Test–retest reliability measures for curve data: an overview with recommendations and supplementary code

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.

Agreement between Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test

“…Both accuracy and precision were examined by using integrated pointwise indices, within each phase of joint kinematics and horizontal CoM measures [17], and also for the reference trial. The accuracy of the IMC system was expressed based on thhe level of agreement between the OSSCA and IMC outputs, by calculating the bias (differences) and 95% limits of the bias [18].…”

Statistical and Practical Significance of Articles at Sports Biomechanics Conferences

“…The descriptive approach is frequently used in sports biomechanics articles because the experimental approach is more difficult compared to the descriptive system and requires to control external variables that affect the dependent variable (23). Generally speaking, in the biomechanical articles, no specified statistical methods for the test-retest reliability are available (24); also, biomechanics tools are expensive, and the processes used for data extraction is usually time-consuming (21). If the experimental method is used, a researcher will use these tools again.…”

Are Multilevel functional models the next step in sports biomechanics and wearable technology? A case study of Knee Biomechanics patterns in typical training sessions of recreational runners

“…The multilevel models have allowed us to calculate the intraclass correlation coefficient between the two interval training sessions taking into account the 20 steps recorded in each session. To the best of our knowledge, this is a novel approach in this area since the traditional approaches previously used to measure reliability rely on the compression of information in the average curve and only beetween two conditions [Pini et al, 2019]. At the same time, we have introduced a new hypothesis test to test the statistical differences between continuous and multilevel running, taking advantage of the representation we obtained with the multilevel model at the second level of the hierarchy.…”