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3 Behavioral Research Program and 4 Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-7344; 5 Department of Epidemiology and Biostatistics, Arnold School of Public Health, and 6 Cancer Prevention and Control Program, University of South Carolina, Columbia, SC 29208; 7 Oregon Research Institute, Eugene, OR 97403; 8 Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029; 9 Department of Nutrition and Food Sciences, University of Rhode Island, Kingston, RI 02881; 10 Departments of Medicine, Clinical and Social Sciences in Psychology, and Psychiatry, University of Rochester, Rochester, NY 14642; 11 Division of Health Promotion and Sports Medicine, Oregon Health & Science University, Portland, OR 97239; 12 Illinois Institute of Technology, Institute of Psychology, Chicago, IL 60616; 13 Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL 60612; 14 Division of Biology and Medicine, Brown University, Providence, RI 02912; 15 School of Nursing, University of Rhode Island, Kingston, RI 02881; 16 Rush University Medical Center, Department of Food and Nutrition Services, Rush Medical Center, Chicago, IL 60612; 17 University of Rochester. Rochester, NY 14642; 18 Rollins School of Public Health, Emory University, Atlanta, GA 30322; 19 Office of Dietary Supplements, NIH, Bethesda, MD 20892-7517; and 20 Department of Nutrition and Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA 02115
* To whom correspondence should be addressed. E-mail: yarocha{at}mail.nih.gov.
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ABSTRACT |
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 TOP ABSTRACT IntroductionMethodsDiscussionLITERATURE CITED |
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Introduction |
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There were 10 working groups formed from BCC grantees. The purpose of this article is to describe the structure and design of the Nutrition Working Group (NWG), which was formed to coordinate data collection and conduct cross-site analyses on dietary intake. The NWG is a multidisciplinary collaboration of representatives from 2 federal entities [the National Cancer Institute (NCI) and the Office of Dietary Supplements in the Department of Health and Human Services) and 7 BCC sites: Emory University, Harvard School of Public Health (HSPH), Illinois Institute of Technology (IIT/Rush), Oregon Health & Science University (OHSU), Oregon Research Institute (ORI), University of Rochester (ROC), and University of Rhode Island (URI), and a data analysis center (University of South Carolina)]. All of the sites in the NWG included a dietary intervention component, either to increase fruit and vegetable intake, to decrease fat intake, or both. Some of the sites also had other intervention components (e.g., smoking cessation, physical activity). Several common dietary assessment tools were used in sites to measure variables of interest at baseline and various follow-ups (see http://www1.od.nih.gov/behaviorchange/measures/measures.htm).
The NWG received additional funding from NCI to examine the properties and measurement characteristics of 2 short dietary assessment methods: the NCI Fruit and Vegetable Screener (FVS) (3,4) and the NCI Percentage Energy from Fat Screener (PFat) (5,6). The values from the screeners were compared with those from multiple dietary 24-h recalls (24HR). Details on the correspondence of the FVS and PFat compared with the 24HR are described elsewhere in this supplement (7,8). The current assessment study of the FVS and the PFat was undertaken because the short, self-reported FFQ or screener is often the most feasible method to assess diet in population-based settings (9). Currently, although the more precise 24HR method is considered the gold standard in national nutrition monitoring and surveillance, screeners have been used successfully to track year-to-year changes in fruit and vegetable intake via the Behavioral Risk Factor Surveillance System (10–12). In addition, a screener has been used to evaluate the effectiveness of 5-a-Day intervention trials and programs among adults (13–15). For continuing surveillance efforts in the US, fruit and vegetable screeners have been used in the National Health Interview Survey (NHIS) 2000 and 2005 (16) and the California Health Interview Survey (CHIS) 2001 and 2005 (17) to track fruit and vegetable intake. However, relatively few measurement studies have been conducted on these screeners. A review was published in 2003 describing validated fruit and vegetable screeners (18), and another review published in 2000 described the validity and reliability of existing fat screeners (19). The cross-site analyses that are presented in this special supplement examine measurement properties of the FVS and PFat (7,8), their sensitivity to detect change (20,21), and the impact of social desirability on responses (22).
