Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention

用于优化产前体重增加干预的控制系统工程

• 批准号: 8557515
• 负责人: Danielle Symons Downs
• 金额: $ 33.13万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8557515
• 关键词: Area; Behavior; Behavior Control; Behavior Therapy; Behavioral; Behavioral Sciences; Biological Models; Biology; Birth; Cardiovascular Diseases; Comorbidity; Computer Simulation; Control Groups; Data; Data Collection; Decision Making; Development; Dietary intake; Eating; Education; Educational process of instructing; Energy Intake; Engineering; Engineering Psychology; Equation; Etiology; Feedback; Focus Groups; Future; Goals; Guidelines; Health; Health Benefit; Health Sciences; Health Technology; Heart Diseases; Hematological Disease; Individual; Individual Differences; Infant; Infant Health; Informal Social Control; Intervention; Kinesiology; Lead; Life Cycle Stages; Lung diseases; Mathematics; Metabolic syndrome; Methods; Mission; Modeling; Modification; Monitor; Mothers; Obesity; Outcome; Overweight; Participant; Physical activity; Physiology; Planning Theory; Population; Population Intervention; Pre-Eclampsia; Pregnancy; Pregnancy Complications; Pregnant Women; Procedures; Psychological Factors; Randomized; Randomized Controlled Trials; Research; Risk; System; Testing; Time; United States National Institutes of Health; Weight; Weight Gain; Weight maintenance regimen; Woman; base; behavioral/social science; cohort; design; dosage; efficacy testing; energy balance; energy density; improved; innovation; intervention program; meetings; multidisciplinary; nutrition; offspring; predictive modeling; pregnant; prenatal; prenatal health; prevent; programs; public health relevance; statistics; therapy design; time use; tool

DESCRIPTION (provided by applicant): Managing gestational weight gain (GWG) offers lifelong health benefits in both mothers and their offspring (e.g., reducing risk of preeclampsia, development of metabolic syndrome, obesity, cardiovascular disease). Because overweight and obese pregnant women (OW/OBPW) often exceed GWG guidelines and have difficulty with managing weight, there is a critical need to identify effective weight management interventions for this population. An individually-tailored intervention that provides OW/OBPW with support for managing GWG on a weekly basis and adapts to their unique needs over pregnancy may be a highly promising way to prevent high GWG. We have synergistically integrated methods/key concepts from the behavioral sciences and control systems engineering to construct a framework for an individually-tailored, behavioral intervention (e.g., components of education, goal-setting, self-monitoring, and engaging in healthy eating/ physical activity [PA] behaviors) to control GWG in OW/OBPW. This intervention has several unique features: (a) individualized treatment to manage GWG on a weekly basis over pregnancy, (b) a validated differential equation model for energy balance to predict GWG trajectories over pregnancy and provide feedback in real- time to adapt treatment as needed, (c) e-health technology to promote self-monitoring and collect data on weight, dietary intake, PA, and psychological factors, and (d) control systems engineering to relate intensive data collected on each participant and dynamical systems modeling to optimize this intervention; in other words, manage GWG in OW/OBPW as effectively and efficiently as possible. The proposed research aims are to first, establish feasibility of delivering this individually-tailored intervention for managing GWG in OW/OBPW by conducting two studies to examine viability of delivering intervention dosages and component sequencing, procedures for self-monitoring of GWG, dietary intake, and PA with e-health technology mechanisms, randomization/retention/data collection procedures with treatment and control groups, and to establish user acceptability. Second, control systems engineering will be used to relate intensive data collected on each participant to a dynamical model that considers how changes in GWG responds to changes in energy intake, PA, and planned/self-regulatory behaviors. We will then make modifications to the intervention and identify a customized intervention plan for each woman; resulting in an optimized (effective and efficient) intervention. We will test the efficacy of this optimized intervention for managing GWG in OW/OBPW in a future randomized controlled trial. Our long-range goal is to make this intervention available to all pregnant women (via e- health technology) to improve the health of mothers and infants and impact the etiology of obesity and cardiovascular disease at a critical time in the life cycle. Thi research compliments the over-reaching goal of NIH to improve maternal/infant health and it is consistent with NHLBI's mission to promote research to reduce the burden of heart, lung, and blood diseases and their related comorbidities worldwide.

描述（由申请人提供）：管理妊娠体重增加（GWG）为母亲及其后代提供终身健康益处（例如，减少子痫前期风险，代谢综合征，肥胖，心血管疾病的发展）。由于超重和肥胖的孕妇（OW/OBPW）通常超过GWG指南，并且在管理体重方面遇到困难，因此迫切需要确定该人群的有效体重管理干预措施。一项单独限制的干预措施，为OW/OBPW提供每周管理GWG的支持，并适应其对怀孕的独特需求，这可能是预防高GWG的一种非常有希望的方法。我们拥有来自行为科学和控制系统工程的协同综合方法/关键概念，以构建一个框架的框架，用于单独计算的行为干预措施（例如，教育的组成部分，目标设定，自我监测，自我监控，并从事健康的饮食/身体活动[PA]行为[PA]行为[PA]行为[PA]行为[PA] 控制GWG/OBPW中的GWG。 This intervention has several unique features: (a) individualized treatment to manage GWG on a weekly basis over pregnancy, (b) a validated differential equation model for energy balance to predict GWG trajectories over pregnancy and provide feedback in real- time to adapt treatment as needed, (c) e-health technology to promote self-monitoring and collect data on weight, dietary intake, PA, and psychological factors, and (d) control systems engineering to relate intensive data collected on each参与者和动态系统建模以优化此干预措施；换句话说，在OW/OBPW中尽可能有效地管理GWG。拟议的研究目的是首先，确定可行性，通过进行两项研究，以检查提供干预剂量和组件测序的可行性，用于自我监控GWG，饮食摄入，饮食摄入量和PA与E-Health Nealth Sechnology Intery Intery Anterions/Data Dapation Controlys，和PA的自我监控的程序，并进行自我监控和PA，并控制型和PA，并控制型和数据，并控制了型和数据。其次，控制系统工程将用于将每个参与者收集的密集数据与一个动态模型联系起来，该模型考虑了GWG的变化如何响应能量摄入，PA和计划/自我调节行为的变化。然后，我们将对干预进行修改，并确定每个妇女的定制干预计划；导致优化（有效且有效）的干预措施。 我们将在未来的随机对照试验中测试这种优化干预措施在OW/OBPW中管理GWG的功效。我们的远程目标是使所有孕妇（通过E-Health Technology）获得这种干预措施，以改善母亲和婴儿的健康状况，并在生命周期的关键时刻影响肥胖和心血管疾病的病因。这项研究称赞NIH可以改善孕产妇/婴儿健康的超越目标，这与NHLBI促进研究以减轻心脏，肺和血液疾病的负担及其全球相关合并症的使命是一致的。