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Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis
JNS-14095; No of Pages 6 Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis Massimiliano Pau a,⁎, Giancarlo Coghe b, Federica Corona a,b, Maria Giovanna Marrosu b, Eleonora Cocco b a b
Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy Multiple Sclerosis Center, Department of Public Health, Clinical and Molecular Medicine University of Cagliari, Cagliari, Italy
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 25 August 2015 Accepted 14 September 2015 Available online xxxx Keywords: Spasticity Multiple Sclerosis (MS) Gait Gait Profile Score (GPS) Motion analysis Kinematics Expanded Disability Status Scale (EDSS) Electromyography (EMG)
a b s t r a c t Purpose: This study proposes to characterize the gait patterns of individuals with Multiple Sclerosis (MS) affected by spasticity using quantitative gait analysis. Method: Cross-sectional study on 38 individuals with MS, 19 affected by lower limb spasticity and 19 not affected, the latter forming the control group. Both groups were evaluated while walking using three-dimensional gait analysis. Spatio-temporal parameters of gait, kinematic data expressed by means of Gait Profile Score (GPS) and Range of Motion (ROM), as well as muscular activation, were evaluated. Results: The results show that spasticity originates a peculiar gait pattern characterized by reduced speed, cadence, stride length, swing phase and increased double support time, but they also reveal specific alterations in kinematics and muscular activation. In particular, significantly higher values of GPS, reduced hip and knee flexion-extension ROM and abnormal activation of the rectus femoris were observed in individuals with spasticity. Conclusions: In people with MS presenting spastic gait, the availability of quantitative data appears crucial in verifying the effectiveness of pharmacologic and rehabilitative treatments, also considering that spasticity scales may not be satisfactory in relating the assessed spasticity with both perception of the patients and the actual body functionalities. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Among the wide spectrum of signs and symptoms associated with Multiple Sclerosis (MS), spasticity (i.e. a velocity-dependent increase in muscle resistance in response to a passive stretch [1]) represents one of the most commonly observed. In fact, it has been estimated that approximately 30–50% of individuals with MS exhibit spasticity following a physical examination or self-report [2–3] even though up to 90% of them result affected by it at a certain point of their disease history [4]. The presence of spasticity can interfere with several everyday activities and, in particular, when lower limbs are involved, gait disorders are likely to occur [3,5]. Moreover, not only self-reported spasticity levels are predictive of the need for assistive devices to ambulate [2] but the more recent scale established to assess the prospects of the impact of spasticity on people with MS (i.e. Multiple Sclerosis Spasticity Scale, MSSS-88 [6]) includes a specific subscale related to walking problems. The negative consequences of spasticity are usually managed by integrating pharmacologic and rehabilitative treatments. However, in some cases, although spasticity appears to be significantly attenuated ⁎ Corresponding author at: Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Piazza d'Armi, 09123 Cagliari, Italy. E-mail address: [email protected] (M. Pau).
after therapy, the possible correspondent functional improvements are subtler or not detectable at all. This may be partly due to the inadequacy of the clinical scales used to assess spasticity [7], although they remain the most widely used tool in a clinical environment. To partly overcome the drawbacks associated with the use of scales, a number of biomechanical tests have been proposed to provide objective measures of spasticity, such as the Wartenberg Pendulum Test [8] and the use of powered devices that force a joint to oscillate. In the first case, the swing of the leg (which is released from a full knee extension position and left free to oscillate) is analyzed, assuming that increasing levels of spasticity would decrease the number and amplitude of oscillations. The latter are basically systems in which a joint is mechanically moved while controlling the applied force or the displacements [9] and the stiffness or the frequency of resonance of the joint measured. Nevertheless, although such systems may actually discriminate between different levels of spasticity with a good degree of reliability and repeatability and the results are well correlated with the Modified Ashworth Scale (MAS), [10] there is little evidence of a direct relationship with walking or other functional measures [11]. In recent times, some attempts have been made to extract information about actual effects of spasticity (and related treatments) on gait of individuals with MS by instrumental assessment of spatio-temporal, kinematic and kinetic parameters. Ørsnes et al. [12] tested 14 MS patients with spasticity before and after an 11-day period of baclofen
http://dx.doi.org/10.1016/j.jns.2015.09.352 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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M. Pau et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
treatment using a treadmill equipped with force platforms. A number of spatio-temporal parameters were analyzed (i.e. stride length, percentages of stance, swing and double support phases) but none of them significantly improved after the treatment, probably owing to small sample size and large inter-individual variations, as hypothesized by the authors. Deterioration of gait kinematics (particularly as regards increased knee flexion at heel strike) and increased activity of leg muscles related to spasticity were observed by Kelleher et al. [13] who employed an optoelectronic system to characterize gait features in people with MS. The perceived impact of spasticity (assessed through the overall rating of the MSSS-88 scale) was found significantly correlated with several spatio-temporal gait parameters (e.g. cadence, velocity, step width and time and double support phase percentage) assessed by means of the GAITRite electronic walkway in a sample of 44 patients with MS, thus suggesting that the impairment of gait quantitatively measured reflects, to some extent, on the self-perception of spasticity impact in everyday activities [14]. Finally, Balantrapu et al. [15] reported that individuals with MS characterized by lower limb spasticity exhibit a poorer performance in walking using ambulatory, kinematic, physiological and perceived measures. Despite the useful information obtained from these investigations, an analysis of the literature shows that the existing quantitative data on the effect of spasticity on gait are not only scanty, but also somewhat scattered as no analyses that integrate kinematics, kinetics, surface electromyography (EMG) and spatio-temporal parameters have been carried out to date. Three-dimensional quantitative gait analysis, although quite widespread in supporting clinicians' decisions in neurologic diseases such as cerebral palsy, stroke and Parkinson's disease, is still relatively little used for MS, since it is considered expensive, timeconsuming and complex to interpret [16–17]. Nevertheless, the use of synthetic indexes such as Gait Variable Score and Gait Profile Score [18], recently validated for MS [19], and successfully applied also in the case of other neurological diseases characterized by the presence of spasticity, such as Cerebral Palsy (CP) [20], allows a reduction of the large amount of kinematic data available from the gait analysis to single values that are easy to understand and simple to use as outcome measures for the assessment of the effectiveness of rehabilitation and pharmacologic treatments. On the basis of the aforementioned considerations, this study aims to quantitatively and objectively assess the impact of lower limb spasticity on gait in people affected by MS who exhibit relevant levels of spasticity by means of three-dimensional gait analysis from which kinematic, spatio-temporal and muscular activation data can be extracted. Our idea is that in individuals affected by MS, the spastic gait pattern is characterized by specific kinematic and muscular activation features. To test this hypothesis, data from gait analysis will be compared with those of a control group to establish what variables appear most sensitive to the presence of spasticity and thus provide a set of indicators that are most likely to effectively link spasticity to the ambulatory function. 2. Methods
according to the 2005 McDonald criteria [21], the ability to independently ambulate with or without an assisting device (i.e. cane, crutches or walking frames) for at least 100 m and the absence of any other condition able to affect gait. EDSS was evaluated for each patient by a MS expert neurologist and the degree of spasticity was self-assessed by participants the day of the laboratory tests using the spasticity Numerical Rating Scale (NRS). The NRS is measured according to the level of spasticity over the preceding 24 h on a 0–10 range, where NRS 0 means “no spasticity” and 10 “the worst possible spasticity” [22]. The main features of the participants are shown in Table 1. The local ethics committee approved the study and all participants signed an informed consent agreeing to participate in the study.
