Static postural control disturbances among the different multiple sclerosis phenotypes: A Neurocom Balance Manager® evaluation study

Multiple Sclerosis and Related Disorders 26 (2018) 46–51

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Static postural control disturbances among the different multiple sclerosis phenotypes: A Neurocom Balance Manager® evaluation study V. Ciminoa,1, CG. Chisarib,1, G. Racitia, A. Pappalardoc, M. Zappiab, F. Pattib,

T

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Centro Neurolesi “Bonino Pulejo”, IRCSS, Messina, Italy Department G.F. Ingrassia, section of Neuroscience, University of Catania, Via S. Sofia n°78, Catania 95123, Italy c Department of Rehabilitation, S.Marta & S.Venera Hospital, Acireale, Catania, Italy a

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Multiple sclerosis Balance board evaluation Neurocom Balance Manager

Background: The computerized stabilometric platform can be used and privileged over clinical scales, as selfadministered questionnaires to asses postural control and balance evaluation in Multiple sclerosis (MS). Aim of our study was to evaluate static postural control assessed by Neurocom Balance Manager® through the modified Clinical Test of Sensory Interaction on Balance (mCTSIB) in relapsing-remitting MS (RRMS), progressive MS (PMS) and CIS, compared to healthy controls (HC). Methods: We screened MS patients consecutively referring to our MS Center at University of Catania, during July 2013–June 2014 diagnosed as CIS, RRMS and PMS. All MS patients underwent clinical and neurological evaluations and a complete postural exam by Neurocom Balance Manager® in order to evaluate Center of Pressure (COP), through mCTSIB. We evaluated the following parameters: Total Path Length-open eyes (TPL-OE), Total Path Length-closed eyes (TPL-CE), Sway Area-open eyes (SA-OE), Sway Area-closed eyes (SA-CE), Mean sway velocity-open eyes (MSV-OE), Mean sway velocity-closed eyes (MSV-CE). Additionally, patients were tested by Berg balance scale (BBS) for balance and Barthel Index (BI) for disability outcomes. Results: Out of 170 MS patients assessed for eligibility, 163 met the inclusion/exclusion criteria and were finally enrolled. All balance parameters were found more impaired in MS group compared to controls and CIS. Moreover, no differences in terms of balance assessment were found between HC and CIS. The correlation analysis showed that BBS was strongly associated to SA-OE, SA-CE, TPL-OE and MSV-OE. We also found a correlation between BI and SA-CE. Conclusion: Our study revealed significant differences among HCs, CIS and MS. MS, especially PMS, exhibit the worst balance performances especially in EC trials. The higher correlation between balance parameters, especially sway area, and BBS score confirmed the reliability and sensibility of mCTSIB assessment in evaluating static postural control in MS patients.

1. Introduction Balance deficits seem to represent a very frequent impairment in Multiple sclerosis (MS) (Martin et al., 2006) poorly investigated and difficult to detect in routine clinical settings. According to several studies reporting on balance in MS patients, the primary mechanisms underlying the postural control deficits are slowed somatosensory conduction and impaired central integration (Cameron et al., 2008). Although, in MS many features may impair postural control: vestibularcerebellar lesions (Prosperini et al., 2011), reduced motor control, abnormal sequencing of muscle contraction (De Souza and Ashburn, 1996), strength and limb-loading asymmetries (Chung et al.,

2008), spasticity (Sosnoff et al., 2011b) and sensory impairment (Cattaneoand Jonsdottir, 2009). Therefore, a single measure could be inadequate to evaluate the overall function of postural control (Horak, 2009). For this reason and given the increasing interest in the recognition of balance impairment at the early stages of the disease, several computerized stabilometric platform systems have been developed in order to measure ground reaction forces generated by a body standing on or moving across them, and to quantify biomechanical parameters of human balance control (Prosperini and Pozzilli, 2013). Thanks to the possibility to provide automatic, time-effective and standardized assessments of balance, static posturography has become a

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Corresponding author. E-mail address: [email protected] (F. Patti). 1 Equally contributed to the manuscript. https://doi.org/10.1016/j.msard.2018.08.024 Received 8 January 2018; Received in revised form 3 August 2018; Accepted 23 August 2018 2211-0348/ © 2018 Elsevier B.V. All rights reserved.

