Healthy children, Strong families: American Indian Communities Preventing Obesity

描述（由申请人提供）：美国印第安人（AI）儿童的肥胖率是美国任何种族或族裔群体中最高的。一旦建立，早期肥胖会持续到晚年，并大大增加未来患心血管疾病和2型糖尿病等慢性疾病的风险。健康的儿童，强大的家庭（HCSF）是一个以社区为基础的，多模式的，儿童早期干预，解决日益严重的AI儿童肥胖问题。HCSF直接让学龄前AI儿童的父母/主要照顾者参与改变家庭健康生活方式。一项最初的小型HCSF试验显示，在超重/肥胖AI儿童中，有希望降低成人BMI和儿童BMI z评分，增加成人/儿童水果/蔬菜摄入量，减少电视/屏幕时间，并增加成人对健康行为改变的自我效能。拟议的研究将使用基于社区的参与性研究方法来加强干预，然后在全国6个不同的农村和城市AI社区进行为期2年的HCSF与对照（儿童安全干预）的双臂交错招募随机试验。将在0、6、12、18和24个月时测量干预结果。主要结果是成人BMI和儿童BMI Z评分。次要结果包括成人/儿童腰围，水果/蔬菜和添加糖的摄入量，电视/屏幕时间，活动，睡眠，家庭环境变化，成人心理社会因素（压力/抑郁）和社交网络的使用。社区将与调查人员合作，加强先前测试的HCSF干预，包括新的交付方法（邮寄课程包，手机辅导/社交网络）和两个研究较少的肥胖决定因素（睡眠和压力），以增加影响和可持续性。这项研究解决了肥胖预防研究中的四个关键空白：学龄前家庭，社交网络支持，压力和睡眠。 目标1将测试HCSF干预在预防和减少AI父母/主要照顾者及其12个月和24个月的儿童（2-5岁）肥胖方面的有效性。目标2将确定HCSF对肥胖和健康行为影响的关键介质，然后将其用于未来以家庭为基础的干预设计。目标3将评估CBPR过程对六个不同人工智能社区的实施和地方干预可持续性的影响。 这项研究的短期目标是为AI家庭开发一种成功的肥胖干预措施， 而且很容易复制长期目标是通过将HCSF纳入AI健康计划来传播干预措施，以确保当地的可持续性和全国性的实用性。在公共卫生影响方面，如果这些假设得到证实，HSCF将成为第一个基于证据的，适应文化的，健康的生活方式干预措施，适用于有幼儿的AI家庭，以解决肥胖流行病。如果成功，它将帮助社区预防和减少肥胖，从而影响城市和农村AI社区的高慢性病率和不断增长的慢性病率。

DESCRIPTION (provided by applicant): Obesity rates among American Indian (AI) children are the highest of any race or ethnic group in the United States. Once established, early obesity persists into later life and greatly increases risk of future chronic diseases such as cardiovascular disease and type 2 diabetes. Healthy Children, Strong Families (HCSF) is a community-based, multimodal, early childhood intervention which addresses the growing problem of AI childhood obesity. HCSF directly involves parents/ primary caregivers of preschool age AI children in making family based healthy lifestyle changes. An initial small HCSF trial showed promise in reducing adult BMI and child BMI z-score in overweight/obese AI children, increasing adult/child fruit/vegetable intake, decreasing TV/screen time, and increasing adult self-efficacy for healthy behavior change. The proposed study will use community-based participatory research methods to enhance the intervention and then conduct a 2-arm staggered-enrollment randomized trial of HCSF vs. control (child safety intervention) in a 2-year design in 6 diverse rural and urban AI communities nationally. Intervention outcomes will be measured at 0, 6, 12, 18, and 24 months. Primary outcomes are adult BMI and child BMI Z-score. Secondary outcomes include adult/child waist circumference, fruit/vegetable and added sugar intake, TV/screen time, activity, sleep, home environment changes, and adult psychosocial factors (stress/depression) and use of social networking. Communities will work with investigators to enhance the previously tested HCSF intervention to include novel methods of delivery (mailed lesson kits with cell phone coaching/ social networking) and two less-studied obesity determinants (sleep and stress) to increase impact and sustainability. The research addresses four key gaps in obesity prevention studies: pre-school age families, social networking support, stress and sleep. Aim 1 will test the effectiveness of the HCSF intervention in preventing and reducing obesity among AI parents/primary caregiver(s) and their children (ages 2-5 years) at 12 and 24 months. Aim 2 will identify key mediators of the effect of HCSF on obesity and health behaviors, which may then be utilized in focusing future family-based intervention design. Aim 3 will evaluate the impact of the CBPR process on implementation and local intervention sustainability in each of the six diverse AI communities. The short term goal for this research is to develop a successful obesity intervention for AI families that is practical and easily replicated. The long-term goal is to disseminate the intervention by incorporating HCSF into AI health programs to ensure local sustainability and utility nationally. In terms of public health impact, if the hypotheses are proven, HSCF will be the first evidence-based, culturally adapted, healthy lifestyles intervention for AI families with young children that addresses the obesity epidemic. If successful, it will assist communities in preventing and reducing obesity, and thereby, impact the high and increasing rates of chronic disease in both urban and rural AI communities.