Gestational Weight Gain Prediction Using Segmented Monitoring
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Solution Overview
Problem
Existing methods struggle to accurately predict gestational weight gain (GWG) in pregnant individuals, as the non-linear growth of the fetus complicates the attribution of weight gain to fat storage versus fetal and placental growth.
Innovation Solution
A method involving frequent weight measurements (at least twice a day) during early pregnancy, combined with gestational age tracking, to generate a historic GWG curve using a specific prediction model. This curve is then used to predict future GWG, allowing for assessment of pregnancy risk and generation of lifestyle recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight measurements are taken frequently (at least twice per day) during early pregnancy, then the accuracy of GWG prediction is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent segments the continuous weight monitoring process into discrete daily measurements taken at specific times (morning and evening). This segmentation transforms a complex continuous monitoring task into manageable discrete data points, reducing processing complexity while maintaining prediction accuracy through the use of a specialized GWG prediction model that accounts for the non-linear nature of fetal growth patterns at different gestational ages
2Measurement precision
If a specific GWG prediction model accounting for gestational age is used, then the prediction accuracy is improved, but the difficulty of detecting and measuring attributable weight gain components increases
Solution Approach 1:
The patent introduces a gestational age-adjusted GWG prediction model as an intermediary that translates raw weight measurements into accurate GWG predictions. This model serves as a mediator that accounts for the non-linear relationship between fetal growth and maternal weight gain at different pregnancy stages, enabling accurate prediction without requiring direct measurement of fetal or placental weight components
Data Source
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AI summary
Disclosed concepts aim to provide schemes, solutions, concepts, designs, methods, and systems pertaining to improved prediction of gestational weight gain (GWG). Specifically, a data series describing weight of the pregnant subject at least twice per day (e.g., after waking up, and before going to sleep) during an early stage of pregnancy (e.g., during the first 30 days of pregnancy, or during the first trimester) is used to determine a historic GWG curve of the subject. As energy balance in pregnancy is defined as energy intake equal to energy expenditure plus dynamic energy storage, a specific GWG prediction model is used to generate the historic GWG curve, rather than a generic weight gain model. From the historic GWG curve, a future GWG value of the subject may be predicted. This future GWG value may be used to assess a pregnancy risk score, and/or to generate lifestyle recommendations for the subject.