Figure 1. Hypothesized Analytical Framework: Efficacy of Behavioral Dietary Interventions to Reduce Cancer Risk
 |
|
|
The Agency for Healthcare Research and Quality (AHRQ), formerly the Agency for Health Care Policy and Research (AHCPR), through its Evidence-Based Practice Centers (EPCs), sponsors the development of evidence reports and technology assessments to assist public- and private-sector organizations in their efforts to improve the quality of health care in the United States. The reports and assessments provide organizations with comprehensive, science-based information on common, costly medical conditions and new health care technologies. The EPCs systematically review the relevant scientific literature on topics assigned to them by AHRQ and conduct additional analyses when appropriate prior to developing their reports and assessments.
To bring the broadest range of experts into the development of evidence reports and health technology assessments, AHRQ encourages the EPCs to form partnerships and enter into collaborations with other medical and research organizations. The EPCs work with these partner organizations to ensure that the evidence reports and technology assessments they produce will become building blocks for health care quality improvement projects throughout the Nation. The reports undergo peer review prior to their release.
AHRQ expects that the EPC evidence reports and technology assessments will inform individual health plans, providers, and purchasers as well as the health care system as a whole by providing important information to help improve health care quality.
We welcome written comments on this evidence report. They may be sent to: Director, Center for Practice and Technology Assessment, Agency for Healthcare Research and Quality, 6010 Executive Blvd., Suite 300, Rockville, MD 20852.
| John M. Eisenberg, M.D. | Douglas B. Kamerow, M.D. |
| Director | Director, Center for Practice and |
| Agency for Healthcare Research and Quality | Technology Assessment |
| Agency for Healthcare Research and Quality |
The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services of a particular drug, device, test, treatment, or other clinical service.
Objectives: This authoritative, systematic review seeks to clarify the existing knowledge base on interventions to alter dietary behavior related to cancer risk and offers directions for future research. Specifically, the review addresses three key questions regarding the efficacy of behavioral interventions for promoting dietary change:
Is there evidence that one such intervention, alone or in combination, is more effective than another in helping individuals or groups modify their diet to consume more fruits and vegetables and less fat?
What is the evidence for the efficacy of dietary interventions by population subgroup, particularly by ethnicity and gender?
What conclusions (if any) can be reached about the cost-effectiveness of these types of interventions?
Search Strategy: To identify articles publishing the results of behavioral dietary interventions, the reviewers performed literature searches in six databases (MEDLINE, EMBASE, PsycINFO, CINAHL, AGELINE, and AGRICOLA) using a variety of relevant terms.
Selection Criteria: The initial search strategy excluded studies on the basis of date and language of publication, location of the study, whether a free-living population was involved, size of the sample, and other factors. Only studies reporting intake of fruits and vegetables and dietary fat as primary dietary outcomes were included. The researchers reviewed 907 articles and retained 104 (representing 92 independent studies). These articles presented results for behavioral interventions conducted in a wide range of settings.
Data Collection and Analysis: For each article analyzed, a team of two abstractors compiled information regarding the study methodology and results, and the article's quality. Re-review and reconciliation was performed by the Scientific Director. After completion of data abstraction for the 104 eligible articles, summary Evidence Tables were generated that present key details and findings for all eligible articles. Using this data, three increasingly inclusive types of analyses were performed: a meta-analysis, a quantitative analysis of the difference in dietary change between intervention and control groups (a differences-in-deltas approach), and a semiquantitative analysis summarizing the statistical significance of the intervention effect. The report explores the overall efficacy of behavioral interventions on dietary outcomes and considers the influence of specific intervention and population characteristics.
Main Results: In the studies that were reviewed, dietary interventions were consistently associated with an increase in fruit and vegetable consumption (with greater increases noted for fruit intake). More than three-quarters of the studies reviewed reported statistically significant increases in fruit and vegetable intake (as a combined variable). Using the differences-in-deltas approach, the reviewers determined that the average increase in fruit and vegetable intake reported was 0.6 servings per day, and consistent decreases were noted in the intake of total fat and saturated fat. The mean change in total fat intake was estimated as a 7.3 percent reduction in the percentage of calories from fat.
Interventions appeared to be more successful at positively changing dietary behavior in populations at risk of (or diagnosed with) disease than in healthy populations. Several dietary intervention components appear to be particularly promising in modifying dietary change favorable to cancer risk reduction: social support, goal setting, small groups, food-related activities, and the incorporation of family components.
Conclusions: The dissimilarity across studies in terms of outcome measures, study design, analysis strategy, and intervention technique makes it difficult to draw broad conclusions about the efficacy or effectiveness of behavioral dietary interventions. For example, investigators tended to employ more intensive interventions with high-risk populations, making it difficult to discern whether population or intervention characteristics were primarily responsible for increased change in dietary behavior. However, some intervention components may hold promise for future research efforts.
Very few studies were appropriately designed or reported to allow interpretation of evidence for the efficacy of the interventions by population subgroup, particularly low-income or ethnic subgroups. Despite increased Federal agency funding for dietary intervention research in underserved and minority populations, a serious deficit still exists in good-quality, published research designed to determine the relative efficacy of different intervention approaches in these groups.
Furthermore, few studies followed participants for more than a year, and those that did often showed a falling off in the initial dietary behavior change achieved. Thus, more research is needed to determine the longer-term effectiveness of dietary interventions in both the general population and important subgroups and to evaluate programs specifically designed to prevent relapse over time.
Finally, no studies that met the review criteria provided data on the cost-effectiveness of dietary interventions. Comparing the cost-effectiveness of current and innovative intervention approaches will be critical to assessing their broader applicability.
This document is in the public domain and may be used and reprinted without permission, except those copyrighted materials noted, for which further reproduction is prohibited without the specific permission of copyright holders.
Ammerman A, Lindquist C, Hersey J, et al. Efficacy of interventions to modify dietary behavior related to cancer risk. Evidence Report/Technology Assessment No. 25 (Contract No. 290-97-0011 to the Research Triangle Institute-University of North Carolina at Chapel Hill Evidence-based Practice Center), AHRQ Publication No. 01-E029. Rockville (MD): Agency for Healthcare Research and Quality. February 2001.
Increasingly, behavioral research is being conducted on the role of dietary change in cancer risk reduction. To implement the findings of such research, researchers are testing numerous dietary interventions in a variety of populations. Despite these efforts, no clear understanding has emerged regarding which interventions are more efficacious in influencing dietary change and for which groups.
To clarify what is known about the efficacy and effectiveness of behavioral interventions in promoting dietary change, the National Cancer Institute (NCI) -- through the Agency for Healthcare Research and Quality (AHRQ) and its Evidence-based Practice Program -- commissioned the Research Triangle Institute-University of North Carolina at Chapel Hill Evidence-based Practice Center (RTI-UNC EPC) to develop a rigorous evidence report on these issues. To discharge this responsibility, we, as members of the RTI-UNC EPC staff, systematically reviewed and synthesized the findings of 104 articles (representing 92 studies) that report the impact of behavioral interventions on dietary outcomes considered to be relevant to cancer risk: dietary fat intake and consumption of fruits and vegetables. In addition to summarizing the existing knowledge base on behavioral interventions and dietary change, we identified limitations in the current literature and outlined directions for future research. As part of this effort, we empaneled an eight-person Technical Expert Advisory Group (TEAG) that provided advice and assistance throughout the project. In addition, the draft evidence report was subjected to an extensive round of external peer review by experts and clinicians in cancer and nutrition and by potential future users of the report.
In this evidence report, we addressed three key questions regarding the efficacy and effectiveness of behavioral dietary interventions for promoting dietary change:
Is there evidence that one type of intervention or combination of interventions is more effective than another in helping individuals or groups modify their diets to consume more fruits and vegetables and less fat?
What is the evidence for the efficacy of dietary interventions by population subgroup, particularly groups defined by ethnicity and sex?
What conclusions (if any) can be reached about the cost-effectiveness of these types of interventions?
In addressing these questions through our review and secondary analysis of published articles, we considered interventions of all types (i.e., individual dietary counseling, group nutrition classes, social support groups) and in all settings (i.e., school, workplace, health care, environmental modifications, community). We included adults, adolescents, and children who were generally healthy-as well as those who were at elevated risk of or diagnosed with cancer, heart disease, or non-insulin-dependent (type 2) diabetes.
Our primary goal was to explore the overall efficacy and effectiveness of behavioral dietary interventions in increasing fruit and vegetable intake and decreasing dietary fat consumption. In addition, we considered the relative effectiveness of interventions in various population subgroups (such as children versus adults, high-risk versus healthy populations) and we explored the impact of key intervention characteristics (including the use of theory, intervention intensity, intervention setting, the use of social support, and the involvement of families in the intervention) on behavior change outcomes.
Our systematic review of the literature involved a mounting a comprehensive literature identification and screening process, abstracting relevant information from eligible articles, grading the quality of the articles, and generating summary Evidence Tables that present key details and findings for all eligible articles.
To identify studies regarding behavioral dietary interventions, we performed literature searches in six databases (MEDLINE, EMBASE, PsycINFO, CINAHL, AGELINE, and AGRICOLA). Primary Medical Subject Headings (MeSH) terms used in the searches included health behavior, attitude toward health, health promotion, behavior change, food habits, fat-restricted diet, diabetic diet, fruit, vegetables, prevent, counsel, cardiovascular disease, cancer, and neoplasms.
Our initial search strategy excluded studies (a) published before 1975 or in languages other than English; (b) conducted outside of North America, Europe, or Australia; (c) conducted with infants, institutionalized populations, or populations with insulin dependent diabetes mellitus; d) with sample sizes of fewer than 40 subjects at followup; and (e) in which dietary intake was externally controlled. Because the primary dietary outcomes we selected were intake of fruits and vegetables and dietary fat, we also excluded studies that did not report results for these outcomes. We included both randomized, controlled trials and nonrandomized studies that had control or comparison group designs. In all, we reviewed 907 articles and retained 104; collectively, these reported the results of 92 studies, which were used as the unit of analysis in our secondary analyses.
A team of trained abstractors completed Data Abstraction Forms, recording information about study methodology, the intervention design, and results. They used Quality Rating Forms to rate the methods, intervention content, and clarity of description for the 92 studies. The Study Director used the abstraction forms and original articles to generate summary Evidence Tables. The Scientific Director and a senior abstractor performed quality control functions; they reviewed information entered into the Evidence Tables and reconciled any discrepancies.
We conducted three sets of secondary analyses:
a meta-analysis (for the outcome of total fat as a percentage of energy intake);
a standardized, quantitative analysis of the differences in dietary changes between intervention and comparison or control groups from baseline to follow-up (which we hereafter refer to as the "differences-in-deltas" approach), conducted for the outcomes of combined fruit and vegetable intake, fruit intake, vegetable intake, total fat as a percentage of energy intake, total fat in grams, and saturated fat as a percentage of energy intake; and
a semiquantitative analysis summarizing the statistical significance of the intervention effects (hereafter, the "summary of significant findings" approach), conducted for the outcomes of combined fruit and vegetable intake, fruit intake, vegetable intake, total fat intake, saturated fat intake, general fat intake scores, specific high-fat foods or cooking practices, and specific low-fat foods or cooking practices.
The primary goal of each of these analytic strategies was to determine the overall effectiveness of dietary interventions in changing dietary behavior. Secondary goals included a determination of the relative effectiveness of different types of interventions for changing dietary behavior in different population subgroups. Together, these analytic approaches represent a spectrum of selectivity, from use of a highly selective class of studies and statistically rigorous methods to assessment of a larger, broadly inclusive set of studies by a simpler analytic technique. Collectively, our unique analytic approach provided some internal validation of the findings, but also pointed to methodologic issues and gaps in the research base of interest to NCI, AHRQ, members of the TEAG, and the clinical and behavioral health community in general.
The currently available literature provided considerable evidence to address our first key question regarding the efficacy or effectiveness of different types or components of interventions in helping individuals or groups modify their dietary intake. These findings are presented below. Very few studies were appropriately designed or reported their findings in a way that permited us to interpret the evidence for the efficacy of interventions by subgroup, particularly low-income or ethnic subgroups. No studies that met our review criteria provided data on the cost-effectiveness of dietary interventions.
Approximately one-third (39/92) of the studies we reviewed reported results of behavioral dietary interventions on fruit and vegetable intake. Based on the small number of studies (14) reporting daily servings of fruits and vegetables as outcomes, and on the high degree of variability across these studies, we concluded that a formal meta-analysis was inappropriate. Therefore, we employed the remaining two analysis strategies in our determination of the impact of interventions on fruit and vegetable intake.
The results of both the differences-in-deltas approach -- based on 17 of 39 studies reporting daily fruit and vegetable intake -- and the summary of significant findings approach -- based on 36 of the 39 studies -- indicated that: (a) dietary interventions were positively associated with changes in fruit and vegetable intake; and (b) when fruit and vegetable intake were measured individually, changes in fruit intake were larger. Although the results from studies that reported significant findings for fruit and vegetable intake varied (depending on the particular outcome measured), the majority of the studies we reviewed reported statistically significant increases in fruit and vegetable intake (either as separate outcomes or combined). Statistically significant increases in fruit and vegetable intake (as a combined variable) were noted in 16 of 22 studies reporting this outcome. Using the differences-in-deltas approach, we determined that the median difference between intervention and control groups in the change in daily servings of fruits and vegetables was +16.6 percentage points. This translates into an average increase in fruit and vegetable intake of 0.6 servings per day.
We were unable to explore the relative effectiveness of interventions on many population subgroups because of the minimum cell size requirement we established for specific analyses. (We determined that a minimum of five studies per cell was necessary to conduct a particular analysis.) Nevertheless, our analyses suggested that interventions were more successful at increasing fruit intake among children and vegetable intake among adults. In addition, interventions conducted among higher disease-risk populations were consistently more likely to report statistically significant increases in fruit and vegetable intake than were studies in general populations. For example, all six of the studies conducted in high-risk populations reported significant intervention effects for fruit intake compared with only eight of 14 studies conducted among general-risk populations. We observed a slightly smaller difference (five of seven high-risk studies compared with five of 14 general population studies) for the outcome of vegetable intake.
Among the specific intervention characteristics we explored, several patterns were evident. Studies employing a theoretical basis were more likely to report statistically significant increases in fruits and vegetable intakes than studies that did not utilize theory (14 of 16 studies using theory reported a significant increase in fruit and vegetable intake, while three of six studies that did not indicate the use of theory reported a significant intervention effect for fruit and vegetable intake). In addition, we observed a linear relationship between study quality and the likelihood of reporting significant findings. Also, the use of social support components was associated with more favorable increases in fruit and vegetable intake (using both analytic strategies). For example, all five studies that included a social support component reported a statistically significant increase in fruit intake, compared with nine of 17 studies not using social support that reported a significant effect for fruit; the differences in the proportion of studies reporting a statistically significant effect for vegetable intake was smaller (three of five studies compared with seven of 17 studies). Finally, studies that employed goal setting and interactive activities involving food were more likely to report statistically significant increases in fruit and vegetable intake, although the magnitude of the increases was not notably higher than that in studies not employing such techniques. For example, among the three fruit and vegetable outcomes we explored, the differences in the proportion of studies reporting statistically significant intervention effects between studies that incorporated a goal setting component and studies that did not ranged from 8 percent to 47 percent. We did not have a large enough pool of articles to be able to explore characteristics such as intervention intensity, setting, mode of delivery, use of individual tailoring, or culturally or ethnically specific interventions.
Nearly 80 percent (80/104) of the articles we reviewed reported results for dietary fat, although these outcomes varied tremendously. In determining the impact of behavioral dietary interventions on decreases in fat intake, we used all three analysis strategies. Based on all three techniques, dietary interventions were positively associated with changes in fat consumption. We observed similar decreases in intake of total fat and saturated fat (the two most commonly reported fat outcomes in the studies we reviewed). The median difference between intervention and control groups in the change in total fat intake (as a percentage of total energy intake) was -15.7 percentage points. This translates into a 7.3 percent reduction in the percentage of calories from fat. Among a subset of articles employing biochemical indicators (e.g., measuring changes in blood cholesterol), the decrease in total fat intake was significantly correlated with concomitant decreases in total blood cholesterol (r = 0.763, p = 0.004). The change in saturated fat was not associated with statistically significant decreases in total blood cholesterol.
The large number of studies reporting results for dietary fat enabled us to explore two moderating population characteristics: age and disease risk status. Across the five sets of fat outcomes that we explored using the summary of significant findings approach, studies conducted in high-risk populations were not consistently more likely to report a statistically significant decrease in fat intake. However, the differences-in-deltas analysis indicated that the magnitude of the change in dietary fat, particularly the reduction in saturated fat, was notably higher among interventions conducted in higher disease-risk populations. For example, the median difference in outcome change between intervention and control groups for saturated fat intake among studies conducted with high-risk populations was -29.3 percentage points; among studies conducted with general-risk populations, the median difference-in-deltas was only -14.5 percentage points. Similarly, 16 of the 17 studies analyzed by this method that focused on high-risk populations reported a significant intervention effect for saturated fat intake, compared with only seven of 10 studies conducted in general-risk populations. The pattern of larger effect sizes being observed among the studies focusing on high disease-risk populations was also evident in our meta-analysis. In addition, interventions conducted among children appeared to be more successful at reducing intake of total fat and less successful at reducing intake of saturated fat than interventions conducted among adults, although a very small number of studies measured fat intake among children (six for total fat intake, five for saturated fat intake).
Unlike the pattern observed for fruit and vegetable outcomes, interventions employing a theoretical framework were not consistently more likely to report significant effects (and the magnitude of the intervention effect was actually lower among studies based on theory). Nor was study quality associated with either the likelihood of reporting significant effects or the magnitude of the intervention effect.
Among the specific intervention characteristics we explored, however, several consistent patterns were evident. The use of social support, small groups, and goal setting appeared particularly effective at reducing intake of dietary fat. Greater proportions of studies employing such strategies reported statistically significant findings, and the magnitude of the change in dietary fat (using the differences-in-deltas approach) appeared higher among these studies. For example, among studies so analyzed that incorporated social support components into the intervention, all seven reported a significant intervention effect for total fat intake, and the median difference-in-deltas was -26.7. Among the studies so analyzed that did not report the use of social support, 83 percent (35/42) reported a statistically significant intervention effect for total fat intake, and the median difference in change between intervention and control groups was only -10.4 percentage points. The differences in the impact of goal setting on total fat intake were less pronounced. Among the studies that used goal setting, 18 of 19 reported a significant intervention effect (with the median difference in change being -18.9 percentage points). Among the studies that did not report the use of goal setting, 80 percent (24/30) reported a significant intervention effect (with the median difference in change being -11.0 percentage points). Although studies that involved families in the interventions and used interactive food-related activities were more likely to report significant decreases in fat intake (for example, all 13 of the studies using a family component reported significant decreases in fat intake, but 80 percent -- 29 of 36 -- of the studies not using a family component reported a significant intervention effect for fat intake), the magnitude of the decrease was not higher than that among the studies that did not incorporate these special features. Finally, although very few studies were designed to be culturally or ethnically specific (to the study sample), our results suggest that such studies reported greater decreases in dietary fat (although we did not have a sufficient number of articles to explore the magnitude of this decrease). For example, all five of the interventions designed to be culturally or ethnically specific reported significant decreases in both total and saturated fat intake, compared with 84 percent (37/44) of the studies that were not designed to be culturally or ethnically specific.
Our evidence review lends support to the notion that a wide variety of dietary interventions delivered in many different settings to individuals of different ages, ethnicities, and genders can have a positive impact on dietary behaviors associated with cancer risk reduction. The large proportion of studies showing favorable outcomes in various situations suggests an overall positive effect, although the potential for publication bias may have influenced the likelihood of identifying positive effects of interventions. Similarly, the language limitations of the literature review present an additional source of potential bias.
The lack of similarity across studies in outcome measures, study design, analysis strategy, and intervention technique makes it difficult to draw broad conclusions about the most efficacious behavioral dietary interventions. Nevertheless, our findings offer insight into intervention components that may hold promise for future research efforts. Several dietary intervention components appear to be promising in modifying dietary change. These factors include social support, goal setting, small groups, food-related activities, and the incorporation of family components. Interventions that included "interactions with food," such as cooking or taste testing, seemed particularly promising in increasing fruit and vegetable intake and reducing fat intake.
