Research in Developmental Disabilities 35 (2014) 2261–2266
Contents lists available at ScienceDirect
Research in Developmental Disabilities
Gait pattern differences in children with unilateral cerebral palsy Andrzej Szopa a, Małgorzata Domagalska-Szopa a,*, Andrzej Czamara b a b
School of Health Sciences, Medical University of Silesia, Medyko´w 12, Katowice 40-752, Poland College of Physiotherapy, Kos´ciuszki 4, Wrocław 50-038, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 6 February 2014 Received in revised form 12 May 2014 Accepted 29 May 2014
Children with cerebral palsy (CP) often have atypical body posture patterns and abnormal gait patterns resulting from functional strategies to compensate for primary anomalies that are directly attributable to damage to the central nervous system. Our previous study revealed two different postural patterns in children with unilateral CP: (1) a pattern with overloading of the affected body side and (2) a pattern with under-loading of the affected side. The purpose of present study was to test whether different gait patterns dependent on weight distribution between the affected and unaffected body sides could be detected in these children. The study included 45 outpatients with unilateral CP and 51 children with mild scoliosis (reference group). The examination consisted of two inter-related parts: paedobarographic measurements of the body mass distribution between the body sides and three-dimensional instrumented gait analysis. Using cluster analysis based on the Gillette Gait Index (GGI) values, three gait patterns were described: a scoliotic gait pattern and two hemiplegic gait patterns, corresponding to overloading/under-loading of the hemi-side, which are the pro-gravitational gait pattern (PGP) and the anti-gravitational gait pattern (AGP), respectively. The results of this study showed that subjects with AGP presented a higher degree of deviation from the normal gait than children with PGP. This proof that there are differences in the GGI between the AGP and PGP could be a starting point to identify kinematic differences between these gaits in a follow-up study. ß 2014 Elsevier Ltd. All rights reserved.
Keywords: Weight bearing Paedobarographic measurements Gillette Gait Index Cluster analysis
1. Introduction Children with cerebral palsy (CP) often have atypical body posture patterns and abnormal gait patterns (Rosenbaum et al., 2007) resulting from functional strategies to compensate for primary anomalies that are directly attributable to damage to the central nervous system. These primary impairments due to this upper motor neuron syndrome can lead, in the longer term, to adaptations in the musculoskeletal system, which are known as secondary impairments, such as inadequate muscle growth, which causes contractures (shortening) of muscles and tendons, bone deformities, misalignment of the joints and excessive fatigue with movement and walking (Rosenbaum et al., 2007). One of the most striking features of CP is the
* Corresponding author. Tel.: +48 32 208 87 12. E-mail addresses: [email protected] (A. Szopa), [email protected] (M. Domagalska-Szopa), [email protected] (A. Czamara). http://dx.doi.org/10.1016/j.ridd.2014.05.020 0891-4222/ß 2014 Elsevier Ltd. All rights reserved.
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variability in its clinical presentation. The diversity of gait deviations observed in children with CP has led to repeated efforts to develop gait classification systems to assist in the diagnosis, clinical decision-making and communication (Hullin, Robb, & Loudon, 1996; Lin, Guo, Su, Chou, & Cherng, 2000; Rodda, Carson, Graham, Galea, & Wolfe, 2004; Stebbins et al., 2004; Sutherland & Davids, 1993; Winters, Gage, & Hicks, 1987). Although the majority of studies have used advanced three-dimensional measurement systems to collect gait data on three planes of motion, most classifications of CP gait have been constructed using only sagittal plane data (Boyd & Graham, 1999; Corry et al., 1998; Koman et al., 2001; Mackey, Lobb, Walt, & Stott, 2003). Although these classifications are used for diagnostic purposes as well as to streamline communication and facilitate clinical decision-making process, including surgical interventions (Dobson, Graham, Baker, & Morris, 2005; Dobson, Morris, Baker, & Graham, 2007; Graham & Selber, 2003; Gage, 1991; Ounpuu, Deluca, & Davis, 2000; Rodda & Graham, 2001), none of the classifications have appeared to be useful in the physiotherapy planning for the children with CP. The term ‘‘gait classification’’ refers to a system that allows for the allocation of gait patterns into groups that can be differentiated from one another based on a set of defined variables. This classification is distinct from gait indices, assessment scores and scales, which score individual gait variables or provide an overall index to quantify the deviations from normal gait without group allocation. Currently, one of the most commonly used indices for quantifying the deviations from normal gait is the Gillette Gait Index (GGI; previously called the Normalcy Index; Gage, 2004; Schutte et al., 2000). The GGI uses a single number to measure the deviation of the patient’s gait from the average gait of a subject without pathology. The GGI values have been standardised for particular diagnostic categories in children with hemiplegia, according to Winter’s classification by Schutte et al. (2000). The average and range of index values were determined for each diagnosis group of hemiplegia: types I, II, III and IV (Schutte et al., 2000). Moreover, the GGI was shown to be efficient in categorising the pathology in children with CP, to be clinically applicable (Assi, Ghanem, Lavaste, & Skalli, 2009; Schutte et al., 2000) and to be correlated with physical functioning (Romei, Galli, Motta, Schwartz, & Crivellini, 2004). Despite the observation that a group of children with hemiplegia (SH) appears to be relatively homogeneous in terms of body posture, our previous study showed that their postural patterns were different (Domagalska, Szopa, & Lembert, 2011). Based on weight bearing between the affected and unaffected body sides, two types of asymmetrical postural patterns were described: (1) the postural pattern with overloading of the affected body side (pro-gravitational postural pattern [PGPP]) and (2) the postural pattern with under-loading of the affected side (anti-gravitational postural pattern [AGPP]; Domagalska et al., 2011). Because the results of previous research have been promising, we decided to test whether different gait patterns corresponding to weight bearing on the hemi-side could be detected in these children. The present study aimed to verify the hypothesis that the degree of deviation of a hemiplegic gait pattern from a normal gait (evaluated by the GGI) depends on the nature of weight bearing on the unaffected or affected body side. Proof that there are differences in the GGI between hemiplegic gait patterns could be a starting point for identifying kinematic differences between these gaits in a follow-up study.
