Abstract
ABSTRACT The purpose of this paper is to provide an overview of available methods for reliability investigations when the outcome of interest is a curve. Curve data, or functional data, is commonly collected in biomechanical research in order to better understand different aspects of human movement. Using recent statistical developments, curve data can be analysed in its most detailed form, as functions. However, an overview of appropriate statistical methods for assessing reliability of curve data is lacking. A review of contemporary literature of reliability measures for curve data within the fields of biomechanics and statistics identified the following methods: coefficient of multiple correlation, functional limits of agreement, measures of distance and similarity, and integrated pointwise indices (an extension of univariate reliability measures to curve data, inclusive of Pearson correlation, intraclass correlation, and standard error of measurement). These methods are briefly presented, implemented (R-code available as supplementary material) and evaluated on simulated data to highlight advantages and disadvantages of the methods. Among the identified methods, the integrated intraclass correlation and standard error of measurement are recommended. These methods are straightforward to implement, enable results over the domain, and consider variation between individuals, which the other methods partly neglect.
Highlights
IntroductionBiomechanical research often involves the collection of large data sets based on integrated three-dimensional kinematics (e.g., motion capture), kinetics (e.g., force plate data), and muscle activation patterns (e.g., electromyography) during variable functional tasks
Biomechanical research often involves the collection of large data sets based on integrated three-dimensional kinematics, kinetics, and muscle activation patterns during variable functional tasks
This review presents and discuss the following methods: integrated pointwise index, functional LoA (FLoA), coefficient of multiple correlation (CMC), as well as measures of distance and similarity
Summary
Biomechanical research often involves the collection of large data sets based on integrated three-dimensional kinematics (e.g., motion capture), kinetics (e.g., force plate data), and muscle activation patterns (e.g., electromyography) during variable functional tasks. Such biomechanical data are high-dimensional and complex (Colyer, Evans, Cosker, & Salo, 2018) but are usually reduced to simple discrete outcome measures. Common examples found in the literature include joint range of motion (Sinsurin, Vachalathiti, Srisangboriboon, & Richards, 2018), maximal angles and moments (Markström, Schelin, & Häger, 2018), ground reaction forces Even when analysing it as functions, the data over the domain are typically discretised, either by a basis representation or, as here, by pointwise evaluations of the functions
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