2.2. Kinematic data collection and processing The acquisition of kinematics associated with the body segments of interest (trunk, pelvis, thigh, shank and foot) as well as the main spatio-temporal parameters of gait (i.e. gait velocity, stride length, step width, cadence, stance, swing and double support phase percentage) was performed using an optoelectronic system composed of eight Smart-D cameras (BTS Bioengineering, Italy) set at a frequency of 120 Hz. Twenty-two spherical retro-reflective passive markers (14 mm diameter) were placed on the skin of individuals' lower limbs and trunk at specific landmarks following the protocol described by Davis et al. [23]. Participants were then asked to walk barefoot at a self-selected speed in the most natural manner possible at least six times on a 10 m walkway, allowing suitable rest times between the trials. The raw data were then processed with the dedicated Smart Analyzer (BTS Bioengineering, Italy) software to calculate the spatiotemporal parameters of gait and the variation of the kinematic parameters of interest within the gait cycle, namely pelvic tilt, rotation and obliquity, hip flexion–extension, adduction–abduction and rotation, knee flexion–extension, ankle dorsiflexion and foot progression. Kinematic data were then summarized separately for each limb using the Gait Variable Score (GVS) and the Gait Profile Score (GPS). This summary measure of gait quality was recently proposed by Baker et al. [18] on the basis of the previously defined Gait Deviation Index. The GPS (expressed in degrees) represents the Root Mean Square (RMS) difference between a patient's data and the mean value obtained from tests performed on the unaffected population calculated for the kinematic variables mentioned above on the whole gait cycle. The RMS difference referring to each of them is defined as the Gait Variable Score (GVS). Higher GVS/GPS scores indicate larger deviations from a hypothetical “normal” gait. Such indexes were found effective in characterizing the gait alterations of MS individuals with a single measure [19]. Finally, the range of motion (ROM) of sagittal plane joints (i.e. hip and knee flexion-extension and ankle dorsiflexion) calculated during the whole gait cycle as the difference between the maximum and minimum value recorded during a trial, was also considered for the analysis.
2.1. Subjects Nineteen patients suffering from relapsing-remitting MS (12 females, 7 males, mean age 54.6 SD 9.5 years) with an EDSS score in the range 3.5–6.5 (mean EDSS 4.4 SD 1.4) and who were referred to the Regional Multiple Sclerosis Centre of Cagliari (Sardinia, Italy) were enrolled in the study after compilation of self-reported questionnaires and a neurological evaluation. A control group (n = 19, 7 females, 12 males, mean age 47.1 SD 11.4 years, EDSS in the range 2.5–4.5, mean EDSS 3.4 SD 0.7) was established among individuals affected by MS currently followed at the same centre, but with no clinical evidence or self-reported spasticity. The main criteria for inclusion in the study were a diagnosis of MS
Table 1 Anthropometric features of participants. Values are expressed as mean ± SD.
Participants # (M, F) Age (years) Height (cm) Body mass (kg) NRS score EDSS score
MS spasticity
MS control group
p-Value
19 (7 M, 12 F) 54.6 ± 9.5 162.9 ± 9.3 59.7 ± 12.8 7.7 ± 1.3 4.4 ± 1.4
19 (12 M, 7 F) 47.1 ± 11.5 167.4 ± 8.5 67.9 ± 16.3 NA 3.4 ± 0.7
– 0.058 0.066 0.061 – b0.001⁎
NA = Not Applicable). ⁎ Denotes statistical difference (p b 0.05).
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
M. Pau et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Table 2 Comparison between the GVS/GPS values for the two groups. Values are expressed as mean ± SD. GPS and GVS scores MS spasticity
MS control group Status p-value
Left
GVS (°)
GPS (°) Pelvic tilt Pelvic rotation Pelvic obliquity Hip flexion–extension Hip abduction–adduction Hip rotation Knee flexion–extension Ankle dorsi-plantarflexion Foot progression
12.01 ± 3.21 8.98 ± 4.01 7.22 ± 3.52 5.02 ± 2.32 14.91 ± 5.98 6.55 ± 3.87 12.34 ± 5.95 17.07 ± 4.70 10.27 ± 3.77 10.27 ± 6.23
Right 11.72 ± 2.72 8.60 ± 3.74 7.17 ± 3.80 4.84 ± 2.20 16.19 ± 5.30 6.58 ± 3.61 13.33 ± 6.18 16.47 ± 3.66 9.16 ± 3.75 9.67 ± 3.24
Left 8.12 ± 1.88 5.43 ± 3.47 4.54 ± 1.64 2.54 ± 0.77 10.93 ± 6.35 3.95 ± 1.50 11.84 ± 4.59 10.06 ± 4.06 5.44 ± 1.67 6.71 ± 3.78
Right 7.77 ± 1.95 5.25 ± 3.40 2.75 ± 1.32 2.47 ± 0.88 11.78 ± 6.09 3.85 ± 1.32 9.47 ± 3.91 10.92 ± 4.23 6.42 ± 2.86 5.86 ± 3.04
Limb p-value b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ 0.003⁎ b0.001⁎ 0.074 b0.001⁎ b0.001⁎ b0.001⁎
0.578 0.741 0.157 0.752 0.436 0.964 0.570 0.893 0.927 0.465
⁎ Denotes statistical difference after Bonferroni correction.
2.3. EMG data collection and processing Surface EMG data were collected simultaneously with kinematic and spatio-temporal data using wireless probes (RT100, BTS Bioengineering, Milano, Italy) attached to three pairs of Ag/AgCl electrodes (Kendall H124SG, Covidien, Dublin, Ireland) that were placed on the rectus femoris, lateral gastrocnemius, and tibialis anterior of each leg following SENIAM recommendations [24], setting the sampling frequency to 1000 Hz. The raw EMG signals collected for each analyzed gait cycle identified to calculate spatio-temporal and kinematic parameters were band-pass filtered (20–450 Hz), full-wave rectified and smoothed with a low pass filter (3 Hz) The signal was normalized to the maximum value recorded during the gait cycle [25] and the overall magnitude of EMG activation was assessed by calculating its root mean square (RMS) value. The mean value of the six trials was then calculated and assumed as representative of the patient.