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76–140 cm; medium 141–165 cm; tall 166–203 cm. For mCTSIB subjects were asked to stand as still as possible with open eyes (OE) and then with closed eyes (CE) with a duration of 10 s for each session; this sequence was performed 3 times. Three trials for each condition were acquired, allowing a suitable rest time between them. For each condition (OE and CE) the best trials (with minimum sway) were chosen for analysis. Neurocom Balance Manager® measures the area of a 95% confidence ellipse of the COP excursion in the anteroposterior (AP) and mediolateral (ML) directions and the average velocity of the COP (Ben Achour Lebib et al., 2006). COP is defined as the point location of the vertical ground reaction force vector. It represents a weighted average of all the pressures over the surface of the area in contact with the ground. The location of the COP under each foot is a direct result of the neural control of the ankle muscles. Increasing plantar flexor activity moves the COP anteriorly, increasing invertor activity moves it laterally. In the literature there is a major misuse of the COP when it is referred to as ‘sway’ (Cofre Lizama et al., 2015; Santos et al., 2008). Force plates generally compute COP location under the feet, assessing balance by measuring COP sway in standing position; the NeuroCom system estimates the center of gravity (COG) displacement, the point of action of the total gravitational force. In upright stance, the COG is positioned at a height corresponding to 55% of the stature, in front of the medial malleolus at a distance equal to 14% of foot length. Platform is able to measure the changes of the angle formed by two lines: the first extending vertically from the COP and the second from the COP through the COG. In static tests, the COG sway outlines the ability to process sensory systems input to maintain balance control; in dynamic testing condition, the limits of stability (LOS) defines the ability to maintain the COG within the base of support during active movements towards predefined targets requiring a maximal leaning position, allowing the assessment of motor component of postural control: poor balance causes COG sway increase in standing still position and may impair the ability to move the COG towards the LOS (Horak, 2009). Dedicated software integrates the force signals, as so a set of outcome measures taken from the COP data were:

reliable tool not only to objectively evaluate balance control in MS but also to carefully select those patients for rehabilitation interventions that may improve balance-related activities and reduce the risk of accidental falls (Prosperini et al., 2013; Zackowski, 2016). In particular, Center of Pressure (COP) related balance measures obtained from laboratory-graded force platforms were found to be useful complements to the clinical scales for the evaluation of stability and risk of falling (Fanchamps et al., 2012; Kalron et al., 2011b). Furthermore, several stabilometric studies have demonstrated that MS patients have a postural sway control significantly poorer than healthy subjects, showing larger oscillations in the frontal and sagittal planes (Daley and Swank, 1981; Prosperini et al., 2013; Prosperini et al., 2011), showing that forceplates systems are more sensitive than clinical scales, self-administered questionnaires and functional assessment scoring tests in detecting balance abnormalities in MS patients (Melillo et al., 2017; Prosperini and Pozzilli, 2013). Moreover, posturographic data on various balance tasks were found to correlate with disability, measured by Expanded Disability Status Scale (EDSS) (Cao et al., 2013; Corporaal et al., 2013), suggesting that proprioceptive transmission deficits are more evident in higher disabled patients. Nevertheless, only few studies assessed postural control declines throughout the disease process and disability progression, rather focusing on rehabilitative intervention or fall prevention (Boes et al., 2012; Fjeldstad et al., 2011; McLoughlin et al., 2015). On the other hand, a proper identification of different postural imbalance among several MS phenotypes and the longitudinal monitoring of potential worsening, may allow clinicians to design the appropriate intervention strategies, including the use of different pharmacological therapies and/or a tailored rehabilitation program. The aim of our study was to evaluate static postural control assessed by Neurocom Balance Manager® through the modified Clinical Test of Sensory Interaction on Balance (mCTSIB) in relapsing-remitting (RRMS), progressive MS (PMS) and clinically isolated syndromes (CIS), compared to healthy controls (HC). 2. Materials and methods

1. Ellipse sway area (mm2): defined as the 95% confidence ellipse for the mean of the COP anterior, posterior, medial and lateral coordinates. 2. COP path length (mm): defined as the absolute length of the COP path movements over the testing period. 3. Sway rate (mm/s): defined as the mean speed of movement of the COP during the testing period.

2.1. Population study Participants were identified from a waiting list of MS patients referring to our MS Center at University of Catania, during July 2013–June 2014. The inclusion criteria were: (a) MS, RRMS and PMS, or CIS diagnosis according to McDonald diagnostic criteria (McDonald et al., 2001; Miller et al., 2005) (b) patients in the remission phase of the disease, (c) ability to walk independently with or without the use of assistive devices. The exclusion criteria were: (a) cognitive or linguistic problems with understanding instructions or filling in self-administered outcome measures; (b) ongoing exacerbation of MS; (c) other neurological disorders interfering with either intervention or testing procedures (e.g. dizziness, diplopia and/or blurred vision, vestibular problems, inability to stand upright with enlarged base of support for at least 30 s) and/or bone/joint pathology with significant functional limitations of movements and (d) EDSS score >6. All MS patients underwent clinical and neurological evaluations. The study followed the Helsinki Declaration of 1964, amended by the 55th General Assembly on October 2008 and was approved by local Hospital Ethic Committee.