The NWG also received supplemental funding from the Office of Dietary Supplements, NCI, and the Office of Behavioral and Social Sciences Research of the NIH to conduct research on multivitamin, vitamin, mineral, botanical, and other supplement use. Signal detection methodology analyses were conducted to determine subgroups of dietary supplement use in the various sites participating in the BCC NWG, and the results are presented in this supplement (23).
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Selection of measures. The BCC NWG was developed during the first grantees meeting, with the intent of identifying common measures across the 7 sites. Measures being used by each of the sites were discussed, as well as the possibility of adding other measures to existing studies depending on the nature of the particular intervention design, content, and the stage of implementation for each intervention. Additional supplemental funding was obtained to evaluate the FVS in all of the sites. In addition, some sites also collected the PFat and the single- or 2-item global fruit and vegetable measure. Each of the individual sites coded its data using a common code book developed by the coordinating center. Site data were sent to the coordination center(s) to be individually grouped and pooled to establish the joint data file. Coordination center responsibilities were shared. For baseline data, the coordination center was at URI, and for longitudinal data, it was at HSPH. Data were checked and cleaned at the coordination centers in conjunction with a third site, the University of South Carolina, where the overall data analyses were conducted.
Dietary assessment instruments. NCI FVS The FVS is a 19-item instrument that assesses daily fruit and vegetable consumption. Six sites administered the standard version querying intake over the past month, whereas 1 site (ORI) queried over a 6-mo timeframe. The instrument prompts the participant to think about all fruits and vegetables consumed that were raw and cooked, eaten as snacks and at meals, eaten at home and away from home, and eaten alone and mixed with other foods. Closed-ended frequencies range from never to 5 or more times a day. In addition, portion size information was collected (e.g., less than 1/2 cup, 1/2 to 1 cup, etc.). The instrument was developed after cognitive testing and was evaluated in 462 adult men and women living throughout the United States (3). Details on the development, scoring, and testing of the FVS can be found elsewhere (3), and it can be accessed electronically (4).
NCI PFat The PFat is composed of 16 questions that ask about usual consumption practices in the past 12 mo and prompts the participant to remember to include breakfast, lunch, dinner, snacks, and eating out. Frequency of intake is asked for 15 categories of food selected in earlier analyses to optimally predict intake of percentage energy from fat. Closed-ended frequencies range from never to 2 or more times a day. Question 16 asks about use of reduced-fat margarine. Portion size is not explicitly asked; however, the scoring algorithms assign gender- and age-specific median portion sizes in grams to each food group asked. Then gender specific regression coefficients relating each food group to percentage energy of fat are applied. Details on the development, scoring, and testing of the PFat can be found elsewhere (5,6). Also, the PFat can be accessed electronically (6).
24HR Estimates of nutrient intake were calculated from multiple telephone-administered 24HR using the Nutrient Data System for Research (NDS-R) developed by the Nutrition Coordinating Center at the University of Minnesota. Data were collected using updated versions of NDS-R (versions 4.03–05 with data-based versions 30–33). All the sites followed a similar protocol for the 24HR. The interviews were conducted by registered dietitians (for Emory and HSPH, by dietitians at the Diet Assessment Center, University of South Carolina; and for ROC, by dietitians at the Diet Assessment Center at Pennsylvania State University; for IIT/Rush, by on-site dietitians; and for URI, by trained interviewers directly supervised by registered dietitians), who were specifically trained in using the multipass interview interface that is an integral component of the NDS-R software. Before the interview, participants were provided with a 2-dimensional food portion guide (24). Each subject was scheduled for 3 nonconsecutive, unannounced 24HR including 1 weekend day. Interviews averaged 20–25 min in length. At baseline, 85% of participants provided 3 24HR, 11% provided 2, and 4% provided 1. Interviews were routinely reviewed for quality assurance, and coding errors were corrected. The NDS-R software has built-in flags that require authorization to enter unusually large quantities of foods consumed, limiting the potential for serious entry errors. Missing items, i.e., those foods not found in NDS-R, were added to the database in consultation with the Nutrition Coordinating Center. At the conclusion of an interview, NDS-R requires the interviewer to categorize the reliability of the subject's report and explain the reasons for this judgment. Any 24HR defined as unreliable by the interviewer was subsequently reviewed and exclusion confirmed by a dietitian with experience in conducting/supervising 24HR.
