Purpose: To assess the effectiveness of applying digital health palliative care to improve symptoms, mood, and quality of life in patients with advanced cancer.
Methods: We searched PubMed, Embase, Web of Science, Cochrane Library (CENTRAL), and CINAHL databases on November 16, 2023, to identify randomized controlled trials of the impact of palliative care applying digital health on patients with advanced cancer. The Cochrane Risk of Bias Tool version 1.0 was used to evaluate the quality of randomized controlled trials.
Results: A total of 20 randomized controlled trials were included, of which 18 were meta-analyzed. Compared with usual care, palliative care applying digital health was effective in improving symptoms (SMD = -0.21, 95% CI: -0.37 to -0.06, P = 0.007) and reducing the intensity of pain (SMD = -0.49, 95% CI: -0.85 to -0.13, P = 0.008) in patients with advanced cancer, but no effective improvement in depressive symptoms, anxiety symptoms, or quality of life was found.
Conclusions: Our systematic review provides evidence that palliative care applying digital health has great potential to improve symptoms in patients with advanced cancer, but more research is needed to explore its impact on mood and quality of life.
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http://dx.doi.org/10.1007/s00520-024-08866-9 | DOI Listing |
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Psychology, Ulster University, Coleraine, United Kingdom.
Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFHealth Psychol Behav Med
January 2025
Department of Psychology, Northumbria University, Newcastle upon Tyne, UK.
Background: Sunburn and intermittent sun exposure elevate melanoma skin cancer risk. Sun protection behaviours, including limiting sun exposure, seeking shade, wearing protective gear, and using sunscreen, help mitigate excessive sun exposure. Smartphone apps present a promising platform to enhance these behaviours.
View Article and Find Full Text PDFFront Oral Health
January 2025
Department of General Dentistry, School of Dental Medicine, Stony Brook University, Stony Brook, NY, United States.
Introduction: Previous studies have shown () esterase is a key mediator of dental composite biodegradation, which can contribute to recurrent caries. This study is to investigate the inhibitory effects of a novel Chemically-Modified-Curcumin (CMC 2.24) on esterase activities and related dental material biodegradation.
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