Search Results (31)

This pilot study (NCT05743686) evaluated the feasibility of a web-based application enabling patients with breast cancer (BC) receiving oral therapy to self-report and manage treatment-related adverse effects (AEs). Patients were enrolled between January and August 2023. Historical controls were used for comparison. Participants used the web-app to self-report AEs daily for 13 weeks, completed the PRO-CTCAE weekly, and completed a questionnaire assessing psychosocial precursor factors associated with treatment adherence. Some patients and healthcare professionals participated in semi-structured interviews. The study included 28 participants and 185 historical controls. Compared with controls, participants had fewer interactions with hospital pharmacists (0 [0–1] vs. 0 [0–7], p = 0.04), with no significant differences in the number of visits, hospitalizations, or modifications to treatment. Concordance between AEs reported via the web-app and PRO-CTCAE was 66.2%. No statistically significant changes were observed in psychosocial precursor factors of treatment adherence following the intervention (all p > 0.05). The qualitative data underscored the generally positive reception of the web-based application among both patients and healthcare professionals. In conclusion, in this small mixed-methods pilot study, monitoring oral cancer therapy-related adverse effects using the web-app was feasible and acceptable, and was associated with a lower frequency of telephone contacts with hospital pharmacists compared with historical usual care. These preliminary findings are exploratory and warrant confirmation in larger, prospectively designed studies. Full article

Physical inactivity is a major preventable cause of non-communicable disease and premature mortality. Mobile health interventions can promote physical activity, but their effectiveness depends on the ability to adapt to user’s context and motivation. Reinforcement learning (RL), particularly contextual bandits (CBs), offers a promising framework for such adaptive personalization. However, in practice, RL-based models face the cold start problem (CSP), due to the lack of initial training data. This study examines whether theory-driven simulated data can mitigate the CSP in training RL systems for personalized physical activity recommendations. A scoping review of 18 empirical studies on the Integrated Behavioral Change Model (IBC) provided population parameters for key constructs, used to simulate 2000 virtual users via multivariate modeling and structural equation calibration. A CB algorithm with an ε-greedy policy was trained with this dataset and compared with data from real world pilot using the Apptivate mHealth web-app (n = 588). Results showed close alignment between simulated and real behaviors. Our findings demonstrate that behaviorally informed synthetic data can effectively be used to train RL algorithms, offering an interpretable, sustainable, scalable, and privacy-safe solution to the CSP in personalized digital health interventions. Full article

The growing demand for resources for production in intensive livestock farming requires research to operate with an environmentally sustainable perspective and respect for animal welfare, promoting circularity in the livestock industry. In this context, animal monitoring plays a key role in livestock management, not only to ensure their well-being but also to preserve the balance of the territory. In particular, early detection of oestrus events is one of the crucial elements in livestock monitoring. This study presents the development and on-farm validation of a low-power oestrus detection system for dairy cows, based on stand-alone smart pedometers (SASPs) connected through a Low-Power Wide-Area Network (LPWAN). The system implements an upgradeable, threshold-based algorithm that analyzes cow motor activity using a 24 h moving-mean approach and three behavioral indicators related to oestrus expression. Data are processed on board and transmitted to a cloud platform for visualization through a farmer-oriented WebApp, without requiring any fixed installation in the barn. The system was tested on a commercial free-stall dairy farm over three experimental campaigns (2021–2023). Oestrus events were validated through farmer visual observation and milk progesterone analysis, used as the reference method. A total of 22 confirmed oestrus events were analyzed. The system achieved a detection rate of 72.7% for certain oestrus events and 86.4% when including probable detections, with a mean oestrus duration of 18.1 ± 2.5 h, consistent with values reported in the literature. The proposed solution demonstrates the feasibility of a transparent, low-computational-cost oestrus detection approach compatible with LPWAN constraints. Its plug-and-play design, reduced infrastructure requirements, and upgradable firmware, although not able to self-update, limiting its potential compared to the machine learning-based methods present in the literature, make it suitable for practical adoption, particularly in farms where conventional connectivity and high-cost commercial systems are limiting factors. Full article

