The study of human locomotion consists of characterizing the shortest repeatable task in walking, the gait cycle. This movement is repeated over and over in order for humans to walk through the world.
In addition to measuring the magnitude and symmetry of gait characteristics, it is important to look at how consistent these measurements are from step to step, known as Gait Variability. Gait variability is defined as, the fluctuation of gait measures between steps. The amount of variability in gait is a sensitive gauge of motor function and motor deficits. One example of this is that variability in STPs is a predictor of fall risk in older adults (Morrison 2018).
While it is clear that measuring the variability of STPs in gait is important, there are several questions that need to be considered:
- Variability within STPs do not show up evenly throughout all measures. Therefore, is variability within certain measures more important than others? (Variability has been reported in at least 11 different STPs, but there is a lack of consensus on the best way to measure this.)
- Should variability of the right and left sides be viewed independently or together?
- Is spatial variability more important than temporal variability or vice versa?
While it is clear that variability has importance, more work is necessary in order to answer these questions.
The second consideration is about degree of variability. Healthy gait does have some variability. For instance, low levels of variability in gait are necessary in order for a person to regulate gait when faced with perturbations in our environment. It is clear that some variability within gait is positive, however, it is less clear at what level variability becomes problematic.
Finally, what is the best measure to quantify gait variability? Standard deviation is one variability measure for STPs, however, it can be sensitive to scale. For example, a standard deviation for step length of 3cm when the mean is 50cm does not have the same implication of variability as a standard deviation of 3cm for stride width when the mean is 8cm. Coefficient of Variation is sensitive to scale but shows very high values when the mean is around 0.
The Enhanced Gait Variability Index
In an attempt to answer these questions, a composite variability score has been proposed. The Gait Variability Index was developed using nine STP weighted by PCA. This measure quantifies the amount of variability observed in an individual and compares this to a reference group. The weighted variability is then transformed into a score with 100 representing the mean GVI and 10 representing 1 standard deviation from the mean (Gouelle et al. 2013). The GVI has been shown to correlate with clinical outcomes in a Friedrich’s ataxia population, is sensitive enough to detect change between ages from childhood through adulthood, and has been shown to be valid when looking at populations with deficits in mobility. While the GVI shows promise as a solution to solve the methodological problems in measuring gait variability, some minor shortcomings have been identified. In response to these shortcomings, the Enhanced Gait Variability Index (eGVI) was established. The eGVI removed some overlapping STP input and addressed directionality in order to strengthen the association between the eGVI and functional outcomes (Gouelle et al. 2017).
Gait Variability is an important area of study within gait analysis as it has already been shown to correlate with fall risk and has clear implications on gait quality. Two gait patterns may have identical magnitudes for mean STPs but if pattern one is consistent and pattern 2 is highly variable, different decisions and interventions may be required. For instance, pattern 2 may be at risk for falling due to the high variability and require a strategy to minimize this risk, such as an assistive device.
It is clear that measuring gait variability is necessary in order to get a full picture of the quality of gait, however, like any measure, it is only as valuable if collected properly. Gait variability is highly related to the quality of the protocol, data capture and processing of the data. While variability data has been shown to be powerful, it is of the utmost importance that a reliable, accurate system is used to collect data with a robust and valid protocol.
See our previous posts:
- Phases of the Gait Cycle
- Using Gait Asymmetry to Define Gait Quality
- How to Measure Spatiotemporal Gait With the Zeno Walkway System
Morrison S, Newell KM. Intraindividual variability of neuromotor function predicts falls risk in older adults and those with type 2 diabetes. Journal of Motor Behavior 2018; published online 14 March 2018 (doi:10.1080/00222895.2018.1440524)
Gouelle A, Mégrot F, Presedo A, Husson I, Yelnik A, Penneçot GF (2013) The gait variability index: a new way to quantify ?uctuation magnitude of spatiotemporal parameters during gait. Gait Posture 38(3):461?465
Gouelle A, Rennie L, Clark DJ, Mégrot F, Balasubramanian CK. Addressing limitations of the Gait Variability Index to enhance its applicability: The enhanced GVI (EGVI). PLOS ONE 2018;13(6):e0198267.