PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Public Health. Author manuscript; available in PMC 2010 August 24.
Published in final edited form as:
PMCID: PMC2696671
NIHMSID: NIHMS226521

Disparities in Dietary Intake, Meal Patterning, and Home Food Environments Among Young Adult Nonstudents and 2- and 4-Year College Students

Melissa C. Nelson, PhD, RD, Nicole I. Larson, PhD, MPH, RD, Daheia Barr-Anderson, PhD, Dianne Neumark-Sztainer, PhD, MPH, RD, and Mary Story, PhD, RD

Abstract

We examined whether young adult meal patterning, dietary intake, and home food availability differed among nonstudents, 2-year college students, and 4-year college students (N = 1687; mean age=20.5 years). Unadjusted analyses showed that few young adults consumed optimal diets and, compared with 4-year college students, nonstudents and 2-year students consumed fewer meals and poorer diets. After controlling for sociodemographics and living arrangements, we found that over half of the observed associations remained significant (P<.05). Nutrition interventions are needed for young adults, particularly specific at-risk groups.

Little is known about eating habits and food practices during young adulthood, and few studies to date have explored the heterogeneity of young adult lifestyles and possible contextual influences on diet. Previous research on young adults has primarily focused on youths attending 4-year colleges,14 with little understanding of diet-related factors among those who have less traditional college experiences (such as those who attend 2-year community or technical colleges) or who do not enroll in college after high school.4 Thus, we sought to examine differences in young adults’ dietary intake, meal patterns, and home Food availability among (1) nonstudents, (2) students at 2-year colleges, and (3) students at 4-year colleges.

METHODS

Data for this cross-sectional analysis were from Project Eating Among Teens II (EAT-II), a longitudinal study of Minnesota adolescents and young adults.5 Our sample consisted of 750 men and 937 women who completed the EAT-II survey and the Youth–Adolescent Food-Frequency Questionnaire at follow-up as young adults (mean age=20.5 years) from 2003 to 2004.

The EAT-II survey included items assessing meal and snack frequency and home food availability. Scores for home food availability were created by summing the availability of specific examples of 5 healthful and 4 unhealthful foods and beverages.68 Scores ranged from 5 to 20 (for healthful foods and beverages) and 4 to 16 (for unhealthful foods and beverages); higher scores indicate greater availability of healthful (or unhealthful) foods and beverages. The food frequency questionnaire was used to assess participants’ overall past-year dietary intake.911

Sociodemographic characteristics were self-reported and included race/ethnicity, socioeconomic status (SES), age, and parental status.12 Student status was defined as not a student, student at a community or technical college (2-year college), or student at a 4-year college. Past-year living arrangements included rented apartment or house, parent’s home, or on campus (including residence hall and fraternity or sorority house).

We used linear regression to estimate differences in diet-related outcomes by student status. Models were examined (1) unadjusted, (2) adjusted for sociodemographics, and (3) additionally adjusted for living arrangement. Because responders to the EAT-II survey were demographically different from nonresponders, all analyses were adjusted for differential response rates using response propensity weights,13 as described elsewhere,14 Analyses were conducted with SAS version 8.2 (SAS Institute, Cary, NC).

RESULTS

Sociodemographic factors varied by student status (Table 1). Among 4-year college students, 38% rented apartments or houses, 25% lived with parents, and 37% lived on campus (data not shown). Among 2-year students, 31% rented, 68% lived with parents, and 1% lived on campus. Among nonstudents, 38% rented and 62% lived with parents.

TABLE 1
Participant Characteristics, by Student Status: Project Eating Among Teens (EAT)-II Survey, Minnesota, 2003–2004

Unadjusted models indicated that most young adults did not meet national recommendations for dietary intake (Table 2).15,16 Four-year college students reported eating meals more frequently and had better dietary intakes than did 2-year students and nonstudents. Nonstudents reported the lowest home availability of healthful foods. Overall, a majority of relationships remained statistically significant (19 of the 25 crude significant associations) in models adjusted for sociodemographic factors, After further adjustment for living arrangement, 13 of the original 25 associations continued to be significant.

TABLE 2
Mean Meal Frequencies, Dietary Intake, and Home Food Availability Among Young Adults, by Student Status: Project Eating Among Teens (EAT)-II Survey, Minnesota, 2003–2004

DISCUSSION

We examined differences in dietary factors among young adults by student status. In general, nonstudents and 2-year college students reported less frequent meals and poorer dietary intake compared with 4-year college students. Most differences were still evident after we controlled for sociodemographic factors, such as race/ethnicity and SES. Although some associations were attenuated after adding contextual factors (i.e., living arrangement) to the model, over half (52%) of the associations originally detected in the crude models remained statistically significant.

In this examination of young adult dietary patterns, crude and adjusted estimates were informative in different ways. Crude estimates indicated which subgroups were most at risk and thus may be the most effective targets for intervention strategies. By contrast, adjusted estimates allowed us to further explore the extent to which the existence of dietary differences across young adult subgroups are independent of sociodemographic factors, which we know are important correlates of both student status and dietary intake. Significant differences observed in adjusted models suggest that nonstudents and 2-year college populations are important targets for nutrition interventions not only because they represent a greater number of racial/ethnic minorities and low-SES groups (who are largely the most at-risk groups for poor dietary intake)17 but also because of other independent lifestyle factors. Virtually no research to date has explored other modifiable lifestyle factors on young adults’ diets; therefore, future research will be critical to informing effective intervention strategies. Examples of other important lifestyle factors could include food access, perceived social norms, social support for healthful eating, stress, and lifestyle management.

