [20,42]. Joint angles are defined as the relative orientations of the different segments of the arm and hand. The importance of this level is even more underlined when considering that
CHAPTER 1 the joint angles (i.e., degrees of freedom, DoF) are abundant, meaning that the number of avail-
able joint angles exceeds the number of joint angles that are minimally necessary to accomplish a task [62–64]. For example, when reaching to a target in the three-dimensional space, the number of joint angles in the arm is larger than three, indicating that multiple joint angle combinations can achieve the same performance. Thus, the abundant DoF of the human body naturally afford variability and lead to variability in joint angles evident in repetitive movements [11].
Ubiquitous variability in joint angles was clearly described in Nicolai Bernstein´s famous experiment of blacksmiths repeatedly hitting a chisel with a hammer [63,65]. He demonstrated that the variabil- ity of the hammer trajectory was smaller than the variability in the joint angle trajectories of the arm across repetitive trials. This shows that there is variability in joint angles that does not affect task performance. In reaching movements, the position of the index finger tip reflects task performance.
Variability in joint angles not affecting task performance is according to the principle of motor abun- dance useful and even vital for many aspects of motor behavior [64,66,67]. It allows movements to be both flexible and stable, because the motor system in its interaction with task constraints can create multiple equivalent solutions [68]. In literature on adults, it has been observed in a wide range of tasks that variability in joint angles not affecting performance is not eliminated but used [67,69–74].
Nevertheless, there are also joint angle configurations that do affect performance, which means that also at the joint angle level there is variability that might result in inconsistent motor behavior [62].
Consequently, motor variability at the joint angle level should not all be treated the same, but rather be separated on the basis of its effect on task performance [62,75,76].
Structure in joint angle variability
The DS theory emphasizes the importance of structure within variability as it presents a window into the underlying processes of motor development [1,2,8,23]. Structuring joint angle variability based on its effect on task performance is especially informative when the task is redundant and affords a manifold of solutions, like it is the case for goal-directed reaching [68]. In the present thesis, the Uncontrolled Manifold (UCM) method was applied to distinguish variability in joint angles that does not affect the position of the index finger from variability that does affect the position of the index finger [62,76–78].
The UCM analysis is a quantitative approach that was developed to examine the structure of variability in joint angle variability [62,67,76,77]. The uncontrolled manifold represents a mathe- matical subspace that corresponds to all combinations of motor elements that preserve the value of a task-specific variable. In reaching, the motor elements are the joint angles, and the mean position of the tip of the index finger is the task-specific variable that is preserved [77,79–81].
Thus, in reaching, the UCM is a manifold in the joint space representing the set of joint angle con- figurations in the arm with the tip of the index finger in one position. Variance in joint angles over repeated trials projected onto the UCM corresponds to the joint angle configurations that do not
affect the mean position of the index finger in space (Vucm, Figure 1, left panel). Variance orthogonal to the UCM subspace, the ORT subspace, corresponds to the joint angle configurations that lead to a deviation from the mean position of the index finger (Vort, Figure 1, right panel). The UCM method has been applied a lot in adult studies [72,77,79,81–83], but only in two developmental studies in the field of quiet stance [19] and walking [24]. In summary, the UCM method structures variability in joint angles and using it in studying development of upper extremity tasks may give novel insight in the processes underlying motor variability.
Figure 1. Variability in joint angles separated based on its effects on task performance in a reaching task.
The Uncontrolled Manifold (UCM) method partitions variability in joint angles in variability that does not affect the position of the index finger (Vucm, left panel) and in variability that does affect the position of the index finger (Vort, right panel).
Developmental Coordination Disorder:
Characteristic features and the role of variability
How to view and measure motor variability is also a hot topic in research on DCD [5,30,31,33].
But first, what is DCD exactly? DCD is an idiopathic condition that is characterized by impaired fine and gross motor coordination [28]. Of all school-aged children, 6% are thought to be affected by DCD. Children with DCD have difficulties performing everyday fine and gross motor tasks at home and in school [84,85]. For example, children with DCD have problems balancing on one leg, directing their arms to the right location to catch a ball or grasping a glass of water without spilling.
