Search Results (181)

Integrated multidisciplinary care is recognised as essential for people living with motor neurone disease (PlwMND) and their families. The values underpinning integrated care, such as person-centredness, respect, empowerment, and co-production, are central to delivering meaningful and comprehensive support. Trust is an essential yet often overlooked element of effective person- and family-centred integrated care, particularly for PlwMND. While specialist multidisciplinary MND clinics represent the benchmark for evidence-based care, many PlwMND and their families depend significantly on local and community-based support services to maintain quality of life. Trust directly influences their engagement with these services and the continuity of care provided. Trust enables understanding of personal priorities and how they change as the disease progresses, ultimately allowing for person-centred care to happen. Trust is necessary to enable service co-production, which is a strong value of integrated care. Research highlights seven key domains of support essential to PlwMND and their carers: practical, social, informational, psychological, physical, emotional, and spiritual. Effective integrated care requires strong relationships built upon trust, shared decision-making, respect for individuality, and clear communication. Furthermore, due to the rapidly progressive nature of MND, care priorities and perceived symptom burdens may shift significantly over short periods, making flexible, temporally sensitive approaches critical. A dynamic, inclusive model of decision-making that fosters autonomy within and regular co-review of needs is recommended. This perspective paper examines how person- and family-centred integrated care is currently being delivered, what is working well, and how these practices can be further strengthened to enhance the care experiences of PlwMND, their families, and the health and social care providers involved. This paper builds on both theoretical knowledge and clinical experience to offer our perspective on the critical role of trust in co-producing integrated care for PlwMND. It brings together the voices of clinicians and researchers, alongside those with lived experience of MND. We propose a diagram of care that embeds the core values of integrated, person-centred care within the specific context of MND. Our aim is to enhance collaborative practices, strengthen cross-sector partnerships, and ultimately improve the care experiences for professionals, PlwMND, and their families. Full article

This study examines whether social relationships influence moral judgment across different types of moral violations and cultural contexts. Drawing on Relationship Regulation Theory, which outlines four relational models—communal sharing, authority ranking, equality matching, and market pricing—we investigate whether moral evaluations vary depending on the relationship between the actor and the victim. Unlike previous research that primarily adopts a third-party perspective, this study uses a first-person approach, focusing on judgments made by individuals directly involved in the moral interaction. Three empirical studies were conducted: Study 1 tests the influence of social relationships on moral judgment using Chinese participants; Study 2 explores how moral judgments differ across various moral domains in relational contexts; and Study 3 compares Chinese and American participants to assess cross-cultural differences in the impact of social relationships on moral evaluation. Across all three studies, the results consistently show that social relationships significantly affect moral judgment, supporting the view that moral evaluations are shaped not only by the nature of the act but also by the relational context in which it occurs. Full article

Cardiovascular diseases (CVDs) are the leading global cause of death, with long-term hospitalization becoming increasingly frequent in advanced or chronic cases. In this context, the interplay between systemic factors such as lipid metabolism, circulating metabolites, gut microbiota, and oral health is gaining attention for its potential role in influencing inflammation, cardiometabolic risk, and long-term outcomes. Despite their apparent independence, these domains are increasingly recognized as interconnected and influential in cardiovascular pathophysiology. Methods: This narrative review was conducted by analyzing studies published between 2015 and 2024 from databases including PubMed, Scopus, and Web of Science. Keywords such as “lipid profile,” “metabolomics,” “gut microbiota,” “oral health,” and “cardiovascular disease” were used. Original research, meta-analyses, and reviews relevant to hospitalized cardiac patients were included. A critical integrative approach was applied to highlight cross-domain connections. Results and Discussion: Evidence reveals significant interrelations between altered lipid profiles, gut dysbiosis (including increased TMAO levels), metabolic imbalances, and oral inflammation. Each component contributes to a systemic pro-inflammatory state that worsens cardiovascular prognosis, particularly in long-term hospitalized patients. Despite isolated research in each domain, there is a paucity of studies integrating all four. The need for interdisciplinary diagnostic models and preventive strategies is emphasized, especially in populations with frailty or immobilization. Conclusions: Monitoring lipid metabolism, metabolomic shifts, gut microbial balance, and oral status should be considered part of comprehensive cardiovascular care. Gut microbiota exerts a dual role in cardiac health: when balanced, it supports anti-inflammatory and metabolic homeostasis; when dysbiotic, it contributes to systemic inflammation and worsened cardiac outcomes. Future research should aim to develop integrative screening tools and personalized interventions that address the multifactorial burden of disease. A systemic approach may improve both short- and long-term outcomes in this complex and vulnerable patient population. Full article

