Are There Sex Differences in Anaerobic Performance Following a Dynamic Warm-Up? A Randomized, Counterbalanced, and Crossover Design Study
Bibliographic record
Abstract
The increasing trend of females participating in elite and historically male-dominated sports has also resulted in a rise in females performing dynamic warm-ups (DWs). DW is a widely used practice in hockey. However, there is scarce evidence describing how DWs impact subsequent anaerobic performance and whether this response differs between sexes. This study aimed to determine sex differences in anaerobic performance when preceded by a DW. Twenty National Collegiate Athletics Association Division-II hockey players (n = 20, 10 female) completed a Wingate Anaerobic Test (WAnT) preceded by a DW or no warm-up in a randomized, counterbalanced order and followed a crossover design. The DW was ∼8 min long and consisted of 13 movements that targeted prime muscles and joints involved in ice skating. The WAnT consisted of a 30-s, maximal effort sprint against 7.5% of the participant’s body mass performed on a cycle ergometer. Peak power output (PPO), relative peak power (RPP), mean anaerobic power (MP), and fatigue index (FI) evaluated anaerobic performance during the WAnT. There were no significant differences between male and female scores following DW. MP was significantly higher in males and females, but PPO, RPP, and FI were not when a DW preceded the WAnT. In both conditions, males had higher PPO and MP than females, while there were no sex differences in RPP and FI. In conclusion, performing a DW before a WAnT improved MP for females and males with no adverse effects on PPO, RPP, and FI. This study suggests that DW might benefit hockey players independently of sex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".