Purpose: The aims of this study were twofold: 1) to identify latent physical performance profiles of motor competence (MC) and cardiorespiratory (CF) and muscular fitness (MF) among school-age children and 2) explore transition probabilities in physical performance profiles over a 2-yr period.
Methods: The present sample comprised 1148 (583 girls, 565 boys) elementary school students (baseline Mage = 11.27 ± 0.32), and data were collected annually (equal intervals) over a period of 2 yr which resulted in a total of three measurements. The measures used were the throwing-catching combination test, 5-leaps and two-legged jumps from side-to-side test (MC), 20-meter shuttle run test (CF), and curl-up and push-up tests (MF). Latent transition analysis was used to identify and track physical performance profiles derived from the measurements of MC, CF, and MF scores.
Results: The key findings were: 1) three physical performance profiles were identified: (a) low (28% of the sample; lowest level in each category), (b) moderate (43% of the sample; higher MC, CF, and MF than low), (c) high (29% of the sample; highest MF); 2) the number of physical performance profiles and probability to belong to a given profile were stable across time; 3) the highest transition probability was found in the high group, where some students had transitioned to the moderate group at T2; and 4) girls were most likely to belong to the low group.
Conclusions: Results demonstrated that children's physical performance profiles are stable from late childhood to early adolescence. This study suggests that the early elementary school years are essential for the development of children's MC and health-related fitness.
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http://dx.doi.org/10.1249/MSS.0000000000002746 | DOI Listing |
Sci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
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January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
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January 2025
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA.
Wearable and implantable bioelectronics that can interface for extended periods with highly mobile organs and tissues across a broad pH range would be useful for various applications in basic biomedical research and clinical medicine. The encapsulation of these systems, however, presents a major challenge, as such devices require superior barrier performance against water and ion penetration in challenging pH environments while also maintaining flexibility and stretchability to match the physical properties of the surrounding tissue. Current encapsulation materials are often limited to near-neutral pH conditions, restricting their application range.
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January 2025
School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
The high performance of two-dimensional (2D) channel membranes is generally achieved by preparing ultrathin or forming short channels with less tortuous transport through self-assembly of small flakes, demonstrating potential for highly efficient water desalination and purification, gas and ion separation, and organic solvent waste treatment. Here, we report the construction of vertical channels in graphene oxide (GO) membrane based on a substrate template with asymmetric pores. The membranes achieved water permeance of 2647 L m h bar while still maintaining an ultrahigh rejection rate of 99.
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January 2025
KAUST Solar Center (KSC), Physical and Engineering Division (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
The controlled growth of two-dimensional (2D) perovskite atop three-dimensional (3D) perovskite films reduces interfacial recombination and impedes ion migration, thus improving the performance and stability of perovskite solar cells (PSCs). Unfortunately, the random orientation of the spontaneously formed 2D phase atop the pre-deposited 3D perovskite film can deteriorate charge extraction owing to energetic disorder, limiting the maximum attainable efficiency and long-term stability of the PSCs. Here, we introduce a meta-amidinopyridine ligand and the solvent post-dripping step to generate a highly ordered 2D perovskite phase on the surface of a 3D perovskite film.
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