War of Attrition: Predicting Dropout from Pediatric Weight Management

消耗战：预测儿童体重管理中的退出

• 批准号: 10022516
• 负责人: Joseph Arnold Skelton
• 金额: $ 58.71万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022516
• 关键词: Address; Adherence; Adult; Advocate; Area; Automobile Driving; Behavior; Body Weight Changes; Calibration; Child; Child Health; Childhood; Clinic; Clinical; Collection; Communities; Data; Data Collection; Data Element; Disadvantaged; Discipline; Disease; Dropout; Drops; Effectiveness; Exposure to; Family; Feasibility Studies; Goals; Health Benefit; High Prevalence; Knowledge; Literature; Measures; Mentored Patient-Oriented Research Career Development Award; Modeling; Morbid Obesity; Obesity; Obesity Epidemic; Outcome; Parents; Participant; Patient Dropouts; Patients; Predictive Factor; Prevention; Preventive; Primary Health Care; Program Effectiveness; Reporting; Reproducibility; Research; Research Personnel; Resources; Risk; Site; Structure; System; Techniques; Testing; Time; Treatment outcome; War; Weight; Weight maintenance regimen; advanced analytics; base; behavior change; cost outcomes; design; evidence base; high risk; improved; improved outcome; innovation; model design; multidisciplinary; obesity in children; obesity treatment; outcome forecast; predictive modeling; prevent; programs; tool; uptake

Abstract The obesity epidemic is one of the foremost threats to the health of children and adults in the U.S today. Multidisciplinary pediatric weight management programs have been deemed effective, with increased access to these programs strongly advocated. However, the effectiveness of these programs is limited by attrition, with dropout ranging from 27-73%, limiting health benefits to children and inefficiently utilizing already scarce resources. Most research in this area is ad hoc, retrospective, observational, and inconsistent in the variables measured, leaving clinicians and researchers little in which to impact this phenomenon. In addition to better understanding the drivers of attrition, being able to predict or forecast dropout holds great potential to improve adherence and outcomes, and modify treatment approaches to best serve the needs of families. We have developed a model that isolates variables associated with attrition from pediatric weight management, with the potential to forecast participant dropout. We have the opportunity to expand and further refine this model through the collection of key data elements and increased study size. Therefore, we are proposing an expanded and refined approach to the study of attrition from pediatric weight management, utilizing the strength of increased participant numbers, acquisition of consistent data elements, and advanced analytic techniques. The overall goal of this project is to increase the accuracy and power of an attrition prediction model we have developed through its installation in additional weight management programs, then demonstrating its internal, external, and temporal validity. Finally, we will test this powerful tool as a means to decrease patient dropout. In Aim 1, we will increase the power and precision of the Outcomes Forecasting System (OFS) through the consistent acquisition of key patient, family, and treatment data, based on our conceptual model of adherence, from three different weight management sites. In Aim 2, external validity will be established through the installation of the OFS at a fourth pediatric weight management. Aim 3 will be an exploratory study of the feasibility and utility of the OFS in pediatric weight management programs to reduce attrition in high-risk patients and families. A greater understanding of patient, family, and disease-specific factors that predict dropout from pediatric weight management can be utilized to prevent attrition. Through the research aims proposed, the OFS will be refined to a level of precision, calibration, and efficiency to be a valuable tool to any weight management program. By identifying the most pertinent factors driving attrition across weight management sites, new avenues for treatment and prevention will be identified. This project will result in a valuable tool, available for dissemination across a diverse array of clinical programs to improve adherence, decrease costs, and improve outcomes.

抽象的 肥胖流行是今天美国儿童和成人健康的最大威胁之一。 多学科的小儿体重管理计划已被视为有效，随着访问的增加 这些计划强烈主张。但是，这些程序的有效性受流失的限制， 辍学率从27-73％不等，将健康益处限制在儿童中，并且利用已经稀少的效率低下 资源。该领域的大多数研究都是临时，回顾性，观察性和不一致的变量 测量，使临床医生和研究人员几乎没有影响这种现象。除了更好 了解流失的驱动因素，能够预测或预测辍学具有巨大的潜力来改善 依从性和结果，并修改治疗方法以最能满足家庭的需求。我们有 开发了一个模型，该模型隔离了与小儿体重管理与流失相关的变量， 潜力预测参与者辍学。我们有机会扩展和进一步完善该模型 通过收集关键数据元素并增加了研究规模。因此，我们正在提议扩展 并利用小儿体重管理研究的损耗研究方法 参与者数量增加，一致的数据元素的获取以及高级分析技术。这 该项目的总体目标是提高我们拥有的流失预测模型的准确性和力量 通过在其他体重管理程序中安装来开发，然后展示其内部， 外部和时间有效性。最后，我们将测试此功能强大的工具，以减少患者辍学的手段。 在AIM 1中，我们将通过（OFS）提高预测系统的能力和精度 基于我们的依从性概念模型，一致地获取关键患者，家庭和治疗数据， 来自三个不同的体重管理网站。在AIM 2中，将通过 在第四个小儿体重管理中安装OFS。 AIM 3将是对 OFS在小儿体重管理计划中的可行性和效用，以减少高风险的流失 患者和家庭。对预测患者，家庭和疾病特异性因素有更深入的了解 小儿体重管理的辍学可以用于防止损耗。通过研究目的 提议，将OFS精制成精确，校准和效率的水平，成为任何有价值的工具 体重管理计划。通过确定跨重量流失的最相关因素 将确定管理站点，治疗和预防的新途径。这个项目将导致 有价值的工具，可用于在各种临床计划中传播以提高依从性， 降低成本并改善结果。