APP-ME: Addressing Place & People MicroEnvironments in weight loss disparities

 描述（由申请人提供）：多中心随机对照试验表明，每减轻2磅体重，3年高血压风险降低20%，3年2型糖尿病风险降低32%。这相当于与戒烟相关的中风风险降低。不幸的是，在追溯到1990年的干预试验中，黑人女性的体重减轻比白色女性少50%。因此，生活方式减肥干预实际上可能会增加发病率的种族差异;黑人妇女死亡的所有5个主要原因都与肥胖有关。城市贫民的肥胖症流行率很高，对城市贫民的肥胖症研究是国际移民组织和卫生与人类健康部的优先事项。从2006年开始，我们与美国第三大安全网健康系统合作，在其8个联邦合格的健康中心实施生活方式减肥干预。该计划也导致黑人女性的体重减轻比白色女性少50%。我们也有一个正在进行的减肥随机对照试验，测试将干预措施送到城市穷人家中的影响。目前还没有结果，但它也是一种以人为中心的生活方式干预。为了改善减肥效果，我们成功地试行了一种新方法来评估与体重相关行为（包括瞬间社交互动和情绪）相一致的微环境。文献非常强调社会微环境在肥胖和减肥差异中的作用。人们往往没有意识到微环境对他们行为的影响。例如，实验表明，当我们和别人一起吃饭时，我们会多消耗70%的卡路里，而且我们基本上没有意识到我们已经这样做了。先前的研究表明，黑人女性的社交网络对减肥的支持程度低于白色女性。这样的社交网络可能会导致更多的肥胖、食物、饮食、久坐不动的活动，以及对这些的接受。事实上，最近一项长达一年的研究对第一年宿舍居民之间的社会互动进行了客观评估，发现与体重增加的其他人的互动次数占一年体重增加方差的三分之一。我们在拟议的研究中寻求评估、比较和测试300名城市贫困黑人和白色妇女的社会和身体微环境以及情绪对不活动和饮食的影响。我们的目标1包括通过GPS，咬计数器和加速度计收集新的数据，以获得位置和行为数据，以及生态瞬时评估和白天重建方法的混合，以获得社会互动和情感数据。在我们的Aim 2随机试验中，这些数据被用来检验关于微环境和体重变化的假设。每个人的目标1数据被用来测试在每个女性的数据表明她容易受到饮食或久坐活动影响的时间和地点，与健康相关的词语和图像形式的信息是否会影响体重变化。这项工作将为下一代减肥计划提供信息，并促进我们对健康和健康差异中自动行为的理解。

 DESCRIPTION (provided by applicant): Multi-site RCTs have shown that every 2lbs of weight loss is associated with a 20% reduction in the 3yr risk of hypertension and a 32% reduction in the 3yr risk of type 2 diabetes. This is equivalent to the stroke risk reduction associated with smoking cessation. Unfortunately, in intervention trials dating back to 1990, black women have experienced 50% less weight loss than white women. Thus, lifestyle weight loss interventions may actually increase racial disparities in morbidity; all 5 leading causes of death among black women are obesity-related. Obesity prevalence among the urban poor is very high and obesity research in the urban poor is an IOM and DHHS priority. Starting in 2006, we worked with the 3rd largest safety-net health system in the U.S. to implement a lifestyle weight loss intervention that operates in its 8 Federally Qualified Health Centers. This program too has resulted in 50% less weight loss in black relative to white women. We also have an ongoing weight loss RCT testing the impact of delivering the intervention into the homes of the urban poor. No outcomes are available yet but it too is a person-centric lifestyle intervention. With a goal of improving weight loss outcomes, we successfully piloted a novel approach to assessing micro- environments that coincide with weight-related behaviors including momentary social interactions and emotions. Literature places a very strong emphasis on the role of social micro-environments in obesity and weight loss disparities. Individuals are often unaware of the influence of micro-environments on their behavior. For example, experiments have shown that we consume up to 70% more calories when eating with others and that we are largely unaware we have done so. Previous research suggests social networks of black women are less supportive of weight loss than are those of white women. Such social networks may lead to more exposures to obesity, food, eating, sedentary activities, and acceptance of these. In fact, a recent year-long study that captured objective assessments of social interactions among first year dormitory residents found the number of interactions with weight gaining others accounted for 1/3rd of the variance in one-year weight gain. We seek in the proposed study to assess, compare, and test the influence of social and physical micro- environments and emotions on inactivity and eating in 300 urban poor black and white women. Our Aim 1 involves novel data collection via GPS, Bite Counters, and accelerometers to obtain location and behavior data and a hybrid of ecological momentary assessment and the day reconstruction method to obtain social interaction and emotion data. In our Aim 2 randomized trial, these data are used to test hypotheses about micro-environments and weight change. Each individual's Aim 1 data are used to test whether messages in the form of health-related words and images at the time and location each woman's data indicate she is vulnerable to eating or sedentary activity affect weight change. This work will inform next-generation weight loss programs and advance our understanding of automatic behaviors in health and health disparities.