Introduction: In the United States, sport is a common form of youth physical activity (PA) with demonstrated health benefits. However, limited longitudinal dataexists on the psychosocial determinants and consequences of youth sport participation. This study examined grade 6 (11-12-year-old) predictors of high school organized sport participation and effects of high school sport participation on age 26 behavior, mental health and wellbeing.
Methods: Structural equation models tested relationships using the National Institute for Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD). Half of the sample was male, and played organized sports at ages 15 and 18. Eighty percent of the sample was white.
Results: Grade 6 predictors of playing high school sport were: child enjoys PA, parent enjoys PA, parent feels physical education (PE) is important, and vigorous PA minutes/week. Playing sports at ages 15 and 18 was associated with better wellbeing, lower depression, increased sport and fitness activity participation. Enjoyment of PA was directly associated with fitness activities at age 26, more than a decade later. High school sport participation at both age 15 and 18 further mediated relationships between enjoyment with wellbeing and depression at age 26.
Discussion: Sport participation is a common accessible means of PA, and participating in sports in high school is associated with better mental health and PA outcomes at age 26. Fostering enjoyment of PA during childhood helps shape PA in early adulthood and adult mental health benefits derived from high school sport participation.
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http://dx.doi.org/10.1080/07853890.2024.2447905 | DOI Listing |
Am J Sports Med
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Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
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Mol Plant
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National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572000, China; State Key Laboratory of Crop Gene Resources and Breeding/ Key laboratory Grain Crop Genetic Resources Evaluation and Utlization Ministry of Agriculture and Rural Affairs, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Yazhouwan National Laboratory, Sanya 572000, China. Electronic address:
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National Forestry and Grassland Administration Engineering Research Centre for Southwest Forest and Grassland Fire Ecological Prevention, College of Forestry, Sichuan Agricultural University, Chengdu, China.
Leaf dry matter content (LDMC) is an important determinant of plant flammability. Investigating global patterns of LDMC could provide insights into worldwide plant flammability patterns, informing wildfire management. We characterised global patterns of LDMC across 4074 species from 216 families, revealing that phylogenetic and environmental constraints influence LDMC.
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Information Materials and Intelligent Sensing Laboratory of Anhui Province, Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiu Long Road, Hefei, 230601, China.
Unipolar barrier architecture is designed to enhance the photodetector's sensitivity by inducing highly asymmetrical barriers, a higher barrier for blocking majority carriers to depressing dark current, and a low minority carrier barrier without impeding the photocurrent flow through the channel. Depressed dark current without block photocurrent is highly desired for uncooled Long-wave infrared (LWIR) photodetection, which can enhance the sensitivity of the photodetector. Here, an excellent unipolar barrier photodetector based on multi-layer (ML) graphene (G) is developed, WSe, and PtSe (G-WSe-PtSe) van der Waals (vdW) heterostructure, in which extremely low dark current of 1.
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College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
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