Variability and Complexity of Knee Neuromuscular Control during an Isometric Task in Uninjured Physically Active Adults: A Secondary Analysis Exploring Right/Left and Dominant/Nondominant Asymmetry

Abstract

Work is needed to better understand the control of knee movement and knee health. Specifically, work is needed to further understand knee muscle force control variability and complexity and how it is organized on both sides of the body. The purpose of this study was to explore side-to-side comparisons of magnitude- and complexity-based measures of knee muscle force control to support future interpretations of complexity-based analyses and clinical reasoning in knee injury control. Participants (male/female n = 11/5) performed constant-force isometric efforts at 50% maximal effort. Force variability was quantified during the constant-force efforts using a coefficient of variation (CV%) and force complexity using approximate entropy (ApEn) and detrended fluctuation analysis (DFA) α. Outcomes were right/left and dominant/nondominant group-level and individual-level comparisons. A limb-symmetry index was calculated for each variable and clinically significant absolute asymmetry was defined (>15%). The only significant side-to-side difference was for right/left DFA α (p = 0.00; d = 1.12). Maximum absolute asymmetries were (right/left, dominant/nondominant): CV 18.2%, 18.0%; ApEn 34.5%, 32.3%; DFA α 4.9%, 5.0%. Different side-to-side comparisons yield different findings. Consideration for how side-to-side comparisons are performed (right/left, dominant/nondominant) is required. Because a significant difference existed for complexity but not variability, this indicates that both complexity-based and magnitude-based measures should be used when studying knee muscle force control.

Measuring complexity of muscle force control: Theoretical principles and clinical relevance in musculoskeletal research and practice

“…Because physiological systems contain multiple anatomical components of different sizes, with each anatomical component containing physiological processes requiring different timeframes, a system's output (i.e., signal) is characterised by constant changes (i.e., fluctuations) across periods-of-time (Lipsitz and Goldberger, 1992). Therefore, quantifying muscle force signal fluctuations is Kandel ( 2013), Lephart et al (2000), Nowak et al (2013), Ostry and Feldman (2003), Pethick et al (2022a), , Williams et al (2001) needed to understand a person's neuromuscular control characteristics better (Clark and Pethick, 2022).…”

“…Muscle force control is one representation of neuromuscular control (Clark and Pethick, 2022) (Table 1). The hardware and software used for measuring peripheral joint muscle force control have been described elsewhere by researchers working with healthy people (Pethick et al, 2015;Vaillancourt and Newell, 2003) and those with disease (Vaillancourt et al, 2001;Chow and Stokic, 2014) and injury (Skurvydas et al, 2011;Hollman et al, 2021).…”

“…An example of a fractal variable is detrended fluctuation analysis (DFA) which quantifies a signal's structural pattern over several timescales (Pethick et al, 2022a;Goldberger et al, 2002;Peng et al, 1994) (Table 2). Because entropy and fractal variables represent different physiological characteristics, both should be used to examine muscle force control signal complexity thoroughly (Clark and Pethick, 2022;Pethick et al, 2022a;Lipsitz and Goldberger, 1992).…”

“…Thus, the measurement of neuromuscular control can be represented by any measurement procedure that characterises some aspect of skeletal muscle activation or force generation via the efferent components of the nervous system (Table 1). The purpose of this article is to present an introduction to technical and measurement principles for quantifying 'complexity' of muscle force control as one representation of neuromuscular control (Clark and Pethick, 2022) (Table 1). Specifically, we review theoretical principles underlying the measurement of complexity of muscle force control and explain its clinical relevance for musculoskeletal scientists and clinicians.…”

Special Issue of New Trends in Neuromechanics and Motor Rehabilitation

“…For healthy individuals, Clark and Pethick [2] explored side-to-side comparisons (i.e., right/left and dominant/nondominant) of variability and complexity measures that potentially estimate knee muscle force control capabilities. The authors found that only right/left detrended fluctuation analysis (DFA) α, a nonlinear tool, indicated side-to-side difference in uninjured healthy people.…”