Purpose: To evaluate the effects of sporting activities, training loads, and athletes' characteristics on sleep among high-level adolescent athletes, in a controlled training and academic environment.
Methods: A total of 128 high-level adolescent athletes (age = 15.2 [2.0] y), across 9 different sports, completed common sleep questionnaires and were monitored daily (7.3 [2.7] d) during a typical in-season training period. Sleep was analyzed using actigraphy and sleep diaries, whereas training load was evaluated using the session rating of perceived exertion, and muscle soreness and general fatigue were reported with the aid of visual analog scales. Separate linear mixed-effects models were fitted, including the athlete as a random effect and the following variables as fixed effects: the sport practiced (categorical predictor), daily training load, age, and sex. Different models were used to compare sleep variables among sports and to assess the influence of training load, age, and sex.
Results: The mean total sleep time was 7.1 (0.7) hours. Swimmers presented increased sleep fragmentation, training loads, perceived muscle soreness, and general fatigue compared with athletes who engaged in other sports. Independent of any sport-specific effects, a higher daily training load induced an earlier bedtime and reduced total sleep time and perceived sleep quality, with higher sleep fragmentation. Moreover, female athletes experienced increased total sleep time and worse sleep quality in response to stress compared with those in males.
Conclusion: In a controlled training and academic environment, high-level adolescent athletes did not achieve the recommended sleep duration. Impaired sleep quality and quantity could be partially explained by increased training loads.
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http://dx.doi.org/10.1123/ijspp.2020-0463 | DOI Listing |
Sensors (Basel)
December 2024
Department of Engineering and Industrial Design, Magdeburg-Stendal University of Applied Sciences, 39110 Magdeburg, Germany.
Inappropriate, excessive, or overly strenuous training of sport horses can result in long-term injury, including the premature cessation of a horse's sporting career. As a countermeasure, this study demonstrates the easy implementation of a biomechanical load monitoring system consisting of five commercial, multi-purpose inertial sensor units non-invasively attached to the horse's distal limbs and trunk. From the data obtained, specific parameters for evaluating gait and limb loads are derived, providing the basis for objective exercise load management and successful injury prevention.
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December 2024
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP.
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December 2024
School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation.
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December 2024
Institute of Railway Research, University of Huddersfield, Huddersfield HD1 3DH, UK.
Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS).
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December 2024
Fundación Centro Tecnológico CTC-Scientific and Technological Park of Cantabria (PCTCAN), Street Isabel Torres Nº 1, 39011 Santander, Spain.
This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally.
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