High-Intensity Cycling Training Necessitates Increased Neuromuscular Demand of the Vastus Lateralis During a Fatiguing Contraction
Bibliographic record
Abstract
Purpose: To examine the effects of a 5-week continuous cycling training intervention on electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships of the vastus lateralis (VL) during a prolonged contraction. Methods: Twenty-four sedentary, young adults performed maximal voluntary contractions (MVCs) and a prolonged isometric trapezoidal contraction at the same absolute 40% MVC for the knee extensors before (PRE) and after training (POSTABS). Individual b- (slopes) and a-terms (y-intercepts) were calculated from the log-transformed electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships during the increasing and decreasing segments of the trapezoid. EMGRMS and MMGRMS was normalized for the 45-s steady torque segment. Results: At PRE, b-terms for the EMGRMS-torque relationships during the linearly decreasing segment were greater than the increasing segment (p < .001), and decreased from PRE to POSTABS (p = .027). a-terms were greater during the linearly increasing than decreasing segment at PRE, while the a-terms for the linearly decreasing segment increased from PRE to POSTABS (p = .027). For the MMGRMS-torque relationships, b-terms during the linearly decreasing segment decreased from PRE to POSTABS (p = .013), while a-terms increased from PRE to POSTABS when collapsed across segments (p = .022). Steady torque EMGRMS increased for POSTABS (p < .001). Conclusion: Although cycling training increased aerobic endurance, incorporating resistance training may benefit athletes/individuals as the alterations in neuromuscular parameters post-training suggest a greater neural cost (EMGRMS) and mechanical output (MMGRMS) to complete the same pre-training fatiguing contraction.
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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) | 0.001 | 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".