To gain the most from intervention research, future studies should assess dietary intake at the individual level and should collect detailed process and psychosocial data to help identify determinants of dietary change. With the emergence of new technologies to enhance health communications, research is urgently needed to evaluate the efficacy of these interventions, either relative to or in combination with more traditional approaches to dietary change. Comparing the cost-effectiveness of these different intervention approaches will be critical to assessing their broader applicability.
Few studies we reviewed followed participants for more than a year, and those that did often showed a falling off in the initial dietary behavior change achieved. More research is needed to determine the longer-term effectiveness of dietary interventions and to evaluate programs specifically designed to encourage the maintenance of change and prevent relapse over time.
Priority funding by federal agencies for dietary intervention research among underserved and minority populations has increased the amount of activity in these areas recently. However, the deficit in good quality, published research designed to determine the relative efficacy of different intervention approaches in these high-risk, hard-to-reach populations remains serious.
It has been estimated that one-third of all cancer mortality in the United States is related to diet.1,2 Based on a convergence of evidence indicating that nutrition plays an important role in the initiation, promotion, and progression of cancer, the National Cancer Institute (NCI) has recommended that Americans decrease fat consumption to 30 percent or less of total calories, increase fiber intake to 20 to 30 grams per day, and increase fruit and vegetable intake to five servings a day (the "5-A-Day" message). The American Cancer Society recommends choosing most foods from plant sources, limiting the intake of high-fat foods -- particularly from animal sources -- and limiting the consumption of alcohol.3 Similar guidelines have been disseminated for prevention of other diseases, such as cardiovascular disease (CVD).4
In the past decade, behavioral research on dietary change for cancer risk reduction has become more prevalent and rigorous. NCI has funded behavior change research at the level of $8.1 million annually,5 with a primary focus on diet. The National Heart, Lung, and Blood Institute (NHLBI) and the National Institute of Digestive Diseases, Diabetes, and Kidney Disease (NIDDK) have also spent millions on dietary intervention studies extending back at least 20 years. Many different intervention channels are being tested in a variety of populations. More recent studies employ theory-based interventions and measure intervening cognitive and psychological variables. In short, significant research is being done on dietary intervention efficacy, and at considerable expense.
Despite these two decades of dietary intervention research, no clear understanding has emerged regarding which interventions are most efficacious or effective in influencing dietary change, and for whom. (Hereafter, effectiveness should be understood to include efficacy in assessments of behavioral interventions.) Of particular concern are minority and underserved populations who appear to be at greatest risk for many cancers and other chronic diseases, yet are often most difficult to reach with screening and prevention programs. These gaps in understanding are the chief motivations for this evidence report.
Dietary behavior change is inextricably linked to social, environmental, cultural, and individual psychosocial and cognitive factors. Unlike in pharmacotherapy, a "single agent" approach to dietary change is neither feasible nor theoretically sound. The challenges are to develop innovative approaches to address the variety of barriers and motivators to change and then to test rigorously the impact of these interventions on dietary intake, biological markers, or both. This multifaceted approach creates many difficulties in evaluating the relative effectiveness of different intervention strategies, which tend not to be directly comparable in terms of objectives, methods, intensity or dose, or outcomes.
Thus, careful thought is needed to refine the research questions of interest, to define the parameters of the literature search and the review criteria, and to carry out a careful synthesis of the literature. These factors all argue for the production of an authoritative systematic review that will clarify the existing knowledge base, point the direction for future research, and lay the groundwork for others to determine which behavioral interventions to adopt, which to revise and adapt, and which (perhaps) to jettison.
As depicted in Figure 1
, the analytic framework
for behavioral dietary interventions extends from existing household
and individual dietary practices to the ultimate health outcome of
interest, cancer prevention.
Our evidence review will stop short of the health outcome per se and
focus instead on the intermediate outcomes of dietary and biomarker
change.
The link between dietary intake and cancer has been extensively studied through epidemiologic research.
With the seminal publication of Doll and Peto (1981) on the causes of cancer, the estimate that approximately 30 percent of all cancers (with a range of 10 percent to 80 percent) could be prevented through diet became commonly accepted.1 Willett (1998) revised these estimates,6 pointing to a 35 percent preventive potential of diet, with a much narrower confidence interval than that of Doll and Peto. The known and potential role of diet in various cancer sites has been reviewed repeatedly, most extensively in the expert report published by the World Cancer Research Fund (WCRF);7 it reviews the growing body of epidemiologic evidence of relationships among individual foods, specific dietary nutrients and nonnutrients, and the occurrence of cancer. It projects that, globally, between one-third and one-half of all cancers of the mouth and pharynx, pancreas, breast, and liver are preventable by diet. The "preventable" estimates are higher for esophageal cancers and reach between two-thirds and three-quarters for all cancers of the stomach, colon, and rectum.
The majority of this evidence is based on observational epidemiologic studies relating self-selected diets to cancer risk. In the search for causes of cancer, the role of diet was initially considered important primarily in the context of artificial colors, sweeteners, and preservatives. More recently, attention has turned to issues relating to fat, fruits and vegetables, and fiber. According to the WCRF report, the most convincing data support the protective effect of fruits and vegetables. Evidence suggests a "possible link" between cancer and low dietary fiber as well as cancer and high total fat intake.7
Clinical trials on dietary influences on carcinogenesis have been restricted largely to single-substance supplements in nonfood form, such as antioxidants (vitamins C, A, and E; beta carotene; selenium). Among the best-known investigations are the Linxan Study in China on a population at high risk of esophageal cancer,8 the beta carotene trials in the United States (the carotene and retinol efficacy trial, CARET, among long-term smokers and people exposed to asbestos)9 and Finland (ATBC study),10 and the Physicians' Health Study.11 These nutrient agents have been generally introduced in pill or placebo form in multifactorial studies among populations at high risk of either occurrence (e.g., persons having polyps or living in areas with extremely high incidence rates) or recurrence (e.g., persons with skin cancer). They are introduced late in life and generally have intervention periods of less than a decade. Some of these studies have yielded surprising results both in the amount of time needed to see an effect (much shorter than expected a priori) and in the direction of effect (some trials showed an increase in risk with supplementation).
Two recent randomized trials failed to support the fiber-adenoma hypothesis. One trial provided wheat-bran fiber supplements,12 and the other intervened with intensive counseling to increase fiber, decrease fat, and increase fruits and vegetables.13 Neither study reported significant intervention effects on recurrence of colorectal adenomas over a 3-year follow-up period.
The inconsistent findings of many diet-cancer observational studies as well as clinical trials raise questions as to the strength of the association, but they may also reflect biases inherent in this type of research. Dietary intake is typically homogeneous among individuals in a specific population and may be confounded by other environmental exposures. Systematic errors associated with dietary assessment methodology are well known. In clinical intervention trials, the wrong active ingredient may have been singled out and may actually be only a marker of the true active ingredient or combination of ingredients in food.6 Possible explanations for null findings include the potential that nutrition factors are more relevant at earlier stages of the carcinogenesis process, and the fact that length of follow-up may be inadequate for the outcome of interest.
In the analytic framework, an individual's pre-intervention dietary intake (Time 1) is influenced by family and household practices (immediate environment) and by the broader environmental factors of food access, culture, and policy. The environmental influences come into play throughout the causal pathway and thus are depicted across the breadth of the diagram. Different categories of interventions, shown as occurring between individual dietary intake and various mediators, can influence dietary change. The ultimate impact of these interventions on dietary change depends greatly on the degree to which they are implemented as planned and on their intensity (or dose). The interventions are likely to first affect psychosocial and cognitive mediating factors such as knowledge and a wide range of attitudes and beliefs derived from health behavior theory. Many psychosocial and cognitive mediators affect the degree to which interventions are received and accepted by the individual. In dietary intervention studies, these factors are often measured to determine whether the intervention is having an impact on intermediate or intervening variables associated with dietary change and to assess which mediators may be most associated with change in outcome measures.
The combined effect of intervention (and its intensity) and psychosocial and cognitive mediators then influences individual dietary intake at Time 2. Measurement challenges make it very difficult to assess "truth" when it comes to self-reported dietary intake (the basis of nearly all dietary assessment measures). Therefore, in dietary intervention studies, biomarkers are increasingly measured in an attempt to validate reported dietary intake.
For dietary interventions to have an impact on long-term cancer risk reduction, they are assumed to promote maintenance of dietary change in compliance with the NCI recommendations over an extended period of time. However, few intervention programs include a long-term maintenance component, and even fewer measure the degree to which positive changes are maintained after the intensive intervention is over.
Ultimately dietary change, maintained over time, should lead to a decreased risk of certain cancers, based on our understanding of the epidemiologic literature. Adverse outcomes of dietary change interventions (shown between mediators and Time 2 dietary intake but possible at other times) are quite rare, but reports have appeared of failure to thrive among infants and young children as a result of excessive fat restriction by parents. With any dietary intervention comes the concern (particularly among young women) that, given the right mix of psychosocial and cognitive factors, dietary restrictions could escalate to eating disorders.
In addition to affecting cancer risk, dietary interventions may lead to changes in other health conditions. The prudent diet advocated to prevent heart disease is low in saturated fat and cholesterol and relatively high in complex carbohydrates, including fruits and vegetables.4 It is similar to the diet recommended for cancer prevention.3 This diet has been advocated for 40 years in various forms to prevent heart disease. Denke (1995) 14 reviewed the effectiveness of this diet to lower blood cholesterol in individuals at usual risk and at high risk for coronary heart disease (CHD). In the individual trials, cholesterol reduction ranged from 4 percent to 17 percent, with greater reductions in the high-risk groups. Cholesterol reductions were lower but often statistically significant in the community intervention trials, ranging from 1 percent to 11 percent. These studies suggest that dietary change consistent with that advocated for the prevention of cancer can be achieved and sustained with appropriate interventions.
An evidence report on the effectiveness of dietary interventions to reduce cancer such as this one-commissioned by NCI from the Research Triangle Institute-University of North Carolina Evidence-based Practice Center (RTI-UNC EPC) through the Evidence-based Practice Program of the Agency for Healthcare Research and Quality -- will provide the foundation for creating and disseminating best public health and clinical practices. Many public health programs and services now incorporate dietary interventions aimed at chronic disease risk reduction, but they struggle with decisions about how to spend scarce resources in a way that maximizes dietary change. Clinical counseling to promote a healthy diet is one of the clinical preventive services addressed by the U.S. Preventive Services Task Force (USPSTF), which is now under the aegis of AHRQ and supported by the RTI-UNC EPC. An evidence report on this topic should further AHRQ's general interest in behavioral counseling of patients, medical informatics, and the design of effective educational tools and behavioral interventions.15 Findings from this report can also help AHRQ extend knowledge about the role of clinicians in promoting behavioral interventions to reduce morbidity and mortality from various chronic diseases.
In addition to providing the foundation for practice guidelines to be developed by others, an evidence report serves an important role in identifying promising areas for continued research as well as holes or gaps that may become the basis for future funding priorities. A particular focus on underserved groups will help to identify whether (a) these groups are currently being included in the interventions tested, (b) intervention programs meet their particular needs and interests, (c) effectiveness measures are appropriate for the target audiences, and (d) intervention effectiveness differs among different subgroups when different intervention strategies are implemented.
The Request for Proposal (RFP) posed four separate key questions. One item specified a multipart typology of behavioral interventions ranging from individual-directed to community-based projects as well as multistrategy endeavors.16-18 The typology described by Rimer (1995) 16 classifies interventions into eight categories: individual-directed (including school, community and worksite settings, and health care settings), system- and physician-directed interventions, access-enhancing interventions, policy-level interventions, media campaigns (including broadcast and print media and point-of-purchase interventions), community-based interventions, multistrategy interventions, and tailored interventions or interventions using emerging technologies. The RFP explicitly drew attention to interventions based on emerging technologies (such as communications and information technologies). In conceptualizing our approach to this evidence report, we are restating the questions in a somewhat more streamlined way as follows:
Is there evidence that one type of intervention or combination of interventions, using a broad typology of behavior interventions and including emerging technologies and approaches, is more effective than another for helping individuals or groups modify their diet to consume more fruits and vegetables and less fat?
What is the evidence by subgroup (e.g., African American, Hispanic, Asian American, Native American) and for males and females within these groups?
What conclusions (if any) can be reached about the cost-effectiveness of these types of interventions?
These key questions generated additional issues involving intervention design and measurement. In critiquing and synthesizing the literature on dietary interventions, we gave these special attention, as discussed in Chapters 2 and 3.
Our review of the literature made it clear that, although the typology of interventions described by Rimer and others represents an excellent characterization of the breadth and scope of existing and needed research in the area, the vast majority of published manuscripts fall into a very limited number of the categories described. This fact dictated that we employ different grouping strategies in our analysis and summary of the literature.
AHRQ guidelines required identification of technical experts in the field of dietary interventions related to cancer. The Technical Expert Advisory Group (TEAG) was expected to contribute to advancing AHRQ's broader goals of (a) creating and maintaining science partnerships and public-private partnerships and (b) meeting the needs of an array of potential customers and users of its products. Thus, it was both an additional resource and a sounding board throughout the project. Our TEAG comprised eight individuals who provided expertise from (a) clinical and public health practice in the area of dietary interventions, (b) private or quasi-public consumer organizations, and (c) the perspective of likely users of this EPC report such as health plans concerned with quality of care and value purchasing, or business coalitions.
We conducted five conference calls with TEAG members. To ensure robust, scientifically relevant work, the TEAG was called on to provide reactions to work in progress and advice on substantive issues or possibly overlooked areas of research. In the early stages of the project, TEAG members assisted us with refining the key questions and analytic framework and provided guidance on the scope of the literature to be included. Later in the project, the TEAG provided guidance on inclusion and exclusion criteria for the literature review, the application of meta-analysis and other analytic strategies, and methods for grouping studies to facilitate comparison. See Appendix B for more detail about the TEAG.
Apart from the TEAG, we also identified external peer reviewers, numbering an additional nine individuals. AHRQ and NCI selected the TEAG members and peer reviewers from a list of nominees. The roster of individual experts and organizations serving as reviewers is found in Appendix C.
The remainder of this evidence report is organized in the following sections. In this Volume 1, Chapter 2 provides details about our literature search and review methodology. Specifically included are the analytical framework for our key clinical questions and our approaches to conducting the systematic review, abstracting data from articles, maintaining quality control, applying a quality rating system to individual articles, and similar details. Chapter 3 provides the results of our analyses, including a meta-analysis for dietary fat outcomes. Chapter 4 provides the concluding discussion, and Chapter 5 offers our recommendations for a research agenda on behavioral dietary interventions specific to cancer reduction. Chapter 6 provides the references cited in the body of the evidence report.
Volume 2 contains our Evidence Tables (Chapter 7) and supporting information. Finally, the complete list of literature considered and used in developing the evidence report (including all articles reviewed in the literature search and all references citations in Chapters 1-7) appears in the bibliography (Chapter 8). The appendices provide acknowledgments (Appendix A), information on our TEAG (Appendix B), the peer reviewers for this report (Appendix C), and our Data Abstraction Form (Appendix D) and Quality Rating Form (Appendix E).
In this chapter, we outline our strategy for identifying and screening articles relevant to determining the impact of behavioral interventions on dietary change related to cancer risk. We describe the process of abstracting relevant information from the eligible articles and generating the summary Evidence Tables, which report key details about the study methodology and findings of the articles we reviewed. Finally, we detail the secondary analysis strategy we employed to explore the efficacy of interventions in changing dietary outcomes.
The first step in the literature identification process involved identifying key search terms and relevant databases on which to perform the literature search. The Literature Review Specialist, in conjunction with the Study Director and the Scientific Director, determined the search terms used in the analysis. During this process, several literature searches revealed the disparate nature of the literature and the necessity of using complex, interactive strategies to narrow the list of potential articles for abstraction. For example, because "intervention" is not included as a MeSH heading, terms such as food habits, health behavior, and health promotion, which are less precise, had to be used. In addition, our search strategy included cardiovascular diseases and other chronic diseases (e.g., diabetes) with similar dietary intervention outcomes. Despite numerous attempts to limit the records to the literature pertaining to dietary interventions rather than general nutritional epidemiology studies, each search produced significant "noise" from the epidemiologic literature. Because many of the abstracts found by a standard literature search did not include adequate information for determining inclusion/exclusion criteria, more than 300 full articles were pulled for review by our Scientific Director and our Study Director in order to select articles that met our criteria.
Searches were performed in the following six databases: (1) MEDLINE , the U.S. National Library of Medicine (NLM) database; (2) EMBASE; (3) PsycINFO; (4) the Cumulative Index to Nursing and Allied Health Literature (CINAHL); (5) AGELINE (produced by the American Association of Retired Persons [AARP]); and (6) AGRICOLA (AGRICultural OnLine Access). This list of databases included PsycINFO as specified by both AHCPR and NCI to ensure that we captured the fullest range of literature on behavioral change.
"Exploding" the search term Diet and/or Nutrition (i,e., including the more-specific terms branching off a primary search term) yielded more than 100,000 results using MEDLINE, EMBASE, and AGRICOLA. However, fewer (about 2,000) articles were identified in the other databases. Because of the unwieldy nature of the initial search, the primary search terms included were (1) health behavior; (2) attitude to health; (3) health promotion; (4) behavior change; (5) food habits; (6) fruit; (7) vegetables; (8) prevent; (9) counsel; (10) cardiovascular disease; (11) cancer; and (12) neoplasms. Other searches specifically focused on diabetes studies (type 2 diabetes) as suggested by our Technical Expert Advisory Group (TEAG).
