With trends toward increasing patient involvement in medical decision-making, decreasing clinic times, and the availability of the Internet, patients and their caregivers are increasingly researching cancer diagnoses online. It is essential for physicians to understand patient Internet usage as it relates to their own health education. Internet usage trends have been studied in various areas, but not in thoracic diseases. This prospective cohort study surveyed 337 thoracic surgery patients and their caregivers with both cancer and non-cancer diagnoses to examine their Internet usage trends. Cancer subjects were more likely to research their condition online if they were younger, had a higher income, had a higher education level, and were currently employed. Only age and income level were predictive for non-cancer subjects. Separately, cancer subjects were more likely to trust information found on the Internet if they had a higher education. Subjects were most likely to conduct research on a hospital website than other websites. These data will be helpful to thoracic surgeons who want to appropriately educate patients and their caregivers and direct them to reliable Internet sources. These data also illustrate the importance of developing trustworthy hospital websites with disease-specific information.
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http://dx.doi.org/10.1007/s13187-015-0934-9 | DOI Listing |
Support Care Cancer
January 2025
Fudan University School of Nursing, Shanghai, China and Fudan University Centre for Evidence-Based Nursing: A Joanna Briggs Institute Centre of Excellence, 305 Fenglin Rd, Shanghai, 200032, China.
Purpose: Aromatase inhibitor-associated musculoskeletal symptoms (AIMSS) are the most common adverse effects experienced by breast cancer patients. This scoping review aimed to systematically synthesize the predictors/risk factors and outcomes of AIMSS in patients with early-stage breast cancer.
Methods: A systematic search was conducted in PubMed, Web of Science, EMBASE, CINAHL, and the China National Knowledge Internet (CNKI) from inception to December 2024 following the scoping review framework proposed by Arksey and O'Malley (2005).
Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
According to recent research, with the ever-increasing use of Internet of Things (IoT) devices, there has arisen an ever-growing need for high-performance yet low-power circuits that can efficiently process information. Quantum-dot Cellular Automata (QCA) has emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) technology due to its great potential in digital design at nanoscale levels on account of very low power consumption and very high processing speed. However, QCA circuits are inherently prone to faults due to variations in manufacturing processes and due to the influence of environmental factors.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
University of Bristol, Bristol, United Kingdom.
Background: Digital health interventions targeting behavior change are promising in adults and adolescents; however, less attention has been given to younger children. The proliferation of wearables, such as smartwatches and activity trackers, that support the collection of and reflection on personal health data highlights an opportunity to consider novel approaches to supporting health in young children (aged 5-11 y).
Objective: This review aims to investigate how smartwatches and activity trackers have been used across child health interventions (for children aged 5-11 y) for different health areas, specifically to identify the population characteristics of those being targeted, describe the characteristics of the devices being used, and report the feasibility and acceptability of these devices for health-related applications with children.
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