Search Results (970)

Augmentative and alternative communication (AAC) enables digital access for individuals with severe motor impairment. Conventional contact-based switches rely on residual voluntary movement, limiting efficiency. We report the clinical application of a novel, researcher-developed noncontact assistive switch, the Augmentative Alternative Gesture Interface (AAGI), in a 39-year-old male with late-stage Duchenne Muscular Dystrophy (DMD) retaining minimal motion. The AAGI converts subtle, noncontact gestures into digital inputs, enabling efficient computer operations. Before intervention, the participant used a conventional mechanical switch, achieving 12 characters per minute (CPM) in a 2 min text entry task and was unable to perform high-speed ICT tasks such as gaming or video editing. After 3 months of AAGI use, the input speed increased to 30 CPM (+2.5-fold), and previously inaccessible tasks became feasible. The System Usability Scale (SUS) improved from 82.5 to 90.0, indicating enhanced usability, whereas the Short Form 36 (SF-36) Social Functioning (+13) and Mental Health (+4) demonstrated meaningful gains. Daily living activities remained stable. This case demonstrates that the AAGI system, developed by our group can substantially enhance communication efficiency, usability, and social engagement in advanced DMD, highlighting its potential as a practical, patient-centered AAC solution that extends digital accessibility to individuals with severe motor disabilities. Full article

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies. Full article

CerMapp is a multi-platform and system application designed to address a critical gap in veterinary public health: the lack of a standardized, national-scale geodatabase for wildlife diseases. This gap has long hindered the effective application of GIS and remote sensing in spatial epidemiology. Currently deployed at the prototype level in Aosta Valley, NW Italy, the application’s core innovation is its ability to generate a structured, analysis-ready data repository, which serves as a foundational resource for One Health initiatives. Developed by the National Reference Center for Wildlife Diseases on the ESRI ArcGIS Survey123 platform v.3.24, CerMapp enables veterinarians, foresters, and wildlife professionals to easily collect and georeference field data, including species, health status, and photographic evidence using flexible methods such as Global Navigation Satellite System or manual map entry. Data collected via CerMapp are stored in a centralized geodatabase, facilitating risk analyses and detailed geospatial studies. This data can be integrated with remote sensing information processed on cloud platforms like Google Earth Engine or within traditional GIS software, contributing to a comprehensive and novel wildlife health registry. By promoting the rational and standardized collection of essential geospatial data, CerMapp data may support predictive disease modeling, risk assessment, and habitat suitability mapping for wildlife diseases, zoonoses, and vector-borne pathogens. Its scalable, user-friendly design ensures alignment with existing national systems like the Italian Animal Disease Information System (SIMAN), making advanced geospatial analysis accessible without requiring specialized digital skills from field operators or complex IT maintenance from institutions. Full article

Digital technologies are revolutionizing health systems worldwide. People with higher digital health literacy are better equipped to access reliable health information, utilize telehealth services, and effectively manage their health through applications. However, a notable digital divide exists for people with intellectual disabilities, and the digitization of healthcare can limit their health opportunities. This scoping review examines the current evidence on digital health literacy among people with intellectual disabilities, emphasizing specific challenges and the need for tailored adaptations. Eleven articles from ten databases were included in the review. Although digital health literacy is becoming increasingly important, it is rarely discussed for people with intellectual disabilities. The term “digital health literacy” is not used, with the exception of one article. However, the focus is mostly on applicability and often at the functional level. The findings underscore that people with intellectual disabilities are underrepresented in research studies and interventions related to digital health literacy. Additionally, the results indicate the lack of a theoretical population-specific framework that focuses on competencies and life experiences. Participation in the digital world is a human right (UN CRPD). Addressing the digital gap is crucial, as improving digital health literacy can lead to better health outcomes, equitable access to health services, and reduced health disparities among people with intellectual disabilities. Based on the results, research directions for developing a population-specific framework for this highly vulnerable group are discussed. Full article