Background: Fibromyalgia (FM) is a chronic condition characterized by widespread pain, fatigue, cognitive difficulties, and psychological symptoms. Patients often present distinct personality traits and psychopathological patterns associated with symptom severity. Objective: To examine psychopathological profiles in FM patients based on functional, physical–somatic, and emotional impairment domains, as well as on cumulative disease severity. Materials and Methods: A cross-sectional study was conducted with 70 women clinically diagnosed with FM at a specialized Fibromyalgia Unit. Psychological functioning was assessed using the Personality Assessment Inventory, and disease impact was measured with the Fibromyalgia Impact Questionnaire. Hierarchical cluster analyses were used to classify participants into mild and severe clusters across FIQ domains, and psychological profiles were compared. Results: Patients with severe functional impairment had more affective dysregulation (76.43 vs. 70.20, p < 0.01) and somatic complaints (85.57 vs. 79.76, p < 0.05) than those with mild impairment. The severe–physical cluster showed greater mood instability, somatization, and suicidal ideation (60.94 vs. 53.61, p < 0.05). The severe–emotional cluster had higher rates of major depression (85.71% vs. 64.28%) and persistent depressive disorder (76.19% vs. 70.61%, p < 0.05). Severe showed more emotional instability and somatization, distinguishing it from mild. Greater cumulative severity intensified depressive and somatic disorders. Discussion: Findings support FM’s biopsychosocial profile, where emotional distress may relate to psychological and physical symptoms, reinforcing the need for personalized, multidisciplinary care and comprehensive assessment. Full article

Background: Mobility limitation is a pivotal but under-documented dimension of disability in Saudi Arabia. Leveraging the 2017 National Disability Survey, this cross-sectional study provides a population-wide profile of mobility-related physical difficulty. Objectives: Five research aims were pursued: (1) estimate national prevalence and severity by sex; (2) map regional differentials; (3) examine educational and marital correlates; (4) characterize cause, duration, and familial context among those with multiple limitations; and (5) describe patterns of assistive-aid and social-service use. Methods: Publicly available aggregate data covering 20,408,362 Saudi citizens were cleaned and analyzed across 14 mobility indicators and three baseline files. Prevalence ratios and χ² tests assessed associations. Results: Overall, 1,445,723 Saudis (7.1%) reported at least one functional difficulty; 833,136 (4.1%) had mobility difficulty, of whom 305,867 (36.7%) had mobility-only impairment. Severity was chiefly mild (35% of cases), with moderate (16%) and severe (7%) forms forming a descending pyramid. Prevalence varied more than threefold across the thirteen regions, peaking in Aseer (9.4%) and bottoming in Najran (2.9%). Mobility difficulty clustered among adults with no schooling (36.1%) and widowed status (18.5%), with sharper female disadvantage in both domains (p < 0.001). Among those with additional limitations, chronic disease dominated etiology (56.3%), and 90.1% had lived with disability for ≥25 years; women were overrepresented in the longest-duration band. Aid utilization was led by crutches (47.7%), personal assistance (25.3%), and wheelchairs (22.6%), while 83.8% accessed Ministry rehabilitation services, yet fewer than 4% used home or daycare support. Conclusions: These findings highlight sizeable, regionally concentrated, and gender-patterned mobility burdens, underscoring the need for education-sensitive prevention, chronic-care management, investment in advanced assistive technology, and distributed community services to achieve Vision 2030 inclusion goals. Full article

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems. Full article

Background: Cognitive heterogeneity in Parkinson’s disease (PD) remains a diagnostic and prognostic challenge, particularly in early stages. In this cross-sectional study, we aimed to identify clinically relevant cognitive subtypes in early PD by integrating neuropsychological profiles with regional brain atrophy assessed via visual MRI scales. Methods: Eighty-one de novo PD patients (≤36 months from diagnosis) and twenty healthy controls underwent 3T MRI with visual atrophy ratings and completed an extensive neuropsychological battery. Results: Using a mixed a priori–a posteriori approach, we defined eight anatomocognitive subtypes reflecting distinct patterns of regional vulnerability: frontosubcortical, posterior cortical, left/right hippocampal, global, and preserved cognition. Specific MRI markers correlated with cognitive deficits in executive, visuospatial, memory, and language domains. Cluster analyses supported subtype validity (AUC range: 0.68–0.95). Conclusions: These results support a practical classification model linking cognitive performance to brain structural changes in early PD. This scalable approach may improve early patient stratification and guide personalized management strategies. Longitudinal studies are needed to assess progression patterns and therapeutic implications. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

