On the assessment of lower-limb muscular power capability
Bibliographic record
Abstract
Purpose: This study assessed the reliability and validity of different methods used to estimate lower-limb muscular power capability based on mechanical variables. For this purpose, vertical jumping was compared with isokinetic knee extensions and with power tests used by practitioners. Methods: Four groups of subjects (N=106) were tested in different conditions. Group-I performed countermovement vertical jumps (CMJ) on a force plate followed by left and right knee extensions on an isokinetic device at 120, 180 and 240 deg · s $^{-1}$ . Group-II performed CMJ trials followed by 20-m sprints, hand-reach jumps and 1RM leg-press testing. Group-III carried out squat jumps (SJ) in addition to CMJ trials. Finally, Group-IV performed the CMJ test and was retested twice after a short inter-session interval (1–4 days) and after a long one (4.5–5 months). The Pearson correlation was used to assess the validity and reliability of CMJ (p ≤ 0.01, **). Results: Mean peak power during CMJ was correlated with sprint time(r=-0.882 $^{**}$ ) and leg-press 1 RM(r=0.797 $^{**}$ ), but less with peak hand-reach height(r=0.695; p ≤ 0.05). Isokinetic knee extension power showed also a significant correlation with CMJ power, but its strength depended on the angular velocity (Isok-120 r=0.702 $^{**}$ ; Isok-180 r=0.737 $^{**}$ ; Isok-240 r=0.599 $^{**}$ ). Test-retests showed a strong correlation after a short interval(r=0.915 $^{**}$ ) and after a long one(r=0.890 $^{**}$ ). Using the SJ technique did not have any effect on reliability (r=0.914 $^{**}$ ). Conclusions: CMJ matches other methods used for testing lower-limb power capability. It is highly reliable and it allows a valid assessment of muscular power. Since CMJ is also simple and accurate to perform, it is the recommended method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".