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Acceptability of the Fit2ThriveMB mHealth physical activity promotion intervention in women with metastatic breast cancer.
Support Care Cancer
January 2025
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400, Chicago, IL, 60611, USA.
Purpose: Increasing physical activity (PA) is safe and associated with improved health outcomes in patients with metastatic breast cancer (MBC). Mobile health (mHealth) PA interventions that allow for remote monitoring and tailoring to abilities may be particularly useful for MBC patients. However, limited data exist on the acceptability of these interventions for MBC patients.
View Article and Find Full Text PDFSurveillance of drug prescribing: why outliers miss their targets - a qualitative study.
BMC Health Serv Res
January 2025
Institute of General Practice/Family Medicine, Philipps-University of Marburg, Karl-Von-Frisch-Straße 4, 35043, Marburg, Germany.
Background: Rising costs are a challenge for healthcare systems. To keep expenditure for drugs under control, in many healthcare systems, drug prescribing is continuously monitored. The Bavarian Drug Agreement (German: Wirkstoffvereinbarung or WSV) for the ambulatory sector in Bavaria (the federal state of Germany) was developed for this purpose.
View Article and Find Full Text PDFFocus group-supported development and psychometric exploration of an instrument to assess perceived physical exertion in nursing students.
BMC Nurs
December 2024
Department of Sport Science and Sport, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Background: The physical demands of nurses during their work and education are high. In addition, shortage in nursing staff increases the individual workload. However, an appropriate tool to measure perceived physical exertion in nursing students is missing.
View Article and Find Full Text PDFHarnessing multimodal approaches for depression detection using large language models and facial expressions.
Npj Ment Health Res
December 2024
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany.
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model.
View Article and Find Full Text PDFIn conversation with Alexander Wlodawer.
FEBS J
December 2024
Federation of European Biochemical Societies, The FEBS Journal, Cambridge, UK.
Alexander Wlodawer is a structural biologist who has made seminal contributions to our understanding of protein structure-function relationships. He obtained his PhD from the University of California, Los Angeles, and has spent the majority of his career at the National Cancer Institute in Frederick, Maryland, where he currently holds a Senior Investigator position at the NCI's Center for Structural Biology. He has been a member of the Editorial Board of The FEBS Journal since 2007.
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