ArticleIFAC-PapersOnLine2018
A "Model-on-Demand" Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions.
Article in IFAC-PapersOnLine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03945266 (Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention), which is not on this map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention
Who cites it
5 citing papers in PubMed.
- Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity.Journal of behavioral medicine · 2021Trial
- Underreporting of Energy Intake Increases over Pregnancy: An Intensive Longitudinal Study of Women with Overweight and Obesity.Nutrients · 2022Article
- Low Resting Energy Expenditure Is Associated with High Gestational Weight Gain Only When Resting Energy Expenditure Fluctuates.Reproductive sciences (Thousand Oaks, Calif.) · 2021Article
- Article
- Low prenatal resting energy expenditure and high energy intake predict high gestational weight gain in pregnant women with overweight/obesity.Obesity research & clinical practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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 e ort and demands less
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Registered trials
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