Objective: This study seeks to analyze the incidence of breastfeeding self-efficacy in postpartum woman who are undergoing prenatal care at the Family Health Strategy Units in the city of Tubarão, Santa Catarina (SC), Southern Brazil, from August to December 2022. Methods: An observational epidemiological study with a cross-sectional design was carried out with puerperal women—either primiparous or multiparous—who were followed in the municipal public network, along with their live-born children delivered at the maternity hospital of Hospital Nossa Senhora da Conceição in Tubarão, SC. Participants agreed to participate in the study. For data collection, an instrument developed by researchers and a validated instrument entitled Breastfeeding Self-Efficacy Scale-Short Form were used to assess the self-efficacy of breastfeeding. Results: The mean score of the Breastfeeding Self-Efficacy Scale (BSES) was 61.75 points (±6.39), indicating high self-efficacy. There was an association between the mean of the general score and/or the domains of the BSES with maternal characteristics. Women with higher education had greater technical mastery (p = 0.010), and those with previous breastfeeding experience and those who breastfed their children during the first hour of life had a higher average in the overall score and in the domains of the scale. In addition, those who planned the pregnancy (p = 0.024) and those who did not receive assistance from the milk bank (p = 0.047) had greater technical domain. Conclusions: In the present study, there was a predominance of high breastfeeding self-efficacy. It was verified that the personal and clinical aspects interfered in the self-efficacy of breastfeeding. Full article

Introduction: As in other crises, during COVID-19 pandemic, journalists were under immense pressure to report precise scientific information in a timely manner, which may have had a negative influence on their mental health. There could be an association between the digital health literacy of journalists and their mental health. The aim of this article was to explore the association between digital health literacy and burnout and depression among journalists in Serbia. Methods: A cross-sectional study was conducted involving a total of 180 journalists working on television with national coverage in Serbia. The main research instrument used was a questionnaire with four sections containing personal demographic information, the Digital Health Literacy Instrument, the Maslach Burnout Inventory-Human Services Survey, and the Beck Depression Inventory. Results: A total of 30% participants were found to have high levels of burnout on the emotional exhaustion (EE) subscale. On the depersonalization (DP) subscale, 10.6% experienced high levels of burnout. On the personal accomplishment (PA) subscale, 38.3% of participants faced high levels of burnout. Multivariate logistic regression analyses showed the association between high burnout on the EE scale and health status (OR: 0.597, 95% CI: 0.375–0.952) and protecting privacy (OR: 0.522, 95% CI: 0.311–0.875). Multivariate logistic regression analysis showed the association between high burnout on the PA scale and information searching (OR: 0.255, 95% CI: 0.124–0.526), sex (OR: 2.594, 95% CI: 1.007–6.68), socioeconomic status (OR: 2.282, 95% CI: 1.133–4.595), and alcohol consumption (OR: 2.188, 95% CI: 1.004–4.769). Multivariate logistic regression analysis showed associations between depression and sex (OR: 0.180, 95% CI: 0.059–0.548), health status (OR: 0.316, 95% CI: 0.160–0.626), the use of anti-anxiety medications (OR: 7.303, 95% CI: 3.167–16.840), information searching (OR: 0.432, 95% CI: 0.191–0.981), and protecting privacy (OR: 0.443, 95% CI: 0.233–0.841). Conclusions: Our study showed a negative association between different domains of burnout, depression, and scores on protecting privacy and information searching scales. Full article

Background/Objectives: Children with above-average cognitive functioning often present complex developmental profiles, combining high cognitive potential with heterogeneous socio-emotional and learning trajectories. Although the cognitive and behavioral characteristics of giftedness have been widely studied in Anglophone countries, evidence remains limited in Southern Europe. This study aimed to investigate the cognitive, academic, and emotional–behavioral profiles of Italian children and adolescents with above-average cognitive functioning, using an inclusive, dimensional approach (IQ > 114). Methods: We analyzed a cross-sectional sample of 331 children and adolescents (ages 2.11–16.5 years), referred for clinical cognitive or behavioral evaluations. Participants were assessed using the WPPSI-III or WISC-IV for cognitive functioning, the MT battery for academic achievement, and the Child Behavior Checklist (CBCL) for emotional and behavioral symptoms. Comparative and correlational analyses were performed across age, gender, and functional domains. A correction for multiple testing was applied using the Benjamini–Hochberg procedure. Results: Gifted participants showed strong verbal comprehension (mean VCI: preschoolers = 118; school-aged = 121) and relative weaknesses in working memory (WM = 106) and processing speed (PS = 109). Males outperformed females in perceptual reasoning (PR = 121 vs. 118; p = 0.032), while females scored higher in processing speed (112 vs. 106; p = 0.021). Difficulties in writing and arithmetic were observed in 47.3% and 41.8% of school-aged participants, respectively. Subclinical internalizing problems were common in preschool and school-aged groups (mean CBCL T = 56.2–56.7). Working memory negatively correlated with total behavioral problems (r = −0.13, p = 0.046). Conclusions: These findings confirm the heterogeneity of gifted profiles and underscore the need for personalized educational and psychological interventions to support both strengths and vulnerabilities in gifted children. Caution is warranted when interpreting these associations, given their modest effect sizes and the exploratory nature of the study. Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