| Search Terms | Results |
|---|---|
| 1. Explode Health Behavior or explode Attitude to Health or explode Health Promotion or explode Health Education or Behavior Change | 132,378 |
| 2. Explode Counseling | 13,181 |
| 3. Explode Computer-Assisted Instruction or Computer-Mediated | 2,813 |
| 4. Explode Internet | 991 |
| 5. Explode Knowledge, Attitudes, Practice | 11,073 |
| 6. 1 or 2 or 3 or 4 or 5 | 146,347 |
| 7. Explode Diabetic Diet or explode Diet or explode Diet, Atherogenic or explode Diet, Fat-Restricted or explode Diet Therapy | 96,021 |
| 8. Explode Food Habits | 7,021 |
| 9. Explode Dietary Fiber or explode Fruit or explode Vegetables or explode Nutrition | 145,762 |
| 10. 7 or 8 or 9 | 182,423 |
| 11. 6 and 10 | 7,693 |
| 12. Limit 11 to (human and English language) | 6,243 |
| 13. Limit 12 to Randomized Controlled Trial | 436 |
| 14. Explode Epidemiologic Study Characteristics | 612,128 |
| 15. 12 and 14 | 872 |
| 16. 13 and 15 | 1,127 |
| 17. Explode Neoplasms | 1,185,861 |
| 18. 16 and 17 | 100 |
| 19. Explode Cardiovascular Diseases | 911,699 |
| 20. 16 and 19 | 223 |
| 21. Explode Diabetes Mellitus, Non-Insulin-Dependent | 17,615 |
| 22. 16 and 21 | 53 |
| 23. 18 or 20 or 22 | 363 |
| Search Terms | Results |
|---|---|
| 1. Diet or Nutrition | 82,584 |
| 2. Counsel | 17,470 |
| 3. Prevent | 178,450 |
| 4. Behavior | 119,342 |
| 5. 2 or 3 or 4 | 303,085 |
| 6. 1 and 5 | 12,559 |
| 7. Cancer | 295,731 |
| 8. 6 and 7 | 2,013 |
| 9. Heart | 267,353 |
| 10. 6 and 9 | 1,492 |
| 11. 8 or 10 | 3,346 |
| 12. Randomized | 73,200 |
| 13. 11 and 12 | 293 |
| Search Terms | Results |
|---|---|
| 1. Explode Diets | 2,323 |
| 2. Explode Nutrition | 1,250 |
| 3. Explode Eating | 2,052 |
| 4. 1 or 2 or 3 | 5,168 |
| 5. Explode Behavior Change | 2,511 |
| 6. Explode Counseling | 19,182 |
| 7. Explode Health Education | 4,677 |
| 8. 5 or 6 or 7 | 25,984 |
| 9. 4 and 8 | 262 |
| 10. Explode Heart | 514 |
| 11. Explode Cardiovascular Disorders | 5,716 |
| 12. 10 or 11 | 6,145 |
| 13. 9 and 12 | 23 |
| 14. Explode Neoplasms | 5,528 |
| 15. 9 and 14 | 10 |
| 16. 13 or 15 | 32 |
| Search Terms | Results |
|---|---|
| 1. Explode Behavior Change | 0 |
| 2. Explode Diets | 0 |
| 3. 1 and 2 | 0 |
| 4. Explode Counseling | 2,676 |
| 5. Explode Nutrition | 7,879 |
| 6. 4 and 5 | 70 |
| 7. 3 not 6 | 0 |
| 8. 6 not 3 | 70 |
| 9. Explode Clinical Trials or explode Random Assignment or explode Prospective Studies or explode Double-Blind Studies or Randomized | 16,084 |
| 10. 8 and 9 | 0 |
| Antibiotics | Results |
|---|---|
| 1. Diet | 521 |
| 2. Nutrition | 1,837 |
| 3. 1 or 2 | 2,104 |
| 4. Counseling | 1,864 |
| 5. Health Education | 805 |
| 6. Behavior Change | 40 |
| 7. 4 or 5 or 6 | 2,638 |
| 8. 3 and 7 | 266 |
| 9. Heart | 923 |
| 10. Cardiovascular | 732 |
| 11. Heart | 923 |
| 12. Cancer | 835 |
| 13. Neoplasms | 20 |
| 14. 9 or 10 or 11 or 12 or 13 | 1,972 |
| 15. 8 and 14 | 15 |
| Search Terms | Results |
|---|---|
| 1. Diet or Nutrition | |
| Diet | 75,758 |
| Nutrition | 202,737 |
| Diet or Nutrition | 241,354 |
| 2. Counseling or Education | |
| Counseling | 1,977 |
| Education | 53,607 |
| Counseling or Education | 54,753 |
| 3. Combine 1 and 2 | |
| 1 | 241,354 |
| 2 | 54,753 |
| 1 and 2 | 20,251 |
| 4. Heart or Cardiovascular | |
| Heart | 10,733 |
| Cardiovascular | 4,440 |
| Heart or Cardiovascular | 13,473 |
| 5. Cancer or Neoplasms | |
| Cancer | 6,431 |
| Neoplasms | 4,183 |
| Cancer or Neoplasms | 9,637 |
| 6. Combine 4 or 5 | |
| 4 | 13,473 |
| 5 | 9,637 |
| 4 or 5 | 22,337 |
| 7. Combine 3 and 6 | |
| 3 | 20,251 |
| 6 | 22,337 |
| 3 and 6 | 1,280 |
| 8. Prevention or Control | |
| Prevention | 17,041 |
| Control | 302,692 |
| Prevention or Control | 315,832 |
| 9. Combine 7 and 8 | |
| 7 | 1,280 |
| 8 | 315,832 |
| 7 and 8 | 521 |
| 10. Randomized | |
| Randomized | 1,613 |
| 11. Combine 9 and 10 | |
| 9 | 521 |
| 10 | 1,613 |
| 9 and 10 | 20 |
| Search Terms | Results |
|---|---|
| 1. Health Behavior or Attitude to Health or Health Promotion or Health Education or Behavior Change | 63,249 |
| 2. Explode Counseling | 13,110 |
| 3. 1 or 2 | 75,169 |
| 4. Explode Diabetic Diet or explode Diet or explode Diet, Atherogenic or explode Diet Therapy | 95,520 |
| 5. Explode Dietary Fiber | 6,451 |
| 6. Explode Fruit | 7,253 |
| 7. Explode Vegetables | 26,404 |
| 8. Explode Food Habits | 6,968 |
| 9. Explode Nutrition | 113,007 |
| 10. Explode Diet, Fat-Restricted | 591 |
| 11. 4 or 5 or 6 or 7 or 8 or 9 or 10 | 181,384 |
| 12. 3 and 11 | 4,984 |
| 13. Limit 12 to (human and English language) | 4,090 |
| 14. Limit 13 to Meta-Analysis | 8 |
| 15. From 14 keep 1-8 | 8 |
| Search Terms | Results |
|---|---|
| 1. Health Behavior or Attitude to Health or Health Promotion or Health Education or Behavior Change | 63,249 |
| 2. Explode Counseling | 13,110 |
| 3. 1 or 2 | 75,169 |
| 4. Explode Diabetic Diet or explode Diet or explode Diet, Atherogenic or explode Diet Therapy | 95,520 |
| 5. Explode Dietary Fiber | 6,451 |
| 6. Explode Fruit | 7,253 |
| 7. Explode Vegetables | 26,404 |
| 8. Explode Food Habits | 6,968 |
| 9. Explode Nutrition | 113,007 |
| 10. Explode Diet, Fat-Restricted | 591 |
| 11. 4 or 5 or 6 or 7 or 8 or 9 or 10 | 181,384 |
| 12. 3 and 11 | 4,984 |
| 13. Limit 12 to (human and English language) | 4,090 |
| 14. Limit 13 to Meta-Analysis | 8 |
| 15. From 14 keep 1-8 | 8 |
| 16. Explode Community Health Nursing or explode Community Health Planning or explode Community Health Services or explode Community Medicine or explode Community Networks or explode Hospitals, Community | 213,314 |
| 17. 13 and 16 | 2,594 |
| 18. Explode Epidemiologic Study Characteristics | 605,476 |
| 19. 17 and 18 | 291 |
| 20. Limit 17 to Randomized Controlled Trial | 158 |
| 21. 19 or 20 | 380 |
| 22. Explode Neoplasms | 1,179,576 |
| 23. 21 and 22 | 41 |
| 24. Explode Heart Diseases | 418,599 |
| 25. Explode Cardiovascular Diseases | 906,722 |
| 26. 24 or 25 | 906,722 |
| 27. 21 and 26 | 99 |
| 28. 23 or 27 | 138 |
| 29. From 28 keep 1-138 | 138 |
| Search Terms | Results |
|---|---|
| 1. Health Behavior or Attitude to Health or Health Promotion or Health Education or Behavior Change | 63,249 |
| 2. Explode Counseling | 13,110 |
| 3. 1 or 2 | 75,169 |
| 4. Explode Diabetic Diet or explode Diet or explode Diet, Atherogenic or explode Diet Therapy | 95,520 |
| 5. Explode Dietary Fiber | 6,451 |
| 6. Explode Fruit | 7,253 |
| 7. Explode Vegetables | 26,404 |
| 8. Explode Food Habits | 6,968 |
| 9. Explode Nutrition | 113,007 |
| 10. Explode Diet, Fat-Restricted | 591 |
| 11. 4 or 5 or 6 or 7 or 8 or 9 or 10 | 181,384 |
| 12. 3 and 11 | 4,984 |
| 13. Limit 12 to (human and English language) | 4,090 |
| 14. Limit 13 to Meta-Analysis | 8 |
| 15. From 14 keep 1-8 | 8 |
| 16. Explode Community Health Nursing or explode Community Health Planning or explode Community Health Services or explode Community Medicine or explode Community Networks or explode Hospitals, Community | 213,314 |
| 17. 13 and 16 | 2,594 |
| 18. Explode Epidemiologic Study Characteristics | 605,476 |
| 19. 17 and 18 | 291 |
| 20. Limit 17 to Randomized Controlled Trial | 158 |
| 21. 19 or 20 | 380 |
| 22. Explode Neoplasms | 1,179,576 |
| 23. 21 and 22 | 41 |
| 24. Explode Heart Diseases | 418,599 |
| 25. Explode Cardiovascular Diseases | 906,722 |
| 26. 24 or 25 | 906,722 |
| 27. 21 and 26 | 99 |
| 28. 23 or 27 | 138 |
| 29. From 28 keep 1-138 | 138 |
| 30. 11 and 22 and 26 | 662 |
| 31. Limit 30 to (human and English language) | 537 |
| 32. Limit 31 to Meta-Analysis | 1 |
| 33. From 32 keep 1 | 1 |
| Search Terms | Results |
|---|---|
| 1. Explode Diabetic Diet or explode Diet or explode Diet, Atherogenic or explode Diet, Fat-Restricted | 12,497 |
| 2. Explode Nutrition | 17,250 |
| 3. Explode Food Habits | 1,183 |
| 4. 1 or 2 or 3 | 20,603 |
| 5. Limit 4 to (human and English language) | 13,186 |
| 6. Explode Neoplasms | 8,125 |
| 7. 5 and 6 | 433 |
| 8. Explode Dietary Fiber | 1,254 |
| 9. Explode Fruit | 1,594 |
| 10. Explode Vegetables | 6,543 |
| 11. 8 or 9 or 10 | 8,698 |
| 12. 7 and 11 | 116 |
| 13. From 12 keep 1-116 | 116 |
| Search Terms | Results |
|---|---|
| 1. Explode Cholesterol or explode Cholesterol, Dietary or explode Hyperlipidemia | 84,821 |
| 2. Limit 1 to (human and English language and year = 1994-1999) | 10,114 |
| 3. Explode Diet or explode Diet Therapy | 93,761 |
| 4. Dietary Advice | 385 |
| 5. 3 or 4 | 94,004 |
| 6. 2 and 5 | 981 |
| 7. Limit 6 to (Clinical Trial or Clinical Trial, Phase I or Clinical Trial, Phase II or Clinical Trial, Phase III or Clinical Trial, Phase IV) | 277 |
| 8. Limit 6 to Randomized Controlled Trial | 200 |
| 9. Explode Clinical Trials or explode Randomized Controlled Trials or Random Allocation or Double-Blind Method | 159,011 |
| 10. 6 and 9 | 101 |
| 11. Explode Single-Blind Method | 4,040 |
| 12. 6 and 11 | 10 |
| 13. 7 or 8 or 10 or 12 | 323 |
| 14. Limit 13 to year = 1997-1999 | 120 |
| 15. From 14 keep 1-120 | 120 |
| Searches | Number of Articles |
|---|---|
| MEDLINE (expanded search) | 363 |
| EMBASE | 293 |
| PsycINFO | 32 |
| CINAHL | 0 |
| AGELINE | 15 |
| AGRICOLA | 20 |
| MEDLINE (meta-analysis and diet counseling) | 8 |
| MEDLINE (counseling) | 138 |
| MEDLINE (meta-analysis and cancer) | 1 |
| MEDLINE (diet and cancer) | 116 |
| MEDLINE (diet therapy) | 120 |
| From all searches | 1,106 |
| Duplicates | 229 |
| Unduplicated articles | 877 |
| From TEAG and review articles | 12 |
| From peer reviewers | 18 |
| Total | 907 |
| Category | Criteria |
|---|---|
| Time period | Publication date of 1975 to present |
| Geographic site of study | North America, Europe, or Australia |
| Publication language | English |
| Study population | Human adults, adolescents, and children (infants excluded) Healthy or high-risk populations (type 1 diabetes populations relying on regimented diets excluded) Noninstitutionalized populations |
| Study design | Randomized controlled trials (RCTs) or non-RCTs (nonequivalent control or comparison group designs) |
| Sample size | >40 subjects at follow-up |
| Settings and intervention | All settings (i.e., inpatient, outpatient, communities, worksites, etc.) All intervention types (i.e., counseling, support groups, classes, etc.) Diet must be freely chosen (i.e., not controlled by the study) |
| Outcomes | Must include fruit and vegetable consumption or dietary fat intake
|
Next, we excluded studies based on population characteristics. The general population on which we focused in this report was human adults, adolescents, and children; therefore, studies on infant populations were excluded. We decided to include both healthy populations and populations at high risk of disease as long as subjects were not restricted to a hospital or chronic care setting. However, populations with type 1 diabetes (insulin-dependent) relying on regimented diets (such as patients with renal disease) were excluded. Type 2 diabetes patients taking insulin were included. Finally, we excluded institutionalized populations (such as prisoners and nursing home residents).
Regarding study design, we included both randomized controlled trials (RCTs) and nonrandomized controlled trials (non-RCTs) that had nonequivalent control or comparison group designs. Sample size was used in our screening; studies with fewer than 40 subjects at follow-up were excluded.
Several characteristics pertaining to the intervention were considered as criteria for inclusion. Interventions of all types (e.g., individual dietary counseling, group nutrition classes, social support groups) and settings (e.g., school, workplace, media, health care setting, policy changes) were included in this review, and no minimum level of intervention intensity or duration was required for inclusion. However, the intervention must have allowed dietary intake to be freely chosen by the participant. Thus, studies that provided prepackaged meals to subjects were excluded.
Because dietary outcomes considered in this report were based on relevance to risk of cancer, we included only studies reporting results for fruit and vegetable intake or dietary fat intake. However, in our abstraction of selected articles, we recorded limited information for fiber intake (this information is presented in the Evidence Tables, which are described in a later section of this chapter). The decision to focus on intake of dietary fat and fruits and vegetables was based on discussions with our TEAG, during which it was generally agreed that the evidence for the relationship between fiber and cancer risk was inconclusive. Other criteria that influenced our decision were the prevalence of dietary outcomes reported in the literature and the extent to which dietary fat and fruits and vegetables are emphasized as dietary goals in behavioral interventions.
Follow-up (i.e., post-intervention) results for these outcomes must have been reported in order for the article to be included. Although results for biochemical indicators (e.g., total cholesterol, low-density lipoprotein cholesterol, and carotenoids) and behavioral mediators (e.g., dietary knowledge, stages of change, dietary self-efficacy) were abstracted in our review of the articles, these outcomes were not necessary for an article to be included.
We did not use dietary measurement methodology as an exclusion criterion, so we identified articles with a wide variety of assessment techniques. All dietary intake data were self-reported in the articles we reviewed, using methodologies such as dietary recalls, food records or diaries, dietary histories, or food frequency questionnaires. Because such techniques are based on self-reported information, they are subject to bias from participant memory and judgment, which can result in underreporting of energy intake (which is particularly problematic among overweight individuals). Indeed, social desirability bias is especially likely to be a source of error in behavioral intervention studies, because participants are taught about the "right" foods to consume and may be less likely to report intake of "unhealthful" foods. Thus, the limitations associated with self-reported dietary intake should be kept in mind when interpreting the results presented in this report.
| Reasons | Number of Articles |
|---|---|
| Not intervention | 253 |
| Not fruit and vegetables, or fat | 175 |
| Not nutrition intervention | 146 |
| Background only/Reviews | 107 |
| Drug study | 34 |
| Diet externally controlled | 8 |
| Editorial | 4 |
| Animal | 4 |
| Age | 1 |
| Other exclusion criteria (e.g., focus on weight loss, special diet for conditions) | 71 |
| Total | 803 |
The data collection process involved abstracting relevant information from the eligible articles and generating summary Evidence Tables that present the key details and findings for the articles. A team of two trained abstractors independently completed a detailed Data Abstraction Form -- which for each eligible article elicited relevant information about the study methodology and results -- and a Quality Rating Form, which rated the quality of the article. The Study Director used the forms and the original articles to generate summary Evidence Tables. Quality control functions were performed by the Scientific Director and a senior abstractor, who re-reviewed the information reported in the Evidence Tables and reconciled discrepancies between the abstractors.
The RTI-UNC EPC used abstractors with two types of backgrounds for the data extraction process: content or clinical expertise and strong methodological skills. The clinical abstractors were Semra Aytur, MPH; Kerry-Ann daCosta, PhD; Denise D. Dickinson, MPH; Alyssa Ghiradelli, MPH, RD; Christine S. Hardy, RD; and Hugh C. Law, RD. All had prior research experience. The methods abstractors were Tracy L. Bouchard-Cyr, MSPH; Nancy A. Davis, MSHE, MPH; Ho-Jui Tung, MPH; Kimberly Truesdale, MSPH; and Carole Toselli, MD.
All abstractors attended two formal training sessions. At the first session we explained the process and goals of the abstraction. Following the training, the abstractors were sent home with an article to review. We then reconvened the group and, through a review of the test article, ensured that the reviewers understood what was expected from their work. For example, we instructed abstractors that when inconsistencies arose between results stated in the text of an article and those presented in tabular form, they were to take data from the text. They were also told to extract precisely what was contained in the article and to reserve any opinions about the contents to notes in the margins. At the completion of the training, the data abstraction process began. The Scientific Director and the UNC Research Coordinator monitored progress. Any problems or questions encountered by the abstractors were routed though the Research Coordinator to the appropriate senior staff member.
Initially we had both a clinical and a methodological abstraction independently completed for each eligible article. The abstractors met to resolve differences, and a reconciled form, with individually completed forms attached, was submitted to the Research Coordinator. However, despite extensive abstractor training, the complexity and high variability in the reporting of behavioral interventions required a comprehensive re-review of all data sources by the Study Director or the Scientific Director. To make the process more time- and cost-efficient while also producing the most accurate reporting of the data, we modified our procedure to have a single trained abstractor complete the abstract form, followed by a detailed review of all information by a senior member of the project team. We found, upon review by the Study Director and the Scientific Director, that this process generated a higher level of accuracy and detail in the summary database while maintaining review of the abstraction by two people.
The Data Abstraction Form (included in Appendix D) was used to extract the relevant information presented in the article and to confirm that the study fulfilled the inclusion criteria described previously. The Study Director and the Scientific Director worked with core staff and the TEAG to develop the Data Abstraction Form. This form was developed with extensive communication between methodologists and researchers. We began by identifying salient study characteristics and dietary outcomes; we then created specific items eliciting information for the following constructs: intervention characteristics, population characteristics, study design, and the statistical results of the study. Specific information abstracted from the articles included the following:
The theoretical framework used in the intervention
The setting of the intervention
Key components of the intervention (e.g., classes, individual counseling, cafeteria modifications)
The delivery of the intervention (e.g., physician, registered dietitian)
The nutrition message of the intervention
Special features of the intervention (e.g., individually tailored components, ethnic specificity)
The intervention duration and intensity
The demographic composition of the sample (including gender, age, race, income, health status)
Study design
Duration of follow-up
Participation and retention rates
Measurement (including information on the validity and reliability of the dietary assessment technique) and statistical results for fruit and vegetable intake
Measurement (including information on the validity and reliability of the dietary assessment technique) and statistical results for dietary fat intake
Statistical results for other dietary outcomes (including fiber- and calcium-related outcomes)
Measurement and statistical results for biochemical outcomes related to fruit and vegetable or dietary fat intake (including plasma carotenoids and blood lipids)
Statistical results for behavioral mediators
The Data Abstraction Form was developed after extensive pretesting by the staff and the abstraction team. To improve the quality of the abstraction process, a comprehensive guide accompanied the final form. After several weeks of using the final version of the form, we decided to have abstractors simply record the table and page numbers for the statistical results on the Data Abstraction Form rather than attempt to transcribe the complete statistical information from the article to the form (with the statistical results entered directly into the Evidence Tables, with re-review by the Scientific Director and a senior abstractor).
During the data abstraction process, the abstractors completed a Quality Rating Form for all eligible articles. The scoring of this form for each article was reviewed by the Study Director, the Scientific Director, or the senior abstractor.
Although no consensus exists on criteria for determining the quality of behavioral intervention research, this issue merits considerable attention because it is likely to influence the degree of credibility surrounding study results. Judging the quality of articles in systematic evidence reports is necessarily multidimensional, including features associated with the quality of the study design and intervention as well as the quality of the write-up itself.
Our approach (as shown in Appendix E) was to identify and rate essential features of both the study description and the methodology, weighing the latter component more heavily. Some degree of subjectivity in our approach to rating the quality of the evidence was unavoidable. Some of our criteria did involve the judgment of the abstractor (and reviewer), and even relatively objective information (e.g., sample size) was not always consistently reported in the articles we reviewed. However, the fact that all quality scores were reviewed by a senior member of the project staff resulted in the consistent application of our scoring procedure.
