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Purpose <p>This systematic review synthesizes current evidence on gait analysis as a diagnostic and prognostic tool for degenerative lumbar spine diseases (DLSD), evaluates the clinical utility of specific gait parameters as biomarkers, and identifies emerging technologies enabling scalable, accessible clinical implementation.</p> Methods <p>A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, and Web of Science (2010–2026). Studies evaluating gait parameters in patients with lumbar spinal stenosis (LSS), lumbar disc herniation (LDH), or lumbar spondylolisthesis were included. Data extraction focused on spatiotemporal gait metrics, kinematic parameters, and diagnostic performance measures.</p> Results <p>A total of 47 studies met the inclusion criteria, comprising 2,847 patients with DLSD and 1,892 healthy controls. Disease-specific gait signatures were identified: LSS demonstrated significant increases in gait asymmetry (+ 131%) and variability (+ 436%), while LDH exhibited marked reductions in gait velocity (-76%) and cadence (-67%). Machine learning classifiers achieved diagnostic accuracy up to 93.18% (AUC 0.97) for differentiating lumbar pathologies. Smartphone-based and wearable sensor technologies showed strong agreement with laboratory-based motion capture systems (ICC &gt; 0.85), offering potential for remote clinical applications.</p> Conclusions <p>Gait analysis provides objective, quantifiable biomarkers that differentiate DLSD subtypes with high diagnostic accuracy. The emergence of smartphone-based video analysis and wearable sensors represents a paradigm shift toward accessible gait assessment. Integration of artificial intelligence with clinical gait databases offers promising directions for decision support systems in spine care.</p>

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Gait analysis in degenerative lumbar spine diseases: clinical applications, biomarkers, and emerging technologies — a systematic review

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Sheyang Xu,
  • Xinglin Liu,
  • Xianglong Meng

摘要

Purpose

This systematic review synthesizes current evidence on gait analysis as a diagnostic and prognostic tool for degenerative lumbar spine diseases (DLSD), evaluates the clinical utility of specific gait parameters as biomarkers, and identifies emerging technologies enabling scalable, accessible clinical implementation.

Methods

A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, and Web of Science (2010–2026). Studies evaluating gait parameters in patients with lumbar spinal stenosis (LSS), lumbar disc herniation (LDH), or lumbar spondylolisthesis were included. Data extraction focused on spatiotemporal gait metrics, kinematic parameters, and diagnostic performance measures.

Results

A total of 47 studies met the inclusion criteria, comprising 2,847 patients with DLSD and 1,892 healthy controls. Disease-specific gait signatures were identified: LSS demonstrated significant increases in gait asymmetry (+ 131%) and variability (+ 436%), while LDH exhibited marked reductions in gait velocity (-76%) and cadence (-67%). Machine learning classifiers achieved diagnostic accuracy up to 93.18% (AUC 0.97) for differentiating lumbar pathologies. Smartphone-based and wearable sensor technologies showed strong agreement with laboratory-based motion capture systems (ICC > 0.85), offering potential for remote clinical applications.

Conclusions

Gait analysis provides objective, quantifiable biomarkers that differentiate DLSD subtypes with high diagnostic accuracy. The emergence of smartphone-based video analysis and wearable sensors represents a paradigm shift toward accessible gait assessment. Integration of artificial intelligence with clinical gait databases offers promising directions for decision support systems in spine care.