Search Results (1,207)

Background/Objectives: Health-related quality of life (HRQoL) is a critical indicator of developmental progress, educational engagement, and psychosocial resilience. By identifying both shared and context-specific differences in HRQoL, we aim to contribute to a more nuanced understanding of well-being that can inform the development of assessment approaches and future research tailored to the diverse contexts in which children with disabilities live and learn. Thus, the purpose of this study is to explore HRQoL and its putative influencing factors among youth with multiple disabilities across two distinct cultural settings, the United States and Iceland. Methods: Participants (N = 26; Icelandic = 50%; Mage = 16.34 ± 2.33 years) completed height, weight, the Test of Perceived Physical Competence (TPPC), Supine-to-Stand (STS), Rapid Assessment of Physical Activity (RAPA), and VISIONS QL. We conducted five, 2 group × 2 sex ANOVA and several independent samples t-tests within groups by sex for our variables of interest. Results: There was a significant difference between Icelandic boys and girls for BMI (p = 0.087, d = 0.65) and STS (p = 0.027, d = 1.04). Conversely, a significant difference was found in the American group between boys and girls for RAPA (p = 0.092, d = 0.81) and TPPC (p = 0.068, d = 0.92). Conclusions: Preliminary findings suggest that patterns in objective and self-reported health indicators may vary by context. These results highlight the importance of considering both measured performance and self-perceived health when examining HRQoL among adolescents with multiple disabilities, while underscoring the need for further research in larger samples to clarify these relationships. Full article

Objectives: Early and accurate identification of frailty is essential for preventing adverse outcomes in older adults. However, existing frailty prediction models often lack reliability, interpretability, and generalizability. Methods: Participants aged 60 years and older between 2011 and 2015 (n = 3419) from the CHARLS were used to develop models, and participants from the CLHLS-HF between 2014 and 2018 (n = 1017) were used for external validation. The frailty was assessed 4 years after baseline in both cohorts by Fried’s Frailty Phenotype (FFP). Six machine learning models were applied to develop prediction models. The SHapley Additive exPlanations (SHAP) method was utilized to explain the final model. Clinical outcomes were evaluated between participants predicted as frail and non-frail by the final model. Results: The XGBoost (AUC = 0.934, 95% CI: 0.921–0.948; F1 = 0.712, 95% CI: 0.686–0.736 in internal validation; AUC = 0.792, 95% CI: 0.750–0.830; F1 = 0.702, 95% CI: 0.652–0.753 in external validation) performed best among six models. Key predictors included lifestyle factors (e.g., instrumental daily living activities, BMI, and self-rated health) and psychological traits (e.g., depression). Participants predicted as frail had significantly elevated risks of falls (OR = 2.11), hospitalization (OR = 1.75), and disability (OR = 1.42). Conclusions: The proposed model provided a robust and interpretable digital tool for predicting frailty among older adults and associated adverse outcomes. Full article

Twice-exceptional (2e) students who demonstrate high potential alongside disability-related needs are frequently underserved in inclusive classrooms, which in part might be related to teachers’ limited preparation. This qualitative phenomenological study aims to explore Saudi teachers’ perceptions of professional development (PD) needs for identifying and supporting 2e students in inclusive public schools. We conducted in-depth semi-structured interviews with 12 teachers (7 male, 5 female; ages 25–55) from four schools in Hail city. Five were general education teachers, three were gifted education teachers, and four were special education teachers. After analyzing the data, the findings revealed three major themes and several subthemes: (a) Understanding the Unique Needs of 2e Students, (b) Barriers to Effective Teacher Training, and (c) Impact of Inadequate Training on Teaching and Learning. The study findings offer insights into the gap between teachers’ conceptual awareness and their practical preparedness, suggesting that practice-oriented training is perceived as a critical need for successful identification and educational support for 2e students in the inclusive classroom. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of death and disability worldwide. Among its subtypes, coronary artery disease (CAD) is the most common and often develops silently, without noticeable symptoms. CAD-related murmurs typically fall below the human hearing threshold, limiting the effectiveness of traditional stethoscope-based auscultation. Currently, the gold standard for CAD diagnosis is coronary angiography, an invasive and expensive procedure usually reserved for symptomatic patients. This highlights the global need for a non-invasive, cost-effective pre-screening tool for asymptomatic CAD detection. Objectives: This study investigates the effectiveness of a wearable vest equipped with multiple digital stethoscopes to detect CAD. By applying signal processing and machine learning to multichannel phonocardiogram (PCG) data, we aim to evaluate the accuracy of CAD detection. We further assess the impact of incorporating patient metadata to enhance model performance. Methods: Data were collected from 40 CAD patients and 40 non-CAD individuals using a wearable vest with seven embedded PCG sensors. Subjects performed 10 s breath-hold recordings in a clinical setting. Linear-frequency cepstral coefficients were extracted from the PCG signals and classified using a support vector machine. Metadata, including body mass index, blood pressure, type 2 diabetes, and hypertension, were integrated to assess performance gains. Results: A combination of four channels achieved an accuracy of 80.44%, a 7% improvement over the best single-channel result. Incorporating metadata increased accuracy to 82.08%. Conclusions: The wearable vest demonstrated promising clinical potential, exceeding a 75% sensitivity-specificity average, and may support accessible, automated CAD screening in future validated settings. Full article