The quality score assigned to each article was a numerical value ranging from 0 to 100. This score was based on the following factors:
Whether the intervention was theoretically based (5 points)
Whether the research design involved random allocation of individuals/units to treatment groups (10 points)
The sample size (10 points)
The duration of follow-up (10 points)
The retention rates (10 points)
The description and validity of the dietary assessment tool (5 points)
Whether changes in biochemical outcomes were explored (5 points)
Whether analysts were blind to the assignment of treatment groups (5 points)
The generalizability of the results (based on the representativeness of the sample and the practicality of the intervention) (10 points)
The quality of the description of the intervention (including relevant details about the setting, components, delivery, duration, and intensity of the intervention) (10 points)
The quality of the description of the study population, recruitment strategy, and inclusion/exclusion criteria (10 points)
The quality of the description of the variable measurement and statistical analysis procedure (10 points)
Methodological features of considerable importance to intervention research in general include study design (with higher quality scores associated with randomized controlled trials), sample size, duration of follow-up, retention rates, whether analysts were blind to treatment assignment, and the generalizability of results. Additional methodological features salient to dietary outcomes include the validity of the dietary assessment tool and the use of biochemical indicators to validate changes in dietary behavior. A unique criterion particularly relevant to behavioral interventions is whether the intervention was based on or guided by a theoretical framework. We incorporated each of these methodological features into our Quality Rating Form; such criteria were worth 70 percent of the total quality score.
In addition to judging the quality of the study methodology, we considered the quality of the written article (in terms of detail and clarity). The quality of the intervention description was particularly important in accurately identifying the type of intervention. Features such as intervention setting (e.g., school, health care setting, media campaign), intervention components (e.g., dietary counseling, support groups, newsletters), delivery mode (e.g., dietitian, physician, regular classroom teacher), and intervention intensity (number of exposures/contacts) were included in our Quality Rating Form. We also rated the article's description of the study population, recruitment strategy, and inclusion/exclusion criteria.
The final component we incorporated in the Quality Rating Form was the article's description of the variable measurement and statistical analysis procedure. Although articles were not excluded on the basis of the dietary assessment technique employed, the description of the methodology and the validity of the assessment tool were used as a component of the quality score assigned to the article; the validity of the measurement tool had to be specified or referenced in order for the article to receive the maximum number of points for that item. Together, the study description features constituted 30 percent of the total quality score calculated for each article. The complete Quality Rating Form is included in Appendix E.
Using the Data Abstraction Form, the Quality Rating Form, and the original article, the Study Director generated summary Evidence Tables, which present a concise summary of key intervention characteristics, methodological details, and statistical results for the 104 articles we reviewed. As mentioned previously, the content of these tables was re-reviewed against the original article by either the Scientific Director or the senior abstractor. In the Evidence Tables, we combined multiple articles reporting results for the same study. Thus, the 104 articles we reviewed represented only 92 independent studies, and each of these 92 studies forms a separate entry in the Evidence Tables.
We decided on the content of the Evidence Tables at an early stage in the project so that we could have all relevant information recorded for each article during the abstraction process. We determined relevant study details through extensive discussions with the RTI-UNC team and the TEAG. The format and general organization of the Evidence Tables were guided by previous RTI-UNC EPC evidence reports.
The Evidence Tables presented in this report are separated by intervention setting, resulting in four tables: school-based interventions, health care interventions (which includes studies in which the intervention was conducted in a health care setting and studies for which subjects were recruited from health care settings), worksite interventions, and community/other interventions (which include interventions conducted in homes, churches, and communities). The content of the Evidence Tables, with a brief description and glossary, is presented in Chapter 7.
The Evidence Tables report information on the intervention setting, subject characteristics (including gender, age, race, and risk status), study design, sample size and retention rates, intervention characteristics (including intervention components, delivery, special features, and nutritional message), duration/intensity of the intervention, and duration of follow-up. The measurement approach (including variables, instrument, and statistical analysis strategy) and statistical results are presented for fruits and vegetables, dietary fats, and biochemical indicators. For other dietary outcomes and behavioral mediators, we provided only a brief description of the significance of the intervention effect. The final piece of information included in the Evidence Tables is the quality score assigned to the article.
In developing a strategy for synthesizing the statistical results presented in the Evidence Tables, it became evident that two major issues would have to be resolved. The first is that the articles in the Evidence Tables reported multiple outcome measures. The second is the variety in the statistical analysis techniques used to determine the significance of various interventions as well as the actual statistics reported in the articles.
Based on input from our TEAG about the dietary outcomes reported most commonly in the nutrition literature, the relevance of dietary outcomes to cancer risk, and current dietary recommendations (which are typically the targeted information in behavioral interventions), we included the following outcomes: for fruits and vegetables, total daily servings of fruits and vegetables, daily servings of fruit, and daily servings of vegetables; for dietary fat, total fat as a percentage of energy intake, saturated fat as a percentage of energy intake, and total fat in grams. However, in the interest of including more studies with a variety of designs, we also analyzed other outcomes related to fruits and vegetables and dietary fats. The definition of fruits and vegetables as outcomes is a topic of considerable debate. The issues include whether high-fat, starchy vegetables such as french fries are counted as vegetables and whether fruit juices are considered servings of fruit. Appropriate serving sizes are similarly a topic of contention. We had to rely on the definitions used in the articles we reviewed (because we could not separate out certain foods and create our own definitions), and this factor led in turn to a lack of standardization in our secondary analyses. Fat outcomes also varied. For example, fat-related behaviors (such as substituting skim milk for whole milk, trimming the fat from meat) and intake of individual foods or food groups (such as high-fat meat, butter, or cream) were commonly reported as outcomes in the studies we reviewed. It was a challenge to identify key outcome variables on which to focus, while still including less commonly reported outcomes in the analyses. As described in further detail below, we developed a three-tiered approach for our secondary analysis of dietary outcomes that used different strategies for "key" outcomes and "nonkey" outcomes.
The second issue (diversity in statistical reporting) also had a major impact on our analysis strategy. In the articles we reviewed, investigators took several approaches to determine the statistical significance of the intervention effect. Common approaches included the following: analyzing the interaction between time and treatment group membership; comparing differences in means between the intervention and control group(s) at follow-up (either with or without controlling for baseline values); and using a repeated measures approach to determine the change in the intervention and control group(s) from baseline to follow-up (with results typically reported separately for the intervention and control group). Concomitant with the diversity in analysis approaches (with multiple approaches often reported for a single article) was nonuniformity in the statistics reported. Mean values were commonly reported among the studies we reviewed, but the reporting of the variance was extremely inconsistent; studies reported standard deviations, standard errors, 95 percent confidence intervals, ranges, or, quite commonly, no indicator of variance at all. Similarly, the significance of effects was inconsistently reported, with actual p values or p value "cut-offs" (e.g., <0.05, <0.01) often unreported, particularly for nonsignificant findings. Thus, because of unique journal requirements and editorial policies (particularly limiting the amount of space available to authors), the statistics necessary to conduct appropriate secondary analyses are often inconsistently reported.
Five additional issues inherent to the design of the studies influenced the level of consistency in statistical reporting: (1) using a cross-over design, (2) including multiple intervention groups in the study, (3) reporting results from multiple follow-up periods, (4) reporting results separately for population subgroups (e.g., males and females), and (5) applying inconsistent dietary measurement techniques. These design and reporting features, while appropriate and desirable for understanding the impact of individual interventions, are difficult to incorporate into secondary analyses and complicated our efforts to synthesize the results of the interventions reviewed.
A final issue, which is an inherent limitation of reviews of published literature and secondary analyses conducted on such literature, is the potential for publication bias. Our review must necessarily rely on previously published, peer-reviewed work, but the pool of articles we reviewed is highly likely to have been biased toward "positive" findings, or findings more likely to support the relationship between behavioral interventions and dietary change (with negative findings being less likely to be submitted or accepted for publication). As a result, our secondary analyses will reflect this bias -- a likelihood that must be considered when interpreting our results.
To accommodate both the statistical diversity and the variety of outcomes reported in the articles we reviewed, we developed a three-tiered secondary analysis strategy that incorporated different analytic techniques for particular outcomes and for specific types of statistical information reported in the articles. As mentioned previously, although we included articles employing consistent units of measurement (such as percentage energy), we could not ensure consistency in dietary assessment techniques because of the wide variety of measurement strategies and often unclear methodological descriptions.
The three tiers of the analysis strategy (described in further detail in the subsequent sections) are the following:
Meta-analysis (based on groupings of articles reporting results for comparable populations) of the change (and variance) between intervention and control groups over time for two outcomes:
total daily servings of fruits and vegetables
daily intake of total fat as a percentage of energy intake
Standardized, quantitative analysis of the change between intervention and control groups from baseline to follow-up for a set of key outcomes:
total daily servings of fruits and vegetables
total daily servings of fruits
total daily servings of vegetables
daily intake of total fat as a percentage of total energy intake
daily intake of saturated fat as a percentage of total energy intake
daily intake of total fat in grams per day
Semiquantitative analysis summarizing whether the reported intervention effect was significant for the following sets of dietary outcomes among all articles:
fruits and vegetables
fruits
vegetables
total fat
saturated fat
general fat intake scores
specific high-fat foods or high-fat behaviors
specific low-fat foods or low-fat behaviors
The primary goal of each of the three secondary analysis strategies was to determine the overall effectiveness of dietary interventions at changing dietary behavior. Secondary goals included a determination of the relative effectiveness of different types of interventions and among different population subgroups at changing dietary behavior. Strategies for determining the relative effectiveness of interventions are discussed in the final section of this chapter.
The first approach in our determination of the efficacy of dietary interventions was a statistical meta-analysis. Our initial step in conducting the meta-analysis was to determine appropriate outcomes on which to focus. Based on our review of the most commonly reported dietary outcomes in the interventions included in our study, as well as discussions with our TEAG regarding common dietary goals emphasized in dietary interventions and dietary outcomes most consistently linked to cancer risk, we selected daily servings of fruits and vegetables and total fat as a percentage of energy intake as the two most appropriate outcomes for our meta-analysis.
The second step of our meta-analysis was to determine the feasibility and utility of conducting a meta-analysis on the outcomes we selected, based on criteria identified by our meta-analysis expert. Specifically, the following essential information must have been reported in the article:
results for both the intervention group(s) and the control group
either (a) means at baseline and follow-up or (b) means at baseline and the mean change between baseline and follow-up
the standard deviation, the standard error, or the 95 percent confidence interval for the means
As described in Chapter 3, we determined that we had an insufficient number of eligible articles to conduct a meta-analysis of fruit and vegetable intake. We based this conclusion primarily on the small number of articles including daily servings of fruits and vegetables as an outcome (n = 14) and on the inadequacy of the statistical reporting among these articles. In addition, statistically eligible articles were not considered substantively comparable enough to merit producing a combined estimate of the intervention effect; the problems were wide variability in population characteristics and in the type and duration of intervention. We did identify a sufficient number of articles reporting the necessary statistical information for total fat (percentage of energy) intake, and thus we conducted a meta-analysis for this outcome.
Of the 45 articles reporting results for total fat (percentage of energy) as an outcome, 27 articles met the statistical criteria for the meta-analysis. Based on the advice of our TEAG, we contacted authors of "excluded" studies to obtain unpublished information (such as standard deviations) that would enable us to include additional articles in the meta-analysis. We contacted 13 authors (of the excluded articles) and received additional information from two authors, resulting in a final pool of 29 articles eligible for the meta-analysis.
The final step was to organize the eligible articles into meaningful groupings for which results could be combined. Our approach was to identify groupings of articles based on the comparability of population characteristics, interventions received, and duration of follow-up. Based on advice and discussion with our TEAG and our meta-analysis expert, we identified the following groupings:
school-based interventions with healthy children
worksite and community interventions with healthy adults
health care setting interventions with healthy adults
health care setting interventions with high-risk adults (including populations at risk of, but not diagnosed with, cancer and type 2 diabetes)
health care setting interventions with adults diagnosed with a disease (including cancer, type 2 diabetes, and cardiovascular disease [CVD])
This classification scheme resulted in our assigning 28 of the 29 articles to one of the five groupings. The remaining article (reporting results from a health care intervention on children diagnosed with type 1 diabetes) did not fit any of the groupings and was not included in the meta-analysis.
We then reviewed each study within the five groupings and found a significant amount of heterogeneity. We determined that the considerable differences in study populations, intervention components, intervention intensity, and follow-up times precluded combining all studies within major groupings. However, we did identify studies within each group that we judged were similar enough to combine. We made the appropriate combinations and plotted them together with the results of the other individual studies to facilitate comparison and interpretation.
Across all studies, the contrast of interest was the difference between intervention and control groups in the change over time in percentage of calories obtained from fat (i.e., the difference in differences). A positive mean effect typically indicates that the decrease in consumption of dietary fat in the intervention was greater than the change in consumption of fat in the comparison group. Standard error estimates for each contrast were also required to construct confidence intervals and for combining results for meta-analysis. For some studies, the standard errors were given, while for others they had to be inferred.
Studies that did not explicitly state the standard error of the
contrast had (1) a test statistic for the contrast (t or F);
(2) the probability of the observed contrast value when the population
value is 0, with
degrees of freedom; or (3) the standard errors for each group by time
mean. When the value of the t statistic was given, the standard
error was estimated by C/t, where C was the observed value of
the contrast.
When an F statistic was given, C/
was
used. Finally, when a probability was given, it was assumed to be from
a two-tailed t-test or an F test with 1 numerator
degree-of-freedom. The relevant
tstatistic was obtained using the inverse of the cumulative
t probability distribution, and that value was applied to the
above equation.
These two methods yielded proper standard errors that use the within-subjects nature of most of the studies.
When only the standard error estimates for each group by time mean were available, obtaining proper contrast standard error estimates was not possible. Observations were treated as independent, and the standard error was estimated using the pooled variance estimate. This method would result in conservative standard error estimates for some studies because observations within subjects would be non-negatively correlated over time. These studies would be underweighted in subsequent meta-analyses as a result of these exaggerated standard error estimates.
For each study, the theoretical distribution of the contrast estimate was assumed to be normal, with the mean equal to the observed value of the contrast and a variance equal to the squared contrast standard error estimate. When appropriate, information from studies was combined using a fixed effects, variance-weighted meta-analysis. The result was an estimate for the common contrast value and an estimated standard error.
The second tier of our analysis strategy involved a systematic analysis of the difference in change from baseline to follow-up (among dietary outcomes) between intervention and control groups (i.e., difference-in-deltas). This approach allowed us to use a common metric (difference in the change between groups, expressed as a percentage) to compare the magnitude of changes in dietary intake across interventions. We decided to focus on six "key" outcomes that are commonly reported across the studies in our Evidence Tables and are particularly relevant to cancer risk. These six outcomes were (1) daily servings of fruits and vegetables, (2) daily servings of fruits, (3) daily servings of vegetables, (4) daily intake of total fat (as a percentage of energy intake), (5) daily intake of saturated fat (as a percentage of energy intake), and (6) daily intake of total fat (grams). In addition to being limited to particular outcomes, this strategy was also limited (by necessity) to articles reporting adequate statistical information for the calculation of this metric.
Thus, the first step of our approach was to identify articles eligible for the calculation of the differences in deltas. The requirements for calculation were that the article must have reported:
results for both the intervention group(s) and the control group
either (a) means at baseline and follow-up or (b) means at baseline and the mean change between baseline and follow-up
The calculation of the difference-in-deltas involved several steps: (1) computing the change in mean intake from baseline to follow-up for the intervention group(s) (expressed as percentage increase or decrease), (2) computing the change in mean intake from baseline to follow-up for the control group (expressed as percentage increase or decrease), and (3) computing the difference in the change between the intervention and control group (expressed as percentage increase or decrease). The difference in delta is based on the mean values reported in the studies we reviewed, and such statistics may have been originally reported as unadjusted or adjusted for covariates. Because we were performing secondary analyses on previously published articles, we had to use the means in the format in which the authors reported them.
| Treatment Group | Total Fat Intake (as a percentage of total energy intake) | ||
|---|---|---|---|
| Baseline | Follow-Up | ||
| Intervention group (n = 6,273) | 38.2 | 33.8 | |
| Control group (n = 6,271) | 38.2 | 38.0 | |
| Difference in change among the intervention group: (38.2 - 33.8)/38.2 = -11.5% * | |||
| Difference in change among the control group: (38.2 - 38.0)/38.2 = -0.5% * | |||
| Difference in % change between the intervention and control group: (-11.5) - (-0.5) = -11.0% | |||
Expressed as a negative value because the change was a decrease in fat intake.
Source: Adapted from Gorder et al.
| Treatment Group | Fruit and Vegetable Intake (servings per day) | ||
|---|---|---|---|
| Baseline | Follow-Up 1 | Follow-Up 2 | |
| Intervention group | 2.61 | 3.96 | 3.20 |
| Control group | 2.51 | 2.28 | 2.21 |
| Difference in change among the intervention group at follow-up 1: (2.61 - 3.96)/2.61 = +51.7% * | |||
| Difference in change among the intervention group at follow-up 2: (2.61 - 3.20)/2.61 = +22.6% * | |||
| Difference in change among the control group at follow-up 1: (2.51 - 2.28)/2.51 = -9.2% ** | |||
| Difference in change among the control group at follow-up 2: (2.51 - 2.21)/2.51 = -12.0% ** | |||
| Difference in % change between the intervention and control group at follow-up 1: (+51.7) - (-9.2) = +60.9% | |||
| Difference in % change between the intervention and control group at follow-up 2: (+22.6) - (-12.0) = +34.6% | |||
Expressed as a positive value because the change was an increase in fruit and vegetable intake.
Expressed as a negative value because the change was a decrease in fruit and vegetable intake.
Source: Adapted from Reynolds et al.
Our general approach was to calculate the differences in deltas for the selected outcomes for all eligible studies. We calculated this metric for all follow-up periods reported in the articles and for all eligible comparisons within an article. For example, if an article presented results for two intervention groups and a control group, we calculated the difference in delta for both intervention groups (i.e., intervention group 1 compared with the control group and intervention group 2 compared with the control group). Similarly, if an article reported results for separate population subgroups (e.g., males and females), we calculated the difference in delta for each group.
Because our primary goal was to derive a summary indicator of the average difference in deltas across all studies, and then among various groupings of studies (such as intervention settings or specific intervention features), we decided to use a single indicator of the difference in percentage change for each study at each reported follow-up period. This strategy prevented the results from a particular study from being counted more than once in the calculation of the overall difference in percentage change for all studies (or various groupings of studies).
In determining how to derive a single percentage change score for each article, we used the following criteria:
For studies reporting results for more than one intervention group, we used the results for the intervention group receiving the most intensive intervention (compared with the control group).
For studies reporting results separately for more than one measurement technique (e.g., reporting total fat as a percentage of energy intake derived from a food frequency questionnaire as well as reporting total fat as a percentage of energy intake derived from a four-day food record), we used the results for the most commonly used measurement technique (typically the food frequency questionnaire), regardless of the validity, reliability, or sensitivity to change of the technique.
For studies reporting results for population subgroups (e.g., males and females), we calculated a single, weighted mean (based on the proportion of the study population in the intervention and control groups) to use in the estimation of the difference in percentage change for the study.
Using these procedures, we developed a single percentage difference in change between intervention and control groups for each study at all reported follow-up periods. We used the differences in deltas calculated for individual studies to create summary measures indicating the effectiveness of interventions for various dietary outcomes. For example, we calculated summary measures indicating the effectiveness of interventions in general at reducing total fat intake or increasing fruit and vegetable intake based on the difference-in-deltas approach.
To present our results (including the overall intervention effect for various dietary outcomes, as well as for specific follow-up periods or among specific intervention settings or other characteristics), we chose the median difference in delta as the appropriate statistic to report because the distribution of differences in deltas calculated for individual studies was skewed. This analytic approach, in relying on a standardized metric, allowed us to compare the intervention effect across a variety of characteristics, such as intervention setting, the risk status of the study population, and specific intervention features. Therefore, for a particular dietary outcome, the median difference-in-deltas for one grouping of articles could be compared with the median difference-in-deltas for another grouping of articles. This in turn enabled us to draw conclusions about the efficacy of the intervention between the two groupings. In short, this technique was used to make relative comparisons about the success of different types of interventions at changing dietary behavior.
