System Identification Approaches for Energy Intake Estimation: Enhancing Interventions for Managing Gestational Weight Gain
System Identification Approaches for Energy Intake Estimation: Enhancing Interventions for Managing Gestational Weight Gain
复制标题
DOI：
10.1109/tcst.2018.2871871
复制
发表时间：
2020-01-01
影响因子：
4.8
通讯作者：
Downs，Danielle Symons
中科院分区：
文献类型：
--
作者：
Guo，Penghong;Rivera，Daniel E.;Downs，Danielle Symons
孕期母亲体重过度增加是一个主要的公共卫生问题，需要新颖有效的妊娠体重管理干预措施。在健康妈妈区 (HMZ) 一项正在进行的干预研究中，能量摄入 (EI) 低报被发现是一个重要的考虑因素，它会干扰干预环境中准确的体重控制评估和能量平衡 (EB) 模型的有效使用。本文提出了一系列解决测量噪声和测量损失的估计方法，以更好地了解 EI 漏报的程度。其中包括从为妊娠体重增加预测而开发的 EB 模型反算 EI、基于卡尔曼滤波的方法（根据间歇测量实时递归估计 EI）以及基于半物理识别原理的方法，该方法具有通过参数化漏报程度来调整未来自我报告 EI 的能力。通过对 HMZ 干预研究获得的参与者数据进行评估来说明这三种方法，结果证明了这些方法在促进体重控制成功方面的潜力。讨论了所提出方法的优缺点，以便为用户在未来的应用中提供见解。
Excessive maternal weight gain during pregnancy represents a major public health concern that calls for novel and effective gestational weight management interventions. In Healthy Mom Zone (HMZ), an on-going intervention study, energy intake (EI) underreporting has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. In this paper, a series of estimation approaches that addresses measurement noise and measurement losses are developed to better understand the extent of EI underreporting. These include back-calculating EI from an EB model developed for gestational weight gain prediction, a Kalman filtering-based approach to recursively estimate EI from intermittent measurements in real time, and an approach based on semiphysical identification principles which features the capability of adjusting future self-reported EI by parameterizing the extent of underreporting. The three approaches are illustrated by evaluating with participant data obtained through the HMZ intervention study, with the results demonstrating the potential of these methods to promote the success of weight control. The pros and cons of the presented approaches are discussed to generate insights for users in the future applications.