Suppr超能文献

Predicting folic acid intake among college students.

作者信息

Lane Susan H, Hines Annette, Krowchuk Heidi

机构信息

Susan H. Lane is an Assistant Professor, Appalachian State University, Boone, NC. She can be reached via e-mail at

出版信息

MCN Am J Matern Child Nurs. 2015 Jan-Feb;40(1):51-7. doi: 10.1097/NMC.0000000000000098.

Abstract

PURPOSE

Annually in the United States, approximately 3,000 babies are born with neural tube defects (NTDs). Folic acid supplementation can reduce NTDs by 50% to 70%. Despite recommendations for folic acid intake, only 30% of women ages 18 to 24 report folic acid supplementation and 6% have knowledge of when to take folic acid. There is little information regarding lifestyle factors that correlate with consuming folic acid. The purpose was to describe folic acid consumption among college students; and explore the relationship between folic acid intake and the variables of: age, gender, year in college, alcohol and tobacco use, and vitamin supplement intake.

STUDY DESIGN AND METHODS

This was a descriptive study with secondary analysis of data from 1,921 college-aged student participants in North Carolina who took part in a pretest/posttest-designed intervention to increase folic acid consumption and knowledge. Surveys included demographic, lifestyle, folic acid knowledge, and consumption questions adapted from the Centers for Disease Control and Prevention questionnaire. Quantitative analyses included descriptive statistics and logistic regression.

RESULTS

Of the 1,921 college students, 83.3% reported taking a vitamin supplement, but only 47.6% stated that the vitamin contained folic acid. A relationship was found between age, year in school, gender, and vitamin intake. Lifestyle variables were not significant predictors of folic acid consumption.

CLINICAL IMPLICATIONS

Identification of variables associated with folic acid intake, marketing, and education can be focused to increase supplementation levels, and ultimately reduce the number of NTDs.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验