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Measurement of the visual contribution to postural steadiness from the COP movement: methodology and reliability
Gait & Posture 22 (2005) 96–106 www.elsevier.com/locate/gaitpost
Measurement of the visual contribution to postural steadiness from the COP movement: methodology and reliability V. Cornilleau-Pe´re`sa,b,*, N. Shabanac, J. Droulezd, J.C.H. Gohe,f, G.S.M. Leef, P.T.K. Chewc a
Labo. de Neurosciences Fonctionnelles et Pathologies, FRE2726, University Lille 2, Lille, France b Singapore Eye Research Institute, Singapore c Department of Ophthalmology, National University of Singapore, Singapore d Labo. de Physiologie de la Perception et de l’Action, CNRS-Colle`ge de France, Paris, France e Division of Bio Engineering, National University of Singapore, Singapore f Department of Orthopedic Surgery, National University of Singapore, Singapore Received 5 November 2003; accepted 6 July 2004
Abstract We studied the reliability of different measures of the visual contribution to postural steadiness by recording the postural sway during standing with eyes open (EO) or eyes closed (EC). The COP trajectory was recorded in 21 subjects aged 42–61 standing on a firm or foam support. The improvement of postural steadiness due to vision was measured with a higher reliability (i.e. lower intra- and inter-subject variabilities) with the sway velocity V, than with the position RMS. Due to the increase of the variability of V and RMS with their own mean values, we quantified the visual contribution to posture by the stabilization ratio (SR), based on a logarithm transform of V or RMS. As compared to the Romberg quotient (EC/EO), SR improved the reliability of the measurement of the visual contribution to posture within individuals, across subjects, and even across different studies in the literature. Our method led to decrease the inter-subject coefficient of variation of this measurement to about 25%, using a foam support. It leads to a similar accuracy in binocular and monocular vision, and it also applies to the quantification of other non-visual sensory contributions to posture. # 2004 Elsevier B.V. All rights reserved. Keywords: Posture; Postural sway; Centre of foot pressure; Vision; Standing; Postural stability; Romberg quotient
1. Introduction In normal conditions, the standing subject oscillates more with eyes closed than with eyes open. In this later condition, the brain combines visual inputs with somatosensory and vestibular information to maintain the upright position. As a major functional role of vision, the maintenance of posture has been largely explored, but remains a critical research topic for several reasons. First, the reduction of the body movements through vision depends on the extent of the cortical representation of the visual stimulus [1], but the neural pathways underlying this phenomenon are still partly * Corresponding author. Present address: Neurosci. Fonctionnelles et Pathologies, CHRU Lille, Clinique Fontan, 6 rue du Pr Laguesse, 59037 Lille, France. Tel.: +33 3 20 91 40 46. E-mail address: [email protected] (V. Cornilleau-Pe´re`s). 0966-6362/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2004.07.009
unknown [2]. Second, visual deficits are associated with the risk of falling, particularly in elderly patients [3–5], because the contribution of vision to postural steadiness increases with age, and is impaired by the presence of blur [6], and by retinal diseases [7–11]. Static posturography analyses the spontaneous movement of body parts, whereas dynamic posturography measures these movements in response to a change in the sensory stimulation (usually a motion of the visual scene or of the foot support). The two methods address complementary questions in postural control, and spontaneous and induced sway velocities seem to covary (correlation coefficients between 0.11 and 0.71 have been reported in one study [12]), although negative correlations have also been reported in young subjects [13]. Dynamic posturography is highly relevant to the study of the postural reactions to changes in the environment, but static
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posturography remains a simpler and cheaper way of studying the multisensory integration in postural stability. It has given good results in (1) predicting falls [3,5,13] (2) delineating the sensory mechanisms underlying postural stabilization [1,12] and their changes with age (3) studying the effect of different pathologies on balance [7,9,14]. The present study focuses on methodological aspects related to the measure of the visual contribution to static posture, in the standing subject. Our goal is to optimize this measure, so as to assess with a maximal precision the consequences of visual deficits on postural steadiness. As discussed later, our approach can be used to study other pathologies, related to losses of proprioception for instance.
Methodological questions addressed in this paper In studying the visual contribution to posture stabilization, most authors record the centre of pressure (COP) of the feet [1,12,15,16]. Others use body markers, often located near or on the head [17–19], or on the hips [20,21]. Although the relationship between the movements of different body points in space has already been documented [22–26], little is known about the reliability of each method for the assessment of postural steadiness. Furthermore, different variables measuring this steadiness have been used in the literature. The most common are the velocity Vof the tracked point (COP or body marker), and the root mean square (RMS) of all positions, which is related to the displacement amplitude. V seems to yield a more accurate measurement of the visual contribution to posture [20,27,28], but the RMS and other amplitude-related variables are still widely used in the recent literature [4,7,29]. All variables measuring the visual contribution to postural steadiness are plagued with a high intra- and inter-subject variability (with standard deviations ranging typically between 25 and 50% of the mean values [30,31]). Therefore, we first aim at comparing the sway velocity and root mean square of positions (RMS) of the COP in their precision to quantify the visual contribution to postural steadiness. Beside the disparities in the methods and measurement variables, a common point in most studies is to use a Romberg quotient (or ‘Romberg’), i.e. the ratio of the variable values obtained in the condition ‘eyes closed’ and ‘eyes open’. This quotient is usually larger than one and quantifies the visual contribution to postural stabilization. Although parametric tests are generally applied to the Romberg, there exists, to our knowledge, no published assessment of the normality of the corresponding distributions. In a second step, we introduce a new operator, called the stabilization ratio (SR), and demonstrate that SR increases the normality and accuracy of the measurement of the visual contribution to postural steadiness, as compared to the Romberg. In addition, this paper explores the following corollary questions:
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(1) a foam plate placed under the subjects’ feet is known to reduce the relative contribution of somatosensory information to posture [30], and increase the precision of the measurement of the visual component. What is the corresponding gain of reliability? (2) postural measurements are usually performed in binocular vision, whereas visual pathologies often differ in their advance between the two eyes. Due to the decrease in the visual field, the visual contribution to posture is reduced in monocular, as compared to binocular vision. What is the corresponding change in reliability of the postural measurements? (3) the Romberg quotient is larger in the antero-posterior direction, than in the lateral direction [16]. Is there an associated change in the reliability of the measurement of the visual contribution to posture? Finally, we illustrate how the use of SR also improves the analysis of other inputs to postural steadiness, such as the somatosensory contribution.
2. Methods 2.1. Subjects Studying the consequences of visual deficits on postural stability often requests to include patients aged 40 and above (for instance in glaucoma, presbyopia, diabetic retinopathy, AMD . . .). Therefore, we chose a mature normal population, composed of 21 subjects aged 42–61, who gave their informed consent to participate. They had no history of vestibular or visual pathology, dizziness or imbalance, neurological or orthopedic problems. Our protocol was approved by the Research and Ethics committee of NUH (Ref. 99/016). 2.2. Apparatus The trajectory of the centre of foot pressure (COP) was recorded on a force platform with four piezoelectric transducers. This force plate (Kistler 9281B) was level with the surrounding floor. 2.3. Visual environment The experiment took place in a normally lit room. We used a short viewing distance and a large visual field, which have been proved to enhance the visual contribution to postural stability [32,33]. The subject stood 0.5 m from a three-panel textured board covering 1808 (horizontal) by 908 (vertical) visual angle. The central panel was frontoparallel, with a fixation mark at the centre. The two side panels were rotated by 1158 around a vertical axis, relative to the frontoparallel plane, toward the subject.
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2.4. Procedure The subjects stood barefoot as still as possible, with arms at the side, feet separated by an angle of 308 and heels placed 5 cm apart. Each session lasted 30 s (10 s of stabilizing time, followed by 20 s recording). In condition ‘eyes open’, the subjects were instructed to look at the central fixation target. The subjects stood directly on the force-plate, or on a slab of rubber foam (6 cm thick, density 40 g/dm3) covered with a thin wooden plate. Each subject participated in five sessions for each condition ‘eyes open’ (EO), ‘eyes closed’ (EC), ‘left eye open’ and ‘right eye open’, with or without foam (FOAM and NO FOAM conditions), making a total of 40 sessions per subject. The order of conditions was randomized. 2.5. Data analysis: posturography The COP signal was acquired at a frequency of 100 Hz, and convoluted with a Hamming filter of width nine. Hence, the signal was low-pass filtered with a cut-off frequency of 15 Hz. From the COP trajectory, we computed the velocities as follows. If Xi and Yi are the COP coordinates at sample i, in the lateral (X) and antero-posterior (Y) directions, we consider the inter-sample monodirectional displacements: DXi ¼ jXi Xi1 j and
DYi ¼ jYi Yi1 j
and the inter-sample total displacement, as projected on a X– Y plane qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Di ¼ ðDX 2i þ DY 2i Þ: DXi, DYi and Di are averaged over a session, and divided by the sample duration, to estimate the mean velocities along the X and Y directions (VX and VY), and the mean global velocity (V). V is equal to the total sway path divided by the recording duration. The root mean squares (RMS) of positions in the X and Y directions are: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðXi Xm Þ2 RMSX ¼ N sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðYi Ym Þ2 RMSY ¼ N where Xm and Ym are the means of the Xi and Yi over the N samples of the session. The global RMS is equal to: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ½ðXi Xm Þ2 þ ðYi Ym Þ2 RMS ¼ N The point of coordinates (Xm, Ym) is the arithmetic mean of all positions. The RMS gives an estimate of the amplitude of
the sway trajectory about this point. We call VX, VY, V, RMSX, RMSY, and RMS ‘sway measurement variables’. A classical computation of the visual contribution to posture is the Romberg quotient (or ‘Romberg’), x(EC)/ x(EO), where x is any of the above variables measured in conditions EC and EO. For instance the Romberg quotient for the velocity is V(EC)/V(EO). In the results below, V and RMS are correlated with their respective variabilities. This led us to consider the logarithmic transform of these variables, which has a decreasing derivative, causing a relative dilatation of range intervals for small, as compared to large x values. Such a logarithmic ‘variance stabilizing’ transformation has been used by others in postural measurements [13]. Here, we use log (1 + x) where x is one of the above variables (in mm/s for the velocity, and in millimetre for the RMS), so as to avoid getting negative logarithms. Within our data analysis, the choice of the additive constant 1 in the logarithm had a negligible influence on the results, so long as this constant remained below the order of magnitude of x (this order of magnitude is 10 mm/s for V, and 10 mm for RMS). The Romberg quotient quantifies the excess of velocity with eyes closed, in reference to the velocity with eyes open. Studying the visual contribution to posture, we chose the velocity with eyes closed as a reference, because it should be similar in normal subjects, and in ophthalmic patients who suffer only from visual problems. Hence, we quantified the visual contribution to postural stability by SR ¼ 1
log ðxðEOÞ þ 1Þ log ðxðECÞ þ 1Þ
A Romberg of 1.2 indicates that the sway is increased by 20% if vision is suppressed, whereas a SR of 20% means that the visual input reduces the logarithm of the sway (in V or RMS) by 20%. 2.6. Data analysis: statistics The Shapiro–Wilk test is used as a criterion of normality, because of its good power properties as compared to a wide range of alternative tests [34]. This test indicates the probability that a distribution differs from a normal distribution. Because of our small sample sizes, we used non-parametric tests (Wilcoxon test for paired samples, Spearman correlation). These tests gave results that were similar to those obtained through the corresponding parametric tests (Student t-test and Pearson correlation). For the handling of confidence intervals of the SR, we used the parametric approach, because of the good scores reached by the SR for the Shapiro–Wilk test. Except if otherwise stated, the significance threshold is p < 0.05. Our goal is to determine which variable (Romberg or SR, calculated from V or RMS) is optimal to measure the visual contribution to postural steadiness, so as to position the
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performance of a patient (or group of patients) relative to our control (normal) group, with a low rate of false alarms. Given our recruitment criteria above, we assume that no subject in our control group presents an abnormality in the visual contribution to posture (our results support this hypothesis, see below). Therefore, we look for the variable that presents the least intra-subject and inter-subject variability in our control group, i.e. the lowest coefficient of variation, equal to the ratio CV = s/M (s: standard deviation, M: mean). The null hypothesis (no contribution of vision to postural steadiness) corresponds to Romberg = 1, or to SR = 0. For sake of simplicity, we replace the Romberg by [Romberg-1], which has the same reference value as SR. Assuming normal distributions, if N is the sample number, the Student t-value for establishing the probability of the null hypothesis is equal to:
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Fig. 1. Histograms of the Romberg quotient and SR for V and RMS in the FOAM and NO FOAM conditions, in binocular vision (N = 21 subjects). Ordinates: number of subjects.
