Warm-up for Sprint Swimming: Race-Pace or Aerobic Stimulation? A Randomized Study
Bibliographic record
Abstract
Neiva, HP, Marques, MC, Barbosa, TM, Izquierdo, M, Viana, JL, Teixeira, AM, and Marinho, DA. Warm-up for sprint swimming: race-pace or aerobic stimulation? A randomized study. J Strength Cond Res 31(9): 2423-2431, 2017-The aim of this study was to compare the effects of 2 different warm-up intensities on 100-m swimming performance in a randomized controlled trial. Thirteen competitive swimmers performed two 100-m freestyle time-trials on separate days after either control or experimental warm-up in a randomized design. The control warm-up included a typical race-pace set (4 × 25 m), whereas the experimental warm-up included an aerobic set (8 × 50 m at 98-102% of critical velocity). Cortisol, testosterone, blood lactate ([La]), oxygen uptake (V[Combining Dot Above]O2), heart rate, core (Tcore and Tcorenet) and tympanic temperatures, and rating of perceived exertion (RPE) were monitored. Stroke length (SL), stroke frequency (SF), stroke index (SI), and propelling efficiency (ηp) were assessed for each 50-m lap. We found that V[Combining Dot Above]O2, heart rate, and Tcorenet were higher after experimental warm-up (d > 0.73), but only the positive effect for Tcorenet was maintained until the trial. Performance was not different between conditions (d = 0.07). Experimental warm-up was found to slow SF (mean change ±90% CL = 2.06 ± 1.48%) and increase SL (1.65 ± 1.40%) and ηp (1.87 ± 1.33%) in the first lap. After the time-trials, this warm-up had a positive effect on Tcorenet (d = 0.69) and a negative effect on [La] (d = 0.56). Although the warm-ups had similar outcomes in the 100-m freestyle, performance was achieved through different biomechanical strategies. Stroke length and efficiency were higher in the first lap after the experimental warm-up, whereas SF was higher after control warm-up. Physiological adaptations were observed mainly through an increased Tcore after experimental warm-up. In this condition, the lower [La] after the trial suggests lower dependency on anaerobic metabolism.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".