The development of postural strategies in children: a factorial design study
 Maurizio Schmid^{1}Email author,
 Silvia Conforto^{1},
 Luisa Lopez^{2},
 Paolo Renzi^{3} and
 Tommaso D'Alessio^{1}
DOI: 10.1186/17430003229
© Schmid et al; licensee BioMed Central Ltd. 2005
Received: 17 December 2004
Accepted: 30 September 2005
Published: 30 September 2005
Abstract
Background
The present study investigates balance control mechanisms, their variations with the absence of visual input, and their development in children from 7 to 11 years old, in order to provide insights on the development of balance control in the pediatric population.
Methods
Posturographic data were recorded during 60 s trials administered on a sample population of 148 primary school children while stepping and then quietly standing on a force plate in two different vision conditions: eyes closed and eyes open. The extraction of posturographic parameters on the quiet standing phase of the experiment was preceded by the implementation of an algorithm to identify the settling time after stepping on the force plate. The effect of different conditions on posturographic parameters was tested with a twoway ANOVA (Age × Vision), and the corresponding eyesclosed/eyesopen (Romberg) Ratios underwent a oneway ANOVA.
Results
Several posturographic measures were found to be sensitive to testing condition (eyes closed vs. eyes open) and some of them to age and anthropometric parameters. The latter relationship did not explain all the data variability with age. An evident modification of postural strategy was observed between 7 and 11 years old children.
Conclusion
Simple measures extracted from posturographic signals resulted sensitive to vision and age: data acquired from force plate made it possible to confirm the hypothesis of the development of postural strategies in children as a more mature selection and reweighting of proprioceptive inputs to postural control in absence of visual input.
Keywords
Postural Control Development ChildrenBackground
Postural control has been studied throughout a century and a half [1], and the development of balance characteristics associated with the emergence and refinement of motor control has been investigated for three decades [2]. Central Nervous System (CNS) responses and developmental changes occurring in the first years of life have been deeply studied by Assaiante [3], and Woollacott and ShumwayCook [4]. The quantitative analysis of human movement and posture has been generally exploited on children population to study biomechanical effects on gross motor skills driven by the presence of diverse pathologies, such as Cerebral Palsy [5–8], Spinal Cord Injury [9], and Muscular Dystrophies [10, 11]. Starting from the work of Williams et al [12], in more recent years researchers extended the application of quantitative posturography to fine cognitive or learning disabilities [13], autism [14, 15], Developmental Coordination Disorder (DCD) [16], Attention Deficit Hyperactivity Disorder (ADHD) [17], and dyslexia [18].
Quantitative posturography can thus be applied to obtain functional markers on fine competencies and their development. For instance, a perturbation in posture with challenges such as a compliant surface [19], or a concurrent cognitive task [20], can help to enlighten possible adjustment strategies or deficiencies, or to monitor balance control variations with age [21]. However, findings obtained from other researchers show some contradictions with the above: as an example, the study of simple orthostatic posture with eyes open has been proven unsuccessful in differentiating controls from autistic patients [15], and children with DCD from controls [16]. Thus, this application field, though promising, needs to be more deeply investigated.
The quantitative analysis of postural control is generally based on data acquired by a force plate that allows one to determine the instantaneous position of the Ground Reaction Force application point, which is referred to as Centre of Pressure (CoP). Several parameters in the time and/or frequency domain [22] are then extracted from these data, or from surrogate functions derived from them [23]. Even if this technique does not allow direct detection of body oscillations, which can be estimated through the use of ad hoc motion analysis systems, the relative simplicity of the set up has encouraged researchers to consider the CoP oscillations as an indirect measure of postural sway [24].
