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Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power.

Pataky, TC, Robinson, MA and Vanrenterghem, J (2016) Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power. PeerJ, 4. ISSN 2167-8359

2016 Pataky PeerJ Region Of Interest + Appendix.pdf - Published Version
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One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA.

Item Type: Article
Uncontrolled Keywords: Biomechanics; Constrained hypotheses; Dynamics; Human movement; Hypothesis testing; Kinematics; Random field theory; Statistical parametric mapping; Time series analysis
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics > QA76 Computer software
Q Science > QH Natural history > QH301 Biology
Divisions: Sport & Exercise Sciences
Publisher: PeerJ
Related URLs:
Date Deposited: 23 Nov 2016 11:02
Last Modified: 04 Sep 2021 12:15
DOI or ID number: 10.7717/peerj.2652
URI: https://researchonline.ljmu.ac.uk/id/eprint/4852
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