These coordination problems might have psycho-social consequences such as low self-esteem and social exclusion [86,87]. The broad bandwidth of problems related to DCD underlines the need of a thorough understanding of the coordination processes in this disorder. Since DCD was first listed in the Diagnostic and Statistical Manual of Mental Disorders, it has received much attention and motor journals have dedicated complete issues to the disorder. Yet, despite this surge of interest, the exact coordination processes that are deviant in this disorder remain unclear.
CHAPTER 1 The majority of investigations into the, still unknown, cause of DCD over the past 25 years adhere
to the view that describes DCD as a brain-based deficit in internal model processing [30,88,89]. As a natural outgrowth of this approach it has been suggested that high system´s noise in children with DCD [31,32,90] impacts sensorimotor control, motor output and predictive control, resulting in high performance variability [26,29,90–93] and in the motor problems seen in children with DCD.
In reaching it has, for example, been shown that errors are larger and movements are performed slower in children with DCD compared to TD children [94–96]. High performance variability in children with DCD is therefore considered to hinder performance [32,89,90]. That is why also in DCD variability is in general construed in negative terms. However, recent literature reviews have pointed out that not all variability might be hindering and that variability should be approached in alternative ways to get more details about what the underlying source of high variability in this disorder might be [5,30,33,97]. There has been a special plea for focus on variability in coordina- tion, which is not surprising considering that DCD is a coordination impairment [98]. This thesis subscribes to this newly stated opinion in the literature and starts with examining the structure of variability in joint angles in children with DCD.
Aim and outline of this thesis
This thesis aims to increase the understanding about developmental changes in motor variability during middle childhood by examining the structure of variability in joint angle configurations over repetitions of trials in goal-directed reaching movements. Chapter two of this thesis examines developmental changes in the structure of joint angle variability in TD children aged between 5 and 10-years of age. It focuses on the joint angle level and the performance level, and therewith aims to give a level-overarching explanation of occurring developmental changes. Chapter 2 also examines how the availability of visual information about the arm influences the developmental trends, which is an important environmental component involved in reaching. Chapter three examines the influence of task constraints on the development of the structure of joint angle variability in 5-to 10-year-old TD children. Adults have been shown to exploit the abundance in their motor system when task demands increase [69,70,74,99]. To assess how children in middle childhood utilize the abundance of their motor system when necessary, a challenging reaching condition is introduced to increase task demands. In chapter four, the structure of variability in joint angle configurations in children with DCD is assessed by comparing children with DCD with age-matched controls. Special emphasis is laid on the role of variability in DCD. Chapter five elaborates on how ideas of the Dynamic Systems approach can positively contribute to the under- standing of developmental changes in reaching during mid-childhood. Ideas on how to continue studying mid-childhood reaching based on the DS theory are presented. Chapter six provides a general discussion of the findings reported in this thesis, including clinical implications and directions for future research.
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CHAPTER 2
Development of reaching during mid-childhood
from a developmental systems perspective
Abstract
Inspired by the Developmental Systems perspective, we studied the development of reaching during mid-childhood (5-10 years of age) not just at the performance level (i.e., endpoint move- ments), as commonly done in earlier studies, but also at the joint angle level. Because the endpoint position (i.e., the tip of the index finger) at the reaching target can be achieved with multiple joint angle combinations, we partitioned variability in joint angles over trials into variability that does not (goal-equivalent variability, Vucm) and that does (non-goal-equivalent variability, Vort) influence the endpoint position, using the Uncontrolled Manifold method. Quantifying this structure in joint angle variability allowed us to examine whether and how spatial variability of the endpoint at the reaching target is related to variability in joint angles and how this changes over development. 6-, 8- and 10-year-old children and young adults performed reaching movements to a target with the index finger. Polynomial trend analysis revealed a linear and a quadratic decreasing trend for the variable error. Linear decreasing and cubic trends were found for joint angle standard deviations at movement end. Vucm and Vort decreased gradually with age, but interestingly, the decrease of Vucm was steeper than the decrease of Vort, showing that the different parts of the joint angle variability changed differently over age. We interpreted these changes in the structure of variability as indi- cating changes over age in exploration for synergies (a family of task solutions), a concept that links the performance level with the joint angle level. Our results suggest changes in the search for synergies during mid-childhood development.
