![[Feedback]](/icons/banner/feedbackACT.gif){kind=icon}
![[For Subscribers]](/icons/banner/subscriberACT.gif){kind=icon}
![[Archive]](/icons/banner/archiveACT.gif){kind=icon}
![[Search]](/icons/banner/searchACT.gif){kind=icon}
![[Contents]](/icons/banner/tocACT.gif){kind=icon}
|
|
|
|
|
||
3 Department of Nutrition and Food Sciences, University of Rhode Island, Kingston, RI 02881; 4 Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029; 5 Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-7344; 6 Departments of Nutrition and Society, Human Development and Health, Harvard School of Public Health, Boston, MA 02115; 7 Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208; 8 Oregon Research Institute, Eugene, OR 97403; 9 Departments of Medicine, Clinical and Social Sciences in Psychology, Psychiatry University of Rochester, Rochester, NY 14642; 10 Division of Health Promotion and Sports Medicine, Oregon Health and Science University, Portland, OR 97239; 11 Illinois Institute of Technology, Institute of Psychology, Chicago, IL 60616; 12 Rush University Medical Center, Department of Food and Nutrition Services, Chicago, IL 60612; 13 Biometry Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7344; 14 Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago IL 60612; and 15 Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-7344
* To whom correspondence should be addressed. E-mail: gwg{at}uri.edu.
 |
ABSTRACT |
|---|
 TOP ABSTRACT IntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
|
Introduction |
|---|
|
TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
Accurate measurement of dietary intake is a major challenge for health promotion research focusing on FV consumption. No "gold standard" provides a reference measurement of true intake, but repeat 24-h dietary recall interviews (24HR) have been the cornerstone of the U.S. national nutrition surveillance system (7). Although there are no recovery biomarkers to quantify FV intake, moderate correlations of serum carotenoids (SC) with dietary FV have been observed in controlled feeding trials (8). However, SC levels reflect metabolic processes also (9) as well as environmental factors (10), thus effectively reducing correlations with dietary intake (7).
In response to the need for program evaluation instruments, several short food frequency instruments assessing only FV intake, without portion size, have been developed, including a single-item instrument. In general, though, these instruments underestimate intake (11). In 1998 the National Cancer Institute (NCI) developed a revised Fruit and Vegetable Screener (FVS) that added explicit portion size questions (12) (http://riskfactor.cancer.gov/diet/screeners/fruitveg/allday.pdf). In a nationwide sample of adults, the FVS produced relatively accurate estimates of FV intake consistent with intake as measured using multiple 24HR (12). Scoring the FVS without portion size questions (frequency alone) led to an underestimation of intake (12,13); although using estimated portion sizes decreased the magnitude of the underestimation, estimated intake remained low compared with 24HR (13,14).
The primary validation studies of the FVS have been conducted in predominantly white, well-educated population samples in dietary assessment research (12–13). The current study was designed to evaluate the FVS among study participants in 5 ethnically and age diverse adult target populations (15). The primary purpose of this article is to assess the correlation between FV intake estimates derived from the FVS as compared with multiple 24HR as well as to assess correlations of the FVS with SC levels among participants enrolled in health promotion trials. Secondary purposes include 1) assessment of the difference in intake estimated by the FVS and intake estimated from 24HR including ability to rank participants according to level of intake (e.g., 24HR intake < 5 servings/d), and 2) exploration of the correlation of alternative scoring procedures for the FVS as well as the 1-item measure with multiple 24HR as well as SC.
|
Materials and Methods |
|---|
|
TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
Dietary assessment. Sites administering 24HR utilized similar multipass methodology; servings of FV were calculated based on gram weights consumed and USDA Pyramid equivalences from USDA's Food and Nutrient Database for Dietary Studies [see Yaroch et al. for a more detailed description (15)].
The NCI FVS is a 19-item instrument querying the frequency of usual consumption of 10 categories of FV over the past month (12) (http://riskfactor.cancer.gov/diet/screeners/fruitveg/allday.pdf). Portion sizes are queried for 9 items: 100% juice, fruit, lettuce salad, French fries/fried potatoes, other white potatoes, cooked dried beans, other vegetables, tomato sauce, and vegetable soups. A single item asking the frequency of consuming "mixtures that included vegetables" was not included in analyses. The screener estimates daily servings of FV using the 1992 USDA Food Guide Pyramid defined servings (16). For this study, all food categories, including French fries, were included. Although designed to be scored using respondent-assessed portion size (frequency x respondent-assessed portion size), the FVS also was scored using frequency alone (FVS-frequency) and using external estimates of portion size with regression-adjusted estimate (FVS-estimate) of servings [
+ β(frequency x estimated portion size based on CSFII age- and gender-specific median portion sizes)] (13,14).