We evaluated the following parameters: Total Path Length-open eyes (TPL-OE), Total Path Length-closed eyes (TPL-CE), Sway Areaopen eyes (SA-OE), Sway Area-closed eyes (SA-CE), Mean Sway Velocity-open eyes (MSV-OE), Mean Sway Velocity-closed eyes (MSVCE). Additionally, patients were tested by Berg balance scale (BBS) for balance and Barthel index (BI) for disability outcomes. BBS assesses static balance; it is a 14-items scale that was designed to measure balance in the elderly population in a clinical setting (Berg et al., 1992). It is also been used to asses postural balance in people with a history of stroke and traumatic brain injury (Berg et al., 1995). The BBS examines individuals’ ability to sit, stand, reach, maintain single-leg stance and turn (Berg et al., 1995). It rates performance on a scale from 0 (cannot perform task) to 4 (normal performance on task). The total possible score ranges from 0 to 56; a score of 45 or below indicates an increased risk of falling (Riddle and Stratford, 1999). The BBS has been also reported as a valid and reliable tool for MS (Cattaneo et al., 2006). The BI is a widely used activity daily leaving scale, with scores ranging from 0 to 100. The top score implies full functional independence, though not necessarily normal status (Mahoney and

2.2. Balance assessment systems All patients and HC underwent a complete postural examination using Neurocom Balance Manager®. Subjects wore disposable plastic shoe covers on bare feet to standardize inputs arising from the somatosensory system; the lateral calcaneus position on the platform was determined by a pre-marked line, according to the subject height: short 47

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MS, especially PMS, exhibited worse balance performances especially in EC tasks compared to other groups, as previously described (Cattaneo.et al., 2014; Chung et al., 2008; Denomme et al., 2014; Fling et al., 2014; Melillo et al., 2017; Porosinska et al., 2010). Complex neurophysiological mechanisms involving multiple systems have been suggested for explaining the different open and closed loop systems that the central nervous system (CNS) employs when controlling balance (Cattaneoand Jonsdottir, 2009; Prosperini and Pozzilli, 2013). In a recent paper, a strong relationship between poor postural control and reduced white matter integrity of the cortical proprioceptive tract was found in MS patients (Fling et al., 2014). Furthermore, our finding of worse balance performances in PMS could be explained by a more severe impairment of the discending/ ascending motor and sensitive corticospinal tracts in these patients respect to RRMS and CIS (Lassmann et al., 2012). Indeed, several studies demonstrated that spinal cord atrophy is correlated with neurological disability (Lin et al., 2003), and damage of this structure and of cerebellum is associated with reduced vibration sensation and balance deficit (Oh et al., 2013; Prosperini et al., 2014). Thus, we hypothesized that the higher burden of brain lesions and spinal cord atrophy of PMS may have the most impact on balance particularly showing worse posturographic indexes compared to the other groups with a dramatic impairment in all balance performances during CE tasks. This explanation is also supported by the correlation between spinal cord afferent conduction time and increased postural response latencies (Cameron et al., 2008). Lesions in the spinal cord may lead to a delay in the afferent proprioceptive information from the muscle spindles to the sensori-motor cortex with a subsequent poor balance corrective response. Moreover, our results are in line with previous reports confirming a positive relationship between posturographic data to level of disability (Boes et al., 2012; Cao et al., 2013; Kalron et al., 2016) and could be also explained by the fact that spasticity, cerebellar and brainstem impairments, more prominent in progressive forms, could more severely affect balance performance and postural control (Soyuer et al., 2006). In particular, muscle weakness and spasticity further compromise the ability to balance by affecting the sequencing and force of muscle contraction (Sosnoff et al., 2011a). In our study, although CIS patients showed overall worse balance performances compared to controls in CE tasks, no statistically significant differences were found in balance indexes between CIS and HC. Contrasting with this evidence, several studies investigating minimally impaired MS patients, showed that balance impairment could be

