A “Step-Ramp-Step” Protocol to Identify Running Speed and Power Associated with the Maximal Metabolic Steady State
Bibliographic record
Abstract
PURPOSE: A previously established Step-Ramp-Step (SRS) exercise protocol was able to accurately predict the work rate associated with the maximal metabolic steady state (MMSS) in cyclists. The purpose of this study was to determine whether a modified SRS protocol could predict the running speed and power associated with the MMSS. METHODS: Fifteen (8 male; 7 female) runners (V̇O 2max 54.5 [6.5] mL·kg -1 ·min -1 ) were recruited for this investigation composed of four to five visits. In the first visit, runners performed a moderate intensity step (MOD), an incremental exercise test, and a heavy intensity step (HVY), on a motorized treadmill. This SRS protocol was used to predict the running speed and power associated with the MMSS (i.e., the SRS-MMSS), where running power was assessed by a wearable device (Stryd) attached to each runner's shoe. Subsequent visits were used to confirm the maximal lactate steady state (MLSS) as a proxy measure of the MMSS (i.e., the MLSS-MMSS) and to validate the SRS-MMSS speed and power estimates. RESULTS: The estimated SRS-MMSS running speed (7.2 [0.6] mph) was significantly lower than confirmed running speed at MLSS-MMSS (7.5 [0.8] mph; bias = 3.6%, P = 0.005); however, the estimated SRS-MMSS running power (241 [35] W) was not different than the MLSS-MMSS confirmed running power (240 [37] W; bias = -0.6%; P = 0.435). V̇O 2 at SRS-MMSS (3.22 [0.49] L·min -1 ) was not different than respiratory compensation point (3.26 [0.58] L·min -1 ; P = 0.430). Similarly, V̇O 2 at MLSS-MMSS (3.30 [0.54] L·min -1 ) was not different than respiratory compensation point ( P = 0.438). CONCLUSIONS: The SRS protocol allows MMSS, as measured by MLSS, to be accurately determined using running power (Stryd), but not speed, in a single laboratory visit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".