The intra-day and inter-day reliability of a 6-second Wingate to determine maximal peak power in endurance-trained athletes
Bibliographic record
Abstract
Determining an athlete's maximal peak power (MPP) is crucial in profiling endurance sports participants. While short (3 to 6 seconds) all-out efforts have been validated for MPP assessment, prior studies mainly involved non-endurance trained athletes. This study aimed to assess the intra- and inter-day reliability of a 6-second Wingate test for MPP determination in endurance athletes. Endurance-trained participants (22 males, 5 females) completed nine 6-second Wingate tests over four days (3 trials at baseline, 2 trials on each subsequent day). Analysis revealed no systematic differences in MPP (F(4.09, 106.3) = 1.88, p = 0.117) or time to peak power (χ2 (8) = 5.23, p = 0.732) across the trials. Reliability, assessed through the intraclass correlation coefficient (ICC) and standard error of measurement (SEM), was excellent across all trials (ICC = 0.95, SEM = 40.0W, SEM% = 3.7%). Absolute reliability improved when selecting the average or the best MPP values from each day (SEM% = 2.7% and 2.9%, respectively). Within-day reliability was consistently rated as excellent, with the best values on the 4th day of tests. No significant differences in MPP values occurred between the first and second 6-second Wingate tests on days 1 to 3, with both trials demonstrating similar reliability values (SEM%: 3.2% vs 2.8%, for the first and second trials, respectively). The test also demonstrated a good sensitivity to detect a meaningful change in MPP values. In conclusion, the 6-second Wingate test proves reliable for determining MPP in endurance-trained athletes. Two trials are recommended on the first day of testing, with a single MPP likely sufficient to determine the athlete's MPP on subsequent days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".