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Nutritional Epidemiology

Adherence to the Polyp Prevention Trial Dietary Intervention Is Associated with a Behavioral Pattern of Adherence to Nondietary Trial Requirements and General Health Recommendations^1,2

³ Epidemiology Research Branch, Division of Epidemiology, Services and Prevention Research, National Institute on Drug Abuse, Bethesda, MD 20892; ⁴ Daston Communications, Chapel Hill, NC 27514; ⁵ Michigan Public Health Institute, Okemos, MI 48864; ⁶ Information Management Services, Inc., Silver Spring, MD 20904; and ⁷ Biometric Research Branch, Division of Cancer Treatment and Diagnosis, ⁸ Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, and ⁹ Laboratory of Cancer Prevention, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892

^* To whom correspondence should be addressed. E-mail: wankek{at}mail.nih.gov.

	ABSTRACT

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

 
This study investigated the factors associated with success in meeting the dietary goals of the Polyp Prevention Trial (PPT), a 4-y low-fat, high-fiber, high-fruit/vegetable dietary intervention. The PPT provided a rare opportunity to assess factors in long-term adherence to a dietary pattern that required changes to multiple aspects rather than a single aspect of diet. Demographics, health indicators, and dietary intake were assessed at baseline and annually for 4 y of follow-up. Participants (n = 833) received dietary and behavioral counseling to support adherence to trial dietary goals. We assessed the association of baseline variables and trial participation with success in meeting dietary goals. Participant adherence to the intervention goals was significantly associated with never smoking, no history of weight gain, and consumption of less fat and more fiber, fruits, and vegetables at trial baseline. Successful participants were also more educated and married, whereas those with the poorest adherence were older. In addition, successful participants demonstrated greater participation throughout the trial, including attendance at counseling sessions, completion of dietary records, and contacts with staff. Of particular interest were the behavioral and demographic characteristics that distinguished the subset of participants who achieved most or all dietary intervention goals across all 4 study years who we termed Super Compliers. These individuals also were more likely to adhere to social norms for healthy lifestyles and demonstrated greater adherence to other aspects of trial participation.

	Introduction

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

This study examined the factors associated with participants achieving the 3 dietary intervention goals of the Polyp Prevention Trial (PPT)¹⁰. The intervention arm of the PPT provided education and counseling to support participants in making their own dietary choices to achieve a low-fat, high-fiber, high-fruit/vegetable diet over the course of 4 y. Although the PPT dietary intervention showed no effect on colorectal adenoma recurrence during the 4 y of the trial (6,7), this intervention provided an opportunity to explore determinants of making and maintaining multiple long-term dietary changes that are consistent with recommendations for good health and chronic disease prevention in a free-living population.

We performed analyses on intervention group participants who completed yearly dietary assessment forms over the course of the study (n = 833), examining a variety of baseline variables and trial behaviors. The study aims were to examine whether prospective data demonstrate that long-term dietary change is associated with: 1) sociodemographic characteristics; 2) baseline health behaviors and indicators; 3) baseline dietary intake of fat, fiber, and fruits/vegetables; and 4) adherence to the other trial requirements, including attendance at intervention counseling sessions, completion of forms and records, and contact with trial staff. We hypothesized that participants who were most successful at making the prescribed dietary changes would be more likely to exhibit a pattern of adherence to both generally accepted health practices at baseline as well as to the other trial intervention program requirements.

