Patellofemoral pain is associated with complex gait deviations captured by an artificial intelligence-derived gait index

Navarro, GJ, de Albuquerque, TAB, Fidelis-de-Paula-Gomes, CA, Ferreira, CL, Correa, JCF, Barton, GJ ORCID: 0000-0002-7214-1967 and Lucareli, PRG (2025) Patellofemoral pain is associated with complex gait deviations captured by an artificial intelligence-derived gait index. Clinical Biomechanics, 132. p. 106735. ISSN 0268-0033

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Abstract

Background
Patellofemoral pain (PFP) in active women is associated with gait adaptations. The Movement Deviation Profile (MDP) summarises deviations in multi-segment kinematic deviation, but it is unclear how the magnitude of deviation relates to pain. We investigated the relationship between MDP and pain intensity during walking.
Methods
In a retrospective, laboratory-based study, we analysed three-dimensional gait data from women with PFP (n = 571) alongside matched asymptomatic controls for reference modelling. Pain intensity was rated on a 0–10 visual analogue scale (VAS). Typical gait was defined from controls; Euclidean distances across 13 kinematic curves yielded the mean MDP (MDPmean). We used linear regression, and the results remained consistent after controlling for the year of data collection and addressing heteroskedasticity in the standard errors.
Findings
In women with PFP, the mean MDP was 13.17° (95 % CI: 12.93°–13.41°), and the mean VAS was 6.03 (95 % CI: 5.91–6.15). Each 1-point increase in VAS corresponded to an approximately 1.99° increase in MDPmean (R2 = 0.92). This relationship remained consistent even after adjusting for the year (β = 1.98°; 95 % CI: 1.93–2.04; p < 0.001), suggesting that changes over time did not influence the observed association.
Interpretation
In women with PFP, higher pain intensity is closely linked to more significant gait deviations, underlining the clinical importance of MDP as a quick measure of movement change. Although the retrospective nature prevents causal conclusions, the strength and consistency of the link indicate that pain level can serve as a useful marker for kinematic deviations during gait analysis.

Item Type:	Article
Uncontrolled Keywords:	0903 Biomedical Engineering; 0913 Mechanical Engineering; 1106 Human Movement and Sports Sciences; Orthopedics; 4003 Biomedical engineering; 4201 Allied health and rehabilitation science; 4207 Sports science and exercise
Subjects:	Q Science > QP Physiology R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Sport and Exercise Sciences
Publisher:	Elsevier BV
Date of acceptance:	8 December 2025
Date of first compliant Open Access:	16 December 2025
Date Deposited:	16 Dec 2025 15:22
Last Modified:	16 Dec 2025 15:22
DOI or ID number:	10.1016/j.clinbiomech.2025.106735
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27729

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