Instrumented assessment of joint neuromechanics in upper motor neuron syndrome: a systematic review of methods, reliability, validity, and clinical relevance

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Abstract Instrumented neuromechanical assessment (I-NMA) is a promising method for quantifying joint neuromechanics in upper motor neuron syndrome (UMNS), using controlled joint manipulation such as stretch- or continuous perturbations. Compared to traditional clinical assessments, I-NMA can provide improved insight by better separating neural and mechanical contributions and increasing objectivity. However, I-NMA has not yet found its way into clinical practice, possibly due to methodological shortcomings. This systematic review analyzed 74 studies on I-NMA using controlled perturbation in UMNS, examining experimental protocols, input–output analyses to quantify neuromechanical outcome parameters, and their reliability and validity. Most studies employed only single-joint (n=64 studies) assessments, solely under passive conditions (n=63), which overlook clinically relevant aspects such as multi-joint interactions and active control. Among neuromechanical parameters, joint stiffness and reflexive resistance were quantified most, whereas joint dampingand intrinsic neural drive contributions remained underrepresented. Four main input–output analysis methods were recognized: System Quantification (n=55), System Modeling (n=12), and System Identification via impulse (IRF, n=7), or frequency response functions (FRF, n=6). The first quantified passive stiffness during a slow joint stretch and reflexive resistance during a fast stretch. The latter three quantified neuromechanical parameters by fitting models, comprising time-delayed reflex components and intrinsic joint stiffness and damping, either directly using input–output data (System Modeling) or via initial nonparametric IRF or FRF estimation. The reliability of quantified parameters showed substantial room for improvement, especially in the minimal detectable change, ranging from 20% to 947% of the parameter value across analyses. I-NMA outcome measures showed stronger validity when assessed against expected UMNS-healthy differences than when assessed against clinical scales. Future I-NMA development may benefit from improved reliability through protocol consistency and optimized analysis, stronger evidence for the valid separation of neural and mechanical constructs, and enhanced clinical relevance by using active and passive assessments, capturing both paresis and hyperresistance.

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