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3 Moores UCSD Cancer Center, University of California, San Diego, La Jolla, CA 92093; 4 Kaiser Permanente Northern California, Division of Research, Oakland, CA 94612 5 Department of Public Health Sciences, University of California, Davis, CA 95616 6 M.D. Anderson Cancer Center, University of Texas, Houston, TX 77030; 7 Center for Health Research, Portland, OR 97227; 8 Roswell Park Cancer Institute, Buffalo, NY 14263; 9 Department of Family and Community Medicine, University of Arizona, Tucson, AZ 85719; 10 Stanford Prevention Research Center, Stanford University, Stanford, CA 94305; and 11 Arizona Cancer Center, University of Arizona, Tucson, AZ 85724
* To whom correspondence should be addressed. E-mail: jppierce{at}ucsd.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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This report describes the dietary change achieved and maintained over 4 y in the Women's Healthy Eating and Living (WHEL) Study, which used an intensive one-on-one intervention delivered over the telephone, complemented by cooking classes and newsletters. In addition to allowing flexible scheduling tailored to individual participants, telephone counseling, with centralized intervention, provided economy of scale (5). At 1 y, we observed substantial medium-term increased intake of vegetables, fruit, and fiber with concurrent reductions in energy intake from fat (6). Because the WHEL Study is testing the hypothesis that dietary pattern will influence breast cancer disease-free survival, the protocol required study participants to maintain the new dietary pattern for a minimum of 4 y (5). This analysis presents the evidence that the WHEL Study intervention resulted in a significant between-group difference in dietary pattern through 4 y. The analysis also considers secular trends in dietary pattern among entry cohorts that may have influenced the absolute levels of dietary intakes achieved and maintained over time.
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Subjects and Methods |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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The WHEL Study enrolled 3088 women from 7 institutions in the south and west regions of the United States within 4 y of breast cancer diagnosis who were aged 18–70 y at diagnosis. The institutional review boards at the 7 clinical sites approved the study protocol and consent forms, and all participants provided written informed consent. Women were randomly assigned either to an intervention group counseled to consume a nutrient-dense, plant-based dietary pattern or to a comparison group that was given nationally recommended dietary guidelines for healthy living (7). Details of the study design and the close comparability of study groups on major socio-demographic and cancer characteristics, including stage, have been reported (5). The mean age at baseline was 52 y, mean BMI was 27.3 kg/m2, over 50% of subjects had attended college, and 14% were from minority subpopulations (African American, Hispanic/Latina, and Asian). The study population eligible for dietary assessment (i.e. those without a study endpoint) at 1 y and 4 y was 2953 and 2719, respectively. Because a significant secular trend for increased energy intake from fat was observed over the 6 y of enrollment from 1995–2000, we divided the participants into 9 consecutive study cohorts, based on their date of study enrollment, for specific analyses to examine these trends in detail.
Intervention and comparison group activities
Intervention group. The WHEL Study protocol has previously been described in detail (5). Briefly, trained counselors delivered a computer-assisted intervention over the telephone, complemented by cooking classes and newsletters. The counseling followed a step-wise, phased approach using strategies adapted from social cognitive theory. In the first phase, usually completed within the first 6 wk, calls every 3–4 d helped the participant build self-efficacy to implement these dietary targets: a daily intake of 5 vegetable servings, 2 cups (473 mL) of 100% vegetable juice (to increase vegetable nutrients without excessive fiber) or vegetable serving equivalents, 3 fruit servings, 30 g of fiber, and 15–20% energy from fat). To maximize intake of protective compounds, participants did not count white potatoes, iceberg lettuce, or fruit juice toward their dietary targets. During phase 2, 6–8 counseling calls at approximately weekly intervals focused on self-monitoring, modifying the environment to support the new dietary pattern, and planning relapse prevention strategies. Phase 3, progressively less intensive through the remainder of the study, focused on maintaining motivation for the dietary pattern and preventing relapse. This phased approach provided assistance when participants needed it the most (8). The mean number of counseling calls per participant was 18 calls in y 1; 6 calls in y 2; 4 calls in y 3; and 3 calls during y 4, for a total of 31 calls over 4 y.
