Repeated anaerobic tests predict performance among a group of advanced CrossFit-trained athletes
Bibliographic record
Abstract
High-intensity functional training (HIFT) (i.e., CrossFit (CF) training) uses a combination of movements and self-selected time periods of work and rest. However, little is known about the physiological responses to an acute bout of HIFT exercise or about the physical parameters that distinguish performance. The purpose of this study was to examine the physiological responses in advanced CF athletes to consecutive Wingate trials with short, active recovery periods. Twenty-nine advanced-level CF-trained athletes volunteered for this study. The participants were required to complete 4 consecutive Wingate anaerobic tests (WAnTs) and a 15-min CF-style workout. Across the 4 WAnT trials, significant (p < 0.001) changes were observed in oxygen consumption, respiratory exchange ratio, and heart rate. Significant (p ≤ 0.001) differences among WAnT trials were observed in all anaerobic performance measures. Compared with all other trials, greater peak power (p < 0.04), relative peak power (p < 0.02), average power (p < 0.001), relative average power (p < 0.001), and total work (p < 0.001), together with a lower fatigue index (p < 0.01), were observed during WAnT 1. Overall, the 4 consecutive WAnT trials resulted in a significant (F = 177.0, p < 0.001) increase in blood lactate response. Stepwise regression revealed that the ability to predict total repetitions completed during the 15-min trial to complete as many repetitions as possible improved as the participants progressed from the first to the third WAnT trial. Our data suggest that, combined with the ability to better maintain performance across high-intensity exercise bouts, the ability to quickly recover between bouts is the most important factor in CF performance.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".