Background/Objectives: Musculoskeletal disorders and their surgical treatment significantly affect global disability, healthcare utilization, and costs. Artificial intelligence (AI) is a key enabler of data-driven musculoskeletal care. Their applications include diagnostic imaging, surgical planning, risk prediction, rehabilitation, and digital health ecosystems. This narrative review synthesizes current evidence on the use of AI in orthopaedics and musculoskeletal care across five areas: diagnostic imaging, surgical planning and intraoperative augmentation, predictive analytics and patient-reported outcomes, rehabilitation intelligence and teleorthopaedics, and system-level management. An additional task is to identify translational gaps and priorities for safe, ethical, and equitable implementation of AI. Methods: A structured narrative review was conducted using targeted searches in PubMed, Scopus, and Web of Science supplemented by semantic and citation-based explorations in Semantic Scholar, OpenAlex, and Google Scholar. The main search period was January 2019 to December 2025. The retrieved peer-reviewed articles were analyzed for clinical relevance to human musculoskeletal care, quantitative outcomes, and the translational implications of the results. From the broader pool of eligible publications, 40 clinically relevant studies were selected for detailed synthesis covering imaging, surgical planning, predictive modeling, rehabilitation, and system-level applications. Owing to the significant heterogeneity in the model architectures, datasets, and endpoints, the results were organized into five predefined thematic areas. Results: The most mature evidence is for AI-assisted detection of bone fractures on radiographs, identification of implants, and use of sizing templates in preoperative planning for arthroplasty, where deep learning systems have achieved expert-level diagnostic performance (e.g., fracture detection sensitivity of approximately 90% and specificity of approximately 92% and implant identification accuracy of 97–99%) and improved the accuracy of preoperative planning compared to conventional templating. AI-based planning increases the likelihood of reducing intraoperative corrections, shortening surgery time, reducing blood loss, and improving the final functional outcomes. Predictive models can support the stratification of risk for complications, rehospitalizations, and patient-reported outcomes, although external validation remains limited and is often single-center at this stage of research. Emerging applications in rehabilitation and teleorthopaedics, including sensor-based monitoring and learning systems integrated with Patient-Reported Outcome Measures (PROMs), are conceptually promising, but are mainly limited to feasibility or pilot studies. Conclusions: AI is beginning to influence musculoskeletal care, moving beyond pattern recognition toward integrated, patient-centered decision support throughout the perioperative and rehabilitation periods. Its widespread use remains constrained by limited multicenter validation, dataset bias, algorithmic opacity, and immature regulatory and governance frameworks. Future work should prioritize prospective multicenter impact studies, repeatable revalidation of local models, integration of PROM and teleorthopedic data with health learning systems, and adaptation to changing regulatory requirements to enable safe, ethical, effective, and equitable implementation in routine orthopedic practice. Full article

Objectives: This longitudinal single-case series evaluated an AI-based routine-learning system as assistive technology (AT) for elite Boccia athletes with severe Cerebral Palsy (CP). The study aimed to provide an innovative outcome measurement approach for individualized monitoring by integrating performance scores and longitudinal kinematic variability indicators. Methods: Three national-level players performed 694 throws over eight weeks. To ensure technical credibility, trials were rated through a consensus-based assessment by a panel of two experts, serving as ground truth for AI modeling. The system utilized a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture to extract 29 kinematic features and perform regression-based scoring, providing real-time augmented feedback. Results: High-baseline tasks maintained stable scores (7–9), while intermediate tasks showed significant score increases, reflecting motor learning transitions. The model achieved a Mean Squared Error of 1.14 and a Mean Absolute Error of 1.13, demonstrating high alignment with expert standards. Training demonstrated stable convergence, with loss reducing from 7.45 to 1.19. Notably, for the most severely impaired athlete, the AI system detected a 4.69% reduction in kinematic variability despite stagnant performance scores. This provides empirical evidence of movement stabilization within the cognitive stage that traditional observation might overlook. Conclusions: The Bi-LSTM system enabled accurate tracking of performance and motor variability, revealing distinct learning curves based on task difficulty. These findings demonstrate the feasibility of AI-enabled motion analysis as an AT for outcome measurement, supporting data-driven coaching where conventional evaluation is constrained by the rarity and severity of disabilities. Full article

