Efficacy of a Novel Digital Platform to Scale-Up a Personalized Prenatal Weight Gain Intervention Using Control Systems Methodology

新型数字平台使用控制系统方法扩大个性化产前体重增加干预的功效

• 批准号: 10562400
• 负责人: Danielle Symons Downs
• 金额: $ 68.53万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10562400
• 关键词: Address; Architecture; Behavior; Birth Weight; Body mass index; COVID-19 pandemic; Categories; Cognitive; Control Groups; Data; Eating; Eating Behavior; Education; Effectiveness; Energy Intake; Future; Gestational Age; Goals; Grant; Guidelines; Health; Health Benefit; Healthy Eating; Individual; Infant; Infant Health; Intake; Intervention; Low Birth Weight Infant; Maternal Health; Measurement; Measures; Methodology; Modeling; Modification; Monitor; Mothers; Obesity; Outcome; Outcome Measure; Outcome Study; Overweight; Persons; Phase; Physical activity; Pregnancy; Pregnant Women; Prenatal care; Qualifying; Randomized; Recommendation; Remote session; Research; Risk; Risk Reduction; Sleep; System; Time; Translational Research; Weight Gain; Woman; attentional control; design; digital platform; dosage; efficacy evaluation; efficacy testing; efficacy trial; energy balance; feasibility trial; gestational weight gain; health care delivery; high body mass index; high risk; infant outcome; innovation; mHealth; novel; offspring; perinatal outcomes; post intervention; prenatal; prepregnancy; primary outcome; randomized trial; remote delivery; restraint; scale up; secondary outcome; simulation; social; social cognitive theory; theories; tool; web interface

PROJECT SUMMARY Pregnant women with overweight/obesity (PW-OW/OB) and high gestational weight gain (GWG) are at increased risk for adverse maternal-infant outcomes. Given this high risk and the rapidly changing landscape of healthcare delivery due to the COVID-19 pandemic, there is a timely need for automated, scalable approaches relying on remote delivery to regulate GWG. Our team addressed this need and developed the Healthy Mom Zone (HMZ) intervention with adapted dosages to regulate GWG. Adaptations give less treatment (e.g., education) to women who can self-regulate GWG within recommendations and more treatment (physical activity/energy intake activities) to women who need more support to regulate GWG. Theory-based components were designed with a multiphase, translational science framework and control systems methodology with the long-term goal to scale- up future use by prenatal clinicians. With R01HL119245, we randomized PW-OW/OB to intervention and control groups and examined feasibility and initial impact of HMZ on GWG. A control system driven by decision rules and a woman’s observed GWG informed when to adapt dosages (GWG within goals, dosage was maintained; GWG above goals, dosage was adapted). Trial feasibility markers showed high compliance, low subject burden and attrition; compliance was better with remove vs. in-person delivery. The HMZ group had 21% lower mean GWG and were more likely to have GWG within goals than controls. Exploratory analyses also showed promise for HMZ to impact secondary maternal-infant outcomes. Given these initial findings, we made refinements to increase efficacy and scalability (e.g., added sleep/eating behavior content, modified all components for remote delivery). We replaced the initial control system with a new automated, model-based predictive control system that forecasts a woman’s future GWG under different possible values of activity/intake behaviors and determinants. We found that it made more efficient decisions to adapt dosages and regulated GWG better than the initial system. With a Penn State seed grant, we built an architecture for a novel digital platform with a web interface that automates the linkage of subject data to the new control system, computes optimized dosage changes across multiple maternal variables, and produces a host of behavior strategies to regulate GWG. The goal of the proposed research is to examine efficacy of the enhanced HMZ 2.0 intervention with new control system/digital platform to regulate GWG and impact maternal-infant outcomes while collecting implementation data to inform future scalability. N=144 PW-OW/OB will be randomized to HMZ 2.0 intervention or attention control groups from ~8-36 weeks gestation. Aim 1 will examine efficacy of HMZ 2.0 on GWG (primary outcome) and activity/intake behaviors and social cognitive determinants between intervention and control groups. Aim 2 will measure pre- to post-intervention differences in secondary maternal sleep/eating behaviors and infant birth weight. Aim 3 will examine impact of implementation markers on HMZ 2.0 efficacy on study outcomes to inform future scalability. Impact of this novel research is an optimized and highly scalable intervention to regulate GWG.

项目摘要 超重/肥胖（PW-OW/OB）和妊娠期体重增加（GWG）的孕妇 母婴不良结局的风险。鉴于这种高风险和医疗保健领域的快速变化， 由于COVID-19大流行，需要及时采用自动化、可扩展的方法， 远程交付以调节GWG。我们的团队解决了这一需求，并开发了健康妈妈区（HMZ） 用合适的剂量进行干预以调节GWG。适应性治疗较少（例如，教育） 谁可以自我调节GWG的建议和更多的治疗（体力活动/能量摄入 活动），以妇女谁需要更多的支持，以规范GWG。基于理论的组件是用 多阶段，转化科学框架和控制系统方法，长期目标是规模化- 产前临床医生的未来使用。使用R 01 HL 119245，我们将PW-OW/OB随机分为干预组和对照组 研究了HMZ对GWG的可行性和初步影响。决策规则驱动的控制系统 并告知妇女观察到的GWG何时调整剂量（GWG在目标内，剂量维持; GWG高于目标，调整剂量）。试验可行性标志物显示依从性高，受试者负担低 和损耗;移除与亲自递送相比，依从性更好。HMZ组的平均水平降低了21%， GWG和更有可能有GWG的目标比对照组。探索性分析也显示出希望 HMZ对次级母婴结局的影响。根据这些初步发现，我们对 提高效率和可伸缩性（例如，添加了睡眠/进食行为内容，修改了所有远程组件 交付）。我们用一个新的自动化的、基于模型的预测控制系统取代了最初的控制系统 预测女性在不同活动/摄入行为可能值下的未来GWG， 决定因素我们发现，它作出了更有效的决定，以适应剂量和监管GWG比 最初的系统。通过宾夕法尼亚州立大学的种子基金，我们为一个新颖的数字平台构建了一个架构， 自动将受试者数据链接到新控制系统的接口， 在多个母亲变量的变化，并产生了一系列的行为策略来调节GWG。的 拟议研究的目标是检查增强的HMZ 2.0干预与新对照的有效性 系统/数字平台，以规范GWG并影响母婴结果，同时收集执行情况 数据，以告知未来的可扩展性。N=144例PW-OW/OB将被随机分配至HMZ 2.0干预或关注组 妊娠约8-36周的对照组。目的1将检查HMZ 2.0对GWG的疗效（主要结局） 干预组和对照组之间的活动/摄入行为和社会认知决定因素。目的2 将测量干预前后次级产妇睡眠/饮食行为和婴儿出生的差异 重量.目标3将检查实施标记对HMZ 2.0疗效对研究结果的影响，以告知 未来的可扩展性这项新研究的影响是一种优化和高度可扩展的干预措施，以调节GWG。