Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention

描述（由申请人提供）：管理妊娠期体重增加（GWG）为母亲及其后代提供终身健康益处（例如，降低先兆子痫、代谢综合征、肥胖症、心血管疾病的发展的风险）。由于超重和肥胖孕妇（OW/OBPW）经常超过GWG指南，并且难以管理体重，因此迫切需要为这一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可能是预防高GWG的非常有希望的方法。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，为个性化定制的行为干预构建了框架（例如，教育、目标设定、自我监控和参与健康饮食/体育活动[PA]行为的组成部分）， 在OW/OBPW中控制GWG。这种干预有几个独特的特点：（a）个体化治疗，以在怀孕期间每周管理GWG，（B）经验证的能量平衡微分方程模型，以预测怀孕期间的GWG轨迹，并提供真实的反馈，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关于体重、饮食摄入、PA和心理因素的数据，以及（d）控制系统工程，将收集到的每个参与者的密集数据与动态系统建模相关联，以优化这种干预;换句话说，尽可能有效地管理OW/OBPW中的GWG。拟议的研究目的是首先，通过进行两项研究来检查提供干预剂量和组分排序的可行性，GWG自我监测程序，饮食摄入量，以及电子健康技术机制的PA，治疗和对照组的随机化/保留/数据收集程序，并建立用户可接受性。第二，控制系统工程将被用来与密集的数据收集每个参与者的动态模型，考虑如何在GWG的变化响应能量摄入量，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，并为每位女性确定定制的干预计划;从而优化（有效和高效）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的干预措施在OW/OBPW中管理GWG的有效性。我们的长期目标是使所有孕妇都能获得这种干预措施（通过电子保健技术），以改善母亲和婴儿的健康，并在生命周期的关键时期影响肥胖和心血管疾病的病因。这项研究赞扬了NIH改善母婴健康的远大目标，也符合NHLBI促进研究以减轻全球心脏、肺和血液疾病及其相关合并症负担的使命。

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.