Background: Stroke recovery is influenced not only by clinical but also sociodemographic factors (SDFs). However, data on how variables such as age, sex, marital status, education, and employment status affect rehabilitation outcomes remain limited, particularly in structured inpatient settings. This study aimed to analyze the impact of key SDFs on functional recovery after stroke. Methods: A retrospective cohort of 289 stroke patients undergoing structured inpatient rehabilitation was analyzed. Functional status was assessed at admission, after three weeks, and at discharge using five standardized outcomes: gait speed (primary outcome), Barthel Index, Berg Balance Scale, Action Research Arm Test, and Ashworth scale. Repeated measures ANOVA and multivariable logistic regression were used to evaluate within-subject changes and associations with SDFs. Results: The cohort consisted predominantly of middle-aged to older adults (58% female, 62% married, 60% retired, 60% with primary education or less). Most patients (88%) had ischemic strokes of moderate severity. Significant improvements were observed across all functional measures. Employed, married, younger, and male patients achieved better outcomes. Interaction models indicated that older and female patients with moderate stroke severity demonstrated greater improvement than younger and male counterparts with milder strokes. Mean gait speed increased by +0.32 m/s, exceeding the minimal clinically important difference (MCID) of 0.16 m/s. Conclusions: Age, sex, marital status, education, and employment status are relevant predictors of stroke rehabilitation outcomes. These findings emphasize the importance of incorporating sociodemographic profiles into individualized rehabilitation planning to optimize functional recovery and reduce disparities among stroke survivors.

Introduction/Aim: In progressive forms of MS, the frequency of pain increases as the disease progresses affecting patients' functional abilities and making the disease much more complex. We conducted a cross-sectional study to examine the association of pain with walking speed and functional abilities in patients who suffer from progressive forms of multiple sclerosis. Material and methods: The cross-sectional study was conducted at the Clinic for rehabilitation "Dr Miroslav Zotović" in the period from January 2020 to May 2023. The research included 55 patients with PPMS and SPMS consecutively admitted to this Clinic for rehabilitation. Demographic and socio-epidemiological data and disease-related data were collected from all the patients. Pain intensity was assessed using Numeric Rating Scale (NRS). Since all patients experienced spasticity, pain was also assessed using the Pain/Discomfort (PD) subscale of the Multiple Sclerosis Spasticity Scale 88 (MSSS-88). The subjective perception of gait impairment was assessed using a subscale of the same questionnaire, MSSS 88, related to walking, namely the Walk (WL). Walking speed was measured by The Timed 25 Foot Walk (T25FW). The functional assessment and all questionnaires were completed in the morning hours over a 24-hour period from the day patients were admitted to rehabilitation. Results: There is a significant strong correlation between WL and P/D (rho=0. 770; p<0.001) and between WL and NRS (rho=0. 825 p<0.001). There is a statistically significant moderate negative correlation between T25FW and NRS pain (p<0.001). There is no statistically significant correlation between T25FW and pain intensity measured by PD (p=0.033). There is a statistically significant correlation between EDSS and pain intensity (NRS p=0.002; PD p=0.006) either. Conclusion: The results of this research indicated a significant negative impact of pain on walking speed and functional disability.

The use of hybrid systems in neurorehabilitation represents a significant advancement in the field, integrating multiple technological and therapeutic approaches to enhance recovery outcomes for patients with neurological impairments. Recent technological advancements have given rise to hybrid neurorehabilitation systems - integrative platforms that combine biological signals with artificial intelligence to deliver more effective, personalized rehabilitation. This multifaceted approach addresses the complex nature of neural damage and plasticity, offering personalized and adaptive rehabilitation protocols. Hybrid systems in neurorehabilitation commonly integrate modalities such as brain-computer interfaces, robotics, functional electrical stimulation and virtual reality. These systems leverage multimodal sensory feedback and advanced control algorithms to promote neuroplasticity, functional reorganization, and motor relearning by actively involving the patient's neural and muscular activity in real-time therapeutic interventions. Clinical studies have demonstrated that hybrid systems can improve motor functions in stroke survivors, spinal cord injury patients, and individuals with neurodegenerative diseases more effectively than conventional rehabilitation alone. These systems offer quantifiable performance metrics, allowing clinicians to tailor interventions and monitor progress objectively. Furthermore, the technology enables intensive, repetitive, and task-specific practice, which are critical factors in neural recovery. Overall, hybrid systems in neurorehabilitation exemplify the convergence of neuroscience, engineering, and clinical therapy, resulting in innovative solutions that maximize functional recovery. Future developments will likely focus on increasing system accessibility, optimizing user interfaces, and integrating machine learning algorithms to refine adaptive rehabilitation strategies further.