Additional analyses using this technique explored the "corroboration" of change in dietary intake with change in biochemical indicators. We calculated the median difference-in-deltas for biochemical outcomes (as well as the previously mentioned dietary outcomes) and then examined the correlation of differences-in-deltas for dietary outcomes with differences-in-deltas for biochemical outcomes. These analyses focused on blood lipids (specifically total blood cholesterol) because of the small number of studies measuring plasma carotenoids. Of the studies we reviewed, only one reported change in plasma carotenoids, 111 precluding our ability to draw conclusions about the corroboration of change in fruit and vegetable intake with biochemical markers.
The final tier of our analysis approach involved a semiquantitative analysis that, in essence, simply summarized whether the investigators reported a statistically significant intervention effect. We performed this type of analysis for all outcomes in the Evidence Tables, and it included almost all of the articles we reviewed. The only criterion for including articles in this third group of analyses was that the statistical significance (at p < 0.05) of the intervention had to have been reported in the article. For each outcome reported in each article, we created a dichotomous indicator of whether the article reported a significant intervention effect.
Although many studies determined the significance of the intervention effect by multiple strategies, at multiple follow-up periods, or among multiple intervention groupings or population subgroups, we classified articles as having had a significant intervention effect if at least one of the results reported in the article was statistically significant. For example, if a study compared the mean fruit intake between an intervention and a control group at three follow-up periods, with results reported separately for males and females, and only the difference between means at follow-up 1 for females was statistically significant, the study would be classified as having had a significant intervention effect (for the outcome of fruit intake). Our classification of the significance of articles was based on the statistical tests that the investigators performed. These included a variety of approaches, such as the p value for parameter estimates for intervention status as a predictor, the group-by-time interaction effect in ANOVA models, t-tests for differences in means, chi-square tests for differences in proportions, and related statistics.
This analysis strategy is likely to overestimate the number of studies that truly observed a significant intervention effect. The reason is that among the studies we reviewed, frequently only p values for significant findings were reported. In addition, studies reporting significant results were more likely to have been submitted and published in the first place, resulting in publication bias. Although subject to some degree of bias, the "summary of significant findings" technique is advantageous because it enabled us to include unique or at least uncommonly reported outcomes that could not be analyzed with either of the previous strategies. In addition, with this approach we could include articles reporting insufficient statistical information (such as means) that otherwise could not be examined with either the meta-analysis or the difference-in-deltas approach. Finally, it accommodated the high degree of variability in various statistical procedures for determining the significance of the intervention effect that we observed in the articles in this report.
Using this technique, we classified all outcomes for each study as having had a significant effect or not. For simplicity in reporting and interpretation, we grouped the outcomes into broader categories. Fruit- and vegetable-related outcomes were classified into three categories:
Fruit and vegetable intake combined (including outcomes such as servings per day of fruits and vegetables, percentage of fruits and vegetables selected in relation to all foods selected, fruit and vegetable consumption scores)
Fruit intake (including outcomes such as servings per day of fruit, grams per day of fruit, fruit consumption scores, percentage of energy from fruit intake)
Vegetable intake (including outcomes such as servings per day of vegetables, grams per day of vegetables, portions per week of vegetables)
Intake of total fat (including outcomes such as percentage of total energy intake from fat, grams of fat)
Intake of saturated fat (including outcomes such as percentage of total energy intake from saturated fat, grams of saturated fat)
General fat intake scores or indices (including "fat scores" based on semiquantitative surveys)
Intake of individual high-fat foods or engagement in specific high-fat eating behaviors or practices (including general "high-fat practices" indices; specific high-fat behaviors such as using saturated fat for frying or eating poultry with the skin on; and intake of specific foods or types of foods such as high-fat meat, fried foods, butter or cream, or high-fat milk)
Intake of individual low-fat foods or engagement in specific low-fat eating behaviors or practices (including general "low-fat practices" indices or scales; specific low-fat behaviors such as trimming the fat from meat; and intake of specific foods or types of foods such as skim milk, low-fat spreads)
When analyzing how effective interventions were for the eight sets of dietary outcomes described above, we reported the proportion (and number) of articles reporting a significant intervention effect. This strategy enabled us to compare the proportion of one type of study (e.g., school-based interventions) reporting a significant intervention effect with the proportion of a different type of study (e.g., community interventions).
Our primary goal (for each of the three secondary analysis strategies described above) was to determine the overall effectiveness of dietary interventions at changing dietary behavior. Our secondary goals included a determination of the relative effectiveness of different types of interventions and among different population subgroups at changing dietary behavior. For example, population characteristics have the potential to moderate the effect of behavioral interventions on dietary change. In addition, key intervention characteristics may influence the magnitude of dietary change. Intervention and population characteristics identified as being particularly important to explore as predictors of the efficacy of interventions included the following:
the age of the population
the risk status of the population
the intervention setting
the delivery mode of the intervention
the intervention intensity
the theoretical basis of intervention
the quality score of the article
whether the intervention included non-nutrition components
specific intervention components
- family component
- social support
- small groups
- interactive activities involving food
- goal setting
- cultural specificity
- individual tailoring
We were able to explore only two population characteristics (age and risk status) because of the limited variability in study populations and the undetailed sample descriptions we encountered in our review of the articles. Although the consideration of gender, ethnicity, or socioeconomic status as moderating variables were a part of the original objectives of this report, we did not encounter sufficient variability in the studies we reviewed to explore these factors.
In our analyses, age indicated simply whether the study population included children (<14 years) or adults (>18 years); none of the studies we reviewed included youth (14 to 18 years) as the target of the intervention. Risk status indicated whether the study population was either at risk of or diagnosed with a particular disease (including CVD, type 2 diabetes, or cancer) or was not at risk.
We examined several intervention characteristics. The articles we reviewed were classified into one of four intervention settings: health care, school-based, worksite, or community/other. The delivery mode of the intervention indicates the role of the individual (or individuals) who delivered the intervention to the subjects. We identified four major delivery modes: self-administered, nonhealth professionals (including classroom teachers, physical education teachers, peers), health professionals (e.g., doctors, nurses), and nutritionists (including dietitians).
For each study in our review, we assigned one of three intensity levels to describe the total dose of the intervention: low, medium, or high. Factors considered in the assignment of the score included number, length, and duration of contacts (with individual contact considered more "intense" than group contacts); the existence of environmental-level interventions (such as alterations in cafeteria or vending machine options); and educational materials or media. Although our classifications were relatively subjective, they permitted us to determine the relative intensity of the interventions based on the limited information that was generally available. We refer to this characteristic as "intensity" in the remainder of the report, but we emphasize that our classification system was quite broad and included any aspects pertaining to the total dosage of the intervention.
The theoretical basis of the intervention involved a simple classification of whether the investigators used a theoretical framework in the design or implementation of the intervention. Because of the inconsistency and lack of detail in the articles we reviewed, this classification does not take into account the degree to which theory was used in the intervention. Rather, it is simply an admittedly crude indicator of whether the authors reported that a specific theory was used in the intervention (this was typically mentioned in either the introduction or the methods section of the articles, in the description of the intervention). Although we classified the articles as having or not having a theoretical basis, we recognize that the true use of theory is likely to be a continuum, rather than a dichotomy, and that in many articles it was extremely difficult to determine whether theory was actually used. In many cases, a strong theoretical base may have actually guided the intervention, but because of journal space limitations and editorial constraints, this fact may not have been evident.
In addition to classifying all articles on the basis of whether theory was used, we decided to explore the relationship between employing a theoretical framework and the significance of effects among recently published articles, given the increasing emphasis on incorporating theory in the design and implementation of behavioral interventions. Our rationale was that distinguishing between interventions that did or did not use theory at a time when awareness of the perceived need to use theory was substantially raised would provide a more appropriate comparison. Thus, we also classified the subset of articles published in or after 1995 as having or not having a theoretical basis.
The quality scores assigned to the articles (described in detail in a previous section of this chapter) ranged from 0 to 100. For use in the analyses, however, they were classified into three evenly distributed categories: low (scores <51), medium (scores ranging from 52 to 61), and high (scores >62). A dichotomous indicator, non-nutrition components, was created to reflect whether an intervention focused exclusively on modifying dietary behavior or if it included ancillary, non-nutrition components (such as physical activity modification, stress management, or smoking cessation).
Finally, we explored the influence of several specific behavioral change strategies in our analyses. Specifically, the articles we reviewed were coded as to whether the interventions included the following: a family component (such as family homework assignments, involving spouses in cooking classes), social support (including support groups), small group sessions, goal setting (and related self-monitoring components), interactive activities involving food (which includes intervention components such as taste testing, cooking classes), cultural or ethnic specificity (in which the intervention either was specifically designed for a particular cultural or ethnic group or included major components that were culturally or ethnically specific), and individual tailoring (which included specifically tailored nutritional messages generated through an interactive process with the intervention participants).
We did not have a large enough number of articles to explore interactions among the intervention and/or population characteristics described above (for instance, the impact of family components combined with goal setting). As a result, the influence of the components we explored cannot be conceptualized as independent effects. This fact, coupled with the likelihood that certain intervention components tended to be closely associated with one another (e.g., nutritionist-led interventions being more likely to incorporate interactive activities involving food), makes findings difficult to interpret. Some degree of confounding is inherent in our analysis plan because many characteristics may indeed be proxies for one another. This limitation should be kept in mind when interpreting the results presented in the following chapter.
We used the population and intervention characteristics described in this section as grouping variables in our three tiers of secondary analyses. For example, using the difference-in-deltas approach, we compared the median difference in change (between intervention and control groups) from one type of study (e.g., school-based interventions) with the median difference in change in another type of study (e.g., worksite interventions). Using the summary of significant effects approach, we compared the proportion of studies reporting significant findings in certain groupings of interest. Because many of the population and intervention characteristics were unevenly distributed among the 92 studies we analyzed (i.e., only a small number of articles employed social support components, making comparisons difficult to conduct), we had to establish minimum "cell sizes" for a particular comparison to be conducted in order to prevent extremely unstable estimates. Thus, we decided that a minimum of five studies had to be in each grouping before we would conduct a particular comparison.
Chapter 3 provides descriptive information about the studies we reviewed (based on the intervention and population characteristics desc ribed in this section). In addition, it presents the results of the three tiers of our secondary analyses.
As discussed in Chapter 2, our literature search and screening procedures resulted in the inclusion of a total of 104 eligible articles (reporting results from 92 studies) in the Evidence Tables. In our secondary analyses, the unit of analysis was the study (not the article); thus our analyses were based on the pool of 92 studies. We classified the interventions by setting; approximately 49 percent were conducted in health care settings,36-87 27 percent were conducted in community settings,98-122 14 percent were conducted in school settings,19-35 and 10 percent were conducted in worksite settings.88-97
The majority of interventions were conducted with adult populations (77 percent, or 71/92 studies); only 16 studies focused on children.19-30, 32-35, 42, 63-64, 99, 109 In five interventions, either the age of the study population was not reported or the study included both children and adults.31,100-102,112 The interventions were equally likely to target high-disease-risk populations (46 percent, or 42/92 studies) and general populations (47 percent, or 43/92 studies). Seven interventions (8 percent) either included both high-risk and healthy populations or did not report information about the risk status of the participants.53-64,82,88, 90,98-99,108
We abstracted detailed information about the content and duration of the interventions. Thirteen percent (12/92) of the interventions were classified as being of low intensity,29,39,60,62,71,78,84-85,92,105,114-115 57 percent (52/92 studies) as medium intensity,23,30-31,34,36,38,40-42,50-51,56-58,61,63-69,72-75,77,79-80,82,88,90-91,93-100,102-104,106-111,113,116-122 and 30 percent (28/92 studies) as being of high intensity.19-22,24-28,32-33,35,37,43-49,52-55,59,70,76,81,83,86-87,89,101,112 Nearly one-half of the interventions exclusively targeted dietary change (and did not include non-nutrition targets, such as exercise, smoking, and stress management). Although the delivery of the intervention was often difficult to determine (with multiple delivery agents being common), the results of our review indicated that 7 percent (6/92) involved self-administered interventions (such as computer modules).92,104,108,118,121-122 Another 27 percent (25/92 studies) were delivered by nonhealth professionals (such as classroom teachers, trained peers)25,19-28,31-35,48,73-74,81,90,95,99-101,105-106,109,113-115, 21 percent (19/92 studies) were delivered by health professionals (including doctors, nurses)29-30,41,49,51,56,58,60,65,71,77,78,82,84-85,88,116,119, and 36 percent (33/92 studies) were delivered by dietitians or nutritionists.36-39,42-47,52-55,57,59,61,63-64,66-70,72,76,79,80,83,86-87,89,93,98,102-103,110,112,120 In 10 interventions (11 percent), the delivery mode was not reported.40,50,62,75,91,94,96-97, 107,111,117
Of the 92 interventions, about one-half (43, or 47 percent) appeared to employ a theoretical framework in the design of the intervention, although, as mentioned previously, we were not able to determine the extent to which theory was involved in the design of the intervention. Because we were interested in the use of theory in recently published articles compared with the earlier generation of articles, we examined the proportion of articles published before and after 1995 that reported the use of theory. Slightly more studies reported the use of theory in or after 1995 (49 percent, or 25/51) than before 1995 (44 percent, or 18/41).
We also abstracted information on "special features" employed in the interventions we reviewed. About 25 percent of the studies (23/92) included a family component (in which either families were the primary target of the intervention or the family of the primary target was involved in some aspects of the intervention).23-28,32-36,39,42,45-47,60,63-64,76,89,96-103,107,112 Eighteen percent (17/92 studies) reported including a social support component in the intervention (e.g., support groups within the intervention).23,45-48,56,76,81,83,86-87,95-97,99-101,109 Twenty-one percent (19/92) of the interventions used small groups in the delivery of nutrition education (or other components of the intervention).30,40,42,44,48,52,54,65,76,81,83,89,95,99-101,107,112-113 Interactive activities involving food (e.g., taste tests, cooking demonstrations) were included in 26 percent -- 24/92 -- of the interventions.20,22-28,32-35,42,45-47,72,88-89,99-101,106-107,109,111-113,116,119 A common feature of the interventions we reviewed was the use of goal setting and self-monitoring (to track the participants' progress on the dietary goals set in the intervention); 33 percent (30/92) of the interventions included this component. 19-20,23,34,40,43-44,52,54-55,57,59-60,70,73-74,76,78,81,83,85,87,89,93,96-97,100-102,109,111,120 Only 10 of the interventions (11 percent) were designed to be ethnically or culturally specific (to the target population).76,87,95,98,101-102,107,112,116,119 Finally, only eight interventions (9 percent) included a tailoring component (which we defined using rather restrictive criteria),73-74,104,108,118-122 such as an interactive computer program that is tailored to the participant's responses.
As discussed in the previous chapter, several of the intervention components we explored are likely to be closely associated with one another (such as the use of theory and goal setting), resulting in a certain degree of confounding. We did not have a large enough pool of interventions to explore the independent effects of specific intervention components (by controlling for the other characteristics), but we did explore the extent to which intervention characteristics tended to overlap. For example, interventions that incorporated the use of theory were more likely also to employ the "special features" described above. Only 53 percent (26/49) of studies lacking an obvious theoretical framework included any special features, whereas 84 percent (36/43) of studies that did report the use of theory included special features. In addition, interventions that used any special feature at all were likely to use several such features. Of the 62 articles that reported any special features, about 42 percent (26) reported the use of only one special feature, and 58 percent (36) reported the use of two or more (with 21 percent employing three, eight percent employing four, and five percent employing five or more special features).
The interventions we reviewed varied in terms of quality (see the previous chapter for a description of our quality rating system). The quality scores assigned to the 92 studies ranged from 25 to 86 points (the theoretical range was 0 to 100). Equal numbers of studies were then classified as being of low,20-21,29,38-40,42,45-47,50,53,56-57,60,62,66-69,71,77,85-96,88,90,94,99,105-107,110,116-117 medium, 22,30-31,36-37,41,43,48-49,51-52,58-59,61,75,79-80,82-83,92,100,102-104,108,111-114,121 or high quality,19,23-28,32-35,44,54,55,63-65,70,72-74,76,78,81,84,87,89,91,93,95-98,101,109,115,118-120,122 based on the distribution of raw quality scores.
Of the dietary outcomes on which we focused in this review (dietary fat and fruit and vegetable intake), fat outcomes were the most commonly reported. Roughly 60 percent (55/92) of the interventions reported results for fat outcomes only, 13 percent (12/92) reported results for fruit and vegetable outcomes only, and 27 percent (25/92) reported results for both dietary fat and fruit and vegetable outcomes. The results for both sets of outcomes are presented in the following section.
The primary purpose of this report was to determine whether behavioral dietary interventions positively influence intake of dietary fat and fruits and vegetables. As described in detail in Chapter 2, we explored this issue using three main analysis strategies: (1) meta-analysis, (2) a standardized analysis of the magnitude of the intervention effect (median differences in effect sizes), and (3) a semiquantitative analysis (proportion of studies) summarizing the statistical significance of the intervention effect. In addition to determining the overall effect of interventions on various dietary outcomes, we considered the impact of interventions among population subgroups and the effect of specific intervention components on changing dietary behavior.
This chapter reports the results separately for fruits and vegetables and for dietary fats. For both sets of outcomes, the results are organized by these three analysis strategies. Within each subsection, we report the overall intervention effect and results by various intervention and population characteristics. We are not giving study-by-study reviews because of our decision to focus on these types of summative analyses.
After reviewing the articles for fruit and vegetable intake, we determined that the small number of articles eligible for a statistical meta-analysis and the diversity in study populations and intervention received did not support the application of this technique. In short, we reviewed all studies reporting results for daily servings of fruits and vegetables (the most commonly reported outcome and the fruit and vegetable outcome presumed to be most closely associated with cancer risk), using criteria developed by our meta-analysis expert that included both parametric and nonparametric methods.
Of the 14 studies including this outcome, we excluded five because of inadequate statistical reporting, which most commonly included lack of an indication of the variance of the estimate. Combining a mean difference requires estimates of standard deviations or standard errors. We reviewed the remaining nine studies for substantive comparability, and we decided that they were too dissimilar to allow a meaningful interpretation of a combined mean effect. For example, the majority of the eight interventions were conducted among special populations (e.g., low-income participants in the Special Supplemental Nutrition Program for Women, Infants, and Children [WIC] program, African-American churchgoers, Girl Scouts, middle-aged male workers), with high variability in population characteristics and the type of intervention conducted. Therefore, we concluded that the few statistically eligible studies were not substantively similar enough to be combined in a meta-analysis.
The second analysis strategy we used to determine the effect of interventions on fruit and vegetable intake was the "difference-in-deltas" approach, which employs a standardized metric indicating the magnitude of change in fruit and vegetable intake between intervention and control groups across the various studies in this report. We calculated the differences in deltas for three outcomes: daily servings of fruits and vegetables, daily servings of fruits, and daily servings of vegetables. The results of this set of analyses are reported separately for fruits, vegetables, and fruits and vegetables combined because we wanted to examine the effects of interventions on fruits and vegetables separately. In addition, we tried to accommodate articles that reported results for fruits and vegetables either combined or separately. These outcomes are largely independent from one another (i.e., the summary results for fruits and vegetables combined are not necessarily the same as the results for fruits and vegetables as individual outcomes summed together), although there is some overlap in the articles reporting results for the various outcomes.
