Evaluation of a Comprehensive School Nutrition Enrichment Intervention (CSNEI) in Rural School Districts

摘要-项目1 在全国范围内，73.6%的成年人超重或肥胖。农村人口和低收入者 家庭肥胖率很高。城乡肥胖率的最大差距 各县都在美国南部。阿肯色州的农村人口比例位居第六(约41%) 肥胖率位居第三位(37.4%)。美国疾病控制与预防中心估计，美国2-19岁的儿童中有18.5%是 太胖了。阿肯色州的高中生肥胖率排名第三(22.1%)，排名第五 对于10-17岁的儿童(20.2%)。肥胖导致糖尿病发病率、癌症发病率和 农村社区的死亡率、心血管疾病发病率和死亡率。此应用程序寻求 资助首次在农村地区评估全面学校营养丰富干预(CSNEI) 学校。主要的研究问题是：与人口结构相似的学区相比， 实施CSNEI，CSNEI政策干预是否改善了肥胖预防结果 农村的K-12学生？“我们将进行配对整群随机试验，前测和重复后测。 在6个学区进行了测试，3个实施了CSNEI，3个匹配了对照学区。评估将会 包括6个学区的约11,500名学生：约5,750名来自CSNEP学区，约5,750名来自Match 比较学区。这项研究将探索种族/民族、性别、年龄、 和经济状况，以了解对肥胖风险最高的人群的影响。我们的具体目标是：目标 1.评估CSNEI对学生亲属(包括年龄和性别)BMI随时间变化的影响。 目的1.B评估CSNEI对#年提供的食物的营养质量的短期和长期影响 学校的饭菜。目标1.C评估CSNEI对学生消费的短期和长期影响 学校用餐中供应的食物。目标1.D评估CSNEI对学生的短期和长期影响 皮肤类胡萝卜素水平，作为水果和蔬菜摄入量的指标。目标2进行实施研究，以 记录在农村学校实施CSNEI的障碍和促进者。我们将完成第一个 在农村学校实施CSNEI研究，这将为确保成功提供关键信息 在学区实施类似的干预措施，将是加快翻译的创新步骤 将证据转化为广泛的实践。综合学校营养干预研究的现有范式 没有适当解决农村儿童的需要和潜在障碍，特别是那些生活在低收入家庭的儿童 家庭。这项研究将通过严格评估农村高中学校的CSNEI来填补证据上的关键空白 有资格享受免费和降价餐饮的学生比例(&gt；50%)；使用配对群 随机设计，比较BMI z得分随时间的变化；并纳入经济状况、年龄(in 几个月)、性别和种族/民族。

SUMMARY – PROJECT 1 Nationally, 73.6% of adults are overweight or obese. Rural populations and persons living in low-income households have high rates of obesity. The greatest disparities in obesity prevalence between urban and rural counties are in the southern United States. Arkansas has the sixth highest proportion of rural population (~41%) and the third highest obesity prevalence (37.4%). The CDC estimates 18.5% of children age 2-19 in the US are obese. Arkansas has the third highest rate of obesity for high school students (22.1%) and the fifth highest rate for children in ages 10-17 (20.2%). Obesity contributes to increased diabetes incidence, cancer incidence and mortality, and cardiovascular disease incidence and mortality among rural communities. This application seeks funding to evaluate a Comprehensive School Nutrition Enrichment Intervention (CSNEI) for the first time in rural schools. The primary research question is: “Compared with demographically similar school districts that did not implement the CSNEI, does the CSNEI policy intervention yield improved obesity prevention outcomes among rural K-12 students?” We will conduct a matched-pairs cluster randomized trial with pre-test and repeated post- tests in 6 school districts, 3 implementing CSNEI and 3 matched comparison school districts. The evaluation will include ~11,500 students in 6 school districts: ~5,750 from CSNEP school districts and ~5,750 from matched comparison school districts. The study will explore heterogeneity of treatment effects for race/ethnicity, sex, age, and economic status to understand effects on populations most at risk for obesity. Our specific aims are: Aim 1.A Evaluate the effects of a CSNEI on students' relative (accounting for age and sex) BMI change over time. Aim 1.B Evaluate the short-term and long-term effects of a CSNEI on the nutritional quality of food served in school meals. Aim 1.C Evaluate the short-term and long-term effects of a CSNEI on students' consumption of food served in school meals. Aim 1.D Evaluate the short-term and long-term effects of a CSNEI on students' skin carotenoid levels, as an indicator of fruit and vegetable intake. Aim 2 Conduct an implementation study to document barriers and facilitators of CSNEI implementation in rural schools. We will complete the first implementation study of a CSNEI in rural schools, which will provide critical information to ensure successful implementation of similar interventions in school districts and will be an innovative step to accelerate translation of evidence into broad practice. Existing paradigms for the study of comprehensive school nutrition interventions do not appropriately address needs and potential barriers of rural children, particularly those living in low-income households. This study will fill critical gaps in evidence by rigorously evaluating a CSNEI in rural schools with high proportions of students eligible for free and reduced price meals (>50%); employing a matched-pairs cluster randomized design to compare changes in BMI z-scores over time; and incorporating economic status, age (in months), sex, and race/ethnicity in the analyses.