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2. Machine learning identifies distinct movement control impairment clusters in patients with chronic neck painŽiva Majcen Rošker, Jernej Rošker, 2026, original scientific article Abstract: atients with chronic neck pain experience various impairments, with reduced movement control suggested as a significant contributing factor. The heterogeneity of this patient population and suboptimal rehabilitation outcomes suggests the existence of latent subgroup characteristics. The aim of this study was to identify distinct groups among patients with neck pain based on the movement control test and pain intensity and to provide information on cluster-specific impairments. 135 patients with idiopathic neck pain performed a movement control test (the Butterfly test) at three difficulty levels and were assessed for pain intensity, providing 13 different parameters (classifiers). Louvain, hierarchical and k-means clustering methods were applied and the number of clusters determined by observing the symmetry and size of silhouette scores. Further, different machine learning algorithms were applied to develop and evaluate a classification framework (based on AUC, classification accuracy, sensitivity, and specificity) and to provide information on individual cluster characteristics using the Shapley Additive Explanations. The k-means and deep learning neural network methods provided the most efficient clustering and classification performance extracting 4 meaningful clusters. Patients between groups differed in the amount of impairment, with cluster 2 and 1 representing the most severe impairments and with clusters 3 and 4 the least severe impairments. Additionally, specific motor control impairments were observed in individual clusters suggesting distinct neck movement control adaptations. Identifying subgroups of patients with neck pain and their specific characteristics based on the results of the Butterfly test may inform future development of targeted rehabilitation strategies.
Keywords: neck pain, kinesthesia, proprioception clustering, machine learning
Published in RUP: 16.03.2026; Views: 634; Downloads: 6
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3. Postural sway in multiple sclerosis patients : interaction of vision, surface, and fatigue effectsŽiga Kozinc, Eva Žura, Gregor Brecl Jakob, 2025, original scientific article Abstract: Introduction: Postural control impairments are common in patients with multiple sclerosis (MS), resulting in postural instability and increased fall risk. Sensory inputs are crucial to maintain balance adequately. Additionally, fatigue is one of the common and most disabling symptoms of MS, possibly contributing to postural deficits. Previous studies have examined the effects of fatigue and altered sensory conditions on postural control in patients with MS. The present study aimed to extend this knowledge by jointly assessing these factors within the same experimental framework, providing additional insight into how fatigue modulates sensory contributions to balance. Methods: A total of 21 patients with MS (age = 41.1 ± 10.1 years; EDSS = 1.9 ± 1.0; disease duration = 6.8 ± 4.9 years) completed balance assessments on firm and compliant surfaces with both eyes open and eyes closed, before and after a 6-min walk test used to induce fatigue. Postural sway was quantified using sway velocity and root mean square (RMS). Results: There was a significant effect of surface on sway velocity (p < 0.001, η2 = 0.60), with a greater sway on the compliant surface compared to the firm surface. Fatigue significantly increased sway RMS (p = 0.023, η2 = 0.23) but did not affect sway velocity (p > 0.05). The absence of visual input (eyes closed) also significantly increased sway RMS (p = 0.001, η2 = 0.46). There was a significant interaction between surface and vision for sway RMS (p < 0.001, η2 = 0.54), with a larger effect of surface instability in the eyes-closed condition. Discussion: Patients with MS face increased challenges in maintaining postural control under conditions of fatigue, surface instability, and lack of visual input. Sway RMS may be more sensitive to these effects than sway velocity.
Keywords: balance control, sensory integration, proprioception, motor impairments, fall prevention, neurological disorders, physical fatigue
Published in RUP: 27.10.2025; Views: 1009; Downloads: 10
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