P40 (continued)

S102 Poster Abstracts Journal of Nutrition Education and Behavior � Volume 42, Number 4S, 2010

Continued on page S103

Outcome Measures and Analysis: The independentvariables were age and university status. The dependentvariable was knowledge of CHD. A 2 (age groups) � 4(university status) analysis of variance was used to comparedifferences in knowledge scores.Results: Knowledge score was represented as percentageanswered correctly on the knowledge section of the survey.The percentage answered correctly for all respondents was71.6% � 14.6. There was a significant difference in correctresponses between age groups (P< .001), whereby the aged35 years and older group answered significantly morecorrect answers than the 18- to 35-year-old group. Therealso was a significant difference in knowledge betweenundergraduate students and graduate students (P < .001),undergraduate students and faculty (P < .001), and under-graduate students and staff (P < .007).Conclusions and Implications: Undergraduate femalestudents and younger women were not as knowledgeableabout the risk of CHD as other women on campus. Theymay benefit from nutrition education programs that em-phasize the role of lifestyle and diet on long-term risk ofCHD.

P41 Do College Students Understand the NCIFruit and Vegetable Screener?Geoffrey Greene, PhD, RD, LDN, gwg@uri.edu;Meghan McCarron, RD, LDN, mccarron0507@gmail.com;Linda Sebelia, MS, RD, LDN, sebelia@mail.uri.edu,University of Rhode Island, NFS Department, 106 RangerHall, Kingston, RI 02881

Objective: To evaluate college students’ ability to use theNCI Fruit and Vegetable Screener, using cognitive inter-views.Design, Setting and Participants: Trained assessorsrecruited 48 students who filled out the NCI Fruit andVegetable Screener (9 fruit and vegetable items with 4quantity categories for each item) and then completedcognitive interviews assessing (1) understanding of thefood item, ‘‘What was the first thing that came to yourmind when you saw __ [food category] on the form?’’and for food items consumed more than once/month,(2) ‘‘Yesterday did you have any _? How much of__ didyou have?’’ (or amount usually consumed if notconsumed yesterday).Outcome Measures and Analysis: Relevant responses,inappropriate responses (eg, fruit drink instead of 100%fruit juice), and other responses were tabulated for eachfruit and vegetable item. Quantity responses were catego-rized as concordant (same quantity), � 1 category andgreater than 1 category.Results: Few responses were inappropriate (0%-12%) Al-though the proportion of relevant responses ranged from6% to 73%, most of the other responses were related tothe food item, eg, 73% referred to a location in which frieswere consumed. Concordance in quantities ranged from41% to 84%. Concordance and � 1 quantity categoryranged from 94% to 100%.

Conclusions and Implications: College students ap-peared to understand items on the instrument, and quan-tity choices on the screener appeared relatively accurate,according to recent intake. Although other studies needto validate the instrument with this population, these cog-nitive interviews suggest the instrument could be used forcollege students, without modification. This project isfunded by RI Agricultural Experiment Station, grantRH00131; USDA CSREES, grant 2005-35215-154121541.

P42 Distribution of Energy and MacronutrientIntakes Among Meals and SnacksEun Ha, PhD, eha@kent.edu; Natalie Caine-Bish, PhD, RD,LD, ncaine@kent.edu; Karen Lowry-Gordon, PhD, RD, LD,klowry@kent.edu, Kent State University, 100 Nixson Hall,School of Health Science, Kent, OH 44242

Objective: To investigate the status of energy and macro-nutrients and their contribution to the meals and snacks ofcollege students.Design, Setting and Participants: A total of 174 col-lege students (146 women, 28 men) participating in thestudy completed personal information forms and 3-daydietary records.Outcome Measures and Analysis: Analysis of variancerepeated measures were performed to determine the differ-ences in the macronutrient intake of each meal and snack.Results: The contribution of carbohydrates to the totalenergy intake was about 48%. Fifteen percent of the totalenergy came from protein and 36% from fat. The averageconsumption of saturated fat was 25.55 � 14.02 g, whichcontributed 11% to the total energy intake. The most en-ergy was consumed during lunch and dinner, significantlyhigher than energy from breakfast, respectively P < .001and P < .001. Energy intake from dinner and an eveningsnack contributed 44.21% of the total energy intake,whereas breakfast and a morning snack provided 17.74%of the total energy.Conclusions and Implications: The study showed thatstudents in the current study consumed the majority of en-ergy at later times of the day, which may raise the concernthat consuming the majority of energy later in the day canperhaps promote gradual weight gain. Meanwhile, break-fast contributed least to energy and all the macronutrientintakes. The study results reinforce the importance ofnutrition education/intervention in this population toaid in distributing energy intake more evenly throughoutthe day and making healthier food choices at dinner todecrease excess energy intake.

P43 Changes in Energy and MacronutrientIntake Among Female College Students AfterClass-based Nutrition Intervention: A PilotStudyEun Ha, PhD, eha@kent.edu; Natalie Caine-Bish, PhD, RD,LD, ncaine@kent.edu; Karen Lowry-Gordon, PhD, RD, LD,klowry@kent.edu, Kent State University, 100 Nixson Hall,School of Health Science, Kent, OH 44242