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Background <p>Idiopathic 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.</p> Methods <p>Twenty-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 <i>t</i>-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.</p> Results <p>Compared 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; <i>p</i> &lt; .001). SPM revealed multi-joint, phase-specific impairments in sagittal and frontal plane kinematics, including reduced pelvic obliquity, diminished hip flexion/extension and abduction, attenuated knee flexion during swing, and blunted ankle plantarflexion at push-off (<i>p</i> &lt; .05). These findings indicate impaired supraspinal coordination consistent with frontal-subcortical motor network dysfunction. Further, the statistical modelling achieved excellent discrimination between patients with iNPH and healthy controls. Due to short-stepped iNPH gait causing multiple consecutive steps on a single force plate, kinetics (i.e., ground reaction forces; joint moments) could not be analysed.</p> Conclusions <p>This 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Richard Mills,
  • Tobias Langheinrich,
  • Liis Uiga,
  • Katherine A. J. Daniels,
  • Sean Maudsley-Barton,
  • Mariam Riaz,
  • Cliff Chen,
  • Owen Thomas,
  • Claire Atherton,
  • Neil D. Reeves,
  • Mats Tullberg

摘要

Background

Idiopathic 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.

Methods

Twenty-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.

Results

Compared 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 < .001). SPM revealed multi-joint, phase-specific impairments in sagittal and frontal plane kinematics, including reduced pelvic obliquity, diminished hip flexion/extension and abduction, attenuated knee flexion during swing, and blunted ankle plantarflexion at push-off (p < .05). These findings indicate impaired supraspinal coordination consistent with frontal-subcortical motor network dysfunction. Further, the statistical modelling achieved excellent discrimination between patients with iNPH and healthy controls. Due to short-stepped iNPH gait causing multiple consecutive steps on a single force plate, kinetics (i.e., ground reaction forces; joint moments) could not be analysed.

Conclusions

This 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.