Background/Objectives: Stroke is a leading cause of long-term disability, and predicting functional outcome at discharge, such as the modified Rankin Scale (mRS), is important for guiding treatment and rehabilitation. Many existing approaches depend on advanced imaging or complex corticospinal tract (CST) segmentation from multi-shell diffusion MRI, limiting clinical feasibility. Automated lesion segmentation is also challenging due to lesion heterogeneity and MRI variability. This study proposes a clinically feasible multimodal MRI pipeline based on routine imaging. Methods: Lesion segmentation models were trained and evaluated on the ISLES 2022 dataset (250 training, 150 test cases). Zero-shot external evaluation was performed on 149 cases from ISLES 2024 using standard MRI sequences only. An ensemble of deep learning models (SEALS, NVAUTO, FACTORIZER) was evaluated on ISLES 2022, while SEALS alone was used for external testing. CST segmentation was performed using TractSeg on single-shell diffusion-weighted imaging. Imaging biomarkers included lesion volume, shape, ADC-based texture features, CST integrity, and lesion–CST overlap. These features were used to train machine learning models for binary mRS prediction at discharge. Results: The ensemble achieved a Dice score of 0.82 on ISLES 2022, while zero-shot evaluation on ISLES 2024 achieved 0.57. In exploratory analysis, CatBoost achieved the highest point estimates (accuracy 0.88, F1-score 0.87, ROC-AUC 0.83). Key predictors included lesion–CST overlap, lesion volume, surface area, dissimilarity, and contrast. Conclusions: This exploratory study demonstrates the feasibility of combining automated lesion segmentation with anatomically informed biomarkers using routine clinical MRI, supporting interpretable stroke outcome modelling and motivating future large-scale validation. Full article

Background/Objective: Mobility Opportunities Via Education (MOVE) is a structured intervention to enhance independent mobility skills in individuals who are non-ambulatory. This study aims at identifying and mapping the literature related to the MOVE programme and to describe its content according to preselected categories, focusing on individuals with non-ambulatory cerebral palsy. Methods: A scoping review was conducted, with thirteen databases searched in May 2024, complemented by reference search and private databases; the search was updated in August 2025. Publications after 1985 were included without restrictions on language, population, or context. Two reviewers independently screened records and extracted data using qualitative content analysis. Results: From 6794 records, 228 publications in 15 languages were included, mainly from the United States and Europe. MOVE was developed in the 1980s during a shift towards age-appropriate, functional interventions for individuals with severe disabilities. It is an early task-specific, activity-based and family-centred approach with retrospectively proposed foundations in dynamic systems theory and motor learning. Implementation follows a structured six-step process, embedding mobility training into daily routines. MOVE has been implemented across populations, settings, and countries, particularly for non-ambulatory individuals with cerebral palsy. Full article

Background: Although traditional rehabilitation methods are effective in promoting recovery for patients with disabilities, some approaches can involve repetitive tasks, making it challenging to maintain high patient engagement and adherence. This can impact the amount of therapy patients receive, which can sometimes limit their overall recovery potential, particularly given constraints in healthcare resources. Extended reality (XR) technologies, which include virtual reality (VR) and augmented reality (AR), offer promising benefits to personalize care and enhance rehabilitation and engagement by increasing motivation and engagement through interactive and immersive environments. Despite these promising advantages, their successful integration in clinical practice has remained limited, partly due to lack of early involvement of clinicians and end-users in the development process. Objective: We aim to provide recommendations for XR rehabilitation technology development, including researchers and industry professionals, to foster more personalized, adoptable and effective tools for patients with neuromusculoskeletal disorders in a clinical setting. Methods: Principles from motor control and game theory are used to describe key features and recommendations for XR rehabilitation technology development to optimize rehabilitation applications in a clinical setting. These recommendations stem from established motor learning and game design principles, a state-of-the-art narrative review of emerging XR rehabilitation literature (2015–2025) and insights from the Ensemble! Program, a living lab where clinicians, researchers, and patients collaborate to explore emerging technologies, including but not limited to serious games using XR technologies. Results: Key design recommendations include strategies for supporting patient motivation, adjusting game difficulty, providing feedback and handling data collection. Conclusions: Integrating motor control and game theory principles into XR rehabilitation technology can help optimize its therapeutic effectiveness and clinical applicability for patients with neuromusculoskeletal conditions. By addressing clinician and patient needs early in the development process, these technologies can be better tailored to meet therapeutic goals and facilitate broader adoption in clinical practice. Full article

