Evidence map›Paper›PMID 42682814›Full record

ArticleFrontiers in bioengineering and biotechnology2026

Stance-phase kinematic descriptors differentiate clinical subgroups in women with moderate knee osteoarthritis.

Mayra A Loayza-Saldaña, Simone Tassani, Laura Tío, Jordi Monfort, Gil Serrancolí, Gemma Piella, Jérôme Noailly

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Article in Frontiers in bioengineering and biotechnology, 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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5 · Who and what money

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7 authors.

Mayra A Loayza-SaldañaDepartment of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
Simone TassaniDepartment of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
Laura TíoInflammation and Cartilage Research Group, Hospital del Mar Research Institute, Barcelona, Spain.
Jordi MonfortInflammation and Cartilage Research Group, Hospital del Mar Research Institute, Barcelona, Spain.
Gil SerrancolíSimulation and Movement Analysis Lab, Universitat Polit `ecnica de Catalunya, Barcelona, Spain.
Gemma PiellaDepartment of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.
Jérôme NoaillyDepartment of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Total knee replacement (TKR) is the standard surgical treatment for severely symptomatic knee osteoarthritis (KOA), but treatment decisions for patients with moderate radiographic severity (Kellgren-Lawrence grades 2-3) remain variable. Radiographic grading and patient-reported outcomes do not fully capture the functional heterogeneity between treatment pathways. Stance-phase kinematics may provide objective descriptors for stratification while remaining more accessible than kinetic measurements. Methods: Sixty-six women with moderate KOA (27 scheduled for TKR, 39 managed conservatively) underwent gait analysis. Stance-phase angles for 12 variables were extracted at three sub-phases (loading response, mid-stance, terminal stance). Group differences were assessed via repeated-measures multifactorial MANOVA. Gait speed was evaluated for confounding (Rothman's criteria) and included as a covariate in MANCOVA. Stance-phase change variables trained a Random Forest classifier with nested cross-validation and exhaustive feature selection. Associations between descriptors and clinical outcomes were examined via univariate logistic regression. Clinical construct consistency was assessed against the Osteoarthritis Initiative dataset. Results: The multivariate analysis identified significant Time Conclusion: Stance-phase kinematics differentiate two groups of women with moderate KOA who share radiographic severity but follow different treatment pathways. Pelvic tilt emerged as the most consistent descriptor across multivariate, individual-level, and pain-related analyses, and the most robust under adjustment for gait speed. Kinematics offer an accessible alternative to kinetic measurements for describing clinical heterogeneity in moderate KOA, although integration with imaging and patient-reported outcomes is necessary before clinical implementation.

Indexed as

gait analysisknee osteoarthritismachine learningpatient stratificationstance-phase kinematics

Identifiers

PMID42682814
PMCPMC13530001

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