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.

项目概要 超重/肥胖 (PW-OW/OB) 和妊娠期体重增加 (GWG) 较高的孕妇的体重增加 鉴于这种高风险和快速变化的医疗保健环境，母婴不良结局的风险。 由于 COVID-19 大流行，迫切需要依赖于自动化、可扩展的方法 远程交付以监管 GWG。我们的团队满足了这一需求并开发了健康妈妈区 (HMZ)。 通过调整剂量进行干预以调节 GWG 调整对女性的治疗（例如教育）较少。 谁可以在建议和更多治疗（体力活动/能量摄入 活动）为需要更多支持来规范 GWG 的女性提供了基于理论的组件。 多相、转化科学框架和控制系统方法，其长期目标是扩大规模 对于 R01HL119245，我们将 PW-OW/OB 随机分为干预组和对照组。 小组并检查了 HMZ 对 GWG 由决策规则驱动的控制系统的可行性和初步影响。 观察到的女性 GWG 告知何时调整剂量（GWG 在目标范围内，维持剂量； GWG 高于目标，剂量进行了调整）。 和自然流失；与现场交付相比，HMZ 组的依从性要低 21%。 GWG 与对照相比更有可能使 GWG 处于目标范围内。探索性分析也显示出希望。 鉴于这些初步发现，我们对 HMZ 影响继发性母婴结局进行了改进。 提高功效和可扩展性（例如，添加睡眠/饮食行为内容、修改远程的所有组件 我们用新的基于模型的自动化预测控制系统替换了最初的控制系统。 预测女性在不同可能的活动/摄入行为值下的未来 GWG，以及 我们发现，与 GWG 相比，它可以更有效地做出调整剂量的决定并更好地调节 GWG。 通过宾夕法尼亚州立大学的种子资助，我们为一个带有网络的新型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，计算优化剂量 多个母亲变量的变化，并产生一系列行为策略来调节 GWG。 拟议研究的目标是检验增强型 HMZ 2.0 干预措施与新控制措施的功效 系统/数字平台，用于规范 GWG 并影响母婴结局，同时收集实施情况 N=144 PW-OW/OB 将被随机分配到 HMZ 2.0 干预或关注。 妊娠 8-36 周的对照组将检查 HMZ 2.0 对 GWG 的功效（主要结果）。 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。 将测量干预前后母亲睡眠/饮食行为和婴儿出生的差异 目标 3 将检查实施标记对 HMZ 2.0 功效对研究结果的影响，以提供信息。 这项新颖研究的影响是对 GWG 进行优化且高度可扩展的干预措施。