(1) Introduction. To improve access times and provide effective treatment to the growing patient population with acute stroke due to large vessel occlusion (LVO), thrombectomy-capable stroke centers (TCSCs) should be made an integral part of hospital infrastructure in Poland. The geographical proximity of thrombectomy-capable centers and recently extended treatment time windows will considerably increase patient numbers, decrease patient disability, and reduce the costs of long-term care. (2) Aim of the study. This study investigates the clinical outcomes, time metrics, and angiographic data of a cohort containing 250 thrombectomy patients at a single TCSC in Poland. We measured performance against data from the national database during two crucial time intervals: at the very beginning of the center’s service and after the involvement of a new operator. This study considers concurrent modifications in qualification guidelines, the TCSC’s transition from a ‘direct-admission-only’ to a ‘drip-and-ship’ model, and the learning curve of the interventional stroke team. (3) Methods. A retrospective analysis was conducted on 250 patients treated from August 2020 to May 2025 at a newly established TCSC. The cohort was dived into 2 subgroups: an initial group of 100 patients, whose treatment corresponded to the involvement of a new, previously trained on-site operator and the establishment of 24/7 service, and a group of 150 patients who received later treatment. Additional comparisons were made between a cohort of directly admitted patients and those treated under the drip-and-ship model. The results compared between patients treated with early and expanded time windows. (4) Results. Significant differences were observed between the first 100 and subsequent 150 patients in terms of admission scheme (97% vs. 70%, p < 0.0001), extended time window treatment (8% vs. 17.3%, p < 0.05), and intravenous thrombolysis treatment (81% vs. 65.3%, p < 0.01). Improvements in time intervals and procedural factors were noted in the second group, reflecting the operator’s increased experience (groin-to-first pass time: 27 vs. 23 min, p < 0.05). A comparative analysis between the direct admission and drip-and-ship models revealed extended time intervals in the latter (door-to-groin: 110 vs. 159 min, p < 0.001; door-to-recanalization: 158 vs. 200 min, p < 0.001; door-to-CT: 9 vs. 16.5 min, p < 0.001; and door-to-IVT: 21 vs. 43 min, p < 0.001). Patients in the extended time window exhibited lower intravenous thrombolysis rates (78.2% vs. 29.4%, p < 0.0001) and prolonged door-to-groin (117.5 vs. 150 min, p < 0.005), door-to-CT (10 vs. 19.5 min, p < 0.01), and door-to-IVT (25 vs. 77.5 min, p < 0.001) times. No significant differences were found in complication rates, clinical outcomes, or mortality between the analyzed subgroups. (5) Conclusions. The present data demonstrate favorable clinical and angiographic results among acute LVO stroke patients at the newly established TCSC, both at the onset of the mechanical thrombectomy service and after the involvement of a newly trained operator. Even when treating patients with prolonged times due to transportation and late window qualification, we observed favorable clinical outcomes and low rates of complications. The results achieved in our TCSC compared with the national data suggest that TCSCs could potentially play an important role within the overall endovascular treatment system for acute ischemic stroke patients in Poland. Full article

Students with autism spectrum disorder (ASD) represent a growing population in U.S. higher education, including those with academic talents and gifts. Our research team has studied these academically talented students with ASD, along with their teachers, parents, counselors, and disability service providers at highly competitive colleges and universities in the United States. Using qualitative methodology and thematic analysis, this study examined factors and experiences relating to how self-perceptions of identification as twice exceptional contributed to academic success among 40 students with ASD attending highly competitive colleges. A focus of this research was the role that participants’ perception of their talents, disabilities, and learning experiences played in their academic success. Findings indicate that slightly under half of the participants believed they had a clear understanding of their academic talents and their ASD during college. Their self-perceptions of ability varied over time and based on various academic and social challenges, but most believed an understanding of their twice-exceptionality was necessary for their academic success. Over time, particularly during their college years, participants learned to better understand their talents and disabilities and to identify which strength-based experiences helped to shape their success. Students’ positive experiences, such as success in advanced, accelerated, and interest-based classes as well as enjoyable extracurricular activities, positively enhanced their self-perceptions of academic abilities and promoted confidence in future educational and career paths. Full article

