Search Results (70)

The increasing prevalence of mental health disorders highlights the need for innovative and accessible interventions. Although existing digital meditation applications offer valuable basic guidance, they often lack interactivity, real-time personalized feedback, and dynamic simulation of real-life scenarios necessary for comprehensive experiential training applicable to daily stressors. To address these limitations, this study developed a novel immersive meditation system specifically designed for deployment within a metaverse environment. The system provides mindfulness practice through two distinct modules within the virtual world. The experience-based module delivers AI-driven social interactions within simulated everyday scenarios, with narrative content dynamically generated by large language models (LLMs), followed by guided inner reflection, thereby forming a scenario–experience–reflection cycle. The breathing-focused module provides real-time feedback through a breath-synchronization interface to enhance respiratory awareness. The feasibility and preliminary effects of this metaverse-based system were explored in a two-week, single-group, pre-test/post-test study involving 31 participants. The participants completed a battery of validated psychological questionnaires assessing psychological distress, mindfulness, acceptance, self-compassion, and self-esteem before and after engaging in the intervention. This study provides exploratory evidence supporting the feasibility and potential of immersive metaverse environments and LLM-based scenario generation for structured mental health interventions, providing initial insights into their psychological impact and user experience. Full article

Background/Objectives: Aging is often accompanied by physical and cognitive decline, affecting older adults’ mobility. Virtual reality (VR) offers innovative opportunities to safely practice everyday tasks, such as street crossing. This study was designed as a feasibility and pilot study to explore acceptance, usability, and preliminary effects of a VR-based road-crossing intervention for older adults. It investigates the use of virtual reality (VR) as an innovative training tool to support senior citizens in safely navigating everyday challenges such as crossing roads. By providing an immersive environment with realistic traffic scenarios, VR enables participants to practice in a safe and controlled setting, minimizing the risks associated with real-world road traffic. Methods: A VR training application called “Wegfest” was developed to facilitate targeted road-crossing practice. The application simulates various scenarios commonly encountered by older adults, such as crossing busy streets or waiting at traffic lights. The study applied a single-group pre-post design. Outcomes included the Timed Up and Go test (TUG), Falls Efficacy Scale-International (FES-I), and Montreal Cognitive Assessment (MoCA). Results: The development process of “Wegfest” demonstrates how a highly realistic street environment can be created for VR-based road-crossing training. Significant improvements were found in the Timed Up and Go test (p = 0.002, d = 0.784) and fall-related self-efficacy (FES-I, p = 0.005). No change was observed in cognitive function (MoCA, p = 0.56). Participants reported increased subjective safety (p < 0.001). Discussion: The development of the VR training application “Wegfest” highlights the feasibility of creating realistic virtual environments for skill development. By leveraging immersive technology, both physical and cognitive skills required for road-crossing can be effectively trained. The findings suggest that “Wegfest” has the potential to enhance the mobility and safety of older adults in road traffic through immersive experiences and targeted training interventions. Conclusions: As an innovative training tool, the VR application not only provides an engaging and enjoyable learning environment but also fosters self-confidence and independence among older adults in traffic settings. Regular training within the virtual world enables senior citizens to continuously refine their skills, ultimately improving their quality of life. Full article

Extended reality (XR)-based rehabilitation is an emerging therapeutic approach that combines real and virtual environments to enhance patient engagement and promote motor and cognitive recovery. Its clinical utility in children with cerebral palsy (CP), particularly regarding gross motor skills, balance, and psychosocial well-being, remains underexplored. This preliminary study aimed to evaluate the potential effects of XR-based rehabilitation on gross motor function, balance, parental stress, and quality of life in children with cerebral palsy. Thirty children with cerebral palsy were randomly assigned to an extended reality training group (XRT, n = 15) or a conventional physical therapy group (CPT, n = 15). Both groups received 30 min sessions, three times per week for 6 weeks. Outcome measures included the Gross Motor Function Measure-88 (GMFM-88), Pediatric Balance Scale (PBS), Functional Independence Measure (FIM), Parenting Stress Index (PSI), and Pediatric Quality of Life Inventory (PedsQL), assessed pre- and post-intervention. A 2 (group) × 2 (time) mixed ANOVA was conducted. The XR group demonstrated improvements in GMFM-88, PBS, and FIM scores, with decreased PSI and increased PedsQL scores. Although most interaction effects were not statistically significant (GMFM-88: η² = 0.035, p = 0.329; PBS: η² = 0.043, p = 0.274), a marginal interaction effect was observed for PSI (p = 0.065, η² = 0.059), suggesting a potential benefit of XR-based rehabilitation in reducing parental stress. This preliminary study indicates that XR-based rehabilitation may provide beneficial trends in motor function and psychosocial health in children with CP, particularly in reducing parental stress. Further studies with larger sample sizes are needed to confirm these findings. Full article

