How to choose and interpret similarity indices to quantify the variability in gait joint kinematics

Roberto Di Marco,Emilia Scalona,Alessandra Pacilli,Paolo Cappa,Claudia Mazzà,Stefano Rossi

doi:10.1080/23335432.2018.1426496

Roberto Di Marco, Emilia Scalona + Show 4 more

Open Access

https://doi.org/10.1080/23335432.2018.1426496

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Abstract

Repeatability and reproducibility indices are often used in gait analysis to validate models and assess patients in their follow-up. When comparing joint kinematics, their interpretation can be ambiguous due to a lack of understanding of the exact sources of their variations. This paper studied four indices (Root Mean Square Deviation, Mean Absolute Variability, Coefficient of Multiple Correlation, and Linear Fit Method) in relation to five confusing-factors: joints’ range of motion, sample-by-sample amplitude variability, offset, time shift and curve shape. A first simulation was conducted to test the mathematics behind each index. A second simulation tested the influence of the curve shape on the indices using a Fourier’s decomposition. The Coefficient of Multiple Correlation and the Linear Fit method Coefficients were independent from the range of motion. Different Coefficients of Multiple Correlation were found among different joints, leading to misinterpretation of the results. The Linear Fit Method coefficients should not be adopted when time shift increases. Root Mean Square Deviation and Mean Absolute Variability were sensitive to all the confusing-factors. The Linear Fit Method coefficients seemed to be the most suitable to assess gait data variability, complemented with Root Mean Square Deviation or Mean Absolute Variability as measurements of data dispersion.

Highlights

Human joint kinematics and dynamics assessed with 3D gait analysis have been proven to be suitable for clinical decision-making, thanks to repeatability and reproducibility studies that validate relevant measurements and modelling techniques (Carson et al 2001; Arnold et al 2013; Benedetti et al 2013; Leigh et al 2014)
This research aims to fill this gap via simulations on both synthetic and experimental data gathered from healthy adults, providing a guide on how to choose the most suitable repeatability and reproducibility indices, and how to interpret the results when dealing with joint kinematic curves
This study presented a comparative analysis of four indices used to assess gait data repeatability and reproducibility, aiming to differentiate the effect of the defined confusing-factors (i.e. joint range of motion (ROM), joint ROM fluctuations (α), offset between curves (O), time shift (τ), curve pattern)

Summary

Introduction

Human joint kinematics and dynamics assessed with 3D gait analysis have been proven to be suitable for clinical decision-making, thanks to repeatability and reproducibility studies that validate relevant measurements and modelling techniques (Carson et al 2001; Arnold et al 2013; Benedetti et al 2013; Leigh et al 2014). According to metrological standards (JCGM 2012), the repeatability is the measurement precision associated with the same operator performing the same procedure on the same group of subjects that in gait analysis quantifies the within- and between-subject variability. The reproducibility, instead, is the measurement precision associated with different operators performing the same procedure on the same group of subjects that quantifies the between-operator variability of the data. Repeatability and reproducibility indices (RI) are influenced by various factors, which lead to limited interpretation of the relevant results. These factors, here indicated as confusing-factors, are: (a) the range of motion of the considered joint (Steinwender et al 2000); (b) the sample

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