Obesity Prevention in Postpartum Women at High-Risk of Cardiovascular Disease

项目摘要 肥胖是心血管（CV）疾病的重要风险因素，影响美国39%的成年人。 肥胖率在妇女中不成比例地上升，并导致糖尿病和 55岁以下女性的CV死亡率。怀孕代表着一个生命的过渡，当许多妇女 体重增加。肥胖的影响在患有先兆子痫的女性中进一步放大，这是一个独立的， 未被充分认识的未来CV疾病的风险因素。迫切需要更好地了解 过度产后体重保留，以提供有效和可扩展的减肥干预措施 在这段时期，处于最高风险的妇女可能特别容易接受生活方式的改变。 社会激励或影响基于关系的行为改变的影响很强 健康行为的动机。游戏设计元素的使用，如目标设定，已经成功地 与社会激励措施相结合，以提高其他疾病环境中的健康行为。核心假设 这项建议的一个重要方面是，使用这些新方法的行为干预，结合现有的 行为减肥计划，将提供一个有效的，可扩展的解决方案，以减少发病率 产后肥胖该提案将利用高级研究人员（Michal Elovitz博士和 Peter Groeneveld）和宾夕法尼亚大学在心血管医学、母胎医学、 和行为经济学。研究将在城市和种族多样化的人口中进行，因为这些 女性最有可能从降低CV风险的干预措施中获益。目标1将使用稳健的统计方法 和机器学习来创建一个临床预测工具，用于在现有的电子 健康记录数据库，其中包含社区一级数据。目标2将使用定性和混合方法， 利用患者反馈确定策略，以加强试点干预的设计。目标3将实施 2-在先兆子痫或妊娠期高血压综合征患者中实现体重减轻的单臂单中心随机临床试验 高血压参与在线行为减肥计划使用远程技术和社会 其他产后妇女的反馈。 这项建议将确定在生命早期阶段将从强化教育中受益最多的妇女。 生活方式的改变，并将测试和完善干预措施，能够传播到产后妇女 远程控制Lewey博士是一位接受过妇女健康和人口科学培训的普通心脏病专家， 妊娠相关心血管疾病的专业知识。她所建议的培训 先进的统计方法、定性分析和行为临床试验设计将使她能够 成为女性心血管健康领域的领导者。在这个项目结束时，她将能够 独立设计，目标和评估行为干预措施，以预防女性心脏病。的 建议的K23的结果将是计划中的R01级应用的宝贵试验工作。

PROJECT SUMMARY Obesity is a significant risk factor for cardiovascular (CV) disease that affects 39% of adults in the U.S. Obesity rates are disproportionately rising among women and contribute to increasing rates of diabetes and CV mortality among women younger than 55 years. Pregnancy represents a life transition when many women gain excess weight. The effect of obesity is further magnified in women with preeclampsia, an independent and underappreciated risk factor for future CV disease. There is a pressing need to better understand predictors of excessive postpartum weight retention in order to deliver effective and scalable weight loss interventions during a period when women at highest risk may be particularly receptive to lifestyle change. Social incentives, or the influences that impact behavior change based on relationships, are strong motivators of healthy behavior. The use of game design elements, such as goal-setting, has been successfully combined with social incentives to enhance healthy behavior in other disease settings. The central hypothesis of this proposal is that a behavioral intervention using these novel approaches, combined with an established behavioral weight loss program, will provide an effective, and scalable solution to reduce the incidence of postpartum obesity. This proposal will leverage mentorship of senior investigators (Drs. Michal Elovitz and Peter Groeneveld) and Penn’s mature research programs in cardiovascular medicine, maternal fetal medicine, and behavioral economics. Research will be conducted in an urban and racially diverse population since these women are most likely to benefit from interventions to reduce CV risk. Aim 1 will use robust statistical methods and machine learning to create a clinical prediction tool for postpartum weight retention in an existing electronic health record database enriched with neighborhood-level data. Aim 2 will use qualitative and mixed methods to identify strategies to enhance the design of a pilot intervention using patient feedback. Aim 3 will implement a 2-arm single site randomized clinical trial to achieve weight loss in women with preeclampsia or gestational hypertension participating in an online behavioral weight loss program using remote technology and social feedback from other postpartum women. This proposal will identify women at an early stage in life who will benefit the most from intensive lifestyle changes and will test and refine interventions that are able to be disseminated to postpartum women remotely. Dr. Lewey is a general cardiologist trained in women’s health and population science with established expertise in pregnancy associated cardiovascular disorders. The training she proposes in advanced statistical methods, qualitative analysis, and behavioral clinical trial design will position her to become a leader in women’s cardiovascular health. By the conclusion of this program, she will be able to independently design, target, and evaluate behavioral interventions to prevent heart disease in women. The results of the proposed K23 will be invaluable pilot work for a planned R01-level application.