Background: Adolescence represents a critical period for growth and athletic development, yet young athletes frequently demonstrate significant gaps in nutritional knowledge that can impair performance and long-term health outcomes. Limited research exists on comprehensive nutrition education interventions for adolescent soccer players in North African populations. Objective: To evaluate both general and sports-specific nutritional knowledge among adolescent soccer players from an elite Tunisian club and assess the feasibility of a digital nutrition intervention using mobile application technology. Methods: A cross-sectional survey was conducted between June and August 2024 among 50 male soccer players aged 11–18 years from Étoile du Sahel club in Sousse, Tunisia. Data were collected via a structured questionnaire comprising sections on basic nutrition knowledge, influences on food choices, sports nutrition knowledge and practices, and demographic information. A pilot digital intervention using the FatSecret app was implemented with 8 participants over 4 weeks, involving meal photo uploads and nutritionist feedback. Results: Participants had a mean age of 15.16 ± 1.55 years, with 92% reporting no formal nutrition education. While 90% correctly identified carbohydrates as the primary energy source, only 2% recognized that fat provides the highest energy density. Significant misconceptions existed regarding sports nutrition: 74% incorrectly believed that consuming protein 2–4 h before an event enhances performance, and only 17% knew the recommended pre-event carbohydrate intake. Food choices were primarily influenced by cravings (80%) and sensory appeal rather than health considerations (20%). The digital intervention demonstrated extremely low engagement, with minimal participation in meal photo uploads. Conclusions: This study reveals critical gaps in both general and sports-specific nutritional knowledge among adolescent soccer players in Tunisia, providing important descriptive information about knowledge distribution in this population. While knowledge deficits are substantial, it is important to acknowledge that this cross-sectional assessment documents only knowledge patterns, without measures of actual dietary intake or athletic performance. The persistent misconceptions and the low feasibility of the digital intervention provide important lessons regarding technology-based approaches to nutrition education in this age group, highlighting challenges in sustained engagement that must be addressed in future intervention design. Full article

Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious dimensions. Psychoanalysis—particularly its constructs of setting, rêverie, and transference—offers a unique epistemological basis for designing therapeutic environments that engage implicit emotional processes. Aim: This paper aims to develop a conceptual framework for modeling IVR-based therapeutic settings inspired by psychoanalytic theory and enhanced through IoT-enabled biosensing technologies. Methods/Approach: We propose a three-layer architecture: (1) a somatic layer involving IoT-based real-time physiological monitoring (e.g., heart rate variability, galvanic skin response, eye-tracking, EEG); (2) a symbolic-narrative layer where the VR environment dynamically adapts to the user’s affective state through immersive visual and auditory stimuli; and (3) a relational layer where AI-driven avatars simulate transferential dynamics. The model is theoretically grounded in psychoanalytic literature and informed by current advances in affective computing and bioengineering. Conclusions: By bridging psychoanalytic metapsychology and bioengineering design, this framework proposes a novel approach to therapeutic IVR systems that move beyond explicit cognition to engage the embodied unconscious. The integration of IoT biosignals enables the mapping and modulation of internal states within a structured symbolic space, opening new pathways for the clinical application of digital psychoanalysis. Full article

Climate change and environmental degradation pose growing threats to health. Despite increasing recognition of these risks, climate-related education and counseling are rarely incorporated into adult inpatient care. A survey-based study was conducted with 250 adult inpatients on the medicine services at Cooper University Health Care (New Jersey) and Maine Medical Center (Maine). Patients received a standardized 30-s educational statement from their physician on the health impacts of air pollution and extreme heat, with introduction to two smartphone applications on air quality and heat conditions. Survey items evaluated patients’ prior awareness of environmental health risks, willingness to use digital monitoring tools, and perceived barriers to use. Descriptive statistics and content analysis were used for data interpretation. Overall, 84% of participants reported awareness of environmental threats to health, indicating high baseline recognition. However, only 50% expressed willingness to adopt smartphone apps as protective tools with barriers including lack of smartphone access, unfamiliarity with technology, and concerns about utility during hospitalization. Twenty-three percent of participants in Maine did not own a smartphone, as compared with 7% in NJ. Despite less smartphone ownership in Maine compared to NJ, participants showed similar willingness to use the suggested apps for monitoring environmental conditions (53% vs. 49.3%). Responses suggested that while patients generally acknowledge climate-related health risks, enthusiasm for technological solutions varies considerably, especially among older and underserved populations. This study highlights a critical gap between awareness of climate health risks and the adoption of digital health tools for self-protection. While brief inpatient education may increase recognition, technology-based interventions alone may not reach all patient groups. Future strategies should focus on accessible, low-barrier methods of environmental health education in clinical care, including integration into inpatient counseling and discharge planning. Addressing technology access gaps and tailoring resources to diverse populations will be essential for advancing climate-related patient education in healthcare settings. Full article

