The effects of verbal cueing for high intended movement velocity on power, neuromuscular activation, and performance
Bibliographic record
Abstract
It is widely believed that lifting heavy loads slowly, but with a conscious intention to move at high velocity, can produce resistance training (RT) adaptations indicative of rapid movements. This study investigated the effects of verbally cued high “intended” movement velocity (HIMV) during RT on neuromuscular and performance outcomes. 20 untrained volunteers (aged 24.2 ± 3.9 years) participated in 3 weeks of knee extension training. Participants were randomly allocated to receive verbal cues focusing on high intended movement velocity, HIMV, or steady and controlled movement, TRAD (traditional training). All other training variables, including actual movement velocity (30° s−1), remained constant. Increase in mean power output at 30° s−1 was greater for TRAD than HIMV (76% and 33%, respectively, P = 0.027). There were main effects for time (but no between‐group differences) for maximal isometric force (+14%, P = 0.003), peak torque at 180° s−1 (+22%, P = 0.006), peak torque at 30° s−1 (+29%, P < 0.001), 3‐repetition maximum (+20%, P < 0.001), and resting corticospinal excitability (+43%, P = 0.017). There were no differences between groups or across time for voluntary activation (P = 0.793), spinal excitability (P = 0.686), or intracortical inhibition (all P > 0.05). HIMV verbal cueing did not produce additional neurophysiological or performance benefits when compared to traditional cueing. Overall, our results demonstrated that verbal cueing did not alter the principle of velocity‐specific adaptation. Cueing that increases the duration of maximal effort may be optimal for maximizing average power output at low speeds.
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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) | 0.000 | 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) | 0.002 | 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".