Changes in Cost of Locomotion Are Higher after Endurance Cycling Than Running When Matched for Intensity and Duration
Bibliographic record
Abstract
INTRODUCTION: Cost of locomotion (C L ) has been shown to increase after endurance running and cycling bouts. The main purpose of this study was to compare, in the same participants, the effect of both modalities on C L when matched for relative intensity and duration. METHODS: Seventeen recreational athletes performed two incremental tests in running and cycling to determine the first ventilatory threshold then two 3-h bouts of exercise at 105% of threshold, with gas exchange measurements taken for 10 min at the start, middle and end of the 3 h to calculate C L . Neuromuscular fatigue during isometric knee extensor contractions and force-velocity profile on a cycle ergometer were assessed before and immediately after the 3-h trials. RESULTS: C L significantly increased at mid (+3.7%, P = 0.006) and end (+7.4%, P < 0.001) of exercise for cycling compared with start, whereas it did not change with time for running. Cardio-respiratory and metabolic variables changed similarly for cycling and running, therefore not explaining the time-course differences in C L between modalities. Changes in C L during cycling correlated significantly with loss of maximal force extrapolated from the force-velocity profile ( r = 0.637, P = 0.006) and changes in cadence ( r = 0.784, P < 0.001). CONCLUSIONS: The type of locomotion influences the effects of exercise on energy cost because 3 h of exercise at the same relative intensity caused a significant increase of cycling C L , and no changes in running C L . The changes in C L in cycling are likely due, at least in part, to fatigue in the locomotor muscles.
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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) | 0.003 | 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".