Dietary behavior change was further reinforced by monthly newsletters and cooking classes. Twelve cooking classes with different educational themes were offered in a participant's first year and these classes were repeated yearly until all participants had completed their first year of the study. These classes provided participants with the opportunity to prepare and taste new recipes that met intervention goals (9). Intervention participants attended a mean of 4 cooking classes during the first year and another 1.5 cooking classes over the next 3 y of study participation. Monthly newsletters complemented the cooking classes and counseling calls for the duration of the intervention; 1 bimonthly newsletter highlighted a food relevant to the dietary pattern and during alternate months, "WHEL News" (tailored to the intervention group) featured a synopsis of relevant scientific literature, recommendations on dining out, supermarket strategies, participant questions and answers, and recipes.
Comparison group. Women randomized to the comparison group were provided with print materials available from the USDA (7) and the National Cancer Institute (10). Cooking classes and newsletters were among the strategies used to promote cohort maintenance. We offered 4 cooking classes each year based on themes unrelated to vegetables, fruit, fiber, and fat (e.g. dietary calcium). On average, comparison group participants attended 1 cooking class in the first year of the study. Participants received the bimonthly "WHEL News" (tailored to the comparison group) that contained study updates, research updates, recipes, and other articles to maintain interest and motivation in the study.
Dietary assessment
The study assessed dietary intake with a multi-method and multi-mode approach that included sets of 24-h dietary recalls (24-HR) as the primary measure and dietary biomarkers (plasma carotenoids) on a 28% random sample identified at baseline (5). 24-HR were collected on the entire study sample at baseline, 1 y, and 4 y. Trained assessors conducted the 24-HR via scheduled telephone calls for 4 selected days (stratified for weekday/weekend day) within 3-wk windows. The Minnesota Nutritional Data System software was used to collect dietary data (NDS-R, 1994–2006, University of Minnesota, Minneapolis) and analyze nutrient intakes (NDS-R version 6.0, 2006). At the baseline clinic visit, a registered dietitian taught participants to assess portion sizes with food models and measuring cups and spoons, paying particular attention to portions of vegetables, fruit, whole grain foods, and legumes. Participants received documentation outlining serving information and dietary assessors reminded them of these when necessary. Using the NDS database, we calculated serving sizes using metric weight. We defined a vegetable serving as one-half cup of cut-up vegetables (90 g), 1 cup of raw leafy green vegetables (55 g); a fruit serving as one-fourth cup of dried fruit (60 g), one-half cup of cut-up fruit (120 g), 1 cup melon (165 g), or 1 medium piece of fruit (120 g); a legume serving as one-half cup cooked legumes (90 g); and a whole grain serving as 1 slice of whole grain bread (50 g) or one-half cup of cooked whole grain cereal (120 g) or the equivalent. Vegetable and fruit juice were reported in 4-oz (120 g) servings of 100% vegetable or 100% fruit juice.
Plasma carotenoid and cholesterol concentration assessment
During clinic visits at baseline, 1 y, and 4 y, participants provided a fasting blood sample, which was separated and stored at –80°C. We quantified plasma carotenoid concentrations using HPLC with a Varian Star 9010, 9050 system as previously described (11). The laboratory participates in the National Institute of Standards and Technology quality assurance program to monitor precision and reliability of carotenoid measurement and the mean day-to-day CV was <6%. As carotenoids are transported in the plasma nonspecifically by cholesterol-rich lipoproteins, cholesterol concentrations can modify the relationship between dietary intake of carotenoids and their plasma concentration and need to be controlled in the analysis. Total plasma cholesterol concentrations were measured with the Kodak Ektachem Analyzer system (Johnson & Johnson Clinical Diagnostics) (12). Standard reference material from the manufacturer was used to validate analytic precision.