2.4. Statistical analysis Differences in muscular activation and kinematic variables acquired during gait and induced by the presence of spasticity were assessed using two-way multivariate analyses of variance (MANOVA) performed using the IBM SPSS Statistics v.20 software (IBM, Armonk, NY, USA). Parametric model assumptions were verified (e.g., normality, homogeneity, and presence of outliers). The independent variables were the individual's status (presence of spasticity or not) and limb (right, left), and the dependent variables were the RMS value of the EMG signal for the three analyzed muscles, the nine GVS scores plus the GPS index, the ROM of hip, knee and ankle and seven spatio-temporal parameters. The level of significance was set at p = 0.05 and effect sizes were assessed using the eta-squared coefficient (η2). Follow-up analyses were conducted using two-way (limb × status) ANOVAs for each
dependent variable, setting the level of significance at p = 0.016 (0.05/3) for EMG and ROM, p = 0.005 (0.05/10) for kinematic data, p = 0.007 (0.05/7) for spatio-temporal parameters, after a Bonferroni adjustment for multiple comparisons. 3. Results 3.1. Kinematic and spatio-temporal gait parameters Table 2 shows the results for the GPS and GVS values calculated for the two groups, while Table 3 reports the spatio-temporal parameters of gait. MANOVA revealed a significant influence of spasticity on gait kinematics [F(10,63) = 7.67, p b 0.001, Wilks λ = 0.45, η2 = 0.55] but not of limb or status per limb interaction. The follow-up, carried out by means of two-way ANOVA (status × limb) on GPS and GVS variables, detected significant effects of status for GPS (p b 0.001) and all the GVS kinematic variables except hip rotation. Similarly, a significant effect of spasticity was found in spatiotemporal parameters, [F(13,60) = 17.25, p b 0.001, Wilks λ = 0.21, η2 = 0.79]. In particular, the follow-up ANOVA showed that all parameters considered, except stance phase percentage and step width, were significantly different in the two groups (p b 0.001). In the case of hip, knee and ankle ROM (Table 4), MANOVA detected a significant effect of spasticity [F(3,70) = 25.45, p b 0.001, Wilks λ = 0.48, η2 = 0.52] and the follow-up analysis revealed that hip and knee flexion-extension ROMs were significantly reduced in the spasticity group (p b 0.001). 3.2. EMG EMG data are reported in Table 5. A significant effect of spasticity on muscular activation was found also in this case [F(3,70) = 17.58, p b 0.001, Wilks λ = 0.57, η2 = 0.43], even though the post-hoc analysis
Table 3 Comparison between spatio-temporal parameters of gait for the two groups. Values are expressed as mean ± SD. Spatio-temporal gait parameters MS spasticity Left Stride length (m) Gait speed (m s−1) Cadence (steps min−1) Step width (m) Stance phase (% of the gait cycle) Swing phase (% of the gait cycle) Double support time (% of the gait cycle)
MS control group Right
0.76 ± 0.18
0.77 ± 0.19 0.42 ± 0.21 69.09 ± 21.96 0.17 ± 0.04 65.48 ± 15.61 67.75 ± 8.68 29.00 ± 5.87 28.97 ± 5.86 22.38 ± 8.29 24.35 ± 15.55
Left
Status p-value
Limb p-value
b0.001⁎ b0.001⁎ b0.001⁎ 0.864 0.021 b0.001⁎ b0.001⁎
0.852 NA NA NA 0.678 0.732 0.577
Right
1.22 ± 0.22 1.23 ± 0.21 1.12 ± 0.25 112.37 ± 14.36 0.17 ± 0.03 61.79 ± 3.59 61.32 ± 4.57 37.32 ± 3.96 36.57 ± 3.58 12.68 ± 3.09 13.10 ± 4.81
⁎ Denotes statistical difference after Bonferroni correction (NA = Not Applicable).
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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Table 4 Comparison between the ROM values for the two groups calculated during the gait cycle. Values are expressed as mean ± SD. Range of motion MS spasticity
Hip flexion–extension (°) Knee flexion–extension (°) Ankle dorsi-plantarflexion (°)
MS control group
Left
Right
Left
Right
34.93 ± 9.75 35.09 ± 16.70 21.21 ± 5.58
32.17 ± 10.47 30.04 ± 17.64 22.24 ± 6.41
45.61 ± 8.09 58.70 ± 6.98 26.05 ± 9.71
46.28 ± 7.58 58.73 ± 8.15 25.62 ± 6.93
Status p-value
Limb p-value
b0.001⁎ b0.001⁎ 0.017
0.413 0.616 0.860
⁎ Denotes statistical difference after Bonferroni correction.
revealed that only activation of the rectus femoris appeared significantly higher in the spasticity group (p b 0.001). 4. Discussion The results of the present study show that in people with MS the presence of moderate to severe spasticity involves distinctive gait features in terms of spatio-temporal and kinematic parameters as well as increased muscular activity in the rectus femoris and decreased ROM in hip and knee joints. Consistent with previous investigations, we observed that spasticity is responsible for a typical gait pattern characterized by reduced velocity, cadence and step length, alteration of the stance/swing phase ratio and increase in double support time [12–15]. Moreover, similar to what was observed by Balantrapu et al. [15], spasticity did not affect the base of support width, thus indicating that this parameter, which is often anecdotally related to an unstable gait, does not reflect the actual impairment of balance due to spasticity [26]. Nevertheless, not only did spatio-temporal parameters appear significantly modified, since the kinematic analysis revealed that the GVS values were 30 to 50% higher in individuals with spasticity, thus indicating that its presence leads to patterns more deviated from physiological gait. Overall, the GPS value of the spasticity group was 50% higher with respect to the control group (11.9° vs. 7.9°) and more than double when compared with those of healthy individuals [19]. In particular, spasticity originates GVS values approximately 45% higher for the pelvis district, 40% in the case of ankle and knee joints and 30% for the hip. A detailed look at the single joint curves shows that such differences are basically originated by a delayed toe-off, reduced hip extension at the mid/terminal stance phase, generalized reduced knee flexion across the whole gait and lack of extension at mid stance cycle, reduced ankle plantar flexion in terminal stance and reduced dorsiflexion in swing (see Fig. 1). These issues are also summarized by the significantly smaller ROM at hip (−35%) and knee (−80%) joints exhibited by individuals with spasticity. A direct comparison of these findings with previous studies is impossible since to our knowledge this is the first study that specifically examines the impact of spasticity on gait kinematics. However, it is noteworthy that Kelleher et al. [13] mentioned the effect of spasticity in a study to generally characterize the gait patterns of people with MS, where two groups of individuals with MS were classified according to the Hauser Ambulation Index and MAS values. The results of their 3D gait analysis suggested that individuals with more severe spasticity (i.e. higher MAS values) exhibit a more deteriorated gait from a
kinematic point of view, especially as regards increased knee flexion at the heel strike and a generalized reduction in ROM of hip, ankle and knee during gait, which appeared more marked at the knee joint, similar to what we observed in the present study. The simultaneous existence of reduced ROM at the knee joint (mainly due to reduced knee flexion at the swing phase) and abnormally increased activity of the rectus femoris that we observed in our spasticity group only, configures the so-called “stiff-knee” gait, which is also typical of other neurological pathologies such as stroke and CP [27–28]. From this point of view, the findings of the present study substantially confirm what was previously observed by Yafit et al. [29] in a smaller cohort of individuals with MS tested using 3D gait analysis and compared with healthy controls. Nevertheless, our results add some new insights by suggesting that such a gait impairment is specifically associated with the presence of spasticity, and not generalizable to all patients. Some limitations of the present study are to be acknowledged: first of all, the disability level as quantified by the EDSS score was significantly different in the two groups, while ideally the analysis should be performed on equally disabled individuals who differ only for the presence of spasticity. However, this issue is shared with most past studies to investigate the effects of spasticity on individuals with MS either generally [2,30] or on specific mobility-related aspects [13,15,26]. As pointed out by the authors, this phenomenon may be justified by the fact that spasticity itself is a factor that has an important impact on overall disability and contributes to increasing it [26,30]. Moreover, spasticity, being a manifestation of pyramidal tract involvement, is significantly correlated with EDSS [31]. It is also noticeable that even when the disability is assessed through patients' self-reports (i.e. using the PDDS scale), a strongly linear relationship between spasticity and disability levels exists [2]. Secondly, the EMG signal might to some extent be biased by muscular tissue changes in orientation that occur during walking due to knee joint angle variations [32]. Future developments of the study should include this effect in the whole EMG signal processing procedure. Finally, it must be recalled that the synthetic measures of gait kinematics (GPS and GVS) also have some limitations. In fact, although they are able to identify a deviation from physiological gait, the direction of such an alteration (e.g. below or above normality) is not provided. Furthermore, unless the single joint angles are examined across the entire gait cycle, it is unknown whether the deviation is due to time-shifts, or if the joint curves deviate in magnitude only [33]. In our specific case, these parameters can quantify how far spasticity shifts the gait pattern from normality with respect to non-spastic
Table 5 Comparison between EMG RMS values for the two groups calculated during the whole gait cycle. Values are expressed as mean ± SD. Muscular activation MS spasticity
Rectus femoris (V) Tibialis anterioris (V) Gastrocnemius lateralis (V)
MS control group
Left
Right
Left
Right
0.644 ± 0.12 0.558 ± 0.09 0.582 ± 0.10
0.639 ± 0.11 0.573 ± 0.10 0.594 ± 0.11
0.504 ± 0.08 0.607 ± 0.06 0.547 ± 0.10
0.493 ± 0.06 0.610 ± 0.08 0.560 ± 0.10
Status p-value
Limb p-value
b0.001⁎ 0.024 0.145
0.720 0.609 0.596
⁎ Denotes statistical difference after Bonferroni correction.