Barthel, 1965). 2.3. Statistical analysis Data are presented as mean and standard deviation or proportion, as appropriate. If the assumptions for F or t-tests were violated, equivalent non-parametric statistics were used. Differences between patients and HCs were tested using the Fisher exact and Mann-Whitney U tests for dichotomous and continuous variables, respectively. The differences, in terms of clinical and postural data, among CIS, RRMS, PMS patients and HC were tested using ANOVA (Bonferroni, post hoc analysis) and Kruskal/Wallis when appropriated. Correlations between demographic and clinical variables, standing balance measures and clinical scales (BBS and BI scores) were also tested using the Spearman rank coefficient, after controlling for sex, age and BMI. P values less than 0.05 in either direction were considered as significant. Analyses were carried out by using the STATA software version 11.2 (Boston and Sumner, 2003). 3. Results Out of 170 MS patients assessed for eligibility, 163 met the inclusion/exclusion criteria and were finally enrolled. Seven patients were excluded because of the presence of vestibular diseases. Clinical and demographical data are shown in Table 1. All balance parameters were found more impaired in MS groups, RRMS and PMS, compared to controls and CIS (Table 2). Moreover, no differences in terms of balance assessment were found between HC and CIS. The correlation analysis showed that an higher EDSS was associated to higher SA-OE and SA-CE (r = 0.51, CI95% 0.30–0.68, p < 0.05 and r = 0.60, CI95% 0.43–0.75, p < 0.01, respectively); no significant correlations were found between EDSS and other stabilometric parameters. Moreover, BBS was strongly associated to SA-OE (Table 3, Fig. 1) and SA-CE (Table 3, Fig. 1), and with TPL-OE and MSV-OE (Table 3). We also found a correlation between BI and SA-CE (Table 3). 4. Discussion The current study investigated whether a system to evaluate mCTSIB, Neurocom Balance Manager®, provides a reliable, objective and valid measure to detect changes of sensory organization abilities and balance control in MS patients among different phenotypes. Our study revealed significant differences among HCs, CIS and MS. Table 1 Clinical and demographical characteristics.

N (%) Female (%) Age; years (mean ± SD) Disease duration; years (mean ± SD) Age at onset; years (mean ± SD) EDSS (mean ± SD) Education level; years (mean ± SD) Height; cm (mean ± SD) 155–160 (%) 161–165 (%) 166–170 (%) 171–175 (%) 176–180 (%) 181–185 (%) BMI (mean ± SD)

HC

CIS

RRMS

PMS

P value

29 (17.8) 19 (65.5%) 32.2 ± 11.2 –

36 (22.1) 22 (61.1) 35.6 ± 9.9 7.5 ± 4.9 26.1 ± 14.4 0.8 ± 1.1 13.6 ± 5.0 163.1 ± 4.7 6 (16.7)a 24 (66.7) 5 (13.9) 1 (2.8)c 0 0 23.8 ± 3.8

88 (53.9) 52 (59.9) 40.3 ± 10.6a 18.6 ± 5.8b 28.8 ± 9.6 2.9 ± 1.2b 12.8 ± 6.2 167.6 ± 5.9 8 (9.1) 16 (18.2)b 27 (30.7) 22 (25) 6 (6.8) 5 (5.7) 24.5 ± 5.1

10 (6.13) 6 (60) 42.4 ± 8.4a 18.2 ± 6.0b 35.2 ± 10.2b,c 4.2 ± 1.8c,b 12.5 ± 6.8 166.6 ± 4.9 1 (10) 3 (30)b 5 (50) b 1 (10) 0 0 23.6 ± 4.0

– n.s. <0.001a <0.001b <0.001c,b <0.001c,b n.s. n.s. <0.05a <0.05b <0.05b <0.05c n.s. n.s. n.s.

– 14.8 ± 5.6 165.5 ± 7.9 2 (6.9) 8 (27.6) 13 (44.8) b 5 (17.2) 1 (3.4) 0 24.6 ± 4.9

HC: Healthy controls, CIS: clinically isolated syndrome; RRMS: relapsing multiple sclerosis; PMS: progressive multiple sclerosis; SD: standard deviation; EDSS: Expanded Disability Status Scale; BMI: body mass index; SD: standard deviation; n.s.: not significant. a versus HC. b versus CIS. c versus RRMS. 48

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Table 2 Clinical and instrumental balance parameters.a HC 2

