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3 Program in Public Health Nutrition, Department of Nutrition and Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA 02115; 4 South Carolina Statewide Cancer Prevention and Control Program, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208; 5 Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI 48109; 6 Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892; 7 Department of Nutrition and Food Sciences, College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI 02881; 8 Health Promotion Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892; 9 Departments of Medicine, Clinical and Social Sciences in Psychology, and Psychiatry, University of Rochester, Rochester, NY 14642; 10 Division of Biology and Medicine, Brown University, Providence, RI 02912; 11 Oregon Research Institute, Eugene, OR 97403; 12 Rush University Medical Center and Rush University College of Health Sciences, Rush Medical Center, Chicago, IL 60612; and 13 Division of Health Promotion and Sports Medicine, Oregon Health & Science University, Portland, OR 97239
* To whom correspondence should be addressed. E-mail: kpeterso{at}hsph.harvard.edu.
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ABSTRACT |
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Introduction |
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Studies investigating the role of diet and health in humans almost always rely on subjects' self-reported dietary intake. Structured questionnaires, such as the FFQ, were developed to estimate usual dietary intake of individuals, the exposure of interest in most epidemiologic investigations (23–27) and nutrition education trials (28). These instruments reduce participant burden and cost in relation to other self-report methods such as 24-h dietary recall interviews (24HR) and food diaries, important considerations in large-scale studies. Abbreviated versions of structured questionnaires aimed at understanding food consumption patterns can focus the inquiry on a short list of candidate foods, further reducing participant burden. Because of their brevity, they also can be adapted for use in lower-literacy populations (29–31).
Increased public health emphasis on monitoring and promoting FV intake as a behavioral outcome has driven the need to understand how various short screeners perform in assessing intake of FV (32–40). A recent review of FV screener methods concluded that instruments with a moderate number of items and those addressing portion size may have greater validity than shorter instruments and those not querying sizes of portions commonly consumed (41). The few studies examining validity of measures to assess FV change as a result of intervention, however, have been confined largely to consideration of cross-sectional, posttest differences between treatment groups (42,43). Self-report biases may vary by subjects' sociodemographic characteristics (44) and can be reduced over time, possibly as participants in health promotion interventions "learn" how to report intakes on structured questionnaires (45). Given their widespread use to evaluate the success of public health efforts to improve diet, it is important to understand how simple, inexpensive dietary screeners estimate changes in FV intake in diverse populations over time and in response to intervention.
This article reports on the performance of the 19-item NCI Fruit and Vegetable Screener (FVS) (34) and a single item on overall FV consumption (1-item) in assessing change in dietary FV intake among adults with diverse sociodemographic characteristics participating in 5 health promotion intervention trials (46). The purpose of this study is to 1) examine the cross-sectional capacity of the FVS and 1-item measure to estimate FV servings relative to multiple 24HR at 2 time points, by gender and treatment group and 2) evaluate the performance of the 2 screeners to measure change in FV intake by comparing mean treatment effects with 24HR at follow-up. In a subgroup of participants, we also examined the correlation of the 3 self-report measures with change in serum carotenoids (SC).
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Methods |
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The Behavior Change Consortium (BCC) consists of 15 intervention studies independently funded to improve the science and practice of health behavior change by linking theory to change in some combination of diet, physical activity, and smoking (47). Seven of 15 studies created the BCC Nutrition Working Group (NWG) (48). As described in detail elsewhere in this supplement, all NWG sites included a dietary intervention component and used common dietary assessment tools, the FVS and the NCI Percentage Energy from Fat Screener, to conduct cross-site analyses examining their performance in diverse participants in health promotion trials (46).
The analytic sample for this report was restricted to 315 subjects at 5 BCC sites with 24HR and FVS data at both baseline and follow-up. Sites were the University of Rhode Island (URI), Harvard School of Public Health (HSPH), Illinois Institute of Technology/Rush University (IIT/Rush), Emory University, and University of Rochester (ROC). Analyses examining the 1-item screener were confined to the subset of 227 participants with complete data for all self-report measures at baseline and follow-up at 4 sites (excluding IIT/Rush, which did not administer the 1-item).
