Predicting fatigue using countermovement jump force-time signatures

PCA can distinguish neuromuscular versus metabolic fatigue

Paul Pao Yen Wu, Nicholas Sterkenburg, Kirsten Everett, Dale W. Chapman, Nicole White, Kerrie Mengersen

Research output: Contribution to journalArticle

Abstract

Purpose: This study investigated the relationship between the ground reaction force-time profile of a countermovement jump (CMJ) and fatigue, specifically focusing on predicting the onset of neuromuscular versus metabolic fatigue using the CMJ. Method: Ten recreational athletes performed 5 CMJs at time points prior to, immediately following, and at 0.5, 1, 3, 6, 24 and 48 h after training, which comprised repeated sprint sessions of low, moderate, or high workloads. Features of the concentric portion of the CMJ force-time signature at the measurement time points were analysed using Principal Components Analysis (PCA) and functional PCA (fPCA) to better understand fatigue onset given training workload. In addition, Linear Mixed Effects (LME) models were developed to predict the onset of fatigue. Results: The first two Principal Components (PCs) using PCA explained 68% of the variation in CMJ features, capturing variation between athletes through weighted combinations of force, concentric time and power. The next two PCs explained 9.9% of the variation and revealed fatigue effects between 6 to 48 h after training for PC3, and contrasting neuromuscular and metabolic fatigue effects in PC4. fPCA supported these findings and further revealed contrasts between metabolic and neuromuscular fatigue effects in the first and second half of the force-time curve in PC3, and a double peak effect in PC4. Subsequently, CMJ measurements up to 0.5 h after training were used to predict relative peak CMJ force, with mean squared errors of 0.013 and 0.015 at 6 and 48 h corresponding to metabolic and neuromuscular fatigue. Conclusion: The CMJ was found to provide a strong predictor of neuromuscular and metabolic fatigue, after accounting for force, concentric time and power. This method can be used to assist coaches to individualise future training based on CMJ response to the immediate session.

Original languageEnglish
Article number0219295
Pages (from-to)1-16
Number of pages16
JournalPLoS One
Volume14
Issue number7
DOIs
Publication statusPublished - 10 Jul 2019

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Principal Component Analysis
Principal component analysis
Fatigue
principal component analysis
Fatigue of materials
athletes
Workload
Athletes
Neuromuscular Agents
Passive Cutaneous Anaphylaxis
Time measurement
methodology

Cite this

Wu, P. P. Y., Sterkenburg, N., Everett, K., Chapman, D. W., White, N., & Mengersen, K. (2019). Predicting fatigue using countermovement jump force-time signatures: PCA can distinguish neuromuscular versus metabolic fatigue. PLoS One, 14(7), 1-16. [0219295]. https://doi.org/10.1371/journal.pone.0219295
Wu, Paul Pao Yen ; Sterkenburg, Nicholas ; Everett, Kirsten ; Chapman, Dale W. ; White, Nicole ; Mengersen, Kerrie. / Predicting fatigue using countermovement jump force-time signatures : PCA can distinguish neuromuscular versus metabolic fatigue. In: PLoS One. 2019 ; Vol. 14, No. 7. pp. 1-16.
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Wu, PPY, Sterkenburg, N, Everett, K, Chapman, DW, White, N & Mengersen, K 2019, 'Predicting fatigue using countermovement jump force-time signatures: PCA can distinguish neuromuscular versus metabolic fatigue', PLoS One, vol. 14, no. 7, 0219295, pp. 1-16. https://doi.org/10.1371/journal.pone.0219295

Predicting fatigue using countermovement jump force-time signatures : PCA can distinguish neuromuscular versus metabolic fatigue. / Wu, Paul Pao Yen; Sterkenburg, Nicholas; Everett, Kirsten; Chapman, Dale W.; White, Nicole; Mengersen, Kerrie.

In: PLoS One, Vol. 14, No. 7, 0219295, 10.07.2019, p. 1-16.

Research output: Contribution to journalArticle

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