	Materials and Methods

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

 
    Overview of PPT design. The data for this study were drawn from the PPT, a 4-y, multi-center, randomized control trial. The design, methods, implementation, and outcome of the PPT were described in detail in previous publications (6–8). The specific goals of the dietary intervention were to limit fat to 20% of energy intake and to consume at least 4.30 g/MJ of fiber (18 g/1000 kcal) and 0.84 servings/MJ of fruits/vegetables (3.5 servings/1000 kcal). Based on report of total energy intake at baseline, as determined by a dietary FFQ, individual goals were calculated for each participant as maximum fat intake, minimum fiber intake, and minimum fruit/vegetable intake. These goals were then communicated to each participant as his/her own target goals for daily intake of fat (consume no more than target g), fiber (meet or exceed target g), and fruits/vegetables (meet or exceed target servings). Participants randomized to the intervention group received extensive behavioral and nutrition counseling and support; success was measured annually with follow-up dietary FFQ. The study was approved by the Institutional Review Boards of the National Cancer Institute and each of the participating centers. All participants provided written informed consent at study entry.

The current study compared participants meeting vs. not meeting the target goals of the dietary intervention throughout the 4 y of the trial. Because the measure of success in meeting the goals of the trial was the FFQ, the main analyses included intervention participants who completed all 4 annual follow-up FFQ. These 833 participants represented 80.3% of those enrolled in the intervention arm at baseline.

    Dietary intervention. Intervention participants engaged in an intensive nutrition education, support, and counseling program, delivered at each clinical center by registered dietitians who were trained in state-of-the-art techniques for facilitating dietary behavior change. The intervention program consisted of 4 key elements: 1) nutrition skill building, 2) behavior modification, 3) self-monitoring, and 4) standardized nutrition and behavior modification materials. During year 1 of the trial, the intervention participants attended 19 counseling sessions. During year 2, the participants attended sessions every other month. Nutritionists also contacted the participants by phone at least once per month to monitor progress and to assist in resolving any adherence difficulties. During years 3 and 4, participants attended sessions quarterly and nutritionists contacted participants at least once monthly by phone. In addition to these sessions and contacts, 3 special intervention campaigns were launched during participant years 2–4 to boost adherence to 1 or more dietary goals. A more complete description of the intervention program and underlying strategies is published elsewhere (7,9,10).

    Baseline questionnaire. Each participant completed a health and lifestyle questionnaire at baseline assessing a variety of sociodemographic variables and dietary, health, and lifestyle practices.

    Dietary assessments. Participants completed an FFQ (11) at baseline and yearly thereafter, which was reviewed by trained staff to ensure proper completion. Staff reviewing FFQ were not involved in that participant's dietary counseling. To serve as a comparison, dietary fat, fiber, and fruit/vegetable intake were also assessed using baseline and yearly follow-up 4-d food records (4DFR) analyzed on a 20% sample of participants, as well as unannounced 24-h dietary recalls (24-HR) administered to a random 10% sample of participants after year 1.

    Serum biomarkers. Concentrations of 5 different carotenoids (-carotene, ß-carotene, lutein/zeaxanthin, cryptoxanthin, and lycopene), -tocopherol and -tocopherol were measured on a random 40% sample of participants at baseline and at each year of follow up using HPLC in fasting serum samples (12,13).

    Follow-up participation. During the trial, staff-documented participant involvement in and adherence to the intervention program, which included attendance at scheduled sessions (including rescheduled sessions), number of no shows, spouse attendance at meetings (number of sessions attended by a spouse or significant other), contacts with trial staff (session attendance plus nutritionist-initiated telephone contacts), and completion of self-monitoring dietary records (fully, partially, or not at all). As the number of scheduled sessions and records differed each year of the trial, the variables were analyzed as a proportion of total scheduled sessions, contacts, records, etc. Spouse attendance, partially completed records, and no shows were dichotomized as "never" vs. "ever," whereas the remainder of trial participation variables were trichotomized as <60%, 60% and <100%, and 100%.

    Dependent variable: measure of adherence with dietary goals. Because the current outcome of interest is participants' achievement of target goals, success at each yearly follow-up was defined as meeting or exceeding the goals communicated to participants at baseline, independent of energy intake at follow-up. Follow-up measures of fat (g), fiber (g), and fruit/vegetable consumption (servings) were therefore calculated as total daily consumption as reported in the annual FFQ. A composite index of success in meeting dietary goals was then determined across years and goals for the entire trial, calculated as a summary score from the 12 goals (3 goals for each of 4 y). Participants were designated as: 1) Poor Compliers = met 0 to 3 goals; 2) Inconsistent Compliers = met 4 to 8 goals; and 3) Super Compliers = met 9 to 12 goals.