Statistical analysis
Means and SEM were computed for all variables of interest, including demographic, clinical, diet, and plasma carotenoid concentrations. Longitudinal data analysis (13) was applied to examine within subject dietary patterns over time. A linear regression model examined the secular trend in fat intakes by WHEL Study entry cohort. To assess between-group differences in dietary intakes, we subtracted the comparison group intake from the intervention group intake and divided this difference by the comparison group intake. We expressed this difference as a percent.
Conservative multiple imputation method. We provide a best estimate of dietary intake for missing data using multiple imputation, which assumes that eligible intervention group women without dietary data after baseline (n = 127) changed their dietary intakes in a manner similar to that of the comparison group. For eligible participants who did provide data after baseline but who did not complete at least 3 24-HR at 1 y or 4 y, we used the expectation maximization algorithm in SAS version 9.1 to estimate dietary intake for the missing data point. Due to linear dependence between some of the dietary variables and resultant singularity in the expectation maximization covariance matrix, we used a 2-step procedure to first impute general categories (vegetables, fruit, fiber, and percent energy from fat) and then subcategories (types of vegetables, fruits, fiber, or fat). Multiple imputations were performed by study group, except for the intervention group women without dietary data after baseline, as indicated above.
Modeling dietary trends over time.
Separate mixed models (13) were fit for each targeted dietary variable using repeated measures for each participant's dietary intake at baseline, 1 y, and 4 y. Study group, time, and group x time interactions were included as fixed effect covariates. Because this method is capable of handling varying amounts of data missing-at-random, only reported dietary intakes were used in the mixed models. The significance level in the mixed models for group x time interaction was set conservatively at P < 0.01 and 
for change within the comparison group was set at P < 0.05. A similar approach was used in a mixed model for plasma carotenoid data.
Secular trend on entry cohorts. To estimate secular trends in dietary fat intake, baseline percent energy intake from fat is reported for distinct participant entry cohorts. Participants randomized during 1995–1996 were combined into the first cohort. Eight additional cohorts were composed of participants randomized in each one-half year between 1997 and 2000. A secular trend was determined to exist if a significant slope of the regression line was observed over time (P < 0.01).
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Results |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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At baseline, 90% of participants provided 24-HR on 4 d and 10% provided 24-HR on 3 d. At both 1 y and 4 y, 76% of eligible participants (those alive without a recurrence or new breast cancer) completed 4 24-HR. The response rate was 90% at 1 y and 86% at 4y and was slightly lower in the intervention group. In eligible participants, 229 women (7.8%) provided no dietary data after baseline. Baseline fat intake did not differ between these nonresponders compared with responders. However, at entry, nonresponders had lower fiber intake (19.0 ± 0.50 vs. 21.5 ± 0.17 g/d; P < 0.01) and lower fruit and vegetable intake (6.5 ± 0.21 serving/d vs. 7.5 ± 0.06 servings/d; P < 0.01) than responders. To account for this nonresponse, we assumed that nonresponders changed their dietary pattern in a similar manner to the comparison group, a conservative imputed data approach for missing data (Table 1). All differences noted below were significant at P < 0.01 in the relevant statistical model unless otherwise specified.
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Fruit intake. At baseline, both groups consumed 3.4 servings per d of fruit for a combined total of 7.3 servings/d of vegetables and fruit. In the comparison group, 1-y fruit intake was similar to baseline and 4-y intake was slightly lower (P < 0.05). The between-group difference in fruit intake at 1 y was 20% and 25% at 4 y. Thus, for the intervention group, total vegetable and fruit intake was 12 servings/d at 1 y and 9.5 servings/d at 4 y. At baseline, both groups consumed just <1 serving/d (103.4 mL) of fruit juice, 17% of which came from citrus fruit. The intervention group decreased their fruit juice consumption throughout the study. Between 1 y and 4 y, the comparison group also decreased their fruit juice consumption so that there was no longer a between-group difference at 4 y.
