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Can Motor Competence And/or Physical Activity Predict Body Composition in Childhood

Sandra Nelson Kaiser

出版	University of Delaware, 2011
URL	http://books.google.com.hk/books?id=km1SnQAACAAJ&hl=&source=gbs_api

註釋

Introduction. The rate of obesity in childhood has risen at an alarming rate in the past two decades. Statistics show an estimated increase of obesity from 8% to 13% in children ages 2-19. Traditionally, researchers have focused on physical activity (PA) as one of the primary variables related to body composition. More recent works have cited motor competence (MC) as a possible variable related to body composition. A conceptual model created by Stodden et al. (2008) discusses the developmental relationship between PA, MC and body composition; citing possible major differences between children in early childhood (EC) and late childhood (LC). The purpose of our study is to a) create an equation to predict body composition for early childhood and late childhood based on motor competence and physical activity variables and b) to compare the new created models to the conceptual model by Stodden et al. (2008). It is hypothesized that regressions with PA as a main variable will be created for EC, while MC and PA will both be used as variables in the regressions, supporting the model by Stodden et al. (2008). Methods: 61 participants were recruited, including 30 EC participants (4.47yrs + .68) and 31 LC participants (11.61yrs + .50). Tests were completed for MC (the Test for Gross Motor Development) and PA (wearing an Actical accelerometer) and body mass index (BMI). Three variables from MC and five variables from PA measures went through a Pearson's or Spearman's rho correlation to determine which variables would be used for each age group. A stepwise regression with a backwards selection was used creating a formula for each group to predict BMI. Results: No regressions were created for the main EC and LC groups or LC-F. Regression equations were created for each of the following groups with excluded outliers: EC-M (BMI =11.462 + [.023*PercTModVig]), EC-F (BMI = 19.099 - [.009*AvgAC]) and LC-M (BMI =34.576 - [.129*Quotient]). Discussion: Specific aim I was partially met. Although not all groups created models, the results were generally in line with what was expected. More importantly, results showed that all EC groups (main EC, EC-F, and EC-M) suggested that PA was a stronger predictor of BMI, compared to MC. All LC groups (LC, LC-M, LC-F) suggested MC to be the stronger predictor of BMI when compared to PA. Specific aim II was also partially met. Although not completely aligned with the Stodden et al. (2008) conceptual model, the results showed a trend similar to the conceptual model. This suggests that future programs in schools and communities should investigate increasing MC-based programs alongside PA-based programs to provide the best opportunities for children throughout childhood.