ReviewEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Gait analysis in degenerative lumbar spine diseases: clinical applications, biomarkers, and emerging technologies - a systematic review.
Review in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
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Abstract
purposeThis 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.
methodsA 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.
resultsA 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.
conclusionsGait 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.