Explore the vast range of titles available.

Patients undergoing surgery often experience stress and anxiety, which can increase complications and hinder recovery. Effective management of these psychological factors is key to improving outcomes. Preoperative anxiety is inversely correlated with the amount of information patients receive, but accessible, personalized support remains limited, especially in preoperative settings. Face-to-face education is often impractical due to resource constraints. Digital health (DH) interventions offer a promising alternative, enhancing patient engagement and empowerment. However, most current tools focus on providing information, overlooking the importance of personalization and psychological support. This study aimed to assess the viability of a DH intervention known as the Adhera CARINAE DH Program. This program is specifically designed to offer evidence-based and personalized stress- and anxiety-management techniques. It achieves this by using a comprehensive digital ecosystem that incorporates wearable devices, mobile apps, and virtual reality technologies. The intervention program also makes use of advanced data-driven techniques to deliver tailored patient education and lifestyle support. A total of 74 patients scheduled for surgery across 4 hospitals in 3 European countries were enrolled in this study from September 2021 to March 2022. Surgeries included cardiopulmonary and coronary artery bypass surgeries, cardiac valve replacements, prostate or bladder cancer surgeries, hip and knee replacements, maxillofacial surgery, and scoliosis procedures. After assessment for eligibility, participants were randomized into 2 groups: the intervention group (n=23) received the Adhera CARINAE DH intervention in addition to standard care, while the control group (n=27) received standard care alone. Psychological metrics such as self-efficacy, self-management, and mental well-being were assessed before and after the intervention, alongside physiological markers of stress. The intervention group demonstrated significant improvements across several psychological outcomes. For example, Visual Analogue Scale Stress at the hospital improved at admission by 5% and at hospital discharge by 11.1% and Visual Analogue Scale Pain at admission improved by 31.2%. In addition, Hospital Anxiety and Depression Scale Anxiety after surgery improved by 15.6%, and Positive and Negative Affect Scale-Negative at hospital admission improved by 17.5%. Overall, patients in the intervention study spent 17.12% less days in the hospital. Besides these individual scores, the intervention group shows more positive relationships among the psychological dimensions of self-efficacy, self-management, and mental well-being, suggesting that the CARINAE solution could have a positive effect and impact on the reduction of stress and negative emotions. Our results provide an important first step toward a deeper understanding of optimizing DH solutions to support patients undergoing surgery and for potential applications in remote patient monitoring and communication. ClinicalTrials.gov NCT05184725; https://clinicaltrials.gov/study/NCT05184725. RR2-10.2196/38536.

[This corrects the article DOI: 10.2196/54049.].

Stress and anxiety are psychophysiological responses commonly experienced by patients during the perioperative process that can increase presurgical and postsurgical complications to a comprehensive and positive recovery. Preventing and intervening in stress and anxiety can help patients achieve positive health and well-being outcomes. Similarly, the provision of education about surgery can be a crucial component and is inversely correlated with preoperative anxiety levels. However, few patients receive stress and anxiety relief support before surgery, and resource constraints make face-to-face education sessions untenable. Digital health interventions can be helpful in empowering patients and enhancing a more positive experience. Digital health interventions have been shown to help patients feel informed about the possible benefits and risks of available treatment options. However, they currently focus only on providing informative content, neglecting the importance of personalization and patient empowerment. This study aimed to explore the feasibility of a digital health intervention called the Adhera CARINAE Digital Health Program, designed to provide evidence-based, personalized stress- and anxiety-management methods enabled by a comprehensive digital ecosystem that incorporates wearable, mobile, and virtual reality technologies. The intervention program includes the use of advanced data-driven techniques for tailored patient education and lifestyle support. The trial will include 5 hospitals across 3 European countries and will use a randomized controlled design including 30 intervention participants and 30 control group participants. The involved surgeries are cardiopulmonary and coronary artery bypass surgeries, cardiac valve replacement, prostate or bladder cancer surgeries, hip and knee replacement, maxillofacial surgery, or scoliosis. The control group will receive standard care, and the intervention group will additionally be exposed to the digital health intervention program. The recruitment process started in January 2022 and has been completed. The primary impact analysis is currently ongoing. The expected results will be published in early 2023. This manuscript details a comprehensive protocol for a study that will provide valuable information about the intervention program, such as the measurement of comparative intervention effects on stress; anxiety and pain management; and usability by patients, caregivers, and health care professionals. This will contribute to the evidence planning process for the future adoption of diverse digital health solutions in the field of surgery. ClinicalTrials.gov NCT05184725; https://www.clinicaltrials.gov/ct2/show/NCT05184725. DERR1-10.2196/38536.

