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Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, United States.
Background: Low back pain (LBP) is highly prevalent and disabling, especially in agriculture sectors. However, there is a gap in LBP prevention and intervention studies in these physically demanding occupations, and to date, no studies have focused on horticulture workers. Given the challenges of implementing interventions for those working in small businesses, self-management offers an attractive and feasible option to address work-related risk factors and manage LBP.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFJMIR Public Health Surveill
January 2025
School of Arts and Media, Wuhan College, Wuhan, China.
Background: The global aging population and rapid development of digital technology have made health management among older adults an urgent public health issue. The complexity of online health information often leads to psychological challenges, such as cyberchondria, exacerbating health information avoidance behaviors. These behaviors hinder effective health management; yet, little research examines their mechanisms or intervention strategies.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Trinity College Dublin, Dublin, Ireland.
Background: Scientific implementation findings relevant to the implementation of internet-delivered cognitive behavioral therapy (iCBT) for depression and anxiety in adults remain sparse and scattered across different sources of published information. Identifying evidence-based factors that influence the implementation of iCBT is key to successfully using iCBT in real-world clinical settings.
Objective: This systematic review evaluated the following: (1) aspects that research articles postulate as important for the implementation of iCBT and (2) aspects relevant to the day-to-day running of iCBT services.
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