Muscle fatigue, pedalling technique and the VO2${{\dot{V}}_{{{{\mathrm{O}}}_{\mathrm{2}}}}}$ slow component during cycling
Bibliographic record
Abstract
Abstract Above the first lactate threshold, the steady‐state is delayed or prevented due to the slow component (). This phenomenon has been associated with muscle fatigue, but evidence for a causal relationship is equivocal. Moreover, little is known about the contribution of pedalling technique adjustments to during fatiguing cycling exercise. Eleven participants completed constant power trials at 10% above the second lactate threshold. Muscle fatigue was assessed, utilizing femoral nerve stimulation and instrumented pedals, while , quadriceps oxygenation, electromyography (EMG) and pedal force components were measured. Correlations between physiological and mechanical variables were estimated at group and individual levels. Group correlations revealed moderate values for with quadriceps twitch force (r = −0.51) and muscle oxygenation (r = −0.52), while weak correlations were observed for EMG amplitude (r = 0.26) and EMG mean power frequency (r = −0.16), and with pedalling mechanical variables such as peak total downstroke force (r = −0.16), minimum total upstroke force (r = −0.16) and upstroke index of effectiveness (r = 0.16). The findings here align with prior literature reporting significant correlations between the magnitude of muscle fatigue and that of , although there was large interindividual variability for all the reported correlations. Considering the heterogeneity in the data, it is difficult to determine the relative impact of pedalling technique adjustments on overall, but the present study opens the possibility that in some cases, increases in secondary to technical adjustments may be ‘superimposed’ on the underlying .
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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 | 0.004 |
| 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".