    Analyses. Baseline characteristics of the intervention participants who were included vs. excluded in the primary analyses were compared using unadjusted t-tests or cross tabulation with chi-square tests. Dietary and serum differences across participant groups categorized by dietary adherence were determined by ANOVA. Associations between the composite index of adherence to dietary goals and the baseline and trial participation variables were analyzed using unadjusted cross-tabulation with chi-square tests, whereas continuous variables were compared using t-tests. Further, polychotomous logistic regression was used to examine the effect of baseline and trial participation variables on the trichotomous outcome variable of dietary success. We used a forward approach in which, starting with no regression terms, terms were entered into the model, 1 by 1, if they were significant at the 0.05 level. We also used a step-wise approach in which, in addition to adding significant effects, terms were removed from the model if they were not significant at the 0.10 level.

A grouped binomial regression was also conducted to determine whether our univariate findings would remain if we included participants with missing FFQ and if we modeled dietary adherence as a continuous rather than trichotomized variable. First, we included all participants in the intervention group with at least 1 FFQ post-baseline (n = 961) rather than limiting the analysis to participants completing all 4 follow-up questionnaires. Adherence was defined as the sample proportion, that is, number of goals met out of total reported, modeled as a linear function (on the logit scale). Then a logistic regression for grouped data (allowing for over-dispersion) was performed for each baseline and trial participation variable, estimating the relation between each participants' proportion of intervention goals met and the covariate of interest. These results were compared with the original analyses to determine whether our results were an artifact of our participant categorization. For all reported tests, a P-value of <0.05 was considered significant.

	Results

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

 
Examination of adherence patterns for each of the dietary goals, fat, fiber, and fruits/vegetables found that a sizeable proportion of participants never met the goal for that dietary component over the 4 y of the trial (fat = 29.7%, fiber = 37.0%, fruit/vegetable = 20.1%) or always met the goal (fat = 21.4%, fiber = 23.7%, fruit/vegetable = 33.4%). However, only 6.8% never achieved a single goal and only 4.4% consistently achieved all 3 goals across 4 y (12 goals total). Therefore, by defining Super Compliance as meeting 9 or more of the 12 goals and Poor Compliance as meeting less than 4, we more optimally captured the extremes of the adherence behavior patterns.

    Comparison of included vs.excluded participants. Because success in meeting the dietary goals of the PPT was determined from yearly FFQ, we compared those with FFQ assessments at all 4 follow-up visits (n = 833) to those in the intervention group not completing all FFQ (n = 204). Of the 1037 intervention participants, the 833 who completed all 4 annual follow-up FFQ differed at baseline from the 204 who did not (Table 1). Those completing all dietary follow-up FFQ were more likely to be white, married, and never smokers. They also reported engaging in more hours of vigorous or moderate exercise and eating less fat and more fiber and fruits/vegetables at baseline. They were less likely to report that they prepared their own meals or purchased their own food and more likely to report eating more meals and snacks per day as compared with those participants who did not complete all follow-up FFQ.

    Comparison of biomarkers. We also examined differences in biomarkers of dietary intake to provide a more objective assessment of some aspects of dietary change reported by participants. We calculated baseline, means (years 1–4) and changes from baseline for serum total carotenoids, -tocopherol, and -tocopherol (Supplemental Table 1). Baseline and mean serum total carotenoid concentrations differed among the 3 adherence groups, but changes in serum total carotenoids did not. Serum -tocopherol, derived mainly from the consumption of cooking oils, differed among the groups at baseline and during the course of the trial, with the Super Compliers having the lowest mean concentrations during the study (geometric mean [95% CI]; 3.7 [3.4, 4.2] µmol/L) and the Poor Compliers having the highest [5.4 (4.9, 5.9) µmol/L]. Also, the change in serum -tocopherol differed among the 3 groups, with the greatest decrease from baseline in the Super Compliers. Serum -tocopherol concentrations and the change from baseline did not differ among the groups.