Background and Objectives: Sleep apnea (OSA) significantly impacts patients’ health, affecting cognitive, emotional, and social functioning, thus reducing overall quality of life (QoL). Despite global research interest, there are limited data on the QoL of Romanian patients with OSA. Aim of study: This study aimed to assess the quality of life of patients diagnosed with OSA using the Sleep Apnea Quality of Life Index (SAQLI), a validated, disease-specific questionnaire. Material and Methods: A cross-sectional study was conducted between January 2021 and February 2022 on 28 adult patients from medical units in Iași (Romania) and Albania. The patients were clinically evaluated and confirmed to have OSA (AHI > 5). QoL was assessed pre-treatment using the SAQLI, covering daily activities, social interactions, emotional status, and symptomatology. Statistical analyses were performed using SPSS v26.0. Results: Most patients had moderate (32.1%) or severe OSA (53.6%). Lower QoL scores correlated with increased disease severity. Significant impairments were observed in daily functioning, social relationships, and emotional well-being. Patients with severe OSA reported the lowest scores across all domains. Conclusions: OSA severely affects quality of life, particularly in patients with moderate to severe forms. Early diagnosis and personalized, multidisciplinary management strategies are essential to improving outcomes and overall patient well-being. Full article

Background: Endometriosis is a chronic, inflammatory, estrogen-dependent gynecological disease in which endometrial-like tissue grows in areas outside the uterus. This condition may significantly influence women’s life course and mental health. Personal, behavioral, social, and environmental factors play a crucial role in predicting these outcomes. The current study aimed to compare the Perceived Life Course Impact (PLCI) and mental health of women with Endometriosis to those without the disease, as well as to explore the factors associated with PLCI and mental health. Methods: This cross-sectional study surveyed 543 Israeli women (270 with Endometriosis, 273 without). Participants completed a validated questionnaire assessing perceptions of life course impact in several life domains (intimacy and relationships, employment, education) and mental health. Multiple regression analyses were used to identify factors associated with PLCI and mental health among participants. Results: Women with Endometriosis reported a significantly greater negative perceived impact on intimacy and relationships, employment, and education, with poorer mental health, as compared to the control group. Women with Endometriosis also experienced higher social negativity and lower social support. Multiple regression analyses indicated social negativity as a strong predictor of negative PLCI. Additionally, menstrual pain, social support, and healthcare accessibility were found to be significant predictors of both PLCI and mental health. Conclusions: Comprehensive care for women with Endometriosis requires a multidisciplinary approach, with interventions focused on improving healthcare accessibility, enhancing social support networks, and mitigating social negativity within interpersonal environments. Full article

Objectives: To identify cognitive domains predictive of dynamic balance performance in older adults and inform targeted cognitive-motor interventions aimed at improving balance and reducing fall risk. Methods: This cross-sectional study used hierarchical multiple regression to analyze relationships between cognitive domains and dynamic balance among 62 community-dwelling older adults (≥65 years). Balance was assessed using the Y Balance Test (YBT) and Timed Up and Go Test (TUG), while cognitive function was measured using the Mini-Mental State Examination (global cognition), Stroop Test (inhibition), N-back Test (working memory), and Deary–Liewald Reaction Time Test (processing speed). Statistical analyses were conducted using SPSS, version 28, with significance set at p < 0.05. Results: Although all cognitive domains correlated with dynamic balance, regression analyses indicated that only global cognition and inhibition were significant predictors. Specifically, global cognition significantly predicted both TUG and YBT performance, whereas inhibition uniquely predicted YBT performance (all p < 0.05). Conclusions: Our findings suggest global cognition and inhibition are key cognitive predictors of dynamic balance in older adults. Assessing these domains could identify individuals at risk of impaired balance, facilitating the design of targeted, personalized cognitive-motor interventions. Future research should investigate cognitively enriched exercise programs, including digital therapeutics and wearable technologies, to effectively target these cognitive domains, enhance balance outcomes, and promote sustained physical activity adherence in aging populations. Full article