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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Fig. 1. Typical example of joint angles of an individual with MS affected by spasticity during gait (F, 49 years old, EDSS = 6.5). Blue curve represents right limb, red curve left limb and the gray curve normality (mean ± SD). (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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individuals and which joints are particularly involved in creating such differences, but any doubt on their interpretation can be removed after detailed screening of the gait analysis graphs. 5. Conclusions The results of the present study suggest that the presence of spasticity originates a clearly identifiable gait pattern in individuals with MS, characterized not only by reduced speed, cadence, stride length, swing phase and increased double support time, but also by specific alterations in kinematic and muscle activation. In particular, the three-dimensional quantitative analysis highlighted larger deviations from physiological gait in comparison with MS patients not affected by spasticity at the level of hip, knee and ankle joints as well as excessive activation of the rectus femoris. These findings show that it is essential to have available a large dataset of quantitative measures of gait that includes kinematic and EMG parameters in addition to spatio-temporal ones to accurately characterize gait impairments consequent to spasticity. Although there are some doubts regarding the complexity of the procedure and the feasibility of its clinical use [17], the quality and amount of information associated with gait analysis is not comparable with any other evaluation technique, and the recent availability of synthetic measures of kinematics, such as GPS/GVS, make data interpretation much easier than in the past. Furthermore, the rapid development of alternative and userfriendlier technologies to assess the kinematics of gait, such as wearable inertial sensors [34–35], should encourage the spread of quantitative methods for the analysis of human movement in clinical settings. Such approaches might also be useful in verifying the effectiveness of pharmacologic and rehabilitative treatments, a critical step in the process of spasticity management, even considering that the results of “traditional” gait measures may be inconsistent with the perception of patients [36] and thus may supply misleading information to the clinician. Declaration of interest The authors report no conflicts of interest. References [1] G.R. Johnson, Outcome measures of spasticity, Eur. J. Neurol. 9 (1) (2002) 10–16. [2] M.A. Rizzo, O.C. Hadjimichael, J. Preiningerova, T. Vollmer, Prevalence and treatment of spasticity reported by multiple sclerosis patients, Mult. Scler. 10 (2004) 589–595. [3] J.K. Haselkorn, S. Loomis, Multiple sclerosis and spasticity, Phys. Med. Rehabil. Clin. N. Am. 16 (2005) 467–481. [4] D.W. Paty, G.C. Ebers, Clinical features, in: D.W. Paty, G.C. Ebers (Eds.), Multiple Sclerosis, F.A. Davis company, Philadelphia, 1998. [5] A.J. Thompson, L. Jarrett, J. Marsden, V.L. Stevenson, Clinical management of spasticity, J. Neurol. Neurosurg. Psychiatry 76 (2005) 459–463. [6] J.C. Hobart, A.A. Riazi, A.J. Thompson, et al., Getting the measure of spasticity in multiple sclerosis: the multiple sclerosis spasticity scale (MSSS-88), Brain 129 (2006) 224–234. [7] J.F. Fleuren, G.E. Voerman, C.V. Erren-Wolters, et al., Stop using the Ashworth Scale for the assessment of spasticity, J. Neurol. Neurosurg. Psychiatry 81 (1) (2010) 46–52 2010. [8] R. Wartenberger, Pendulousness of the legs as a diagnostic test, Neurology 1 (1951) 18–24.
[9] E.G. Walsh, Thixotropy: a time dependent stiffness, In: Muscles Masses and Motion, MacKeith Press, London 1996, pp. 78–102. [10] R.T. Katz, G.P. Rovai, C. Brait, et al., Objective quantification of spastic hypertonia: correlation with clinical findings, Arch. Phys. Med. Rehabil. 73 (1992) 339–347. [11] H.K. Graham, Pendulum test in cerebral palsy, Lancet 355 (2000) 2184. [12] G.B. ørsnes, P.S. sørensen, T.K. Larsen, et al., Effect of baclofen on gait in spastic MS patients, Acta Neurol. Scand. 101 (2000) 244–248. [13] K.J. Kelleher, W. Spence, S. Solomonidis, et al., The characterization of gait patterns of people with multiple sclerosis, Disabil. Rehabil. 32 (15) (2010) 1242–1250. [14] S. Balantrapu, B.M. Sandroff, J.J. Sosnoff, et al., Perceived impact of spasticity is associated with spatial and temporal parameters of gait in multiple sclerosis, ISRN Neurol. (2012) (675431). [15] S. Balantrapu, J.J. Sosnoff, J.H. Pula, et al., Leg spasticity and ambulation in multiple sclerosis, Mult. Scler. Int. (2014) 649390 2014. [16] U. Givon, G. Zeilig, A.A. Achiron, Gait analysis in multiple sclerosis: characterization of temporal–spatial parameters using GAITRite functional ambulation system, gait posture 29 (1) (2009) 138–142. [17] M.H. Cameron, J.M. Wagner, Gait abnormalities in multiple sclerosis: pathogenesis, evaluation, and advances in treatment, Curr. Neurol. Neurosci. Rep. 11 (5) (2011) 507–515. [18] R. Baker, J.L. McGinley, M.H. Schwartz, et al., The gait profile score and movement analysis profile, gait posture 30 (3) (2009) 265–269. [19] M. Pau, G. Coghe, C. Atzeni, et al., Novel characterization of gait impairments in people with multiple sclerosis by means of the gait profile score, J. Neurol. Sci. 345 (2014) 159–163. [20] H.M. Rasmussen, D.B. Nielsen, N.W. Pedersen, et al., Gait deviation index, gait profile score and gait variable score in children with spastic cerebral palsy: intra-rater reliability and agreement across two repeated sessions, gait posture 42 (2) (2015) 133–137. [21] C.H. Polman, S.C. Reingold, G. Edan, et al., Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald criteria”, Ann. Neurol. 