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. Data collection involved regular instructor meetings documenting student experiences, challenges, and developmental trajectories through qualitative analysis emphasizing contextual interpretation. While students reported general satisfaction with chatbot integration, implementation revealed three critical complexities: temporal misalignment between AI consultation and real-time discussion dynamics; feedback inflation creating disconnects between AI reassurance and classroom reception; and unintended progression from supportive scaffolding toward technological dependency. Individual outcomes varied, with some students developing independence while others increased reliance on external validation. AI-assisted participation interventions demonstrate both promise and limitations, requiring careful consideration of classroom dynamics. Effective implementation necessitates rehearsal-based rather than validation-focused applications, emphasizing human mentorship and community-centered approaches that preserve educational autonomy while leveraging technological scaffolding strategically. Full article

Background: Professional knowledge and experience of healthcare organization went through continuous change and development with the progression of COVID-19 pandemic waves. However, carefully developed guidelines for cardiopulmonary resuscitation (CPR) remained largely unchanged regardless of the epidemic situation, with the largest change being a more prominent bioethical approach. It would be possible to further improve the quality of CPR by systematic data collection, the facilitation of prospective studies, and further development of the methodology based on this evidence, as well as by providing information and developing provisions on interventions with expected poor outcomes, and ultimately by refusing resuscitation. Methods: This study involved the critical collection and analysis of literary data originating from the Web of Science and PubMed databases concerning bioethical aspects and the efficacy of CPR during the COVID-19 pandemic. Results: According to the current professional recommendation of the European Resuscitation Council (ERC), CPR should be initiated immediately in case of cardiac arrest in the absence of an exclusionary circumstance. One such circumstance is explicit refusal of CPR by a well-informed patient, which in practice takes the form of a prior declaration. ERC prescribes the following conjunctive conditions for do-not-attempt CPR (DNACPR) declarations: present, real, and applicable. It is recommended to take the declaration as a part of complex end-of-life planning, with the corresponding documentation available in an electronic database. The pandemic has brought significant changes in resuscitation practice at both lay and professional levels as well. Incidence of out-of-hospital resuscitation (OHCA) did not differ compared to the previous period, while cardiac deaths in public places almost halved during the epidemic (p < 0.001) as did the use of AEDs (p = 0.037). The number of resuscitations performed by bystanders and by the emergency medical service (EMS) also showed a significant decrease (p = 0.001), and the most important interventions (defibrillation, first adrenaline time) suffered a significant delay. Secondary survival until hospital discharge thus decreased by 50% during the pandemic period. Conclusions: The COVID-19 pandemic provided a significant impetus to the revision of guidelines. While detailed methodology has changed only slightly compared to the previous procedures, the DNACPR declaration regarding self-determination is mentioned in the context of complex end-of-life planning. The issue of safe environment has come to the fore for both lay and trained resuscitators. Future Directions: Prospective evaluation of standardized methods can further improve the patient’s autonomy and quality of life. Since clinical data are controversial, further prospective controlled studies are needed to evaluate the real hazards of aerosol-generating procedures. Full article

Malnutrition is a widespread problem in hospitalized patients, which significantly impacts clinical outcomes, quality of life, and healthcare costs. Despite its well-documented consequences, it remains underdiagnosed and inadequately managed in many healthcare settings. Even with recent progress, key challenges remain, including inconsistent use of standardized nutritional screening tools and practices, insufficient professional training, and resource limitations. A multidisciplinary approach involving physicians, dietitians, nurses, and pharmacists is crucial for early detection, timely intervention, and prevention of malnutrition-related complications. The sustainability of a multidisciplinary model requires overcoming logistical and financial barriers, including the integration of technology for real-time monitoring, standardized screening protocols, and specific professional training. Regional initiatives, such as the establishment of the Clinical Nutrition Network of Lombardy (Italy), reported and discussed in this article, have made strides in improving nutritional care by promoting scientific networking and standardized practices across hospitals. This approach may not only improve patient outcomes but also reduce long-term healthcare costs by shortening hospital stays and preventing readmissions. For this model to be effective and sustainable, collaboration among healthcare providers, policymakers, and researchers is essential to promote an integrated, cost-effective approach to managing nutritional risk throughout the continuum of care. Full article