Background/Objectives: COPD is a leading cause of death in the US, with higher morbidity and mortality in rural areas that lack specialized pulmonary care. Mobile health (mHealth) tools, including remote spirometry, may fill this gap, yet healthcare provider (HCP) perspectives on utility and implementation of remote spirometry and mHealth for COPD management in these settings remain unexplored. This study aimed to examine HCPs’ perspectives of mHealth with remote spirometry to inform future implementation in rural and low medical access settings. Methods: Five HCPs working in rural or medically limited settings in South Carolina participated in a deliberative discussion focus group. A semi-structured interview guide was used to explore insights into feasibility, clinical utility, and implementation needs. Transcripts were analyzed using thematic analysis to identify facilitators, barriers, and implementation considerations. Results: Participants reported that remote spirometry and mHealth had potential to support COPD treatment, increase healthcare access, and support self-management. Key facilitators identified were access to smartphones, potential for individualized COPD care, and visual tools for patient engagement. Barriers included risk of time and workload burden, data information overload, and technological limitations. Participants emphasized the need for team training, thoughtful integration into existing workflows, customizable data displays, and robust support for patient onboarding. Conclusions: Providers viewed mHealth applications with remote spirometry as a valuable tool with potential to improve COPD care but identified critical implementation needs. Participants emphasized that successful integration would require structured support, user-centered design, and attention to reimbursement and workflow alignment to enhance sustainability and patient/provider engagement. Full article

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

Background: Adults with congenital heart disease (CHD) are living longer but face increasing comorbidities. Sleep is a key health determinant, yet objective data in CHD remain limited. This study compared sleep characteristics of adults with CHD and controls using wearable technology and a Health Application Programming Interface (API). Methods: A total of 175 CHD patients (33.1 ± 10.3 years, 49.2% women) and 52 controls (34.4 ± 12.4 years, 40.4% women) completed seven continuous days of wrist-worn Garmin Vivosmart^® 5 during routine follow-up at the TUM Klinikum Deutsches Herzzentrum. Sleep duration, phases, Sleep Scores, and weekday-weekend differences were analyzed, and multivariate models examined clinical and demographic predictors. Results: Total sleep duration and rapid eye movement (REM) sleep did not differ between groups. CHD patients had more deep sleep (83 ± 19 vs. 75 ± 16 min, p = 0.004) but lower Sleep Scores (74 ± 9 vs. 77 ± 9, p = 0.041). Within CHDs, deep sleep was higher on weekends than on weekdays (p = 0.033). Multivariate analyses showed no overall group effect, but age (p = 0.016), sex (p = 0.013), and body mass index (BMI; p < 0.001) significantly predicted sleep outcomes. Regression analyses in CHDs revealed female sex associated with longer REM sleep (p < 0.001), while higher BMI consistently predicted poorer outcomes. Disease severity was linked to lower Sleep Scores. Conclusions: Sleep in CHDs is broadly comparable to controls, but BMI, sex, and disease severity significantly shape outcomes. The additional variability between weekends and weekdays and a higher risk of sleep-disordered breathing, according to the literature, underscores that sleep is an underestimated target for prevention and clinical care in CHD. Full article

Intelligent crack detection and displacement measurement are critical for evaluating the health status of dams. Earth-fill dams, composed of fragmented independent material particles, are particularly vulnerable to climate changes that can exacerbate cracking and displacement. Existing crack segmentation methods often suffer from discontinuous crack segmentation and misidentification due to complex background noise. Furthermore, current skeleton line-based width measurement techniques demonstrate limited accuracy in processing complex crack patterns. To address these limitations, this study introduces a novel three-step approach for crack detection in earth-fill dams. Firstly, an enhanced YOLOv8-CGA crack segmentation method is proposed, incorporating a Cascaded Group Attention (CGA) mechanism into YOLOv8 to improve feature diversity and computational efficiency. Secondly, image processing techniques are applied to extract sub-pixel crack edges and skeletons from the segmented regions. Finally, an adaptive skeleton fitting algorithm is developed to achieve high-precision crack width estimation. This approach effectively integrates the pattern recognition capabilities of deep learning with the detailed delineation strengths of traditional image processing. Additionally, dam crest displacements and crack zone strain field are measured via the digital image correlation (DIC) method. The efficacy and robustness of the proposed method are validated through laboratory experiments on an earth-fill dam model, demonstrating its potential for practical structural health monitoring (SHM) applications in a changing climate. Full article