Single- or 2-item global fruit and vegetable measure In addition to the FVS, a single-item or 2-item global self-assessment of servings of fruits and vegetables usually eaten was assessed. The 1-item question was closed ended and asked some derivation of: "How many servings of fruits and vegetables do you eat each day?" The question also included serving size definitions such as "a serving is 1/2 cup of cooked vegetables, 1 cup of salad, a piece of fruit, 3/4 cup of 100% fruit juice." The 2-item measure asked about fruit and vegetable consumption separately (as 2 questions).
Other common variables assessed. Sociodemographic variables Each site assessed sociodemographic variables using their own instruments. Information on gender, age, education, ethnicity and race, employment status, yearly family income, and marital status was collected. Categories were similar among sites, but if needed, recoding was performed by URI for baseline data. For racial/ethnic categories in particular, categories were developed during NWG meetings, and sites recoded data to meet these categories: white, not of Hispanic origin; black/African American, not of Hispanic origin; Hispanic; Asian or Pacific Islander; American Indian/Alaskan Native; Portuguese (for URI); or other. For analyses, the 3 last categories were collapsed into "other."
BMI
The BMI was calculated as self-reported weight (kg) divided by self-reported height (m)2. For analyses, both the continuous measure of BMI and BMI groupings were used. The cutoffs used to categorize BMI are derived from the WHO recommendations. Weight status classified individuals with a BMI below 18.5 kg/m2 as underweight, those between 18.5 and 24.9 kg/m2were normal weight, 25 to 29.9 kg/m2were overweight, and 
30 kg/m2 were obese (25).
Smoking status All the sites assessed cigarette smoking status using common measures (26). Sites were able to choose from 3 different modules: the "economy" model for studies not looking at change over time in smoking but just collecting status and current stage of change information, the "midsize" model for studies looking at change in smoking but not conducting an intervention, and the "luxury" model for studies looking at change in smoking and conducting a smoking intervention.
Carotenoid assessment in serum
The levels of 5 major carotenoids (lutein with zeaxanthin, β-cryptoxanthin, lycopene, 
-carotene, and β-carotene) were determined by a previously described, well-established method (27,28). In brief, serum samples (aliquots of 200 µL) were deproteinized with equal volumes of absolute ethanol containing internal standard (retinyl acetate, 0.5 mg/L). The mixture was extracted twice with 2 mL hexane (containing 0.01% BHT), and the combined extracts were completely evaporated under vacuum and reconstituted with 50 µL stabilized ethyl ether and 150 µL HPLC mobile phase (methanol:acetonotrile:tetrahydrofuran, 50:45:5, v:v:v). Ten microliters of this extract was injected onto a Waters NovaPak C18 column (Chicago, IL) and eluted isocratically with the described mobile phase at 1 mL/min. Waters 490 Programmable Multiwavelength Detector was used to detect carotenoids at 450 nm. The detection limits are 4 nm for lutein and 9 nm for the other carotenoids. The analytes were quantified by comparing their peaks with the appropriate external standards and correcting for the recovery of added internal standard. The reliability of the assay was confirmed with blind control samples in large epidemiological studies (29,30). This laboratory is a reference laboratory for the National Institute of Standards and Technology (Gaithersburg, MD) quality assurance program for carotenoids (31).
Statistical analyses
Descriptive statistics were analyzed separately for each site, as well as being summarized overall across sites. SAS version 9 was used for statistical analyses.