CHAPTER 2
Introduction
The development of goal-directed reaching is important as reaching is involved in many manual everyday life actions. Refinement of reaching skills occurs during mid-childhood development (5 to 10-years of age), a relevant developmental period which has often been overlooked. Until now, the few studies that did examine mid-childhood development have solely focused on the performance level of reaching; showing for example that reaching movements become more accurate and less variable with increasing age [1–4] and that reaching movements can be better adjusted to sudden changes in target location [5–9]. Doing so, these studies did not emphasize the contribution and development of other levels of analysis involved in reaching, such as for instance the joint level (see for an exception Schneiberg et al. [10]). This might be attributed to the theoretical approach underlying these studies, such as the information processing approach or the computational neu- roscience perspective (i.e., internal models and representations). Following these approaches, studies tacitly assumed that there is a single process (i.e., feedback/feedforward mechanisms) or component (i.e., representation) in the system that controls motor behavior [2,11–13]. This idea implicitly entails that developmental changes in reaching follow directly from developmental changes in the single process or component. That is why examining changes in performance over age was considered sufficient to understand developmental changes. Even though these studies and approaches have gathered important knowledge about mid-childhood development, we pro- pose that if one wants to understand the full range and complexity of developmental changes, one should depart from studying just one level and should distinguish the development of individual levels contributing to reaching (see for detail criticism of earlier studies Chapter 5).
A perspective that views changes in motor behavior not as a reflection of changes in a single process or component, but as resulting from the interaction of multiple changing components acting on different levels of the system is the Developmental Systems (DS) perspective [14–17]. Importantly, the system is not confined to the body, but includes the full action-perception cycle. Automatically, this means that the environment and the task are equally important parts of the system. The DS theory´s starting point is that over development each component of the system changes on its own time-scale. This means that the current behavioral state of the system results from the inter- action among all components. Hence, if one or multiple components change, the behavior might change. From this perspective a great deal of understanding about the acquisition of reaching skills during early-infancy has been achieved [18–22]. Here, we noted the lack of using ideas from the DS perspective on the study of reaching development during mid-childhood (Chapter 5). Following the DS perspective, understanding developmental trends requires examining not only variables at the performance level, but also variables at other levels and their relation. We took a first step in filling in this gap by focusing on development at the level of the joint angles in the arm and how it relates to the development at the performance level (i.e., kinematics of movements of the end- point, which is in the current study the tip of the index finger).
Joint angles are defined as the relative orientations of the different segments of the arm and hand (finger, wrist, elbow and shoulder joints). The wrist has for example two joint angles: the wrist can flex or extend and abduct or adduct. An important characteristic of the joint angles is that they are abundant [23]. This means that there are more joint angle combinations available than necessary to accomplish the task of reaching (i.e., reaching the 3D target position in space with the tip of the index finger) [23–25]. For example, imagine sitting in front of a table and keeping your index finger tip at one position on the table. It is possible to move your arm while keeping the index finger tip on the same position on the table, meaning that you use different joint angle configurations that all accomplish this task. Thus, abundance allows for variability in joint angles over repetitions of reaching trials. The main goal of this study is to examine how this variability in joint angles changes during mid-childhood when performing a reaching task and how it affects the performance of the endpoint at the reaching target, i.e. endpoint position variability. Studying such variability, and in particular the structure within it, is generally considered an important way to reveal underlying developmental processes [26–30].
How can structure in joint angle variability be assessed? An often used approach in the literature is to parse joint angle variability over repetitions of trials into two parts [23,25,31]: (1) One part is the joint angle variabilty that does not affect task success, meaning that joint angles co-vary to stabilize the endpoint around its mean position (goal-equivalent variability). This part of the joint angle variability is the variability demonstrated in the earlier example of moving the arm while keeping the index finger tip at one position on the table. (2) The other part is the joint angle variability that causes the endpoint to deviate from its mean position, shifting the end-point away from the mean position (non-goal-equivalent variability). This variablity over repetitions of trials therewith results in error around the mean position, usually seen in endpoint position variability around the target. Examining the structure in joint angle variability allows to examine the relation between joint angles and the index finger position in spatial measures at the reaching target. We used the Uncontrolled Manifold (UCM) method to quantify the structure in joint angle variabilty [23,25,31–33]. The UCM method partitions variability in joint angles over repetitions of trials into goal-equivalent variability (Vucm) and non-goal-equivalent variability (Vort). Higher levels of Vucm than Vort correspond to a relatively invariant, stable value of the performance (i.e., index finger position).