Dietary fiber. Study groups consumed a mean of 21 g/d of dietary fiber at baseline (approximately two-thirds insoluble fiber). Dietary fiber intake was unchanged at 1 y in the comparison group and declined slightly (P < 0.05) at 4 y, although the proportion of insoluble fiber intake did not change. Dietary fiber intake increased in the intervention group, resulting in between-group differences of 38% at 1 y and 30% at 4 y,with no difference in proportions of total fiber that was insoluble.
Energy intake from fat.
At baseline, both groups had similar percentages of energy intake from fat (
28.5%), with the mean energy from saturated fat below 10%. The comparison group maintained this intake at 1 y but increased intake to 31.3% by 4 y (P < 0.05). The between-group difference was –20% at 1 y and –13% at 4 y, with little difference in the proportion of subcategories of fat intake.
Secular trends in fat intake at baseline.
The 1995–1996 entry cohort (Fig. 1) consumed 27% energy intake from fat at baseline. There was a positive trend of 0.8% per year across entry cohorts (P < 0.0001) so the last entry cohort (2nd half of 2000) reported consuming >30% of energy intake from fat. The secular trend for an increase in fat intake in entry cohorts may explain the significant increase in this variable in the comparison group at 4 y (Table 1). Figure 2 compares fat intake between study groups after stratifying the sample into halves by study enrollment date. In both these higher and lower fat intake groups, a similar intervention effect was observed with between-group differences of 3–5% in fat intake across all follow-up time points.
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Both groups had similar plasma carotenoid concentrations (2.4 µmol/L) at baseline (Table 2). Plasma carotenoid concentrations in the comparison group were relatively unchanged at 1 y and 4 y. The between-group difference was 66% at 1 y and 42% at 4 y.
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Discussion |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Initially, the decrease in dietary fat intake achieved by WHEL appears lower than that reported by the large Women's Intervention Nutrition Study, which focused on reducing energy intake from fat to 15% (18). In a completers-only analysis, the Women's Intervention Nutrition Study reported an 8% maintained between-group difference in absolute energy from fat. However, dietary data were reported for only 64% of the intervention group and 74% of control participants at 3 y. If even modest proportions of nonrespondents were no longer following the intervention, the between-group difference would be substantially reduced.
A significant secular trend for increased energy intake from fat was observed in study participants over the 6-y enrollment period from 1995 to 2000. The increase averaged almost 1 percentage point per year and may reflect trends in popular diets (e.g. low-carbohydrate diets) (19) or results of observational studies questioning the role of dietary fat in breast cancer (20). The study intervention had an equivalent impact on those randomized in the early and late years of the study, suggesting that the intervention approach can effectively encourage people with quite different dietary patterns to make major changes.
The main intervention strategy was telephone counseling, delivered by trained counselors using a computer-based protocol and supervised by a registered dietitian (9). This one-on-one communication mode was most intensive in the early months of the intervention and the contact schedule was modeled after previously successful face-to-face interventions (21). The counseling was complemented by cooking classes and newsletters. The cooking classes appeared to impose a substantial burden on some participants, as intervention participants attended only one-third of the available classes in the first year (9). However, these cooking classes complemented the telephone counseling intervention, because the dietary counselors used them as motivational tools and discussion points to help maintain participant interest.
Monthly newsletters highlighted the continued importance of food choices and provided recipes specific to the dietary pattern. As telephone counseling contacts decreased over time, these newsletters assumed a greater role in helping participants maintain the study dietary pattern. Other research has shown that monthly newsletters alone (22) or in conjunction with an initial interview and follow-up (23) can be a cost-effective way of achieving small increases in fruit and vegetable consumption. In this study, we think that newsletters helped prevent relapse, given that the intervention group sustained significant dietary change even when newsletters served as the primary intervention tool. Thus, we consider all 3 intervention components, delivered on a schedule aimed at meeting the participant's need for assistance, as key to achieving and maintaining a major change in dietary pattern. Further research is needed to identify the level of behavior change that such an intervention can achieve in lower-risk populations.
