Strength And Power Asymmetries In The Knee Extensors And Flexors
Bibliographic record
Abstract
Asymmetries in motor function can evolve from habitual use of the dominant side; and in athletes, through the demands of sport. Asymmetries between limbs are known to increase injury risk; however, less is known about power asymmetries, especially in the lower limb. PURPOSE: To determine strength and power asymmetries in the upper leg muscles, and explore the relationship to limb dominance. METHODS: Twelve right-footed participants (6 M; 6 F; right kicking; Waterloo footedness score +10.8±4.4) were recruited. Muscle thickness (MT) was measured using β-mode ultrasound on the vastus lateralis (VL) and biceps femoris (BF) of both legs. Maximal voluntary isometric (MVC) strength and velocity dependent contractions (power) were completed on a NORM dynamometer for knee flexion (KF) and knee extension (KE). Electromyography (EMG) recordings were acquired from the VL and BF, and normalized to MVC. Asymmetry scores were calculated using the equation: (dominant leg - nondominant leg/stronger leg) x 100, with the absolute score representing the absolute asymmetry. RESULTS: No significant differences in MT were found between legs (p=0.16). Absolute asymmetries were significantly different than zero (p<0.05) for KE (7.2±5.1%) and KF (11.5±7.1%) strength, and KE (13.9±10.2%) and KF (11.7±8.3%) power. Asymmetries related to dominance (positive scores favour the dominant) were only significant for KE power (p<0.05; 11.1±13.5%). Overall for both tasks, power asymmetries (12.8±9.3%) were larger than strength (9.3±6.1%) (p<0.05). Normalized EMG revealed a task (KE vs. KF) x muscle (agonist vs. antagonist) interaction (p<0.05). Agonist activation was similar between tasks but KE had greater antagonist activation (0.94±0.60 of MVC) than KF (0.23±0.12 of MVC) (p<0.05). There was no difference between legs for agonist or antagonist activity (p=0.27), or the timing of antagonist onset (p=0.79). CONCLUSION: Contrasting absolute asymmetries revealed much larger strength and power asymmetries compared to asymmetries factoring in limb dominance. Overall there was greater power than strength asymmetry in the upper legs, suggesting explosive tasks may expose larger side-to-side differences than static tasks. These findings suggest substantial leg asymmetries exist but may not always favour the dominant leg.
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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 | 0.001 | 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) | 0.004 | 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".