ArticleIFAC-PapersOnLine2017
State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions.
Article in IFAC-PapersOnLine, 2017. 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 4 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
4 citing papers in PubMed.
- Sports Energy Consumption Evaluation Based on Improved Adaptive Weighted Data Fusion Energy-Saving Algorithm.Computational intelligence and neuroscience · 2022Article
- Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2020Article
- 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
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The growing prevalence of obesity and related health problems warrants immediate need for effective weight control interventions. Quantitative energy balance models serve as powerful tools to assist in these interventions, as a result of their ability to accurately predict individual weight change based on reliable measurements of energy intake and energy expenditure. However, the data collected in most existing weight interventions is self-monitored; these measurements often have significant noise or experience losses resulting from participant non-adherence, which in turn, limits accurate model estimation. To address this issue, we develop a Kalman filter-based estimation algorithm for a practical scenario where on-line state estimation for weight, or energy intake/expenditure is still possible despite correlated partial data losses. To account for non-linearities in the models, an algorithm based on extended Kalman filtering is also developed for sequential state estimation in the presence of missing data. Simulation studies are presented to illustrate the performance of the algorithms and the potential benefits of these techniques in real-life interventions.
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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.