Reliability of time-to-exhaustion and selected psycho-physiological variables during constant-load cycling at the maximal lactate steady-state
Bibliographic record
Abstract
The maximal lactate steady-state (MLSS) is frequently assessed for prescribing endurance exercise intensity. Knowledge of the intra-individual variability of the MLSS is important for practical application. To date, little is known about the reliability of time-to-exhaustion and physiological responses to exercise at MLSS. Twenty-one healthy men (age, 25.2 (SD 3.3) years; height, 1.83 (0.06) m; body mass, 78.9 (8.9) kg; maximal oxygen uptake, 57.1 (10.7) mL·min −1 ·kg −1 ) performed 1 incremental exercise test, and 2 constant-load tests to determine MLSS intensity. Subsequently, 2 open-end constant-load tests (MLSS 1 and 2) at MLSS intensity (3.0 (0.7) W·kg −1 , 76% (10%) maximal oxygen uptake) were carried out. During the tests, blood lactate concentrations, heart rate, ratings of perceived exertion (RPE), variables of gas exchange, and core body temperature were determined. Time-to-exhaustion was 50.8 (14.0) and 48.2 (16.7) min in MLSS 1 and 2 (mean change: −2.6 (95% confidence interval: −7.8, 2.6)), respectively. The coefficient of variation (CV) was high for time-to-exhaustion (24.6%) and for mean (4.8 (1.2) mmol·L −1 ) and end (5.4 (1.7) mmol·L −1 ) blood lactate concentrations (15.7% and 19.3%). The CV of mean exercise values for all other parameters ranged from 1.4% (core temperature) to 8.3% (ventilation). At termination, the CVs ranged from 0.8% (RPE) to 11.8% (breathing frequency). The low reliability of time-to-exhaustion and blood lactate concentration at MLSS indicates that the precise individual intensity prescription may be challenging. Moreover, the obtained data may serve as reference to allow for the separation of intervention effects from random variation in our sample.
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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 | 0.002 | 0.009 |
| 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) | 0.000 | 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 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".