Background/Objectives: Type 2 diabetes (T2D) prevalence is rising worldwide. Despite numerous efforts to address the condition, many initiatives fall short due to limited consumer engagement. Involving people with lived experience in healthcare design is increasingly recognized as an effective strategy for improving diabetes management. Aim: To understand the unmet needs of people with T2D in self-managing their condition through the establishment of a Consumer Reference Group (CRG). Methods: The CRG was established using a standardized approach advised by Monash Partners, with specific terms of reference for consumer engagement. A face-to-face training workshop was conducted to develop consumers’ capacity to co-design T2D interventions. Two focus groups were held to explore consumers’ unmet needs and propose potential solutions. An inductive thematic analysis was performed. Results: Ten adults (three females/seven males; 58–78 years old) with T2D participated. Four main themes emerged: (1) misinformation; (2) limited guidance; (3) challenges in self-management; and (4) gaps in prevention and screening. Participants reported difficulties in maintaining motivation, balancing T2D management with other life commitments, and addressing mental health concerns. They reported feeling misinformed and inadequately supported by healthcare professionals and diabetes organizations, often relying on conflicting sources of information. Participants from culturally and linguistically diverse (CALD) backgrounds noted a lack of tailored dietary information. Proposed solutions included better training for primary care providers, reinforcing the role of diabetes nurse educators, expanding mental health support, and collaborating with CALD communities to provide culturally appropriate dietary information. Conclusions: These insights are critical for developing consumer-driven interventions that are responsive to the real-world needs of people living with T2D. Full article

Background/Objectives: The practice of physical activity contributes to obtaining adequate values of body composition and physical fitness, which is beneficial for people’s health. However, a large part of the adult population does not comply with the recommendations for physical activity, due to factors such as lack of time and nearby sports venues. Immersive virtual reality is a tool that allows individuals to immerse themselves in a simulated world and perceive visual, auditory, and tactile sensations. Its use in physical activity interventions favors exercise in situations that, due to time and space, could be limited in real life. The objective of this case report is to measure the impact on body composition and physical fitness of an exercise program executed through immersive virtual reality. Methods: The design is a case study with a quantitative approach developed through a physical activity intervention with immersive virtual reality in which body composition was evaluated considering fat and muscle components, and physical fitness considering cardiorespiratory fitness, speed-agility, and hand grip strength, through pre- and post-testing. The physical exercise program based on immersive virtual reality lasted 8 weeks. The subject of the study was a 24-year-old man, a second-year student of Pedagogy in Physical Education at a Chilean university, with no previous experience in this virtual tool. Results: The results indicate that for body composition the study subject decreased the fat component and slightly improved the musculature, while for physical fitness cardiorespiratory fitness and speed-agility improved, but manual grip strength decreased. Conclusions: It is concluded that the training developed through immersive virtual reality proves to be a tool that can promote improvements in body composition and physical fitness; it is necessary to carry out more research to validate the potential of this instrument as a means of contributing to the health of the population. Full article

Background: Therapeutic hypothermia (TH) represents a critical therapeutic intervention for patients with cardiac arrest, although treatment efficacy and prognostic factors may vary between individuals. Precise, personalized outcome predictions can empower better clinical decisions. Methods: In this multi-center retrospective cohort study involving nine medical centers in Taiwan, we developed machine learning algorithms to predict neurological outcomes in patients who experienced cardiac arrest with shockable rhythms and underwent TH. The study cohort comprised 209 patients treated between January 2014 and September 2019. The models were trained on patients’ pre-treatment characteristics collected during this study period. The optimal artificial neural network (ANN) model was interpretable using the SHapley Additive exPlanations (SHAP) method. Results: Among the 209 enrolled patients, 79 (37.80%) demonstrated favorable neurological outcomes at discharge. The ANN model achieved an area under the curve value of 0.9089 (accuracy = 0.8330, precision = 0.7984, recall = 0.7492, specificity = 0.8846) for outcome prediction. SHAP analysis identified vital predictive features, including the dose of epinephrine during resuscitation, diabetes status, body temperature at return of spontaneous circulation (ROSC), whether the cardiac arrest was witnessed, and diastolic blood pressure at ROSC. Using real-life case examples, we demonstrated how the ANN model provides personalized prognostic predictions tailored to individuals’ distinct profiles. Conclusion: Our machine learning approach delivers personalized forecasts of TH outcomes in cardiac arrest patients with shockable rhythms. By accounting for each patient’s unique health history and cardiac arrest event details, the ANN model empowers more precise risk stratification, tailoring clinical decision-making regarding TH prognostication and optimizing personalized treatment planning. Full article