CIS

RRMS

PMS b,c

89.5 ± 18.5

p value b,c,d

SA-OE; mm

7.4 ± 1.3

19.8 ± 4.1

45.7 ± 14.7

SA-CE; mm2

11.2 ± 3.2

14.3 ± 9.8

65.5 ± 15.1b,c

123.4 ± 16.8b,c,d

TPL-OE; mm TPL-CE; mm MSV-OE; deg/s

37.1 ± 12.2 57.0 ± 19.6 3.7 ± 1.2

46.4 ± 9.3 64.9 ± 21.9 5.6 ± 0.9

89.6 ± 16.4b,c 130.9 ± 22.6b,c 10.8 ± 3.4b,c

164.7 ± 32.2b,c,d 245.6 ± 45.9b,c,d 19.6 ± 5.5b,c,d

MSV-CE; deg/s

5.7 ± 2.0

6.9 ± 2.2

13.1 ± 12.6b,c

27.0 ± 12.6b,c

Berg Balance Scale Barthel Index

– –

52.3 ± 3.0 84.3 ± 9.7

44.6 ± 7.3b 87.3 ± 9.6b

32.6 ± 9.2b,d 62.3 ± 4.3b,d

<0.001b <0.01c <0.05d <0.001b <0.001c <0.01d <0.001b,c,d <0.001b,c,d <0.001b,c <0.01d <0.001b <0.001c <0.05b <0.05b <0.01d

a

All data are expressed as mean ± SD. HC: Healthy controls, CIS: clinically isolated syndrome; RMS: relapsing multiple sclerosis; PMS: progressive multiple sclerosis; TPL-OE: Total Path Length-open eyes, TPL-CE: total path length-closed eyes, SA-OE: sway area-open eyes, SA-CE sway area-closed eyes, MSV-OE: mean sway velocity-open eyes, MSV-CE: mean sway velocity-closed eyes; SD: standard deviation; n.s.: not significant. b vs HC. c vs CIS. d versus RRMS.

that taller subjects significantly exceeded smaller subjects for EO path length and EC velocity (Melillo et al., 2017). Our CIS patients were younger than RRMS and PMS and previous studies have revealed differences between young and elderly people balance performances as the result of the loss of postural control and muscle strength with age (Yamako et al., 2017). Furthermore, RRMS and PMS reported the similar disease duration; this selection bias could be probably due, firstly to the hospital-based and not population based nature of the study. In the present report, we observed that EC tasks were found more impaired than those performed with EO. As widely known, proper balance control is based on the vision, vestibular and somatosensory systems and it is reasonable that, when one system is impaired, the others usually attempt to compensate (Creath et al., 2008). Therefore, MS patients assessed with EC had to rely more on the somatosensory and vestibular systems; if one or more of these components are deteriorated, as frequently occurs in highly disabled MS patients, postural control could be affected (Creath et al., 2008; Kalron et al., 2016). These statements are in line with previous reports investigating balance capabilities with and without vision in MS (Denomme et al., 2014; Kalron et al., 2016; Prosperini et al., 2014). A recent research examining the relationship between posturography measures and brain damage reported that the extent of the COP path length under visual stimulation indicated different patterns of damage in the cerebellum and spinal cord compared to the COP path length at EC (Prosperini et al., 2014). Whether the instability in MS is particularly evident in the absence of vision suggests that instability is largely the result of deficits in proprioceptive feedback (Rougier et al., 2007); indeed, as a consequence of a slow spinal somatosensory conduction, MS patients receive delayed and/or distorted proprioceptive feedback of postural displacements leading to late, incorrect, or absent compensatory postural adjustments (Cameron et al., 2008). In our study the higher correlation between balance parameters and BBS score confirmed the reliability and sensibility of mCTSIB assessment in evaluating static postural control (Prosperini and Pozzilli, 2013). In particular SA measurements seem to be more highly associated to BBS clinical evaluation. This study is limited by the cross-sectional design and the small sample size, which could reduce the statistical power of our findings and the chance to stratify patients according to clinical conditions in further detail. Secondly we did not discriminate between primary and

Table 3 Correlation analysisa between mCTSIB indexes and clinical balance parameters.

2

SA-OE; mm

SA-CE; mm2 TPL-OE; mm TPL-CE; mm MSV-OE; deg/s MSV-CE; deg/s

Berg Balance Scale

Barthel Index

−0.70 (−0.41, 0.001) −0.79 (−0.39, <0.001) −0.62 (−0.51, −0.63 (−0.40, −0.31 (−0.15, −0.61 (−0.36,

−0.81;

−0.51 (−0.13, −0.56; n.s.)

