The validity and reliability of quadriceps twitch force as a measure of skeletal muscle fatigue while cycling
Bibliographic record
Abstract
The measurement of skeletal muscle fatigue in response to cycling exercise is commonly done in isometric conditions, potentially limiting its ecological validity, and creating challenges in monitoring the time course of muscle fatigue across an exercise bout. This study aimed to determine if muscle fatigue could be reliably assessed by measuring quadriceps twitch force evoked while pedaling, using instrumented pedals. Nine participants completed three laboratory visits: a step incremental test to determine power output at lactate threshold, and on separate occasions, two constant-intensity bouts at a power output 10% above lactate threshold. Femoral nerve electrical stimulation was applied to elicit quadriceps twitch force both while pedaling (dynamic) and at rest (isometric). The test-retest reliability of the dynamic twitch forces and the agreement between the dynamic and isometric twitch forces were evaluated. Dynamic twitch force was found to have excellent reliability in an unfatigued state (intraclass correlation coefficient (ICC) = 0.920 and mean coefficient of variation (CV) = 7.5%), and maintained good reliability at task failure (ICC = 0.846 and mean CV = 11.5%). When comparing dynamic to isometric twitch forces across the task, there was a greater relative decline in the dynamic condition (P = 0.001). However, when data were normalized to the 5 min timepoint when potentiation between conditions was presumed to be more similar, this difference disappeared (P = 0.207). The reliability of this method was shown to be commensurate with the gold standard method utilizing seated isometric dynamometers and offers a new avenue to monitor the kinetics of muscle fatigue during cycling in real time.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".