Abstract
Intrinsic gait variability (GV), i.e., fluctuations in the regularity of gait patterns between repetitive cycles, is inherent to the sensorimotor system and influenced by factors such as age and pathology. Increased GV is associated with gait impairments in individuals with cerebral palsy (CP) and has been mainly studied based on spatiotemporal parameters. The present study aimed to describe kinematic GV in young people with CP and its associations with clinical impairments [i.e., passive range of motion (pROM), muscle weakness, reduced selective motor control (selectivity), and spasticity]. This retrospective study included 177 participants with CP (age range 5–25 years; Gross Motor Function Classification System I-III) representing 289 clinical gait analyses [n = 172 for unilateral CP (uCP) vs. 117 for bilateral CP (bCP)]. As variability metrics, Root Mean Square Deviation (RMSD) for nine lower-limb kinematic parameters and Gait Standard Deviation (GaitSD) – as composite score of the kinematic parameters – were computed for the affected (unilateral = uCP) and most affected side (bilateral = bCP), respectively, as defined by clinical scores. GaitSD was then computed for the non/less-affected side for between leg comparisons. Uni- and multivariate linear regressions were subsequently performed on GaitSD of the affected/most affected side with all clinical impairments (composite scores) as independent variables. Highest RMSD were found in the transverse plane (hip, pelvis), for distal joints in the sagittal plane (knee, ankle) and for foot progression. GaitSD was not different between uCP and bCP (affected/most affected side) but higher in the non-affected vs. affected side in uCP. GaitSD was associated with age (p < 0.001), gait deviation index (GDI) (p < 0.05), muscle weakness (p < 0.001), selectivity (p < 0.05), and pROM (p < 0.001). After adjustment for age and GDI, GaitSD remained associated with muscle weakness (uCP: p = 0.003, bCP: p < 0.001) and selectivity (bCP: p = 0.024). Kinematic GV can be expressed as global indicator of variability (GaitSD) in young people with CP given the strong correlation of RMSD for lower-limb kinematic parameters. In terms of asymmetry, increased variability of the non-affected vs. affected side may indicate contralateral compensation mechanisms in uCP. Notably muscle weakness (uCP, bCP) and selectivity (bCP) – but not spasticity – were associated with GaitSD. Further studies need to explore the clinical relevance of kinematic GV in CP to support the interpretation of clinical gait analyses and therapeutic decision-making.
Highlights
Clinical Gait Analysis (CGA) is fundamental for the clinical management of pathological gait deviations
The aims of this study were to investigate kinematic GV in children and young adults with unilateral and bilateral spastic Cerebral palsy (CP) while (1) describing the pathology specific GV patterns of nine lower limbs kinematic variables and (2) identifying the explanatory variables of the variability pattern observed based on clinical impairment scores
352 participants (686 CGA) with CP were screened: 41 (145 CGA) did not meet the following inclusion criteria: age between 5 and 25 years old, (>1 year between 2 CGA, >1 year after surgery, >6 m after botulinum toxin injection (BTX)); 119 (222 CGA) did not have available 3D data or walked with external aids; 9 (18 CGA) did not have > = 5 valid gait cycles and 6 (12 CGA) had missing clinical data (Figure 1)
Summary
Clinical Gait Analysis (CGA) is fundamental for the clinical management of pathological gait deviations. In contrast to simple variability measures (such as standard deviation and coefficient of variation) used to quantify data dispersion at specific instances of the gait cycle, more advanced variability metrics – such as the RMSD (Picerno et al, 2008) for unidimensional parameters, and GaitSD (Sangeux et al, 2016) as an overall index of kinematic GV – can characterize whole within-stride variability to quantify the similarity of curve patterns along the whole gait cycle (Di Marco et al, 2018) Association of these curve based metrics with clinical impairments could facilitate the interpretation of treatment efficacy on an individual basis
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