Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics.

Marco Beato,Benjamin Carty,Kevin L De Keijzer,Mark Connor

doi:10.3389/fphys.2019.00780

Marco Beato, Benjamin Carty + Show 2 more

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https://doi.org/10.3389/fphys.2019.00780

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Abstract

The aim of this study was to assess the sensitivity of accelerometer-derived metrics for monitoring fatigue during an intermittent exercise protocol. Fifteen university students were enrolled in the study (age 20 ± 1 years). A submaximal intermitted recovery test (Sub-IRT) with a duration of 6 min and 30 s (drill 1) was performed. In order to increase the participants’ fatigue, after that, a repeated sprint protocol (1×6 maximal 20 m sprints) was performed. Following that, participants repeated the Sub-IRT (drill 2) to evaluate the external and internal training load (TL) variations related to fatigue. Apex 10 Hz global navigation satellite system (GNSS) units were used to collect the variables total distance (TD), high metabolic distance (HMD), relative velocity (RV), average metabolic power (MP), heart rate maximal (HRmax) and mean (HRmean), muscular (RPEmus) and respiratory rating of perceived exertion (RPEres), dynamic stress load (DSL), and fatigue index (FI). A Bayesian statistical approach was used. A likelihood difference (between drill 1 and drill 2) was found for the following parameters: TD (BF10 = 0.33, moderate per H0), HMD (BF10 = 1.3, anecdotal), RV (BF10 = 0.29, moderate per H0), MP (BF10 = 1.3, anecdotal), accelerations (BF10 = 1.6, anecdotal ), FI (BF10 = 4.7, moderate), HRmax (BF10 = 2.2, anecdotal), HRmean (BF10 = 4.3, moderate), RPEmus (BF10 = 11.6, strong), RPEres (BF10 = 3.1, moderate), DSL (BF10 = 5.7, moderate), and DSL•m−1 (BF10 = 4.3, moderate). In conclusion, this study reports that DSL, DSL•m−1, and FI can be valid metrics to monitor fatigue related to movement strategy during a standardized submaximal intermittent exercise protocol.

Highlights

The quantification of training load (TL) is important for improving fitness adaptations and reducing the risk of injury in team sports (Rowell et al, 2018)
Considering the intermittent-activity profile of many team sports (Coratella et al, 2016), heart rate (HR) should be taken into consideration, and where possible integrated with external TL variables, to exhaustively describe a players’ TL demands (Akubat et al, 2014)
Bayesian analysis related to differences between drill 1 vs. drill 2 reported the following strength of the evidence: total distance covered (TD) (BF10 = 0.33, moderate per H0), high metabolic distance (HMD) (BF10 = 1.3, anecdotal), relative velocity (RV) (BF10 = 0.29, moderate per H0), metabolic power (MP) (BF10 = 1.3, anecdotal), accelerations (BF10 = 1.6, anecdotal), heart rate maximal (HRmax) (BF10 = 2.2, anecdotal), HRmean (BF10 = 4.3, moderate), RPEmus (BF10 = 11.6, strong), RPE was quantified for both respiratory (RPEres) (BF10 = 3.1, moderate), dynamic stress load (DSL) (BF10 = 5.7, moderate), DSLm−1 (BF10 = 4.3, moderate), and fatigue index (FI) (BF10 = 4.7, moderate)

Summary

Introduction

The quantification of training load (TL) is important for improving fitness adaptations and reducing the risk of injury in team sports (Rowell et al, 2018). Monitoring Fatigue by Accelerometry-Derived Metrics metrics during training sessions and matches, such as total distance covered (TD), high-speed running (HSR), and sprint running (Beato et al, 2018b). This approach consists of evaluating the TL demands by segmenting locomotion into bands based on speed thresholds. Considering the nature of team sports, were high-intensity mechanical actions are generally required (Zamparo et al, 2015), recent research suggests that accelerometer-based TL variables should be integrated with other established internal and external TL variables (Akubat et al, 2014)

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