URI, HSPH, and ROC used a single-item global self-assessment of servings of FV usually eaten (17), and Emory used a 2-item assessment (1 for fruits, the other for vegetables; items were summed for analysis) (18). Response categories ranged from 0 to 6 or more daily (coded as 6). Emory asked this range for both items; for consistency, daily servings were truncated at 6.
Biochemical assessment.
Blood was collected from an antecubital vein after an overnight fast, centrifuged at 1500 x g, and refrigerated for 15 to 20 min. After the centrifugation, 0.5 to 1.0 mL serum was transferred into a cryotube. Samples were stored at –70°C to –80°C until shipment (express mail, packed in dry ice) to a laboratory at the University of Illinois at Chicago. Levels of 5 major carotenoids (-carotene, β-carotene, β-cryptoxanthin, lutein/zeaxanthin, and lycopene) were determined by an established method (19). The reliability of the assay was previously confirmed with blind control samples with coefficients of variability 5 to 6% (20). The laboratory is a reference laboratory for the National Institute of Standards and Technology's (Gaithersburg, MD) quality assurance program for carotenoids (21). Consistent with prior biomarker studies, results are reported without lycopene (10).
Statistical analyses. Before analysis, 24HR and FVS were square-root transformed, and SC was log transformed. Statistical tests are based on transformed values and are back-transformed for presentation. One extreme (>3 times the interquartile range above quartile 3) value of FV intake was identified for FVS and excluded. All primary analyses are presented separately by site and gender (as these factors were significant modifiers of agreement based on –2 log likelihood ratios). Comparisons of mean FV intake were tested using analysis of variance (ANOVA; Proc GLM in SAS 9.1). A measurement error model (ME) was applied in this study, as described in Freedman et al. (22), to deattenuate estimated correlations between the multiple 24HR and other instruments and SC.
Correlations between the various dietary measures and SC were estimated for each site and gender using Proc Corr (SAS 9.1). Partially adjusted correlations controlling for site and age, and fully adjusted correlations, in addition controlling for smoking, multivitamin use, and BMI, were estimated (23). A limitation of the study was the inability to adjust correlations for serum cholesterol (10) because serum cholesterol was not assessed at all sites.
|
Results |
|---|
|
TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
60 y) and education (49% 
high school/GED) (Table 1).
|
5 servings/d (0.11 to 0.63 men; 0.20 to 0.62 women) but a high negative predictive value to detect 24HR intake < 5 servings/d (0.64 to 0.85 men; 0.83 to 0.95 women).
|
|
|
Discussion |
|---|
|
TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
The overestimation of intake found with FVS relative to 24HR was associated with a high negative predictive value indicating that the instrument would be effective in identifying subjects with a low intake. However, in studies where subjects with intake above a specific predetermined threshold are excluded, the relatively low positive predictive values (indicative of overreporting of FV intake) suggest that many potentially eligible subjects would be excluded. This could increase the pool of subjects needing to be screened.
Correlations between 24HR intake and FVS in women were similar to previous findings, but the FVS produced higher correlations than alternative methods, in contrast to Thompson (12). For men, correlations of the FVS with 24HR were lower than those found by Thompson (12); correlations with alternative scoring methods and the 1-item were too low to be useful for dietary assessment.
Servings of FV as estimated by 24HR showed modest to moderate correlations with SC for both genders, as has been found previously (25); however, correlations between FVS and SC were moderate among women and low and nonsignificant in men. Future research is recommended before using the FVS to estimate dietary carotenoid intake in men. In women, correlations between the FVS and SC were higher than correlations between SC and other scoring methods or 1-item, indicating that portion size adjustment may be useful.
In summary, in these diverse study populations, the FVS did not perform as well as in prior cross-sectional studies. One reason may be that BCC participants were recruited into behavioral intervention studies as opposed to surveillance studies. It is unknown how generalizable the results of our study are to other intervention studies, especially given the wide variation in instrument correspondence among sites. This study found that participants overestimated intakes with the FVS, sometimes substantially, suggesting a positive bias. Correlations between the FVS and 24HR intake and between the FVS and SC were low among men, although they were moderate among women. Overall, obtaining portion size estimates from respondents seemed to improve correlations with 24HR, although the additional self-reported information also appeared to exacerbate tendencies to overestimate. Utility of the instrument for detecting change in intake during the course of intervention studies is addressed elsewhere in this supplement (26).
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
2 Author disclosures: G. W. Greene, K. Resnicow, C. F. E. Thompson, K. E. Peterson, T. G. Hurley, J. R. Hebert, D. J. Toobert, G. C. Williams, D. L. Elliot, T. Goldman Sher, A. Domas, D. Midthune, M. Stacewicz-Sapuntzakis, A. L. Yaroch, and L. Nebeling, no conflicts of interest.
