The correlation between lower limb spasticity and proprioceptive dysfunction in post-stroke patients

To investigate the correlation between lower limb spasticity and proprioception in stroke patients, to analyze the influencing factors of spasticity, and to evaluate the predictive value of musculoskeletal ultrasound parameters on spasticity.

A cross-sectional study was used to enroll 80 stroke patients admitted to the Rehabilitation Center of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from October 2024 to April 2025. The degree of lower limb spasticity was evaluated by the modified Ashworth Scale (MAS), and the proprioceptive assessment module of the Pro-kin balance system was used to quantitatively detect the mean trajectory error (ATE) and the average weight-bearing asymmetry (AWA). Musculoskeletal ultrasound was used to detect the spasmodic side and the healthy gastrocnemius muscle, and the longitudinal and transverse ultrasound images were obtained, and the length of muscle fibers, medial head pinnate angle and muscle thickness were measured. Spearman correlation analysis was used to explore the correlation between MAS score and various parameters, a multiple linear regression model was constructed to analyze the influencing factors of spasticity, and the predictive performance of ultrasound parameters was evaluated by receiver operating characteristic (ROC) curve.

Compared with the healthy side, the muscle fiber length, medial head pinnate angle and muscle thickness of the gastrocnemius muscle on the spasticity side were significantly reduced (p < 0.05). The mean trajectory difference of proprioceptive parameters was (65.83 ± 13.11) %, and the average weight-bearing force difference was (2.41 ± 0.46) kg. Correlation analysis showed that MAS spasticity score was statistically significantly related to proprioceptive parameters and muscle structure parameters (p < 0.05), and multiple linear regression analysis showed that muscle feather angle (β = 0.362), muscle fiber length (β = -0.157), muscle thickness (β = -0.230), mean trajectory error (β = 0.329) and average weight-bearing strength difference (β = 0.260) constituted independent influencing factors for MAS score (adjusted R²  = 0.787, P<0.05). ROC curve analysis showed that the area under the curve of muscle pinnate angle predicting spasticity (MAS ≥ 2 grade) was the largest (AUC = 0.850, 95%CI: 0.729-0.972), which was better than muscle fiber length (AUC = 0. 840) and muscle thickness (AUC = 0.838).

There is a significant correlation between lower limb spasticity and proprioceptive and muscle structure parameters in stroke patients, and proprioception and muscle structure parameters are the key factors affecting spasticity, and musculoskeletal ultrasound can be used as a quantitative evaluation tool for lower limb spasticity in stroke.

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