Background: This study aims to analyze the changes of approximately 1 month in fatigue, sleep, and mood in athletes after returning to training following infection with the COVID-19 Omicron strain and provide recommendations for returning to training after infection.
Methods: Two hundred and thirty professional athletes who had returned to training after being infected with COVID-19 in December 2022 were recruited to participate in three tests conducted from early January 2023. The second test was completed approximately 1 week after the first, and the third was completed about 2 weeks after the second. Each test consisted of completing scales and the exercise-induced fatigue measure. The scales included a visual analog scale, the Athens Insomnia Scale for non-clinical application, and the Depression-Anxiety-Stress scale. The exercise task was a six-minute stair climb test, and athletes evaluated subjective fatigue levels before and after exercise using another Visual Analog Scale and the Karolinska Sleepiness Scale.
Results: After returning to training, athletes' physical fatigue decreased initially but increased as training progressed. Cognitive fatigue did not change significantly. The exercise task led to elevated levels of physical fatigue after a longer duration of training. Sleep quality problems decreased rapidly after the start of training but remained stable with prolonged training. Depression levels continued to decline, while anxiety levels only reduced after a longer duration of training. Stress levels decreased rapidly after the start of training but did not change with prolonged training.
Conclusion: Athletes who return to training after recovering from COVID-19 experience positive effects on their fatigue, sleep, and mood. It is important to prioritize anxiety assessment and interventions during the short period after returning and to continue monitoring fatigue levels and implementing recovery interventions over a longer period of time.
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http://dx.doi.org/10.7717/peerj.15580 | DOI Listing |
Cogn Emot
January 2025
Equipe de Recherche Contextes et Acteurs de l'Education (ERCAé), Université d'Orléans, Orléans, France.
Recent research has revealed the widespread effects of emotion on cognitive functions and memory. However, the influence of emotional valence on verbal short-term memory remains largely unexplored, especially in children. This study measured the effect of emotional valence on word immediate serial recall in 4-6-year-old French children ( = 124).
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Center for Community-Engaged Artificial Intelligence, School of Science & Engineering, Tulane University, New Orleans, LA, United States.
There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. Public health practitioners and researchers have historically innovated in areas like vaccination and sanitation but have been slower in adopting emerging technologies such as generative AI. However, with increasingly complex funding, programming, and research requirements, the field now faces a pivotal moment to enhance its agility and responsiveness to evolving health challenges.
View Article and Find Full Text PDFATS Sch
January 2025
Critical Care Medicine Department, National Institutes of Health, Bethesda, Maryland.
Rapid accumulation of knowledge and skills by trainees in the intensive care unit assumes prior mastery of clinically relevant core physiology concepts. However, for many fellows, their foundational physiology knowledge was acquired years earlier during their preclinical medical curricula and variably reinforced during the remainder of their undergraduate and graduate medical training. We sought to assess the retention of clinically relevant pulmonary physiology knowledge among pulmonary and critical care medicine (PCCM) and critical care medicine (CCM) fellows.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.
View Article and Find Full Text PDFJ Palliat Med
January 2025
Pain and Palliative Care, Medical Superspeciality Hospital, Kolkata, India.
Acute leukemia (AL) affects patients' well-being and inflicts substantial symptom burden. We evaluated palliative care needs and symptom burden in adult patients with AL from diagnosis through fourth week of induction chemotherapy. Newly diagnosed adult patients with AL scheduled for curative-intent treatments, prospectively completed Functional Assessment of Cancer Therapy-Leukemia questionnaire at diagnosis and postinduction therapy.
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