2. Materials and methods This study was approved by the Local Ethical Committee. The patients and their parents provided informed consent before data collection. 2.1. Subjects From all of 111 children with a diagnosis of unilateral CP, resident in our geographical region, who were the outpatients of the local paediatric rehabilitation centres, 57 met the inclusion criteria for study of three-dimensional instrumented gait analysis. Although the exclusion criteria included lower limb surgery in the previous 6 months and botulinum toxin A injection into the lower limb in the previous 6 months, appointments were arranged over the period of data collection to minimise these exclusion. However, out of 57 eligible, identified individuals, a representative sample of 45 participated in study: 17 girls and 28 boys; deficits occurred on the right side in 29 patients and on the left side in 16 patients; the mean age was 9 years 5 months old (range, 7 years 4 months to 12 years 2 months; SD = 2.11); 75.5% at Level I and 24.5% at Level II on the Gross Motor Function Classification System. All subjects met the following criteria: (1) older than 7 years of age (to minimise the incidence of instability of kinematic parameters), (2) able to walk without assistive mobility devices and orthoses, (3) able to follow verbal directions, (4) no surgical procedures in the lower extremities and (5) no dislocation of the hip. The subjects with CP had the following additional criteria: (1) diagnosis of SH, (2) non-use of pharmacological agents at the time of the study and (3) no spasticity management 6 months before the evaluation. Fifty-one children with mild scoliosis (scoliosis curve 10–208), range of lateral curvature of 11 208 (mean, 188) and a mean age of 9 years 2 months old (range, 7 years, 5 months to 12 years, 3 months; SD = 1.99) were recruited as references. All of them were outpatients at a local Centre for Corrective Gymnastics. Statistical analysis confirmed that the patient demographic characteristics were similar in both groups.
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2.2. Methods Our study consisted of two inter-related parts: 1. Paedobarographic measurements of the distribution of body mass between the body sides and 2. Three-dimensional instrumented gait analysis (3DGA).
2.2.1. Testing procedure: paedobarographic measurements Analyses of the weight distribution on the base of support (BOS) between the affected and unaffected body sides and the centre of pressure (COP) were simultaneously conducted. A force plate pressure distribution measurement system, PDM-S (Zebris Medizintechnik GmbH, Isny, Germany), with FootPrint software was applied for these types of paedobarographic measurements. Each measurement was recorded three times (three trials, each lasting 30 sec with a 30-sec pause between trials), and the mean value from three trials was taken as value of weight distribution on the right and left sides in the reference subjects and on the unaffected/affected body sides in children with hemiplegia for additional analysis. Based on the above-mentioned specifications, the Index of Asymmetry (AI) of weight distribution between the right and left body sides was calculated for the references. The standard deviation was used as a criterion to define the asymmetry of weight distribution on the affected/unaffected body sides in children with SH (AI > 9.83%) to create four SH subgroups (LL, RR, RL and LR) and two reference subgroups (NR and NL; Domagalska-Szopa & Szopa, 2013). Based on the AI of weight distribution on the left and right body sides, the children with mild scoliosis were divided into two subgroups (Table 1). Based on both the AI of weight distribution on the unaffected/affected body sides and side of hemiplegia (to exclude the effect of right side/left side hemiplegia on the GGI values), the hemiplegic children were divided into four subgroups (Table 1). 2.2.2. Three-dimensional gait analysis (3DGA) Three-dimensional instrumented gait analysis (3DGA) was performed using the Compact Measuring System for 3D realtime motion analysis (CMS-HS 3D) and WinGait software (Zebris Medizintechnik GmbH, Germany). The CMS HS 3D system is based on 15 active ultrasonic markers (five triplets of ultrasound markers; Kocsis, Kiss, Knoll, & Jurak, 2000). Before the gait analysis, we identified the following anatomical landmarks using an instrumented pointer: hip joint centre, knee rotation centre (internal and external), ankle rotation centre (internal and external), forefoot landmark (between the second and third metatarsals) and rear foot (heel). We recorded the gait data as the subjects walked on a treadmill (Alfa XL, Kettler, Germany). Before data collection, all the subjects had the opportunity to practice walking on the treadmill. By trial and error, the speed of the treadmill was adjusted to the ability of each child, such that his or her gait was the most natural. The subjects were required to perform a minimum of 3 min of walking on the treadmill, without shoes or assistive devices. Using double-sided adhesive tape, we attached and placed bilateral markers on the skin. Depending on each subject’s walking ability, five to eight gait cycles were recorded. Separate measurements for the right and left gait cycles for each subject were obtained. The data were collected from the most representative trial for each side. The data collected were reported using WinGait software. To characterise the gait patterns, we used the GGI, which is a single number, derived from gait kinematics and spatiotemporal parameters that quantify the deviation of a pathological gait from the normal gait. Two experienced physical therapists selected both the weight-bearing image and gait cycles for analysis. The weightbearing image and gait cycle that were most characteristic of the child—in both experts’ opinions—were chosen for further analysis. When their assessments differed, the senior author (D.-S.,M.) selected the image and cycle that were analysed. The accuracy of their evaluations was then analysed. The intraclass correlation coefficient (ICC) with 95% confidence interval was used to measure the overall intraobserver and interobserver agreement. The intraobserver agreement was calculated separately for each paedobarographic measurements (PT) and gait analysis (GGI for affected lower limb), based on two examinations performed by the same two assessors in each group (SH and reference; 10 subjects each; 40 examinations in total). The interobserver agreement was calculated (for the same subjects) for the two assessors. For the analysis, a mean ICC value of 0.80 and above reflected excellent reliability, that between 0.70 and 0.79 indicated good reliability, and that below 0.70 reflected poor to moderate reliability. Table 1 Participants divided into subgroups taking into account the tendency to overload the left body side (right/left) in children with mild scoliosis and both the side of hemiplegia and weight distribution on the unaffected/affected body sides in children with hemiplegia. Postural patterns
