Identification of gait patterns in individuals with cerebral palsy using multiple correspondence analysis.
Seven quick gait scores split CP patients into four walking types that guide goal setting.
01Research in Context
What this study did
The team used a math tool called multiple correspondence analysis. It shrinks many gait numbers into a few key scores.
They fed the tool 16 gait variables from 122 people with cerebral palsy. The goal was to spot clear walking types.
What they found
Seven gait scores explained most of the differences. These scores sorted the group into four walking profiles.
Each profile had its own mix of knee, ankle, and trunk motion. The labels give clinicians a quick picture of how a person walks.
How this fits with other research
O'Sullivan et al. (2018) show that crouch gait in bilateral CP gets worse over time. Fahmie et al. (2013) profiles fit inside that review, giving names to the patterns Rory tracks.
Saether et al. (2014) used trunk sensors and also found four key metrics. Their balance scores line up with the trunk-related scores in Fahmie et al. (2013), even though the tools differ.
Heyrman et al. (2014) link poor seated trunk control to extra thoracic motion during gait. Fahmie et al. (2013) trunk scores echo that link, showing the same deficit shows up in both lab and clinic tests.
Why it matters
You can run the seven-item checklist while watching a child walk. If the scores cluster in one profile, you know which joints need the most work and can pick goals that match. No force plates or EMG needed—just a stopwatch, goniometer, and sharp eyes.
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02At a glance
03Original abstract
Great importance has been placed on the development of gait classification in cerebral palsy (CP) to assist clinicians. Nevertheless, gait classification is challenging within this group because the data is characterized by a high-dimensionality and a high-variability. Thus, the aim of this study was to analyze without a priori, a database of clinical gait analysis (CGA) of CP patients, using multiple correspondence analysis (MCA). A retrospective search, including biomechanical and clinical parameters was done between 2006 and 2012. One hundred and twenty two CP patients were included in this study (51 females and 71 males, mean age ± SD: 14.2 ± 7.5 years). Sixteen biomechanical spatio-temporal and kinematic parameters were included in the analysis. This data was transformed by a fuzzy window coding based on the distribution of each parameter in three modalities: low, average and high. Afterward, a MCA was used to associate parameters and to define classes. From this, seven most explicative gait parameters used to characterize gait of CP patients were identified: maximal hip extension, hip range, knee range, maximal knee flexion at initial contact, time of peak knee flexion, and maximal ankle dorsiflexion in stance phase and in swing phase. Moreover, four main profiles of CP patients have been defined from the multivariate approach: an apparent equinus gait group (the most similar of the control group with diplegic and hemiplegic patients with a GMFCS 1), a true equinus gait group (the youngest group with diplegic and some hemiplegic patients with a GMFCS 1), a crouch gait group (the oldest group with a majority of diplegic and rare hemiplegic patients with a GMFCS 2) and a jump knee gait group (the greatest level of global spasticity of the lower limbs with a majority of diplegic and rare hemiplegic patients with a GMFCS 2). Thus, this study showed the feasibility of the MCA in order to characterize and classify a large database of CP patients.
Research in developmental disabilities, 2013 · doi:10.1016/j.ridd.2013.05.002