SC were collected at 4 sites (URI, IIT/Rush, Emory, ROC). ROC carotenoid data were excluded because use of a different laboratory and procedures might have resulted in noncomparable SC estimates. Carotenoids were not obtained at follow-up at IIT/Rush. The analytic subgroup for comparing self-report measures with SC data comprised the subset of 134 participants from 2 study sites (Emory and URI) with complete biochemical data and 24HR, FVS, and 1-item.
Instruments
The FVS is a 19-item instrument querying the frequency of usual consumption of 10 categories of FV over the past month (34). Portion sizes are asked for 9 categories: 100% juice, fruit, lettuce salad, French fries/fried potatoes, other white potatoes, cooked dried beans, other vegetables, tomato sauce, and vegetable soups. A single question that asked the frequency of consuming "mixtures that included vegetables" did not ask portion size, and was not analyzed. FV servings are quantified in terms of the 1992 Food Guide Pyramid (49). For fruits, a serving was defined as a whole fruit, 1/2 cup cut-up fruit, or 3/4 cup juice (1 cup = 0.237L). For vegetables, a serving was defined as 1 cup (0.237L) raw leafy vegetables, such as lettuce; 1/2 cup other vegetables; or 3/4 cup vegetable juice. Frequency and portion size reports were used to estimate self-reported individual fruit and vegetable consumption in Pyramid servings (50). Subjects with either incomplete frequency or portion size data were excluded from analyses. Standard scoring procedures [frequency x respondent-assessed portion size (RPS)] were used to calculate servings estimated by the FVS (50).
The 1-item is based either on a single FV question (at ROC, URI, and HSPH) (51) or 1 question each on fruit and vegetables (Emory) (39), which were summed to reflect total FV intake (40,46). The measure asks: "How many servings of fruits and vegetables do you usually eat each day?" and specifies "a serving is 1/2 cup of cooked vegetables, 1 cup of salad, a piece of fruit, 3/4 cup of 100% fruit juice." Responses range from 0 to 6 or more for the single item; 6 or more were coded as 6. Emory used 0–6 or more servings as the range both for fruits and for vegetables; for consistency across sites, the maximum number of servings at Emory was truncated at 6 for FV combined.
Multiple 24HR were administered by telephone to respondents by trained interviewers. Data collection and processing for Emory and HSPH were performed by the Diet Assessment Unit of the Cancer Prevention and Control Program at the University of South Carolina; for ROC by the Diet Assessment Center at Pennsylvania State University; and for URI and IIT/Rush by their staff. All sites followed the same protocol, including a preinterview mailing of a 2-dimensional food portion guide (52) and 3 nonconsecutive, unannounced 24HR over a 3-wk period, including 1 weekend day. Most (61%) of the analytic sample had 3 24HR at both baseline and follow-up, and 90% had at least 2.
The Minnesota Nutrient Data System (NDS) for Research (versions 4.05_33 and 4.06_34) was used to conduct, code, and process the 24HR (53). Interviews were conducted by trained interviewers using the multipass approach interface of NDS. Interviews were reviewed by supervisors, and all missing items were added with consultation from the Minnesota Nutrition Coordinating Center. Coding quality was checked with built-in systems that flag extreme values. All individual recalls defined as unreliable by the interviewer were reviewed and exclusion confirmed by a dietitian with experience in conducting/supervising NDS recalls and an author with similar credentials (G. W. Greene). In most cases, reasons for determining that a recall was unreliable were listed in the interviewers' notes and included examples such as "subject appeared confused," and "sick/vomiting all day." Only subjects with at least 1 valid recall were included in analyses.
24HR-derived Pyramid servings of FV were estimated through the USDA Continuing Survey of Food Intakes by Individuals (CSFII) 1994–96 survey database (46), which provides the FV servings per 100 g for each of >5000 food codes. Foods reported in the BCC and coded using NDS were linked to identical or similar food codes in the USDA database.