58 (6) (2005) 840–846. [22] J.T. Farrar, A.B. Troxel, C. Stott, et al., Validity, reliability, and clinical importance of change in a 0–10 numeric rating scale measure of spasticity: a post hoc analysis of a randomized, double-blind, placebo-Controlled trial, Clin. Ther. 30 (5) (2008) 974–985. [23] R.B. Davis, S. Õunpuu, D. Tyburski, et al., A reduction technique, Hum. Mov. Sci. 10 (5) (1991) 575–587. [24] H. Hermens, B. Freriks, R. Merletti, et al., European Recommendations for Surface Electromyography, Roessingh Research and Development, Enschede, NL, 1999, ISBN 90-75452-15-2. [25] J.F. Yang, D.A. Winter, Electromyographic amplitude normalization methods: improving their sensitivity as diagnostic tools in gait analysis, Arch. Phys. Med. Rehabil. 65 (1984) 517–521. [26] J.J. Sosnoff, E. Gappmaier, A. Frame, et al., Influence of spasticity on mobility and balance in persons with multiple sclerosis, J. Neurol. Phys. Ther. 35 (3) (2011) 129–132. [27] S.R. Goldberg, S. Ounpuu, A.S. Arnold, et al., Kinematic and kinetic factors that correlate with improved knee flexion following treatment for stiff-knee gait, J. Biomech. 39 (4) (2006) 689–698. [28] I. Campanini, A.A. Merlo, B. Damiano, A method to differentiate the causes of stiff-knee gait in stroke patients, gait posture 38 (2) (2013) 165–169. [29] D. yafit, aA. achiron, M. Dolev, et al., Characteristics of stiff knee gait in patients with multiple sclerosis, Mult Scler 19 (11) (2013) S545–S546. [30] M.P. Barnes, R.M. Kent, J.K. Semlyen, et al., Spasticity in multiple sclerosis, Neurorehabil. Neural Repair 17 (1) (2003) 66–70. [31] A.G. Gil, C.G. González, S.P. Peralta, et al., Spasticity perception in MS is not influenced by other concomitant symptoms like sleep, fatigue, and emotional or cognitive disorders, Mult scler 19 (7) (2013) 989. [32] D. Farina, R. Merletti, M. Nazzaro, Effect of joint angle on EMG variables in leg and thigh muscles, IEEE Eng. Med. Biol. Mag. 20 (6) (2001) 62–71. [33] K. Schweizer, J. Romkes, M. Coslovsky, et al., The influence of muscle strength on the gait profile score (GPS) across different patients, gait Posture 39 (1) (2014) 80–85. [34] A.A. Muro-de-la-Herran, B. Garcia-Zapirain, A.A. Mendez-Zorrilla, Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications, sensors (Basel) 14 (2) (2014) 3362–3394. [35] J.C. van den Noort, A.A. Ferrari, A.G. Cutti, et al., Gait analysis in children with cerebral palsy via inertial and magnetic sensors, Med. Biol. Eng. Comput. 51 (4) (2013) 377–386. [36] J. Chan, A.A. Winter, M. Palit, et al., Are gait and mobility measures responsive to change following botulinum toxin injections in adults with lower limb spasticity? Disabil. Rehabil. 35 (12) (2013) 959–967.
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis Massimiliano Pau a,⁎, Giancarlo Coghe b, Federica Corona a,b, Maria Giovanna Marrosu b, Eleonora Cocco b a b
Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy Multiple Sclerosis Center, Department of Public Health, Clinical and Molecular Medicine University of Cagliari, Cagliari, Italy
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 25 August 2015 Accepted 14 September 2015 Available online xxxx Keywords: Spasticity Multiple Sclerosis (MS) Gait Gait Profile Score (GPS) Motion analysis Kinematics Expanded Disability Status Scale (EDSS) Electromyography (EMG)
a b s t r a c t Purpose: This study proposes to characterize the gait patterns of individuals with Multiple Sclerosis (MS) affected by spasticity using quantitative gait analysis. Method: Cross-sectional study on 38 individuals with MS, 19 affected by lower limb spasticity and 19 not affected, the latter forming the control group. Both groups were evaluated while walking using three-dimensional gait analysis. Spatio-temporal parameters of gait, kinematic data expressed by means of Gait Profile Score (GPS) and Range of Motion (ROM), as well as muscular activation, were evaluated. Results: The results show that spasticity originates a peculiar gait pattern characterized by reduced speed, cadence, stride length, swing phase and increased double support time, but they also reveal specific alterations in kinematics and muscular activation. In particular, significantly higher values of GPS, reduced hip and knee flexion-extension ROM and abnormal activation of the rectus femoris were observed in individuals with spasticity. Conclusions: In people with MS presenting spastic gait, the availability of quantitative data appears crucial in verifying the effectiveness of pharmacologic and rehabilitative treatments, also considering that spasticity scales may not be satisfactory in relating the assessed spasticity with both perception of the patients and the actual body functionalities. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Among the wide spectrum of signs and symptoms associated with Multiple Sclerosis (MS), spasticity (i.e. a velocity-dependent increase in muscle resistance in response to a passive stretch [1]) represents one of the most commonly observed. In fact, it has been estimated that approximately 30–50% of individuals with MS exhibit spasticity following a physical examination or self-report [2–3] even though up to 90% of them result affected by it at a certain point of their disease history [4]. The presence of spasticity can interfere with several everyday activities and, in particular, when lower limbs are involved, gait disorders are likely to occur [3,5]. Moreover, not only self-reported spasticity levels are predictive of the need for assistive devices to ambulate [2] but the more recent scale established to assess the prospects of the impact of spasticity on people with MS (i.e. Multiple Sclerosis Spasticity Scale, MSSS-88 [6]) includes a specific subscale related to walking problems. The negative consequences of spasticity are usually managed by integrating pharmacologic and rehabilitative treatments. However, in some cases, although spasticity appears to be significantly attenuated ⁎ Corresponding author at: Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Piazza d'Armi, 09123 Cagliari, Italy. E-mail address: [email protected] (M. Pau).
