Mechanical Efficiency of Treadmill Running Exercise: Effect of Anaerobic-Energy Contribution at Various Speeds
Bibliographic record
Abstract
OBJECTIVES: Mechanical efficiency (ME) describes the ratio between mechanical (PMECH) and metabolic (PMET) power. The purpose of the study was to include an estimation of anaerobic energy expenditure (AnE) into the quantification of PMET using the accumulated oxygen deficit (AOD) and to examine its effect on the value of ME in treadmill running at submaximal, maximal, and supramaximal running speeds. METHODS: Participants (N = 11) underwent a graded maximal exercise test to determine velocity at peak oxygen uptake (vVO2peak). On 4 separate occasions, subjects ran for 6 min at speeds corresponding to 50%, 70%, 90%, and 110% of vVO2peak. During each testing session, PMET was measured from pulmonary oxygen uptake (VO2p) using open-circuit spirometry and was quantified in 2 ways: from VO2p and an estimate of AnE (from the AOD method) and from VO2p only. PMECH was determined from kinematic analyses. RESULTS: ME at 50%, 70%, 90%, and 110% of vVO2peak was 59.9% ± 11.9%, 55.4% ± 12.2%, 51.5% ± 6.8%, and 52.9% ± 7.5%, respectively, when AnE was included in the calculation of PMET. The exclusion of AnE yielded significantly greater values of ME at all speeds: 62.9% ± 11.4%, 62.4% ± 12.6%, 55.1% ± 6.2%, and 64.2% ± 8.4%; P = .001 (for 50%, 70%, 90%, and 110% of vVO2peak, respectively). CONCLUSIONS: The data suggest that an estimate of AnE should be considered in the computation of PMET when determining ME of treadmill running, as its exclusion leads to overestimations of ME values.
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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 | 0.001 |
| 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) | 0.001 | 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".