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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".