Incorporating Sprint Training With Endurance Training Improves Anaerobic Capacity and 2,000-m Erg Performance in Trained Oarsmen
Bibliographic record
Abstract
A 2,000-m time-trial performance, aerobic capacity, and anaerobic capacity were assessed in 16 trained oarsmen after sprint interval training (SIT) replaced a portion of an endurance-based training program (EBTSIT) vs. an endurance-based program alone (EBTAlone). The EBTSIT involved 10 SIT sessions over 4 weeks, in addition to 12 continuous exercise sessions, 2 anaerobic threshold exercise sessions, and 4 strength training sessions. The EBTAlone consisted of 20 continuous, 6 anaerobic threshold, 2 interval exercise sessions, and 8 strength training sessions. Time-trial performance (2,000-m erg performance) improved with EBTSIT (baseline = 414.6 ± 18.5, post = 410.6 ± 17.5 seconds; p < 0.001) but only approached significance in EBTAlone (baseline = 413.0 ± 27.7, post = 411.4 ± 27.9 seconds; p = 0.06). In a 60-second "all-out" anaerobic capacity test, peak power output (PPO) increased significantly with EBTSIT (PPO: EBTSIT: baseline = 566 ± 82, post = 623 ± 60 W; p = 0.02) but not with EBTAlone (EBTAlone: baseline = 603 ± 81, post = 591 ± 123 W; p = 0.59). Changes in average power output (APO) also approached significance (p = 0.07) (APO: EBTSIT: baseline = 508 ± 48, post = 530 ± 52 W; EBTAlone: baseline = 532 ± 55, post = 533 ± 68 W). Neither group experienced any change in aerobic capacity ((Equation is included in full-text article.)or ventilatory threshold; p ≥ 0.16). We conclude that replacing a portion of EBT with SIT can improve both 2,000-m erg performance and anaerobic capacity, while maintaining aerobic fitness in trained oarsmen. Incorporating SIT within endurance training programs may be useful during periods of low-volume training, to improve performance without sacrificing aerobic capacity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".