Obesity Prevention in Early Life (OPEL): Risk Screening and Targeted Intervention

考虑到一旦出现肥胖就很难逆转，人们越来越关注初级肥胖 在生命周期早期预防肥胖。在第一个阶段，几乎没有人尝试过预防肥胖 生命中的岁月。这份提案总结了一项为期5年的职业发展指导计划，该计划与 多方法研究项目，旨在改善对婴儿和儿童的识别和潜在治疗 学步儿童肥胖风险高。我的长期目标是通过识别风险最高的婴儿来预防肥胖 并为他们提供有效的、以家庭为中心的干预，针对可改变的生命过程因素。我的 基于我之前的研究的中心假设是，在肥胖发作之前识别出有肥胖风险的婴儿 不健康的体重增加将使早期干预成为可能。我的研究计划是：(1)建立风险预测模型 通过连接三个现有的数据系统，这些系统结合了出生证明，上下文- 水平和健康结果数据；(2)测试前瞻性地将这些数据联系起来以验证目标1的可行性 当代临床队列中24个月期间的肥胖风险预测模型；以及(3)确定 以家庭为重点的风险沟通的最佳方法，以防止体重过度增加和 以人为中心的设计方法在婴幼儿肥胖中的应用。在我的职业发展过程中 在我导师的计划和指导下，我将在流行病学和儿科健康的基础上进行扩展 服务研究，以发展机器学习、健康信息学、数据集成、定性 方法、以人为本的设计和行为改变。总而言之，这一研究和教育目标 提案将为我作为独立人士竞争额外资金提供必要的基础 调查员。具体地说，我将在我的K01奖的第四年向NIDDK寻求R03级别的赠款资金，以测试 在目标3中开发的沟通策略，并与家庭合作，以修改现有的行为 改变婴儿期使用的干预措施。到这个奖项结束时，我就可以申请资金了 从NIDDK进行一项强大的R01级别研究，该研究结合了经过验证的预测模型，家族- 有针对性的沟通策略，以及修改的干预措施，以确定是否可以有效地预防 那些被认定为肥胖风险最高的婴儿和蹒跚学步的儿童。这方面的研究将会有所帮助 确保以高效、符合成本效益的方式部署预防工作，并为以下人员所接受 我需要他们。我将在儿童健康服务机构亚伦·E·卡罗尔博士的指导下完成这项工作 研究人员，以及拥有机器学习、健康信息学、数据等专业知识的多学科教员团队 整合和监督、定性研究、以人为中心的设计方法、行为改变和 儿童肥胖症。鉴于我过去的研究效率，目前我非常适合完成这项研究 指导团队、开放访问医疗保健数据以及已建立的临床决策支持基础架构 在印第安纳大学医学院。

Given the difficulty of reversing obesity once present, there has been increasing focus on the primary prevention of obesity early in the lifecourse. Few attempts have been made to prevent obesity during the first years of life. This proposal summarizes a 5-year program of mentored professional development tied to a multi-method research project intended to improve the identification and potential treatment of infants and toddlers with high risk for obesity. My long-term goal is to prevent obesity by identifying infants at greatest risk and providing for them an effective, family-centered intervention that targets modifiable, life course factors. My central hypothesis, based on my prior research, is that identifying infants at risk for obesity prior to the onset of unhealthy weight gain will enable early intervention. My research plan aims to: (1) create risk prediction models for obesity at age 24 months by linking three existing data systems that combine birth certificate, contextual- level, and health outcome data; (2) test the feasibility of linking these data prospectively to validate the Aim 1 obesity risk prediction models over a 24-month period within a contemporary, clinical cohort; and (3) identify best approaches for family-focused risk communication regarding the prevention of excessive weight gain and obesity in infants and toddlers using a human-centered design approach. Through my career development plan and guidance from my mentors, I will expand upon a foundation in epidemiology and pediatric health services research to develop expertise in machine learning, health informatics, data integration, qualitative methods, human-centered design, and behavior change. Together, the research and educational aims of this proposal will provide me with the necessary groundwork to compete for additional funding as an independent investigator. Specifically, I will seek R03-level grant funding from the NIDDK in year 4 of my K01 award to test the communication strategy developed in Aim 3 and to partner with families to modify an existing behavioral change intervention for use in infancy. By the end of this award, I will be well positioned to apply for funding from the NIDDK to conduct a robust R01-level study that combines the validated prediction models, family- focused communication strategy, and modified intervention to determine whether we can effectively prevent obesity in those infants and toddlers identified as being at the highest risk. This line of research will help ensure that prevention efforts are deployed in an efficient, cost-effective manner and accepted by those who need them. I will accomplish this work under the mentorship of Dr. Aaron E. Carroll, a child health services researcher, and a multidisciplinary team of faculty with expertise machine learning, health informatics, data integration and surveillance, qualitative research, human-centered design approaches, behavior change, and childhood obesity. I am ideally suited to complete this research due to my past research productivity, current mentorship team, open access to health care data, and the established clinical decision support infrastructure at the Indiana University School of Medicine.