−0.83;

−0.60 (−0.40, <0.01) −0.22 (−0.15, −0.28 (−0.16, −0.35 (−0.11, −0.28 (−0.13,

−0.79; −0.77; −0.39; −0.70;

0.01) 0.01) n.s.) 0.01)

−0.81; −0.29; −0.33; −0.43; −0.36;

n.s.) n.s.) n.s.) n.s.)

a data are expressed as r coefficient of Spearman (95%CI; p value). mCTSIB: modified Clinical Test of Sensory Interaction on Balance; TPL-OE: Total Path Length-open eyes, TPL-CE: total path length-closed eyes, SA-OE: sway areaopen eyes, SA-CE sway area-closed eyes, MSV-OE: mean sway velocity-open eyes, MSV-CE: mean sway velocity-closed eyes.

present even when clinical evaluation is normal (Corporaal et al., 2013; Fanchamps et al., 2012; Kalron et al., 2011a; Melillo et al., 2017). Our lack of statistical differences between CIS patients and control subjects on the performance indices computed by mCTSIB, apparently contradicting the results shown by previous studies (Grassi et al., 2017; Shiravi et al., 2017), could be justified considering the relatively small sample size and the different level of disability of patients involved in these studies. In our study, we included CIS patients with lower disability (mean EDSS 0.8) and shorter diseases duration compared to other groups and no history of falls, who probably would have required more challenging balance trials to reveal subtle difficulties in maintenance of upright stance. Indeed, in a recent study, controls and MS did not differ in velocity and path length in EO and EC trials; whilst significant differences were found for the LOS test (the subjects were asked to lean far, straight and fast as possible toward targets placed forward, on the right and left and to maintain the maximal leaning position until the end of the trial), demonstrating that postural indices evaluated in perturbed conditions show higher sensitivity respect to common static tests in discriminating and quantifying postural performance in MS patients (Melillo et al., 2017). Moreover, in our population, CIS patients were slightly smaller that MS patients. Stratifying our sample according to range of height we found that CIS group showed a higher percentage of patients with ranges of height of 155–165 compared to other groups. This could influence the balance outcomes, given 49

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Fig. 1. Correlation analysis between BBS and sway area at open eyes and close eyes parameter. BBS: Berg balance scale score; SA-OE: Sway Area open eyes; SA-CE: Sway Area closed eyes; r: Spearman correlation.

secondary progressive MS, precluding us from detecting any difference in postural control between these groups. Finally we did not test any dynamic condition, preventing to assess the influence of the motor component on postural control.

carrying out rehabilitative trainings tailored to each MS patient, according to the specific CNS structure more closely associated with the patient's balance deficit in order to ameliorate balance and reducing risk of falls.

5. Conclusion

Acknowledgment We would like to thank D. Veca, A. Russo, F. Zagari.

The balance assessment with Neurocom Balance Manager® could provide objective measures of balance function, supported by the higher correlation between BBS score and balance parameters, especially in CE tasks. Moreover we confirmed that PMS showed worse postural control compared to other groups suggesting the involvement of more complex neurophysiological mechanisms and networks in different open and closed loop systems of the balance control. Further studies on larger and balanced samples are needed to widely clarify the role of dynamic and static stabilometric platform in characterizing balance performance in among different MS forms. In particular, more exhaustive posturographic batteries evaluating many different variables should be adopted to increase sensitivity of the balance assessment (e.g. tests on foam, dual tasks, LOS test, evaluation of more indexes). Finally, future studies should evaluate balance impairment integrating posturographic parameters and clinical measures, involve wider population stratified according to the MS symptoms and disease forms, and extend long-term follow-up. The knowledge of different grade of balance impairment among MS phenotypes may be helpful for

Conflict of interest Chisari CG, Cimino V, Raciti G and Pappalardo A reports no disclosures. Prof Zappia M has received honoraria for speaking activities by Bayer Schering, Biogen, Merck, Novartis, TEVA and Sanofi Aventis; he also served as advisory board member the following companies: Bayer Schering, Biogen, Merck, Novartis; he received grant for congress participation from Bayer Schering, Biogen Idec, Merck, Novartis, Sanofi Aventis and TEVA. Prof Patti F has received honoraria for speaking activities by Bayer Schering, Biogen, Merck, Novartis, Roche, TEVA and Sanofi Aventis; he also served as advisory board member the following companies: Bayer Schering, Roche, Biogen, Merck, Novartis; he was also funded by Pfeizer and FISM for epidemiological studies; finally he received grant for congress participation from Bayer Schering, Roche, Biogen, Merck, Novartis, Sanofi Aventis and TEVA. 50

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