Gait Analysis in Neurologic Disorders: Methodology, Applications, and Clinical Considerations.

Gait and balance disorders are a leading cause of morbidity, mortality, and disability in central and peripheral neurologic disorders. Neurologic gait disorders are classically evaluated with a clinical examination and visual pattern recognition. Gait patterns such as a parkinsonian or ataxic gait may have distinct etiologies and are assessed using the neurologic physical examination and validated clinical scales. Technological advances have made gait analysis more accessible, allowing precise objective measurement of gait and balance deficits. Gait analysis may be more sensitive at detecting change compared with a physical examination alone. It has the potential to augment clinical diagnosis, track disease progression, and evaluate response to therapies in clinical trials. Additional applications of gait analysis include early disease screening, discriminating between conditions that have similar gait profiles, and use of quantified gait parameters to predict future outcomes. Numerous devices are now available to conduct gait performance measurements in the laboratory, clinic, or real-world settings. With the rapid growth of gait analysis technology and application of artificial intelligence to these data, there are clinical and research implications that should be carefully considered when evaluating patients with neurologic disorders. Important factors include the clinical or research question, reliability and validity of the method used, and the effect of environmental and patient factors. Knowledge of gait analysis technology is essential for clinicians to implement this tool in clinical practice or research and critically analyze literature on the topic in neurologic diseases. In this narrative review, we provide an overview of normal gait function, an appraisal of available gait analysis technologies (merits and applications), implications for clinicians and researchers, recent advances in gait analysis for neurologic disorders, and future considerations.