Definition
N
%
NL NR LL RR LR RL
Mild scoliosis and the tendency to overload the right body side Mild scoliosis and the tendency to overload the left body side Left side hemiplegia and the tendency to overload the affected body side Right side hemiplegia and the tendency to overload the affected body side Left side hemiplegia and the tendency to overload the unaffected body side Right side hemiplegia and the tendency to overload the unaffected body side
28 23 10 13 6 16
29.2 23.8 10.5 13.5 6.3 16.7
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Every outcome of the PT examination demonstrated good to very high levels of intraobserver agreement for both groups, with the ICC for all variables ranging from 0.76 to 0.95 for CP and from 0.79 to 0.97 for the references. The ICC values indicated a very high level of interobserver agreement among the assessors, with the ICC ranging from 0.90 to 0.97 for both groups. The GGI demonstrated good interobserver (0.70–0.77) and intraobserver (0.68–0.75) repeatability for both groups. With respect to both the within and between assessor agreement, the children with scoliosis showed lower variability in the GGI than the hemiplegic children. 2.3. Statistical analysis The GGI for each gait cycle of individuals from the SH and reference groups was calculated (separately for each leg) using the procedure described by Schutte et al. (2000). The GGI was calculated based on 16 selected gait parameters taken from the objective gait analysis data, including the following: stance phase, expressed as the percentage of the gait cycle; walking speed, normalised to the leg length; cadence; mean pelvic tilt; pelvic range of motion on the sagittal plane; mean pelvic rotation; minimum hip flexion; hip range of movement in the sagittal plane; peak abduction in swing; mean hip rotation in stance; knee flexion at initial contact; time to peak knee flexion in swing, expressed as the percentage of the gait cycle; knee range of movement on the sagittal plane; peak dorsiflexion in stance and swing; and mean foot progression. Non-hierarchical k-means clustering was used in the selection of GGI, assuming three clusters (Davidson, 2002). K-means clustering is used when there have already been hypotheses concerning the number of clusters in the cases or variables. Because gait pattern was assumed to depend on weight-bearing patterns (pattern with almost symmetrical weight-bearing, an asymmetrical pattern with hemi-side overloading and a pattern with hemi-side under-loading), the identification of three clusters in the cluster analysis was justified. The mean and SD values of the GGI were calculated for the entire group and for each of the three clusters, and these values were compared among the subgroups. Analysis of variance (ANOVA, Tukey’s post-hoc test) was used to detect the differences in the GGI among the three clusters. Only significant differences (P < 0.05) among clusters were described. 3. Results In accordance with the cluster analysis results, 18 (18.7%) participants were classified into Cluster 1, whereas 28 (22.3%) were included in Cluster 2, and 57 (60%) were included in Cluster 3 (Table 2). There were significant differences among the means of the various clusters for both GGIs, as shown in Table 3. Tukey’s post hoc test revealed the GGIs calculated for both lower limbs: right or affected and left or unaffected reliably differentiated Cluster 1 from both Clusters 2 and 3 as well as Cluster 2 from Cluster 3 via their cluster means (each P < 0.001). Cluster 1 was predominantly characterised by subgroups LL and RR of the SH group (Table 4). Cluster 2 was predominantly characterised by subgroups RL and LR of the SH group (Table 4). Cluster 3 was predominantly characterised by subgroups NR and NL of the reference group (Table 4). Three gait patterns, in terms of the degree of deviation from a normal gait, were recognised by the GGI calculation. One (Cluster 3, n = 57) approached the normal gait patterns of the references (100%). All the GGI values were within the normal Table 2 GGI scores represent the mean values for the right* and left* lower limbs in the reference group and the unaffected** and affected*** lower limb in hemiplegic children in particular clusters. GGI
Cluster
N
%
Mean
SD
Minimum
Maximum
Right/affected
1 2 3 1 2 3
18 27 57 18 27 57
18.7 22.3 60.0 18.7 22.3 60.0
163.9*** 74.5* 15.04** 94.5*** 31.7* 15.3**
63.32*** 21.9* 7.9** 21.5*** 12.9* 9.19**
86.4*** 44.5* 3.8** 61.7*** 10.7* 4.74**
347.4*** 131.7* 31.8** 128.8*** 73.1* 45.4**
Left/unaffected
Table 3 ANOVA results and differences between the means of the various clusters for GGI of both lower limbs. GGI
Groups
Sum of squares
df
Mean squared
F
P
Right/affected
Between Within Total Between Within Total
24,1584.51 12,0792.3 60,219.9 62,348.30 31,174.15 14,142.89
2 93 95 2 93 95
647.52
186.55
.00000
152.07
204.99
.00000
Left/unaffected
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Table 4 Non-hierarchical k means clustering. Subgroup
NR NL RR RL LR LL Total
Cluster 1
Cluster 2
Cluster 3
Total
N
%
N
%
N
%
N
%
0 0 1 11 4 2 18
0.00 0.00 7.69 68.75 66.67 20.00 18.7
0 0 8 5 2 6 21
0.00 0.00 61.54 31.25 33.33 60.00 22.3
23 28 4 0 0 2 57
100.00 100.00 30.77 0.00 0.00 20.00 59
23 28 13 16 6 10 96
25 29 13 17 6 10 100.00
Two subgroups of children with scoliosis – NR, the tendency to overload the right body side, and NL, the tendency to overload the left body side – and four subgroups of children with CP – RR, right side hemiplegia and the tendency to overload the affected body side; RL, right side hemiplegia and the tendency to overload the unaffected body side; LL, left side hemiplegia and the tendency to overload the affected body side and LR, left side hemiplegia and the tendency to overload the unaffected body side.
range (15.04, SD = 7.9). The other two patterns included only patients with SH. The first pattern (Cluster 2) was presented by children with a tendency to overload the affected body side (RR + LL; 14 of 21 subjects), whereas the second pattern (Cluster 1) characterised children with a tendency to overload the unaffected body side (RL + LR; 13 of 18 subjects). Two gait patterns in children with SH were defined as follows: 1. the pro-gravitational gait pattern (PGP) with the tendency to overload the affected body side (Cluster 2) and 2. the anti-gravitational gait pattern (AGP) with the tendency to under-load the affected body side (Cluster 1).