Biochemical assessment
Blood was collected from an antecubital vein after an overnight fast by a certified laboratory technician. The blood was centrifuged at 1500 x g and refrigerated for 15 to 20 min. Once centrifuged, 0.5 to 1.0 mL serum was transferred into a 1-mL cryotube. Samples were stored in a sample box at –70°C to –80°C until shipment. Samples packed in dry ice were shipped by Express Mail to a laboratory at the University of Illinois at Chicago. The levels of 5 major carotenoids (
- and β-cryptoxanthin, lutein, zeaxanthin, and lycopene) were determined by an established method (54). Consistent with validations of multiple self-report measures with biomarkers among participants in health promotion trials, results are reported without lycopene (39,40). The reliability of this assay has been confirmed with blind control samples in large epidemiological studies, in which coefficients of variability were 5 to 6% for all carotenoids measured (55,56). Similar quality assurance was not performed with SC samples used in this report. Nevertheless, the University of Illinois at Chicago laboratory is a reference laboratory for the National Institute of Standards and Technology's (Gaithersburg, MD) quality assurance program for carotenoids (57).
Treatment condition
Details on the design and implementation of the behavior change interventions from the 5 BCC sites contributing data for these analyses are described elsewhere in this supplement (46). These sites all targeted behavioral change in FV intake and measured this outcome using the same instrument (FVS). Increasing intake of fruits and vegetables was a primary behavioral target at 3 sites, URI, Emory, and HSPH. The intervention at the remaining 2 sites targeted reduction in dietary fat intake and, because dietary fat intake is inversely associated with fruit and vegetable intake (58), the intervention encouraged increased intake of FV. So, increasing intake of FV was also considered a major component of the dietary intervention at ROC and IIT. At all 5 sites, alternative intervention behavioral targets or no-treatment controls were included in the design. Thus, a variable indicating whether or not increased FV intake was a behavioral objective of the intervention was created. All other intervention conditions, i.e., those that addressed only smoking or physical activity, were classified as control.
Statistical analysis
Descriptive statistics on sociodemographic characteristics, smoking, and weight status were computed for the 315 participants who had at least 1 24 HR and the FVS at both baseline and follow-up and on the subset of 227 participants with complete information on all 3 self-report instruments, i.e., 1-item, FVS and 24 HR. Outliers for FV servings were defined as values more than or less than 3 times the interquartile (Q3–Q1) range for distributions at baseline and follow-up; none was found. Because FV values were positively skewed, FV servings for all instruments were square-root transformed to achieve a more normal distribution. To ease interpretation, analyses conducted on the transformed scale were subsequently back-transformed for presentation in tables. Means estimated on the transformed scale are statistically equivalent to medians on the original scale.
Cross-sectional comparisons. First, to examine the cross-sectional performance of the 2 screeners relative to the 24HR over time, we compared differences and deattenuated correlations of FV servings estimated from the FVS (n = 315) and the 1-item (n = 227) at baseline and follow-up. The means and SD for daily FV servings were computed on the square-root-transformed scale for the 24 HR, FVS, and 1-item measure, at baseline and at follow-up. Results are presented on the original scale by gender and by gender and treatment group. When the square-root-transformed FV values were used, the differences between each of the 2 screeners and the 24 HR (i.e., FVS – 24HR; 1-item – 24HR) were calculated at each time point, and a paired t test was used to test whether the difference was significantly different from 0 after controlling for site. The individual instrument mean and the difference are presented on the back-transformed scale.
To estimate deattenuated cross-sectional correlations at baseline and at follow-up, we used a measurement error (ME) model (59) in which the multiple, nonconsecutive 24HR was the reference instrument. The model assumes that the 24HR is unbiased on the square-root-transformed scale and contains only within-person error at baseline and at follow-up. ME models were run for each gender. Because of potential confounding, these analyses were adjusted for age and site.
