scholarly journals Racial/Ethnic Differences in Diet Quality and Eating Habits Among WIC Pregnant Women: Implications for Policy and Practice

2019 ◽  
Vol 34 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Alla M. Hill ◽  
Danielle L. Nunnery ◽  
Alice Ammerman ◽  
Jigna M. Dharod

Purpose: One of the major federal food assistance programs, the Special Supplemental Program for Women, Infants, and Children (WIC), serves approximately 1.5 million low-income pregnant women per year; however, limited information is available on their dietary habits. This is critical because low-income women are at higher risk of gaining excess weight during pregnancy. Thus, the study objectives were to (1) determine the overall diet quality of WIC pregnant women and (2) examine diet quality and eating behaviors by race/ethnicity and other sociodemographics. Design: This was a cross-sectional study. Setting: One of the 3 WIC offices in a north-central county in North Carolina, USA. Sample: Pregnant women (n = 198) in the second trimester. Measures: Interviews included sociodemographics, food security, diet, and eating behaviors. Diet quality was assessed by the Healthy Eating Index (HEI) 2010 scores. Analysis: Descriptives, bivariate analysis, and multivariate analysis. Results: Average participant age was 26 years, and the mean HEI-2010 score was 56 of maximum score of 100. Specifically, African American women consumed significantly lower servings of whole grains (β = −1.71; 95% CI: −3.10 to −0.32; P < .05) and dairy (β = −1.42; 95% CI: −2.51 to −0.33; P < .05) compared with non-Hispanic white women. Hispanic women scored higher in daily intake of fruits (β = 0.98; 95% CI: 0.17-1.79; P < .05) and for consuming empty calories in moderation (β = 1.57; 95% CI: 0.06-3.09; P < .05). Frequency of intake of fast foods/outside meals was higher among African American women (57%, P = .025). Conclusion: Efforts are warranted to promote optimal nutrition among WIC pregnant women. Specifically, African American women are highly vulnerable to poor dietary habits during pregnancy. Further investigation of barriers/facilitators for healthy eating is necessary to address nutrition disparities among WIC pregnant women.

2008 ◽  
Vol 139 (2) ◽  
pp. 359-364 ◽  
Author(s):  
Kristen M. Hurley ◽  
Sarah E. Oberlander ◽  
Brian C. Merry ◽  
Margaret M. Wrobleski ◽  
Ann C. Klassen ◽  
...  

2015 ◽  
Vol 34 (5) ◽  
pp. 416-424 ◽  
Author(s):  
Terryl J. Hartman ◽  
Regine Haardörfer ◽  
Laura L. Whitaker ◽  
Ann Addison ◽  
Maria Zlotorzynska ◽  
...  

2005 ◽  
Vol 14 (10) ◽  
pp. 2293-2301 ◽  
Author(s):  
Lori B. Frank ◽  
Louis S. Matza ◽  
Dennis A. Revicki ◽  
Joyce Y. Chung

2006 ◽  
Vol 38 (5) ◽  
pp. 317-318 ◽  
Author(s):  
Peter S. Houts ◽  
Sharada Shankar ◽  
Ann C. Klassen ◽  
Ellen B. Robinson

2020 ◽  
Vol 11 (2) ◽  
pp. 96-102
Author(s):  
Krishna Mohandas ◽  
L. Prema

The food habits of global population has been evolving in such a way that makes unhealthy foods cheaper and widely available and healthy foods costly and less available. Being surrounded by such foods and living in an environment with lesser requirement for physical activity is the primary reason for the pandemic explosion in overweight and obesity. This study is an attempt to analyze the quality of diet with an aim to study the significance of Alternate Healthy Eating Index (AHEI) in predicting the quality of dietary intake. Methodology: The study was conducted in 66 respondents (44 females and 22 males) aged 18-65 years with BMI between 23 kg/m2 to 50 kg/m2. The respondent’s data were collected using a pretested standard questionnaire. The nutrient consumption was calculated from the 24 hour recall and the AHEI scores were derived from recall and food use frequency data. The data were analysed using SAS software. Results: The intake of Energy, protein, fat and carbohydrates were more than their requirement while intake of fibre was not meeting the requirement. The AHEI scores obtained ranged from 36 to 76 with a mean value of 55.6 ± 9.54. A positive linear association for AHEI with BMI (0.0362) and energy intake (0.13) was established through Pearson’s correlation while the association was negative with BMR (-0.14). Paired t test comparing AHEI against the difference between intake and requirement of macronutrients revealed that when the diet quality was good (as indicated by AHEI>51), the difference in intake exhibited a significant linear relationship with p values <0.001 while no relation was established when the diet quality was poor. Conclusion: AHEI encompasses all nutrients and food groups relevant to metabolic health and it can be used as a good tool to assess the quality of dietary habits of overweight and obese subjects.


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