This study used a multi-method, multi-mode approach to assessing dietary intakes. In addition to 24-HR data and biomarker of vegetable and fruit intake, the study used an FFQ. A subsample investigation indicated that both self-report measures captured significant differences in targeted components of the dietary pattern (24). We investigated the validity and systematic error of both self-report measures for carotenoid-rich vegetable and fruit consumption (25) and confirmed the choice of 24-HR as the primary self-report measure. Measurement error in dietary self-report is well known (26) and the lack of an adequate biomarker of other elements of the dietary pattern, such as fiber intake, is a limitation to the study. Importantly, in a subsample, the intervention was associated with significant changes in plasma lipid concentrations consistent with the self-reported changes in carbohydrate and fat intakes (27).
In summary, the WHEL Study participants generally were already following a healthy dietary pattern at baseline. An innovative, multi-component intervention achieved and maintained a major change in that dietary pattern to one with 10 vegetable and fruit servings/d, 25 g/d of fiber, and 27% energy from fat. This resulted in large between-group differences in the randomized arms of the study. This effect attests to the efficacy of an intensive telephone counseling protocol adapted from the principles of social learning theory for dietary behavior change. Of greatest importance, the level of dietary change achieved and maintained in this study is sufficient to provide a definitive test of the role of dietary pattern in additional breast cancer events.
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FOOTNOTES |
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2 Author disclosures: J. P. Pierce, V. A. Newman, L. Natarajan, S. W. Flatt, W. K. Al-Delaimy, B. J. Caan, J. A. Emond, S. Faerber, E. B. Gold, R. A. Hajek, K. Hollenbach, L. A. Jones, N. Karanja, S. Kealey, L. Madlensky, J. Marshall, C. Ritenbaugh, C. L. Rock, M. L. Stefanick, C. Thomson, L. Wasserman, and B. A. Parker, no conflicts of interest.
Manuscript received 8 May 2007. Initial review completed 13 June 2007. Revision accepted 19 July 2007.
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LITERATURE CITED |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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1. Rock CL, Flatt SW, Natarajan L, Thomson CA, Bardwell WA, Newman VA, Hollenbach KA, Jones L, Caan BJ, et al. Plasma carotenoids and recurrence-free survival in women with a history of breast cancer. J Clin Oncol. 2005;23:6631–8.
2. Goodwin PJ, Ennis M, Pritchard KI, Koo J, Trudeau ME, Hood N. Diet and breast cancer: evidence that extremes in diet are associated with poor survival. J Clin Oncol. 2003;21:2500–7.
3. Kroenke CH, Fung TT, Hu FB, Holmes MD. Dietary patterns and survival after breast cancer diagnosis. J Clin Oncol. 2005;23:9295–303.
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5. Pierce JP, Faerber S, Wright FA, Rock CL, Newman V, Flatt SW, Kealey S, Jones VE, Caan BJ, et al. A randomized trial of the effect of a plant-based dietary pattern on additional breast cancer events and survival: the Women's Healthy Eating and Living (WHEL) Study. Control Clin Trials. 2002;23:728–56.[Medline]
6. Pierce JP, Newman VA, Flatt SW, Faerber S, Rock CL, Natarajan L, Caan BJ, Gold EB, Hollenbach KA, et al. Telephone counseling intervention increases intakes of micronutrient- and phytochemical-rich vegetables, fruit and fiber in breast cancer survivors. J Nutr. 2004;134:452–8.
7. USDA/DHHS. Dietary guidelines for Americans. Washington, DC: U.S. Department of Health and Human Services; 1995. Home Health and Garden Bulletin no. 232.
8. Zhu SH, Pierce JP. A new scheduling method for time-limited counseling. Prof Psychol Res Pr. 1995;26:624–5.
9. Newman VA, Thomson CA, Rock CL, Flatt SW, Kealey S, Bardwell WA, Caan BJ, Pierce JP. Achieving substantial changes in eating behavior among women previously treated for breast cancer: an overview of the intervention. J Am Diet Assoc. 2005;105:382–91.[Medline]
10. National Cancer Institute. Eat 5 fruits and vegetables a day. Washington, DC: National Cancer Institute; 1995.
11. Pierce JP, Natarajan L, Sun S, Al-Delaimy W, Flatt SW, Kealey S, Rock CL, Thomson CA, Newman VA, et al. Increases in plasma carotenoid concentrations in response to a major dietary change in the Women's Healthy Eating and Living Study. Cancer Epidemiol Biomarkers Prev. 2006;15:1886–92.
