Giovanni Spallone, Letizia Mancini, Arianna Carnevale, Stefano Campi, Alessandro de Sire, Emiliano Schena, Pieter D'Hooghe, Michael T Hirschmann, Rocco Papalia, Umile Giuseppe Longo
{"title":"Step-by-step insight into gait analysis: A narrative review unlocking knee biomechanics.","authors":"Giovanni Spallone, Letizia Mancini, Arianna Carnevale, Stefano Campi, Alessandro de Sire, Emiliano Schena, Pieter D'Hooghe, Michael T Hirschmann, Rocco Papalia, Umile Giuseppe Longo","doi":"10.1002/ksa.70067","DOIUrl":null,"url":null,"abstract":"<p><p>Gait analysis offers a powerful tool for clinical and orthopaedic decision-making. By quantifying spatiotemporal, kinematic and kinetic parameters during walking, it provides a dynamic window into joint function that static imaging cannot capture. Despite its potential, gait analysis remains largely confined to specialised centres, with limited integration in clinical pathways, mainly due to its perceived complexity and lack of standardisation. This narrative review aims to bridge that gap through a step-by-step approach to guide orthopaedic surgeons, sports medicine physicians and musculoskeletal clinicians in understanding and interpreting key biomechanical markers relevant to common knee pathologies, such as osteoarthritis and anterior cruciate ligament injury. Particular attention is given to how deviations in parameters like joint angles and moments, dynamic alignment and centre of pressure trajectories can offer actionable insights into disease progression, treatment response and surgical planning. The urgent need for standardised protocols, encompassing marker placement, biomechanical modelling and data processing, is also underscored, as they are essential to ensure reproducibility and facilitate clinical translation. By clarifying the clinical meaning of gait metrics, this review empowers healthcare professionals to integrate dynamic functional data into everyday decision-making and move towards more personalised, biomechanically informed care. Level of Evidence: Level IV.</p>","PeriodicalId":520702,"journal":{"name":"Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA","volume":" ","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/ksa.70067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Gait analysis offers a powerful tool for clinical and orthopaedic decision-making. By quantifying spatiotemporal, kinematic and kinetic parameters during walking, it provides a dynamic window into joint function that static imaging cannot capture. Despite its potential, gait analysis remains largely confined to specialised centres, with limited integration in clinical pathways, mainly due to its perceived complexity and lack of standardisation. This narrative review aims to bridge that gap through a step-by-step approach to guide orthopaedic surgeons, sports medicine physicians and musculoskeletal clinicians in understanding and interpreting key biomechanical markers relevant to common knee pathologies, such as osteoarthritis and anterior cruciate ligament injury. Particular attention is given to how deviations in parameters like joint angles and moments, dynamic alignment and centre of pressure trajectories can offer actionable insights into disease progression, treatment response and surgical planning. The urgent need for standardised protocols, encompassing marker placement, biomechanical modelling and data processing, is also underscored, as they are essential to ensure reproducibility and facilitate clinical translation. By clarifying the clinical meaning of gait metrics, this review empowers healthcare professionals to integrate dynamic functional data into everyday decision-making and move towards more personalised, biomechanically informed care. Level of Evidence: Level IV.