In this article we highlight and discuss how norm-challenging pedagogy in music education can be encouraged and executed from three different angles. We primarily focus on activities such as democratic learning situations for pupils and teachers, to be explored as safe and brave spaces. With a starting point in norm-critical pedagogy, we explore the possibility of using norm-challenging pedagogy as, through and in music educational settings. Norm-challenging pedagogy as music education can challenge dominant ways of assimilating, processing, and expressing knowledge, whereas norm-challenging pedagogy through music education concerns how traditional views on, for example, gender, race, or disability identities can be challenged through music activities. Norm-challenging pedagogy in music education critically reflects on who has the right to learn and express themselves musically and in what ways, related to gender, race or disability. The article is based on a phenomenological view of aesthetic experience and music education as a life of equal value, where de Beauvoir’s concepts of freedom, facticity, and ambiguity constitute crucial analytical concepts. The author’s own experiences of ambiguous norm-challenging situations as, through and in music education will be used and discussed in relation to the philosophical framework. The results of the exploration will be critically reflected upon in relation to organisational, collegial, didactic and relational aspects of music education. Full article

The pursuit of disabled students’ inclusion in higher education remains a significant global concern, particularly in developing nations where systemic and institutional barriers persist. Despite progressive legislative and policy frameworks promoting inclusive education, Kenyan and Pakistani universities continue to encounter structural, financial, and attitudinal challenges that hinder equal participation in learning and research for disabled students. This study aims to identify, analyze, and prioritize the complementary support strategies necessary for disabled students’ inclusion in learning and research opportunities in both Kenyan and Pakistani higher education institutions. Employing a quantitative research design, data were gathered through structured questionnaires distributed among disabled students in institutions of higher learning. The data were analyzed using the fuzzy synthetic evaluation (FSE) approach, which integrates fuzzy logic with descriptive statistics to objectively determine the weight, level of agreement, and internal consistency of the identified support strategies. Among the six support strategies, Physical Facility Support emerged as the most crucial in Pakistan, followed by Attitudinal and Community Support. On the other hand, the Kenyan group indicated Policies and Advocacy as the most essential support strategy for disabled students’ inclusion in higher education. The findings underscore that the two countries differ in how they prioritize support strategies for the inclusion of students with disabilities. This study contributes theoretically by advancing the application of the FSE model within inclusion research, offering a rigorous, data-driven framework for understanding multidimensional support strategies for disabled students. Full article

This study addresses the enduring controversy surrounding the diagnostic criteria for Specific Learning Disabilities (SLD) following the publication of the DSM-5, which is related to their definition. The aim of this study is to review and compare the diagnostic criteria of different classification systems and analyse differences in the identification and evaluation criteria of SLD. To this end, a search of the scientific literature was conducted through ERIC, PsycInfo (Proquest) and Web of Science spanning 2013 to 2024. Fifteen records published in English and focused on school-age children (primary education) were included. The studies address issues in reading, writing and mathematics, using different diagnostic criteria and tools. The findings highlight the need for multidimensional, validated assessments, as well as the importance of early identification to improve access to resources and tackle socio-emotional and motivational factors. Full article

Background: Ischemic stroke (IS) is a major cause of disability and mortality worldwide, with mitochondrial dysfunction playing a critical role in its pathogenesis. This study aimed to identify immune-related mitochondrial biomarkers associated with IS and evaluate their diagnostic potential. Methods: IS-related gene expression datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified from the GSE58294 dataset, followed by functional enrichment analysis, immune infiltration assessment, and weighted gene co-expression network analysis (WGCNA). Immune-related mitochondrial genes were screened using the MITOCARTA 3.0 database. Four machine learning algorithms—random forest (RF), support vector machine (SVM), generalized linear model (GLM), and extreme gradient boosting (XGB)—were applied to identify hub genes. External validation was performed using the GSE16561 dataset, and RT-qPCR confirmed key gene expression. Functional enrichment and single-cell RNA sequencing analyses explored biological pathways and cellular localization. Results: Five key genes (ECHDC3, EPHX2, SPTLC2, MSRB2, and TK2) were identified, among which ECHDC3, EPHX2, and SPTLC2 showed strong diagnostic potential (AUC > 0.7). These genes were significantly enriched in apoptosis, JAK-STAT, MAPK, and VEGF signaling pathways and were closely associated with neutrophil infiltration. Single-cell analysis revealed increased immune cell populations and distinct expression patterns of key genes in the ischemic mouse brain. Conclusions: This study identifies novel immune-related mitochondrial biomarkers for IS, providing insights into its pathogenesis and offering potential targets for early diagnosis and therapeutic intervention. Full article