The search for performance related factors in biomechanics

Introduction: The identification of performance related factors (PRFs) is a major goal in sports biomechanics. However, PRFs identified across studies are inconsistent and this might be explained by the limitations of discrete point\ud analysis, which is commonly used. New data analysis techniques involving continuous waveform analysis (e.g. functional principal component analysis, fPCA) have been suggested, but their use in biomechanics is not widespread, and they also have limitations. Another problem in current studies is the general use of a single group analysis design. The primary aim of this work was to develop and test a novel, enhanced method of continuous waveform analysis. A secondary aim was to examine the benefits of a subgroup analysis design, when identifying performance related factors.\ud \ud Methods: A self-developed data analysis technique (Analysis of Continuous Phase; ACP) was compared to fPCA and discrete point analysis in their ability to identify PRFs in the countermovement jump (CMJ). In addition, the generally used single group design was compared to a subgroup analysis design.\ud \ud Results: The ability to explain jump height was: ACP (99 %), fPCA (79 %) and discrete point analysis (23 %). ACP identified previously hidden PRFs in CMJs and was able to explain inconsistencies in previous studies that used discrete point analysis. The subgroup analysis demonstrated a better ability to describe jump height than the single group analysis (+10 %) and indicated that different subgroups / individuals have different PRFs.\ud \ud Conclusion: The findings of this work demonstrate large advantages of continuous data analysis, especially the novel ACP method, and in employing a subgroup analysis. Furthermore, findings indicate that discrete point analysis\ud and single group analysis are sources of inconsistencies in previous experiments on the CMJ