文章基本信息

标题：A “Model-on-Demand” Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions ⁎
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作者：Penghong Guo ; Daniel E. Rivera ; Abigail M. Pauley 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：144-149
DOI：10.1016/j.ifacol.2018.09.105
语种：English
出版社：Elsevier
摘要：AbstractEnergy intake underreporting is a frequent concern in weight control interventions. In prior work, a series of estimation approaches were developed to better understand the issue of underreporting of energy intake; among these is an approach based on semi-physical identification principles that adjusts energy intake self-reports by obtaining a functional relationship for the extent of underreporting. In this paper, this global modeling approach is extended, and for comparison purposes, a local modeling approach based on the concept of Model-on-Demand (MoD) is developed. The local approach displays comparable performance, but involves reduced engineering effort and demands lessa prioriinformation. Cross-validation is utilized to evaluate both approaches, which in practice serves as the basis for selecting parsimonious yet accurate models. The effectiveness of the enhanced global and MoD local estimation methods is evaluated with data obtained fromHealthy Mom Zone,a novel gestational weight intervention study focused on the needs of obese and overweight women.
关键词：KeywordsSemi-physical IdentificationModel-on-DemandEstimationWeight Interventions