Asymmetry Should Be Considered the Norm,Not the Exception: Neuromuscular Asymmetries in Knee Flexors and Extensor Assessed Through a Multimetric Approach

Abstract

Background:

The level of interlimb asymmetry varies significantly across outcome measures, resulting in poor agreement in categorizing participants as (a)symmetric. Several researchers have discussed the need for an individual approach to data analysis and the need to perform multiple tests.

Hypothesis:

Limb dominance does not consistently influence muscle function, and the direction of asymmetry may show low consistency across different muscle groups and across the various metrics applied to the same muscles.

Study Design:

Cross-sectional.

Level of Evidence:

Level 4.

Methods:

A total of 71 subjects visited the laboratory once to undergo assessments for knee extensors (KEs) and knee flexors (KFs) separately for the dominant and nondominant limbs. Maximal voluntary force (MVF), rate of force development (RFD) at various time intervals (50, 100, 150 ms) and at its peak (RFD_peak), and RFD-scaling factor (RFD-SF) were obtained. Approximate entropy (ApEn), coefficient of variation, and detrended fluctuation analysis (DFAα) of force signal were also calculated during sustained submaximal contractions. Analyses of variance or Wilcoxon signed-rank test assessed differences in each metric between muscle groups and limbs. Kappa coefficients (κ) were calculated to determine the level of agreement for the direction of asymmetry.

Results:

Except for RFD₁₅₀ (d = 0.305) and RFD-SF (r_rb = 0.280) for KF, no differences between dominant and nondominant limb were observed. All κ values were null, slight or fair (all κ ≤ 0.23).

Conclusion:

The dominant limb is not necessarily stronger than the nondominant limb. The direction of asymmetry varies depending on the metric and muscle considered. Our findings corroborate that asymmetries are ubiquitous in physical performance with neuromuscular asymmetries observed in 75% of subjects.

Clinical Relevance:

These findings highlight the necessity of comprehensive assessment across a multimetrics approach, identifying imbalances and tailoring personalized programs to address performance asymmetries effectively.