Background: Anxiety disorders are common in childhood, yet access to cognitive behavioral therapy (CBT) is often limited. Internet-delivered CBT (iCBT) can help overcome these barriers, but evidence in younger children remains scarce. This pilot study describes the development and preliminary evaluation of an Italian iCBT platform for children with mild to moderate anxiety. Methods: Five children aged 8–12 years and their caregivers were recruited through pediatricians. Eligibility was assessed using the MASC-2 and a psychiatrist interview. Each child completed a supervised session with the WebApp, which delivers CBT modules combining psychoeducation, cognitive restructuring, relaxation, and gamified activities. Usability was evaluated using the ita-MAUQ, observation, and interviews. Results: All participants completed the session without dropouts. Mean ita-MAUQ scores were consistently above the midpoint, with the highest ratings for interface design and satisfaction. Children appreciated the interactive, game-like features, while caregivers valued the clarity and practicality of content. Qualitative feedback indicated good comprehensibility and engagement, with suggestions for improving navigation flow and language adaptation. No adverse events occurred. Conclusions: This pilot study supports the feasibility, safety, and acceptability of the new iCBT platform and provides essential insights for its refinement and future large-scale clinical trials. Full article

In this study, a diverse collection of images of myxozoans from the genera Henneguya and Myxobolus was created, providing a practical dataset for application in computer vision. Four versions of the YOLOv5 network were tested, achieving an average precision of 97.9%, a recall of 96.7%, and an F1 score of 97%, demonstrating the effectiveness of MLens in the automatic detection of these parasites. These results indicated that machine learning has the potential to make microparasite detection more efficient and less reliant on manual work in parasitology. The beta version of the MLens showed strong performance, and future improvements may include fine-tuning the WebApp hyperparameters, expanding to other myxosporean genera, and refining the model to handle more complex optical microscopy scenarios. This work presented a significant advancement, opening new possibilities for the application of machine learning in parasitology and substantially accelerating parasite detection. Full article

The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users. Full article

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO₂, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance. Full article

Objectives: The aim was to evaluate the influence of weather parameters on disease activity assessed by Routine Assessment of Patient Index Data (RAPID) scores via a Web-based smartphone application (WebApp). Methods: Correlation of changes of temperature (change of temperature, °C) and air pressure (change of air pressure, hPa) two days prior to and weekly self-assessment of disease activity by RAPID-3 scores over three months. To define background noise and quadrants of weather changes, we defined a central quadrant ± 2 hPa and ± 2° C, called E1. Based on this inner square, four quadrants were defined: A1 = sector left side above with increasing temperature and air pressure (improving weather); B1 = sector right side above; C1 = decreasing temperature and air pressure sector right side down (worsening weather); and D1 = sector left side down. Alterations of RAPID-3 scores analyzed changes in disease activity compared to RAPID-3 scores detected one week in advance. Results: Eighty patients were included in the analysis (median RA duration, 4.5 years; age, 57 years; 59% female). Median disease activity was 2.8 as assessed by DAS 28. In total, 210 time points were analyzed for quadrant A1, 164 for quadrant B1, 160 for quadrant C1, 196 for quadrant D1, and 145 for the inner square E1 were found during follow-up. The middle square E1 was balanced between increasing or decreasing values for RAPID scores. The odds for increasing RAPID scores were 1.33 (95% confidence interval CI: 1.0–1.78) for patients with ameliorating weather conditions which improve or alleviate unfavorable or adverse conditions (A1) compared to 0.98 (CI: 0.67–1.45) for worsening weather (C1) as defined by temperature and air pressure. Conclusions: On average, more patients developed a slight increase of disease activity if they were in the quadrant with increasing temperature and air pressure (improving weather). Thus, no correlation between the worsening of the weather and changing RAPID-3 scores was found. Full article

This article dwells on the role of gamified digital tools in promoting environmental self-awareness and action. In particular, it unfolds the outreach of a web application, developed within the European GoBeEco project, aimed at encouraging users to adopt ecological and sustainability habits. In this article, the focus is on the implementation of the project in Portugal, and, therefore, the data presented in the results reflect the involvement of participants on a national level. Overall, more than two dozen participants were involved in the validation of the application, which comprised three evaluation phases involving the distribution of questionnaires and the organisation of a focus group aimed at assessing the role of GoBeEco in fostering sustainable personal change and also evaluating specifically the role that gamified elements played in that change. Results show that the application had a very positive impact on the users and helped mitigate the well-documented gap between sustainable awareness and action, and, also, that the gamification strategy contributed to that purpose. We argue that the potential of these applications in Portugal is latent but still has room for growth. In this sense, the study also highlights future paths for the development and implementation of these tools, based on the features most valued by users—access to statistical data, examples from real life, gamified and fun elements, and focus on daily, individual actions, among others. Full article

Background: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, “Article” and “Conference paper”, mostly constituting the total of retrieved documents, highlighting that the term “ChatGPT” becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT’s applications for public health by outlining pivotal themes and uncovering emerging trends. Full article