Because we focused on only three outcomes (based on specific measurement units), we excluded from this analysis studies reporting other fruit- and vegetable-related outcomes and studies that did not report statistical information necessary for the calculation of the differences in deltas. In the end, we used 17 of the 39 studies reporting results for fruit- and vegetable-related outcomes.
| Outcomes | Median (range) (n = number of studies) | References |
|---|---|---|
| Fruits and Vegetables (servings/day) | +16.6 (-3.7 to +60.9) (n = 12) | [2334-359194-97109113115, 119-120] |
| Fruits (servings/day) | +16.9 (0 to +73.4) (n = 9) | [23, 34-35, 96-97, 108111, 118-119, 121] |
| Vegetables (servings/day) | +5.7 (-17.2 to +153.2) (n = 9) | [23, 34-35, 96-97, 108111, 118-119, 121] |
| Outcomes | Follow-Up 1 | Follow-Up 2 | ||
|---|---|---|---|---|
| Median (range) | Refs. | Median (range) | Refs. | |
| Fruits and Vegetables (servings/day) | +16.8 (+7.2 to +60.9) (n = 6) | [34-35, 95-97, 109, 115] | +6.7 (-9.7 to +34.6) (n = 6) | [34-35, 95-97, 109, 115] |
| Fruits (servings/day) | +24.1 (0 to +73.4) (n = 5) | [34-35, 87, 96-97, 111] | +27.4 (+5.7 to +57.3) (n = 5) | [34-35, 87, 96-97, 111] |
| Vegetables (servings/day) | +25.8 (+5.7 to +153.2) (n = 5) | [34-35, 87, 96-97, 111] | +19.2 (+1.7 to +128.2) (n = 5) | [34-35, 87, 96-97, 111] |
Refs = references, n = number of studies.
The next step of the difference-in-deltas analysis involved examining the magnitude of the intervention effect across various population and intervention characteristics. We required a minimum of five studies in each cell for any given comparison, which eliminated any comparisons for fruits and vegetables as individual outcomes. In addition, the small number of articles eligible for the difference-in-deltas analysis for fruit and vegetable intake (combined) that focused on children or high-risk populations also prevented the determination of any population differences in change in fruit and vegetable intake.
| Grouping Characteristics | Median Difference in Change in Fruit and Vegetable Intake (servings/day) | |
|---|---|---|
| Median | Refs. | |
| Intervention Characteristics | ||
| Social support | ||
| Yes | +17.3 (-3.7 to +18.6) (n = 5) | [23, 95-97, 109, 119] |
| No | +15.9 (+6.9 to +60.9) (n = 7) | [34-35, 9194113115, 120] |
| Interactions with food | ||
| Yes | +14.9 (-3.7 to +60.9) (n = 6) | [23, 34-35, 109113, 119] |
| No | +16.8 (+6.9 to +31.8) (n = 6) | [91, 94-97, 115, 120] |
| Goal setting | ||
| Yes | +12.5 (-3.7 to +22.9) (n = 5) | [2324, 96-97, 115, 120] |
| No | +17.3 (+6.9 to +60.9) (n = 7) | [3591, 94-95, 113115, 119] |
Refs. = references, n = number of studies.
None of the three intervention strategies appeared to be associated with a greater magnitude of change in intervention groups compared with control groups for daily servings of fruit and vegetable intake. Interventions that incorporated social support components, food-related activities, or goal-setting components appeared to be no more successful at increasing fruit and vegetable intake than were interventions that did not employ such strategies. Unfortunately, the small number of articles eligible for the difference-in-deltas analyses and the limited variability in other intervention characteristics precluded further comparisons.
Our final approach to determining the efficacy of dietary interventions at increasing fruit or vegetable intake was the most inclusive in that it accommodated the diversity of outcomes and statistical reporting evident in the articles we reviewed. Indeed, although this strategy is less standardized than is the difference-in-deltas approach, and although it does not allow for determination of the magnitude of the intervention effect, it has one primary advantage -- it permitted us to include 36 of the 39 articles reporting fruit- and/or vegetable-related outcomes. In addition, it enabled us to explore the influence of several population and intervention characteristics at promoting change in fruit and vegetable intake that we were not able to consider using the difference-in-deltas approach (because of the small number of articles eligible for that analysis). As described in Chapter 2, we categorized the various outcomes measures reported in the studies into three sets of outcomes (fruit and vegetable intake, fruit intake, and vegetable intake), regardless of the specific units of measurement, and simply summarized whether studies reported a significant intervention effect for each of these outcomes.
| Grouping Characteristics | Outcomes Analyzed | |||||
|---|---|---|---|---|---|---|
| Fruits and Vegetables (n = 22) | Fruits (n = 22) | Vegetables (n = 23) | ||||
| % of articles | Refs. | % of articles | Refs. | % of articles | Refs. | |
| Total | 77% (17/22) | [20, 23-28, 31-35, 90-92, 94-97, 107, 109-110, 113-115, 119-120] | 64% (14/22) | [22-28, 31, 33-35, 37, 45-47, 66-69, 82, 87, 96-97, 103, 105, 107-108, 111, 117-119, 121] | 43% (10/23) | [22-28, 31, 33-35, 37, 45-47, 58, 66-69, 82, 87, 96-97, 103, 105, 107-108, 111, 117-119, 121] |
| Population Characteristics | ||||||
| Age | ||||||
| Children | 75% (6/8) | [20, 23-28, 32-35, 109] | 67% (4/6) | [22-28, 33-35] | 33% (2/6) | [22-28, 33-35] |
| Adults | 77% (10/13) | [90-92, 94-97, 107, 110, 113-115, 119-120] | 60% (9/15) | [37, 45-47, 66-69, 82, 87, 96-97, 103, 105, 107-108, 111, 117-119, 121] | 50% (8/16) | [37, 45-47, 58, 66-69, 82, 87, 96-97, 103, 105, 107-108, 111, 117-119, 121] |
| Risk status | ||||||
| General risk | -- | -- | 57% (8/14) | [22-28, 31, 33-35, 87, 105, 107, 117-119, 121] | 36% (5/14) | [22-28, 31, 33-35, 87, 105, 107, 117-119, 121] |
| High risk | -- | -- | 100% (6/6) | [37, 45-47, 66-69, 96-97, 103, 111] | 71% (5/7) | [37, 45-47, 58, 66-69, 96-97, 103, 111] |
| Intervention Characteristics | ||||||
| Theoretical basis | ||||||
| Yes | 88% (14/16) | [20, 23, 31-35, 91, 95-97, 109, 113-115, 119-120] | 70% (7/10) | [22-23, 31, 33-35, 96-97, 108, 111, 119] | 50% (5/10) | [22-23, 31, 33-35, 96-97, 108, 111, 119] |
| No | 50% (3/6) | [24-28, 90, 92, 94, 107, 110] | 58% (7/12) | [24-28, 37, 45-47, 66-69, 82, 87, 103, 105, 107, 117-118, 121] | 38% (5/13) | [24-28, 37, 45-47, 58, 66-69, 82, 87, 103, 105, 107, 117-118, 121] |
| Among articles published in 1995 or later: | ||||||
| Yes | 93% (13/14) | [31-35, 91, 95-97, 106, 109, 113-115, 119-120] | 100% (8/8) | [32, 91, 95, 109, 113-115, 120] | 25% (2/8) | [24-28, 66-69, 82, 87, 107, 117-118, 121] |
| No | 50% (3/6) | [24-28, 90, 92, 94, 107, 110] | 50% (4/8) | [24-28, 66-69, 82, 87, 107, 117-118, 121] | 63% (5/8) | [31, 33-35, 96-97, 108, 111, 119] |
| Quality Score | ||||||
| Low | -- | -- | 40% (2/5) | [45-47, 66-69, 105, 107, 117] | 0% (0/5) | [45-47, 66-69, 105, 107, 117] |
| Medium | -- | -- | 50% (4/8) | [22, 31, 37, 82, 103, 108, 111] | 44% (4/9) | [22, 31, 37, 58, 82, 103, 108, 111, 121] |
| High | -- | -- | 89% (8/9) | [23-28, 33-35, 87, 96-97, 118-119] | 67% (6/9) | [23-28, 33-35, 87, 96-97, 118-119] |
| Inclusion of non-nutrition components | ||||||
| Yes | 80% (4/5) | [20, 24-28, 31, 90, 94] | 71% (5/7) | [22, 24-28, 31, 37, 45-47, 82, 103] | 38% (3/8) | [22, 24-28, 31, 37, 45-47, 58, 103] |
| No | 76% (13/17) | [23, 32-35, 91-92, 95-97, 107, 109-110, 113-115, 119-120] | 60% (9/15) | [22, 33-35, 66-69, 87, 96-97, 105, 107-108, 111, 117-119, 121] | 47% (7/15) | [23, 33-35, 66-69, 87, 96-97, 105, 107-108, 111, 117-119, 121] |
| Family component | ||||||
| Yes | 75% (6/8) | [23-28, 32-35, 96-97, 107] | 78% (7/9) | [23-28, 33-35, 45-47, 96-97, 103, 107] | 44% (4/9) | [23-28, 33-35, 45-47, 96-97, 103, 107] |
| No | 79% (11/14) | [20, 31, 90-95, 109-110, 113-115, 119-120] | 54% (7/13) | [22, 31, 37, 66-69, 82, 87, 105, 108, 111, 117-119, 121] | 43% (6/14) | [22, 31, 37, 58, 66-69, 82, 87, 105, 108, 111, 117-119, 121] |
| Social support | ||||||
| Yes | 80% (4/5) | [23, 95-97, 109, 119] | 100% (5/5) | [23, 45-47, 87, 96-97, 119] | 60% (3/5) | [23, 45-47, 87, 96-97, 119] |
| No | 76% (13/17) | [20, 24-28, 33-35, 90-92, 94, 107, 110, 113-115, 120] | 53% (9/17) | [22, 24-28, 31, 33-35, 37, 66-69, 82, 103, 105, 107-108, 111, 117-118, 121] | 39% (7/18) | [22, 24-28, 31, 33-35, 37, 58, 66-69, 82, 103, 105, 107-108, 111, 117-118, 121] |
| Interactions with food | ||||||
| Yes | 82% (9/11) | [20, 23-28, 32-35, 107, 109, 113, 119] | 70% (7/10) | [22-28, 33-35, 45-47, 107, 111, 119] | 40% (4/10) | 22-28, 33-35, 45-47, 107, 111, 119 |
| No | 73% (8/11) | [31, 90-92, 94-97, 110, 114-115, 120] | 58% (7/12) | [31, 37, 66-69, 82, 87, 96-97, 103, 105, 108, 117-118, 121] | 46% (6/13) | [31, 37, 58, 66-69, 82, 87, 96-97, 103, 105, 108, 117-118, 121] |
| Goal setting | ||||||
| Yes | 83% (5/6) | [20, 23, 34, 96-97, 109, 120] | 80% (4/5) | [23, 34, 87, 96-97, 111] | 80% (4/5) | [23, 34, 87, 96-97, 111] |
| No | 75% (12/16) | [24-28, 31-33, 35, 90-92, 94-95, 107, 110, 113-115, 119] | 59% (10/17) | [22, 24-28, 31, 33, 35, 37, 45-47, 66-69, 103, 105, 107-108, 117-119, 121] | 33% (6/18) | [22, 24-28, 31, 33, 35, 37, 45-47, 58, 66-69, 82, 103, 105, 107-108, 117-119, 121] |
Refs. = references, n = number of studies.
Although we did not have enough articles to compare combined fruit and vegetable intake by differences in the risk status of the study population, a clear pattern was evident for the separate fruit and vegetable outcomes. Interventions conducted on high-risk populations were nearly twice as likely to report a significant intervention effect as were interventions conducted on general populations for both fruit (6/6 studies compared with 8/14 studies) and vegetable (5/7 studies for high-risk populations compared with 5/14 general-risk studies) intake.
In exploring whether significant changes in fruit and vegetable intake would be associated with various intervention characteristics, we detected several interesting findings. First, studies employing a theoretical framework were consistently more likely to report a significant intervention effect than studies that did not. Keeping in mind that each dietary category consisted of 22 to 23 studies, 20 percent to 30 percent more studies using a theoretical framework reported significant findings than did studies not reporting the use of theory. This pattern was evident for the combined and separate fruit and vegetable outcomes. Among the subset of articles published in 1995 or later, which ranged from 16 to 20 per dietary category, the differences in the proportions reporting significant effects between articles that did or did not use theory were still evident for combined fruit and vegetable intake and for fruit intake as a separate outcome. For fruit and vegetable intake (combined) and fruit intake (individually), nearly twice as many studies employing theory reported significant effects as did studies not using a theoretical framework. Interestingly, we observed the opposite pattern with vegetable intake (i.e., more than twice as many articles not using a theoretical framework reported significant findings as did the articles that did use theory).
A dose-response relationship between the quality of the study and the likelihood of significant findings being reported was evident for both fruit and vegetable intake (as separate outcomes); increasingly greater proportions of studies classified as being of "high" quality reported a significant intervention effect. Among the high-quality interventions, 6/9 to 8/9 studies reported significant findings for the two outcomes; for low-quality interventions, the range was only 0/5 to 2/5 studies.
The presence of non-nutrition components in the interventions was not consistently associated with the likelihood of reporting a significant intervention effect. Roughly similar proportions of studies reported effects for combined fruit and vegetable intake (4/5 studies with non-nutrition components compared with 13/17 studies without such components). By contrast, slightly more studies including non-nutrition components reported significant findings for fruit intake (5/7 studies with non-nutrition components versus 9/15 without), and the reverse pattern was evident for vegetable intake (3/8 studies versus 7/15).
We were able to explore the effectiveness of several specific intervention strategies (or "special features") aimed at increasing fruit and vegetable intake. Specifically we considered the impact of including a family component in the intervention, employing social support, incorporating interactive activities involving food, and goal setting.
Our results indicated that family components were not consistently associated with more favorable fruit and vegetable intake, although this feature was positively associated with fruit intake (7/9 studies using a family component reported significant changes in fruit intake compared with 7/13 studies not using this component). Including a social support component in the intervention did appear to be positively associated with more favorable results, particularly when fruit and vegetable intakes were measured as separate outcomes. The results for interactive activities involving food were not as consistent. Although a greater proportion of studies including food activities reported significant effects for fruit and vegetable intake (combined) and fruit intake, this pattern was not evident for vegetable intake (for this outcome, interventions that included food-related activities were less successful at increasing vegetable intake). Finally, goal setting was consistently associated with more favorable intervention effects for all three outcomes. The differences between studies that did and did not include goal setting as a part of the intervention were particularly pronounced when fruit and vegetable intake were measured separately. Thirteen of 16 articles that included goal setting reported significant changes in fruit and/or vegetable intake; for vegetable intake alone, this proportion is particularly notable because it is nearly twice as high (4/5 studies, or 80 percent) as the proportion (10/23 studies, or 43 percent) found in the overall pool of articles reporting results for change in vegetable intake.
This section considers the efficacy of behavioral dietary interventions for reducing intake of dietary fats. Our analyses included the same set of strategies reported in the previous section: (1) a meta-analysis, (2) median differences in effect sizes, and (3) a summary of the proportion of studies reporting significant intervention effects. Within each subsection, we report the overall intervention effect and results by various intervention and population characteristics.
| Group and Study Citation | Mean Difference in Change (% energy) | Confidence Interval |
|---|---|---|
| Group 1. School-Based Interventions with Healthy Children | ||
| Walter et al., 19 5 years follow-up | 2.90 | 1.73 - 4.07 |
| Luepker et al.,24; Lytle et al, 25 2.5 years follow-up | 1.80 | 0.82 - 2.78 |
| Reynolds et al.,35 2 years follow-up | 1.79 | -0.13 - 3.71 |
| Pooled school-based interventions | 2.19 | 1.49 - 2.89 |
| Group 2. Worksite/Community Interventions with Healthy Adults | ||
| Fitzgibbon et al.,64 females, 12 weeks | 5.40 | -0.77 - 11.57 |
| Stolley et al.,112 females, 12 weeks | 6.70 | 3.45 - 9.95 |
| Pooled females, 12 weeks | 6.40 | 3.52 - 9.28 |
| Strychar et al.,93 males and females, one-time | 0.90 | -1.26 - 3.06 |
| Tilley et al.,96-97 males and females, 1 year follow-up | 0.90 | 0.31 - 1.49 |
| Tilley et al.,96-97 males and females, 2 years follow-up | 0.50 | -0.28 - 1.28 |
| Group 3. Healthcare Setting Interventions with Healthy Adults | ||
| Simkin-Silverman et al.,70 females, group sessions, 6 months | 10.10 | 8.79 - 11.41 |
| Coates et al.,87 females, group sessions, 6 months | 11.00 | 10.18 - 11.82 |
| Pooled females, group sessions, 6 months | 10.80 | 10.09 - 11.51 |
| Rankinen et al.,62 males, counseling 6 months | -0.30 | -3.93 - 3.33 |
| Rankinen et al.,62 males, counseling and exercise, 6 months | 4.50 | 1.46 - 7.54 |
| Coates et al.,87 females, group sessions, 12 months | 11.30 | 10.26 - 12.34 |
| Beresford et al.,78 males and females, booklet, 12 months | 1.20 | 0.73 - 1.67 |
| Group 4. Healthcare Setting Interventions with High-Risk Adults | ||
| Lee-Han et al.,38 6 months counseling at 6 months follow-up | 12.15 | 7.47 - 16.83 |
| Insull et al.,44 6 months counseling at 6 months follow-up | 17.30 | 15.16 - 19.44 |
| Simon et al.,83 6 months counseling at 6 months follow-up | 17.40 | 14.60 - 20.20 |
| Pierce et al.,111 6 months counseling at 6 months follow-up | 6.80 | 2.88 - 10.72 |
| Pooled, 6 months counseling at 6 months follow-up | 15.30 | 13.83 - 16.77 |
| Lee-Han et al.,38 12 months counseling at 12 months follow-up | 10.74 | 5.33 - 16.15 |
| Insull et al.,44 12 months counseling at 12 months follow-up | 15.80 | 13.57 - 18.03 |
| McKeown-Eyssen et al.,61 12 months counseling at 12 months follow-up | 11.90 | 6.80 - 17.00 |
| Simon et al.,83 12 months counseling at 12 months follow-up | 15.90 | 12.94 - 18.86 |
| Pierce et al.,111 12 months counseling at 12 months follow-up | 6.60 | 2.48 - 10.72 |
| Pooled, 12 months counseling at 12 months follow-up | 13.90 | 12.41 - 15.39 |
| Insull et al.,44 24 months counseling at 24 months follow-up | 14.30 | 11.95 - 16.65 |
| Boyd et al.,72 24 months counseling at 24 months follow-up | 11.60 | 9.23 - 13.97 |
| Pooled, 24 months counseling at 24 months follow-up | 13.00 | 11.33 - 14.67 |
| McKeown-Eyssen et al.,61 12 months counseling at 24 months follow-up | 9.20 | 3.89 - 14.51 |
| Boyd et al.,72 12 months counseling at 7-8 years follow-up | 3.60 | 0.70 - 6.50 |
| Group 5. Health Care Interventions with Disease Diagnosed Adults | ||
| Glasgow et al.,40 Type 2 diabetes, 2-3 months follow-up | 4.50 | -2.69 - 11.69 |
| Glasgow et al.,52 Type 2 diabetes, 2-3 months follow-up | 3.90 | -0.65 - 8.45 |
| Agurs-Collins et al.,76 Type 2 diabetes, 2-3 months follow-up | 6.00 | 2.00 - 10.00 |
| Pooled, Type 2 diabetes, 2-3 months follow-up | 5.00 | -2.02 - 12.02 |
| Glasgow et al.,52 Type 2 diabetes, 6 months follow-up | -2.50 | -7.11 - 2.11 |
| Agurs-Collins et al.,76 Type 2 diabetes, 6 months follow-up | 3.00 | -1.55 - 7.55 |
| Campbell et al.,43 Type 2 diabetes, 6 months follow-up | 7.00 | 2.41 - 11.59 |
| Pooled, Type 2 diabetes, 6 months follow-up | 2.60 | 0.07 - 5.13 |
| Laitinen et al.,57 Type 2 diabetes, 12 months follow-up | 3.00 | -1.55 - 7.55 |
| Ornish et al.,48 Cardiovascular disease, 12 months follow-up | 24.10 | 17.08 - 24.10 |
| Chlebowski et al.,55 Cancer, 6 months follow-up | 10.70 | 8.21 - 13.19 |
| Kristal et al.,81 Cancer, 6 months follow-up | 8.40 | 5.66 - 11.14 |
| Pooled, Cancer, 6 months follow-up | 9.70 | 7.86 - 11.54 |
| Kristal et al.,81 Cancer, 12 months follow-up | 7.20 | 4.26 - 10.14 |
| Glasgow et al.,73-74 computer-assisted, 3 months follow-up | 3.00 | -0.53 - 6.53 |
| Glasgow et al.,73-74 computer-assisted, 1 year follow-up | 2.00 | -1.76 - 5.76 |
| Turnin et al.,104 computer-assisted, 6 months follow-up | 2.00 | -1.45 - 5.45 |
Three studies investigated school-based interventions on healthy elementary and middle-school children.19,24,25,35 Each study included classroom education plus other nutrition- and health-related activities, such as cafeteria modification, smoking prevention, and exercise programs. The timing and extent of the classroom nutrition education differed among the studies, as did the types of non-nutrition components.