pffiffiffiffi pffiffiffiffi M N t¼ N ¼ s CV
significance in 13 of 16 cases. Hence, this transform led to a better stability in the variability of the sway measurement.
Therefore, for a given N, the best variable for measuring the visual contribution to posture presents the lowest CV, i.e. the highest t-value associated to the lowest probability for the null hypothesis. When the sample number is small, the calculus of the mean and standard deviation is very sensitive to outliers. Therefore, we also define a ‘non parametric’ coefficient of variation equal to CVnp = (Q75 Q25)/Med, where Med is the median, Q75 and Q25 are the 75% and 25% quartiles.
3.2. Normality of the Romberg and SR
3. Results
From the mean V and RMS over five sessions, we obtained a Romberg and SR for each subject and condition (FOAM/NO FOAM). In binocular vision, the corresponding histograms are plotted in Fig. 1. In each condition, the distributions did not differ significantly from normality, except for the Romberg of RMS, in monocular vision only (Table 2). SR never differed significantly from normality. In 10 of the 12 cases presented in Table 2, the Shapiro–Wilk test value was smaller for the Romberg, than for SR, which indicates that the distributions were closer to normality in the latter case.
3.1. Correlation of V and RMS with their intra-subject variability
3.3. Inter-subject variability
Because of the small number of sessions (five), we calculated the range (difference between the maximum and minimum values) of V and RMS for each subject, rather than their standard deviation. The means of V and RMS were significantly correlated with their respective ranges in all cases but one (Table 1). For the log (x + 1) transform of these variables, the correlations were weaker and lacked
In the NO FOAM condition, [Romberg-1] and SR had lower variation coefficients when they were calculated from V, than from RMS (Table 3). Therefore, V leads to a better accuracy than RMS in measuring the visual contribution to posture. In the FOAM condition, this is also found in monocular vision, but not in binocular vision, where the two variables were similar in this regard.
Table 1 Spearman coefficient of correlation between the mean M of different variables, and their range (difference between the maximum and minimum values) Variables
V and range RMS and range log (V + 1) and range log (RMS + 1) and range
NO FOAM
FOAM
EC
‘Left eye open’
‘Right eye open’
0.60 0.75 0.27 0.25
0.69 0.45 0.23 0.07
0.66 0.61 0.17 0.32
EO
EC
‘Left eye open’
‘Right eye open’
EO
0.62 0.35 0.28 0.09
0.53 0.92 0.14 0.78
0.66 0.80 0.27 0.53
0.73 0.47 0.17 0.12
0.58 0.83 0.15 0.48
M and the range are calculated over five sessions for each subject. The correlation is established for 21 subjects. Significant coefficients (at p < 0.05) are in bold
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100 Table 2 Results of the Shapiro–Wilk test for normality Shapiro–Wilk test W(p)
No foam
EO
V RMS V RMS V RMS
‘Left eye open’ ‘Right eye open’
Foam
Romberg
SR
0.938 0.924 0.857 0.945 0.898 0.925
0.993 0.978 0.944 0.975 0.945 0.972
(0.195) (0.104) (0.006) (0.277) (0.032) (0.112)
(0.999) (0.889) (0.266) (0.845) (0.270) (0.774)
Romberg
SR
0.968 0.952 0.957 0.954 0.963 0.941
0.978 0.971 0.958 0.932 0.959 0.965
(0.691) (0.378) (0.459) (0.402) (0.585) (0.226)
(0.892) (0.753) (0.472) (0.152) (0.491) (0.624)
The value W of the test, and the associated probability that the distribution differs from normality are indicated for each variable (Romberg, SR) calculated from V or RMS, in the FOAM and NO FOAM conditions.
The coefficient of variation was systematically lower (1) for SR than for [Romberg-1], (2) for the FOAM condition than for the NO FOAM condition. Hence, the reduction of the proprioceptive feedback by the foam led to an enhancement of the reliability in measuring the visual contribution to posture. In all cases, the probability of the null hypothesis (no visual contribution to posture, Wilcoxon matched pair test) was lower than 1.E-4. A major factor of variability in the quantification of the visual contribution to posture is the large variability observed in the reference condition EC. Therefore, our measurement of this contribution should be as independent as possible from the sway magnitude in the EC condition. Table 4 indicates the positive correlation between the Romberg in RMS or V, with RMS(EC) or V(EC), respectively. This correlation was weaker for SR, and usually not significant. It follows that SR is more independent than the Romberg from the measurement of V or RMS at the reference level in condition EC. It is of clinical interest to estimate the type of abnormality that our measure can detect, either for a single patient, or for a group of patients. For the most sensitive measurement (SR(V) in the FOAM condition), we calculated the drop in
SR (in percent of the mean) which is considered as abnormal with a false alarm rate below 2.5%. In binocular vision, 95% of the population had a SR(V) ranging between 0.1 and 0.3 in the FOAM condition (M 1.96s, where M is the mean at 0.2, and s the standard deviation at 0.049). Hence, a patient will be declared as abnormal if SR is below 0.1, i.e. if his visual contribution to posture (SR) is reduced by more than 50% relative to the normal group. In the case of a homogeneous patient population, the mean SR(V) had a confidence interval of half-width 1.96s/HN. Assuming that s is similar for the normals and patients, the mean SR can be considered as abnormal if it is reduced by 10%, relative to the normal value (i.e. if it is below 0.18). Similarly, in monocular vision, we found that a patient will be considered as abnormal if SR is below 51 % of the normal mean. A group of patients will be considered as abnormal if the mean SR has dropped by more than 11% of its normal value. Supporting the hypothesis that our control group is homogeneous, Fig. 1 shows the absence of any single individual who would show a low SR(V) relative to the whole group in the FOAM condition, in binocular vision. Also, no subject had a SR(V) below the inferior limit of the
Table 3 Comparison of [Romberg-1] and SR for the measure of the visual contribution to posture No foam
Foam
V Romberg-1 (A) Mean ‘Left eye open’ 0.47 ‘Right eye open’ 0.46 EO 0.64 EOX 0.61 0.67 EOY (B) Coefficients of variation (%) ‘Left eye open’ 63.7 ‘Right eye open’ 70.0 EO 55.7 EOX 58.5 EOY 59.3
RMS
V
RMS
SR
Romberg-1
SR
Romberg-1
SR
Romberg-1
SR
0.13 0.12 0.16 0.18 0.18
0.42 0.40 0.53 0.54 0.54
0.13 0.13 0.16 0.18 0.18
0.76 0.79 0.96 0.93 0.99
0.17 0.18 0.20 0.22 0.23
0.67 0.66 0.97 0.93 1.07
0.18 0.18 0.24 0.26 0.27
45.1 52.1 39.5 39.4 44.5
75.0 80.2 61.7 78.5 54.6
62.8 66.4 47.8 58.7 46.2
34.0 31.9 32 36.5 33.2
28.4 23.6 24.4 26 24.4
45.0 40.0 34 34.4 42.6
34.3 32.2 23.3 24.5 27.9
(A): Mean [Romberg-1] and SR, as obtained by averaging the individual values of the 21 subjects in monocular vision (left or right eye open) or binocular vision (EO). EOX and EOY are the lateral and antero-posterior sway component (in V or RMS) in condition EO. (B): Corresponding coefficients of inter-subject variation.
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Table 4 Spearman correlation coefficients between V(EC) (or RMS(EC)) and the Romberg or SR obtained in different conditions Condition NO FOAM
FOAM
*
EO ‘Left eye open’ ‘Right eye open’ EO ‘Left eye open’ ‘Right eye open’
V(EC) and Romberg(V) *
0.59 0.55* 0.61* 0.09 0.01 0.29
V(EC) and SR(V)
RMS(EC) and Romberg(RMS)
RMS(EC) and SR(RMS)
0.42 0.39 0.50* 0.18 0.22 0.00
0.42 0.41 0.47* 0.18 0.33 0.34
0.28 0.32 0.36 0.07 0.12 0.16
Significant correlation.
