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Table of Contents
Year : 2022  |  Volume : 5  |  Issue : 2  |  Page : 106-111

Quantitative gait analysis in patients with spinocerebellar ataxia—An explorative analysis

1 Physiotherapy Center, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
2 Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
3 Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India

Date of Submission28-Oct-2021
Date of Decision20-Dec-2021
Date of Acceptance28-Jan-2021
Date of Web Publication21-Jul-2022

Correspondence Address:
Dr. V SelvaGanapathy
Senior Physiotherapist, Physiotherapy Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bengaluru - 560029, Karnataka
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/AOMD.AOMD_52_21

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BACKGROUND: Quantitative gait analysis is aimed at quantifying the degree of gait impairment in a patient. It helps to estimate the severity, track the prognosis, and identify the treatment effect in patients. There is a paucity of studies assessing gait characteristics in patients with spinocerebellar ataxia (SCA) using instrumental gait analysis. Here, we aim to identify the gait characteristics in patients with SCA and compare them with age-matched healthy individuals. METHODS: In this retrospective cross-sectional study, we analyzed the gait analysis data of patients with SCA from May 2018 to January 2020 in the gait and balance laboratory of the Physiotherapy Center in NIMHANS and compared them with age-matched controls from the existing database. The data were analyzed using an independent t-test. RESULTS: Each group consisted of 49 subjects. The SCA group had a mean age of 37.88 ± 13.25 years and the control group has a mean age of 40.88 ± 14.57 years, with a male to female ratio of 1:0.96 and 5:2, respectively. A significant difference was observed in all gait parameters (p < 0.001) between the SCA and control groups, except for swing time (p = 0.396). The SCA group demonstrated reduced velocity and cadence compared to the control group. The values of spatial parameters were reduced in the SCA group, with increased temporal parameters along with the base of support. The coefficient of variation was significantly increased in the SCA group, and the highest value was recorded for step length (10.45 ± 7.14). CONCLUSION: Patients with SCA demonstrated significant deviation in gait parameters from the normal values. The increased step-to-step variability in this patient population suggests an increased risk of falls. Identifying the changes in gait parameters at an early stage may help in planning the rehabilitation of patients with SCA, with focus on fall prevention strategies by targeting improvements in gait variability.

Keywords: Gait analysis, gait variability, spinocerebellar ataxia

How to cite this article:
James TT, SelvaGanapathy V, Kamble N, Dhargave P, Pal PK, Muralidharan K. Quantitative gait analysis in patients with spinocerebellar ataxia—An explorative analysis. Ann Mov Disord 2022;5:106-11

How to cite this URL:
James TT, SelvaGanapathy V, Kamble N, Dhargave P, Pal PK, Muralidharan K. Quantitative gait analysis in patients with spinocerebellar ataxia—An explorative analysis. Ann Mov Disord [serial online] 2022 [cited 2023 May 29];5:106-11. Available from: https://www.aomd.in/text.asp?2022/5/2/0/351585

  Introduction Top

Gait disturbances as a result of cerebellar disorders are characterized by variations in the temporal and spatial parameters and an increased base of support, which remarkably influence the patients’ abilities to perform daily activities with increased fall risk, leading to poor quality of life.[1],[2] Spinocerebellar ataxia (SCA) is a hereditary degenerative disorder leading to a combination of clinical presentations including dysmetria, dyssynergia, dysdiadochokinesia, dysrhythmia, and intentional tremor.[3] The ataxic gait, also called drunkard gait, is caused by deficits in interlimb coordination, dynamic limb control, and trunk and limb kinematics, leading to irregular timing and amplitude of steps.[4],[5] The cerebellum is responsible for the feedback and feed-forward control of balance and the modulation of sensorimotor interactions, leading to adaptations in motor performance.[6] A lesion on the cerebellum impacts the spatial and temporal activation of muscles, leading to specific impairments in gait.

Patients with equilibrium and balance deficits demonstrated more remarkable gait variations than those with coordination deficits.[5] In addition, increased step-to-step variations of parameters are observed in patients with SCA. While Serrao et al.[7] suggested this to be an adaptive and compensatory mechanism leading to step-by-step adjustment, Gabell et al.[8] suggested the influence of the gait patterning process for the variability in step length and stride time; however, the deterioration of balance mechanisms influences the variability in stride width and doubles support time. Increased gait variability was found to be directly correlated with fall risk.[2]

Instrumented gait analysis has been found to be accurate in detecting subtle variations during clinical assessment.[9] It helps clinicians identify the problems in advance, which augments the clinical examination in estimating the severity, tracking the prognosis, treatment effect, and initiating gait modification and fall reduction strategies. However, studies comparing the gait characteristics of patients with SCA with age-matched healthy control subjects using instrumented gait analysis are lacking. The main objective of our study is to quantify gait characteristics in patients diagnosed with SCA and compare the same with healthy controls using the GAITRite Walkway system with a relatively large sample of patients compared to previous studies.

