Predicting Energy Balance in Midlife Women

描述(申请人提供)：这项研究的目的是测试分段分析预测中年女性能量平衡的可行性。超重和肥胖是女性中年患2型糖尿病的主要危险因素。对于女性来说，这是一个关键时期，因为在不考虑能量平衡的情况下，随着基础代谢率随着年龄的增长而下降，存在与年龄相关的体重增加的可能性。没有高度预测性的理论方法来预测能量平衡，这些方法充分解决了社会认知因素和环境背景下的情景因素之间的相互作用。基于不同饮食情况(情况/环境)的态度因素(社会认知)的细分分析可用于将一大群不同类型的中年妇女细分为较小的同质部分，以便进行有效的量身定做的干预。该项目的目标是：1)使用大声思考和焦点小组数据创建两个测量工具(态度问卷和进餐时间日记)，2)使用基于这些工具收集的数据的细分分析，以确定不同类别的女性，这些态度由社会认知因素(态度细分)和不同的饮食情况(需求状态)决定，这些态度由构成食物选择的理性和情境/情感需求驱动，3)测量能量平衡(饮食记录、活动监测和间接热量测量)，以及4)使用多元回归分析来确定不同国家的态度细分需求是否可以预测能量平衡。基于该矩阵的分段是更好地理解能量平衡的一种创新策略。这些结果可用于开发与特定女性群体高度相关的量身定制的干预措施，从而维持能量平衡，从而防止超重和与之相关的2型糖尿病风险。

DESCRIPTION (provided by applicant): The purpose of this study is to test the feasibility of segmentation analyses to predict energy balance in midlife women. Overweight and obesity are major risk factors for the development of type 2 diabetes in women in midlife. This is a critical time period for women because without regard for energy balance, there is the potential for age-related weight gain as basal metabolic rate decreases with age. Highly predictive theoretical approaches are not available for the prediction of energy balance that adequately address the interplay between social cognitive factors and situational factors in the context of the environment. Segmentation analyses based on attitudinal factors (social cognitive) across eating situations (situation/environment) can be used to subdivide a large heterogeneous group of midlife women into smaller homogeneous segments that allow for effective tailored interventions. Project aims are to: 1) use think aloud and focus group data to create two measurement instruments (attitudinal questionnaire and eating occasions diary), 2) use segmentation analyses based on data collected with these instruments to identify distinct segments of women based on their prevailing attitudes as determined by social cognitive factors (Attitudes Segments) and distinct eating situations (Needs States) driven by the rational and situational/emotional needs that underlie food choices, 3) measure energy balance (diet records, activity monitor and indirect calorimetry), and 4) use multiple regression analyses to determine whether segmentation by Attitudes Segments across Needs States is predictive of energy balance. Segmentation based on this matrix is an innovative strategy to better understand energy balance. The results can be used to develop tailored interventions that are highly relevant to specific segments of women resulting in maintenance of energy balance, thereby preventing overweight and associated risk for type 2 diabetes.