95% probability interval. This limit was 0.1 for EO, 0.086 for ‘left eye open’ and ‘right eye open’. The latter two conditions did not differ significantly in their V, RMS, Romberg and SR (Wilcoxon matched paired test at Z < 1.41 and p > 0.16). Finally, we verified the absence of outliers in scatterplots relating the variables for the ‘left eye open’ and ‘right eye open’ conditions, suggesting a good homogeneity between the left and right eye populations, in terms of their visual contribution to postural steadiness. 3.4. Effect of the foam on the X & Y sway components For sake of simplicity, we analysed the X and Y sways only in binocular vision. The presence of foam increased VY, RMSX and RMSY in similar proportions (35–43% in EC, 15–18% in EO), but its main effect was on VX, which increases by 49% (EC) and 36% (EO). This particular increase of VX had the following consequence. Subjects swayed more in the Y, than in the X direction, in terms of V and RMS in the NO FOAM condition. On foam, this was maintained for RMS, but not for V(VY/VX was then reduced and not significantly different from one). Hence, the weakening of proprioceptive inputs through a foam support increased mainly the lateral velocity, but preserved the Y/X elongation of the RMS (Fig. 2). 3.5. Inter-subject variability of the X & Y sway components Fig. 3 plots the different variables (RMSX, RMSY, VX, VY), and their log(x + 1) transform, in all conditions. The standard
Fig. 2. The XY anisotropy in COP sway for different conditions indicated in abscissae. Circles: ratio of VY (antero-posterior) by VX (lateral). Squares: ratio of RMSY by RMSX.
deviations are more constant across conditions for the log transform (right panel) than for the raw values (left panel). Second, the log transform enhances the parallelism between the lines. Hence, we find that vision tends to decrease equally the logarithm of V and RMS in X and Y, which supports the relevance of the log transform for sway variables. Using the global variables (V or RMS) rather than their respective X and Y components led to similar measurement of the visual contribution to posture, in terms of magnitude and coefficient of variation (Table 3). Nevertheless, the use of the global variables seems advisable, because their coefficients of variation tend to be similar or lower than that of the X or Y measurements. All Romberg quotients exceeded one, except RMSY for one subject in the NO FOAM condition. However, we do not consider this as a sign of a visual destabilization of posture, because all other three measurements of the sway (RMSX, VX, VY) had a Romberg above one, as well as all variables measured in the FOAM condition. Rather, we attribute this observation to the noise in RMS measurements. 3.6. Measurement of the increase of the somatosensory contribution in the NO FOAM, versus FOAM, condition So far, we focused on the visual contribution to postural steadiness. It is possible to apply our approach to the comparison between other pairs of experimental conditions. For instance we can measure the increase of the somatosensory contribution, from the FOAM to the NO FOAM condition, by using the Romberg and SR operators (see methods), where we replace x(EC) by x(FOAM) and x(EO) by x(NO FOAM), respectively. Again the coefficients of variation were lower when using V, rather than RMS, (in global values, or for their directional components), and for SR than for [Romberg-1] (Table 5), in conditions EC and EO. Also, the reliability of the measurement of the sway decay due to the somatosensory change is higher in the absence of visual input, than with vision. SR(VX) is above SR(VY), SR(RMSX) and SR(RMSY) in Table 5, again suggesting that the main effect of the somatosensory input induced by our firm support (as compared to the foam support) is to reduce VX. Again it seems optimal to use the global measurement SR(V), which has a coefficient of variation lower or similar to that of VX and VY.
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Fig. 3. X (lateral) and Y (antero-posterior) components of the sway velocity and RMS, averaged over 21 subjects, for different conditions indicated in abscissae. Left graph: raw values. Right graph: logarithmic values (for instance log (VX + 1)).
Table 5 Evaluation of the contribution of the somatosensory input in the NO FOAM condition, in reference to the FOAM condition, with eyes open or eyes closed Eyes open
Eyes closed
V Romberg-1 (A) Mean Global 0.31 X 0.46 Y 0.21 (B) Coefficient of variation (%) Global 61.1 X 56.0 Y 105.2
RMS SR 0.10 0.15 0.07 51.1 41.8 104.6
Romberg-1 0.54 0.53 0.59 94.7 100.0 126.3
V SR
Romberg-1 .07 .08 .07
91.6 104.9 132.8
0.59 0.78 0.45 45.7 53.9 42.3
RMS SR 0.14 0.19 0.13 38.2 42.7 37.5
Romberg-1 0.19 0.19 0.20 48.9 61.5 48.6
SR 0.15 0.16 0.18 39.1 51.5 39.8
(A): [Romberg-1] and SR are calculated for each subject and averaged over 21 subjects. (B): Corresponding coefficients of inter-subject variation. Each line corresponds to the global variable (V or RMS) or to its X or Y component.
4. Summary of the results (1) V leads generally to more reliable measurement of the visual contribution to postural steadiness than RMS. (2) Replacing the Romberg quotient by SR systematically enhanced the reliability of the measure of the visual contribution to posture, within and across individuals. (3) In monocular or binocular vision, a patient group will be declared as abnormal at p < 0.025, if the mean SR in the FOAM condition drops by more than 10%, relative to the mean value found for our normal population. This threshold is at 50% for declaring a single patient as abnormal. (4) Subjects swayed generally more in the Y (anteroposterior), than in the X (lateral) direction in all conditions (except on foam, where VX and VY are similar). This anisotropy differed in the FOAM and NO FOAM conditions. However, the reliability of the
measurement of the visual contribution to posture was similar for the X, Y and global measurements.
5. Discussion 5.1. The use of different variables for characterizing postural steadiness Classical static posturography uses global variables such as V, RMS or the sway area to quantify the sway magnitude. More complex analyses have been proposed, such as (1) the calculus of structural variables describing the fine structure of the stabilogram, for instance the time interval between one peak and another [35] (2) the extraction of variables from the Fourier transform of the stabilogram, for instance the power density at different temporal frequencies [14,22] (3) stochastic modeling and diffusion analysis [36], as well
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as fractal dynamics [37–39]. Most often, the complex variables show significant differences between EC and EO conditions, but it is not clear yet whether they are more reliable than classical global variables for the quantitative comparison of subject populations or conditions [35]. Koles and Castelein [25] find that the COP power spectrum component due to the ankle leads to an inter-subject coefficient of variation of 44% for the difference between EC and EO (firm support), which is close to what we obtain for SR(V) in the NO FOAM condition. The high interest of the complex variables listed above is to provide insights into the mechanisms of the sway generation, and their associated neural structures [14]. Among global variables, V and RMS have been compared, in terms of their reliability to distinguish between subject groups and conditions. Sway magnetometry [40] showed a smaller intra-individual coefficient of variation for [Romberg-1](V) than for [Romberg-1](RMS). This was confirmed at an interindividual level [20] with a coefficient of variation of [Romberg-1] of about 50% for V, and more than 100% for other amplitude related variables, in full agreement with our NO FOAM results. For the COP sway, V has been reported as the most discriminating variable for age-related differences [28]. V is systematically higher in EC than in EO, which is not the case for other variables (maximum antero-posterior or lateral displacements, RMS) in half of the subjects studied by Collins and De Luca [36]. Sakaguchi et al [41] find a much larger dispersion of the Romberg quotient for RMS than for V. Latash et al [42] find that Romberg quotients are larger for V (1.19) than for RMS (1), or for the sway area (1.02), but high Romberg quotients have been reported elsewhere for the sway area (2.19 in [43]; 1.55 in [29]). In one study only [44], a significant difference between conditions EC and EO is found for the COP RMS, but not for V. However, this is obtained for a heel-to-toe tandem position, rather than with the normal stance used in all other studies, i.e. feet positioned side by side. Hence, our findings agree with a majority of studies to declare V as more reliable than amplitude-related variables (RMS, sway area . . .) for discriminating between conditions and subject populations. 5.2. The use of different techniques for measuring postural steadiness Among the rare studies comparing the reliability of different techniques of sway recording, Fitzgerald et al [20] measured the hip motion through magnetometry, and the COP motion on a forceplate. Magnetometry yielded a variation coefficient for the Romberg quotient of about 54%, comparable to ours (Romberg(V) in the NO FOAM condition). However, with the forceplate, the reliability of the Romberg quotient was poor (variation coefficient above 200%), and the Romberg(V) did not differ significantly from one, in contrast with what we find, and what is found by others (Table 6). This discrepancy might be explained by the
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sampling frequency of 5 Hz used by Fitzgerald et al, which is low in comparison of values above 20 Hz in the other studies. The relationship between the COP movement, and the movement of other body segments is complex [24–26], and we can wonder whether the optimal properties of SR(V) would also hold for the analysis of the motion of other body segments. In a control experiment, we analysed simultaneously the head and COP sways in eight subjects, using a Vicon system with three cameras capturing the 3D trajectory of an infrared marker stuck on the subject’s forehead. In agreement with others [41,45,46] [49], we found high similarities between the head and COP trajectories, with significant correlations between the head and COP variables (V and RMS, correlation coefficients above 0.69). The coefficients of variation for the measure of the visual contribution to posture (Romberg or SR) were also similar for the head and COP. The main differences between head and COP trajectories were a slight shift in their frequency domains (toward low temporal frequencies for the head), and different Y/X distortions in V. Overall, we retrieved the main conclusions above for the head sway, namely that V is a more reliable measurement than RMS, and that SR leads to a lower coefficient of variation than [Romberg-1], for measuring the visual contribution to posture. 5.3. Does visual destabilization exist in normal subjects? One of our 21 subjects presents a slight visual destabilization effect (Romberg < 1 or SR < 0) for the sway RMS. This effect is not confirmed for V, and could be due to the higher variability found for RMS, as compared to V. Similarly, using the RMS of the COG sway angle, Turano et al [7] find a Romberg quotient below one in three of 20 normal subjects. In Lacour et al [29], 46% of normal subjects have equal or larger sway areas in condition EO, than in condition EC. Their histogram of Romberg quotients shows two categories of subjects, with weak or strong visual contribution to posture, respectively. This result is confirmed in another study [36] using the sway amplitude. With equivalent experimental conditions, we do not observe such bimodal distributions in our Romberg (RMS) histograms (Fig. 1). Such discrepancy is important to resolve, because the presence of a significant proportion of normal subjects showing little or no visual contribution to postural steadiness would impair the assessment of the effect of visual deficits on posture. So far, there exists no explanation to these contradictory results. However, the use of the sway amplitude, rather than V, seems to favor the observation of ‘non visual’ subjects because (1) the two studies showing ‘non visual’ subjects use amplitude related variables, whereas the studies using V (listed in Table 6) do not mention them, and (2) in magnetometry, the Romberg quotient is below one for 13–23% of the sway-area measurements, but only for 1% of the V measurements [27,40], and (3) Collins and De Luca [36] indicate that the