Virtual reality (VR) has re-emerged as a low-cost, highly accessible consumer product, and training on simulators is rapidly becoming standard in many industrial sectors. However, the available systems are either focusing on gaming context, featuring limited capabilities or they support only content creation of virtual environments without any rapid prototyping and modification. In this project, we propose a code-free, visual scripting platform to replicate gamified training scenarios through rapid prototyping and VR software design patterns. We implemented and compared two authoring tools: a) visual scripting and b) VR editor for the rapid reconstruction of VR training scenarios. Our visual scripting module is capable of generating training applications utilizing a node-based scripting system, whereas the VR editor gives user/developer the ability to customize and populate new VR training scenarios directly from the virtual environment. We also introduce action prototypes, a new software design pattern suitable to replicate behavioral tasks for VR experiences. In addition, we present the training scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum. Finally, a user-based evaluation of the proposed solution indicated that users—regardless of their programming expertise—can effectively use the tools to create and modify training scenarios in VR.

Therapeutic virtual reality (VR) has emerged as an effective treatment modality for cognitive and physical training in people with mild cognitive impairment (MCI). However, to replace existing nonpharmaceutical treatment training protocols, VR platforms need significant improvement if they are to appeal to older people with symptoms of cognitive decline and meet their specific needs. This study aims to design and test the acceptability, usability, and tolerability of an immersive VR platform that allows older people with MCI symptoms to simultaneously practice physical and cognitive skills on a dual task. On the basis of interviews with 20 older people with MCI symptoms (15 females; mean age 76.25, SD 5.03 years) and inputs from their health care providers (formative study VR1), an interdisciplinary group of experts developed a VR system called VRADA (VR Exercise App for Dementia and Alzheimer's Patients). Using an identical training protocol, the VRADA system was first tested with a group of 30 university students (16 females; mean age 20.86, SD 1.17 years) and then with 27 older people (19 females; mean age 73.22, SD 9.26 years) who had been diagnosed with MCI (feasibility studies VR2a and VR2b). Those in the latter group attended two Hellenic Association Day Care Centers for Alzheimer's Disease and Related Disorders. Participants in both groups were asked to perform a dual task training protocol that combined physical and cognitive exercises in two different training conditions. In condition A, participants performed a cycling task in a lab environment while being asked by the researcher to perform oral math calculations (single-digit additions and subtractions). In condition B, participants performed a cycling task in the virtual environment while performing calculations that appeared within the VR app. Participants in both groups were assessed in the same way; this included questionnaires and semistructured interviews immediately after the experiment to capture perceptions of acceptability, usability, and tolerability, and to determine which of the two training conditions each participant preferred. Participants in both groups showed a significant preference for the VR condition (students: mean 0.66, SD 0.41, t =8.74, P<.001; patients with MCI: mean 0.72, SD 0.51, t =7.36, P<.001), as well as high acceptance scores for intended future use, attitude toward VR training, and enjoyment. System usability scale scores (82.66 for the students and 77.96 for the older group) were well above the acceptability threshold (75/100). The perceived adverse effects were minimal, indicating a satisfactory tolerability. The findings suggest that VRADA is an acceptable, usable, and tolerable system for physical and cognitive training of older people with MCI and university students. Randomized controlled trial studies are needed to assess the efficacy of VRADA as a tool to promote physical and cognitive health in patients with MCI.

We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first time, the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines (MGEs), that are based on the same approach but often present these notions in a black-box approach. It is designed to actively utilize software design patterns, under an extensible open-source approach. Although Elements provides a modern (i.e., shader-based as opposed to fixed-function OpenGL), simple to program approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts.

Virtual reality (VR) has re-emerged as a low-cost, highly accessible consumer product, and training on simulators is rapidly becoming standard in many industrial sectors. However, the available systems are either focusing on gaming context, featuring limited capabilities or they support only content creation of virtual environments without any rapid prototyping and modification. In this project, we propose a code-free, visual scripting platform to replicate gamified training scenarios through rapid prototyping and VR software design patterns. We implemented and compared two authoring tools: a) visual scripting and b) VR editor for the rapid reconstruction of VR training scenarios. Our visual scripting module is capable to generate training applications utilizing a node-based scripting system whereas the VR editor gives user/developer the ability to customize and populate new VR training scenarios directly from the virtual environment. We also introduce action prototypes, a new software design pattern suitable to replicate behavioral tasks for VR experiences. In addition, we present the training scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum. Finally, a user-based evaluation of the proposed solution indicated that users - regardless of their programming expertise - can effectively use the tools to create and modify training scenarios in VR.