We started by computing and plotting a difference-in-difference estimate of percentage fat intake (i.e., the difference in percentage points between the change over time in the intervention group and the change over time in the comparison group) for each study. The estimates for the school-based interventions are reported in Table 21 and plotted in Figure 2
. The mean estimates were very close in magnitude, ranging from
1.8 to 2.9 percentage points. We then computed and plotted a pooled
estimate. The mean of the pooled estimate indicated an improvement in
fat intake by 2.2 percentage points (95 percent confidence interval
[CI], 1.5 to 2.9 percentage points). In other words, on average,
children in school-based interventions showed a 2.2 percentage point
greater change (e.g., greater decrease) in consumption of
dietary fat than did children in the comparison group.Four studies examined worksite or community interventions conducted with healthy adults.93,96-97,107,112 Two of these studies included low-income mothers and their children aged 7 to 12 years, but our analysis included only the mothers' data.107,112 The intervention components of all four studies included nutrition education or group meetings, although curriculum, duration, and added components all varied. The two studies of low-income mothers reported short-term outcomes only (i.e., the difference in the percentage fat intake immediately after the conclusion of the 12-week intervention). The third study was a one-time brief education session for which the investigators reported change in fat intake at 16 to 20 weeks following the intervention.93 The final study had fewer nutrition sessions than did the studies for mothers, and the sessions were spread out over the first year, with newsletters sent out quarterly in the second year.96-97
We combined the results for the two studies of low-income mothers (which had short-term follow-up) and compared the pooled result with the results of the other two studies shown separately. The mean estimates are reported in Table 21 and plotted inFigure 3
. The pooled difference in fat intake at
12 weeks was statistically significant, with a mean of 6.4 percentage
points (95 percent CI, 3.5 to 9.3 percentage points). The effect for
the two studies with low-income mothers, in which the intervention
consisted of 60- to 90-minute classes once a week for 12 weeks, was
significantly greater than the effects for two worksite studies
(discussed in the following
paragraph) with less intensive interventions.
The study of a worksite intervention program that had five nutrition sessions over 1 year, reinforced by a monthly newsletter, found a small (1.0 percentage point), albeit significant, effect at the 1-year follow-up, but this effect was no longer statistically significant by the end of the second year.96-97 A study of a brief worksite program (consisting of a 20-minute education session and a mail reminder after 6 weeks) did not find statistically significant differences in effects.93
Four studies were conducted with healthy adults drawn from patient lists in different health care settings.62,70,78,87 The interventions ranged in intensity from a self-help booklet with a brief physician message to 2 years of dietary counseling reinforced by ongoing group sessions. Two studies included only females,70,87 and one included only males.62 The studies also had different follow-up times (ranging from 3 months to 1.5 years).
We combined the estimates of the change in percentage of fat intake at 6 months of follow-up for the two studies that included only women. In both cases, the intervention was group counseling sessions conducted over at least 6 months. As indicated in Table 21 and Figure 4
, the
combined estimate has a mean effect of 10.8 percentage points (95
percent CI, 10.1 to 11.5 percentage points). One of these studies
continued the sessions for another 6 months.87 For this study, the difference in the
changes between the intervention group and the comparison group was
statistically significant and roughly of the same magnitude after a
full year as after the first 6 months.
The intervention targeted to men that provided only nutrition advice appeared to have no significant impact at 6 months. However, when the intervention was coupled with an exercise program, the investigators observed a statistically significant change of 4.5 percentage points (95 percent CI, 1.5 to 7.5 percentage points).
The nutrition booklet provided to adults by physicians with a brief nutrition message had a small but statistically significant effect -- a mean effect of 1.3 percentage points (95 percent CI, 0.7 to 1.7 percentage points).
Eight studies investigated the effect of nutrition education on fat intake among high-risk adult patients from various health care settings.38,44,61,72,79,80,83,111 Again the populations, interventions, and follow-up times differed markedly from study to study. Upon further investigation, we dropped one study because it was conducted with adults at high risk for type 2 diabetes (participants in the remaining studies were at high risk for cancer) and because the intervention was less intensive than those in the other studies.80
We combined the effect size (i.e., difference-in-change) estimates for (1) four studies that investigated the effect of at least 6 months of dietary counseling reported at the end of 6 months,38,44,83,111 (2) five studies that investigated the effect of at least 12 months of counseling reported at the end of 12 months,38,44,61,83,111 and (3) two studies that investigated the effect of 2 years of counseling at the end of 2 years. 61,79
As indicated in Figure 5
, our analyses
suggested that the major benefit of nutritional counseling was evident
within 6 months. The studies of interventions in medical settings that
employed nutritional counseling with high-risk adults found mean
effects after 6 months of 15.3 percentage points (95 percent CI, 13.8
to 16.8 percentage points). Continuing counseling for longer than 6
months helped to maintain these effects but did not appear to result
in further improvements. After 12 months of nutritional counseling,
the mean effect size was 13.9 percentage points; after 24 months of
counseling, it was 13.0 percentage points. (The differences between
these results and the 6-month results were not statistically
significant.) Another study also suggested that continuing
intervention can help to maintain behavioral change.44 Estimates from a study providing 12 months
of counseling indicated that without continuous counseling, the effect
had started to wear off by the end of the second year of follow-up
(decreasing from 13.0 percentage points to 9.2 percentage
points). Nonetheless, despite a decrease over time, the effects of a
similar intervention in another study were still evident after 7 to 8
years of follow-up.72 After 7 years,
the
impact remained statistically significant, with a mean effect size of 3.6 percentage points (95 percent CI, 0.7 to 6.5 percentage points).In this grouping, we included seven studies conducted with patients with type 2 diabetes,40,43,52,57,76,73-74 two studies conducted with cancer patients,55,81 and one study conducted with patients with cardiovascular disease.48 Because the interventions for the three diseases were not sufficiently comparable, we did not combine the effect size estimates across diseases. Two studies of computer-assisted interventions for cancer patients were dropped from this group after further consideration because their interventions were very different from the others and from one another.73-74,104
We combined the results for (1) three studies among type 2 diabetes patients receiving less than 3 months of group sessions with various other activities,40,43,76 (2) three studies among type 2 diabetes patients receiving similar interventions and reporting results at 6-months follow-up,43,52,76 and (3) two studies conducted with cancer patients receiving less than 6 months of group sessions with other activities at 6-months follow-up.55,81 The mean difference-in-differences for the final grouping are reported in Table 21 and plotted in Figure 6
.
The results for the type 2 diabetes patient interventions were all quite low. Only the 6-month follow-up estimate was statistically significant (mean effect of 2.6 percentage points; 95 percent CI, 0.07 to 5.1 percentage points).
In contrast, after 6 months, the studies with cancer patients had an effect of 9.7 percentage points (95 percent CI, 7.8 to 11.6 percentage points). The CVD patients who received intensive nutrition education (including a week-long retreat and biweekly group sessions for a year) had a significant mean effect of 24.1 percentage points (95 percent CI, 17.1 to 31.1 percentage points).48
The results of this meta-analysis lend support to the proposition that these interventions can promote decreases in total fat intake. In addition, our findings suggest that behavioral interventions appear to be more successful with higher-risk populations -- although we observed that interventions with higher-risk populations are often more intense than interventions with lower-risk populations.
The greatest effects were observed among populations that had been diagnosed with potentially life-threatening diseases such as cancer and CVD. We observed the largest effect size (a difference in improvement after 12 months of 24.1 percentage points) among patients diagnosed with CVD. However, this intervention was also particularly intense, including a week-long retreat and two group meetings per week over 1 year, suggesting that the intensity of the intervention may have been more influential than the risk status of the population at promoting dietary change.
We also observed mean effects at 6 months of 15.3 percentage points among women at high risk for cancer who were participating in counseling and monthly sessions. Similarly, among women diagnosed with cancer who were involved in counseling and ongoing support sessions, we observed mean effects at 1 year of 9.7 percentage points.
The biggest departure from the pattern of larger effects being observed among high-risk populations was that individuals diagnosed with type 2 diabetes had a mean effect of only 2.6 percentage points. Part of the reason for this relatively lower effect is likely the fact that this set of interventions was not as intense or long lasting as the intervention conducted with cancer and CVD patients. Beyond this, we also speculate that type 2 diabetes may be perceived by patients more as a chronic condition than as a life-threatening illness, and that this may be associated with the lower efficacy of interventions with this population.
Although interventions conducted outside health care settings appeared to have modest effects, in many cases the interventions themselves were less intense. However, such interventions may have the potential to reach large numbers of people, and preventive interventions may benefit from working with general populations who can be reached through worksite, community, and school settings.
Finally, our results seem to indicate that interventions that have multiple components (e.g., counseling plus materials rather than materials alone) are more effective; furthermore, continuing reinforcement of interventions may be associated with maintenance of changes in behavior. For instance, even though the effects of interventions were often apparent after 6 months, interventions that extended for a longer period of time (through the use of ongoing group sessions) were largely able to maintain those changes. This suggests that research into the various components of interventions and the maintenance of behavior change may be fruitful areas for continuing research.
Our next analysis strategy for determining the efficacy of dietary interventions on fat intake involved the difference-in-deltas approach, which we applied to the three most commonly reported fat outcomes: total fat (percentage of energy intake), total fat (grams), and saturated fat (percentage of energy intake). This analysis included 34 of the 80 articles reporting results for dietary fat outcomes.
Similar to the analyses for fruits and vegetables, we calculated the median differences in change in fat intake between the intervention and control groups. In addition to indicating the magnitude of the intervention effect for total fat and saturated fat outcomes that had not been included in the meta-analysis just described, this approach enabled us to include more studies reporting total fat than were eligible for the meta-analysis (because the metric we calculated does not require an indication of the variance of the estimate).
| Outcomes | Median (range) (n = number of studies) | References |
|---|---|---|
| Total fat (% energy) | -15.7 (-76.4 to -1.0) (n = 33) | [19, 35, 38, 41, 43-48, 52, 54-55, 57, 61-62, 70, 72-74, 76, 78-83, 87, 89, 91, 93, 96-97, 104, 107, 111-112] |
| Total fat (grams/day) | -38.0 (-74.0 to -15.9) (n = 7) | [48, 55, 61, 63-64, 81, 83, 87] |
| Saturated fat (% energy) | -14.5 (-41.9 to +0.5) (n = 16) | [19, 35, 38, 45-47, 55, 57, 62, 70, 73-74, 76, 79, 82, 87, 93, 107, 112] |
| Outcomes | Follow-Up 1 | Follow-Up 2 | ||
|---|---|---|---|---|
| Median (range) | Refs. | Median (range) | Refs. | |
| Total fat (% energy) | -19.5 (-44.0 to -2.7) (n = 13) | [24-28, 35, 38, 40, 44, 61, 76, 78, 81, 83, 87, 96-97, 111] | -21.7 (-48.3 to -2.6) (n = 13) | [24-28, 35, 38, 40, 44, 61, 76, 78, 81, 83, 87, 96-97, 111] |
| Saturated fat (% energy) | -13.4 (-38.0 to -7.3) (n = 6) | [35, 38, 73-74, 76, 87] | -12.1 (-33.2 to +1.1) (n = 6) | [35, 38, 73-74, 76, 87] |
Refs. = references, n = number of studies.
| Grouping Characteristics | Median Difference in Change | |||
|---|---|---|---|---|
| Total fat (% of energy) | Saturated fat (% of energy) | |||
| Median | Refs. | Median | Refs. | |
| Population Characteristics | ||||
| Risk status | ||||
| General risk | -8.0 (-27.9 to -1.0) (n = 12) | [19, 35, 41, 62, 70, 78, 87, 89, 91, 93, 107, 112] | -14.5 (-31.6 to +0.5) (n = 8) | [19, 35, 62, 70, 87, 93, 107, 112] |
| High risk | -20.0 (-76.4 to -3.5) (n = 20) | [38, 43-48, 52, 54-55, 57, 61, 72-74, 76, 79, 80-81, 83, 96-97, 104, 111] | -29.3 (-41.9 to -10.0) (n = 7) | [38, 45-47, 55, 57, 73-74, 76, 79] |
| Intervention Characteristics | ||||
| Theoretical basis | ||||
| Yes | -8.2 (-32.2 to -1.0) (n = 13) | [19, 35, 43, 52, 55, 76, 78, 81, 89, 91, 93, 96-97, 111] | -10.0 (-29.3 to +0.5) (n = 5) | [19, 35, 55, 76, 93] |
| No | -18.0 (-76.4 to -3.8) (n = 20) | [38, 44-48, 54, 57, 61-62, 70, 72-74, 79-80, 82-83, 87, 104, 107, 112] | -18.4 (-41.9 to -6.6) (n = 11) | [38, 45-47, 57, 62, 70, 73-74, 79, 82, 87, 107, 112] |
| Quality score | ||||
| Low | -15.7 (-31.6 to -8.6) (n = 5) | [38, 45-47, 57, 62, 107] | -- | -- |
| Medium | -17.6 (-76.4 to -3.8) (n = 12) | [41, 43, 48, 52, 61, 79-80, 82-83, 104, 111-112] | -- | -- |
| High | -9.2 (-44.2 to -1.0) (n = 16) | [19, 35, 44, 54, 55, 70, 72-74, 76, 78, 81, 87, 89, 91, 93, 96-97] | -- | -- |
| Non-nutrition components | ||||
| Yes | -16.3 (-76.4 to -3.2) (n = 15) | [19, 41, 43, 45-48, 52, 55, 57, 62, 70, 76, 80-82, 89] | -16.9 (-31.6 to -6.6) (n = 8) | [19, 45-47, 55, 57, 62, 70, 76, 82] |
| No | -13.4 (-44.2 to -1.0) (n = 18) | [35, 38, 44, 54, 61, 72-74, 78-79, 83, 87, 91, 93, 96-97, 104, 107, 111-112] | -12.6 (-41.9 to +0.5) (n = 8) | [35, 38, 73-74, 79, 87, 93, 107, 112] |
| Family component | ||||
| Yes | -11.0 (-26.7 to -3.2) (n = 7) | [35, 45-47, 76, 89, 96-97, 107, 112] | -10.2 (-29.8 to -7.3) (n = 5) | [35, 45-47, 76, 107, 112] |
| No | -17.6 (-76.4 to -1.0) (n = 26) | [19, 38, 41, 43-44, 48, 52, 54-55, 57, 61-62, 70, 72-74, 78-83, 87, 91, 93, 104, 111] | -18.4 (-41.9 to +0.5) (n = 11) | [19, 38, 55, 57, 62, 70, 73-74, 79, 82, 87, 93] |
| Social support | ||||
| Yes | -26.7 (-76.4 to -3.5) (n = 7) | [45-48, 76, 81, 83, 87, 96-97] | -- | -- |
| No | -10.4 (-44.2 to -1.0) (n = 26) | [44, 52, 54-55, 57, 61, 80, 82, 89, 91, 93, 104, 107, 111-112] | -- | -- |
| Small groups | ||||
| Yes | -19.2 (-76.4 to -3.2) (n = 10) | [44, 48, 52, 54, 76, 81, 83, 89, 107, 112] | -- | -- |
| No | -8.7 (-34.1 to -1.0) (n = 23) | [19, 35, 38, 41, 43, 45-47, 55, 57, 61-62, 70, 72-74, 78-80, 82, 87, 91, 93, 96-97, 104, 111] | -- | -- |
| Interactions with food | ||||
| Yes | -11.0 (-26.7 to -3.2) (n = 7) | [35, 45-47, 72, 89, 107, 111-112] | -- | -- |
| No | -17.8 (-76.4 to -1.0) (n = 26) | [19, 38, 41, 43-44, 48, 52, 54-55, 57, 61, 62, 70, 73-74, 76, 78-83, 87, 91, 93, 96-97, 104] | -- | -- |
| Goal setting | ||||
| Yes | -18.9 (-44.2 to -2.7) (n = 18) | [19, 43-44, 52, 54-55] | -14.4 (-31.6 to +0.5) (n = 8) | [19, 55, 57, 70, 73-74, 76, 87, 93] |
| No | -11.0 (-76.4 to -1.0) (n = 15) | [35, 38, 41, 45-48, 61-62, 72, 79-80, 82, 91, 104, 107, 112] | -16.0 (-41.9 to -6.6) (n = 8) | [35, 38, 45-47, 62, 79, 82, 107, 112] |
Refs. = references, n = number of studies.
Unlike the pattern among fruit and vegetable outcomes, the use of a theoretical framework in designing or implementing the intervention was not associated with the magnitude of the intervention effect using the difference-in-deltas approach. In fact, studies employing a theoretical framework reported substantially lower decreases in total fat and saturated fat intake than studies not using theory (by 8 to 10 percentage points).
Contrary to our expectations, studies employing intervention components other than dietary education (e.g., physical activity components, smoking components) had slightly higher decreases in both total and saturated fat than did studies that focused solely on nutrition (our rationale was that among studies with non-nutrition components, the nutrition message would be diluted by the ancillary components). However, the differences in this grouping were quite small.
Of the special intervention features, only social support and goal setting appeared effective at reducing intake of total fat. Goal setting was not associated with a greater decrease in saturated fat, however. Interventions that incorporated social support components had substantially larger decreases in total fat (-26.7 percentage points) than did interventions not incorporating social support (-10.4 percentage points). An insufficient number of articles prevented us from exploring the impact on saturated fat. The use of family components and interactive activities involving food were actually associated with smaller decreases in fat intake than the decreases observed among studies that did not incorporate these components into the intervention.
One advantage of measuring biochemical outcomes among participants in behavioral interventions is the ability to determine whether physiological changes accompany self-reported behavioral changes in dietary fat intake. As part of our review, we abstracted information for two biochemical outcomes associated with intake of dietary fat: total blood cholesterol (TC) and low-density lipoprotein (LDL) cholesterol. However, because an insufficient number of articles reported results for LDL cholesterol, this report presents only the results for our secondary analyses of the relationship between change in dietary fat intake and TC. For studies reporting results for both fat intake and TC, we conducted a separate series of analyses to determine the relationship between change in dietary fat intake and change in biochemical indicators over the intervention period. This series of analyses used the difference-in-deltas approach -- that is, we calculated the difference in change in TC between intervention and control groups.
| Comparison | Total Blood Cholesterol Median (range) | Fat Intake Median (range) | Refs. |
|---|---|---|---|
| Total Cholesterol and Total Fat (%age of energy) | -3.4 (-18.9 to +0.1) (n = 12) | -18.4 (-76.4 to -2.7) (n = 12) | [19, 43, 48, 55, 57, 63-64, 72-74, 76, 80, 82, 87, 93] |
| Total Cholesterol and Saturated Fat (%age of energy) | -2.9 (-7.8 to -0.3) (n = 8) | -12.4 (-29.3 to +0.5) (n = 8) | [19, 55, 57, 73-74, 76, 82, 87,93] |
Refs. = references, n = number of studies.
We also explored the linear bivariate relationship between difference-in-deltas for biochemical outcomes and the difference-in-deltas for fat outcomes using Pearson correlations. Because of the small number of studies reporting results for both biochemical and dietary fat-related outcomes for which difference-in-deltas could be calculated, we decided to focus primarily on two relationships: change in TC compared with change in total fat and change in TC compared with change in saturated fat. While the goal of disease risk reduction is typically to lower LDL (but not high-density lipoprotein cholesterol [HDL]) cholesterol levels through reductions in saturated fat intake, an insufficient number of studies prevented us from examining other comparisons; for example, only three studies reported both total fat and either TC or LDL cholesterol,48,76,80 only one study reported both saturated fat and LDL cholesterol,76 and only two studies reported both total fat (grams) and LDL cholesterol.48,63-64
The results of the correlation analyses suggest that whereas change in total fat intake does appear to be associated with decreases in TC, changes in saturated fat are not. The correlation coefficient for the relationship between the difference-in-deltas for total fat and TC was 0.763 (p = 0.004). In contrast, the correlation between saturated fat and TC was -0.066 (p = 0.877).