An additional goal of this study was to evaluate whether and how the availability of visual informa- tion about the arm influenced the structure in joint angle variability. The DS perspective states that the behavior of the system results from the interaction of not only the components of the person, but also from the environment and task [34]. An important environmental component involved in reaching movements is visual information about the hand and arm. Previous developmental studies that focused on the performance of the reach found that vision influenced end-point error (i.e., more errors in no vision conditions) and even influenced the developmental trend [1,2,13]. It
CHAPTER 2 could also impact joint angle variability because of the required reliance on proprioceptive infor-
mation when no visual information is available. We therefore asked whether vision availability is an equally large constraint in reaching at each age during development.
In line with the DS perspective, we focused on changes in the relation of the performance level and the joint angle level in goal-directed reaching movements during mid-childhood development. We do so because even though index-finger movements are brought about by joint angles, this does not mean that there is a direct relation between the two; only the structure in the variability can characterize such relation. We therefore examined the developmental trend of the structure in joint angle variability across 6-, 8-, and 10-year-old children and adults. By that we aimed, in line with the DS perspective, to increase the understanding of reaching by using a level-overarching explanation.
Doing so, we focused on developmental trends of spatial variables of endpoint position variability (i.e. constant and variable error), joint angle variability (i.e. standard deviation) and structure in joint angle variability (Vucm and Vort) at the end-point of the movement. From earlier studies, we know that both the constant and variable error decrease with age [13,35]. Vort should relate to the error variables of the index finger because this is the joint angle variability that influences the index finger position. We therefore expected a decrease in Vort over age. Vucm, on the other hand, does not influence the index finger position, meaning that the developmental trend of this variable does not have to be related to the trend of the performance of the index finger. We proposed two competing hypotheses for Vucm: Either Vucm decreases with age or Vucm stays similar across age groups.
Method
Participants
40 typically developing children, recruited from local sport clubs and schools around the univer- sity, and 15 young adults participated (age range in years/months = 19/2 - 28/5). Children were distributed in three groups based on their age resulting in a group of twelve 6-year-olds (age range in years/months = 5/9 - 6/5), a group of fifteen 8-year-olds (age range in years/months = 7/6 - 8/6) and a group of thirteen 10-year-olds (age range in years/months = 9/7 - 10/5). All children and adults were right-handed.
Ethics statement
The local ethics committee of the Center for Human Movement Sciences (University Medical Center Groningen) approved the study that was conducted according to the principles expressed in the Declaration of Helsinki. Adult participants and children’s parents or legal guardians provided written informed consent prior to the experiment.
Movement-ABC-2 test
The Movement Assessment Battery for children-2nd Edition (MABC-2) was performed by all chil- dren prior to the experiment to test their motor development [36]. The MABC-2 test provides an indication of motor functioning across fine and gross motor tasks for children aged 3 to 16 years.
The test consists of three age-related item-sets, measuring manual dexterity (three items), aiming and catching (two items), and balance (three items). Children get a score on each item, which are then transformed into standard scores, ranging from 1-19. A percentile equivalent for the total test score is used as outcome measure which ranges from 0.1 to 99.9. A typical development is indicated by a score above the 16th percentile. Adults received no motor assessment, because the MABC-2 has no norm for adults. Instead adults were asked whether they had any neurological diseases, recent injuries or musculoskeletal problems in the neck, shoulder, arm or hand regions, which was not the case.
Apparatus
3D position data of all segments of the right arm were collected with two Optotrak 3020 system sensors (Northern Digital, Waterloo, Canada). To obtain joint angles of the shoulder, elbow, wrist, and index finger, six rigid bodies (each with three LED markers) were placed on the participants’
right arm and the trunk [37]. Five triangular shaped rigid bodies were attached to the sternum, to the flat part of the acromion, to the upper arm just below the insertion of the deltoid, to the lower arm proximal to the ulnar and radial styloid, and to the dorsal surface of the hand (Figure 1). The sixth rigid body was placed on the index finger so that it splinted the finger to prevent motion of the inter-phalangeal joints (i.e., the finger was considered as one segment in the analysis). Two dif- ferent sizes were used; matching the length of the index finger of adults and children, respectively.