Measures of FV change. Second, to evaluate capacity of the FVS and the 1-item to measure change in FV intake relative to the 24HR, we compared mean treatment effects estimated by each of the 3 self-report methods. The ME model was extended to an ANCOVA approach that regresses posttest values on a design variable for group assignment while controlling for pretest values (60). Least-squares means and 95% CIs were generated from the model (square root data were back-transformed) for FV servings from the 24HR, FVS, and 1-item, comparing intervention and control participants overall and by gender. To assess whether bias in servings estimated from the FVS was consistent over the range of intakes, we computed Bland-Altman plots comparing the difference in the change (Post-Pre) in FV servings between the 24HR and FVS with the mean change across both the FVS and 24HR (average of 24HR Post-Pre servings and FVS Post-Pre servings), adapting methods described elsewhere (31).
SC. For the third aim, we used SC, a group of nonrecovery biomarkers shown to correlate with fruit and vegetable intake (61) and that are not subject to biases associated with self-report measures (62). We first computed deattenuated correlations between change in FV servings estimated from 24HR, FVS, and 1-item with change in SC, excluding lycopene (n = 134). Triangulation of multiple self-report methods has been explored to improve estimates of usual "true intake" (40). We averaged servings estimated from unique combinations of 2 instruments and from all 3 self-report instruments and then correlated the changes in mean FV servings with change in SC.
Significance levels for testing were set at P 
0.05. All analyses were conducted using SAS software (Version 9.1; SAS Institute, Cary, NC).
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Results |
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25 kg/m2) than women (72.1%), and more reported they were currently smoking (25.8 and 17.1%, respectively). The 227 subjects with complete data, including the 1-item, were overrepresented by women (81.5% compared with 70.5%) and African Americans (38.3% compared with 30.8%) and non-smokers (92.1% compared with 78.7%). A greater proportion of men in the sample of 227 were aged >60 y (76.2% compared with 54.8%), but fewer were overweight or obese (73.9% compared with 83.8%). The 134 subjects with SC measures reflected the sociodemographic characteristics of Emory and URI study populations, described elsewhere in this supplement (46).
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0.05) of FVS with 24HR estimates was seen among male and female control subjects combined (0.31 baseline, 0.50 follow-up), whereas improvement for 1-item was evident only for males (0.03 baseline, 0.43 follow-up). Similar relationships were found when the FVS analyses shown in Tables 2 and 3 were restricted to 227 subjects (data not shown).
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0.05 with change in SC in men. |
Discussion |
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Frequency questionnaires with a greater number of items on FV have been shown to result in larger cross-sectional estimates of FV intake than shorter instruments (63). Similarly, we found FVS overestimated mean intake relative to 24HR. So it is plausible that the larger number of items in FVS, in comparison to the 1-item, contributed to relative overestimates. Consistent with results examining social desirability bias (64), overall changes in bias from baseline to follow-up for both instruments were not statistically significant. A post-test validation of a FFQ and 2 similar screeners among multiethnic participants in a 5-a-Day intervention trial found, however, that both an 8-item FVS (without portion-size questions) and the 1-item resulted in underestimates relative to 24HR of 1.6–1.7 FV servings compared with a 0.7 serving underestimate derived from a full-length FFQ (43). In a sample of 486 adults of whom 90% were white and 79% had a high school education, FVS underestimated FV servings relative to multiple 24HR in men [median 5.8 (24HR) vs. 5.0 (FVS) servings] but overestimated servings in women [4.2 (24HR) vs. 5.0 (FVS)] (34).
The cross-sectional deattenuated correlation of FVS with 24HR recalls improved significantly over time among control, but not intervention, subjects. Findings are consistent with the notion that participants learn to improve intake estimates with repeat measurement. Most cross-sectional validations of FFQ and structured dietary questionnaires have found that correlations and other measures of association with criterion measures improve at the second administration (23,31,45,65). Lack of such improvement among intervention subjects may reflect a countervailing influence of report bias caused by the intervention itself. However, for the 1-item, improvement in correlations was seen only among males in the intervention group, the same group in whom the social desirability bias emerged over time (64).
