Three km Track Time Trial Performance Changes after HIIT in Competitive Cross-Country Skiers
Bibliographic record
Abstract
Background: Limited research surrounding sex differences in fatigue and performance after high intensity interval training (HIIT) exists in the field setting. While lab-based protocols provide a controlled environment, physiological mechanisms of fatigue and performance response in competitive athletes are best measured by time-trial (TT). Few studies, however, have investigated fatigue in TT performance while controlling for the menstrual cycle (MC). Objective: The study investigated the influence of sex and MC phase on 3km track TT performance after a HIIT session in Competitive Cross Country (XC) skiers. Methods: A quasi-experimental design was employed and athletes over the age of 16 years (30F, 9M), were recruited from across Canada and completed 3 days of testing/training: a 3km track TT on Day 1 and Day 3; and a HIIT session (4-8x, 800m) on Day 2. MC phase was verified by ovulation testing and salivary hormone samples; athletes were classified as either “Low Hormone” or “High Hormone” for analysis. Results: An overall improvement in performance from pre- to post-HIIT TTs occurred (p<0.01). No significant differences in TT performance after HIIT were observed between sexes (p=0.16) or MC phase (p=0.26). Conclusion: These results indicate that competitive XC skiers will likely experience an improvement in TT performance after a bout of HIIT. Coaches and athletes should plan their workouts prior to testing accordingly to maximize TT performance. These results also suggest that no special adjustments need to be considered for differences in performance after HIIT between sexes and MC phase, although further studies with a greater sample size and repeated testing are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".