Nineteen bony landmarks were digitized using a standard pointer device [37] and were linked to the position of the LED markers on the rigid bodies.
Figure 1 shows the experimental setup. The task was performed at a black table (height = 72 cm), in which a large television screen (Panasonic, 62*111 cm) was horizontally mounted presenting the task display that was developed using Presentation (Neurobehavioral systems, Berkely, CA). Light- ing of the room could be controlled to manipulate visual feedback of the arm and hand. Participants sat in a chair adjusted to their height and the length of their arm, so that the elbow had a 90-degree angle and was at the same height as the table, keeping the relation between table and participant similar across participants. For children, we used a chair (Tripp Trapp, Stokke, Sweden) of which also the plate for the feet could be adjusted so that children could sit with their legs resting on this plate. The back of the chair was extended in height with a board so that participants’ trunk could gently be strapped against it to prevent movements of the torso, but allowing free movements of the shoulder and elbow joints. To keep the start posture of the upper extremity as similar as pos- sible over trials, the olecranon of the right arm had to be placed on a marked location on an elbow rest that was positioned at a comfortable height on the right side of the participant (Figure 1).
CHAPTER 2
Figure 1. Experimental setup. Bird’s-eye view on a participant sitting at the experimental table. The partic- ipant was gently strapped to the chair (grey straps). The posture represents the start position of each trial.
The elbow of the participant was placed on the elbow rest and the tip of the index finger was positioned on the start position. If the participant was a child, an experimenter was sitting next to the child. Triangles and the rectangle on the finger represent rigid bodies (the rigid body on the sternum cannot be seen).
Procedure and design
After anthropometrics were measured, markers were attached and participants were seated at the table. Prior to testing, participants completed three practice trials to be sure that participants understood the instructions. In the case of a child, an experimenter sat next to the child to ensure that the hand and the arm were in the required position at the beginning of each trial, and that the child was attentive to the task (Figure 1).
At the beginning of each trial the start location was illuminated (red dot, 1cm diameter) and partic- ipants were instructed to touch the start location with the tip of their right index finger while the elbow was positioned on the elbow rest (elbow left the rest to reach the target). A target location (green dot, 2cm diameter) appeared and participants pointed as quickly and accurately as possible to the target location, according to instructions. The trial ended with holding the tip steady on the target location for a short period of time. The start location (located 10 cm away from the body) and target location were displayed at the midline of the screen in the depth direction (which was aligned to the body midline). Reaching distance was 30% of the average arm-length of norm values of the concerned age group (18.5cm in 6-year-olds, 20.5cm in 8-year-olds, 22.5cm in 10-year-olds, and 28.0cm in adults, according to Gerver & De Bruin [38].
Reaching movements in two conditions were performed: (1) Reaching to the target with normal room lighting so that visual information about the position of the arm was available (vision con- dition) and (2) reaching to the target in the dark so that the position of the arm and hand could not be seen (no-vision condition). Note, the illuminated target was visible in both conditions.
In total, the experimental session consisted of 60 trials (30 in each condition). We needed this high number of trials to get a proper approximation of the uncontrolled manifold (see next sec- tion) under the assumption that children’s behavior was rather variable. Participants completed two blocks of 15 trails in each condition that were separated by short breaks (around 3 minutes).
Blocks were presented in a random order.
Data analysis
For all analysis, customized data analysis programs were developed in Matlab (MathWorks; Natick, Massachusetts). To determine the initiation and the termination of the reaching movement, a backward (movement initiation) and forward (movement termination) search was performed from the maximum in the velocity profile of the forward direction (x-direction) of the index finger until a threshold of 5 cm/s, respectively. The first points below threshold were taken as the initiation and termination of the reaching movement, respectively. Note that all dependent variables were analyzed at the instant of movement termination because reaching the target was the only con- straint in the current study.
All variables were analyzed with repeated measures ANOVAs using SPSS version 20.0 (IBM, Armonk, New York). All repeated measures ANOVAs had age-group (6, 8, 10-year-old children, and adults) as between-subject factor. For the factor age-group we were interested in the developmental trend, therefore we tested linear, quadratic, and cubic contrasts. Note, the age intervals between age-groups were not similar, i.e., the interval between 10-year-old children and the adults was much larger than the age interval between the other age groups. Therefore, a linear statistical trend does not mean that the developmental trend over ages is also linear. We took this into account when interpreting the results. We also report the omnibus test of the factor age-group for completeness.