after therapy, the possible correspondent functional improvements are subtler or not detectable at all. This may be partly due to the inadequacy of the clinical scales used to assess spasticity [7], although they remain the most widely used tool in a clinical environment. To partly overcome the drawbacks associated with the use of scales, a number of biomechanical tests have been proposed to provide objective measures of spasticity, such as the Wartenberg Pendulum Test [8] and the use of powered devices that force a joint to oscillate. In the first case, the swing of the leg (which is released from a full knee extension position and left free to oscillate) is analyzed, assuming that increasing levels of spasticity would decrease the number and amplitude of oscillations. The latter are basically systems in which a joint is mechanically moved while controlling the applied force or the displacements [9] and the stiffness or the frequency of resonance of the joint measured. Nevertheless, although such systems may actually discriminate between different levels of spasticity with a good degree of reliability and repeatability and the results are well correlated with the Modified Ashworth Scale (MAS), [10] there is little evidence of a direct relationship with walking or other functional measures [11]. In recent times, some attempts have been made to extract information about actual effects of spasticity (and related treatments) on gait of individuals with MS by instrumental assessment of spatio-temporal, kinematic and kinetic parameters. Ørsnes et al. [12] tested 14 MS patients with spasticity before and after an 11-day period of baclofen
http://dx.doi.org/10.1016/j.jns.2015.09.352 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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treatment using a treadmill equipped with force platforms. A number of spatio-temporal parameters were analyzed (i.e. stride length, percentages of stance, swing and double support phases) but none of them significantly improved after the treatment, probably owing to small sample size and large inter-individual variations, as hypothesized by the authors. Deterioration of gait kinematics (particularly as regards increased knee flexion at heel strike) and increased activity of leg muscles related to spasticity were observed by Kelleher et al. [13] who employed an optoelectronic system to characterize gait features in people with MS. The perceived impact of spasticity (assessed through the overall rating of the MSSS-88 scale) was found significantly correlated with several spatio-temporal gait parameters (e.g. cadence, velocity, step width and time and double support phase percentage) assessed by means of the GAITRite electronic walkway in a sample of 44 patients with MS, thus suggesting that the impairment of gait quantitatively measured reflects, to some extent, on the self-perception of spasticity impact in everyday activities [14]. Finally, Balantrapu et al. [15] reported that individuals with MS characterized by lower limb spasticity exhibit a poorer performance in walking using ambulatory, kinematic, physiological and perceived measures. Despite the useful information obtained from these investigations, an analysis of the literature shows that the existing quantitative data on the effect of spasticity on gait are not only scanty, but also somewhat scattered as no analyses that integrate kinematics, kinetics, surface electromyography (EMG) and spatio-temporal parameters have been carried out to date. Three-dimensional quantitative gait analysis, although quite widespread in supporting clinicians' decisions in neurologic diseases such as cerebral palsy, stroke and Parkinson's disease, is still relatively little used for MS, since it is considered expensive, timeconsuming and complex to interpret [16–17]. Nevertheless, the use of synthetic indexes such as Gait Variable Score and Gait Profile Score [18], recently validated for MS [19], and successfully applied also in the case of other neurological diseases characterized by the presence of spasticity, such as Cerebral Palsy (CP) [20], allows a reduction of the large amount of kinematic data available from the gait analysis to single values that are easy to understand and simple to use as outcome measures for the assessment of the effectiveness of rehabilitation and pharmacologic treatments. On the basis of the aforementioned considerations, this study aims to quantitatively and objectively assess the impact of lower limb spasticity on gait in people affected by MS who exhibit relevant levels of spasticity by means of three-dimensional gait analysis from which kinematic, spatio-temporal and muscular activation data can be extracted. Our idea is that in individuals affected by MS, the spastic gait pattern is characterized by specific kinematic and muscular activation features. To test this hypothesis, data from gait analysis will be compared with those of a control group to establish what variables appear most sensitive to the presence of spasticity and thus provide a set of indicators that are most likely to effectively link spasticity to the ambulatory function. 2. Methods
according to the 2005 McDonald criteria [21], the ability to independently ambulate with or without an assisting device (i.e. cane, crutches or walking frames) for at least 100 m and the absence of any other condition able to affect gait. EDSS was evaluated for each patient by a MS expert neurologist and the degree of spasticity was self-assessed by participants the day of the laboratory tests using the spasticity Numerical Rating Scale (NRS). The NRS is measured according to the level of spasticity over the preceding 24 h on a 0–10 range, where NRS 0 means “no spasticity” and 10 “the worst possible spasticity” [22]. The main features of the participants are shown in Table 1. The local ethics committee approved the study and all participants signed an informed consent agreeing to participate in the study.
2.2. Kinematic data collection and processing The acquisition of kinematics associated with the body segments of interest (trunk, pelvis, thigh, shank and foot) as well as the main spatio-temporal parameters of gait (i.e. gait velocity, stride length, step width, cadence, stance, swing and double support phase percentage) was performed using an optoelectronic system composed of eight Smart-D cameras (BTS Bioengineering, Italy) set at a frequency of 120 Hz. Twenty-two spherical retro-reflective passive markers (14 mm diameter) were placed on the skin of individuals' lower limbs and trunk at specific landmarks following the protocol described by Davis et al. [23]. Participants were then asked to walk barefoot at a self-selected speed in the most natural manner possible at least six times on a 10 m walkway, allowing suitable rest times between the trials. The raw data were then processed with the dedicated Smart Analyzer (BTS Bioengineering, Italy) software to calculate the spatiotemporal parameters of gait and the variation of the kinematic parameters of interest within the gait cycle, namely pelvic tilt, rotation and obliquity, hip flexion–extension, adduction–abduction and rotation, knee flexion–extension, ankle dorsiflexion and foot progression. Kinematic data were then summarized separately for each limb using the Gait Variable Score (GVS) and the Gait Profile Score (GPS). This summary measure of gait quality was recently proposed by Baker et al. [18] on the basis of the previously defined Gait Deviation Index. The GPS (expressed in degrees) represents the Root Mean Square (RMS) difference between a patient's data and the mean value obtained from tests performed on the unaffected population calculated for the kinematic variables mentioned above on the whole gait cycle. The RMS difference referring to each of them is defined as the Gait Variable Score (GVS). Higher GVS/GPS scores indicate larger deviations from a hypothetical “normal” gait. Such indexes were found effective in characterizing the gait alterations of MS individuals with a single measure [19]. Finally, the range of motion (ROM) of sagittal plane joints (i.e. hip and knee flexion-extension and ankle dorsiflexion) calculated during the whole gait cycle as the difference between the maximum and minimum value recorded during a trial, was also considered for the analysis.
2.1. Subjects Nineteen patients suffering from relapsing-remitting MS (12 females, 7 males, mean age 54.6 SD 9.5 years) with an EDSS score in the range 3.5–6.5 (mean EDSS 4.4 SD 1.4) and who were referred to the Regional Multiple Sclerosis Centre of Cagliari (Sardinia, Italy) were enrolled in the study after compilation of self-reported questionnaires and a neurological evaluation. A control group (n = 19, 7 females, 12 males, mean age 47.1 SD 11.4 years, EDSS in the range 2.5–4.5, mean EDSS 3.4 SD 0.7) was established among individuals affected by MS currently followed at the same centre, but with no clinical evidence or self-reported spasticity. The main criteria for inclusion in the study were a diagnosis of MS
Table 1 Anthropometric features of participants. Values are expressed as mean ± SD.
Participants # (M, F) Age (years) Height (cm) Body mass (kg) NRS score EDSS score
MS spasticity
MS control group
p-Value
19 (7 M, 12 F) 54.6 ± 9.5 162.9 ± 9.3 59.7 ± 12.8 7.7 ± 1.3 4.4 ± 1.4
19 (12 M, 7 F) 47.1 ± 11.5 167.4 ± 8.5 67.9 ± 16.3 NA 3.4 ± 0.7
– 0.058 0.066 0.061 – b0.001⁎
NA = Not Applicable). ⁎ Denotes statistical difference (p b 0.05).
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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Table 2 Comparison between the GVS/GPS values for the two groups. Values are expressed as mean ± SD. GPS and GVS scores MS spasticity
MS control group Status p-value
Left
GVS (°)
GPS (°) Pelvic tilt Pelvic rotation Pelvic obliquity Hip flexion–extension Hip abduction–adduction Hip rotation Knee flexion–extension Ankle dorsi-plantarflexion Foot progression
12.01 ± 3.21 8.98 ± 4.01 7.22 ± 3.52 5.02 ± 2.32 14.91 ± 5.98 6.55 ± 3.87 12.34 ± 5.95 17.07 ± 4.70 10.27 ± 3.77 10.27 ± 6.23
Right 11.72 ± 2.72 8.60 ± 3.74 7.17 ± 3.80 4.84 ± 2.20 16.19 ± 5.30 6.58 ± 3.61 13.33 ± 6.18 16.47 ± 3.66 9.16 ± 3.75 9.67 ± 3.24
Left 8.12 ± 1.88 5.43 ± 3.47 4.54 ± 1.64 2.54 ± 0.77 10.93 ± 6.35 3.95 ± 1.50 11.84 ± 4.59 10.06 ± 4.06 5.44 ± 1.67 6.71 ± 3.78
Right 7.77 ± 1.95 5.25 ± 3.40 2.75 ± 1.32 2.47 ± 0.88 11.78 ± 6.09 3.85 ± 1.32 9.47 ± 3.91 10.92 ± 4.23 6.42 ± 2.86 5.86 ± 3.04
Limb p-value b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ 0.003⁎ b0.001⁎ 0.074 b0.001⁎ b0.001⁎ b0.001⁎
0.578 0.741 0.157 0.752 0.436 0.964 0.570 0.893 0.927 0.465
⁎ Denotes statistical difference after Bonferroni correction.
