Connections among family socioeconomic status, aerobic fitness, executive function, and the positive experiences of childhood physical activity

被引:0
|
作者
Becker, Derek R. [1 ]
Pedonti, Sarah F. [1 ]
Grist, Cathy [1 ]
Watson, Myra [1 ]
机构
[1] Western Carolina Univ, Dept Human Serv, Cullowhee, NC 28723 USA
关键词
Family socioeconomic status; Positive childhood experiences; Structured and unstructured physical activities; Executive function; Aerobic fitness; CARDIORESPIRATORY FITNESS; ACADEMIC-ACHIEVEMENT; SELF-REGULATION; CARDIOVASCULAR-DISEASE; PRESCHOOL-CHILDREN; SEDENTARY TIME; BODY FATNESS; HEALTH; SPORT; ASSOCIATIONS;
D O I
10.1016/j.jecp.2024.106147
中图分类号
B844 [发展心理学(人类心理学)];
学科分类号
040202 ;
摘要
A family's socioeconomic status (SES) can be linked to a child's physical and cognitive health, with children from low-SES families often experiencing poor developmental outcomes. Early positive childhood experiences that include structured and unstructured physical activities (SUPAs) offer a potential avenue to promote positive health and cognitive development during early childhood. However, prior to school entry, it is not well-understood whether SES is related to participation in SUPAs or how SUPAs relate to early health and cognitive indicators such as aerobic fitness and executive function (EF). Children (N = 99) aged 3 to 5 years were recruited from 17 classrooms in seven center-based prekindergartens. In fall and spring, children were assessed on EF using the Head-Toes-Knees-Shoulders task and aerobic fitness was assessed with the 20-m shuttle run test. Family SES significantly predicted SUPAs and fall and spring fitness, with SUPAs and spring fitness significantly predicting spring EF. Partial support for an indirect relationship between SES and EF through SUPAs was also found. Results suggest that family SES could play a role in predicting participation in SUPAs and aerobic fitness, with SUPAs and aerobic fitness linked to EF during pre-kindergarten. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页数:15
相关论文
empty
未找到相关数据