Repeatability and validation of Gait Deviation Index in children: Typically developing and cerebral palsy
Introduction
Given the different gait patterns and pathologies in children with cerebral palsy (CP), a global analysis is essential in clinical practice. For this purpose, estimation of gait deviations from normative values is required and is helpful to improve therapeutic interventions.
In the CP pathology, three dimensional (3D) gait analysis for ambulant children and clinical evaluations are commonly used as assessments and are part of the international standard of CP care [1]. Even with 3D gait analysis, which is becoming an essential tool to assess ambulant children with CP [2], it is sometimes difficult to define objectively the amount of abnormalities and the degree by which an abnormal gait deviates from normal patterns. Three dimensional (3D) gait analysis provides a large amount of interdependent data and variables corresponding to different gait patterns [3]. The quantity and complexity of the data have pushed many authors to describe indices based on 3D gait analysis, developed primarily to evaluate clinical changes after a therapeutic intervention like the hip flexor index (HFI) [4] and the Gillette Gait Index (GGI) [5]. The GGI is calculated using discrete parameters incorporating 16 kinematic and temporal distance variables, chosen arbitrarily, allowing us to describe and to quantify the amount of pathology in an individual's gait pattern, and its repeatability has been evaluated [6]. These tools ignore the relation that exists between gait variables, contrary to other indices, described later, like the Gait Profile Score (GPS) [3] and the Gait Deviation Index (GDI) [7]. The GDI is an alternative to the GGI, measuring the subject's gait deviation from a normative database. It is calculated using kinematic variables, studied point by point, during the entire gait cycle. It is a scaled distance between 9 kinematic parameters of pathological gait and the average of 9 kinematic parameters of normal gait (group of typically developing (TD) children) [8]. The GDI is represented as a single number: when the number decreases, clinical involvement increases, and when the number increases, the gait profile is closer to a normal profile (≥100).
Many authors were interested in studying the correlation between indices calculated from the computerized gait analysis and clinical evaluation tools [9], [10], [11]. Molloy [12] studied the correlation between the GDI and clinical functional measures like the Gross Motor Function Measure (GMFM) and Gross Motor Function Classification system (GMFCS) and concluded that the GDI is a valid tool to describe motor impairments in CP.
However, for clinical use, it would be interesting to evaluate the repeatability of the GDI, by the test–retest method, since there is no previous study on this subject. Moreover, the method of calculation of the GDI involves kinematic curves and their transformation into vectors, matrices, euclidian distances and singular value decomposition [7]. Kinematics are subject to errors in 3D gait analysis and these errors can lead to uncertainties on the value of the GDI. It would be interesting to evaluate the propagation of errors during the calculation of the GDI.
The aim of this study is to evaluate the repeatability of the GDI within typically developing (TD) children. The error propagation during the calculation of the GDI was evaluated by applying Monte Carlo simulations on TD children and children with CP.
Section snippets
Samples
A retrospective study was conducted on our 3D gait data and clinical assessments performed between 2006 and 2012. One hundred ninety one (N = 191) children were referred to our gait laboratory for an orthopedic evaluation, an orthotic intervention or for a baseline gait assessment. Forty-nine (N = 49) TD children formed our asymptomatic database with a mean age of 10.3 years (SD = 3, minimum = 5 years, maximum = 15 years) [6]. One hundred thirty four (N = 134) children with the diagnosis of spastic
GDI for children with CP
The demographic characteristics of the children with CP and TD children are summarized in Table 1. The GDI was calculated separately for each lower limb. The GDI was normally distributed in each GMFCS level for children with CP and for TD children. The mean value of the GDI according to GMFCS levels is represented in Table 2. The mean value of the GDI decreases when the GMFCS increases. The histogram of the GDI is shifted toward 100 when the GMFCS decreases (Fig. 1 (left)). The mean values of
Discussion
In this study, the repeatability of the GDI for the 16 TD children who completed the exam twice was evaluated. The error propagation on GDI calculation for 49 TD subjects and 134 spastic children with CP was studied by applying Monte Carlo simulations. Correlation between the GDI and the GMFCS was assessed.
The test–retest study on TD subjects did not show any statistical differences between the mean values of the GDI in the first and second session (p > 0.05). This result gives further evidence
Conflict of interest statement
All authors disclose any financial and personal relationships with other people or organisations that could inappropriately influence this work.