When dealing with posturographic measures, the detection of the stabilization time after stepping on the force plate is crucial: the majority of the parameters used to define the postural ability are summary measures, and their application is based on the assumption of stationarity, in that the statistical properties of the underlying data do not significantly change over time. In presence of a transitory response to an event, such as standing up from a chair or stepping on the force plate, this assumption cannot be considered as valid. Thus the transitory response should be excluded from the analysis. By analysing the first and second order moment of the CoP trajectory, Carroll and Freedman [25] estimated this nonstationary interval to be about 20 seconds long. This assumption can be however challenged by considering that the transitory phase due to a similarly demanding perturbation, such as the Sit to Stand task, has been estimated in about 3 seconds [26]. Carpenter et al. [27] showed that the first order moment of the CoP Power Spectral Density could give insights on the duration of the transitory response.
A significant age dependence of the postural measures has been demonstrated [28, 29]: from a longitudinal study, Kirschenbaum et al. [30] showed that the control strategy to maintain balance does not follow a simple linear relationship with age, but a steplike transition at the age of 6 to 8 years occurs. This hypothesis can be linked to a clear rise in normalized stability limits to adult levels at age 7, as calculated by Riach and Starkes [31] by asking children to lean as far as they could in the four directions (forward, backward, left, and right) while standing. These results suggest that, at that age, the exploratory behaviour is reached, and thus the child has to work with a new strategy, which takes into account both open loop and closed loop components of balance control. By analysing postural responses to unpredicted translations of the base of support, Sundermier et al. [32] hypothesized that the development of postural control follows the maturation of fine competencies in muscle coordination.
A variety of posturographic parameters have been shown to depend on biomechanical and anthropometric factors, such as height or weight [33], and when extracting the CoP mean amplitude on a sample population ranging from 7 to 80 years, Peterka showed no changes with age if normalization with height was performed [34].
Thus, the question remains as to whether there is any reliable marker extracted from posturographic data that can give insights on the development of balance control, and whether age significantly affects posturographic data or changes as simply the result of anthropometric factors. Aim of the present study is to investigate mechanisms involved in the development of postural stability by attempting to answer these questions.
Methods
Participants
148 children were selected from classes of three different grades in one primary school, after obtaining proper informed consent from parents and teachers to participate in the study. None of the children had educational needs or certified disabilities. After the collection of height and weight, they were screened with a threesided testing procedure: Quantitative Posturography, Physical Examination for Neurological Subtle Signs (PANESS), and Teachers' Rating. For the present study, PANESS Assessment [35] and Teachers' Rating were used for inclusion criteria for the sample population, and by excluding subjects outside 10^{th}90^{th} percentile, the resulting sample size for data analysis on Quantitative Posturography was reduced to 107 children, divided into three age groups (n = 41 for Seven Years' Group, Y7, n = 38 for Nine Years' Group, Y9, and n = 28 for Eleven Years' Group, Y11). Table 1 summarizes data on participants, and Table 2 provides information on PANESS and Teachers' Rating.
Population anthropometric data
Age Group  Y7  Y9  Y11 