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Table 6 Comparison between different studies measuring the COP velocity in subjects standing on a firm or foam support
Firm Support Baloh et al (1994) [12] Baloh et al (1994) [12] Straube et al (1988) [30] Straube et al (1988) [30] Hufschmidt et al (1980) [31] Paulus & Zihl (1989) [16] Teasdale et al (1991) [47] Teasdale et al (1991) [47] Baratto et al (2002) [35] Current expt NO FOAM Coefficient of variationa Foam support Straube et al (1988) [30] Straube et al (1988) [30] Straube et al (1990) [6] Teasdale et al (1991) [47] Teasdale et al (1991) [47] Guerraz et al (2000) [45] Guerraz et al (2000) [9] Current expt FOAM Coefficient of variationa Support F(1,14), (p) Age F(1,14), (p)
Age range (years)
Velocity eyes open (mm/s)
Velocity eyes closed (mm/s)
26.6 6 79.6 4 20–40 40–60
9.16 13.68 8.17 10.5 10.67 6.24 10.0 14.3 9.8 8.29
15.13 24.22 14 16.17 16.83 12.44 17.2 28.1 12.7 13.47
1.65 1.77 1.71 1.54 1.58 1.99 1.72 1.97 1.30 1.62
0.17 0.17 0.18 0.14 0.15 0.24 0.17 0.19 0.09 0.17
24.3%
30.3%
29.6%
22.3%
11.67 20.5 11.17 12.1 17.5 5.42 10.06 10.81
45 71.67 43.33 20.9 39.5 10.1 25.62 20.77
3.86 3.50 3.88 1.73 2.26 1.86 2.55 1.92
0.34 0.28 0.34 0.17 0.21 0.23 0.27 0.20
37.4%
56.1%
b
20–50 21–22 70–80 47 17 42–61
20–40 40–60 27 3.4 21–22 70–80 mean 35 mean 40.5 42-61
Romberg
53.9% 10.70 (0.005) 0.45 (0.513)
SR
25.2% 12.96 (0.003) 2.09 (0.169)
In the nine references indicated in the left column, the total sway velocity V is estimated from the average lateral and antero-posterior velocities VL and VA by: V ¼ sqrtðV 2L þ V 2A Þ. The Romberg and SR are estimated from the average values of V. In our study, V, the Romberg and SR are calculated for each subject and then averaged. The ‘inter-study’ coefficients of variation are indicated for each support. The two bottom lines give the results of an ANOVA on the Romberg and SR given in the table, with factors ‘age’ (below 40 or above 40), and ‘support’ (foam or firm). The interactions between the two factors are not given, for they are not significant. a The coefficients of variation are calculated for [Romberg-1] and for SR. b The subjects in this study have been put in the category ‘below 40’, but we verified that this choice hardly affected the results in the ANOVA, and did not change the conclusions given in the text.
weak visual stabilizers for sway amplitude still have larger V(EC) than V(EO). 5.4. Using SR, rather than the Romberg quotient All our results support the use of SR, as a more reliable quantification of the visual contribution to posture than the Romberg quotient, within or among subjects. The reason is that the variability of Vor RMS increases with the magnitude of these variables (as found previously [12,30,40]), which effect is strongly weakened by the application of a log transform. Let us confront our results to nine anterior studies reporting the average COP velocity for normal subjects of different age ranges (Table 6) in conditions EC and EO, on firm and foam supports. From these velocities, we estimate the Romberg quotients and SR. For firm or foam supports, the ‘across study’ coefficient of variation is lower for SR than for [Romberg-1]. Hence, grouping all these studies yields a more significant assessment of the visual contribution to posture through SR, than through the Romberg
quotient. A similar result is obtained when using the nonparametric coefficient of variation (Section 2). This does not mean that SR confounds the effect of conditions more than the Romberg quotient. Grouping the subject populations by age into a ‘young’ category (below 40), and an ‘old’ category (above 40), we performed an ANOVA on [Romberg-1] and SR, using two factors (‘age’ and ‘support’). The bottom line of Table 6 indicates that SR is still more reliable than the Romberg quotient at finding differences in the contribution of vision, between the foam and firm support, and between age groups (although significance is not reached in this latter case). Table 6 also indicates that our results on the COP measurements are in good quantitative agreement with many previous studies. Interestingly, other methods of postural recording yield SR values that are comparable to those listed in Table 6. With magnetometry [27], SR(V) is about 0.15– 0.16 on a firm support, although hip velocity is about two or three times slower than COP velocity. The recording of head movements on a foam support leads to a SR(V) of about 0.30 [45], which is well in the range of the values of Table 6.
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5.5. Comparing monocular vs binocular vision
References
As reported earlier [32,45], the sway magnitude is smaller in binocular, than in monocular vision. This is compatible with the fact that the postural steadiness decreases as the visual stimulation area increases [1]. However, we find that the difference between monocular and binocular SR(V) is small in terms of magnitude (on foam, SR(V) is 0.20 in binocular and 0.17–0.18 in monocular vision) and reliability (the coefficients of variation are about 25%). Hence, we may plan the measurement of losses in postural stability with a similar accuracy in monocular and binocular vision.
[1] Straube A, Krafczyk S, Paulus W, Brandt T. Dependence of visual stabilization of postural sway on the cortical magnification factor of restricted visual fields. Exp Brain Res 1994;99:501–6. [2] Duffy CJ, Wurtz RH. Optic flow, posture and the dorsal visual pathway. In: Ono T, McNaughton BL, Molotchnikoff S, Rolls ET, Nishijo H, editors. Perception Memory and Emotion: Frontier in Neuroscience. Cambridge: Elsevier Science; 1996. p. 63–77. [3] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988;319: 1701–7. [4] Anand V, Buckley JG, Scally A, Elliott DB. Postural stability changes in the elderly with cataract simulation and refractive blur. Invest Ophthalmol Vis Sci 2003;44(11):4670–5. [5] Harwood RH. Visual problems and falls. Age Ageing 2001;30(Suppl 4):13–8. [6] Straube A, Paulus W, Brandt T. Influence of visual blur on objectmotion detection, self-motion detection and postural balance. Behav Brain Res 1990;40:1–6. [7] Turano KA, Herdman SJ, Dagnelie G. Visual stabilization of posture in retinitis pigmentosa and in artificially restricted visual fields. Invest Ophthalmol Vis Sci 1993;34:3004–10. [8] Turano KA, Dagnelie G, Herdman SJ. Visual stabilization of posture in persons with central visual field loss. Invest Ophthalmol Vis Sci 1996;37:483–91. [9] Guerraz M, Shallo-Hoffmann J, Yarrow K, Thilo KV, Bronstein AM, Gresty MA. Visual control of postural orientation and equilibrium in congenital nystagmus. Invest Ophthalmol Vis Sci 2000;41(12):3798– 804. [10] Di Girolamo S, Di Nardo W, Cosenza A, Ottaviani F, Dickmann A, Savino G. The role of vision on postural strategy evaluated in patients affected by congenital nystagmus as an experimental model. J Vestib Res 1999;9(6):445–51. [11] Elliott DB, Patla AE, Flanagan JG, Spaulding S, Rietdyk S, Strong G, et al. The Waterloo Vision and Mobility Study: postural control strategies in subjects with ARM. Ophthalmic Physiol Opt 1995;15(6):553–9. [12] Baloh RW, Fife TD, Zwerling L, Socotch T, Jacobson K, Bell T, et al. Comparison of static and dynamic posturography in young and older normal people. J Am Geriatr Soc 1994;42:405–12. [13] Maki BE, Holliday PJ, Fernie GR. Aging and postural control: a comparison of spontaneous- and induced-sway balance tests. J Am Geriatr Soc 1990;38:1–9. [14] Yokoyama K, Araki S, Nishikitani M, Sato H. Computerized posturography with sway frequency analysis: application in occupational and environmental health. Ind Health 2002;40(1):14–22. [15] Kapteyn TS, Bles W, Njiokiktjien CJ, Kodde L, Massen CH, Mol JM. Standardization in platform stabilometry being a part of posturography. Agressologie 1983;24:321–6. [16] Paulus W, Zihl J. Visual stabilisation of posture in a case with selective disturbance of movement vision after bilateral brain damage: real and apparent motion cues. Clin Vision Sci 1989;4:367–71. [17] Fearing FS. The factors influencing static equilibrium. J Comp Physiol Psychol 1924;4:90–121. [18] Tokita T, Miyata H, Matsuoka T, Taguchi T, Shimada R. Correlation analysis of the body sway in standing posture. Agressologie 1976;17: 7–16. [19] Koncek V, Pollak VA, Yu AM. Analysis of stability of human upright posture based upon the measurement of the head sway. Med Prog Technol 1993;19:55–60. [20] Fitzgerald JE, Murray A, Elliott C, Birchall JP. Comparison of body sway analysis techniques. Assessment with subjects standing on a stable surface. Acta Otolaryngol 1994;114(2):115–9. [21] Fitzgerald JE, Murray A, Elliott C, Birchall JP. Comparison of balance assessment by sway magnetometry and force platforms. Arch Otolaryngol Head Neck Surg 1993;119(1):41–6.
5.6. Assessing the role of different sensory inputs As described in the results, our approach applies to the evaluation of other sensory contribution to posture. For instance, neurological patients can be compared to normal subjects by using the SR quantifying the change of the somatosensory contribution between two conditions (firm and foam support for instance). More generally, the question arises whether the use of relative changes in the sway velocity between two conditions (EC/EO or FOAM/NO FOAM) is necessary to reduce the inter-subject variability in posture studies. In the literature on the effect of pathologies on postural steadiness, both approaches can be found. Some authors study the differences between the sway magnitude of patients and controls [4,29,42], whereas others use the intercondition change in sway magnitude [8,9]. We propose that the use of the logarithmic transform of the sway variables would lead to an improved accuracy in the inter-condition or inter-population comparison of the postural measurements.
