Time to exhaustion at intermittent maximal lactate steady state is longer than continuous cycling exercise
Bibliographic record
Abstract
The maximal lactate steady state (MLSS) represents a submaximal intensity that may be important in prescribing both continuous and interval endurance training. This study compared time to exhaustion (TTE) at MLSS in continuous and intermittent (i.e., with pauses) exercise, investigating whether physiological variables differ between these exercise modes. Fourteen trained male cyclists volunteered for this investigation and performed an incremental test, several 30-min tests to determine two MLSS intensities (continuous and discontinuous protocol), and two randomized tests until exhaustion at MLSS intensities on a cycle ergometer. The intermittent or discontinuous protocol was performed using 5 min of cycling, with an interval of 1 min of passive rest. TTE at intermittent MLSS was 24% longer than TTE at continuous exercise (67.8 ± 14.3 min vs. 54.7 ± 10.9 min; p < 0.05; effect sizes = 1.04), even though the absolute power output of intermittent MLSS was higher than continuous (268 ± 29 W vs. 251 ± 29 W; p < 0.05). Additionally, the total mechanical work done was significantly lower at continuous exercise than at intermittent exercise. Likewise, regarding cardiorespiratory and metabolic variables, we observed greater responses during intermittent exercise than during continuous exercise at MLSS. Thus, for endurance training prescription, this is an important finding to apply in extensive interval sessions at MLSS. This result suggests that interval sessions at discontinuous MLSS should be used instead of continuous MLSS, as discontinuous MLSS allows for a larger amount of total work during the exhaustion trial.
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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) | 0.000 | 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".