Significantly higher values of the GGI for both the affected and unaffected lower limbs were noted in the subjects included in Cluster 1 (Table 2). 4. Discussion Children with unilateral CP show a variety of gait patterns. Cluster analysis was used for differentiation not only between the gait patterns of children with mild scoliosis and the pathological gait patterns of children with unilateral CP but also within hemiplegic gait patterns. Two different gait patterns, in terms of the degree of deviation of a pathological gait from a normal gait in children with SH, were recognised. One characterised children with a tendency to overload the affected body side and was known as the PGP, and the other, the AGP, was characteristic of children with a tendency to under-load the affected side. During walking, the PGP gives the impression of ‘‘submission’’ to the force of gravity by overloading the affected side (similar to a unilateral crouched gait), whereas the AGP seems to present excessive activity against gravity by under-loading the affected side (similar to a unilateral equinus gait). Clear differences in the GGI of both the affected and unaffected body sides were noted between these gait patterns. The children who presented the AGP showed two times higher GGI measurements for the unaffected leg and three times higher GGI measurements for the affected leg than children with the PGP. The results of this study showed that subjects with AGP presented a much higher degree of deviation from normal gait than children with PGP. As reported by Schutte et al. (2000) and Gage (2004), more severe diagnoses, on average, would correspond to more gait abnormalities and, thus, result in higher index scores. This confirmation is consistent with the results of our study. We observed that the mean index values increased with the level of involvement from Type I to IV hemiplegia. In contrast, no overlap was found between the index values for the references and the individuals with gait abnormalities, within the unaffected and affected body sides in hemiplegics and within the hemiplegic patients. In the present study, the mean GGI values were significantly different between the PGP and AGP. We used cluster analysis for the construction of the hemiplegic gait classification. Cluster analysis is a tool for demonstrating the associations and structure in data, which, although not previously evident, are sensible and useful when discovered. The advantage of using this statistical method is that it provides a systematic and structured approach based on objective gait analysis data. As a result of cluster analysis in the present study, we recognised two categories of gait patterns in children with hemiplegia, which were consistent not only with the characteristic of weight-bearing on the unaffected body side but also with their postural patterns (PGPP and AGPP), which were recognised in our previous study (DomagalskaSzopa & Szopa, 2013). The presented study was allowed for patients with the ability to walk without assistive devices. Additionally the lower limb surgery in the past was an exclusion criterion of the study. That is the reason why only a part of children with unilateral CP population were eligible for inclusion to study of 3D gait analysis. Despite these limitations the obtained finding clearly suggests that the distribution of body mass between the affected and unaffected body sides determines the nature of the compensatory mechanism, which was typical for both the postural and gait pattern in children with unilateral CP. This protocol describes a study that, to the best of our knowledge, is the first attempt to classify gait patterns while accounting for
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the characteristic of weight-bearing on the unaffected or affected body side, using a cohort of children with unilateral CP. Therefore identified differences of GGI between hemiplegic gait patterns should be a starting point for seeking kinematic differences between them in a follow-up study. 5. Conclusion The present study was based on the GGI calculation using selected spatio-temporal parameters and sagittal-, coronal- and transverse-plane kinematics for the pelvis, hip, knee and ankle. The recognition of two different gait patterns in terms of the degree of their deviation from a normal gait in children with unilateral CP could be an essential step toward defining kinematic differences between these gaits in a follow-up study. Then, the AGP and PGP definitions will enable many specialists, including physiotherapists, to differentiate these gaits in children with hemiplegia into clinically significant categories that would assist in treatment decision-making. Conflict of interest The authors declare that they have no conflicts of interest. References Assi, A., Ghanem, I., Lavaste, F., & Skalli, W. (2009). Gait analysis in children and uncertainty assessment for Davis protocol and Gillette Gait Index. Gait & Posture, 30, 22–26. Boyd, R. N., & Graham, H. K. (1999). Objective measurement of clinical findings in the use of botulinum toxin A for the management of children with cerebral palsy. European Journal of Neurology, 6, S23–S35. Corry, I. S., Cosgrove, A. P., Duffy, C. M., McNeill, S., Taylor, T. C., & Graham, H. K. (1998). Botulinum toxin A compared with stretching casts in the treatment of spastic equinus: A randomised prospective trial. Journal of Pediatric Orthopedics, 18, 304–311. Davidson, I. (2002). Understanding K-means non-hierarchical clustering. Albany, NY: SUNY. Dobson, F., Graham, H. K., Baker, R., & Morris, M. E. (2005). Multilevel orthopaedic surgery in group IV spastic hemiplegia. Journal of Bone and Joint Surgery British Volume, 87, 548–555. Dobson, F., Morris, M. E., Baker, R., & Graham, H. K. (2007). Gait classification in children with cerebral palsy: A systematic review. Gait & Posture, 25, 