Background: Preconception care (PCC) is a key element of preventive reproductive health, aiming to optimise maternal and child outcomes by addressing biomedical, behavioural, psychosocial, and genetic risks before conception. International frameworks provide clear guidance, yet implementation in many low- and middle-income countries remains inconsistent. Methods: A structured narrative review was conducted across PubMed, Web of Science, Cochrane Library, and Google Scholar, focusing on literature published between 2010 and 2025. Eligible sources included empirical studies, clinical guidelines, policy documents, and high-quality grey literature from health authorities. Quality, relevance, and applicability were assessed, with particular emphasis on European and Bulgarian contexts. Results: Evidence from diverse settings demonstrates that PCC interventions—such as chronic disease management, vaccination, lifestyle optimisation, and expanded carrier screening (ECS)—can reduce adverse pregnancy outcomes and prevent severe genetic disorders. Effective international models integrate PCC into primary care, leverage digital health tools, and ensure equitable access through public funding. In Bulgaria, PCC remains underdeveloped: genetic screening is not part of routine care, there are no national guidelines or surveillance systems, and only ~4% of women initiate folic acid supplementation before pregnancy. NGOs and EU-funded digital initiatives provide partial outreach but cannot replace state-supported services. Conclusions: Bulgaria urgently requires a coordinated national PCC strategy, incorporating standardised guidelines, provider training, digital platforms, and phased ECS introduction. Strengthening PCC delivery can reduce preventable maternal and neonatal morbidity, advance reproductive justice, and enhance the long-term sustainability of public health systems. These findings support the development of a publicly funded, guideline-driven national PCC strategy with phased introduction of expanded carrier screening under NHIF to improve equity and long-term system sustainability. Full article

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

Background/Objectives: The increasing use of electronic devices is reshaping lifestyle by offering new avenues for health behavior change. These tools provide to monitor health, fitness, and nutrition, promoting healthier lifestyles to prevent non-communicable diseases (NCDs). This umbrella review (conducted according to PRISMA 2020 guidelines, registered on PROSPERO CRD42024511141) assesses the effectiveness of wearable devices and mobile applications in improving healthy lifestyle behaviors to mitigate the risk of NCDs. Methods: Systematic reviews and meta-analyses (n = 27) focusing on digital tools for health behavior change were analyzed, with emphasis on their integration into daily life and their impact on health outcomes, including body weight, metabolic and anthropometric parameters, and dietary quality. Results and Conclusions: Interventions leveraging gamification, social interaction, and goal-setting (6/27) have shown greater efficacy in improving body-nutrition profile. The integration of eHealth technologies holds transformative potential for preventive healthcare and positive biology. These tools can contribute to healthier lifestyles, extended life expectancy, and reduced healthcare costs, although current limitations exist, including data accuracy, privacy concerns, and sustaining user engagement over time. Full article

Background: Neurological disorders are the leading cause of disability, affecting over three billion people worldwide. Amyotrophic lateral sclerosis (ALS) is among the most feared and uniformly fatal neurodegenerative diseases, with no therapy capable of restoring lost function. Methods: We report the first application of therapeutic fever to ALS using Computerized Brain-Guided Intelligent Thermofebrile Therapy (CBIT²). This fully noninvasive treatment, delivered through an FDA-approved computerized platform, digitally reengineers the 1927 Nobel Prize-recognized malarial fever therapy into a modern treatment guided by the Brain–Eyelid Thermoregulatory Tunnel. CBIT² induces therapeutic fever through synchronized hypothalamic feedback, activating heat shock proteins, which are known to restore proteostasis and neuronal function. Case presentation: A 56-year-old woman was diagnosed with progressive ALS at the Mayo Clinic, with electromyography (EMG) demonstrating fibrillation and fasciculation indicative of denervation corroborated by neurological and MRI findings; the patient was informed that she had an expected survival of three to five years. A neurologist from Northwestern University confirmed the diagnosis and thus maintained the patient on FDA-approved ALS drugs (riluzole and edaravone). Her condition rapidly worsened despite pharmacological treatment, and she underwent CBIT², resulting in (i) electrophysiological reversal with complete disappearance of denervation; (ii) biomarker correction, including reductions in neurofilament and homocysteine, IL-10 normalization (previously linked to mortality), and robust HSP70 induction; (iii) restoration of gait, swallowing, respiration, speech, and cognition; (iv) reconstitution of tongue structure; and (v) return to complex motor tasks, including golf, pickleball, and swimming. Discussion: This case provides the first documented evidence that ALS can be reversed through digitally reengineered fever therapy aligned with thermoregulation, which induces heat shock response and upregulates heat shock proteins, resulting in the patient no longer meeting diagnostic criteria for ALS and discontinuation of ALS-specific medications. Beyond ALS, shared protein-misfolding pathology suggests that CBIT² may extend to Alzheimer’s, Parkinson’s, and related disorders. By modernizing this Nobel Prize-recognized therapeutic principle with computerized precision, CBIT² establishes a framework for large-scale clinical trials. A century after fever therapy restored lost brain function and so decisively reversed dementia paralytica such that it earned the 1927 Nobel Prize in Medicine, CBIT² now safely harnesses the therapeutic power of fever through noninvasive, intelligent, brain-guided thermal modulation. Amid a global brain health crisis, fever-based therapies may offer a path to preserve thought, memory, movement, and independence for the more than one-third of humanity currently affected by neurological disorders. Full article