    Univariate associations. We conducted chi-square analyses to explore the relation between baseline variables and participation in the intervention program with levels of adherence among participants who completed all FFQ (Table 3). Participants' reported level of adherence was found to significantly differ by the following sociodemographics: age, education, marital status; baseline health indicators: weight pattern since age 18, smoking status; baseline dietary intake: energy-adjusted fat, fiber, and fruit/vegetable consumption. Super Compliers, were more educated and married and were more likely to report a history of stable or lost weight during adulthood and of never smoking. Those with lower levels of adherence were older, with participants aged 69–85 y showing the highest levels of "poor" adherence. Additionally, Super Compliers were more likely to be in the lowest quartile of fat intake and the highest quartiles of fiber and fruits/vegetables at baseline.

    Polychotomous logistic regression. The baseline and trial participation variables were then modeled as potential predictors of overall dietary adherence through polychotomous logistic regression. All baseline and trial participation variables listed in the Methods section were allowed in the selection process. Both stepwise and forward-entry models were conducted, yielding identical results (Table 4). All variables included in the final model were identified as significantly associated with the outcome in previous chi-square analyses (see Table 3), with the exception of "primary shopper," where a trend was demonstrated for participants who bought their own food to report less success in meeting the goals of the trial when controlling for all other variables entered into the model (Table 4).

	Discussion

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

 
Most dietary interventions have focused on a single dietary component such as fat and/or have spanned a time period of 1 y or less (1). The PPT is unique with its 3 simultaneous dietary goals sustained over a 4-y period. Participants reporting the greatest success in meeting the dietary goals of the PPT also demonstrated greater adherence to social norms for healthy lifestyles and adherence to the other trial intervention program requirements. We termed these participants "Super Compliers" as their pattern of adherence to intervention goals over the 4 trial years tended to generalize to other behavioral domains. Super Compliers were more likely to report no history of significant weight gain/fluctuation or smoking. They were more likely to be married, obtain greater educational levels, and report healthy eating habits, consuming less fat and more fiber, fruits/vegetables at baseline, while still making the greatest overall changes in these dietary components during the trial. They demonstrated greater general participation throughout the trial, including attendance, contacts with staff, and completion of dietary records.

The measure of success in meeting dietary goals was through self-report using the FFQ. Because this may be subject to over- or under-reporting (15–19), we assessed dietary intake using 2 separate random subcohorts of all PPT participants. In 1 subcohort of 20% of all participants, we analyzed 4DFR yearly. For 24-HR we selected a different 10% random sample each year after year 1. All 3 types of dietary assessments showed similar intake and demonstrate a larger change in fat, fiber, and fruits/vegetables in the Super Compliers.

Because self-reported dietary intake is subjective, validation of dietary measures is of great interest (20,21). However, there are no clearly unambiguous biomarkers of fat, fiber, or fruit/vegetable intake. Although serum carotenoids have been shown to be a marker of carotenoid-rich fruits and vegetables, not all fruits and vegetables contain carotenoids. Although the intervention group increased their consumption of fruits and vegetables, the major increases were from low-carotenoid sources, with the actual percent of carotenoid-rich fruits/vegetables decreasing during the trial (7). The intent of the dietary intervention in the PPT was not to enhance carotenoid intake but to increase overall fruit/vegetable intake, increase dietary fiber, and decrease fat. A similar finding was demonstrated when an increase in fruit/vegetable intake from 3.3 to 5.2 servings/d (excluding potatoes) during a low-fat intervention was not sufficient to significantly increase blood carotenoid concentrations (22). However, when carotenoid-rich fruits/vegetables are emphasized and the goals are 9 servings/d or greater, increases in plasma carotenoids can be substantial and plasma carotenoids can be used as a marker of fruit/vegetable intake (23–26).