6. Conclusion In agreement with many previous studies, our results support the use of the COP velocity as a reliable and probably optimal indicator of the sway magnitude in the standing subject. As compared to the Romberg quotient, the SR is a more stable indicator of the visual contribution to posture, for the statistical comparison of different visual or somatosensory conditions, or of subject populations. By using SR(V), it is possible to plan the evaluation of the postural consequences of visual deficits in monocular and binocular vision.
Acknowledgments This study was supported by the National Medical Research Council of Singapore, through grants NMRC/ 0528/2001 and SERI/PG/99-01/000016, by the National University of Singapore (PhD Grant for Noor Shabana), and by the French Embassy in Singapore.
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[34] Shapiro SS, Wilk MB, Chen HG. A comparative study of various tests for normality. J Am Stat Assoc 1968;63:1343–72. [35] Baratto L, Morasso PG, Re C, Spada G. A new look at posturographic analysis in the clinical context: sway-density versus other parameterization techniques. Motor Control 2002;6(3):246–70. [36] Collins JJ, De Luca CJ. The effects of visual input on open-loop and closed-loop postural control mechanisms. Exp Brain Res 1995;103(1): 151–63. [37] Blaszczyk JW, Klonowski W. Postural stability and fractal dynamics. Acta Neurobiol Exp 2001;61(2):105–12. [38] Doyle DLA. Dugan EL, Humphries B, Newton RU. Discriminating between elderly and young using a fractal dimension analysis of centre of pressure. Intl J Med Sciences 2004;1:11–20. [39] Katz MJ, George EB. Fractals and the analysis of growth path. Bull Math Biol 1985;47:273–86. [40] Elliott C, Murray A. Repeatability of body sway measurements; dayto-day variation measured by sway magnetometry. Physiol Meas 1998;19(2):159S–64S. [41] Sakaguchi M, Taguchi K, Ishiyama T, Netsu K, Sato K. Relationship between head sway and center of foot pressure sway. Auris Nasus Larynx 1995;22:151–7. [42] Latash ML, Ferreira SS, Wieczorek SA, Duarte M. Movement sway: changes in postural sway during voluntary shifts of the center of pressure. Exp Brain Res 2003;150(3):314–24. [43] Njiokiktjien C, de Rijke W. The recording of Romberg’ test and its application in neurology. Agressologie 1972;13(Suppl C):1–7. [44] Hunter MC, Hoffman MA. Postural control: visual and cognitive manipulations. Gait Posture 2001; 13(1):41–48. [45] Guerraz M, Sakellari V, Burchill P, Bronstein AM. Influence of motion parallax in the control of spontaneous body sway. Exp Brain Res 2000;131(2):244–52. [46] Guerraz M, Thilo KV, Bronstein AM, Gresty MA. Influence of action and expectation on visual control of posture. Brain Res Cogn Brain Res 2001;11(2):259–66. [47] Teasdale N, Stelmach GE, Breunig A. Postural sway characteristics of the elderly under normal and altered visual and support surface conditions. J Gerontol 1991;46(6):B238–44.
Measurement of the visual contribution to postural steadiness from the COP movement: methodology and reliability V. Cornilleau-Pe´re`sa,b,*, N. Shabanac, J. Droulezd, J.C.H. Gohe,f, G.S.M. Leef, P.T.K. Chewc a
Labo. de Neurosciences Fonctionnelles et Pathologies, FRE2726, University Lille 2, Lille, France b Singapore Eye Research Institute, Singapore c Department of Ophthalmology, National University of Singapore, Singapore d Labo. de Physiologie de la Perception et de l’Action, CNRS-Colle`ge de France, Paris, France e Division of Bio Engineering, National University of Singapore, Singapore f Department of Orthopedic Surgery, National University of Singapore, Singapore Received 5 November 2003; accepted 6 July 2004
Abstract We studied the reliability of different measures of the visual contribution to postural steadiness by recording the postural sway during standing with eyes open (EO) or eyes closed (EC). The COP trajectory was recorded in 21 subjects aged 42–61 standing on a firm or foam support. The improvement of postural steadiness due to vision was measured with a higher reliability (i.e. lower intra- and inter-subject variabilities) with the sway velocity V, than with the position RMS. Due to the increase of the variability of V and RMS with their own mean values, we quantified the visual contribution to posture by the stabilization ratio (SR), based on a logarithm transform of V or RMS. As compared to the Romberg quotient (EC/EO), SR improved the reliability of the measurement of the visual contribution to posture within individuals, across subjects, and even across different studies in the literature. Our method led to decrease the inter-subject coefficient of variation of this measurement to about 25%, using a foam support. It leads to a similar accuracy in binocular and monocular vision, and it also applies to the quantification of other non-visual sensory contributions to posture. # 2004 Elsevier B.V. All rights reserved. Keywords: Posture; Postural sway; Centre of foot pressure; Vision; Standing; Postural stability; Romberg quotient
1. Introduction In normal conditions, the standing subject oscillates more with eyes closed than with eyes open. In this later condition, the brain combines visual inputs with somatosensory and vestibular information to maintain the upright position. As a major functional role of vision, the maintenance of posture has been largely explored, but remains a critical research topic for several reasons. First, the reduction of the body movements through vision depends on the extent of the cortical representation of the visual stimulus [1], but the neural pathways underlying this phenomenon are still partly * Corresponding author. Present address: Neurosci. Fonctionnelles et Pathologies, CHRU Lille, Clinique Fontan, 6 rue du Pr Laguesse, 59037 Lille, France. Tel.: +33 3 20 91 40 46. E-mail address: [email protected] (V. Cornilleau-Pe´re`s). 0966-6362/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2004.07.009
unknown [2]. Second, visual deficits are associated with the risk of falling, particularly in elderly patients [3–5], because the contribution of vision to postural steadiness increases with age, and is impaired by the presence of blur [6], and by retinal diseases [7–11]. Static posturography analyses the spontaneous movement of body parts, whereas dynamic posturography measures these movements in response to a change in the sensory stimulation (usually a motion of the visual scene or of the foot support). The two methods address complementary questions in postural control, and spontaneous and induced sway velocities seem to covary (correlation coefficients between 0.11 and 0.71 have been reported in one study [12]), although negative correlations have also been reported in young subjects [13]. Dynamic posturography is highly relevant to the study of the postural reactions to changes in the environment, but static
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posturography remains a simpler and cheaper way of studying the multisensory integration in postural stability. It has given good results in (1) predicting falls [3,5,13] (2) delineating the sensory mechanisms underlying postural stabilization [1,12] and their changes with age (3) studying the effect of different pathologies on balance [7,9,14]. The present study focuses on methodological aspects related to the measure of the visual contribution to static posture, in the standing subject. Our goal is to optimize this measure, so as to assess with a maximal precision the consequences of visual deficits on postural steadiness. As discussed later, our approach can be used to study other pathologies, related to losses of proprioception for instance.
Methodological questions addressed in this paper In studying the visual contribution to posture stabilization, most authors record the centre of pressure (COP) of the feet [1,12,15,16]. Others use body markers, often located near or on the head [17–19], or on the hips [20,21]. Although the relationship between the movements of different body points in space has already been documented [22–26], little is known about the reliability of each method for the assessment of postural steadiness. Furthermore, different variables measuring this steadiness have been used in the literature. The most common are the velocity Vof the tracked point (COP or body marker), and the root mean square (RMS) of all positions, which is related to the displacement amplitude. V seems to yield a more accurate measurement of the visual contribution to posture [20,27,28], but the RMS and other amplitude-related variables are still widely used in the recent literature [4,7,29]. All variables measuring the visual contribution to postural steadiness are plagued with a high intra- and inter-subject variability (with standard deviations ranging typically between 25 and 50% of the mean values [30,31]). Therefore, we first aim at comparing the sway velocity and root mean square of positions (RMS) of the COP in their precision to quantify the visual contribution to postural steadiness. Beside the disparities in the methods and measurement variables, a common point in most studies is to use a Romberg quotient (or ‘Romberg’), i.e. the ratio of the variable values obtained in the condition ‘eyes closed’ and ‘eyes open’. This quotient is usually larger than one and quantifies the visual contribution to postural stabilization. Although parametric tests are generally applied to the Romberg, there exists, to our knowledge, no published assessment of the normality of the corresponding distributions. In a second step, we introduce a new operator, called the stabilization ratio (SR), and demonstrate that SR increases the normality and accuracy of the measurement of the visual contribution to postural steadiness, as compared to the Romberg. In addition, this paper explores the following corollary questions:
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(1) a foam plate placed under the subjects’ feet is known to reduce the relative contribution of somatosensory information to posture [30], and increase the precision of the measurement of the visual component. What is the corresponding gain of reliability? (2) postural measurements are usually performed in binocular vision, whereas visual pathologies often differ in their advance between the two eyes. Due to the decrease in the visual field, the visual contribution to posture is reduced in monocular, as compared to binocular vision. What is the corresponding change in reliability of the postural measurements? (3) the Romberg quotient is larger in the antero-posterior direction, than in the lateral direction [16]. Is there an associated change in the reliability of the measurement of the visual contribution to posture? Finally, we illustrate how the use of SR also improves the analysis of other inputs to postural steadiness, such as the somatosensory contribution.
2. Methods 2.1. Subjects Studying the consequences of visual deficits on postural stability often requests to include patients aged 40 and above (for instance in glaucoma, presbyopia, diabetic retinopathy, AMD . . .). Therefore, we chose a mature normal population, composed of 21 subjects aged 42–61, who gave their informed consent to participate. They had no history of vestibular or visual pathology, dizziness or imbalance, neurological or orthopedic problems. Our protocol was approved by the Research and Ethics committee of NUH (Ref. 99/016). 2.2. Apparatus The trajectory of the centre of foot pressure (COP) was recorded on a force platform with four piezoelectric transducers. This force plate (Kistler 9281B) was level with the surrounding floor. 2.3. Visual environment The experiment took place in a normally lit room. We used a short viewing distance and a large visual field, which have been proved to enhance the visual contribution to postural stability [32,33]. The subject stood 0.5 m from a three-panel textured board covering 1808 (horizontal) by 908 (vertical) visual angle. The central panel was frontoparallel, with a fixation mark at the centre. The two side panels were rotated by 1158 around a vertical axis, relative to the frontoparallel plane, toward the subject.