N  41  38  28 
Age (yrs)  7.0 ± 0.3 (Range 6.5–7.5)  9.0 ± 0.3 (Range 8.0–9.8)  11.0 ± 0.3 (Range 10.5–12.0) 
Height (m)  1.22 ± 0.06  1.34 ± 0.07  1.46 ± 0.06 
Weight (kg)  25.3 ± 4.7  32.5 ± 7.1  43.1 ± 8.7 
BMI (kg/m^{2})  17.0 ± 2.1  18.0 ± 2.8  20.0 ± 3.1 
Teachers' Rating and PANESS Assessment
Teachers' Rating  

Cluster  Definition  Score 
Read and Write  reading: speed and correctness writing: tract quality and correctness oral language production (vocabulary richness and fluency and structure)  Scoring 0–3 0 is best score 
Arithmetics  Arithmetics text: reading and placing numbers Arithmetcs logic: operations Sequences: understands and repeats sequences days, months, alphabets and multiplication tables  Scoring 0–3 0 is best score 
Attention and Movement  Motor activity in the gym/garden: follow instructions without confusing leftright, in/out Motor activity in class: from being able to sit still, to fine movements to gross movements he cannot avoid Attention: attention span  Scoring 0–3 0 is best score 
Behavior:  creativity: having many interests Social behavior: being integrated in class group and having friends Team working: following group rules Autonomy: not needing continuous instructions  Scoring 0–3 0 is best score 
PANESS*  
Cluster  Definition  Score 
Errors  errors on tiptoe walking errors on heel walking errors on nosefinger (right) errors on nosefinger (left)  scoring 0–3, depending on total number of errors (oscillations or falls during walking, misses or wrong fingers during other tests) 
Precision  Indexlittle tapping on thumb (right) Indexlittle tapping on thumb (left) Tandem walking  sequence of movements is correct from index to little with no repetitions or misses independently of rhythm Scoring 0–3. 
Rhythm  Indexlittle tapping on thumb (right) Indexlittle tapping on thumb (left) Tandem walking  the self chosen rhythm is kept during task independently of misses of repetitions. Scoring 0–3. 
Procedure
A posturographic test was performed, which consisted of 2 tests of upright stance (lasting 60 seconds each) corresponding to two different conditions: standing with eyes open (EO), and standing with eyes closed (EC). Between tests an interval of 2 minutes was allowed.
Participants were asked to select a comfortable sidebyside feet position, with their arms relaxed, then make a step forward and position themselves in the middle of the force plate, as indicated by stickers, maintaining a quiet stance. Data acquisition started immediately prior to the subject stepping on the force plate. Illumination and noise were kept under control: diffuse artificial illumination of approximately 40 lux, no remarkable fixed sound sources, experiment performed during lesson time.
Relevant force and torque components were lowpass filtered (corner frequency 20 Hz, 8^{th} order elliptical filter, stopband attenuation 80 dB at 30 Hz, attenuation slope 135 dB/octave) and fed to an AD converter (100 samples/s, DAQCard™AI16E4, by National Instruments Corporation), and then processed to obtain the Centre of Pressure trajectories in both antero/posterior and medio/lateral directions, CoP = {CoP_{AP}(t), CoP_{ML}(t)}. The maximum of the vertical component of the ground reaction force marked the subject's stepping on the force plate.
Feature Extraction
Posturographic Parameters Definition
Posturographic Parameter  Acronym  Definition 

Mean Velocity  MV 

Mean Amplitude  MA 

Sway Area  SA 

Mean Frequency  MF 

Mean Power Frequency{AP, ML}  MPF_{{AP, ML}} 

Centroidal Frequency {AP, ML}  CF_{{AP, ML}} 

Frequency at 95% {AP, ML}  F95_{{AP, ML}} 

 
T represents the total time for processing (30 s), and CoP_{{AP, ML}} are considered as purged of their mean value 
All PPs were calculated by retaining the first 30 seconds after T_{set}. Four of them can be directly extracted from the CoP trajectory, while the remaining six are used to characterize the shape of the Power Spectral Density: in particular, the Mean Power Frequency and the Centroidal Frequency are respectively representative of the barycentre and the dispersion of the Power Distribution in the frequency domain, i.e. the Power Spectral Density. F95% is finally representative of the overall breadth of the Spectrum.
PPs underwent statistical analysis, and, for each of them, the corresponding Romberg Ratio (RR), defined as the EC condition measure divided by the EO measure, was also computed and fed to statistics, as described in the following.
Statistical Analysis
All PPs were analyzed through a twoway ANOVA, with vision (EO vs. EC) and age as factors. Each condition was then separately analyzed for parameters exhibiting age effect, in the following way: Bartlett's test verified homogeneity of variances, and for parameters exhibiting different variances, Welch's ANOVA was run instead of traditional ANOVA; a Post Hoc Test for trend was also applied to different age groups.
For the whole population sample, possible relationships between PPs (dependent variables) and selected subjectspecific parameters (predictors) were sought to test if differences were dependent on anthropometric factors, such as body mass (m), height (h), and body mass index (BMI = m/h^{2}). The linear correlation between parameters and predictors was measured through the Pearson productmoment coefficient of correlation (r), and deemed reliable if a twotailed test of significance applied to this coefficient, had p ≤ 0.05. The percentage of each PP variance that can be explained by each reliable predictor was then calculated, and denoted as σ_{exp}^{2}.
Then, to test changes for significant interaction between age and vision, the Romberg Ratios (RR) for each parameter underwent a oneway ANOVA, with age as factor.
Results
As far as the differential analysis is concerned, most of the PPs were affected by vision, partly as a function of age: the effect of vision was statistically significant in MV, SA, MA, and in all the spectral parameters. This effect was more evident in amplitude parameters, thus confirming that, regardless of age, CoP displacement and velocity increased without visual input.
TwoWay ANOVA pvalues for posturographic parameters
PP  Age  Vision  Interaction 