  Materials and Methods Top

We adopted a retrospective cross-sectional study design to identify the differences in gait parameters in patients with SCA and healthy individuals. Patients were diagnosed with SCA by the consultant neurologist at NIMHANS through clinical or genetic analysis and were referred to the physiotherapy center of the institution for rehabilitation. Data of the quantitative gait analysis performed for these patients were retrieved from the center’s gait and balance laboratory for analysis. The data were available for all patients assessed from May 2018 to January 2020. The data of the age-matched control group were obtained from within the database during the same period.

The gait parameters of the subjects were assessed using the GAITRite® Electronic Walkway System (CIR Systems Inc., Clifton, New Jersey). The system consists of a 7-meter sensor-enabled walkway that detects spatiotemporal parameters. The subjects were familiarized with the test procedure through verbal commands and one trial session before the test. They were asked to initiate their walk 1 meter ahead of the walkway and terminate it 1 meter after the walkway to nullify the acceleration and deceleration effects of the gait cycle. The subjects were asked to walk at a comfortable speed and were not given any commands during the test to avoid bias. The gait parameters used for analysis in this study are velocity, cadence, step time, step length, stride time, stride length, stride velocity, heel-to-heel base of support, swing time, stance time, and double support time.[10]

The variability between independent footfalls was analyzed using the coefficient of variation (CV), which is measured by dividing the standard deviation of each parameter by its mean value expressed in percentage.[11] The coefficient of variation of step length, step time, stride length, stride time, stride velocity, swing time, stance time, double support time, and heel-to-heel base of support were analyzed between the SCA and control groups. The graphical representation of the output from the GAITRite software is illustrated in [Figure 1]. Descriptive statistics were performed to analyze the demographic details. The difference between the SCA and control groups was analyzed using an independent t-test after using Levene’s test to assess the homogeneity of variance. A value of p < 0.05 was considered to be statistically significant.
Figure 1: Graphical representation of the output from GAITRite for a single SCA patient and a control

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  Results Top

We scrutinized a total of 98 subjects for gait analysis data, with 49 subjects in each group. The age range was 20–75 years in the SCA group and 23–72 years in the control group. The SCA group consisted of 35 men and 14 women and the control group had 25 men and 24 women. The descriptive analysis of the demographic details is presented in [Table 1]. The mean age of onset, duration, the International Cooperative Ataxia Rating Scale score, and subtypes of SCA were available for only 24 patients ([Table 1]).
Table 1: Demographic details of the study population

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Homogeneity was maintained between both groups in terms of the demographic characteristics. The analysis of various gait parameters under consideration in the SCA and control groups is presented in [Table 2]. Significant difference was observed for all the gait parameters except for swing time in the SCA and control groups. The mean swing time for the SCA group was 0.43 ± 0.55 seconds and that of the control group was 0.42 ± 0.03 seconds (p = 0.396). The spatial parameters such as step length and stride length were reduced in the SCA group with a mean difference of -13.74 cm and -27.99 cm, respectively (p < 0.001). The temporal parameters such as step time, stride time, swing time, and stance time were significantly higher in the SCA group than the control group. Velocity, cadence, and stride velocity parameters were lower in the SCA group than the control group ([Figure 2]). The base of support was 15.08 ± 6.51 cm in the SCA group and 10.29 ± 3.71 cm in the control group, demonstrating a significant increase (p < 0.001) in the SCA group.
Table 2: Quantitative gait analysis of the SCA and control groups

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Figure 2: Graphical representation of mean velocity and cadence between the SCA and control groups

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The coefficient of variation of the spatial and temporal parameters was <5% and heel-to-heel base of support was <25% in the control group, which was acceptable.[2] The SCA group demonstrated significant step-to-step variability in parameters assessed by the coefficient of variation, with the highest significant variation observed for step length (10.45 ± 7.14 vs. 3.98 ± 2.47, p < 0.001; [Figure 3]). The coefficient of variation of heel-to-heel base of support between the SCA and control groups was not significant (p = 0.067).
Figure 3: Graphical representation of mean coefficient of variation of step length and step time between the SCA and control groups

