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

生命早期肥胖预防 (OPEL)：风险筛查和针对性干预

• 批准号: 9912764
• 负责人: Erika Rose Cheng
• 金额: $ 13.96万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912764
• 关键词: Address; Age; Age-Months; Award; Behavioral; Belief; Birth Certificates; Child; Child Health Services; Childhood; Clinical; Communication; Communities; Computers; Data; Data Set; Databases; Decision Support Systems; Development; Development Plans; Early Intervention; Early identification; Electronic Health Record; Ensure; Epidemiology; Faculty; Family; Foundations; Funding; Future; Geographic Information Systems; Goals; Grant; Health Services Research; Health Status; Human; Indiana; Infant; Information Systems; Infrastructure; Intervention; K-Series Research Career Programs; Lead; Life; Life Cycle Stages; Link; Machine Learning; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Modeling; National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; Obesity; Obesity Epidemic; Outcome; Parents; Perception; Positioning Attribute; Pregnancy Histories; Prevention; Preventive Intervention; Primary Prevention; Process; Productivity; Public Health Informatics; Publishing; Qualitative Methods; Qualitative Research; Records; Research; Research Methodology; Research Personnel; Research Project Grants; Risk; Risk Factors; Source; Testing; Toddler; Universities; Weight; Weight Gain; Work; approach behavior; base; behavior change; career development; clinical decision support; cohort; cost effective; data access; data integration; data warehouse; design; epidemiologic data; health care availability; health data; healthy weight; high risk; improved; infancy; innovation; machine learning method; medical schools; multidisciplinary; obesity in children; obesity prevention; obesity risk; predictive modeling; prevent; programs; prospective; risk prediction model; screening; sociodemographics; tool

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.

鉴于肥胖一旦出现就很难逆转，人们越来越关注原发性肥胖。 在生命的早期预防肥胖。在第一次世界大战期间， 多年的生活。该提案总结了一项为期5年的专业发展计划， 一项旨在改善婴儿的识别和潜在治疗的多方法研究项目， 肥胖风险高的幼儿。我的长期目标是通过识别最危险的婴儿来预防肥胖 并为他们提供有效的、以家庭为中心的干预措施，以可改变的生命过程因素为目标。我 根据我先前的研究，中心假设是，在婴儿肥胖症发作之前， 不健康的体重增加可以及早干预。我的研究计划旨在：（1）建立风险预测模型 针对24个月大的肥胖症，将三个现有的数据系统连接起来， 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 在当代临床队列中24个月期间的肥胖风险预测模型;以及（3）识别 关于预防体重过度增加的以家庭为重点的风险沟通的最佳方法， 使用以人为本的设计方法，在婴儿和幼儿肥胖。通过我的职业发展 计划和指导，我将扩大在流行病学和儿科健康的基础 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。总之，本研究的研究和教育目的 这项提案将为我提供必要的基础，以争取额外的资金作为一个独立的 调查员具体来说，我将在我的K 01奖的第4年从NIDDK寻求R 03级赠款资金，以测试 在目标3中制定的沟通策略，并与家庭合作，以改变现有的行为 在婴儿期使用的改变干预。在这个奖项结束时，我将很好地申请资金 从NIDDK进行一个强大的R 01级研究，结合经验证的预测模型，家庭， 集中的沟通策略和修改干预，以确定我们是否可以有效地防止 婴儿和幼儿肥胖的风险最高。这一系列的研究将有助于 确保预防工作以高效率、具有成本效益的方式部署，并为那些 需要他们。我将在Aaron E博士的指导下完成这项工作。卡罗尔，一个儿童健康服务 研究员，以及具有机器学习，健康信息学，数据等专业知识的多学科教师团队 整合和监督，定性研究，以人为本的设计方法，行为改变， 儿童肥胖症我非常适合完成这项研究，由于我过去的研究生产力，目前 指导团队，开放医疗保健数据访问，以及已建立的临床决策支持基础设施 在印第安纳州大学医学院。