Three-dimensional kinematic gait signatures of idiopathic normal pressure hydrocephalus: a biomechanical framework toward objective diagnosis.

Research overview

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BackgroundIdiopathic Normal Pressure Hydrocephalus (iNPH) is a leading cause of reversible gait disturbance in older adults, yet diagnosis and treatment selection remain limited by low-sensitivity clinical assessments. Conventional tests such as the 10-metre walk and Timed Up-and-Go capture overall performance but overlook the multidimensional, joint-level alterations that characterise iNPH gait. This study aimed to deliver the first comprehensive, three-dimensional (3D) biomechanical characterisation of iNPH gait to inform more objective diagnostic frameworks.MethodsTwenty-three participants with clinically diagnosed iNPH and eighteen age-matched healthy controls underwent baseline gait analysis (iNPH: pre tap test) consisting of overground walking using a 3D optoelectronic motion capture system with integrated force plates. Spatiotemporal parameters were compared using independent-samples t-tests, while continuous kinematic (i.e., joint angle) waveforms for the pelvis, hip, knee, and ankle were analysed using one-dimensional statistical parametric mapping (SPM). A binary logistic regression model was developed to classify participants as iNPH or healthy control.ResultsCompared with controls, individuals with iNPH demonstrated markedly slower gait speed (-60%), shorter stride length (-52%), and greater stride width (+ 64%), each with large effect sizes (1.5-3.3; p ConclusionsThis study suggests the first detailed biomechanical signatures of gait in iNPH, identifying spatiotemporal and joint-specific alterations that together define a central coordination deficit. Beyond confirming known gait slowing, these results delineate kinematic markers with translational potential as digital biomarkers for diagnosis and postoperative monitoring. By advancing gait analysis from simple bedside tests to detailed quantification, 3D motion capture offers a rigorous, reproducible framework that may improve diagnostic sensitivity in patients with iNPH. However, larger future studies should include clinically more heterogeneous cohorts and incorporate relevant disease mimics (e.g., Parkinson's disease, progressive supranuclear palsy) to determine differentiation capabilities of the predictive modelling.

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