Training Load Quantification in Women's Elite Football: A Season-Long Prospective Cohort Study.
Ulrik B KarlssonMarkus VagleHåvard WiigLive Steinnes LutebergetPublished in: International journal of sports physiology and performance (2023)
The aim of this study was to investigate (1) if there are differences in training load and intensity between the different training days within a microcycle and (2) if training load and intensity within the different training days are stable over the course of a season. Data were collected over a full season from a team in the women's premier division in Norway. External load (total distance, high-speed-running distance, sprint distance, and the combined number of accelerations and decelerations [ACCDEC]) was assessed using a 10-Hz GPS system with a built-in accelerometer. Internal load was assessed through session rating of perceived exertion, which was multiplied with session duration (session rating of perceived exertion-load). Training days were classified in relation to their proximity to the upcoming match day (MD): MD - 4, MD - 3, MD - 2, and MD - 1. Contents on these days were standardized according to a weekly periodization model followed by the coaching staff. Differences between training days were analyzed using a linear mixed-effects model. All training days were significantly different from each other across multiple variables. ACCDEC values were highest on MD - 4 (147.5 [13.0] ACCDEC count), and all distance variables were highest on MD - 3. All measures of training load were significantly reduced from MD - 3 to MD - 2 (effect size [ES] = 1.0-4.1) and from MD - 2 to MD - 1 (ES = 1.6-4.3). A significant negative effect across the season was observed for session rating of perceived exertion-load and ACCDEC (ES = 0.8-2.1). These results provide evidence that elite female football teams can be successful in differentiating training load between training days when implementing a weekly periodization approach.
Keyphrases
- molecular dynamics
- virtual reality
- high intensity
- high speed
- mental health
- physical activity
- machine learning
- social support
- magnetic resonance imaging
- magnetic resonance
- body composition
- transcranial direct current stimulation
- resistance training
- high resolution
- contrast enhanced
- big data
- metabolic syndrome
- working memory
- insulin resistance
- artificial intelligence