The appeal of Mixed Reality (MR) digital games arouses interest among researchers and education specialists who since their recent proliferation, they have been trying to introduce their motivating potential in learning contexts. Our main research question in this work focuses on whether MR digital games can, via novel Presence (feeling of 'being and doing there' in a virtual or augmented world) and MR gamification (dynamics, mechanics, components) support and foster future learning and teaching, to address a wide variety and variation of educational contexts. To accomplish the above, a consistent computational framework that supports new types of Mixed-Reality Serious Games and Gamification (MRSGs) is established in this work that features MR gesture-based and game-based learning. The introduced term Mixed Reality Serious Games (MRSGs), refers to digital mini game-shells that allow the learners and teachers to sense the feeling of 'Presence' experienced under a novel MR educational learning framework, in both Virtual Reality (VR) as well as Augmented Reality (AR) formal and informal learning. The former (VR) allows for the unique feeling of 'being there' and 'doing there' in the virtual world, that will be transforming the overall game-based learning experience, via latest innovations as well as recent progress in low-cost h/w Head Mounted Displays (HMDs). The latter (AR) blends real and virtual elements so that the 3D virtual element is registered accurately in the real world and interacted freely by the learner via various mobile displays, including smart glasses, natural, gesture-based interaction (mobile RGB and RGB-D), MR virtual characters and gamified learning processes. Game-based learning also involves the incorporation of games into lessons. The principal aim of applying games in education is to increase students' engagement and motivation. In our case studies we provide two mini MRSGs in VR and AR that accompany the primary school history class and particular the period of the Minoan Civilization, as manifested by the archaeological site of Ancient Knossos in Heraklion, Greece. The AR MRSG implements a desktop-based holographic application using the Meta-AR glasses. This MRSG consist of three mini game shells in which the student has to complete various learning tasks by using gesture-based interaction. These tasks consist of puzzle-based and constructional games tasks. The VR MRSG implements a similar approach of a Virtual tour in Knossos with quests that student need to accomplish to collect rewards. The student has to fully explore the palace, interact with special characters and complete their quests.These MRSGs are a first attempt to formally study latest h/w and s/w advances in Mixed Reality technologies, applied in gesture-based and game-based learning in both formal and informal educational contexts. Moreover we compared the gamification elements for each enabling technology and define the gamification dynamics, mechanics and components that need to be utilized in each MR environment.

In this work, a new and innovative way of spatial computing that appeared recently in the bibliography called True Augmented Reality (AR), is employed in cultural heritage preservation. This innovation could be adapted by the Virtual Museums of the future to enhance the quality of experience. It emphasises, the fact that a visitor will not be able to tell, at a first glance, if the artefact that he/she is looking at is real or not and it is expected to draw the visitors' interest. True AR is not limited to artefacts but extends even to buildings or life-sized character simulations of statues. It provides the best visual quality possible so that the users will not be able to tell the real objects from the augmented ones. Such applications can be beneficial for future museums, as with True AR, 3D models of various exhibits, monuments, statues, characters and buildings can be reconstructed and presented to the visitors in a realistic and innovative way. We also propose our Virtual Reality Sample application, a True AR playground featuring basic components and tools for generating interactive Virtual Museum applications, alongside a 3D reconstructed character (the priest of Asinou church) facilitating the storyteller of the augmented experience.

This work presents the introduction of UniSG^GA, a novel integrated scenegraph structure, that to incorporates behavior and geometry data on a 3D scene. It is specifically designed to seamlessly integrate Graph Neural Networks (GNNs) and address the challenges associated with transforming a 3D scenegraph (3D-SG) during generative tasks. To effectively capture and preserve the topological relationships between objects in a simplified way, within the graph representation, we propose UniSG^GA, that seamlessly integrates Geometric Algebra (GA) forms. This novel approach enhances the overall performance and capability of GNNs in handling generative and predictive tasks, opening up new possibilities and aiming to lay the foundation for further exploration and development of graph-based generative AI models that can effectively incorporate behavior data for enhanced scene generation and synthesis.