Utilization of a Diet Optimization Model in Ensuring Adequate Intake among Pregnant Women in Selangor, Malaysia.

Int J Environ Res Public Health

Dietetic Programme, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia.

Published: November 2019

AI Article Synopsis

  • Achieving nutritional needs for pregnant women in rural or suburban areas, while honoring local food traditions, is challenging.
  • A study used linear programming to create affordable diet plans from local Malaysian foods, ensuring they met recommended nutrient intakes.
  • While the optimized menus improved overall dietary adequacy, deficiencies in iron and niacin highlighted the need for potential supplementation.

Article Abstract

Achieving nutritional requirements for pregnant women in rural or suburban households while maintaining the intake of local and culture-specific foods can be difficult. Usage of a linear programming approach can effectively generate diet optimization models that incorporate local and culturally acceptable menus. This study aimed to determine whether a realistic and affordable diet that achieves recommended nutrient intakes for pregnant women could be formulated from locally available foods in Malaysia. A cross-sectional study was conducted to assess the dietary intake of 78 pregnant women using a 24-h dietary recall and a 3-day food record. A market survey was also carried out to estimate the cost of raw foods that are frequently consumed. All linear programming analyses were done using Excel Solver to generate optimal dietary patterns. Our findings showed that the menus designed from diet optimization models using locally available foods would improve dietary adequacy for the seven food groups based on the Malaysian Dietary Guidelines 2010 (MDG 2010) and the 14 nutrients based on Recommended Nutrient Intake 2017 (RNI 2017) in pregnant women. However, inadequacies remained for iron and niacin, indicating that these nutrients may require supplementation.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927000PMC
http://dx.doi.org/10.3390/ijerph16234720DOI Listing

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