Baseline elements
BCC NWG site description. The BCC sites participating in the NWG implemented a wide variety of randomized controlled intervention trials designed to increase physical activity, improve nutrition, reduce tobacco dependence, or some combination of these in diverse populations that varied by age, gender, educational level, race/ethnicity, health status, and reproductive status. Data were collected from 1999 to 2004. Study participants included adult smokers, postpartum women, African-American adults who attended church, firefighters, community-dwelling older adults, sedentary postmenopausal women with type 2 diabetes, and adults diagnosed with coronary artery disease (Table 1). Table 1 also provides additional details on each intervention trial, including the theoretical framework used, primary outcomes, and study designs. As primary dietary outcomes, 4 sites (HSPH, OHSU, ORI, and IIT/Rush) targeted both increased fruit and vegetable consumption and decreased fat consumption; 2 sites targeted only increased fruit and vegetable consumption (Emory and URI), and 1 site (ROC) targeted decreased fat consumption. Most of the interventions incorporated multiple theories or frameworks for behavior change, with the most utilized theories, models, and frameworks being Social Cognitive Theory (HSPH, OHSU, ORI), the Transtheoretical Model (HSPH, URI, IIT/Rush), Self-Determination Theory (ROC and IIT/Rush), Social Ecological Framework (HSPH and ORI), and Motivational Interviewing (Emory, OHSU). The various intervention designs also are shown in Table 1, but additional detail on all of the 7 intervention sites participating in the NWG of the BCC can be found in a special issue of Health Education Research (2).
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The PFat was administered at all 7 sites except IIT/Rush; data available per site ranged from 79% (Emory) to 92% (URI). At all sites but Emory, the baseline PFat was administered after enrollment into the intervention study but before randomization. At Emory, the PFat was administered after randomization. The instrument was self-administered at 4 sites (Emory, OHSU, ORI, and ROC) and interviewer-administered in 2 sites (URI, HSPH). Participants at the Emory site were not asked about rice consumption, so a different set of regression coefficients (available upon request) was needed to estimate percentage energy from fat intakes for this site.
Five sites (URI, HSPH, IIT/Rush, Emory, and ROC) collected 24HR data, with available data being the lowest for Emory (21%) and the highest for URI (99%). Interviews were conducted over a 2-wk period for HSPH, Emory, IIT/Rush, and ROC and over a 3-wk period for URI. All participants were blinded to randomization status at baseline except the Emory participants. Also, the 24HR was conducted after the NCI screeners had been administered except for Emory, where the sequence was reversed. The median and range of days between consecutive recalls are, respectively: URI 14, 119; HSPH 5, 16; IIT/Rush 10, 70; Emory 5, 16; and ROC 6, 40.
Four sites (URI, HSPH, OHSU, ROC) used a single-item global self-assessment of servings of fruits and vegetables usually eaten, and 1 site (Emory) used a 2-item self-assessment (1 item for fruits usually eaten and 1 item for vegetables usually eaten; fruits and vegetables were summed for analysis). The frequencies ranged from 0 to 6 servings or more, except for OHSU, which ranged from 0 to 10 or more. For purposes of consistency across sites, the maximum number of servings assessed by this method was truncated at 6 servings, to match those sites that asked the question with 6 or more as the maximum amount allowed. The data available were very complete for this measure, ranging from 95% (ROC) to 99% (URI and OHSU).
Five of the sites used the "economy" model for assessing smoking status, with 2 sites using 1 question (URI and Emory), another using 2 questions (HSPH), and 2 sites using 3 questions (OHSU and IIT/Rush) to assess current smoking status. One site, ORI, used a modified version of the midsized model. ROC was conducting an intervention to reduce tobacco dependence and therefore used the luxury model. An example of a 1-item question queried whether or not a participant currently smoked, didn't currently smoke, or had quit more than 6 mo ago (e.g., URI). In contrast, Emory asked their 1-item question as, "How many DAYS in the LAST MONTH did you smoke a cigarette?" and had closed-ended responses from "NONE" to "20–31" in the past month. Another site (HSPH) asked participants whether >100 cigarettes were smoked in a lifetime (yes/no) and if they had smoked even a puff in the last year (yes/no). Regardless of the exact wording and number of questions asked, URI recoded all the sites to a "YES/NO" option for current cigarette smoking.
Five sites (URI, OHSU, ORI, IIT/Rush, Emory) collected serum carotenoids using common collection methodology and laboratory assays. The lowest percentage of participants with carotenoid data was at IIT/Rush (64%), and the highest percentage was at ORI (99%).