Acknowledgment
The authors would like to thank Ziad El Bakouny for his assistance in the preparation for the manuscript.
References (18)
- et al.
A gait analysis data collection and reduction technique
Hum Movement Sci
(1991) - et al.
The gait profile score and movement analysis profile
Gait Posture
(2009) - et al.
A tool for quantifying hip flexor function during gait
Gait Posture
(2000) - et al.
An index for quantifying deviations from normal gait
Gait Posture
(2000) - et al.
Gait analysis in children and uncertainty assessment for Davis protocol and Gillette Gait Index
Gait Posture
(2009) - et al.
The Gait Deviation Index: a new comprehensive index of gait pathology
Gait Posture
(2008) - et al.
Further evidence of validity of the Gait Deviation Index
Gait Posture
(2010) - et al.
Tibio-femoral joint constraints for bone pose estimation during movement using multi-body optimization
Gait Posture
(2011) - et al.
Tibialis anterior tendon shortening in combination with Achilles tendon lengthening in spastic equinus in cerebral palsy
Gait Posture
(2011)
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2020, Clinical BiomechanicsCitation Excerpt :The range of mean ages reported across all 68 studies on acaCP was 2 to 17 years, and for TDP was 3.2 to 15 years. The GMFCS levels of acaCP ranged from 1 to 3 across the 37 studies (Bakir et al., 2013; Barber et al., 2017; Bartonek et al., 2016; Bourgeois et al., 2014; Bulea et al., 2017; Cappellini et al., 2016; Choi et al., 2011; Degelaen et al., 2016; Delabastita et al., 2016; Eek et al., 2017; Goudriaan et al., 2018; Iosa et al., 2013; Ishihara and Higuchi, 2014; Kalsi et al., 2016; Klotz et al., 2014; Kruger et al., 2017; Kurz et al., 2012, 2015b; Malone et al., 2015; Malt et al., 2016; Massaad et al., 2014; Meyns et al., 2012a, 2016, 2014, 2017, 2012b; Pauk et al., 2016; Petersen et al., 2012; Rumberg et al., 2016; Saether et al., 2014; Tavernese et al., 2016; Taweetanalarp et al., 2011; Van de Walle et al., 2012; Wahid et al., 2015; Wallard et al., 2014; Wang et al., 2015; Zanin et al., 2018) which reported this measure. Overground walking was reported in 61 studies (Abel et al., 2003; Al-Abdulwahab and Al-Khatrawi, 2009; AlAbdulwahab, 2011; Bakir et al., 2013; Balaban et al., 2012; Barber et al., 2017; Bartonek et al., 2016; Bosmans et al., 2016; Bourgeois et al., 2014; Bruijn et al., 2011, 2013; Cappellini et al., 2016; Carriero et al., 2009; Chang et al., 2011; Chen et al., 2009; Choi et al., 2011; Degelaen et al., 2016; Delabastita et al., 2016; Eek et al., 2017; Feng et al., 2014; Goudriaan et al., 2018; Gross et al., 2013; Iosa et al., 2013; Ishihara and Higuchi, 2014; Kalsi et al., 2016; Kiernan et al., 2014; Kim et al., 2014; Klotz et al., 2014; Kruger et al., 2017; Kurz et al., 2015b,b; Langerak et al., 2008; Malone et al., 2015; Malt et al., 2016; Massaad et al., 2014; Meyns et al., 2012a, 2016, 2014, 2017; Mileti et al., 2016; Pauk et al., 2016; Rumberg et al., 2016; Saether et al., 2014; Steinwender et al., 2000; Svehlík et al., 2010; Tavernese et al., 2016; Taweetanalarp et al., 2011; Thompson et al., 1998; Tuzson et al., 2003; Van de Walle et al., 2012; Wahid et al., 2015; Wallard et al., 2014; Wang et al., 2015; White et al., 2005, 1999; Wong et al., 2004, 2005, 2000; Zanin et al., 2018; Zwick et al., 2010; Krzak et al., 2015; Meyns et al., 2012b) out of which one study (Tuzson et al., 2003) reported fast-walking speed, six studies (Bruijn et al., 2011; Delabastita et al., 2016; Kurz et al., 2015b; Meyns et al., 2012a, 2016, 2012b) reported both preferred and fast-walking speed conditions and one study (Gross et al., 2013) reported preferred, slow and fast-walking speed conditions.