140–152. Domagalska, M., Szopa, A., & Lembert, D. (2011). A descriptive analysis of abnormal postural patterns in children with hemiplegic cerebral palsy. Medical Science Monitor, 17, 110–116. Domagalska-Szopa, M., & Szopa, A. (2013). Body posture asymmetry differences between children with mild scoliosis and children with unilateral cerebral palsy. BioMed Research International, 2013, ID462094. Gage, J. R. (1991). Gait analysis in cerebral palsy (clinics in developmental medicine). London, UK: MacKeith Press. Gage, J. R. (2004). The treatment of gait problems in cerebral palsy. London, UK: MacKeith Press. Graham, H. K., & Selber, P. (2003). Musculoskeletal aspects of cerebral palsy. Journal of Bone and Joint Surgery British Volume, 85, 157–166. Hullin, M. G., Robb, J. E., & Loudon, I. R. (1996). Gait patterns in children with hemiplegic spastic cerebral palsy. Journal of Pediatric Orthopedics Part B, 5, 247–251. Kocsis, L., Kiss, R. M., Knoll, Z., & Jurak, M. (2000). BUTE’s ultrasound-based measuring technique and model for gait analysis. Facta Universitatis Physical Education and Sport, 1, 1–13. Koman, L. A., Brashear, A., Rosenfeld, S., Chambers, H., Russman, B., Rang, M., et al. (2001). Botulinum toxin type A neuromuscular blockade in the treatment of equinus foot deformity in cerebral palsy: A multicenter, open-label clinical trial. Pediatrics, 108, 1062–1071. Lin, C. J., Guo, L. Y., Su, F. C., Chou, Y. L., & Cherng, R. J. (2000). Common abnormal kinetic patterns of the knee in gait in spastic diplegia of cerebral palsy. Gait & Posture, 11, 224–232. Mackey, A. H., Lobb, G. L., Walt, S. E., & Stott, N. S. (2003). Reliability and validity of the Observational Gait Scale in children with spastic diplegia. Developmental Medicine and Child Neurology, 45, 4–11. Ounpuu, S., Deluca, P., & Davis, R. B. (2000). Gait analysis. In B. Neville & R. Goodman (Eds.), Congenital hemiplegia (clinics in developmental medicine) (pp. 81–97). London, UK: MacKeith Press. Rodda, J. M., Carson, L., Graham, H. K., Galea, M. P., & Wolfe, R. (2004). Sagittal gait patterns in spastic diplegia. Journal of Bone and Joint Surgery British Volume, 86, 251–258. Rodda, J., & Graham, H. K. (2001). Classification of gait patterns in spastic hemiplegia and spastic diplegia: A basis for a management algorithm. European Journal of Neurology, 8, 98–108. Romei, M., Galli, M., Motta, F., Schwartz, M., & Crivellini, M. (2004). Use of the normalcy index for the evaluation of gait pathology. Gait & Posture, 19, 85–90. Rosenbaum, P., Paneth, N., Leviton, A., Goldstein, M., Bax, M., Damiano, D., et al. (2007). A report: The definition and classification of cerebral palsy. 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Contents lists available at ScienceDirect
Research in Developmental Disabilities
Gait pattern differences in children with unilateral cerebral palsy Andrzej Szopa a, Małgorzata Domagalska-Szopa a,*, Andrzej Czamara b a b
School of Health Sciences, Medical University of Silesia, Medyko´w 12, Katowice 40-752, Poland College of Physiotherapy, Kos´ciuszki 4, Wrocław 50-038, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 6 February 2014 Received in revised form 12 May 2014 Accepted 29 May 2014
Children with cerebral palsy (CP) often have atypical body posture patterns and abnormal gait patterns resulting from functional strategies to compensate for primary anomalies that are directly attributable to damage to the central nervous system. Our previous study revealed two different postural patterns in children with unilateral CP: (1) a pattern with overloading of the affected body side and (2) a pattern with under-loading of the affected side. The purpose of present study was to test whether different gait patterns dependent on weight distribution between the affected and unaffected body sides could be detected in these children. The study included 45 outpatients with unilateral CP and 51 children with mild scoliosis (reference group). The examination consisted of two inter-related parts: paedobarographic measurements of the body mass distribution between the body sides and three-dimensional instrumented gait analysis. Using cluster analysis based on the Gillette Gait Index (GGI) values, three gait patterns were described: a scoliotic gait pattern and two hemiplegic gait patterns, corresponding to overloading/under-loading of the hemi-side, which are the pro-gravitational gait pattern (PGP) and the anti-gravitational gait pattern (AGP), respectively. The results of this study showed that subjects with AGP presented a higher degree of deviation from the normal gait than children with PGP. This proof that there are differences in the GGI between the AGP and PGP could be a starting point to identify kinematic differences between these gaits in a follow-up study. ß 2014 Elsevier Ltd. All rights reserved.
Keywords: Weight bearing Paedobarographic measurements Gillette Gait Index Cluster analysis
1. Introduction Children with cerebral palsy (CP) often have atypical body posture patterns and abnormal gait patterns (Rosenbaum et al., 2007) resulting from functional strategies to compensate for primary anomalies that are directly attributable to damage to the central nervous system. These primary impairments due to this upper motor neuron syndrome can lead, in the longer term, to adaptations in the musculoskeletal system, which are known as secondary impairments, such as inadequate muscle growth, which causes contractures (shortening) of muscles and tendons, bone deformities, misalignment of the joints and excessive fatigue with movement and walking (Rosenbaum et al., 2007). One of the most striking features of CP is the
* Corresponding author. Tel.: +48 32 208 87 12. E-mail addresses: [email protected] (A. Szopa), [email protected] (M. Domagalska-Szopa), [email protected] (A. Czamara). http://dx.doi.org/10.1016/j.ridd.2014.05.020 0891-4222/ß 2014 Elsevier Ltd. All rights reserved.