Neonatal respiratory distress syndrome (RDS) is a common and potentially life-threatening condition in preterm infants, primarily due to surfactant deficiency. Early and accurate diagnosis is critical to guide timely interventions such as surfactant administration and respiratory support. Traditionally, chest X-rays have been used for diagnosis, but lung ultrasound (LUS) has gained prominence due to its non-invasive, radiation-free, and bedside applicability. Compared to chest X-rays and CT scans, LUS demonstrates superior sensitivity and specificity in diagnosing RDS, particularly in identifying surfactant need and predicting CPAP failure. Additionally, LUS offers real-time imaging without radiation exposure, an advantage over other modalities. However, its broader adoption is limited by challenges in standardizing training, ensuring diagnostic reproducibility, and validating scoring systems, especially in resource-limited settings. This narrative review aims to evaluate the role of LUS in the diagnosis and management of neonatal RDS over the past decade, focusing on its clinical utility, scoring systems, and emerging applications. We reviewed the literature from 2013 to 2023, focusing on studies evaluating LUS’ diagnostic accuracy, scoring systems, and its potential role in guiding surfactant therapy and predicting CPAP failure. Despite its benefits, addressing the variability in operator expertise and integrating artificial intelligence to enhance usability are crucial for ensuring LUS’ efficacy across diverse clinical environments. Future research should prioritize standardizing training and scoring protocols to facilitate wider implementation and optimize neonatal respiratory care outcomes. Full article

Writing is first-order instrumental learning that develops throughout the life cycle, a complex process evolving from early childhood education. The identification of risk predictors of dysgraphia at age 5 has the potential to significantly reduce the impact of graphomotor difficulties in early primary school, which affects handwriting performance to such an extent that it can become illegible. Building on established scientific literature, this study focuses on screening processes, with particular attention to writing requirements. This paper proposes a novel prevention and intervention system based on new technologies for teachers and educators or therapists. Specifically, it presents a pilot study testing an innovative tactile device to analyze graphomotor performance and motor coordination in real time. The research explores whether this haptic device can be used as an effective pedagogical aid for preventing graphomotor issues in children aged 5 to 6 years. The results showed a high level of engagement and usability among young participants. Furthermore, the quality of graphomotor traces, respectively executed by children after virtual and physical training, were comparable, supporting the use of the tool as a complementary training resource for the observation and enhancement of graphomotor processes. Full article

Background: Enhancing working memory performance in cognitively and physically healthy individuals is a popular area of research. The results from a large number of studies have now been summarized in multiple meta-analyses. In these reviews, various training methods have been examined, including mindfulness training, adaptive working memory training, physical activity training, and video game training, to examine whether working memory capacity can be improved. This report aggregated the results of these meta-analyses using second-order meta-analytic approaches to ascertain the extent to which working memory functioning can be enhanced in healthy adults. Methods: A total of six meta-analyses of randomized controlled trials that compared working memory interventions to a control group were included in the analyses. These studies were identified after systematically searching three electronic databases: APA PsycInfo, ERIC and Medline. Collectively, the meta-analyses investigated the effects of cognitive programs, mindfulness, video games and physical activity on working memory. Only meta-analyses undertaken with healthy adults aged between 18 and 55 years were included in the report. Results: The results revealed an average improvement in working memory across the included studies compared to the control groups. The findings indicated a small yet significant enhancement in working memory, with a standardized mean difference of 0.335 (95% CI [0.223; 0.447], p < .001). Further analysis tests for superiority effects between the different working memory training programs revealed no significant differences between intervention effect sizes. Conclusion: Based on the findings, it can be concluded that the working memory capacity of healthy adults can be improved through training. However, the effect size is small, so the utility of this type of training in real-life improvements in cognition may be minimal. The evidence does not indicate that one type of working memory training is superior to another. Full article

