TY - JOUR
T1 - Influence of proximal trunk borne load on lower limb countermovement joint dynamics
AU - Liew, Bernard X.W.
AU - Helwig, Nathaniel E.
AU - Morris, Susan
AU - Netto, Kevin
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - Vertical jumping involves coordinating the temporal sequencing of angular motion, moment, and power across multiple joints. Studying the biomechanical coordination strategies that differentiates loaded from unloaded vertical jumping may better inform training prescription for athletes needing to jump with load. Common multivariate methods (e.g. Principal Components Analysis) cannot quantify coordination in a dataset with more than two modes. This study aimed to identify coordinative factors across four modes of variation using Parallel Factor (Parafac2) analysis, which may differentiate unloaded (body weight [BW]) from loaded (BW + 20% BW) countermovement jump (CMJ). Thirty-one participants performed unloaded and loaded CMJ. Three-dimensional motion capture with force plate analysis was performed. Inverse dynamics was used to quantify sagittal plane joint angle, velocity, moment, and joint power across the ankle, knee, and hip. The four-mode data were as follows: Mode A was jump cycle (101 cycle points), mode B was participant (31 participants by two load), mode C was joint (two sides by three joints), and mode D was variable (angle, velocity, moment, power). Three factors were extracted, which explained 95.1% of the data's variance. Only factors one (P = 0.001) and three (P < 0.001) significantly differentiated loaded from unloaded jumping. The body augmented hip-dominant at the start, and both hip and ankle dominant behaviors at the end of the ascending phase of the CMJ, but kept knee-dominant behavior invariant, when jumping with a 20% BW load. By studying the variation across all data modes, Parafac2 provides a holistic method of studying jumping coordination.
AB - Vertical jumping involves coordinating the temporal sequencing of angular motion, moment, and power across multiple joints. Studying the biomechanical coordination strategies that differentiates loaded from unloaded vertical jumping may better inform training prescription for athletes needing to jump with load. Common multivariate methods (e.g. Principal Components Analysis) cannot quantify coordination in a dataset with more than two modes. This study aimed to identify coordinative factors across four modes of variation using Parallel Factor (Parafac2) analysis, which may differentiate unloaded (body weight [BW]) from loaded (BW + 20% BW) countermovement jump (CMJ). Thirty-one participants performed unloaded and loaded CMJ. Three-dimensional motion capture with force plate analysis was performed. Inverse dynamics was used to quantify sagittal plane joint angle, velocity, moment, and joint power across the ankle, knee, and hip. The four-mode data were as follows: Mode A was jump cycle (101 cycle points), mode B was participant (31 participants by two load), mode C was joint (two sides by three joints), and mode D was variable (angle, velocity, moment, power). Three factors were extracted, which explained 95.1% of the data's variance. Only factors one (P = 0.001) and three (P < 0.001) significantly differentiated loaded from unloaded jumping. The body augmented hip-dominant at the start, and both hip and ankle dominant behaviors at the end of the ascending phase of the CMJ, but kept knee-dominant behavior invariant, when jumping with a 20% BW load. By studying the variation across all data modes, Parafac2 provides a holistic method of studying jumping coordination.
KW - Jumping
KW - Kinematics
KW - Kinetics
KW - Load carriage
KW - Multivariate statistics
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U2 - 10.1016/j.jbiomech.2018.08.009
DO - 10.1016/j.jbiomech.2018.08.009
M3 - Article
C2 - 30126721
AN - SCOPUS:85051680730
SN - 0021-9290
VL - 79
SP - 223
EP - 226
JO - Journal of Biomechanics
JF - Journal of Biomechanics
ER -