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
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| Autores: | , , , |
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| Formato: | texto |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2023 |
| Descripción: | 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. |
| País: | Portal de Revistas UNA |
| Institución: | Universidad Nacional de Costa Rica |
| Repositorio: | Portal de Revistas UNA |
| Lenguaje: | Español |
| OAI Identifier: | oai:ojs.www.una.ac.cr:article/16578 |
| Acceso en línea: | https://www.revistas.una.ac.cr/index.php/mhsalud/article/view/16578 |
| Palabra clave: | global positioning system training load fatigue recovery football Sistema global de posicionamiento carga de entrenamiento fatiga recuperación fútbol sistema de posicionamento global carga de treinamento fadiga recuperação futebol |