Manual handling is a major risk factor for work-related musculoskeletal disorders and one of the leading causes of disability-adjusted life years globally, necessitating multifaceted risk reduction measures. One potential intervention for manual handling tasks is work technique training assisted by augmented feedback on biomechanical exposures. However, there is a research gap regarding its effectiveness specifically for manual handling tasks in both real work environments and controlled settings, as well as its ability to induce retained reductions in biomechanical exposure. The gap was investigated using a rapid review comprising a literature search using two databases and 11 reviews/overviews to identify studies from the past 20 years, up to studies published by 1 June 2024. Sixteen studies were identified, with 14 of them being of high or moderate methodological quality and were included. Three studies were conducted in real work environments and eleven in controlled settings. Most studies (n = 9) used auditory feedback, followed by vibration feedback (n = 6). In real work environments, the evidence for the effectiveness of sensor-based augmented feedback in reducing biomechanical exposure during administration was considered to be inconsistent and very limited directly after administration. For longer periods after administration, ranging from one week to more than six months, there is currently no evidence demonstrating the effectiveness of the feedback. In controlled settings, there was strong evidence for its effectiveness during and immediately after administration, and limited evidence for effectiveness up to six months post-administration when considering the tasks included in the training. Future research needs are discussed. Full article

Forest fires pose a significant threat to ecosystems, property, and human life, making their early and accurate detection crucial for effective intervention. This study presents a novel, lightweight approach to real-time forest fire detection that is optimized for resource-constrained devices like drones. The method integrates multi-task knowledge distillation, transferring knowledge from a high-performance DenseNet201 teacher model that was trained on a hierarchically structured wildfire dataset. The dataset comprised primary classes (fire vs. non-fire) and detailed subclasses that account for confounding elements such as smoke, fog, and reflections. The novelty of this approach lies in leveraging knowledge distillation to transfer the deeper insights learned by the DenseNet201 teacher model—specifically, the auxiliary task of recognizing the confounding elements responsible for false positives—into a lightweight student model, enabling it to achieve a similar robustness without the need for complex architectures. Using this distilled knowledge, we trained a MobileNetV3-based student model, which was designed to operate efficiently in real-time while maintaining a low computational overhead. To address the challenge of false positives caused by visually similar non-fire elements, we introduced the Confounding Element Specificity (CES) metric. This novel metric, made possible by the hierarchical structure of the wildfire dataset, is unique in its focus on evaluating how well the model distinguishes actual fires from the confounding elements that typically result in false positives within the negative class. The proposed approach outperformed the baseline methods—including single-task learning and direct multi-task learning—achieving a primary accuracy of 93.36%, an F1-score of 91.57%, and a higher MES score, demonstrating its enhanced robustness and reliability in diverse environmental conditions. This work bridges the gap between advanced deep learning techniques and practical, scalable solutions for environmental monitoring. Future research will focus on integrating multi-modal data and developing adaptive distillation techniques to further enhance the model’s performance in real-time applications. Full article

Background/objectives: Overweight and obesity are growing concerns that also affect nursing staff, healthcare professionals that play a critical role in public health awareness and intervention. This study aimed to define the health parameters associated with body weight, analyze if there is an erroneous self-perception of overweight/obesity through the distortion of body weight perception, and determine the predictive factors of body weight distortion. Methods: A cross-sectional study of 224 nursing students gathered anthropometric and demographic data. Self-perceived body weight was assessed using Stunkard and Stellar’s scale. Descriptive statistics and multinomial logistic regression identified significant predictors of weight distortion. Results: The analysis found that men reported greater weight discrepancies than women. Specifically, 57% of the men (28 of 49 participants) underestimated their real BMI, while only 23% of the women (40 of 175 participants) did so. Age, sex, and weight classification emerged as significant explanatory variables for the distortion of body weight perception. Conclusions: The findings indicate a significant vulnerability among nursing students to the misperception of their own body weight status, highlighting the need for targeted training strategies. These strategies should focus on correcting misperceptions of obesity among health professionals throughout their working life to improve future obesity prevention efforts for society. Full article