Effects of Novel Supramaximal Interval Training Versus Continuous Training on Performance in Preconditioned Collegiate, National, and International Class Rowers
Bibliographic record
Abstract
This investigation compared supramaximal oxygen uptake interval training with continuous training in collegiate, national, and international class rowers. It was hypothesized that 6 supramaximal intensity sessions over 11 days would increase power on selected power measures. After 8 weeks of training for a new season, 10 heavyweight and 6 lightweight rowers were randomized into 2 groups. A ramp test to limit of tolerance to determine peak aerobic power (6 females: 25 W·min; 10 males: 30 W·min) and an all-out 3-minute test to determine peak power, 60-second power, critical power, and work above critical power (W') were performed before and after training. A supramaximal training session consisted of 10 cycles of 10-second work (140% peak aerobic power):5-second recovery followed by 8 minutes of active recovery, and repeated 6 times. The continuous group performed predominantly moderate intensity (below lactate threshold) training. All training was performed on rowing ergometers. Critical power increased pre-to-post supramaximal (Δ7%) and continuous training (Δ9%), respectively (336 ± 59W to 360 ± 59W; 290 ± 73W to 316 ± 74W; p ≤ 0.05), whereas the mean power output from all performance measures increased only after supramaximal training (Δ7%) (464 ± 158W to 496 ± 184W; p ≤ 0.05). Testing also revealed decreased W' (Δ21%) and 60-second power (Δ4%) pre-to-post continuous training only (p ≤ 0.05). No differences (p > 0.05) in peak aerobic power or peak power were observed pre-to-post training in either group. In conclusion, after an 8-week preconditioning period, supramaximal interval training preserved anaerobic capacity compared with predominantly continuous training and elicited similar increases in critical power in rowers.
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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 | 0.000 |
| 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 | 0.000 |
| 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".