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.

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.

Background and Objective: Active aging is influenced by various factors, including chronic diseases, multimorbidity, functional limitations, and disabilities. The presence of these factors might lead to greater dependence on caregivers and could present potential barriers to community engagement. Physical functioning might be considered as one of the vital components for healthier aging experience promotion and support in elderly people. This study aimed to assess self-perceived general health and related health characteristics among the elderly population in Serbia, with a focus on varying degrees of functional limitations, as well as to analyze the predictors of physical functioning limitations in relation to gender. Materials and Methods: This population-based modeling study included a representative sample of 3540 elderly individuals aged above 65 years from Serbia. We employed a dual approach to model the four distinct difficulty levels related to the two groups of limitations of physical functioning (PF1 and PF2) for both genders. The PF1 focused on walking half a kilometer on level ground without the assistance of any mobility aids, and the PF2 navigated a set of 12 steps of ascent and descent: Model 1: inability to execute PF1, Model 2: some/a lot of difficulty in PF1, Model 3: inability to execute PF2, and Model 4: some/a lot of difficulty in PF2. Further variables were evaluated: self-perceived general health, long-lasting health problems, and chronic diseases/chronic conditions. Logistic regression analysis was performed to assess predictors of physical functioning. The models’ performance was presented. Results: Significant predictors were as follows: self-perceived general health (Model 1 (male OR: 8.639; female OR: 3.569); Model 2 (male OR: 2.759; female OR: 2.277); Model 3 (male OR: 24.290; female OR: 5.090); Model 4 (male OR: 3.256; female OR: 2.152)); long-lasting general health (Model 1 (female OR: 2.867); Model 3 (female OR: 3.602)); pulmonary diseases (Model 2 (male OR: 2.036); Model 4 (male OR: 1.976; female OR: 1.756)); musculoskeletal diseases (Model 1 (female OR: 1.537); Model 2 (male OR: 1.397; female OR: 1.410); Model 3 (male OR: 1.954; female OR: 1.739); Model 4 (male OR: 1.531; female OR: 1.483)); and other chronic diseases (Model 3 (male OR: 2.215)). Conclusions: Bad self-perceived general health and pulmonary and musculoskeletal diseases were predictors of functional disability in both genders of elderly individuals, while long-lasting health problems were predictors of functional disability in females and other chronic diseases were predictors in elderly males.