This paper presents a method for the detection of wakeful state, rapid eye movement sleep (REM), light sleep (N1&N2) and deep sleep (N3&N4) based on cardiorespiratory parameters. Experiments were conducted with data of 625 subjects without sleep-disordered breathing selected from the SHHS dataset. Compared to previous studies, our method considers results of neighboring epochs classification and epoch position over record time. The method demonstrates Cohen's kappa of 0.57 ± 0.13 and the accuracy of 71.4 ± 8.6 %. The results might contribute to the development of screening tools for diagnostics, prevention, and management of sleep disorders.
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http://dx.doi.org/10.1109/EMBC.2016.7591477 | DOI Listing |
J Med Internet Res
January 2025
Department of Rehabilitation Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Sarcopenia is closely associated with a poor quality of life and mortality, and its prevention and treatment represent a critical area of research. Resistance training is an effective treatment for older adults with sarcopenia. However, they often face challenges when receiving traditional rehabilitation treatments at hospitals.
View Article and Find Full Text PDFSports Med Open
January 2025
GALENO Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cadiz, Avenida República Saharaui s/n, 11519, Puerto Real, Cádiz, Spain.
Background: While there is evidence on the validity and reliability of field-based physical fitness tests in children, adolescents and adults, there is limited evidence to provide feasibility and safety data on the application and performance of the existing field-based physical fitness tests.
Objectives: (i) To examine the feasibility and safety of existing field-based physical fitness tests used in people of all ages and (ii) to establish a comprehensive view of criterion-related validity, reliability, feasibility and safety based on scientific evidence for the existing field-based physical fitness tests in adults.
Methods: The search was conducted through the electronic databases MEDLINE (via PubMed) and Web of Science (all databases) for published studies from inception to 31 January 2023.
Brain Sci
December 2024
Sport and Human Movement Science Research Group (SaHMS), Department of Sport Science, Nord University, 7600 Levanger, Norway.
Background/objectives: High-intensity interval training (HIIT) alternates short periods of intense exercise with recovery, effectively enhancing cardiorespiratory fitness, endurance, and strength in various populations. Concurrently, brain-derived neurotrophic factor (BDNF) supports neuronal resilience and activity-dependent plasticity, which are vital for learning and memory. This study aims to systematically review changes in BDNF levels in response to HIIT, with three primary objectives: evaluating the benefits of HIIT for BDNF modulation, assessing methodological quality and the risk of bias in reviewed studies, and identifying patterns in BDNF response based on HIIT protocols and population characteristics.
View Article and Find Full Text PDFBMJ Mil Health
January 2025
Academic Department of Military Medicine, Royal Centre for Defence Medicine, Birmingham, UK
Introduction: Abnormal cardiorespiratory symptoms and investigative findings in service personnel typically result in prolonged investigation and occupational restriction. This analysis aimed to assess the impact of the xford ilitary Cardiopulmonary xercise Testing linic (OMEC), which investigates such symptoms and findings, on occupational recommendations.
Methods: A service evaluation was conducted on all OMEC attendances over a 5-year period.
Shock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
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