MV   (0.44)  ** (p < 0.001)   (0.99) 
SA   (0.15)  ** (p < 0.001)   (0.50) 
MA  * (0.014)  ** (p < 0.001)   (0.31) 
MF   (0.18)   (0.15)   (0.40) 
MPF_{ML}   (0.82)   (0.13)   (0.23) 
CF_{ML}   (0.89)  * (0.022)   (0.46) 
F95_{ML}   (0.42)  * (0.036)   (0.28) 
MPF_{AP}   (0.18)  * (0.046)   (0.14) 
CF_{AP}  * (0.034)  * (0.013)   (0.24) 
F95_{AP}  * (0.030)  * (0.009)  * (0.032) 
Effect of age on Posturographic Parameters
PP  Age  Bartlett's Test  Test for Trend 

MA (EO)   (0.22)  * (0.046)   (0.21) 
MA (EC)  ** (0.0037)  ** (0.0003)  * (0.01) 
CF_{AP} (EO)   (0.27)  * (0.044)   (0.38) 
CF_{AP} (EC)   (0.10)   (0.417)  * (0.035) 
F95_{AP} (EO)   (0.51)   (0.929)   (0.70) 
F95_{AP} (EC)  ** (0.005)   (0.704)  ** (0.002) 
Anthropometric effect on posturographic parameters
PP  Mass  Height  BMI  

p  σ_{exp}^{2}  p  σ_{exp}^{2}  p  σ_{exp}^{2}  
MPF_{AP} (EC)   (0.061)    * (0.040)  4.0%   (0.40)   
CF_{AP} (EC)  * (0.013)  5.8%  * (0.009)  6.3%   (0.18)   
F95_{AP} (EC)  * (0.0055)  7.1%  * (0.0058)  7.0%   (0.0945)   
Romberg Ratios: effect of age
RR  Age  Welch's Test  Test for Trend 

SA   (0.35)   (0.30)   (0.49) 
MA   (0.14)   (0.053)   (0.051) 
MPF_{AP}  * (0.025)  * (0.045)  * (0.020) 
CF_{AP}   (0.13)  (0.24)   () 
F95_{AP}  ** (0.0012)  * (0.015)  ** (0.0014) 
Discussion
A large number of posturographic measures were sensitive to the testing condition (i.e. eyes open vs. eyes closed). If the trajectory of the CoP can be considered as an indirect measure of postural sway, and thus a marker for the control of stance, the presented results confirm the wellknown thesis that visual input contribution plays a relevant role in postural stabilization. From the results on MV, SA, and MA, it is indeed possible to state that, with eyes closed, the CoP displacement and velocity increased relative to eyes open. It is known that also young adults can improve postural performance by using visual targets [38], and that closing eyes affects postural measures [22]. Ratios between EC and EO in the present study, however, were rather different from those obtained by Prieto [22] on young adults: restricting the analysis to time domain measures, thus including MF which is a surrogate parameter for time domain measures, similar ratios resulted for MV, SA, and MF. On the other hand, MA ratios tended to young adults' figures only at 11 years, while remaining higher for the other ages. For the frequency domain measures, all RR on both CF and F95 revealed higher values than young adults [22], while no comparison was possible for MPF, which is by definition different from the Median Frequency computed by Prieto. Moreover, Prieto removed very low frequency (f < 0.15 Hz) shares to spectral measures, and thus a comparison could be affected by this choice.
Conclusion
The obtained results are in favour of a non monotonic development of postural strategies in children, slightly dependent on anthropometric factors: the role of vision clearly varies within the studied age range, and probably the maturation of balance control is not yet complete, even at the age of 11. Finally, another question is to be unveiled: is the maturation of balance control paralleled by a corresponding change in cognitive processes? The application of dual tasks, such as a concurrent cognitive one, in the execution of quiet stance trials could help in providing information on this issue.
Declarations
Acknowledgements
The authors are indebted to Prof. Aurelio Cappozzo, who provided the force plate for the experiments, to PsyD Annalisa Conte, for her help in data collection, and to the anonymous reviewers for their constructive feedbacks and comments. The help of the class teachers of the "Istituto Comprensivo Indro Montanelli" is greatly acknowledged. Work partially supported by MIUR.
Authors’ Affiliations
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