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  Discussion Top

Studies analyzing gait variables in patients with SCA utilizing instrumental gait analysis are scarce. To the best of our knowledge, ours is the only study to include the maximum number of subjects with SCA and compare them with age-matched controls. The assessment of gait parameters was performed as per the typical gait pattern of patients with SCA, which demonstrates a wide-based, slowed, and short-stepped gait to compensate for balance and coordination deficits during the motor task of walking. The values of the spatial parameters were reduced in the SCA group compared to the control group, while the temporal parameters were increased. Of note, the difference in swing time between the SCA and control groups was not significant. It has been noted that the patients with SCA tend to have a predominant anteroposterior instability than a mediolateral one while standing.[9] The reduced cadence, increased stance, and double support time can be considered as compensatory mechanisms to the balance deficit to ensure a stable walking pattern.[12] Reduced speed and cadence help individuals maintain balance during the single-leg stance without deviating from the motion trajectory.[4]

The intermediate cerebellum, interpositus nucleus, and lateral cerebellum function differently in controlling limb dynamics, postural adaptations, and motor control during gait.[5] Deficits in these functions are observed in accordance with the area affected in patients with SCA. Therefore, the variations in gait parameters are considered to be the consequence of structural malfunctioning when compared to the age-matched controls.

Human ambulation is a collection of repetitive gait cycles. It is widely acknowledged that approximately 60% of a gait cycle contributes to the stance phase and the remaining 40% contributes to the swing phase. This rhythmic movement represents gait harmony. Iosa et al.[13],[14],[15] have extensively studied this harmony in gait cycle and suggested it to be similar in value to the golden ratio (ϕ), which was first proposed by Euclid in the 3rd century BC. The ratio of stance to swing phase (gait ratio) in a healthy individual is regarded to be close to the golden ratio value (1.618034). Serrao et al.[16] considered this as a convergence attractor state with minimal energy expenditure and variability.

The gait ratio of the control group in our study was 1.69 ± 0.21, which is similar in value to the golden ratio, while the SCA group had a gait ratio of 1.94 ± 0.49, demonstrating a significant deviation from the normal value (p = 0.001). Gait ratio remarkably deviates from the normal value in pathological gaits, leading to reduced efficiency. Increased trunk and upper body oscillations with altered limb dynamics in patients with cerebellar ataxia leads to an increased dependency on compensatory mechanisms, leading to increased energy expenditure and variability. We found that the swing time in both groups was not significantly different, which is similar to that observed in other studies.[5],[11],[12] We suggest that the variation identified in gait ratio in the SCA group is due to increased stance time compared to that in the control group, which may be a compensatory strategy to maintain dynamic equilibrium during ambulation.

We identified a significant increase in all the coefficient of variation parameters for gait, which represents the step-to-step variations. Several authors have reported increased variability in patients with SCA,[1],[12],[17] which is considered to be one of the hallmark features of gait assessment in this population. This, in turn, directly correlates with an increased risk of falls. The reasons for these variations include imprecise execution of movement; irregular amplitude, timing, and generation of force during a multijoint movement; incoordination due to the cerebellar lesion; and disturbance in the sensory feedback and integration of sensory cues.[4],[5],[12],[18] The deficits in the dynamic balance are considered to be related to the increased variability in gait parameters, while the reduced spatial and increased temporal parameters are associated with a compensatory mechanism for impaired postural control and instability of the trunk.[9],[19] The presence of a wide base of support in patients with SCA is to compensate for the wide oscillations of the center of mass and dynamic instability that occur during a single-leg stance. These mechanisms, in turn, reduce the velocity and cadence in patients with SCA.

The findings of our study can be used to guide the rehabilitation of patients with SCA. The exercise protocols should focus on strategies to reduce fall risk. Gait training with the aid of audiovisual feedback may be useful in reducing the variations in the gait parameters. Despite these promising findings, our study has some limitations. The onset, duration, and subtypes of SCA were available only for 24 patients, restricting further subgroup analysis. Future studies with a subgroup analysis of the duration may provide insights into the early changes in gait parameters in patients with SCA compared to healthy controls.

In conclusion, the gait parameters of patients with SCA markedly vary from those of age-matched controls. The increased step-to-step variation in patients with SCA is higher than that in the healthy controls, which is one of the key features in gait assessment. Since the variation in gait parameters are linked to falls, it needs to be considered when prescribing drugs or exercises for a favorable prognosis in the patient population.



