Abstract
This study focuses on providing meaningful information to vulnerable road users (VRUs) to support their objectives and perceptions while navigating urban spaces, employing a novel route planning concept. Through three focus group sessions, a comprehensive survey was conducted to identify the needs and concerns of VRUs, leading to the development of an integrated and collaborative mobile application for active mobility. The application encompasses route calculation, prioritizing safety, comfort, civic participation, and empathy. The solution aims to bridge citizen users and city managers, facilitating alerts, historical information on safety and comfort, and collaborative problem-solving and sharing of urban attractions. A prototype of the concept was developed and extensively tested by potential users, and subjective evaluation and feedback demonstrated the usefulness and added value of the integrated and collaborative approach. This study highlights the proposed solution relevance and differentiation from official alerts, user experiences, and civic participation, positioning it as a comprehensive solution for active mobility. © 2025 Elsevier B.V., All rights reserved.

Abstract
Introduction: The importance of investigating innovative technologies to improve patient rehabilitation is fundamental in the current context of healthcare. This highlights the need to map the technological resources used in the rehabilitation of adult burn patients. Methods: A scoping review was conducted according to the parameters set by the Joanna Briggs Institute (JBI) guidelines and structured using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and MetaAnalyses for Scoping Reviews). The scientific literature search covered various databases: Medline, CINAHL, SportDiscus, Psychology & Behavioral Sciences Collection, Scopus, SciELO, and the Cochrane Library. The inclusion criteria considered studies related to the use of technological resources in the rehabilitation of burn patients. The research was conducted until November 2024. Results: A total of 19 articles published between 2000 and 2024 were included. The technological resources analyzed included virtual reality (10 studies), exergames (6 studies), exoskeletons (4 studies), and augmented reality (1 study). These resources primarily aimed to promote motor functionality, increase muscle strength, and enhance joint range of motion. Conclusion: The technologies applied to the rehabilitation of burn patients represent a promising advancement, with the potential to transform the paradigm of rehabilitation, making it more interactive. Future research should focus on a detailed analysis of the long-term benefits and on integrating these technologies into standard rehabilitation protocols.

Abstract
To address the challenges of self-care in oncology, gamification emerges as an innovative strategy to enhance health literacy and self-care among individuals with oncological disease. This study aims to explore and map how gamification can promote health literacy for self-care of oncological diseases. A scoping review was conducted following the Joanna Briggs Institute guidelines and the PRISMA-ScR Checklist developed for scoping reviews. A comprehensive search strategy was employed across MEDLINE (R), CINAHL (R), Scopus (R), and Web of Science (R) databases, with keywords focusing on oncological patients and gamification tools applied to self-management, from inception to December 2023. Thirty studies published between 2011 and 2023 were included, with a total of 1,118 reported participants. Most interventions (n = 21) focused on the development of mobile applications. The most frequent gamification elements included customizable avatars, rewards, social interaction, quizzes, and personalized feedback. The interventions primarily targeted health literacy and patient education, symptom monitoring, management of side effects, pain control, and adherence to medication and nutrition regimens. The integration of gamification elements into digital health solutions for oncology is expanding and holds promises for supporting health literacy and self-care. Further studies, preferably longitudinal, are needed to assess the effectiveness and impact of these interventions across different oncological populations and clinical settings.

Abstract
Objective: This scoping review synthesized and mapped the breadth of the existing literature on technological resources used to support individuals undergoing hemodialysis treatment. Methods: Following the methodological guidelines of the Joanna Briggs Institute (JBI) for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist, comprehensive searches were conducted across the following databases: MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Scopus, Scientific Electronic Library Online (SciELO), MedicLatina, and the Cochrane Central Register of Controlled Trials, with no time restrictions. Results: Thirty-nine studies conducted between 2003 and 2023 met the inclusion criteria. These studies covered a range of technological innovations developed specifically for hemodialysis treatment, including virtual reality, exergames, websites, and mobile applications. These technologies were designed with diverse objectives: to facilitate physical exercise, optimize dietary and medication management, improve disease adherence and management, and promote self-efficacy and self-care in patients. Conclusions: The review revealed a wide range of technological resources available to hemodialysis patients. These digital solutions show great potential to transform care by promoting more engaged and personalized health practices. Although this study did not directly assess the impact of these technologies, it provides a solid foundation for future investigations that can explore in-depth how such innovations contribute to effective disease management and improvement in clinical outcomes.

Abstract
BackgroundIndividuals with spinal cord injury (SCI) face complex and ongoing rehabilitation needs. In this context, mobile health applications have emerged as promising tools to support self-management and rehabilitation.ObjectiveTo map and characterize mobile applications specifically developed to support rehabilitation of individuals with SCI.MethodsA scoping review was conducted in accordance with PRISMA-ScR guidelines. A systematic search was performed across five electronic databases (PubMed, Scopus, Web of Science, and CINAHL). Studies published between 2015 and 2024 describing the use of mobile applications in the rehabilitation of adults with SCI were included.ResultsA total of 24 studies were included. We synthesized the identified applications descriptively into four domains: self-management and health education; gamification and motivation for physical rehabilitation; monitoring and prevention of secondary complications; and assistive technology and advanced rehabilitation. A consistent adoption of user-centered design principles was observed. Despite high levels of reported usability, challenges remain regarding long-term engagement, technological complexity, and sustained adherence.ConclusionMobile applications represent a promising complementary resource to support rehabilitation and health management in individuals with SCI. However, more robust longitudinal studies with larger sample sizes are required to assess the clinical impact and long-term feasibility of these interventions.

Abstract
Traffic state prediction is critical to decision-making in various traffic management applications. Despite significant advancements in Deep Learning (DL) models, such as Long Short-Term Memory (LSTM), Graph Neural Networks (GNN), and attention-based transformer models, multi-step predictions remain challenging. The state-of-the-art models face a common limitation: the predictions' accuracy decreases as the prediction horizon increases, a phenomenon known as error accumulation. In addition, with the arrival of non-recurrent events and external noise, the models fail to maintain good prediction accuracy. Deep Reinforcement Learning (DRL) has been widely applied to diverse tasks, including optimising intersection traffic signal control. However, its potential to address multi-step traffic prediction challenges remains underexplored. This study introduces an Actor-Critic-based adapted DRL method to explore the solution to the challenges associated with multi-step prediction. The Actor network makes predictions by capturing the temporal correlations of the data sequence, and the Critic network optimises the Actor by evaluating the prediction quality using Q-values. This novel combination of Supervised Learning and Reinforcement Learning (RL) paradigms, along with non-autoregressive modelling, helps the model to mitigate the error accumulation problem and increase its robustness to the arrival of non-recurrent events. It also introduces a Denoising Autoencoder to deal with external noise effectively. The proposed model was trained and evaluated on three benchmark traffic flow and speed datasets. Baseline multi-step prediction models were implemented for comparison based on performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal that the proposed method outperforms the baselines by achieving average improvements of 0.26 to 21.29% in terms of MAE and RMSE for up to 24 time steps of prediction length on the three used datasets, at the expense of relatively higher computational costs. On top of that, this adapted DRL approach outperforms traditional DRL models, such as Deep Deterministic Policy Gradient (DDPG), in accuracy and computational efficiency.