Measuring complexity of muscle force control: Theoretical principles and clinical relevance in musculoskeletal research and practice. (April 2023) - Clark, Nicholas C.; Pethick, Jamie; Falla, Deborah
Abstract: Musculoskeletal conditions affect bones, joints, and muscles of the locomotor system and are a leading cause of disability worldwide. This suggests that current musculoskeletal rehabilitation techniques fail to target the characteristics (e.g., physiological/physical/psychological) most influential for long-term musculoskeletal health. To identify whether a physiological characteristic is impaired, it must be measured. In neuromuscular control, traditional research approaches use magnitude-based measurements (e.g., peak force/standard deviation of force/coefficient of variation of force). However, magnitude-based measurements miss 'hidden information' regarding a physiological system's status across time. To better identify physiological characteristics that are clinically-important for long-term musculoskeletal health, other measurement approaches currently less applied in musculoskeletal research may be helpful. The purpose of this article is to present an introduction to technical and measurement principles for quantifying the 'complexity' of muscle force control as one representation of peripheral joint neuromuscular control. Complexity measurements are time-based and consider the irregular temporal structure of physiological signals. We review theoretical principles underlying measuring complexity of muscle force control and explain its clinical relevance for musculoskeletal scientists and clinicians. The principles include sensorimotor control of peripheral joints, muscle force signal construction and features, muscle force control measurement procedures, and variability and complexity variables. We propose the potential utility of measuring the complexity of muscle force control for diagnosing sensorimotor system impairment and prognosis following musculoskeletal disease or injury. This article will serve as an educational asset and a scientific resource that will inform future research directions to optimise rehabilitation for people with peripheral joint disease and injury. Highlights: Musculoskeletal (MSK) conditions are a leading cause of disability worldwide. MSK rehabilitation fails to target most influential physiological characteristics. Variability (magnitude-based) measures miss hidden physiological information. Complexity (time-based) measures may uncover hidden sensorimotor impairments. Complexity measures may be useful for diagnosis and prognosis for MSK conditions.