Analyses examining the capacity of screeners to capture treatment effects yielded significantly higher FVS estimates in the intervention than control groups at follow-up overall as well as by gender compared with 24HR. Among 315 subjects, FVS differences were significant both overall and within gender but not when repeated in the sample of 227. Thus, FVS may be prone to type I error, i.e., detecting an effect when one does not exist. The 1-item showed no significant treatment effects, consistent with 24HR. Because no significant change was observed using 24HR, and no independent evidence corroborated an intervention effect, it was not possible to assess the ability of either screener to detect behavioral change in FV intake.
No FV change score from any of the 3 self-report measures was significantly correlated with change in SC. In general, the correlations averaged 
0. Because carotenoids are not recovery biomarkers, and there was no large intervention effect, this should not be too surprising.
There are several limitations to the study. The reference criterion, 24HR, is associated with error (62). Three days may be insufficient to estimate usual intake, and 24HR may be susceptible to underreporting (63) because of variations in respondents' cognitive ability and other forms of bias. Thus, the true correlation coefficients of the 2 types of screeners may be higher than that reported here. Deattenuation remedies reliability limitations of recalls but not other forms of bias; to the extent that bias is correlated across instruments, coefficients may be misleadingly large. Using SC as a criterion measure can be questioned because levels reflect carotenoid status rather than dietary intake, and these may be influenced by factors such as smoking, alcohol consumption, or dietary supplement use (66,67). Another study limitation is the variation in samples in which instruments were analyzed, ranging from 315 when comparing FVS to 24HR to 134 when comparing all self-report measures to SC. Most FVS results in the sample of 315 were largely identical when these analyses were repeated in the sample of 227 participants with complete data for the 1-item measure. However, the nonsignificant treatment effect found with the FVS in the smaller sample, compared with the significant treatment effect found in the larger sample, highlights the importance of sample characteristics on performance. In the 315 participants for whom significant treatment effects were found, there were greater proportions of whites, smokers, and overweight individuals than in the subset of 227. The sample for this study was predominantly white or African American and middle-aged and older. Although site was controlled in all analyses, this covariate is an incomplete proxy for a variety of differences in sociodemographic and lifestyle characteristics that could be associated with bias in different self-reports. Sample size disallowed examination of differences across sites.
Despite the utility of FV screeners and their widespread use in evaluating health promotion efforts to achieve public health goals for FV consumption, little work has evaluated the performance of these tools using longitudinal data. Because cost may be a practical limitation to use of 24HR, population-based interventions often must rely on short FV screeners to understand whether or not they have made a difference. Automated self-administered 24HR systems are currently being developed (68) that may make multiple 24HR a more feasible evaluation option. This study represents a significant achievement in testing the commonly used FVS and 1-item to estimate change in FV consumption relative to 24HR in study populations with diverse sociodemographic characteristics participating in 5 intervention trials. Cross-sectional deattenuated correlations of FVS and the 1-item with 24HR, at baseline and follow-up, were modest and of similar magnitude: 0.4–0.5. It is encouraging that measures of concordance did not worsen appreciatively over time. Continued testing in larger samples representative of key U.S. population groups, especially in the context of a successful intervention, would help to advance the field.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Author disclosures: 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, D. J. Toobert, A. Domas, D. L. Elliot, J. Hardin, and L. Nebeling, no conflicts of interest.
14 Abbreviations used: 1-item, question on overall fruit and vegetable consumption; 24HR, 24-h dietary recall interviews; BCC, Behavior Change Consortium; CSFII, Continuing Survey of Food Intakes by Individuals; FV, fruits and vegetables; FVS, NCI Fruit and Vegetable Screener; HSPH, Harvard School of Public Health; IIT/Rush, Illinois Institute of Technology/Rush University; ME, measurement error; NCI, National Cancer Institute; ROC, University of Rochester; SC, serum carotenoids; URI, University of Rhode Island.
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