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It is noteworthy that the populations were mostly overweight or obese with percentages of overweight/obese as assessed by BMI ranging from 62% of the population for Emory and ROC to 92% of the ORI population. This is not surprising because many of the interventions recruited groups with a particular health condition(s) that placed them at greater risk for having comorbidities such as obesity (e.g., ORI recruited postmenopausal women with type II diabetes and IIT/Rush recruited adults diagnosed with coronary heart disease). However, some of the other sites recruited participants who may have been at increased risk for obesity (e.g., community-dwelling older adults from URI, African-American women from Emory, and postpartum Hispanic women from HSPH). Yet, these participants were not recruited specifically on the basis of having a particular health condition. But with their increased risk for overweight and obesity, it was not necessarily surprising that these populations had mean BMI values over 25 kg/m2. One noteworthy exception, surprisingly, was OHSU's population of firefighters (mean BMI = 27.4).
There were some limitations to the current study. First, there were missing data, which may create bias if those that are missing are not representative or are not missing at random. In addition, we were unable to present information on missing data for each measure and by site because some sites do not have those details. Another issue was the coordination of data/information across the 7 sites. Different methodologies existed for some of the sites; there was no common coding guide; and the questions were sometimes asked slightly differently (as in the case of the single-item fruit and vegetable measure). Next, there were some cases where data from sites could not be used because of the lack of collection of all measures. For instance, 1 site (IIT/Rush) collected 24HR but did not administer the PFat. This resulted in a restriction of the sample size in many of the analyses. However, this inconsistency in common measures collected was mainly because the NWG was formed after the individual research was under way, making it difficult to collaborate on and unify all the screening tools. In future studies, this coordination should occur in advance. Another limitation was that there was increased overall participant burden across sites, given that many of the sites had added measures to participate in this activity. In addition, it was not possible to get the overall results summarized in a timely fashion because the sites completed data collection at varying times. Last, some data were collected for variables such as physical activity behavior and biochemical lipids but were not able to be reported collectively because of inconsistent measures and/or scoring.
A strength of the study is the wide diversity in each site's target population, although this is a potential limitation from an internal validity point of view. The unique clustering within each site of factors such as gender, age, and ethnicity, which are known or thought to be related to the validity of self-report data, required that statistical analyses be conducted stratified by site and gender (and aggregated after concerns about heterogeneity had been addressed). In some cases, this reduced the power of the analyses because of small sample sizes. It is difficult to draw broad conclusions about the efficacy of dietary intervention trials because of the lack of studies with common outcome measures. This effort is an attempt to address this challenge using trials with different interventions and populations but with many common dietary intake measures. To enhance the integration of research in this field, more of the BCC NWG kind of cross-collaboration is warranted.
In summary, this article presents the baseline design elements and sample characteristics from 1 of the first reports of a series of articles focusing on dietary methodologies that draws from diverse populations across the U.S. population. As such, it is the largest study of its type. It is desired that the findings presented herein be useful to those who use such methods in their work and that it will prompt continued interest in testing methods appropriate for a range of lifestyle-related interventions.
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2 Author disclosures: 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, T. Goldman Sher, M. Stacewicz-Sapuntzakis, J. Salkeld, S. Rossi, A. Domas, H. McGregor, C. DeFrancesco, F. McCarty, R. B. Costello, and K. E. Peterson no conflicts of interest.
21 Abbreviations used: 24HR, 24-hour recall diet interviews; BCC, Behavior Change Consortium; FVS, Fruit and Vegetable Screener; HSPH, Harvard School of Public Health; IIT/Rush, Illinois Institute of Technology; LDL-C, LDL cholesterol; NCI, National Cancer Institute; NDS-R, Nutrient Data System for Research; NWG, Nutrition Working Group; OHSU, Oregon Health & Science University; ORI, Oregon Research Institute; PFat, NCI Percentage Energy from Fat Screener; ROC, University of Rochester; URI, University of Rhode Island
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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]
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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]
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