12. Shirey TL. Development of a layered-coating technology for clinical chemistry. Clin Biochem. 1983;16:147–55.[Medline]
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14. USDHHS. Healthy People 2010; 2000 [cited 2006 May 17]. Available from: http://www.healthypeople.gov/Publications/
15. Caan B, Sternfeld B, Gunderson E, Coates A, Quesenberry C, Slattery ML. Life After Cancer Epidemiology (LACE) Study: a cohort of early stage breast cancer survivors (United States). Cancer Causes Control. 2005;16:545–56.[Medline]
16. Wayne SJ, Lopez ST, Butler LM, Baumgartner KB, Baumgartner RN, Ballard-Barbash R. Changes in dietary intake after diagnosis of breast cancer. J Am Diet Assoc. 2004;104:1561–8.[Medline]
17. Pomerleau J, Lock K, Knai C, McKee M. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr. 2005;135:2486–95.
18. Chlebowski RT, Blackburn GL, Thomson CA, Nixon DW, Shapiro A, Hoy MK, Goodman MT, Giuliano AE, Karanja N, et al. Dietary fat reduction and breast cancer outcome: interim efficacy results from the Women's Intervention Nutrition Study. J Natl Cancer Inst. 2006;98:1767–76.
19. Stern L, Iqbal N, Seshadri P, Chicano KL, Daily DA, McGrory J, Williams M, Gracely EJ, Samaha FF. The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial. Ann Intern Med. 2004;140:778–85.
20. Hunter DJ, Willett WC. Nutrition and breast cancer. Cancer Causes Control. 1996;7:56–68.[Medline]
21. Chlebowski RT, Blackburn GL, Buzzard IM, Rose DP, Martino S, Khandekar JD, York RM, Jeffery RW, Elashoff RM, et al. Adherence to a dietary fat intake reduction program in postmenopausal women receiving therapy for early breast cancer. The Women's Intervention Nutrition Study. J Clin Oncol. 1993;11:2072–80.
22. Lutz SF, Ammerman AS, Atwood JR, Campbell MK, DeVellis RF, Rosamond WD. Innovative newsletter interventions improve fruit and vegetable consumption in healthy adults. J Am Diet Assoc. 1999;99:705–9.[Medline]
23. Richards A, Kattelmann KK, Ren C. Motivating 18- to 24-year-olds to increase their fruit and vegetable consumption. J Am Diet Assoc. 2006;106:1405–11.[Medline]
24. Thomson CA, Giuliano A, Rock CL, Ritenbaugh CK, Flatt SW, Faerber S, Newman V, Caan B, Graver E, et al. Measuring dietary change in a diet intervention trial: comparing food frequency questionnaire and dietary recalls. Am J Epidemiol. 2003;157:754–62.
25. Natarajan L, Flatt SW, Sun X, Gamst AC, Major JM, Rock CL, Al-Delaimy W, Thomson CA, Newman VA, et al. Validity and systematic error in measuring carotenoid consumption with dietary self-report instruments. Am J Epidemiol. 2006;163:770–8.
26. Freedman LS, Potischman N, Kipnis V, Midthune D, Schatzkin A, Thompson FE, Troiano RP, Prentice R, Patterson R, et al. A comparison of two dietary instruments for evaluating the fat-breast cancer relationship. Int J Epidemiol. 2006;35:1011–21.
27. Rock CL, Flatt SW, Thomson CA, Stefanick ML, Newman VA, Jones L, Natarajan L, Pierce JP, Chang RJ, et al. Plasma triacylglycerol and HDL cholesterol concentrations confirm self-reported changes in carbohydrate and fat intakes in women in a diet intervention trial. J Nutr. 2004;134:342–7.
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