To our knowledge, ours is the first study of its kind in over a decade.18 Strengths of the study include the use of data on a broad array of diet-related factors and the diverse study sample. However, the sample, drawn from a cohort of former Minnesota high school students, may have limited generalizability. Furthermore, although validated food frequency questionnaires are a well-recognized dietary assessment method for large-scale studies, obtaining accurate estimates of intake frequency and serving sizes can be difficult.

Overall, our findings indicate that few young adults are consuming optimal diets. Although students at 4-year colleges reported the best dietary intake, they were, on average, far from meeting national dietary recommendations.15,16 Thus, effective health promotion efforts are needed for all young adults.

Acknowledgments

This study was supported by the Maternal and Child Health Bureau, Health Resources and Services Administration, Department or Health and Human Services (grant R40 MC 00319). Additional salary support was provided by the Obesity Prevention Center at the University Minnesota and the National Cancer Institute (Award K07CA126837).

Footnotes

Contributors

M.C Nelson conceptualized the analysis plan and wrote the article. N.I. Larson conducted the data analysis. D.Neumark-Sztainer and M. Story conceptualized the larger Project EAT-II study design and oversaw data collection. All authors contributed to the interpretation of results and revision of the article.

Note. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Human Participant Protection

All study procedures were approved by the University or Minnesota institutional review board. Completion and return of the EAT-II mail-in survey implied written consent to study participation

Contributor Information

Melissa C. Nelson, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis.

Nicole I. Larson, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis.

Daheia Barr-Anderson, School of Kinesiology, University of Minnesota, Minneapolis.

Dianne Neumark-Sztainer, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis.

Mary Story, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis.

References

1. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Weight changes, exercise and dietary patterns during freshman and sophomore years of college. J Am Coll Health. 2005;53(6):245–251. [PubMed]
2. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Changes in weight and health behaviors from freshman through senior year of college. J Nutr Educ Behav. 2008;40(1):39–42. [PubMed]
3. Levitsky DA, Halbmaier CA, Mrdjenovic G. The freshman weight gain: a model for the study of the epidemic of obesity. Int J Obes Relat Metab Disord. 2004;28(11):1435–1442. [PubMed]
4. Nelson M, Story M, Larson N, Neumark-Sztainer D, Lytle L. Emerging adulthood and college-aged youth; an overlooked age for weight-related behavior change. Obesity (Silver Spring) 2008;16(10):2205–2211. [PubMed]
5. Neumark-Sztainer D, Wall M, Guo J, Story M, Haines J, Eisenberg M. Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents: how do dieters fare 5 years later? J Am Diet Assoc. 2006;106(4):559–568. [PubMed]
6. French SA, Story M, Neumark-Sztainer D, Fulkerson JA, Hannan P. Fast food restaurant use among adolescents. Int J Obes Relat Metab Disord. 2001;25(12):1823–1833. [PubMed]
7. Boutelle K, Fulkerson J, Neumark-Sztainer D, Story M, French S. Fast food for family meals: relationships with parent and adolescent food intake, home food environment, and weight status. Public Health Nutr. 2007;10(1):16–23. [PubMed]
8. Larson NI, Neumark-Sztainer DR, Harnack LJ, Wall MM, Story MT, Eisenberg ME. Fruit and vegetable intake correlates during the transition to young adulthood. Am J Prev Med. 2008;35(1):33–37. [PubMed]
9. Rockett HR, Breitenbach M, Frazier AL, et al. Validation of a youth/adolescent food frequency questionnaire. Prev Med. 1997;26(6):808–816. [PubMed]
10. Rockett HR, Wolf AM, Colditz GA. Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. J Am Diet Assoc. 1995;95(3):336–340. [PubMed]
11. Perks S, Roemmich J, Sandow-Pajewski M, et al. Alterations in growth and body composition during puberty. IV. Energy intake estimated by the Youth-Adolescent Food-Frequency Questionnaire: validation by the doubly labeled water method. Am J Clin Nutr. 2000;72:1455–1460. [PubMed]
12. Neumark-Sztainer D, Story M, Hannan PJ, Croll J. Overweight status and eating patterns among adolescents: where do youths stand in comparison with the healthy people 2010 objectives? Am J Public Health. 2002;92(5):844–851. [PubMed]
13. Little R. Survey nonresponse adjustments for estimates of means. Int Stat Rev. 1986;54:137–139.
14. Neumark-Sztainer D, Wall M, Guo J, Story M, Haines J, Eisenberg M. Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents. J Am Dent Assoc. 2006;106(4):559–568. [PubMed]
15. US Department of Health and Human Services and US Department of Agriculture. Dietary Guidelines for Americans, 2005. 6. Washington, DC: US Government Printing Office; 2005.
16. United States Department of Agriculture. MyPyramid. [Accessed December 16, 2008]. Web site. Available at: http://mypyramid.gov.
17. US Department or Health and Human Services. Healthy People 2010. [Accessed December 9, 2008]. Available at: http://www.healthypeople.gov.
18. Youth Risk Behavior Surveillance. National College Health Risk Behavior Survey–United States, 1995. MMWR CDC Surveill Summ. 1997;46(6):1–56. [PubMed]