A protocol for measuring the direct effect of cycling on neuromuscular control of running in triathletes
Bibliographic record
Abstract
The direct effects of cycling on movement and muscle recruitment patterns (neuromuscular control) during running are unknown but critical to success in triathlon. We outline and test a new protocol for investigating the direct influence of cycling on neuromuscular control during running. Leg movement (three-dimensional kinematics) and muscle recruitment (surface electromyography, EMG) were compared between a control run (no prior exercise) and a 30-min transition run that was preceded by 20 min of cycling. We conducted three experiments investigating: (a) the repeatability (between-day reliability) of the protocol; (b) the ability of the protocol to investigate, in highly trained national or international triathletes, the direct influence of cycling on neuromuscular control during running independent of neuromuscular fatigue; and (c) the ability of the protocol to provide a control, or baseline, measure of neuromuscular control (determined using a measure of stability) without causing fatigue. Kinematic and EMG measures of neuromuscular control during running showed moderate to high repeatability: mean coefficients of multiple correlation for repeatability of EMG and kinematics were 0.816 +/- 0.014 and 0.911 +/- 0.031, respectively. The protocol provided a robust baseline measure of neuromuscular control during running without causing neuromuscular fatigue (coefficients of multiple correlation for stability of EMG and kinematics were 0.827 +/- 0.023 and 0.862 +/- 0.054), while EMG and force data provided no evidence of fatigue. The protocol outlined here is repeatable and can be used to measure any direct influence of cycling on neuromuscular control during running.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".