Serum -tocopherol, the predominate form of vitamin in dietary fats and oils, has been shown to be higher in those with a poor-quality diet compared with those with an excellent diet (27,28). Additionally, the decrease in -tocopherol in Super Compliers is consistent with the recent findings from the Women's Health Initiative (WHI), which showed that women randomized to the low-fat intervention had significantly lower mean concentrations of -tocopherol in plasma relative to those without low-fat intervention; -tocopherol did not change (29).

Previous research shows some consistency with our findings. Women's adherence to a low-fat diet in the Women's Health Trial feasibility study was associated with educational session attendance, baseline dietary fat consumption, and college education (30,31). The WHI Study Group (32) subsequently reported women's adherence to a low-fat dietary pattern was associated with white race, higher income, and group session attendance, whereas older age and obesity were associated with poorer adherence. Further analysis of the WHI data indicated that a 10% increase in session attendance predicted a 1.2% decrease in percent energy from fat (33). This analysis also found that participation in the dietary intervention mediated the effect of lower mental health scores on dietary adherence, and increases in physical functioning predicted increased session attendance, further highlighting the clustering of these behaviors. The Multiple Risk Factor Intervention Trial found that men's adherence to a low-fat diet was associated with white race, lower BMI, nondrinking, nonsmoking, fewer stressful life events, and eating out less often (34). They also found that adherence improved with increasing age, with their oldest category cut-off at 55 y, consistent with our findings that optimal adherence occurred in participants in the 55-y to 61-y age quartile. With further age increases, we found decreased adherence. Other studies have also demonstrated that adherence to dietary interventions increases with self-monitoring (35,36). In the general population, a large nationally representative survey (37) found that women typically adhered to healthy lifestyle practices more than men and that adherence improved with increasing age, education, and income.

Although the PPT, with its large sample size, provided a rare opportunity to assess health behavior adherence over a long time period (4 y) with multiple dietary changes (3 goals), some caveats should be noted. The PPT participants were mostly white, well educated, and over the age of 50 y. Participants had a colorectal adenoma removed before enrollment, providing a salient motivation for dietary change. Diet change was made within the context of a clinical trial, supported by trial staff, counseling sessions, and follow-up monitoring. Therefore, results may not be indicative of rates or predictors of success for interventions prescribed by physicians or attempted through self-help programs. Additionally, many variables involve self-report, including physical activity, weight pattern, and dietary patterns like number of daily meals/snacks typically eaten. Recall ability or bias in self-assessment may be differentially distributed across compliance categories.

These findings do, however, inform dietary behavior change and may be applicable to preventive medicine strategies more generally, facilitating an understanding of associated factors related to dietary change. This may assist in devising better procedures for dietary interventions and adherence to clinical trial regimens (38,39). As behaviors tend to cluster into patterns that generalize across similar behavioral domains (37,40), it is important to identify patterns that predict adoption of health recommendations. The information gleaned from these investigations promises to shed light on patient adherence in research trials and physician practice. Understanding adherence patterns may also inform medical and health research findings more broadly by identifying the factors that confound associations seen in observational epidemiology but not subsequently replicated in large-scale randomized controlled trials (41). A myriad of health-related behaviors cluster together and confound our best attempts to delineate cause-and-effect associations. Studies with motivated or convenience samples or studies that selectively recruit those most likely to adhere may have better outcomes due to higher proportions of Super Compliers, whereas studies with high attrition are likely to cull the poorest compliers, biasing the results in all instances. Perhaps the most promising way to make progress is in directly studying this clustering and the resulting patterns of behavior. The PPT Super Complier findings illustrate the importance of behavioral and contextual factors in determining motivation and potential for success in modifying diet or maintaining healthy eating patterns.