16 Abbreviations used: 24HR, 24-h recalls; FV, fruit and vegetables; FVS, NCI Fruit and Vegetable Screener; SC, serum carotenoids; Emory, Emory University; HSPH, Harvard School of Public Health; IIT/Rush, Illinois Institute of Technology, Rush University; NCI, National Cancer Institute; OHSU, Oregon Health & Science University; ORI, Oregon Research Institute; ROC, University of Rochester; URI, University of Rhode Island; USDA, United States Department of Agriculture.
17 Women in the IIT/Rush sample were excluded from the analytical sample because of small numbers (n = 5).
18 Although ROC also collected SC, ROC data were excluded from analyses because their use of a different laboratory resulted in noncomparable SC estimates.
19 IIT/Rush did not administer the 1-item screener.
|
LITERATURE CITED |
|---|
|
TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
|---|
1. Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, Colditz GA, Rosner B, Spiegelman D, Willett WC. Fruit and vegetable intake and risk of major chronic disease. J Natl Cancer Inst. 2004;96:1577–84.
2. US Dept of Health and Human Services, US Dept. of Agriculture, US Dietary Guidelines Advisory Committee. Dietary guidelines for Americans, 2005. 6th ed. Washington, DC: GPO; 2005.
3. US Dept of Health and Human Services. Healthy people 2010. Washington, DC: US Dept of Health and Human Services, US GPO, Supt of Documents; 2000.
4. US Dept of Agriculture, US Dept of Health and Human Services. Nutrition and your health: Dietary guidelines for Americans. 5th ed. Washington, DC: US Dept of Agriculture, US Dept of Health and Human Services; 2000.
5. Potter J, Finnegan J, Guinard JX, Huerta E, Kelder SH, Kristal A, Kumanyika S, Li R, Motsinger B, et al. 5 A Day for Better Health Program evaluation report. Bethesda, MD: National Institutes of Health, National Cancer Institute; 2000.
6. Cook A, Friday J. CNRG table set 3.0: pyramid servings intakes in the United States 1999–2002, 1 day. World Wide Web; 2005.
7. Thompson FE, Subar A. Dietary assessment methodology. In Coulston AM, Rock CL, Monsen ER, eds. Nutrition in the prevention and treatment of disease. San Diego, CA: Academic Press; 2001.
8. Bowen PE, Garg V, Stacewicz-Sapuntzakis M, Yelton L, Schreiner RS. Variability of serum carotenoids in response to controlled diets containing six servings of fruits and vegetables per day. Ann NY Acad Sci. 1993;691:241–3.[Medline]
9. Potischman N. Biologic and methodologic issues for nutritional biomarkers. J Nutr. 2003;133: Suppl 3:875S–80S.
10. Mayne ST, Cartmel B, Silva F, Kim CS, Fallon BG, Briskin K, Zheng T, Baum M, Shor-Posner G, Goodwin WJ Jr. Plasma lycopene concentrations in humans are determined by lycopene intake, plasma cholesterol concentrations and selected demographic factors. J Nutr. 1999;129:849–54.
11. Thompson FE, Kipnis V, Subar AF, Krebs-Smith SM, Kahle LL, Midthune D, Potischman N, Schatzkin A. Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate daily number of servings of fruit and vegetables. Am J Clin Nutr. 2000;71:1503–10.
12. Thompson FE, Subar AF, Smith AF, Midthune D, Radimer KL, Kahle LL, Kipnis V. Fruit and vegetable assessment: performance of 2 new short instruments and a food frequency questionnaire. J Am Diet Assoc. 2002;102:1764–72.[Medline]
13. Thompson FE, Midthune D, Subar AF, Kahle LL, Schatzkin A, Kipnis V. Performance of a short tool to assess dietary intakes of fruits and vegetables, percentage energy from fat and fibre. Public Health Nutr. 2004;7:1097–105.[Medline]
14. Thompson FE, Midthune D, Subar AF, McNeel T, Berrigan D, Kipnis V. Dietary intake estimates in the National Health Interview Survey, 2000: methodology, results, and interpretation. J Am Diet Assoc. 2005;105:352–63.[Medline]
15. Yaroch AL, Nebeling L, Thompson FE, Hurley TG, Hebert JR, Toobert DJ, Resnicow K, Greene GW, Elliot DL, et al. Baseline design elements and sample characteristics for seven sites participating in the Nutrition Working Group of the Behavior Change Consortium. J Nutr. 2008;138:185S–192S.
16. US Dept of Agriculture Center for Nutrition Policy and Promotion. The food guide pyramid. Home and garden bulletin No. 252. Washington, DC: USDA; 1996.
