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: 8720059
• 负责人: Danielle Symons Downs
• 金额: $ 32.42万
• 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 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8720059
• 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; Differential Equation; Eating; Education; Educational process of instructing; Energy Intake; Engineering; Engineering Psychology; Etiology; Feedback; Focus Groups; Future; Goals; Guidelines; Health; Health Benefit; Health Sciences; 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; Technology; Testing; Time; United States National Institutes of Health; Weight; Weight Gain; Weight maintenance regimen; Woman; base; behavioral/social science; cohort; design; dosage; eHealth; 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）：管理妊娠期体重增加（GWG）可为母亲及其后代带来终生健康益处（例如，降低先兆子痫、代谢综合征、肥胖、心血管疾病的风险）。由于超重和肥胖孕妇 (OW/OBPW) 通常超出 GWG 指南并且难以控制体重，因此迫切需要为该人群确定有效的体重管理干预措施。为 OW/OBPW 提供每周管理 GWG 的支持并适应其在怀孕期间的独特需求的个性化干预措施可能是预防高 GWG 的一种非常有前途的方法。我们协同整合了行为科学和控制系统工程的方法/关键概念，构建了一个个性化的行为干预框架（例如，教育、目标设定、自我监控和健康饮食/身体活动的组成部分）活动 [PA] 行为） 在 OW/OBPW 中控制 GWG。这种干预措施有几个独特的特点：(a) 个性化治疗，在怀孕期间每周管理 GWG，(b) 经过验证的能量平衡微分方程模型，可以预测怀孕期间的 GWG 轨迹，并实时提供反馈，以根据需要调整治疗(c) 电子健康技术，以促进自我监测并收集有关体重、饮食摄入量、PA 和心理因素的数据，以及 (d) 控制系统工程，将每个参与者收集的密集数据与动态系统建模联系起来，以优化这种干预；换句话说，尽可能有效和高效地管理 OW/OBPW 中的 GWG。拟议的研究目标是，首先，通过开展两项研究来检验提供干预剂量和成分排序的可行性、GWG 自我监测程序、饮食摄入量、和 PA，采用电子医疗技术机制、治疗组和对照组的随机化/保留/数据收集程序，并建立用户可接受性。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动态模型联系起来，该模型考虑 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。然后我们将对干预措施进行修改，并为每位女性制定定制的干预计划；从而实现优化（有效且高效）的干预。 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 的有效性。我们的长期目标是（通过电子健康技术）向所有孕妇提供这种干预措施，以改善母亲和婴儿的健康，并在生命周期的关键时刻影响肥胖和心血管疾病的病因。这项研究赞扬了 NIH 改善孕产妇/婴儿健康的远大目标，并且与 NHLBI 的使命相一致，即促进研究以减轻全球心脏、肺和血液疾病及其相关合并症的负担。