Training Monitoring With GPS Data and Subjective Measures of Fatigue and Recovery in Honduran Soccer Players During a Preparatory Period for Tokyo 2020/2021 Olympic Games

Authors

DOI:

https://doi.org/10.15359/mhs.20-2.3

Keywords:

global positioning system, training load, fatigue, recovery, football

Abstract

Background: Training monitoring is essential to optimize performance. Therefore, methodologies that improve the preparation of national teams in events such as the Olympic Games should be documented.

Purpose: To determine whether GPS data, in combination with subjective measures of well-being, fatigue, and recovery, are appropriate for load monitoring during a preparatory period for the Olympic Games.

Methodology: Twenty-two under-23 professional players participated during 5 micro-cycles and 27 training sessions. External load data was collected via a global positioning system (GPS): Total distance (DT), performance zones Z0 (0-15 km/h), Z1 (15.1-18 km/h), Z2 (18.1 -24 km/h), Z3 (>24.1 km/h), maximum speed (km/h), accelerations (>2.5m/s2) and decelerations (<2.5m/s2). Also, the internal load was obtained through subjective measures of Rating Perceived Exertion (RPE), Total Quality Recovery (TQR), Readiness to Train (RTT%) obtained from the sleep quality, muscle pain, energy levels, mood, stress, food quality, and health. The subjective rate of fatigue-recovery (F-R) was then calculated. An ANOVA test, Principal Component Analysis (PCA), and multiple linear regression were applied.

Results: the variables DT (p=0.00 ES=0.22), Z0 (p= 0.00 TE=0.08), Z2 (p=0.00 ES= 0.05), maximum speed (p= 0.00 ES=0.42), sum of acceleration and deceleration (p=0.00 ES=0.08) and values relative to load/min (p=0.00 ES=0.17) were identified as variables more sensitive to load change between micro-cycles.  RTT% and subjective rate F-R showed a moderate effect size (p=0.04 ES=0.06 and p=0.06 ES=0.06), but were sensitive to change between micro-cycles. PCA extracted 15 GPS variables and 11 subjective variables that explained 78% of the training load variance.

Conclusion: Using GPS data together with subjective measures involved in fatigue-recovery may be a good strategy to monitor the training load in soccer players.

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Published

2023-03-30

How to Cite

Training Monitoring With GPS Data and Subjective Measures of Fatigue and Recovery in Honduran Soccer Players During a Preparatory Period for Tokyo 2020/2021 Olympic Games. (2023). MHSalud: Revista En Ciencias Del Movimiento Humano Y Salud, 20(2), 1-18. https://doi.org/10.15359/mhs.20-2.3

How to Cite

Training Monitoring With GPS Data and Subjective Measures of Fatigue and Recovery in Honduran Soccer Players During a Preparatory Period for Tokyo 2020/2021 Olympic Games. (2023). MHSalud: Revista En Ciencias Del Movimiento Humano Y Salud, 20(2), 1-18. https://doi.org/10.15359/mhs.20-2.3

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