This article proposes the integration of two novel aspects into the production of 3D-printed customized wrist-hand orthoses. One aspect involves the material, particularly Colorfabb varioShore thermoplastic polyurethane (TPU) filament with an active foaming agent, which allows adjusting the 3D-printed orthoses’ mechanical properties via process parameters such as printing temperature. Consequently, within the same printing process, by using a single extrusion nozzle, orthoses with varying stiffness levels can be produced, aiming at both immobilization rigidity and skin-comfortable softness. This capability is harnessed by 3D-printing the orthosis in a flat shape via material extrusion-based additive manufacturing, which represents the other novel aspect. Subsequently, the orthosis conforms to the user’s upper limb shape after secure attachment, or by thermoforming in the case of a bi-material solution. A dedicated design web app, which relies on key patient hand measurement input, is also proposed, differing from the 3D scanning and modeling approach that requires engineering expertise and 3D scan data processing. The evaluation of varioShore TPU orthoses with diverse designs was conducted considering printing time, cost, maximum flexion angle, comfort, and perceived wrist stability as criteria. As some of the produced TPU orthoses lacked the necessary stiffness around the wrist or did not properly fit the palm shape, bi-material orthoses including polylactic acid (PLA) inserts of varying sizes were 3D-printed and assessed, showing an improved stiffness around the wrist and a better hand shape conformity. The findings demonstrated the potential of this innovative approach in creating bi-material upper limb orthoses, capitalizing on various characteristics such as varioShore properties, PLA thermoforming capabilities, and the design flexibility provided by additive manufacturing technology. Full article

Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and processing quality. In addition, as far as we know, there are relatively few industrial case studies on the real applications of remote monitoring and maintenance systems that include both conventional and advanced maintenance techniques under the context of Industry 4.0. Addressing these issues, an industrial case study on the monitoring and maintenance service system for a robot-driven carbon block polishing service system is presented, including its application background and engineering problems, software/hardware architecture and running logic, the monitoring and maintenance-related enabling techniques, and the configuration and operation workflows of the system in the form of screenshots of the functional WebAPPs of the software system. The case study can provide real examples and references for the industrial application of remote monitoring and maintenance service systems on industrial product service systems under the context of Industry 4.0. Advanced techniques such as the Industrial Internet of Things, digital twins, deep learning, and edge/cloud/fog computing have been applied to the system. Full article

In this paper, we have presented the design and implementation of a blockchain-based approach for ensuring reliable supply chain management for commodities transported through smart containers. To administer interactions between the sender and receiver, our developed system makes use of the Ethereum blockchain’s smart contract features. Smart containers equipped with Internet of Things (IoT)-enabled sensors are used to monitor shipping conditions to check predefined shipping requirements. Smart contracts on Ethereum are used to automate payments, validate receivers, and give refunds in the case of violation of predefined requirements. We have also implemented our designed front-end decentralized WebApp and wallet that allows the sender and receiver to communicate with Ethereum smart contracts. Full article

This study empirically examines the effects of the acceptance of e-learning Web-apps by student on the learning outcomes achieved with their use. With this objective, two theoretically recognized purposes for use these apps were tested in a blended learning model, as a way to change the traditional face-to-face classrooms activities (apps we called ICTf) and as a virtual evaluation platform in learning (ICTv apps). The data was collected through online surveys from university students of a blended Master’ degree, enrolled in different specialties. PLS-SEM analysis of the data was performed. A proportion of the variance of student learning outcomes was explained by the level of ICTv acceptance. However, the positive effect of the ICTf acceptance on this performance was not significant. Heterogeneity was observed in students’ ratings on the acceptance of the Web-apps by different master’s specialties, and it was higher in ICTf than in ICTv. Our research highlights the important role that the acceptance of use of electronic learning resources plays in boosting their effective learning performance. Full article

While personal characteristics have been evaluated as determinants of dietary choices over the years, only recently studies have looked at the impact of eating context. Examining eating context, however, can be challenging. Here, we propose the use of a web-app for the Ecological Momentary Assessment of dietary habits among 138 college students from Catania (Italy) and therefore for examining the impact of eating context on dietary choices. Eating away from home was associated with lower odds of consuming vegetables, fruits, and legumes and higher odds of consuming processed meat, salty snacks, and alcoholic drinks compared with eating at home. Eating in the company of other people was associated with higher odds of consuming vegetables, red meat, fish, legumes, milk, and sugar-sweetened beverages and lower odds of consuming nuts than eating alone. This study proposed a new way to capture and assess how eating environment might affect dietary habits. Based on our results, meal location and social context have significant effects on the dietary choices of college students, pointing to the need to incorporate these aspects into further epidemiological studies. Full article