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2.4. Procedure The subjects stood barefoot as still as possible, with arms at the side, feet separated by an angle of 308 and heels placed 5 cm apart. Each session lasted 30 s (10 s of stabilizing time, followed by 20 s recording). In condition ‘eyes open’, the subjects were instructed to look at the central fixation target. The subjects stood directly on the force-plate, or on a slab of rubber foam (6 cm thick, density 40 g/dm3) covered with a thin wooden plate. Each subject participated in five sessions for each condition ‘eyes open’ (EO), ‘eyes closed’ (EC), ‘left eye open’ and ‘right eye open’, with or without foam (FOAM and NO FOAM conditions), making a total of 40 sessions per subject. The order of conditions was randomized. 2.5. Data analysis: posturography The COP signal was acquired at a frequency of 100 Hz, and convoluted with a Hamming filter of width nine. Hence, the signal was low-pass filtered with a cut-off frequency of 15 Hz. From the COP trajectory, we computed the velocities as follows. If Xi and Yi are the COP coordinates at sample i, in the lateral (X) and antero-posterior (Y) directions, we consider the inter-sample monodirectional displacements: DXi ¼ jXi Xi1 j and
DYi ¼ jYi Yi1 j
and the inter-sample total displacement, as projected on a X– Y plane qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Di ¼ ðDX 2i þ DY 2i Þ: DXi, DYi and Di are averaged over a session, and divided by the sample duration, to estimate the mean velocities along the X and Y directions (VX and VY), and the mean global velocity (V). V is equal to the total sway path divided by the recording duration. The root mean squares (RMS) of positions in the X and Y directions are: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðXi Xm Þ2 RMSX ¼ N sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðYi Ym Þ2 RMSY ¼ N where Xm and Ym are the means of the Xi and Yi over the N samples of the session. The global RMS is equal to: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ½ðXi Xm Þ2 þ ðYi Ym Þ2 RMS ¼ N The point of coordinates (Xm, Ym) is the arithmetic mean of all positions. The RMS gives an estimate of the amplitude of
the sway trajectory about this point. We call VX, VY, V, RMSX, RMSY, and RMS ‘sway measurement variables’. A classical computation of the visual contribution to posture is the Romberg quotient (or ‘Romberg’), x(EC)/ x(EO), where x is any of the above variables measured in conditions EC and EO. For instance the Romberg quotient for the velocity is V(EC)/V(EO). In the results below, V and RMS are correlated with their respective variabilities. This led us to consider the logarithmic transform of these variables, which has a decreasing derivative, causing a relative dilatation of range intervals for small, as compared to large x values. Such a logarithmic ‘variance stabilizing’ transformation has been used by others in postural measurements [13]. Here, we use log (1 + x) where x is one of the above variables (in mm/s for the velocity, and in millimetre for the RMS), so as to avoid getting negative logarithms. Within our data analysis, the choice of the additive constant 1 in the logarithm had a negligible influence on the results, so long as this constant remained below the order of magnitude of x (this order of magnitude is 10 mm/s for V, and 10 mm for RMS). The Romberg quotient quantifies the excess of velocity with eyes closed, in reference to the velocity with eyes open. Studying the visual contribution to posture, we chose the velocity with eyes closed as a reference, because it should be similar in normal subjects, and in ophthalmic patients who suffer only from visual problems. Hence, we quantified the visual contribution to postural stability by SR ¼ 1
log ðxðEOÞ þ 1Þ log ðxðECÞ þ 1Þ
A Romberg of 1.2 indicates that the sway is increased by 20% if vision is suppressed, whereas a SR of 20% means that the visual input reduces the logarithm of the sway (in V or RMS) by 20%. 2.6. Data analysis: statistics The Shapiro–Wilk test is used as a criterion of normality, because of its good power properties as compared to a wide range of alternative tests [34]. This test indicates the probability that a distribution differs from a normal distribution. Because of our small sample sizes, we used non-parametric tests (Wilcoxon test for paired samples, Spearman correlation). These tests gave results that were similar to those obtained through the corresponding parametric tests (Student t-test and Pearson correlation). For the handling of confidence intervals of the SR, we used the parametric approach, because of the good scores reached by the SR for the Shapiro–Wilk test. Except if otherwise stated, the significance threshold is p < 0.05. Our goal is to determine which variable (Romberg or SR, calculated from V or RMS) is optimal to measure the visual contribution to postural steadiness, so as to position the
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performance of a patient (or group of patients) relative to our control (normal) group, with a low rate of false alarms. Given our recruitment criteria above, we assume that no subject in our control group presents an abnormality in the visual contribution to posture (our results support this hypothesis, see below). Therefore, we look for the variable that presents the least intra-subject and inter-subject variability in our control group, i.e. the lowest coefficient of variation, equal to the ratio CV = s/M (s: standard deviation, M: mean). The null hypothesis (no contribution of vision to postural steadiness) corresponds to Romberg = 1, or to SR = 0. For sake of simplicity, we replace the Romberg by [Romberg-1], which has the same reference value as SR. Assuming normal distributions, if N is the sample number, the Student t-value for establishing the probability of the null hypothesis is equal to:
99
Fig. 1. Histograms of the Romberg quotient and SR for V and RMS in the FOAM and NO FOAM conditions, in binocular vision (N = 21 subjects). Ordinates: number of subjects.
pffiffiffiffi pffiffiffiffi M N t¼ N ¼ s CV
significance in 13 of 16 cases. Hence, this transform led to a better stability in the variability of the sway measurement.
Therefore, for a given N, the best variable for measuring the visual contribution to posture presents the lowest CV, i.e. the highest t-value associated to the lowest probability for the null hypothesis. When the sample number is small, the calculus of the mean and standard deviation is very sensitive to outliers. Therefore, we also define a ‘non parametric’ coefficient of variation equal to CVnp = (Q75 Q25)/Med, where Med is the median, Q75 and Q25 are the 75% and 25% quartiles.
3.2. Normality of the Romberg and SR
3. Results
From the mean V and RMS over five sessions, we obtained a Romberg and SR for each subject and condition (FOAM/NO FOAM). In binocular vision, the corresponding histograms are plotted in Fig. 1. In each condition, the distributions did not differ significantly from normality, except for the Romberg of RMS, in monocular vision only (Table 2). SR never differed significantly from normality. In 10 of the 12 cases presented in Table 2, the Shapiro–Wilk test value was smaller for the Romberg, than for SR, which indicates that the distributions were closer to normality in the latter case.
3.1. Correlation of V and RMS with their intra-subject variability
3.3. Inter-subject variability
Because of the small number of sessions (five), we calculated the range (difference between the maximum and minimum values) of V and RMS for each subject, rather than their standard deviation. The means of V and RMS were significantly correlated with their respective ranges in all cases but one (Table 1). For the log (x + 1) transform of these variables, the correlations were weaker and lacked
In the NO FOAM condition, [Romberg-1] and SR had lower variation coefficients when they were calculated from V, than from RMS (Table 3). Therefore, V leads to a better accuracy than RMS in measuring the visual contribution to posture. In the FOAM condition, this is also found in monocular vision, but not in binocular vision, where the two variables were similar in this regard.
Table 1 Spearman coefficient of correlation between the mean M of different variables, and their range (difference between the maximum and minimum values) Variables
V and range RMS and range log (V + 1) and range log (RMS + 1) and range
NO FOAM
FOAM
EC
‘Left eye open’
‘Right eye open’
0.60 0.75 0.27 0.25
0.69 0.45 0.23 0.07
0.66 0.61 0.17 0.32
EO
EC
‘Left eye open’
‘Right eye open’
EO
0.62 0.35 0.28 0.09
0.53 0.92 0.14 0.78
0.66 0.80 0.27 0.53
0.73 0.47 0.17 0.12
0.58 0.83 0.15 0.48
M and the range are calculated over five sessions for each subject. The correlation is established for 21 subjects. Significant coefficients (at p < 0.05) are in bold
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100 Table 2 Results of the Shapiro–Wilk test for normality Shapiro–Wilk test W(p)
No foam
EO
V RMS V RMS V RMS
‘Left eye open’ ‘Right eye open’
Foam
Romberg
SR
0.938 0.924 0.857 0.945 0.898 0.925
0.993 0.978 0.944 0.975 0.945 0.972
(0.195) (0.104) (0.006) (0.277) (0.032) (0.112)
(0.999) (0.889) (0.266) (0.845) (0.270) (0.774)
Romberg
SR
0.968 0.952 0.957 0.954 0.963 0.941
0.978 0.971 0.958 0.932 0.959 0.965
(0.691) (0.378) (0.459) (0.402) (0.585) (0.226)
(0.892) (0.753) (0.472) (0.152) (0.491) (0.624)
The value W of the test, and the associated probability that the distribution differs from normality are indicated for each variable (Romberg, SR) calculated from V or RMS, in the FOAM and NO FOAM conditions.