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variability in its clinical presentation. The diversity of gait deviations observed in children with CP has led to repeated efforts to develop gait classification systems to assist in the diagnosis, clinical decision-making and communication (Hullin, Robb, & Loudon, 1996; Lin, Guo, Su, Chou, & Cherng, 2000; Rodda, Carson, Graham, Galea, & Wolfe, 2004; Stebbins et al., 2004; Sutherland & Davids, 1993; Winters, Gage, & Hicks, 1987). Although the majority of studies have used advanced three-dimensional measurement systems to collect gait data on three planes of motion, most classifications of CP gait have been constructed using only sagittal plane data (Boyd & Graham, 1999; Corry et al., 1998; Koman et al., 2001; Mackey, Lobb, Walt, & Stott, 2003). Although these classifications are used for diagnostic purposes as well as to streamline communication and facilitate clinical decision-making process, including surgical interventions (Dobson, Graham, Baker, & Morris, 2005; Dobson, Morris, Baker, & Graham, 2007; Graham & Selber, 2003; Gage, 1991; Ounpuu, Deluca, & Davis, 2000; Rodda & Graham, 2001), none of the classifications have appeared to be useful in the physiotherapy planning for the children with CP. The term ‘‘gait classification’’ refers to a system that allows for the allocation of gait patterns into groups that can be differentiated from one another based on a set of defined variables. This classification is distinct from gait indices, assessment scores and scales, which score individual gait variables or provide an overall index to quantify the deviations from normal gait without group allocation. Currently, one of the most commonly used indices for quantifying the deviations from normal gait is the Gillette Gait Index (GGI; previously called the Normalcy Index; Gage, 2004; Schutte et al., 2000). The GGI uses a single number to measure the deviation of the patient’s gait from the average gait of a subject without pathology. The GGI values have been standardised for particular diagnostic categories in children with hemiplegia, according to Winter’s classification by Schutte et al. (2000). The average and range of index values were determined for each diagnosis group of hemiplegia: types I, II, III and IV (Schutte et al., 2000). Moreover, the GGI was shown to be efficient in categorising the pathology in children with CP, to be clinically applicable (Assi, Ghanem, Lavaste, & Skalli, 2009; Schutte et al., 2000) and to be correlated with physical functioning (Romei, Galli, Motta, Schwartz, & Crivellini, 2004). Despite the observation that a group of children with hemiplegia (SH) appears to be relatively homogeneous in terms of body posture, our previous study showed that their postural patterns were different (Domagalska, Szopa, & Lembert, 2011). Based on weight bearing between the affected and unaffected body sides, two types of asymmetrical postural patterns were described: (1) the postural pattern with overloading of the affected body side (pro-gravitational postural pattern [PGPP]) and (2) the postural pattern with under-loading of the affected side (anti-gravitational postural pattern [AGPP]; Domagalska et al., 2011). Because the results of previous research have been promising, we decided to test whether different gait patterns corresponding to weight bearing on the hemi-side could be detected in these children. The present study aimed to verify the hypothesis that the degree of deviation of a hemiplegic gait pattern from a normal gait (evaluated by the GGI) depends on the nature of weight bearing on the unaffected or affected body side. Proof that there are differences in the GGI between hemiplegic gait patterns could be a starting point for identifying kinematic differences between these gaits in a follow-up study.
2. Materials and methods This study was approved by the Local Ethical Committee. The patients and their parents provided informed consent before data collection. 2.1. Subjects From all of 111 children with a diagnosis of unilateral CP, resident in our geographical region, who were the outpatients of the local paediatric rehabilitation centres, 57 met the inclusion criteria for study of three-dimensional instrumented gait analysis. Although the exclusion criteria included lower limb surgery in the previous 6 months and botulinum toxin A injection into the lower limb in the previous 6 months, appointments were arranged over the period of data collection to minimise these exclusion. However, out of 57 eligible, identified individuals, a representative sample of 45 participated in study: 17 girls and 28 boys; deficits occurred on the right side in 29 patients and on the left side in 16 patients; the mean age was 9 years 5 months old (range, 7 years 4 months to 12 years 2 months; SD = 2.11); 75.5% at Level I and 24.5% at Level II on the Gross Motor Function Classification System. All subjects met the following criteria: (1) older than 7 years of age (to minimise the incidence of instability of kinematic parameters), (2) able to walk without assistive mobility devices and orthoses, (3) able to follow verbal directions, (4) no surgical procedures in the lower extremities and (5) no dislocation of the hip. The subjects with CP had the following additional criteria: (1) diagnosis of SH, (2) non-use of pharmacological agents at the time of the study and (3) no spasticity management 6 months before the evaluation. Fifty-one children with mild scoliosis (scoliosis curve 10–208), range of lateral curvature of 11 208 (mean, 188) and a mean age of 9 years 2 months old (range, 7 years, 5 months to 12 years, 3 months; SD = 1.99) were recruited as references. All of them were outpatients at a local Centre for Corrective Gymnastics. Statistical analysis confirmed that the patient demographic characteristics were similar in both groups.
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2.2. Methods Our study consisted of two inter-related parts: 1. Paedobarographic measurements of the distribution of body mass between the body sides and 2. Three-dimensional instrumented gait analysis (3DGA).