17. Laforge RG, Greene GW, Prochaska JO. Psychosocial factors influencing low fruit and vegetable consumption. J Behav Med. 1994;17:361–74.[Medline]
18. Resnicow K, Odom E, Wang T, Dudley WN, Mitchell D, Vaughan R, Jackson A, Baranowski T. Validation of three food frequency questionnaires and 24-hour recalls with serum carotenoid levels in a sample of African-American adults. Am J Epidemiol. 2000;152:1072–80.
19. Stacewicz-Sapuntzakis M, Bowen P, Kikendall J, Burgess M. Simultaneous determination of serum retinol and various carotenoids: their distribution in middle-aged men and women. J Micronutr Anal. 1987;3:27–45.
20. Potischman N, Herrero R, Brinton LA, Reeves WC, Stacewicz-Sapuntzakis M, Jones CJ, Brenes MM, Tenorio F, de Britton RC, Gaitan E. A case-control study of nutrient status and invasive cervical cancer. II. Serologic indicators. Am J Epidemiol. 1991;134:1347–55.
21. Duewer DL, Kline MC, Sharpless KE, Thomas JB, Stacewicz-Sapuntzakis M, Sowell AL. NIST micronutrients measurement quality assurance program: characterizing individual participant measurement performance over time. Anal Chem. 2000;72:3611–9.[Medline]
22. Freedman LS, Carroll RJ, Wax Y. Estimating the relation between dietary intake obtained from a food frequency questionnaire and true average intake. Am J Epidemiol. 1991;134:310–20.
23. Hebert JR, Hurley TG, Hsieh J, Rogers E, Stoddard AM, Sorensen G, Nicolosi RJ. Determinants of plasma vitamins and lipids: the Working Well Study. Am J Epidemiol. 1994;140:132–47.
24. Resnicow K, Jackson, A, Blissett D, Wang T, McCarty F, Rahotep S, Periasamy S. Results of the Healthy Body Healthy Spirit Trial. Health Psycho. 2005;24:339–48.
25. Boeing H, Bohlscheid-Thomas S, Voss S, Schneeweiss S, Wahrendorf J. The relative validity of vitamin intakes derived from a food frequency questionnaire compared to 24-hour recalls and biological measurements: results from the EPIC pilot study in Germany. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol. 1997;26: Suppl 1:S82–90.
26. Peterson KE, Hebert JR, Hurley TG, Resnicow K, Thompson FE, Greene GW, Shaikh AR, Yaroch AL, Williams GC, Salkeld J, et al. Accuracy and precision of two short screeners to assess change in fruit and vegetable consumption among diverse populations participating in health promotion intervention trials. J Nutr. 2008;138:218S–225S.
This article has been cited by other articles:
|
|
 |
|
  S. A. A. Beresford, L. M. Klesges, and H. R. H. Rockett Introduction J. Nutr., January 1, 2008; 138(1): 183S - 184S. [Full Text] [PDF]
|
|
|
|
|
|
A. L. Yaroch, L. Nebeling, F. E. Thompson, T. G. Hurley, J. R. Hebert, D. J. Toobert, K. Resnicow, G. W. Greene, G. C. Williams, D. L. Elliot, et al. Baseline Design Elements and Sample Characteristics for Seven Sites Participating in the Nutrition Working Group of the Behavior Change Consortium J. Nutr., January 1, 2008; 138(1): 185S - 192S. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. E. Davis, K. Resnicow, A. A. Atienza, K. E. Peterson, A. Domas, A. Hunt, T. G. Hurley, A. L. Yaroch, G. W. Greene, T. Goldman Sher, et al. Use of Signal Detection Methodology to Identify Subgroups of Dietary Supplement Use in Diverse Populations J. Nutr., January 1, 2008; 138(1): 205S - 211S. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. E. Peterson, J. R. Hebert, T. G. Hurley, K. Resnicow, F. E. Thompson, G. W. Greene, A. R. Shaikh, A. L. Yaroch, G. C. Williams, J. Salkeld, et al. Accuracy and Precision of Two Short Screeners to Assess Change in Fruit and Vegetable Consumption among Diverse Populations Participating in Health Promotion Intervention Trials J. Nutr., January 1, 2008; 138(1): 218S - 225S. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. R. Hebert, T. G. Hurley, K. E. Peterson, K. Resnicow, F. E. Thompson, A. L. Yaroch, M. Ehlers, D. Midthune, G. C. Williams, G. W. Greene, et al. Social Desirability Trait Influences on Self-Reported Dietary Measures among Diverse Participants in a Multicenter Multiple Risk Factor Trial J. Nutr., January 1, 2008; 138(1): 226S - 234S. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