If the assumption of sphericity was violated, the Greenhouse–Geisser correction was applied. The level of significance was set at α < 0.05. Generalized eta-squared, η2G, [39] was used to calculate effect sizes, and interpreted according to Cohen’s recommendation of 0.02 for a small effect, 0.13 for a medium effect, and 0.26 for a large effect [40]. Only results with an effect size larger than 0.02 were reported. For the three children groups, a one-way ANOVA was conducted to test effects of age on the M-ABC percentile scores.
Performance level
For each trial, the constant error (CE; mean difference between target position and tip position of the index finger at movement termination) and the variable error (VE; within-subject standard
CHAPTER 2
33
deviation of CE) was calculated. Even though we focused primarily on spatial variables, we also calculated movement time (MT; time from movement initiation to movement termination). These variables were analyzed with a repeated measures ANOVA with visual condition (vision, no-vision) as within-subject factor and the polynomial contrasts of age-group. Moreover, to ensure that the results regarding spatial variability (main outcome measures) were not confound by differences over age in speed-accuracy tradeoff, we calculated linear regressions between MT and accuracy for each individual participant. The intercept and the slope of the regression lines were analyzed with a repeated measures ANOVA with visual condition (vision, no-vision) as within-subject factor and age-group as between subject factor.
Joint angle level
We examined the following joint angles of the right arm: shoulder plane of elevation (SPE), shoul- der elevation (SE), shoulder inward–outward rotation (SIOR), elbow flexion–extension (EFE), elbow pronation–supination (EPS), wrist flexion–extension (WFE), wrist abduction–adduction (WAA), index finger flexion–extension (FFE), and index finger abduction–adduction (FAA). Joint angles were calculated as proposed in the ISB standardization proposal for the upper extremity by Wu et al. [41]. Standard deviation (SD) of the joint angles at movement termination over 30 repeated reaching trials was calculated. For each joint angle, a repeated measures ANOVA on joint angle SD was performed with visual condition (vision, no-vision) within-subject factor and the polynomial contrasts of age-group.
Structure in joint angle variability
The UCM analysis was performed as described previously [23,25,32,42]. The UCM analysis was com- puted for the moment of movement termination from 30 reaching trials (N). Elemental variables were defined as the joint angles of the shoulder, elbow, wrist, and finger resulting in a 9-degree of freedom (DoF) system. The position of the endpoint was selected as performance variable (3-DoF).
The relation between changes in elemental variables and changes in the performance variable were computed based on a 3D forward kinematics model and united in a Jacobian (J) matrix [32,42].
Its null-space was used as a linear approximation of the UCM. The variance components Vucm and Vort were computed by projecting the total variance in joint space onto the null-space of J and the orthogonal complement, respectively. Equations 1 and 2 show the computation of Vucm and Vort, where tr denotes the trace of a matrix, J denotes the Jacobian matrix, C denotes the covariance matrix of all joint angles, n denotes the dimension of the joint space (n = 9) and d denotes the dimension of the task space (d = 3). Vucm and Vort were normalized by its number of DoF.
Eq (1)
Eq (2)
𝑉𝑉"#$=𝑡𝑡𝑡𝑡(𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛(𝐽𝐽).∗ 𝐶𝐶 ∗ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛(𝐽𝐽))
𝑛𝑛 − 𝑑𝑑
𝑉𝑉45.=𝑡𝑡𝑡𝑡(((𝐽𝐽.)6).∗ 𝐶𝐶 ∗ (𝐽𝐽.)6) 𝑑𝑑
𝑉𝑉"#$=𝑡𝑡𝑡𝑡(𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛(𝐽𝐽).∗ 𝐶𝐶 ∗ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛(𝐽𝐽))
𝑛𝑛 − 𝑑𝑑
𝑉𝑉45.=𝑡𝑡𝑡𝑡(((𝐽𝐽.)6).∗ 𝐶𝐶 ∗ (𝐽𝐽.)6) 𝑑𝑑