2.3. EMG data collection and processing Surface EMG data were collected simultaneously with kinematic and spatio-temporal data using wireless probes (RT100, BTS Bioengineering, Milano, Italy) attached to three pairs of Ag/AgCl electrodes (Kendall H124SG, Covidien, Dublin, Ireland) that were placed on the rectus femoris, lateral gastrocnemius, and tibialis anterior of each leg following SENIAM recommendations [24], setting the sampling frequency to 1000 Hz. The raw EMG signals collected for each analyzed gait cycle identified to calculate spatio-temporal and kinematic parameters were band-pass filtered (20–450 Hz), full-wave rectified and smoothed with a low pass filter (3 Hz) The signal was normalized to the maximum value recorded during the gait cycle [25] and the overall magnitude of EMG activation was assessed by calculating its root mean square (RMS) value. The mean value of the six trials was then calculated and assumed as representative of the patient.
2.4. Statistical analysis Differences in muscular activation and kinematic variables acquired during gait and induced by the presence of spasticity were assessed using two-way multivariate analyses of variance (MANOVA) performed using the IBM SPSS Statistics v.20 software (IBM, Armonk, NY, USA). Parametric model assumptions were verified (e.g., normality, homogeneity, and presence of outliers). The independent variables were the individual's status (presence of spasticity or not) and limb (right, left), and the dependent variables were the RMS value of the EMG signal for the three analyzed muscles, the nine GVS scores plus the GPS index, the ROM of hip, knee and ankle and seven spatio-temporal parameters. The level of significance was set at p = 0.05 and effect sizes were assessed using the eta-squared coefficient (η2). Follow-up analyses were conducted using two-way (limb × status) ANOVAs for each
dependent variable, setting the level of significance at p = 0.016 (0.05/3) for EMG and ROM, p = 0.005 (0.05/10) for kinematic data, p = 0.007 (0.05/7) for spatio-temporal parameters, after a Bonferroni adjustment for multiple comparisons. 3. Results 3.1. Kinematic and spatio-temporal gait parameters Table 2 shows the results for the GPS and GVS values calculated for the two groups, while Table 3 reports the spatio-temporal parameters of gait. MANOVA revealed a significant influence of spasticity on gait kinematics [F(10,63) = 7.67, p b 0.001, Wilks λ = 0.45, η2 = 0.55] but not of limb or status per limb interaction. The follow-up, carried out by means of two-way ANOVA (status × limb) on GPS and GVS variables, detected significant effects of status for GPS (p b 0.001) and all the GVS kinematic variables except hip rotation. Similarly, a significant effect of spasticity was found in spatiotemporal parameters, [F(13,60) = 17.25, p b 0.001, Wilks λ = 0.21, η2 = 0.79]. In particular, the follow-up ANOVA showed that all parameters considered, except stance phase percentage and step width, were significantly different in the two groups (p b 0.001). In the case of hip, knee and ankle ROM (Table 4), MANOVA detected a significant effect of spasticity [F(3,70) = 25.45, p b 0.001, Wilks λ = 0.48, η2 = 0.52] and the follow-up analysis revealed that hip and knee flexion-extension ROMs were significantly reduced in the spasticity group (p b 0.001). 3.2. EMG EMG data are reported in Table 5. A significant effect of spasticity on muscular activation was found also in this case [F(3,70) = 17.58, p b 0.001, Wilks λ = 0.57, η2 = 0.43], even though the post-hoc analysis
Table 3 Comparison between spatio-temporal parameters of gait for the two groups. Values are expressed as mean ± SD. Spatio-temporal gait parameters MS spasticity Left Stride length (m) Gait speed (m s−1) Cadence (steps min−1) Step width (m) Stance phase (% of the gait cycle) Swing phase (% of the gait cycle) Double support time (% of the gait cycle)
MS control group Right
0.76 ± 0.18
0.77 ± 0.19 0.42 ± 0.21 69.09 ± 21.96 0.17 ± 0.04 65.48 ± 15.61 67.75 ± 8.68 29.00 ± 5.87 28.97 ± 5.86 22.38 ± 8.29 24.35 ± 15.55
Left
Status p-value
Limb p-value
b0.001⁎ b0.001⁎ b0.001⁎ 0.864 0.021 b0.001⁎ b0.001⁎
0.852 NA NA NA 0.678 0.732 0.577
Right
1.22 ± 0.22 1.23 ± 0.21 1.12 ± 0.25 112.37 ± 14.36 0.17 ± 0.03 61.79 ± 3.59 61.32 ± 4.57 37.32 ± 3.96 36.57 ± 3.58 12.68 ± 3.09 13.10 ± 4.81
⁎ Denotes statistical difference after Bonferroni correction (NA = Not Applicable).
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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Table 4 Comparison between the ROM values for the two groups calculated during the gait cycle. Values are expressed as mean ± SD. Range of motion MS spasticity
Hip flexion–extension (°) Knee flexion–extension (°) Ankle dorsi-plantarflexion (°)
MS control group
Left
Right
Left
Right
34.93 ± 9.75 35.09 ± 16.70 21.21 ± 5.58
32.17 ± 10.47 30.04 ± 17.64 22.24 ± 6.41
45.61 ± 8.09 58.70 ± 6.98 26.05 ± 9.71
46.28 ± 7.58 58.73 ± 8.15 25.62 ± 6.93
Status p-value
Limb p-value
b0.001⁎ b0.001⁎ 0.017
0.413 0.616 0.860
⁎ Denotes statistical difference after Bonferroni correction.
