A “Model-on-Demand” Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions

Penghong Guo, Daniel E. Rivera, Abigail M. Pauley, Krista S. Leonard, Jennifer S. Savage, Danielle S. Downs

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Energy 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 less a priori information. 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 from Healthy Mom Zone, a novel gestational weight intervention study focused on the needs of obese and overweight women.

Original languageEnglish (US)
Pages (from-to)144-149
Number of pages6
Journal18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018
Issue number15
StatePublished - Jan 1 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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