Strat{é}gies de gestion de la chaleur et performances sportives de haut niveau: {é}clairage psychophysiologique et recommandations appliqu{é}es
Objectives: This article sheds light on the different heat stress management strategies, common or innovative, in order to analyze those that would be the most suitable and effective for athletes who have to compete in humid and/or hot environments.News: The Paris summer Olympics Games in 2024 will take place from July 26th to September 8th with a high risk for athletes to practice their sport at high temperatures, thereby imposing physiological (cardiovascular, ventilatory, thermoregulatory, etc.) and psychological (early mental fatigue, decreased motivation, discomfort, etc.) which can have a major negative impact on their performance.Prospects and projects: To perform in a hot environment, it is now recommended to use strategies, in particular active acclimatization which promotes physiological but also psychological adaptations. Similarly, fluid management and cooling techniques have potentially beneficial effects on physiological factors but their psychological consequences are still poorly understood and need to be investigated. Finally, mental strategies (goal setting, mental imagery, positive self-talk, music, etc.) or cognitive training in the heat can limit poor performance in this condition. The effects of combining physical and mental techniques, as well as innovative strategies such as cold suggestion, are also being investigated.Conclusion: For each strategies presented, the scientific work has enabled the development of practical recommendations for athletes, coaches and mental trainers in order to allow them to physiologically and psychologically anticipate the effects of high relative humidity and/or high temperature.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Learning to Generate Explanation from e-Hospital Services for Medical Suggestion
Explaining the reasoning of neural models has attracted attention in recent years. Providing highly-accessible and comprehensible explanations in natural language is useful for humans to understand model’s prediction res…
Explanation GenerationMulti-Task LearningA Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks
Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the…
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models
The performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tu…
Hyperparameter OptimizationBayesian Uncertainty Quantification for Anaerobic Digestion models
Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian proc…
Inductive BiasUncertainty QuantificationAI-Driven Scenarios for Urban Mobility: Quantifying the Role of ODE Models and Scenario Planning in Reducing Traffic Congestion
Urbanization and technological advancements are reshaping urban mobility, presenting both challenges and opportunities. This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic cong…
Autonomous VehiclesManagement