The coefficient of variation was systematically lower (1) for SR than for [Romberg-1], (2) for the FOAM condition than for the NO FOAM condition. Hence, the reduction of the proprioceptive feedback by the foam led to an enhancement of the reliability in measuring the visual contribution to posture. In all cases, the probability of the null hypothesis (no visual contribution to posture, Wilcoxon matched pair test) was lower than 1.E-4. A major factor of variability in the quantification of the visual contribution to posture is the large variability observed in the reference condition EC. Therefore, our measurement of this contribution should be as independent as possible from the sway magnitude in the EC condition. Table 4 indicates the positive correlation between the Romberg in RMS or V, with RMS(EC) or V(EC), respectively. This correlation was weaker for SR, and usually not significant. It follows that SR is more independent than the Romberg from the measurement of V or RMS at the reference level in condition EC. It is of clinical interest to estimate the type of abnormality that our measure can detect, either for a single patient, or for a group of patients. For the most sensitive measurement (SR(V) in the FOAM condition), we calculated the drop in
SR (in percent of the mean) which is considered as abnormal with a false alarm rate below 2.5%. In binocular vision, 95% of the population had a SR(V) ranging between 0.1 and 0.3 in the FOAM condition (M 1.96s, where M is the mean at 0.2, and s the standard deviation at 0.049). Hence, a patient will be declared as abnormal if SR is below 0.1, i.e. if his visual contribution to posture (SR) is reduced by more than 50% relative to the normal group. In the case of a homogeneous patient population, the mean SR(V) had a confidence interval of half-width 1.96s/HN. Assuming that s is similar for the normals and patients, the mean SR can be considered as abnormal if it is reduced by 10%, relative to the normal value (i.e. if it is below 0.18). Similarly, in monocular vision, we found that a patient will be considered as abnormal if SR is below 51 % of the normal mean. A group of patients will be considered as abnormal if the mean SR has dropped by more than 11% of its normal value. Supporting the hypothesis that our control group is homogeneous, Fig. 1 shows the absence of any single individual who would show a low SR(V) relative to the whole group in the FOAM condition, in binocular vision. Also, no subject had a SR(V) below the inferior limit of the
Table 3 Comparison of [Romberg-1] and SR for the measure of the visual contribution to posture No foam
Foam
V Romberg-1 (A) Mean ‘Left eye open’ 0.47 ‘Right eye open’ 0.46 EO 0.64 EOX 0.61 0.67 EOY (B) Coefficients of variation (%) ‘Left eye open’ 63.7 ‘Right eye open’ 70.0 EO 55.7 EOX 58.5 EOY 59.3
RMS
V
RMS
SR
Romberg-1
SR
Romberg-1
SR
Romberg-1
SR
0.13 0.12 0.16 0.18 0.18
0.42 0.40 0.53 0.54 0.54
0.13 0.13 0.16 0.18 0.18
0.76 0.79 0.96 0.93 0.99
0.17 0.18 0.20 0.22 0.23
0.67 0.66 0.97 0.93 1.07
0.18 0.18 0.24 0.26 0.27
45.1 52.1 39.5 39.4 44.5
75.0 80.2 61.7 78.5 54.6
62.8 66.4 47.8 58.7 46.2
34.0 31.9 32 36.5 33.2
28.4 23.6 24.4 26 24.4
45.0 40.0 34 34.4 42.6
34.3 32.2 23.3 24.5 27.9
(A): Mean [Romberg-1] and SR, as obtained by averaging the individual values of the 21 subjects in monocular vision (left or right eye open) or binocular vision (EO). EOX and EOY are the lateral and antero-posterior sway component (in V or RMS) in condition EO. (B): Corresponding coefficients of inter-subject variation.
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101
Table 4 Spearman correlation coefficients between V(EC) (or RMS(EC)) and the Romberg or SR obtained in different conditions Condition NO FOAM
FOAM
*
EO ‘Left eye open’ ‘Right eye open’ EO ‘Left eye open’ ‘Right eye open’
V(EC) and Romberg(V) *
0.59 0.55* 0.61* 0.09 0.01 0.29
V(EC) and SR(V)
RMS(EC) and Romberg(RMS)
RMS(EC) and SR(RMS)
0.42 0.39 0.50* 0.18 0.22 0.00
0.42 0.41 0.47* 0.18 0.33 0.34
0.28 0.32 0.36 0.07 0.12 0.16
Significant correlation.
95% probability interval. This limit was 0.1 for EO, 0.086 for ‘left eye open’ and ‘right eye open’. The latter two conditions did not differ significantly in their V, RMS, Romberg and SR (Wilcoxon matched paired test at Z < 1.41 and p > 0.16). Finally, we verified the absence of outliers in scatterplots relating the variables for the ‘left eye open’ and ‘right eye open’ conditions, suggesting a good homogeneity between the left and right eye populations, in terms of their visual contribution to postural steadiness. 3.4. Effect of the foam on the X & Y sway components For sake of simplicity, we analysed the X and Y sways only in binocular vision. The presence of foam increased VY, RMSX and RMSY in similar proportions (35–43% in EC, 15–18% in EO), but its main effect was on VX, which increases by 49% (EC) and 36% (EO). This particular increase of VX had the following consequence. Subjects swayed more in the Y, than in the X direction, in terms of V and RMS in the NO FOAM condition. On foam, this was maintained for RMS, but not for V(VY/VX was then reduced and not significantly different from one). Hence, the weakening of proprioceptive inputs through a foam support increased mainly the lateral velocity, but preserved the Y/X elongation of the RMS (Fig. 2). 3.5. Inter-subject variability of the X & Y sway components Fig. 3 plots the different variables (RMSX, RMSY, VX, VY), and their log(x + 1) transform, in all conditions. The standard
Fig. 2. The XY anisotropy in COP sway for different conditions indicated in abscissae. Circles: ratio of VY (antero-posterior) by VX (lateral). Squares: ratio of RMSY by RMSX.
deviations are more constant across conditions for the log transform (right panel) than for the raw values (left panel). Second, the log transform enhances the parallelism between the lines. Hence, we find that vision tends to decrease equally the logarithm of V and RMS in X and Y, which supports the relevance of the log transform for sway variables. Using the global variables (V or RMS) rather than their respective X and Y components led to similar measurement of the visual contribution to posture, in terms of magnitude and coefficient of variation (Table 3). Nevertheless, the use of the global variables seems advisable, because their coefficients of variation tend to be similar or lower than that of the X or Y measurements. All Romberg quotients exceeded one, except RMSY for one subject in the NO FOAM condition. However, we do not consider this as a sign of a visual destabilization of posture, because all other three measurements of the sway (RMSX, VX, VY) had a Romberg above one, as well as all variables measured in the FOAM condition. Rather, we attribute this observation to the noise in RMS measurements. 3.6. Measurement of the increase of the somatosensory contribution in the NO FOAM, versus FOAM, condition So far, we focused on the visual contribution to postural steadiness. It is possible to apply our approach to the comparison between other pairs of experimental conditions. For instance we can measure the increase of the somatosensory contribution, from the FOAM to the NO FOAM condition, by using the Romberg and SR operators (see methods), where we replace x(EC) by x(FOAM) and x(EO) by x(NO FOAM), respectively. Again the coefficients of variation were lower when using V, rather than RMS, (in global values, or for their directional components), and for SR than for [Romberg-1] (Table 5), in conditions EC and EO. Also, the reliability of the measurement of the sway decay due to the somatosensory change is higher in the absence of visual input, than with vision. SR(VX) is above SR(VY), SR(RMSX) and SR(RMSY) in Table 5, again suggesting that the main effect of the somatosensory input induced by our firm support (as compared to the foam support) is to reduce VX. Again it seems optimal to use the global measurement SR(V), which has a coefficient of variation lower or similar to that of VX and VY.
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Fig. 3. X (lateral) and Y (antero-posterior) components of the sway velocity and RMS, averaged over 21 subjects, for different conditions indicated in abscissae. Left graph: raw values. Right graph: logarithmic values (for instance log (VX + 1)).
Table 5 Evaluation of the contribution of the somatosensory input in the NO FOAM condition, in reference to the FOAM condition, with eyes open or eyes closed Eyes open
Eyes closed
V Romberg-1 (A) Mean Global 0.31 X 0.46 Y 0.21 (B) Coefficient of variation (%) Global 61.1 X 56.0 Y 105.2
RMS SR 0.10 0.15 0.07 51.1 41.8 104.6
Romberg-1 0.54 0.53 0.59 94.7 100.0 126.3
V SR
Romberg-1 .07 .08 .07
91.6 104.9 132.8
0.59 0.78 0.45 45.7 53.9 42.3
RMS SR 0.14 0.19 0.13 38.2 42.7 37.5
Romberg-1 0.19 0.19 0.20 48.9 61.5 48.6
SR 0.15 0.16 0.18 39.1 51.5 39.8
(A): [Romberg-1] and SR are calculated for each subject and averaged over 21 subjects. (B): Corresponding coefficients of inter-subject variation. Each line corresponds to the global variable (V or RMS) or to its X or Y component.
4. Summary of the results (1) V leads generally to more reliable measurement of the visual contribution to postural steadiness than RMS. (2) Replacing the Romberg quotient by SR systematically enhanced the reliability of the measure of the visual contribution to posture, within and across individuals. (3) In monocular or binocular vision, a patient group will be declared as abnormal at p < 0.025, if the mean SR in the FOAM condition drops by more than 10%, relative to the mean value found for our normal population. This threshold is at 50% for declaring a single patient as abnormal. (4) Subjects swayed generally more in the Y (anteroposterior), than in the X (lateral) direction in all conditions (except on foam, where VX and VY are similar). This anisotropy differed in the FOAM and NO FOAM conditions. However, the reliability of the
measurement of the visual contribution to posture was similar for the X, Y and global measurements.
