Training load, mileage, and perceived exertion as a predictive model of injury and illness in women`s soccer
(Trainingsbelastung, Laufleistung und empfundene Anstrengung als Vorhersagemodell für Verletzungen und Erkrankungen im Frauenfußball)
This study examined the relationship between training load, mileage, and session rating of perceived exertion (s-RPE) as predictors of injury and illness in Division I women`s soccer players. Twenty-four athletes were monitored over a 13-week season including 69 athlete exposures (49 training sessions and 20 matches). Internal and external load were measured during each athlete exposure. Player injury and illness status were documented daily by medical staff and categorized as healthy, medical attention, or time-loss. Associations between athlete exposures and injury/illness status were analyzed using a mixed-effects ordinal logistic regression model with player ID as a random intercept. A total of 1560 athlete observations were included. Higher daily mileage was associated with increased odds of injury or illness (OR = 1.67, 95% CI: 1.19-2.34). Training load was associated with reduced odds of injury or illness, with each unit increase lowering the odds by 42% (OR = 0.58, 95% CI: 0.41-0.83). Session-RPE was not significantly associated with injury or illness (OR = 0.96, 95% CI: 0.65-1.42). These findings indicate that accumulated mileage elevates injury and illness risk, while structured increases in training load enhance athlete resilience, and reduce injury and illness risk. Monitoring both internal and external workload provides performance staff with a practical approach to optimize training stress, augment recovery, and prepare athletes for the demands of competition in women`s soccer.
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| Schlagworte: | |
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| Notationen: | Spielsportarten Biowissenschaften und Sportmedizin |
| Veröffentlicht in: | Sports |
| Sprache: | Englisch |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://doi.org/10.3390/sports13110411 |
| Jahrgang: | 13 |
| Heft: | 11 |
| Seiten: | 411 |
| Dokumentenarten: | Artikel |
| Level: | hoch |