revealed that only activation of the rectus femoris appeared significantly higher in the spasticity group (p b 0.001). 4. Discussion The results of the present study show that in people with MS the presence of moderate to severe spasticity involves distinctive gait features in terms of spatio-temporal and kinematic parameters as well as increased muscular activity in the rectus femoris and decreased ROM in hip and knee joints. Consistent with previous investigations, we observed that spasticity is responsible for a typical gait pattern characterized by reduced velocity, cadence and step length, alteration of the stance/swing phase ratio and increase in double support time [12–15]. Moreover, similar to what was observed by Balantrapu et al. [15], spasticity did not affect the base of support width, thus indicating that this parameter, which is often anecdotally related to an unstable gait, does not reflect the actual impairment of balance due to spasticity [26]. Nevertheless, not only did spatio-temporal parameters appear significantly modified, since the kinematic analysis revealed that the GVS values were 30 to 50% higher in individuals with spasticity, thus indicating that its presence leads to patterns more deviated from physiological gait. Overall, the GPS value of the spasticity group was 50% higher with respect to the control group (11.9° vs. 7.9°) and more than double when compared with those of healthy individuals [19]. In particular, spasticity originates GVS values approximately 45% higher for the pelvis district, 40% in the case of ankle and knee joints and 30% for the hip. A detailed look at the single joint curves shows that such differences are basically originated by a delayed toe-off, reduced hip extension at the mid/terminal stance phase, generalized reduced knee flexion across the whole gait and lack of extension at mid stance cycle, reduced ankle plantar flexion in terminal stance and reduced dorsiflexion in swing (see Fig. 1). These issues are also summarized by the significantly smaller ROM at hip (−35%) and knee (−80%) joints exhibited by individuals with spasticity. A direct comparison of these findings with previous studies is impossible since to our knowledge this is the first study that specifically examines the impact of spasticity on gait kinematics. However, it is noteworthy that Kelleher et al. [13] mentioned the effect of spasticity in a study to generally characterize the gait patterns of people with MS, where two groups of individuals with MS were classified according to the Hauser Ambulation Index and MAS values. The results of their 3D gait analysis suggested that individuals with more severe spasticity (i.e. higher MAS values) exhibit a more deteriorated gait from a
kinematic point of view, especially as regards increased knee flexion at the heel strike and a generalized reduction in ROM of hip, ankle and knee during gait, which appeared more marked at the knee joint, similar to what we observed in the present study. The simultaneous existence of reduced ROM at the knee joint (mainly due to reduced knee flexion at the swing phase) and abnormally increased activity of the rectus femoris that we observed in our spasticity group only, configures the so-called “stiff-knee” gait, which is also typical of other neurological pathologies such as stroke and CP [27–28]. From this point of view, the findings of the present study substantially confirm what was previously observed by Yafit et al. [29] in a smaller cohort of individuals with MS tested using 3D gait analysis and compared with healthy controls. Nevertheless, our results add some new insights by suggesting that such a gait impairment is specifically associated with the presence of spasticity, and not generalizable to all patients. Some limitations of the present study are to be acknowledged: first of all, the disability level as quantified by the EDSS score was significantly different in the two groups, while ideally the analysis should be performed on equally disabled individuals who differ only for the presence of spasticity. However, this issue is shared with most past studies to investigate the effects of spasticity on individuals with MS either generally [2,30] or on specific mobility-related aspects [13,15,26]. As pointed out by the authors, this phenomenon may be justified by the fact that spasticity itself is a factor that has an important impact on overall disability and contributes to increasing it [26,30]. Moreover, spasticity, being a manifestation of pyramidal tract involvement, is significantly correlated with EDSS [31]. It is also noticeable that even when the disability is assessed through patients' self-reports (i.e. using the PDDS scale), a strongly linear relationship between spasticity and disability levels exists [2]. Secondly, the EMG signal might to some extent be biased by muscular tissue changes in orientation that occur during walking due to knee joint angle variations [32]. Future developments of the study should include this effect in the whole EMG signal processing procedure. Finally, it must be recalled that the synthetic measures of gait kinematics (GPS and GVS) also have some limitations. In fact, although they are able to identify a deviation from physiological gait, the direction of such an alteration (e.g. below or above normality) is not provided. Furthermore, unless the single joint angles are examined across the entire gait cycle, it is unknown whether the deviation is due to time-shifts, or if the joint curves deviate in magnitude only [33]. In our specific case, these parameters can quantify how far spasticity shifts the gait pattern from normality with respect to non-spastic
Table 5 Comparison between EMG RMS values for the two groups calculated during the whole gait cycle. Values are expressed as mean ± SD. Muscular activation MS spasticity
Rectus femoris (V) Tibialis anterioris (V) Gastrocnemius lateralis (V)
MS control group
Left
Right
Left
Right
0.644 ± 0.12 0.558 ± 0.09 0.582 ± 0.10
0.639 ± 0.11 0.573 ± 0.10 0.594 ± 0.11
0.504 ± 0.08 0.607 ± 0.06 0.547 ± 0.10
0.493 ± 0.06 0.610 ± 0.08 0.560 ± 0.10
Status p-value
Limb p-value
b0.001⁎ 0.024 0.145
0.720 0.609 0.596
⁎ Denotes statistical difference after Bonferroni correction.
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
M. Pau et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Fig. 1. Typical example of joint angles of an individual with MS affected by spasticity during gait (F, 49 years old, EDSS = 6.5). Blue curve represents right limb, red curve left limb and the gray curve normality (mean ± SD). (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)
Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352
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individuals and which joints are particularly involved in creating such differences, but any doubt on their interpretation can be removed after detailed screening of the gait analysis graphs. 5. Conclusions The results of the present study suggest that the presence of spasticity originates a clearly identifiable gait pattern in individuals with MS, characterized not only by reduced speed, cadence, stride length, swing phase and increased double support time, but also by specific alterations in kinematic and muscle activation. In particular, the three-dimensional quantitative analysis highlighted larger deviations from physiological gait in comparison with MS patients not affected by spasticity at the level of hip, knee and ankle joints as well as excessive activation of the rectus femoris. These findings show that it is essential to have available a large dataset of quantitative measures of gait that includes kinematic and EMG parameters in addition to spatio-temporal ones to accurately characterize gait impairments consequent to spasticity. Although there are some doubts regarding the complexity of the procedure and the feasibility of its clinical use [17], the quality and amount of information associated with gait analysis is not comparable with any other evaluation technique, and the recent availability of synthetic measures of kinematics, such as GPS/GVS, make data interpretation much easier than in the past. Furthermore, the rapid development of alternative and userfriendlier technologies to assess the kinematics of gait, such as wearable inertial sensors [34–35], should encourage the spread of quantitative methods for the analysis of human movement in clinical settings. Such approaches might also be useful in verifying the effectiveness of pharmacologic and rehabilitative treatments, a critical step in the process of spasticity management, even considering that the results of “traditional” gait measures may be inconsistent with the perception of patients [36] and thus may supply misleading information to the clinician. Declaration of interest The authors report no conflicts of interest. References [1] G.R. Johnson, Outcome measures of spasticity, Eur. J. Neurol. 9 (1) (2002) 10–16. [2] M.A. Rizzo, O.C. Hadjimichael, J. Preiningerova, T. Vollmer, Prevalence and treatment of spasticity reported by multiple sclerosis patients, Mult. Scler. 10 (2004) 589–595. [3] J.K. Haselkorn, S. Loomis, Multiple sclerosis and spasticity, Phys. Med. Rehabil. Clin. N. Am. 16 (2005) 467–481. [4] D.W. Paty, G.C. Ebers, Clinical features, in: D.W. Paty, G.C. Ebers (Eds.), Multiple Sclerosis, F.A. Davis company, Philadelphia, 1998. [5] A.J. Thompson, L. Jarrett, J. Marsden, V.L. Stevenson, Clinical management of spasticity, J. Neurol. Neurosurg. Psychiatry 76 (2005) 459–463. [6] J.C. Hobart, A.A. Riazi, A.J. Thompson, et al., Getting the measure of spasticity in multiple sclerosis: the multiple sclerosis spasticity scale (MSSS-88), Brain 129 (2006) 224–234. [7] J.F. Fleuren, G.E. Voerman, C.V. Erren-Wolters, et al., Stop using the Ashworth Scale for the assessment of spasticity, J. Neurol. Neurosurg. Psychiatry 81 (1) (2010) 46–52 2010. [8] R. Wartenberger, Pendulousness of the legs as a diagnostic test, Neurology 1 (1951) 18–24.
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Please cite this article as: M. Pau, et al., Effect of spasticity on kinematics of gait and muscular activation in people with Multiple Sclerosis, J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.09.352