Development and validation of a new model of desirable dietary pattern (N-DDP) score for Chinese diets

Public Health Nutr. 2014 Mar;17(3):519-28. doi: 10.1017/S1368980012005629. Epub 2013 Feb 1.

Abstract

Objective: To develop a new model of desirable dietary pattern (N-DDP) score for Chinese diets and to validate it against the nutrient-rich foods (NRF) index.

Design: The N-DDP score model followed the principles of the traditional DDP (T-DDP) score model (DDP-China for 2000) proposed in 1991 and of food grouping in the dietary pagoda for Chinese residents in 2007, and made detailed ratings by expressing the food weight coefficient, reasonable maximum limit of the score and an algorithm of the deserved score for each group of foods after considering current nutritional problems of Chinese residents. The N-DDP score model was validated against the NRF9·3 index with linear regression analysis and compared with the T-DDP score model. Settings One set of dietary data was extracted from the diet recommended by the dietary pagoda for Chinese residents in 2007 and the literature on dietary surveys in China. The other two sets of dietary data were from a dietary survey in 2011. DDP scores for all three dietary data sets were calculated with the N-DDP score model and the T-DDP score model.

Subjects: All items of dietary records in the three dietary data sets were included in the present study.

Results: All DDP scores obtained with the N-DDP score model were positively correlated (P = 0·000) with the NRF9·3 index. DDP scores obtained with the N-DDP score model had higher R 2 with the NRF9·3 index than those of the T-DDP score model, as well as higher β values.

Conclusions: It can be considered that the N-DDP score is a more accurate and convenient tool to evaluate current individual and group diet for Chinese residents.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms
  • China
  • Diet / standards*
  • Diet Records*
  • Energy Intake
  • Female
  • Food / classification*
  • Food / statistics & numerical data
  • Humans
  • Linear Models
  • Male
  • Nutrition Assessment*
  • Nutritive Value*
  • Regression Analysis
  • Reproducibility of Results