A control-based observer approach for estimating energy intake during pregnancy.

Area of Science:

• Obstetrics and Gynecology
• Control Systems Engineering
• Nutritional Science

Background:

• Gestational weight gain outside Institute of Medicine guidelines presents risks to maternal and fetal health.
• Behavioral interventions for managing gestational weight gain, such as Healthy Mom Zone (HMZ), rely on self-reported energy intake, which is often inaccurate.
• Accurate energy intake monitoring is essential for effective intervention outcomes.

Purpose of the Study:

• To present a novel control systems approach for estimating energy intake during pregnancy.
• To evaluate the efficacy of Internal Model Control (IMC) and Model Predictive Control (MPC) observer formulations for energy intake estimation.
• To assess the performance of the proposed method using both hypothetical and real participant data.

Main Methods:

• Development of an energy balance model incorporating physical activity and energy intake.
• Formulation of two control-based observer methods: Internal Model Control (IMC) and Model Predictive Control (MPC).
• Application and validation of the methods on data from a hypothetical participant and four participants in the Healthy Mom Zone (HMZ) intervention.

Main Results:

• The control systems approach demonstrated effectiveness in estimating energy intake during pregnancy.
• Both IMC and MPC formulations showed promise, with generally optimal results observed when estimating intake over a weekly period.
• The method provides a more robust estimation of energy intake compared to traditional self-reporting.

Conclusions:

• Control systems offer a viable and effective method for improving the accuracy of energy intake estimation in gestational weight management programs.
• This approach can enhance the effectiveness of behavioral interventions like HMZ by providing more reliable data on participant energy consumption.
• Further research can explore real-time application and integration of these control-based estimation techniques into digital health platforms for pregnancy.