Separate and combined effects of time of day and verbal instruction on knee extensor neuromuscular adjustments
Bibliographic record
Abstract
We examined the effects of time of day and verbal instruction, separately and combined, on knee extensor neuromuscular adjustments, with special reference to rapid muscle force production capacity. Ten healthy male participants performed 4 experimental trials in counterbalanced order: morning “hard-and-fast” instruction, evening hard-and-fast instruction, morning “fast” instruction, and evening fast instruction. During each experimental trial, neuromuscular performance was assessed from the completion of 6 maximal isometric voluntary contractions (rest = 2 min) of the knee extensors with concomitant quadriceps surface electromyography recordings. For each contraction, we determined maximal voluntary force (Fmax), maximal rate of force development (RFDmax) and associated maximal electromechanical delay (EMDmax), and maximal rate of muscle activation (RMAmax). Globally, oral temperature (+2.2%), Fmax (+4.9%) and accompanying median frequency (+6.6%)/mean power frequency (+6.0%) as well as RFDmax (+13.5%) and RMAmax (+15.5%) were significantly higher in the evening than morning (p < 0.05). Conversely, evening in reference to morning values were lower for EMDmax (–4.3%, p < 0.05). Compared with a hard-and-fast instruction, RFDmax (+30.6%) and corresponding root mean square activity (+18.6%) were globally higher using a fast instruction (p < 0.05), irrespectively of the time of day. There was no significant interaction effect of time of day and verbal instruction on any parameter, except for EMDmax (p = 0.028). Despite diurnal variation in maximal or explosive force production of knee extensors and associated neuromuscular parameters, these adjustments occurred essentially independently of the verbal instruction provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".