Purpose: To assess relationships between objective sleep characteristics, external training loads, and subjective ratings of well-being in elite Australian football (AF) players.
Methods: A total of 38 elite male AF players recorded objective sleep characteristics over a 15-day period using an activity monitor. External load was assessed during main field sessions, and ratings of well-being were provided each morning. Canonical correlation analysis was used to create canonical dimensions for each variable set (sleep, well-being, and external load). Relationships between dimensions representing sleep, external load, and well-being were quantified using Pearson r.
Results: Canonical correlations were moderate between pretraining sleep and external training load (r = .32-.49), pretraining sleep and well-being (r = .32), and well-being and posttraining sleep (r = .36). Moderate to strong correlations were observed between dimensions representing external training load and posttraining sleep (r = .31-.67), and well-being and external training load (r = .32-.67). Player load and Player load 2D (PL2D) showed the greatest association to pretraining and posttraining objective sleep characteristics and well-being. Fragmented sleep was associated with players completing the following training with a higher PL2D.
Conclusions: Maximum speed, player load, and PL2D were the common associations between objective sleep characteristics and well-being in AF players. Improving pretraining sleep quality and quantity may have a positive impact on AF players' well-being and movement strategy during field sessions. Following training sessions that have high maximum speed and PL2D, the increased requirement for sleep should be considered by ensuring that subsequent sessions do not start earlier than required.
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http://dx.doi.org/10.1123/ijspp.2019-0061 | DOI Listing |
Abdom Radiol (NY)
January 2025
Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China.
Background: To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer.
Methods: This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set.
J Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.
View Article and Find Full Text PDFBiol Psychiatry Cogn Neurosci Neuroimaging
January 2025
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Brain and Cognitive Science at the McGovern Institute for Brain Research at Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Psychology, Northeastern University. Electronic address:
The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, goal-directed cognition, self-projection, and theory of mind. Over recent years, there has been a surge in examining its functional connectivity, particularly its relationship with frontoparietal networks (FPN) involved in top-down attention, executive function, and cognitive control. The fluidity in switching between these internal and external modes of processing-highlighted by anti-correlated functional connectivity-has been proposed as an indicator of cognitive health.
View Article and Find Full Text PDFMed Image Anal
January 2025
Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
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