2.2.1. Testing procedure: paedobarographic measurements Analyses of the weight distribution on the base of support (BOS) between the affected and unaffected body sides and the centre of pressure (COP) were simultaneously conducted. A force plate pressure distribution measurement system, PDM-S (Zebris Medizintechnik GmbH, Isny, Germany), with FootPrint software was applied for these types of paedobarographic measurements. Each measurement was recorded three times (three trials, each lasting 30 sec with a 30-sec pause between trials), and the mean value from three trials was taken as value of weight distribution on the right and left sides in the reference subjects and on the unaffected/affected body sides in children with hemiplegia for additional analysis. Based on the above-mentioned specifications, the Index of Asymmetry (AI) of weight distribution between the right and left body sides was calculated for the references. The standard deviation was used as a criterion to define the asymmetry of weight distribution on the affected/unaffected body sides in children with SH (AI > 9.83%) to create four SH subgroups (LL, RR, RL and LR) and two reference subgroups (NR and NL; Domagalska-Szopa & Szopa, 2013). Based on the AI of weight distribution on the left and right body sides, the children with mild scoliosis were divided into two subgroups (Table 1). Based on both the AI of weight distribution on the unaffected/affected body sides and side of hemiplegia (to exclude the effect of right side/left side hemiplegia on the GGI values), the hemiplegic children were divided into four subgroups (Table 1). 2.2.2. Three-dimensional gait analysis (3DGA) Three-dimensional instrumented gait analysis (3DGA) was performed using the Compact Measuring System for 3D realtime motion analysis (CMS-HS 3D) and WinGait software (Zebris Medizintechnik GmbH, Germany). The CMS HS 3D system is based on 15 active ultrasonic markers (five triplets of ultrasound markers; Kocsis, Kiss, Knoll, & Jurak, 2000). Before the gait analysis, we identified the following anatomical landmarks using an instrumented pointer: hip joint centre, knee rotation centre (internal and external), ankle rotation centre (internal and external), forefoot landmark (between the second and third metatarsals) and rear foot (heel). We recorded the gait data as the subjects walked on a treadmill (Alfa XL, Kettler, Germany). Before data collection, all the subjects had the opportunity to practice walking on the treadmill. By trial and error, the speed of the treadmill was adjusted to the ability of each child, such that his or her gait was the most natural. The subjects were required to perform a minimum of 3 min of walking on the treadmill, without shoes or assistive devices. Using double-sided adhesive tape, we attached and placed bilateral markers on the skin. Depending on each subject’s walking ability, five to eight gait cycles were recorded. Separate measurements for the right and left gait cycles for each subject were obtained. The data were collected from the most representative trial for each side. The data collected were reported using WinGait software. To characterise the gait patterns, we used the GGI, which is a single number, derived from gait kinematics and spatiotemporal parameters that quantify the deviation of a pathological gait from the normal gait. Two experienced physical therapists selected both the weight-bearing image and gait cycles for analysis. The weightbearing image and gait cycle that were most characteristic of the child—in both experts’ opinions—were chosen for further analysis. When their assessments differed, the senior author (D.-S.,M.) selected the image and cycle that were analysed. The accuracy of their evaluations was then analysed. The intraclass correlation coefficient (ICC) with 95% confidence interval was used to measure the overall intraobserver and interobserver agreement. The intraobserver agreement was calculated separately for each paedobarographic measurements (PT) and gait analysis (GGI for affected lower limb), based on two examinations performed by the same two assessors in each group (SH and reference; 10 subjects each; 40 examinations in total). The interobserver agreement was calculated (for the same subjects) for the two assessors. For the analysis, a mean ICC value of 0.80 and above reflected excellent reliability, that between 0.70 and 0.79 indicated good reliability, and that below 0.70 reflected poor to moderate reliability. Table 1 Participants divided into subgroups taking into account the tendency to overload the left body side (right/left) in children with mild scoliosis and both the side of hemiplegia and weight distribution on the unaffected/affected body sides in children with hemiplegia. Postural patterns
Definition
N
%
NL NR LL RR LR RL
Mild scoliosis and the tendency to overload the right body side Mild scoliosis and the tendency to overload the left body side Left side hemiplegia and the tendency to overload the affected body side Right side hemiplegia and the tendency to overload the affected body side Left side hemiplegia and the tendency to overload the unaffected body side Right side hemiplegia and the tendency to overload the unaffected body side
28 23 10 13 6 16
29.2 23.8 10.5 13.5 6.3 16.7
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Every outcome of the PT examination demonstrated good to very high levels of intraobserver agreement for both groups, with the ICC for all variables ranging from 0.76 to 0.95 for CP and from 0.79 to 0.97 for the references. The ICC values indicated a very high level of interobserver agreement among the assessors, with the ICC ranging from 0.90 to 0.97 for both groups. The GGI demonstrated good interobserver (0.70–0.77) and intraobserver (0.68–0.75) repeatability for both groups. With respect to both the within and between assessor agreement, the children with scoliosis showed lower variability in the GGI than the hemiplegic children. 2.3. Statistical analysis The GGI for each gait cycle of individuals from the SH and reference groups was calculated (separately for each leg) using the procedure described by Schutte et al. (2000). The GGI was calculated based on 16 selected gait parameters taken from the objective gait analysis data, including the following: stance phase, expressed as the percentage of the gait cycle; walking speed, normalised to the leg length; cadence; mean pelvic tilt; pelvic range of motion on the sagittal plane; mean pelvic rotation; minimum hip flexion; hip range of movement in the sagittal plane; peak abduction in swing; mean hip rotation in stance; knee flexion at initial contact; time to peak knee flexion in swing, expressed as the percentage of the gait cycle; knee range of movement on the sagittal plane; peak dorsiflexion in stance and swing; and mean foot progression. Non-hierarchical k-means clustering was used in the selection of GGI, assuming three clusters (Davidson, 2002). K-means clustering is used when there have already been hypotheses concerning the number of clusters in the cases or variables. Because gait pattern was assumed to depend on weight-bearing patterns (pattern with almost symmetrical weight-bearing, an asymmetrical pattern with hemi-side overloading and a pattern with hemi-side under-loading), the identification of three clusters in the cluster analysis was justified. The mean and SD values of the GGI were calculated for the entire group and for each of the three clusters, and these values were compared among the subgroups. Analysis of variance (ANOVA, Tukey’s post-hoc test) was used to detect the differences in the GGI among the three clusters. Only significant differences (P < 0.05) among clusters were described. 3. Results In accordance with the cluster analysis results, 18 (18.7%) participants were classified into Cluster 1, whereas 28 (22.3%) were included in Cluster 2, and 57 (60%) were included in Cluster 3 (Table 2). There were significant differences among the means of the various clusters for both GGIs, as shown in Table 3. Tukey’s post hoc test revealed the GGIs calculated for both lower limbs: right or affected and left or unaffected reliably differentiated Cluster 1 from both Clusters 2 and 3 as well as Cluster 2 from Cluster 3 via their cluster means (each P < 0.001). Cluster 1 was predominantly characterised by subgroups LL and RR of the SH group (Table 4). Cluster 2 was predominantly characterised by subgroups RL and LR of the SH group (Table 4). Cluster 3 was predominantly characterised by subgroups NR and NL of the reference group (Table 4). Three gait patterns, in terms of the degree of deviation from a normal gait, were recognised by the GGI calculation. One (Cluster 3, n = 57) approached the normal gait patterns of the references (100%). All the GGI values were within the normal Table 2 GGI scores represent the mean values for the right* and left* lower limbs in the reference group and the unaffected** and affected*** lower limb in hemiplegic children in particular clusters. GGI