5. Discussion 5.1. The use of different variables for characterizing postural steadiness Classical static posturography uses global variables such as V, RMS or the sway area to quantify the sway magnitude. More complex analyses have been proposed, such as (1) the calculus of structural variables describing the fine structure of the stabilogram, for instance the time interval between one peak and another [35] (2) the extraction of variables from the Fourier transform of the stabilogram, for instance the power density at different temporal frequencies [14,22] (3) stochastic modeling and diffusion analysis [36], as well
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as fractal dynamics [37–39]. Most often, the complex variables show significant differences between EC and EO conditions, but it is not clear yet whether they are more reliable than classical global variables for the quantitative comparison of subject populations or conditions [35]. Koles and Castelein [25] find that the COP power spectrum component due to the ankle leads to an inter-subject coefficient of variation of 44% for the difference between EC and EO (firm support), which is close to what we obtain for SR(V) in the NO FOAM condition. The high interest of the complex variables listed above is to provide insights into the mechanisms of the sway generation, and their associated neural structures [14]. Among global variables, V and RMS have been compared, in terms of their reliability to distinguish between subject groups and conditions. Sway magnetometry [40] showed a smaller intra-individual coefficient of variation for [Romberg-1](V) than for [Romberg-1](RMS). This was confirmed at an interindividual level [20] with a coefficient of variation of [Romberg-1] of about 50% for V, and more than 100% for other amplitude related variables, in full agreement with our NO FOAM results. For the COP sway, V has been reported as the most discriminating variable for age-related differences [28]. V is systematically higher in EC than in EO, which is not the case for other variables (maximum antero-posterior or lateral displacements, RMS) in half of the subjects studied by Collins and De Luca [36]. Sakaguchi et al [41] find a much larger dispersion of the Romberg quotient for RMS than for V. Latash et al [42] find that Romberg quotients are larger for V (1.19) than for RMS (1), or for the sway area (1.02), but high Romberg quotients have been reported elsewhere for the sway area (2.19 in [43]; 1.55 in [29]). In one study only [44], a significant difference between conditions EC and EO is found for the COP RMS, but not for V. However, this is obtained for a heel-to-toe tandem position, rather than with the normal stance used in all other studies, i.e. feet positioned side by side. Hence, our findings agree with a majority of studies to declare V as more reliable than amplitude-related variables (RMS, sway area . . .) for discriminating between conditions and subject populations. 5.2. The use of different techniques for measuring postural steadiness Among the rare studies comparing the reliability of different techniques of sway recording, Fitzgerald et al [20] measured the hip motion through magnetometry, and the COP motion on a forceplate. Magnetometry yielded a variation coefficient for the Romberg quotient of about 54%, comparable to ours (Romberg(V) in the NO FOAM condition). However, with the forceplate, the reliability of the Romberg quotient was poor (variation coefficient above 200%), and the Romberg(V) did not differ significantly from one, in contrast with what we find, and what is found by others (Table 6). This discrepancy might be explained by the
103
sampling frequency of 5 Hz used by Fitzgerald et al, which is low in comparison of values above 20 Hz in the other studies. The relationship between the COP movement, and the movement of other body segments is complex [24–26], and we can wonder whether the optimal properties of SR(V) would also hold for the analysis of the motion of other body segments. In a control experiment, we analysed simultaneously the head and COP sways in eight subjects, using a Vicon system with three cameras capturing the 3D trajectory of an infrared marker stuck on the subject’s forehead. In agreement with others [41,45,46] [49], we found high similarities between the head and COP trajectories, with significant correlations between the head and COP variables (V and RMS, correlation coefficients above 0.69). The coefficients of variation for the measure of the visual contribution to posture (Romberg or SR) were also similar for the head and COP. The main differences between head and COP trajectories were a slight shift in their frequency domains (toward low temporal frequencies for the head), and different Y/X distortions in V. Overall, we retrieved the main conclusions above for the head sway, namely that V is a more reliable measurement than RMS, and that SR leads to a lower coefficient of variation than [Romberg-1], for measuring the visual contribution to posture. 5.3. Does visual destabilization exist in normal subjects? One of our 21 subjects presents a slight visual destabilization effect (Romberg < 1 or SR < 0) for the sway RMS. This effect is not confirmed for V, and could be due to the higher variability found for RMS, as compared to V. Similarly, using the RMS of the COG sway angle, Turano et al [7] find a Romberg quotient below one in three of 20 normal subjects. In Lacour et al [29], 46% of normal subjects have equal or larger sway areas in condition EO, than in condition EC. Their histogram of Romberg quotients shows two categories of subjects, with weak or strong visual contribution to posture, respectively. This result is confirmed in another study [36] using the sway amplitude. With equivalent experimental conditions, we do not observe such bimodal distributions in our Romberg (RMS) histograms (Fig. 1). Such discrepancy is important to resolve, because the presence of a significant proportion of normal subjects showing little or no visual contribution to postural steadiness would impair the assessment of the effect of visual deficits on posture. So far, there exists no explanation to these contradictory results. However, the use of the sway amplitude, rather than V, seems to favor the observation of ‘non visual’ subjects because (1) the two studies showing ‘non visual’ subjects use amplitude related variables, whereas the studies using V (listed in Table 6) do not mention them, and (2) in magnetometry, the Romberg quotient is below one for 13–23% of the sway-area measurements, but only for 1% of the V measurements [27,40], and (3) Collins and De Luca [36] indicate that the
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Table 6 Comparison between different studies measuring the COP velocity in subjects standing on a firm or foam support
Firm Support Baloh et al (1994) [12] Baloh et al (1994) [12] Straube et al (1988) [30] Straube et al (1988) [30] Hufschmidt et al (1980) [31] Paulus & Zihl (1989) [16] Teasdale et al (1991) [47] Teasdale et al (1991) [47] Baratto et al (2002) [35] Current expt NO FOAM Coefficient of variationa Foam support Straube et al (1988) [30] Straube et al (1988) [30] Straube et al (1990) [6] Teasdale et al (1991) [47] Teasdale et al (1991) [47] Guerraz et al (2000) [45] Guerraz et al (2000) [9] Current expt FOAM Coefficient of variationa Support F(1,14), (p) Age F(1,14), (p)
Age range (years)
Velocity eyes open (mm/s)
Velocity eyes closed (mm/s)
26.6 6 79.6 4 20–40 40–60
9.16 13.68 8.17 10.5 10.67 6.24 10.0 14.3 9.8 8.29
15.13 24.22 14 16.17 16.83 12.44 17.2 28.1 12.7 13.47
1.65 1.77 1.71 1.54 1.58 1.99 1.72 1.97 1.30 1.62
0.17 0.17 0.18 0.14 0.15 0.24 0.17 0.19 0.09 0.17
24.3%
30.3%
29.6%
22.3%
11.67 20.5 11.17 12.1 17.5 5.42 10.06 10.81
45 71.67 43.33 20.9 39.5 10.1 25.62 20.77
3.86 3.50 3.88 1.73 2.26 1.86 2.55 1.92
0.34 0.28 0.34 0.17 0.21 0.23 0.27 0.20
37.4%
56.1%
b
20–50 21–22 70–80 47 17 42–61
20–40 40–60 27 3.4 21–22 70–80 mean 35 mean 40.5 42-61
Romberg
53.9% 10.70 (0.005) 0.45 (0.513)
SR
25.2% 12.96 (0.003) 2.09 (0.169)
In the nine references indicated in the left column, the total sway velocity V is estimated from the average lateral and antero-posterior velocities VL and VA by: V ¼ sqrtðV 2L þ V 2A Þ. The Romberg and SR are estimated from the average values of V. In our study, V, the Romberg and SR are calculated for each subject and then averaged. The ‘inter-study’ coefficients of variation are indicated for each support. The two bottom lines give the results of an ANOVA on the Romberg and SR given in the table, with factors ‘age’ (below 40 or above 40), and ‘support’ (foam or firm). The interactions between the two factors are not given, for they are not significant. a The coefficients of variation are calculated for [Romberg-1] and for SR. b The subjects in this study have been put in the category ‘below 40’, but we verified that this choice hardly affected the results in the ANOVA, and did not change the conclusions given in the text.
weak visual stabilizers for sway amplitude still have larger V(EC) than V(EO). 5.4. Using SR, rather than the Romberg quotient All our results support the use of SR, as a more reliable quantification of the visual contribution to posture than the Romberg quotient, within or among subjects. The reason is that the variability of Vor RMS increases with the magnitude of these variables (as found previously [12,30,40]), which effect is strongly weakened by the application of a log transform. Let us confront our results to nine anterior studies reporting the average COP velocity for normal subjects of different age ranges (Table 6) in conditions EC and EO, on firm and foam supports. From these velocities, we estimate the Romberg quotients and SR. For firm or foam supports, the ‘across study’ coefficient of variation is lower for SR than for [Romberg-1]. Hence, grouping all these studies yields a more significant assessment of the visual contribution to posture through SR, than through the Romberg
quotient. A similar result is obtained when using the nonparametric coefficient of variation (Section 2). This does not mean that SR confounds the effect of conditions more than the Romberg quotient. Grouping the subject populations by age into a ‘young’ category (below 40), and an ‘old’ category (above 40), we performed an ANOVA on [Romberg-1] and SR, using two factors (‘age’ and ‘support’). The bottom line of Table 6 indicates that SR is still more reliable than the Romberg quotient at finding differences in the contribution of vision, between the foam and firm support, and between age groups (although significance is not reached in this latter case). Table 6 also indicates that our results on the COP measurements are in good quantitative agreement with many previous studies. Interestingly, other methods of postural recording yield SR values that are comparable to those listed in Table 6. With magnetometry [27], SR(V) is about 0.15– 0.16 on a firm support, although hip velocity is about two or three times slower than COP velocity. The recording of head movements on a foam support leads to a SR(V) of about 0.30 [45], which is well in the range of the values of Table 6.
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5.5. Comparing monocular vs binocular vision
References
As reported earlier [32,45], the sway magnitude is smaller in binocular, than in monocular vision. This is compatible with the fact that the postural steadiness decreases as the visual stimulation area increases [1]. However, we find that the difference between monocular and binocular SR(V) is small in terms of magnitude (on foam, SR(V) is 0.20 in binocular and 0.17–0.18 in monocular vision) and reliability (the coefficients of variation are about 25%). Hence, we may plan the measurement of losses in postural stability with a similar accuracy in monocular and binocular vision.
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5.6. Assessing the role of different sensory inputs As described in the results, our approach applies to the evaluation of other sensory contribution to posture. For instance, neurological patients can be compared to normal subjects by using the SR quantifying the change of the somatosensory contribution between two conditions (firm and foam support for instance). More generally, the question arises whether the use of relative changes in the sway velocity between two conditions (EC/EO or FOAM/NO FOAM) is necessary to reduce the inter-subject variability in posture studies. In the literature on the effect of pathologies on postural steadiness, both approaches can be found. Some authors study the differences between the sway magnitude of patients and controls [4,29,42], whereas others use the intercondition change in sway magnitude [8,9]. We propose that the use of the logarithmic transform of the sway variables would lead to an improved accuracy in the inter-condition or inter-population comparison of the postural measurements.
6. Conclusion In agreement with many previous studies, our results support the use of the COP velocity as a reliable and probably optimal indicator of the sway magnitude in the standing subject. As compared to the Romberg quotient, the SR is a more stable indicator of the visual contribution to posture, for the statistical comparison of different visual or somatosensory conditions, or of subject populations. By using SR(V), it is possible to plan the evaluation of the postural consequences of visual deficits in monocular and binocular vision.
Acknowledgments This study was supported by the National Medical Research Council of Singapore, through grants NMRC/ 0528/2001 and SERI/PG/99-01/000016, by the National University of Singapore (PhD Grant for Noor Shabana), and by the French Embassy in Singapore.
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