Abstract
The purpose of this pilot study was to compare walking speed, an important component of gait, in the laboratory and daily life, in young individuals with cerebral palsy (CP) and with typical development (TD), and to quantify to what extent gait observed in clinical settings compares to gait in real life. Fifteen children, adolescents and young adults with CP (6 GMFCS I, 2 GMFCS II, and 7 GMFCS III) and 14 with TD were included. They wore 4 synchronized inertial sensors on their shanks and thighs while walking at their spontaneous self-selected speed in the laboratory, and then during 2 week-days and 1 weekend day in their daily environment. Walking speed was computed from shank angular velocity signals using a validated algorithm. The median of the speed distributions in the laboratory and daily life were compared at the group and individual levels using Wilcoxon tests and Spearman’s correlation coefficients. The corresponding percentile of daily life speed equivalent to the speed in the laboratory was computed and observed at the group level. Daily-life walking speed was significantly lower compared to the laboratory for the CP group (0.91 [0.58–1.23] m/s vs 1.07 [0.73–1.28] m/s, p = 0.015), but not for TD (1.29 [1.24–1.40] m/s vs 1.29 [1.20–1.40] m/s, p = 0.715). Median speeds correlated highly in CP (p < 0.001, rho = 0.89), but not in TD. In children with CP, 60% of the daily life walking activity was at a slower speed than in-laboratory (corresponding percentile = 60). On the contrary, almost 60% of the daily life activity of TD was at a faster speed than in-laboratory (corresponding percentile = 42.5). Nevertheless, highly heterogeneous behaviors were observed within both populations and within subgroups of GMFCS level. At the group level, children with CP tend to under-perform during natural walking as compared to walking in a clinical environment. The heterogeneous behaviors at the individual level indicate that real-life gait performance cannot be directly inferred from in-laboratory capacity. This emphasizes the importance of completing clinical gait analysis with data from daily life, to better understand the overall function of children with CP.
Highlights
The World Health Organization (WHO) has emphasized the need to consider both capacity, defined as what a person can do in a standardized environment, and performance, defined as what a person does in his/her habitual environment, to describe a person’s activity
The results found for children with cerebral palsy (CP), in line with those of our previous study assessing multiple gait characteristics (Carcreff et al, 2020) are not in total accordance with previous studies that used dissimilar metrics to compare gait performance with gait capacity
Some solutions could be to include only frequently repeated walking bouts to eliminate unique behaviors or events from the analysis (Wang and Adamczyk, 2019); to use technological developments such as multimodal sensing (e.g., GPS, barometric pressure, microphone, weather records, etc.) to be more precise regarding the contexts, e.g., discriminate between indoor and outdoor, even and irregular surface, or straight and curved path, detect load carriage, a surrounding crowd or weather conditions (Wang and Adamczyk, 2019). This pilot study revealed that the assessment of walking speed in real-life conditions through inertial measurement units (IMU)-based wearable sensors worn on the shanks and thighs was feasible and relevant to highlight the differences between a young individual’s performance and his capacity
Summary
The World Health Organization (WHO) has emphasized the need to consider both capacity, defined as what a person can do in a standardized environment, and performance, defined as what a person does in his/her habitual environment, to describe a person’s activity. In children with cerebral palsy (CP), gait capacity assessments are a mainstay of clinical evaluation (Gerber et al, 2019) These children, who present lifelong motor disabilities (O’Shea, 2008), are regularly assessed in clinical settings through diverse functional tests, such as the Gross Motor Function Measure (GMFM) (Alotaibi et al, 2014) and the 6-Min Walk Test (Enright, 2003), or through an exhaustive assessment of gait deviations using 3D clinical gait analysis (Armand et al, 2016; Carcreff et al, 2016). If proven to be efficient (i.e., using objective performance measurement tools), clinicians might focus on adapting the environment and working on personal factors (e.g., with self-efficacy training) especially when capacity plateaus despite intensive training
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