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The Longitudinal Study of the Environmental Determinants of Children Obesity: Analysis by the Ecological Model
|Abstract:||國內兒童肥胖的比例已經超過25%，高於亞洲各國。學者認為調控社區及學校的飲食與身體活動環境是最有效的兒童肥胖防治策略，但是要改善那些環境卻沒有一致的看法。 擬以生態理論模式以學校為單位，用地理資訊系統收集學校1.6公里半徑可步行範圍內，影響飲食與身體活動的社區及校內環境資料。追蹤學齡早期兒童之肥胖比例。用SPSS 統計軟體以集群分析將學區環境分成不同型態；運用邏輯斯回歸分析預測肥胖比率較高學校之最佳環境型態；並以多層回歸模式分析環境變遷對不同世代學生身體質量指數的預測力。研究目的如下： 第一年：物理環境對兒童肥胖比例之影響分析。 1. 建構全國2664所小學附近社區可能影響小學生肥胖比例之地理資訊。 2. 分析學校內之物理環境對小學生肥胖比例的影響。 3. 建構影響小學生肥胖比例之最佳物理環境模式。 第二年：時空變遷對兒童肥胖比例之影響分析。 1. 分析物理環境對不同世代小學生肥胖比例的影響。 2. 分析物理環境變遷對小學生肥胖比例的影響。 第三年：多元環境對兒童肥胖比例之影響分析。 1. 綜合分析經濟、政策、社會文化、物理環境對小學生肥胖比例的影響。 2. 分析物理環境對不同世代小學生中期肥胖比例的影響。 3. 分析物理環境變遷對小學生中期肥胖比例的影響。|
The prevalence of childhood obesity of Taiwan is over 25%. Health providers perceived that changing the food and physical-activity environments in communities and schools would be the most effective way to support their clinical obesity-prevention efforts. The previous studies of the environment and childhood obesity identified inconsistencies in measurements, and noted that these studies rarely studied both diet and physical activity. An ecological framework which is transdisciplinary and multilevel by nature is recognized as the most promising approach for studying this problem. The purpose of this project is to perform a longitudinal research using an ecological framework to study the etiology of childhood obesity. Contextual factors are assessed in a childhood cohort including school and community data collected within a radius of 1 mile to follow the trend of the percentage of obesity rate by the definition of the International Obesity Task Force. We will use the SPSS 11.0 for statistical analysis. Cluster analysis will be used to classify the pattern of environment. Logistic regression will be used to figure out the best model of prediction the highest quarter of percentage of obesity. We will use the multilevel model to analyze the longitudinal data to explore the relationship of a set of independent variables with the growth trajectory of the dependent variable, BMI. A growth curve model captures two aspects of the data: (1) the starting point and the trajectory followed by the response variable, which is measured repeatedly on each individual (within-subject growth); and (2) the extent to which both starting points and trajectories vary separately and simultaneously as a function of other independent variables that are used to differentiate individual growth (between-subject). In this study, a three-level hierarchical model will be used. Repeated observations of each individual child are considered nested within a child. The objectives are: The first year: To analyze the relationship of physical environment and children obesity. 1. Establish the geographic information of neighborhood environments around 2664 elementary schools. 2. Collect the physical environmental determinants of obesity students in 2664 elementary schools. 3. Figure out the best predicting model of physical environments for higher obesity prevalence of school. The second year: To analyze the children obesity rate affected by the transition of physical environment. 1. Analyze the effects on school children obesity rate by the physical environment in different cohorts. 2. Analyze the effects on the children obesity rate by the transition of physical environment. The third year: To analyze the effects on children obesity rate by multiple environmental factors. 1. Comprehensively analyze the environmental determinants of physical, ecological, policy, social/culture factors of the obesity rate of the elementary schools. 2. Analyze the midterm effects on obesity rate by schools and physical environment in different cohorts. 3. Analyze the midterm effects on the children obesity rate and by the transition of physical environment.
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