Effect of a Prior Force-Velocity Test Performed with Legs on Subsequent Peak Power Output Measured with Arms or Vice Versa
Bibliographic record
Abstract
The aim of this study was to examine whether measurement of peak anaerobic power (Wpeak) by force-velocity test using the arms or the legs influenced the performance obtained when the opposite muscle group was tested. Ten trained male throwers (age: 20.6 +/- 2; stature: 1.82 +/- 0.06 m; and body mass: 85.5 +/- 17.2 kg) performed, on separate days, 2 Monark cycle-ergometer protocols comprising (a) arm cranking (A1) followed by a leg cycling (L2) force-velocity test (series A-L) and (b) a leg cycling (L1) followed by an arm cranking (A2) force-velocity test (series L-A). On each day, 8 minutes of seated rest separated the 2 force-velocity tests. Arterialized capillary blood was collected from the finger tips for blood lactate analysis at rest and at the end of each force-velocity test. Wpeak-A1 and Wpeak-A2 were similar (8.1 +/- 1.7 and 8.6 +/- 1.5 W.kg, respectively). Wpeak-L1 and Wpeak-L2 were 14.0 +/- 3 and 13.4 +/- 2.8 W.kg (NS). Blood [La] increased significantly after each force-velocity test (p < 0.001), but peak blood [La] did not differ significantly between L1 (6.6 +/- 1.2) and L2 (6.2 +/- 1.4 mmol.L) or between A1 (7.2 +/- 1.0) and A2 (7.4 +/- 1.6 mmol.L). In this population, force-velocity tests performed using the legs or the arms did not induce a significant decrease in force-velocity determinations of peak anaerobic power performed subsequently with the opposite muscle group. In strength-trained athletes, the force-velocity approach can thus be used to measure the peak power output of both the legs and the arms in a single laboratory session, without adversely affecting estimates of an athlete's performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".