A three-minute all-out test performed in a remote setting does not provide a valid estimate of the maximum metabolic steady state
Bibliographic record
Abstract
Abstract Purpose The three-minute all-out test (3MT), when performed on a laboratory ergometer in a linear mode, can be used to estimate the heavy–severe-intensity transition, or maximum metabolic steady state (MMSS), using the end-test power output. As the 3MT only requires accurate measurement of power output and time, it is possible the 3MT could be used in remote settings using personal equipment without supervision for quantification of MMSS. Methods The aim of the present investigation was to determine the reliability and validity of remotely performed 3MTs (3MTR) for estimation of MMSS. Accordingly, 53 trained cyclists and triathletes were recruited to perform one familiarisation and two experimental 3MTR trials to determine its reliability. A sub-group (N = 10) was recruited to perform three-to-five 30 min laboratory-based constant-work rate trials following completion of one familiarisation and two experimental 3MTR trials. Expired gases were collected throughout constant-work rate trials and blood lactate concentration was measured at 10 and 30 min to determine the highest power output at which steady-state $${{\dot{V}O}}_{2}$$ V ˙ O 2 (MMSS- $${{\dot{V}O}}_{2}$$ V ˙ O 2 ) and blood lactate (MMSS-[La−]) were achieved. Results The 3MTR end-test power (EPremote) was reliable (coefficient of variation, 4.5% [95% confidence limits, 3.7, 5.5%]), but overestimated MMSS (EPremote, 283 ± 51 W; MMSS- $${{\dot{V}O}}_{2}$$ V ˙ O 2 , 241 ± 46 W, P = 0.0003; MMSS-[La−], 237 ± 47 W, P = 0.0003). This may have been due to failure to deplete the finite work capacity above MMSS during the 3MTR. Conclusion These results suggest that the 3MTR should not be used to estimate MMSS in endurance-trained cyclists.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".