	Appendix

TOP ABSTRACT Introduction Materials and Methods Results Discussion Appendix LITERATURE CITED

 
The members of the Polyp Prevention Study Group participated in the conduct of the PPT. However, the data presented in this manuscript and the conclusions drawn from them are solely the responsibility of the listed coauthors.

National Cancer Institute: Schatzkin A, Lanza E, Corle D, Freedman LS, Clifford C, Tangrea J; Bowman Gray School of Medicine: Cooper MR, Paskett E (currently Ohio State Comprehensive Cancer Center), Quandt S, DeGraffinreid C (currently Ohio State Comprehensive Cancer Center), Bradham K, Kent L, Self M, Boyles D, West D, Martin L, Taylor N, Dickenson E, Kuhn P, Harmon J, Richardson I, Lee H, Marceau E; University of New York at Buffalo: Lance MP (currently Arizona Cancer Center), Marshall JR (currently Roswell Park Cancer Center), Hayes D, Phillips J, Petrelli N, Shelton S, Randall E, Blake A, Wodarski L, Deinzer M, Melton R; Edwards Hines, Jr. Hospital, Veterans Administration Medical Center: Iber FL, Murphy P, Bote EC, Brandt-Whittington L, Haroon N, Kazi N, Moore MA, Orloff SB, Ottosen WJ, Patel M, Rothschild RL, Ryan M, Sullivan JM, Verma A; Kaiser Foundation Research Institute: Caan B, Selby JV, Friedman G, Lawson M, Taff G, Snow D, Belfay M, Schoenberger M, Sampel K, Giboney T, Randel M; Memorial Sloan-Kettering Cancer Center: Shike M, Winawer S, Bloch A, Mayer J, Morse R, Latkany L, D'Amato D, Schaffer A, Cohen L; University of Pittsburgh: Weissfeld J, Schoen R, Schade RR, Kuller L, Gahagan B, Caggiula A, Lucas C, Coyne T, Pappert S, Robinson R, Landis V, Misko S, Search L; University of Utah: Burt RW, Slattery M, Viscofsky N, Benson J, Neilson J, McDivitt R, Briley M, Heinrich K, Samowitz W; Walter Reed Army Medical Center: Kikendall JW, Mateski DJ, Wong R, Stoute E, Jones-Miskovsky V, Greaser A, Hancock S, Chandler S; Data and Nutrition Coordinating Center (Westat): Cahill J, Hasson M, Daston C, Brewer B, Zimmerman T, Sharbaugh C, O'Brien B, Cranston L, Odaka N, Umbel K, Pinsky J, Price H, Slonim A; Central Pathologists: Lewin K (University of California, Los Angeles), Appelman H (University of Michigan); Laboratories: Bachorik PS, Lovejoy K (Johns Hopkins University); Sowell A (Centers for Disease Control); Data and Safety Monitoring Committee: Greenberg ER (chair) (Dartmouth Medical School); Feldman E (Augusta, Georgia); Garza C (Cornell University); Summers R (University of Iowa); Weiand S (through June 1995) (University of Minnesota); DeMets D (beginning July 1995) (University of Wisconsin).

	ACKNOWLEDGMENTS

 
The authors thank Grace Lee for her contributions in the preparation of this manuscript. We also thank the PPT Study Group (see Appendix) for their outstanding contribution to this project.

	FOOTNOTES

 
¹ Supported by the Intramural Research Program, National Cancer Institute, NIH, Bethesda, MD; Dr. Wanke was supported by a National Cancer Institute postdoctoral fellowship while working on this project.

² Supplemental Table 1 is available with the online posting of this paper at jn.nutrition.org.

¹⁰ Abbreviations used: 4DFR, 4-d food record; 24HR, 24-h dietary recall; PPT, Polyp Prevention Trial; WHI, Women's Health Trial.

Manuscript received 24 July 2006. Initial review completed 5 August 2006. Revision accepted 15 November 2006.

	LITERATURE CITED

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