The use of non-linear tools to analyze the variability of force production as an index of fatigue: A systematic review

Abstract

Background: Fatigue is a process that results in a decreased ability to produce force, and which could eventually affect performance and increase the risk of injury. Force variability analysis has been proposed to describe the level of fatigue with the purpose of detecting the development of fatigue. Variability is credited to play a functional and adaptive role through which the components of a system self-organize to solve a motor problem. Non-linear tools have been applied to analyze the variability of physiological signals, revealing that the structure of motor fluctuations provides relevant information about the functional role of variability. It has been suggested that the presence of lower complexity in the variability structure could reveal a less functional and adaptative state (e.g., ageing or illness). In the last years, an increased number of studies have applied these techniques to force variability analysis in relation to fatigue.Objective: To provide an overview of the current knowledge on the use of non-linear tools on force variability as a fatigue index.Methods: Following PRISMA guidelines, a systematic search of SPORTDiscus, Scopus, Web of Science and PubMed was carried out. Studies included were: a) original studies that analyzed the effect of fatigue on humans during an action focused on force production; b) published studies with their title and abstract in English; c) studies that applied non-linear tools on a signal directly related to force production.Results: Twenty-five studies were included in this review. The relationship between fatigue and the complexity of force variability, the type of action and relative intensity, the nature of the signal and the non-linear tools used, and the methods of data acquisition and processing were identified.Conclusion: The articles reviewed suggest that fatigue leads to a decrease in complexity mostly in isometric contractions, but this is not as clear in dynamic contractions. This fatigue-induced loss of complexity seems to be a result of changes in the nervous system at the central level, albeit triggered by peripheral mechanisms. It should be noted that non-linear tools are affected by the relative intensity of contraction, non-stationarity, and the acquisition and treatment of the signal.

Assessing Motor Variability during Squat: The Reliability of Inertial Devices in Resistance Training

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Effects of Mechanical Vibration during an Incremental Slide Board Skating Test on Physiological and Movement Variability Parameters

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Influence of the Slope and Gate Offset on Movement Variability and Performance in Slalom Skiing

“…However, in some cases, different time series can exhibit the same standard deviation even if they do not share the same structure in time [33,34]. This limitation can be addressed with the use of non-linear tools, such as entropy, since it quantifies the amount of regularity and unpredictability of point-to-point fluctuations in large sets of time-series data and it is suitable for dealing with the complexity of biological systems [34][35][36][37]. Sample entropy (SampEn), Multiscale entropy (MSE), and Approximate entropy (ApEn) are the most popular methods for assessing data regularity in health and sports sciences [9,35,38,39].…”