Cluster
N
%
Mean
SD
Minimum
Maximum
Right/affected
1 2 3 1 2 3
18 27 57 18 27 57
18.7 22.3 60.0 18.7 22.3 60.0
163.9*** 74.5* 15.04** 94.5*** 31.7* 15.3**
63.32*** 21.9* 7.9** 21.5*** 12.9* 9.19**
86.4*** 44.5* 3.8** 61.7*** 10.7* 4.74**
347.4*** 131.7* 31.8** 128.8*** 73.1* 45.4**
Left/unaffected
Table 3 ANOVA results and differences between the means of the various clusters for GGI of both lower limbs. GGI
Groups
Sum of squares
df
Mean squared
F
P
Right/affected
Between Within Total Between Within Total
24,1584.51 12,0792.3 60,219.9 62,348.30 31,174.15 14,142.89
2 93 95 2 93 95
647.52
186.55
.00000
152.07
204.99
.00000
Left/unaffected
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Table 4 Non-hierarchical k means clustering. Subgroup
NR NL RR RL LR LL Total
Cluster 1
Cluster 2
Cluster 3
Total
N
%
N
%
N
%
N
%
0 0 1 11 4 2 18
0.00 0.00 7.69 68.75 66.67 20.00 18.7
0 0 8 5 2 6 21
0.00 0.00 61.54 31.25 33.33 60.00 22.3
23 28 4 0 0 2 57
100.00 100.00 30.77 0.00 0.00 20.00 59
23 28 13 16 6 10 96
25 29 13 17 6 10 100.00
Two subgroups of children with scoliosis – NR, the tendency to overload the right body side, and NL, the tendency to overload the left body side – and four subgroups of children with CP – RR, right side hemiplegia and the tendency to overload the affected body side; RL, right side hemiplegia and the tendency to overload the unaffected body side; LL, left side hemiplegia and the tendency to overload the affected body side and LR, left side hemiplegia and the tendency to overload the unaffected body side.
range (15.04, SD = 7.9). The other two patterns included only patients with SH. The first pattern (Cluster 2) was presented by children with a tendency to overload the affected body side (RR + LL; 14 of 21 subjects), whereas the second pattern (Cluster 1) characterised children with a tendency to overload the unaffected body side (RL + LR; 13 of 18 subjects). Two gait patterns in children with SH were defined as follows: 1. the pro-gravitational gait pattern (PGP) with the tendency to overload the affected body side (Cluster 2) and 2. the anti-gravitational gait pattern (AGP) with the tendency to under-load the affected body side (Cluster 1).
Significantly higher values of the GGI for both the affected and unaffected lower limbs were noted in the subjects included in Cluster 1 (Table 2). 4. Discussion Children with unilateral CP show a variety of gait patterns. Cluster analysis was used for differentiation not only between the gait patterns of children with mild scoliosis and the pathological gait patterns of children with unilateral CP but also within hemiplegic gait patterns. Two different gait patterns, in terms of the degree of deviation of a pathological gait from a normal gait in children with SH, were recognised. One characterised children with a tendency to overload the affected body side and was known as the PGP, and the other, the AGP, was characteristic of children with a tendency to under-load the affected side. During walking, the PGP gives the impression of ‘‘submission’’ to the force of gravity by overloading the affected side (similar to a unilateral crouched gait), whereas the AGP seems to present excessive activity against gravity by under-loading the affected side (similar to a unilateral equinus gait). Clear differences in the GGI of both the affected and unaffected body sides were noted between these gait patterns. The children who presented the AGP showed two times higher GGI measurements for the unaffected leg and three times higher GGI measurements for the affected leg than children with the PGP. The results of this study showed that subjects with AGP presented a much higher degree of deviation from normal gait than children with PGP. As reported by Schutte et al. (2000) and Gage (2004), more severe diagnoses, on average, would correspond to more gait abnormalities and, thus, result in higher index scores. This confirmation is consistent with the results of our study. We observed that the mean index values increased with the level of involvement from Type I to IV hemiplegia. In contrast, no overlap was found between the index values for the references and the individuals with gait abnormalities, within the unaffected and affected body sides in hemiplegics and within the hemiplegic patients. In the present study, the mean GGI values were significantly different between the PGP and AGP. We used cluster analysis for the construction of the hemiplegic gait classification. Cluster analysis is a tool for demonstrating the associations and structure in data, which, although not previously evident, are sensible and useful when discovered. The advantage of using this statistical method is that it provides a systematic and structured approach based on objective gait analysis data. As a result of cluster analysis in the present study, we recognised two categories of gait patterns in children with hemiplegia, which were consistent not only with the characteristic of weight-bearing on the unaffected body side but also with their postural patterns (PGPP and AGPP), which were recognised in our previous study (DomagalskaSzopa & Szopa, 2013). The presented study was allowed for patients with the ability to walk without assistive devices. Additionally the lower limb surgery in the past was an exclusion criterion of the study. That is the reason why only a part of children with unilateral CP population were eligible for inclusion to study of 3D gait analysis. Despite these limitations the obtained finding clearly suggests that the distribution of body mass between the affected and unaffected body sides determines the nature of the compensatory mechanism, which was typical for both the postural and gait pattern in children with unilateral CP. This protocol describes a study that, to the best of our knowledge, is the first attempt to classify gait patterns while accounting for
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the characteristic of weight-bearing on the unaffected or affected body side, using a cohort of children with unilateral CP. Therefore identified differences of GGI between hemiplegic gait patterns should be a starting point for seeking kinematic differences between them in a follow-up study. 5. Conclusion The present study was based on the GGI calculation using selected spatio-temporal parameters and sagittal-, coronal- and transverse-plane kinematics for the pelvis, hip, knee and ankle. The recognition of two different gait patterns in terms of the degree of their deviation from a normal gait in children with unilateral CP could be an essential step toward defining kinematic differences between these gaits in a follow-up study. Then, the AGP and PGP definitions will enable many specialists, including physiotherapists, to differentiate these gaits in children with hemiplegia into clinically significant categories that would assist in treatment decision-making. 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