Author contribution

Concepts: TT James, V SelvaGanapathy; Design: TT James, V SelvaGanapathy, N Kamble; Definition of intellectual content: TT James, V SelvaGanapathy, N Kamble, P Dhargave, P Pal and K Muralidharan; Literature search: TT James; Data acquisition: TT James, V SelvaGanapathy; Data analysis: TT James, V SelvaGanapathy, N Kamble; Statistical analysis: TT James, V SelvaGanapathy; Manuscript preparation: TT James, V SelvaGanapathy; Manuscript editing: TT James, V SelvaGanapathy, N Kamble; Manuscript review: P Dhargave, P Pal and K Muralidharan.

The manuscript has been read and approved by all the authors, that the requirements for authorship as stated earlier in this document have been met, and that each author believes that the manuscript represents honest work,

Ethical compliance statement

The procedures followed were in accordance with the ethical standards.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Serrao M, Chini G, Casali C, Conte C, Rinaldi M, Ranavolo A, et al. Progression of gait ataxia in patients with degenerative cerebellar disorders: A 4-Year follow-up study. Cerebellum 2017;16:629-37.  Back to cited text no. 1
Wuehr M, Schniepp R, Ilmberger J, Brandt T, Jahn K. Speed-dependent temporospatial gait variability and long-range correlations in cerebellar ataxia. Gait Posture 2013;37:214-8.  Back to cited text no. 2
Holmes G. The cerebellum of man. Brain 1939;62:1-30.  Back to cited text no. 3
Ebersbach G, Sojer M, Valldeoriola F, Wissel J, Müller J, Tolosa E, et al. Comparative analysis of gait in Parkinson’s disease, cerebellar ataxia and subcortical arteriosclerotic encephalopathy. Brain 1999;122:1349-55.  Back to cited text no. 4
Ilg W, Golla H, Thier P, Giese MA. Specific influences of cerebellar dysfunctions on gait. Brain 2007;130:786-98.  Back to cited text no. 5
Martino G, Ivanenko YP, Serrao M, Ranavolo A, d’Avella A, Draicchio F, et al. Locomotor patterns in cerebellar ataxia. J Neurophysiol 2014;112:2810-21.  Back to cited text no. 6
Serrao M, Pierelli F, Ranavolo A, Draicchio F, Conte C, Don R, et al. Gait pattern in inherited cerebellar ataxias. Cerebellum 2012;11:194-211.  Back to cited text no. 7
Gabell A, Nayak USL. The effect of age on variability in gait. J Gerontol 1984;39:662-6.  Back to cited text no. 8
Buckley E, Mazzà C, Mcneill A. A systematic review of the gait characteristics associated with cerebellar ataxia. Gait Posture 2018;60:154-63.  Back to cited text no. 9
Cir Systems Inc. The GAITRite Electronic Walkway: Measurements & Definitions. 2006. p. 1-16.  Back to cited text no. 10
Palliyath S, Hallett M, Thomas SL, Lebiedowska MK. Gait in patients with cerebellar ataxia. Mov Disord 1998;13:958-64.  Back to cited text no. 11
Stolze H, Klebe S, Petersen G, Raethjen J, Wenzelburger R, Witt K, et al. Typical features of cerebellar ataxic gait. J Neurol Neurosurg Psychiatry 2002;73:310-2.  Back to cited text no. 12
Iosa M, Bini F, Marinozzi F, Paolucci S, Martino A, Sonia C. Stability and harmony of gait in patients with subacute stroke. J Med Biol Eng 2016;36:635-43.  Back to cited text no. 13
Iosa M, De Bartolo D, Morone G, Boffi T, Mammuccari E, Vannozzi G, et al. Gait phase proportions in different locomotion tasks: The pivot role of golden ratio. Neurosci Lett 2019;699:127-33.  Back to cited text no. 14
Iosa M, Fusco A, Marchetti F, Morone G, Caltagirone C, Paolucci S, et al. The golden ratio of gait harmony: Repetitive proportions of repetitive gait phases. Biomed Res Int 2013;2013:918642.  Back to cited text no. 15
Serrao M, Chini G, Iosa M, Casali C, Morone G, Conte C, et al. Harmony as a convergence attractor that minimizes the energy expenditure and variability in physiological gait and the loss of harmony in cerebellar ataxia. Clin Biomech 2017;48:15-23.  Back to cited text no. 16
Buckley C, Alcock L, McArdle R, Ur Rehman RZ, Del Din S, Mazzà C, et al. The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: Insights from gait and postural control. Brain Sci 2019;9:1-21.  Back to cited text no. 17
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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2]


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