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Influence of proximal trunk borne load on lower limb countermovement joint dynamics.
J Biomech. 2018 10 05; 79:223-226.JB

Abstract

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.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Liew BXW
Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, UK. Electronic address: LiewB@adf.bham.ac.uk.
Helwig NE
Department of Psychology, University of Minnesota, Minneapolis, MN, USA; School of Statistics, University of Minnesota, Minneapolis, MN, USA.
Morris S
School of Physiotherapy and Exercise Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia.
Netto K
School of Physiotherapy and Exercise Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia.

MeSH

AdultAthletesBiomechanical PhenomenaFemaleHumansJointsLower ExtremityMaleMovementTorsoWeight-Bearing

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30126721

Citation

Liew, Bernard X W., et al. "Influence of Proximal Trunk Borne Load On Lower Limb Countermovement Joint Dynamics." Journal of Biomechanics, vol. 79, 2018, pp. 223-226.
Liew BXW, Helwig NE, Morris S, et al. Influence of proximal trunk borne load on lower limb countermovement joint dynamics. J Biomech. 2018;79:223-226.
Liew, B. X. W., Helwig, N. E., Morris, S., & Netto, K. (2018). Influence of proximal trunk borne load on lower limb countermovement joint dynamics. Journal of Biomechanics, 79, 223-226. https://doi.org/10.1016/j.jbiomech.2018.08.009
Liew BXW, et al. Influence of Proximal Trunk Borne Load On Lower Limb Countermovement Joint Dynamics. J Biomech. 2018 10 5;79:223-226. PubMed PMID: 30126721.
* Article titles in AMA citation format should be in sentence-case
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, Y1 - 2018/08/13/ PY - 2018/01/20/received PY - 2018/06/28/revised PY - 2018/08/10/accepted PY - 2018/8/22/pubmed PY - 2019/5/18/medline PY - 2018/8/22/entrez KW - Jumping KW - Kinematics KW - Kinetics KW - Load carriage KW - Multivariate statistics SP - 223 EP - 226 JF - Journal of biomechanics JO - J Biomech VL - 79 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. SN - 1873-2380 UR - https://www.unboundmedicine.com/medline/citation/30126721/Influence_of_proximal_trunk_borne_load_on_lower_limb_countermovement_joint_dynamics_ DB - PRIME DP - Unbound Medicine ER -