ArticleProceedings of the ... American Control Conference. American Control Conference2016
Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain.
Article in Proceedings of the ... American Control Conference. American Control Conference, 2016. 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 13 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
13 citing papers in PubMed, 17 citations in OpenAlex.
- 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
- Gestational Weight Gain Intervention Impacts Determinants of Healthy Eating and Exercise in Overweight/Obese Pregnant Women.Journal of obesity · 2018Trial
- Healthy Mom Zone Adaptive Intervention With a Novel Control System and Digital Platform to Manage Gestational Weight Gain in Pregnant Women With Overweight or Obesity: Study Design and Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- 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
- System Identification Approaches For Energy Intake Estimation: Enhancing Interventions For Managing Gestational Weight Gain.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2020Article
- Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods.Psychology of sport and exercise · 2019Article
- Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions.Journal of medical Internet research · 2018Article
- Individually Tailored, Adaptive Intervention to Manage Gestational Weight Gain: Protocol for a Randomized Controlled Trial in Women With Overweight and Obesity.JMIR research protocols · 2018Article
- A Dynamical Systems Model of Intrauterine Fetal Growth.Mathematical and computer modelling of dynamical systems · 2018Article
- A "Model-on-Demand" Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions.IFAC-PapersOnLine · 2018Article
- State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions.IFAC-PapersOnLine · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Excessive gestational weight gain (i.e., weight gain during pregnancy) is a significant public health concern, and has been the recent focus of novel, control systems-based interventions. This paper develops a control-oriented dynamical systems model based on a first-principles energy balance model from the literature, which is evaluated against participant data from a study targeted to obese and overweight pregnant women. The results indicate significant under-reporting of energy intake among the participant population. A series of approaches based on system identification and state estimation are developed in the paper to better understand and characterize the extent of under-reporting; these range from back-calculating energy intake from a closed-form of the energy balance model, to a constrained semi-physical identification approach that estimates the extent of systematic under-reporting in the presence of noise and possibly missing data. Additionally, we describe an adaptive algorithm based on Kalman filtering to estimate energy intake in real-time. The approaches are illustrated with data from both simulated and actual intervention participants.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.