In short, the differences between intervention and control groups in reducing intake of total fat observed among the studies we reviewed are substantiated by a significant correlation with concomitant reductions in TC. The difference-in-deltas for saturated fat were not associated with similar reductions in TC. The small number of eligible studies may have influenced our ability to detect a statistically significant correlation between saturated fat intake and TC. In addition, the significant correlation between TC and total fat intake likely reflects the fact that total fat is a marker of saturated fat intake, because intake of total fat independent of the relative content of different types of fatty acids is not thought to be associated with blood cholesterol. Finally, the significant correlation between TC and total fat observed in our analyses may reflect the fact that energy balance influences plasma cholesterol, and because we were not able to adjust for the influence of weight change in our secondary analyses, the association may simply reflect an energy deficit (regardless of the actual composition of the diet).123
The final set of analyses summarize the likelihood that investigators reported a significant intervention effect for dietary fat outcomes. To accommodate the tremendous variability in the measurement of dietary fat and to include as many articles (and outcomes) as possible in our analyses, we grouped dietary fat variables into five major sets of outcomes (see Chapter 2): (1) intake of total fat, (2) intake of saturated fat, (3) general fat intake, (4) intake of individual high-fat foods or specific high-fat eating behaviors or practices, and (5) intake of individual low fat foods or specific low-fat eating behaviors or practices. For each grouping, we calculated the percentage of studies reporting a significant intervention effect and then compared differences among various population and intervention characteristics. This analysis strategy included approximately 74 of the 80 articles reporting results for dietary fat outcomes.
| Grouping Characteristics | Outcomes Analyzed | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total fat intake (n = 49) | Saturated fat intake (n = 30) | General fat intake scores (n = 17) | Intake of high-fat foods/high-fat practices (n = 16) | ||||||
| % of studies | Refs. | % of studies | Refs. | % of studies | Refs. | % of studies | Refs. | ||
| Total | 86% (42/49) | [19, 24-28, 33, 35-38, 40, 42-55, 57, 61, 63-64, 66-74, 76-82, 84, 87, 89, 91, 93-94, 96-97, 101, 104, 106-107, 110-112] | 87% (26/30) | [19, 24-28, 33, 35-37, 43-47, 50, 54-55, 57-58, 63-64, 66-74, 76-77, 79, 82, 87, 93, 98, 107, 110, 112] | 76% (13/17) | [59-60, 65, 78, 90, 100-102, 105, 108, 116-118, 121-122] | 88% (14/16) | [31, 37, 41, 45-47, 53, 58, 66-69, 71, 73-74, 88, 92, 99-100, 103, 105, 108] | |
| Population Characteristics | |||||||||
| Age | |||||||||
| Children | 100% (6/6) | [19, 24-28, 33, 35, 42, 63-64] | 60% (3/5) | [19, 24-28, 33, 35, 63-64] | -- -- | -- -- | -- -- | -- -- | |
| Adults | 83% (34/41) | [36-38, 40, 43-55, 57, 61, 66-74, 76-82, 84, 87, 89, 91, 93-94, 96-97, 101, 104, 106-107, 110-112] | 88% (22/25) | [36-37, 43-47, 50, 54-55, 57-58, 66-74, 76-77, 79, 82, 87, 93, 98, 107, 110] | -- -- | -- -- | -- -- | -- -- | |
| Risk status | |||||||||
| General risk | 88% (15/17) | [1, 6, 11, 13,27, 42, 49, 55, 58, 60, 62, 64, 71, 76-77, 80, 82] | 70% (7/10) | [19, 24-28, 33, 35, 70, 87, 93, 107, 110, 112] | 80% (8/10) | [78, 100-102, 105, 116-118, 121-122] | 60% (3/5) | [31, 41, 92, 100, 105] | |
| High risk | 83% (25/30) | [36-38, 40, 42-50, 52-55, 57, 61, 66-69, 71-74, 76-77, 79-81, 94, 96-97, 104, 111] | 94% (16/17) | [36-37, 43-47, 50, 54-55, 57-58, 66-69, 71-74, 76-77, 79] | 60% (3/5) | [30, 56, 59, 60, 65] | 100% (8/8) | [37, 45-47, 53, 58, 66-69, 71, 73-74, 103] | |
| Intervention Characteristics | |||||||||
| Theoretical basis | |||||||||
| Yes | 100% (20/20) | [19, 33, 35, 40, 43, 52, 55, 63-64, 71, 76, 78, 81, 84, 89, 91, 93, 96-97, 101, 106, 111] | 80% (8/10) | [19, 33, 35, 43, 55, 63-64, 71, 76, 93, 98] | 78% (7/9) | [56, 59, 78, 100-102, 108, 116, 122] | 80% (4/5) | [31, 71, 99, 100, 108] | |
| No | 76% (22/29) | [24-28, 36-38, 42, 44-51, 53-54, 57, 61, 66-70, 72-74, 77, 79-80, 82, 87, 94, 104, 107, 110, 112] | 90% (18/20) | [24-28, 36-37, 44-47, 50, 54, 57-58, 66-70, 72-74, 77, 79, 82, 87, 107, 110, 112] | 75% (6/8) | [30, 60, 65, 90, 105, 117-118, 121] | 91% (10/11) | [37, 41, 45-47, 53, 58, 66-69, 73-74, 88, 92, 103, 105] | |
| Among subset of articles published in 1995 or later: | |||||||||
| Yes | 100% (12/12) | [33, 35, 71, 76, 78, 81, 84, 91, 93, 96-97, 106, 111] | 80% (4/5) | [33, 35, 71, 76, 93] | -- -- | -- -- | -- -- | -- -- | |
| No | 78% (11/14) | [24-28, 66-70, 72-74, 77, 79-80, 82, 87, 94, 107, 110, 112] | 83% (10/12) | [24-28, 66-70, 72-74, 77, 79, 82, 87, 107, 110, 112] | -- -- | -- -- | -- -- | -- -- | |
| Quality score | |||||||||
| Low | 64% (9/14) | [38, 40, 42, 45-47, 50, 53, 57, 66-69, 71, 77, 94, 106-107, 110] | 75% (6/8) | [45-47, 50, 57, 66-69, 71, 77, 107, 110] | 83% (5/6) | [56, 90, 105, 116-117] | -- -- | -- -- | |
| Medium | 86% (12/14) | [36-37, 43, 48-49, 51-52, 61, 79-80, 82, 104, 111-112] | 100% (7/7) | [36-37, 43, 58, 79, 82, 112] | 67% (4/6) | [30, 59, 100, 102, 108, 121] | -- -- | -- -- | |
| High | 100% (21/28) | [19, 24-28, 33, 35, 44, 54-55, 63-64, 70, 72-74, 76, 78, 81, 84, 87, 89, 91, 93, 96-97, 101] | 87% (13/15) | [19, 24-28, 33, 35, 44, 54-55, 63-64, 70, 72-74, 76, 87, 93, 98] | 80% (4/5) | [65, 78, 101, 118, 122] | -- -- | -- -- | |
| Non-nutrition components | |||||||||
| Yes | 81% (17/21) | [19, 24-28, 36-37, 43, 45-50, 52, 55, 57, 70, 76-77, 80-82, 89, 94, 101] | 87% (13/15) | [19, 24-28, 36-37, 43, 45-47, 50, 55, 57-58, 70, 76-77, 82, 98] | 78% (7/9) | [30, 56, 59, 65, 90, 100-102, 116] | 88% (7/8) | [31, 37, 41, 45-47, 58, 88, 100, 103] | |
| No | 89% (25/28) | [33, 35, 38, 40, 42, 44, 51, 53-54, 61, 63-64, 66-69, 71-74, 78-79, 84, 87, 91, 93, 96-97, 104, 106-107, 110-112] | 87% (13/15) | [33, 35, 44, 54, 63-64, 66-69, 71-74, 79, 87, 93, 107, 110, 112] | 75% (6/8) | [60, 78, 105, 108, 117-118, 121-122] | 88% (7/8) | [53, 66-69, 71, 73-74, 92, 99, 105, 108] | |
| Family component | |||||||||
| Yes | 100% (13/13) | [24-28, 33, 35-36, 42, 45-47, 63-64, 76, 89, 96-97, 101, 107, 112] | 90% (9/10) | [24-28, 33, 35-36, 45-47, 63-64, 76, 98, 107, 112] | -- -- | -- -- | -- -- | -- -- | |
| No | 80% (29/36) | [19, 37-38, 40, 43-44, 48-55, 61, 66-74, 77-82, 84, 87, 91, 93-94, 104, 106, 110-111] | 85% (17/20) | [19, 37, 43-44, 50, 54-55, 57-58, 66-74, 77, 79, 82, 87, 93, 110] | -- -- | -- -- | -- -- | -- -- | |
| Social support | |||||||||
| Yes | 100% (7/7) | [45-48, 76, 81, 87, 96-97, 101] | -- -- | -- -- | -- -- | -- -- | -- -- | ||
| No | 83% (35/42) | [19, 24-28, 33, 35-38, 40, 42-44, 49-55, 57, 61, 63-64, 66-74, 77-80, 82, 84, 89, 91, 93-94, 104, 106-107, 110-112] | -- -- | -- -- | -- -- | -- -- | -- -- | ||
| Small groups | |||||||||
| Yes | 100% (12/12) | [40, 42, 44, 48, 52, 54, 76, 81, 89, 101, 107, 112] | 100% (5/5) | [44, 54, 76, 107, 112] | -- -- | -- -- | -- -- | -- -- | |
| No | 81% (30/37) | [19, 24-28, 33, 35-38, 43, 45-47, 49-51, 53, 55, 57, 61, 63-64, 66-74, 77-80, 82, 84, 87, 91, 93-94, 96-97, 104, 106, 110-111] | 84% (21/25) | [19, 24-28, 33, 35-37, 43, 45-47, 50, 55, 57-58, 63-64, 66-74, 77, 79, 82, 87, 93, 98, 110] | -- -- | -- -- | -- -- | -- -- | |
| Food interactions | |||||||||
| Yes | 100% (12/12) | [24-28, 33, 35, 42, 45-47, 72, 89, 101, 106-107, 111-112] | 86% (6/7) | [24-28, 33, 35, 45-47, 72, 107, 112] | -- -- | -- -- | -- -- | -- -- | |
| No | 81% (30/37) | [19, 36-38, 40, 43-44, 48-55, 57, 61, 63-64, 66-71, 73-74, 76-82, 84, 87, 91, 93-94, 96-97, 104, 110] | 87% (20/23) | [19, 36-37, 43-44, 50, 54-55, 57-58, 63-64, 66-71, 73-74, 76-77, 79, 82, 87, 93, 98, 110] | -- -- | -- -- | -- -- | -- -- | |
| Goal setting | |||||||||
| Yes | 95% (18/19) | [19, 40, 43-44, 52, 54-55, 57, 70, 73-74, 76, 78, 81, 87, 89, 93, 96-97, 101, 111] | 91% (10/11) | [19, 43-44, 54-55, 57, 70, 73-74, 76, 87, 93] | 100% (6/6) | [59-60, 78, 100-102] | -- -- | -- -- | |
| No | 80% (24/30) | [24-28, 33, 35-38, 42, 45-51, 53, 61, 63-64, 66-69-71-72, 77, 79-80, 82, 84, 91, 94-94, 104, 106-107, 110, 112] | 84% (16/19) | [24-28, 33, 35-37, 45-47, 50, 58, 63-64, 66-69, 71-72, 77, 79, 82, 98, 107, 110, 112] | 64% (7/11) | [30, 56, 65, 90, 105, 108, 116-118, 121-122] | -- -- | -- -- | |
| Cultural specificity | |||||||||
| Yes | 100% (5/5) | [76, 87, 101, 107, 112] | 100% (5/5) | [76, 87, 98, 107, 112] | -- -- | -- -- | -- -- | -- -- | |
| No | 84% (37/44 ) | [19, 24-28, 33, 35-38, 40, 42-55, 57, 61, 63-64, 66-74, 77-82, 84, 89, 91, 93-94, 96-97, 104, 106, 110-111 | 84% (21/25 ) | [19, 24-28, 33, 35-37, 43-47, 50, 54-55, 57-58, 63-64, 66-74, 77, 79, 82, 93, 110] | -- -- | -- -- | -- -- | -- -- | |
Refs. = references, n = number of studies.
Very few studies explored changes in fat intake among children. We were only able to compare age differences in changes in total fat and saturated fat intake. While a greater proportion of interventions conducted with children reported significant reductions in total fat intake (around 17 percent more studies), slightly fewer reported reductions in saturated fat intake (28 percent fewer studies). However, the extremely small number of studies reporting results for children requires that caution be used in interpreting these results.
Insufficient variation among the studies we reviewed prevented us from comparing the influence of intervention settings, delivery mode, or intensity on any of the fat outcomes explored in this tier of analyses. However, we were able to determine the effects of several other intervention characteristics. Among the total pool of articles we reviewed, the use of a theoretical framework in designing or implementing the intervention was not consistently associated with the significance of findings. However, studies that used a theoretical framework were clearly more likely to report significant reductions in total fat intake (20/20 studies) than studies not employing theory (22/29 studies, or 76 percent). This same pattern was evident when examining the subset of articles published in 1995 or later (all 12 studies using theory compared with 11/14 studies that did not). We observed the opposite pattern, however, for saturated fat and outcomes measuring intake of high-fat foods (or eating practices); for these outcomes, interventions reporting a theoretical framework were slightly less likely to report significant findings. For example, 100 percent of studies with some theoretical underpinnings produced a significant intervention effect for total fat, compared with 76 percent of those not reporting a theoretical framework.
The quality score assigned to the studies we reviewed was associated with the likelihood of reporting significant findings for intake of total fat in a dose-response fashion (9/14 low-quality studies reported a significant intervention effect, 12/14 medium-quality studies, and 100 percent -- 21/21 -- of high-quality studies). We did not observe this pattern for intake of saturated fat (although high-quality studies were more likely than low-quality studies to report significant decreases in saturated fat) or for general fat intake scores (for which low-quality studies were the most likely to report significant findings). An insufficient number of articles and limited variability in quality scores precluded this comparison for the high-fat and low-fat intake-related outcomes.
Equal proportions of articles that focused exclusively on dietary change or included non-nutrition components reported significant intervention effects for all sets of fat outcomes. While a higher percentage of "nutrition only" interventions reported significant reductions in total fat intake than did studies that targeted non-nutrition behaviors, this difference was slight (i.e., only eight percent more articles reported significant findings for total fat intake).
We had a large enough number of studies (and variation within the studies) to consider the effects of several specific strategies employed in the studies. Several of these strategies appeared particularly effective at producing changes in dietary behavior. Interventions that included family components were substantially more likely to report significant reductions in total fat intake (13/13 studies compared with 29/36 -- 81 percent) and slightly more likely to report significant reductions in saturated fat intake (9/10 studies compared with 17/20). Similar to the findings based on the difference-in-deltas approach, interventions that incorporated social support and used small groups to provide nutrition education produced greater changes in intake of total fat and saturated fat. The use of small groups was also associated, though to a lesser extent, with reductions in saturated fat intake. We observed a positive association between reductions in total fat intake and the inclusion of interactive activities involving food (such as taste tests and cooking classes). This pattern was not evident for saturated fat intake (with equal proportions of articles reporting significant effects for saturated fat).
Goal setting was consistently associated with a greater likelihood of reporting significant intervention effects among the studies eligible for this analysis. Though the differences for total and saturated fat intake were small, interventions that incorporated goal setting (or self-monitoring) to promote dietary change were more likely to report significant reductions in these fat outcomes. The differences in the proportion of studies reporting significant reductions in fat using general measures of fat intake were substantial (all six studies that employed goal setting reported significant effects, compared with 7/11 studies that did not include this strategy). Finally, the impact of culturally or ethnically specific interventions was consistently associated with reductions in dietary fat, although we were only able to make this comparison for two sets of outcomes (total and saturated fat intake). All five studies that were designed to be culturally or ethnically specific (to the study population) reported significant decreases in total or saturated fat intake.
Approximately one-third of the studies we reviewed reported results of behavioral dietary interventions on fruit and vegetable intake. Based on the small number of studies reporting such information and the high degree of variability across these studies, we concluded that a formal meta-analysis was inappropriate. Therefore, we employed the remaining two analysis strategies in our determination of the impact of interventions on fruit and vegetable intake. The results of both the differences-in-deltas approach and the summary of significant findings approach indicated that (a) dietary interventions were positively associated with changes in fruit and vegetable intake and (b) when these outcomes are measured individually, changes in fruit intake are more notable. While the range of studies reporting significant findings for fruit and vegetable intake varied (depending on the particular outcome measured), the vast majority of the studies we reviewed reported significant increases in fruit and vegetable intake (either as separate outcomes or combined). More than three-fourths of the studies in our review reported significant increases in fruit and vegetable intake (as a combined variable). Using the differences-in-deltas approach, we determined that the average increase in fruit and vegetable intake reported in the studies we reviewed was 0.6 servings per day.
While we were unable to explore the relative effectiveness of interventions on many population subgroups (because of the minimum cell size requirement we established for specific analyses to be conducted), our analyses suggested that interventions were more successful at increasing fruit intake among children and vegetable intake among adults. In addition, interventions conducted among higher disease risk populations were consistently more likely to report significant increases in fruit and vegetable intake.
Among the specific intervention characteristics we explored, several patterns were evident. Studies employing a theoretical basis were more likely to report significant increases in intake of fruits and vegetables than were studies that did not use theory. In addition, we observed a linear relationship between study quality (using procedures described in Chapter 2) and the likelihood of reporting significant findings. Also, the use of social support components in the interventions we reviewed was associated with greater increases in fruit and vegetable intake (using both analysis strategies). Finally, while studies that used goal setting and interactive activities involving food were more likely to report significant increases in fruit and vegetable intake, the magnitude of the increases was not notably higher than that in studies not employing such techniques. We did not have a large enough pool of articles to explore characteristics such as intervention intensity, setting, mode of delivery, or use of individually tailored or culturally/ethnically specific interventions.
Nearly 90 percent of the articles we reviewed reported results for dietary fat, although there was a tremendous amount of variability in these outcomes. In determining the impact of behavioral dietary interventions on decreases in fat intake, we used all three analysis strategies. Based on all three techniques, dietary interventions were positively associated with changes in fat consumption. We observed similar decreases in intake of total fat and saturated fat (the two most commonly reported fat outcomes in the studies we reviewed). Among a subset of articles employing biochemical indicators (i.e., measuring changes in blood cholesterol), the decrease in total fat intake was significantly correlated with concomitant decreases in total blood cholesterol (r = 0.76). The change in saturated fat was not corroborated by decreases in total blood cholesterol.
The large number of studies reporting results for dietary fat enabled us to explore two moderating population characteristics: age and risk status. Although studies conducted among high-risk populations were not consistently more likely to report significant decrease in fat intake (across the five sets of fat outcomes we explored using the summary of significant findings approach), the differences-in-deltas analysis indicated that the magnitude of the change in dietary fat was notably higher among interventions conducted with higher-risk populations, particularly the reduction in saturated fat. The pattern of greater effects being observed among the studies focusing on high disease risk populations was also evident in our meta-analysis. In addition, interventions conducted among children appeared to be more successful at reducing intake of total fat and less successful at reducing intake of saturated fat than were interventions conducted among adults; however, only a very small number of studies measured fat intake among children.
Unlike the pattern observed for fruit and vegetable outcomes, interventions employing a theoretical framework were not consistently more likely to report significant effects (and the magnitude of the intervention effect was actually lower among studies using theory). Nor was study quality associated with the likelihood of reporting significant effects or the magnitude of the intervention effect. Among the specific intervention characteristics we explored, however, several consistent patterns were evident. The use of social support, small groups, and goal setting appeared particularly effective at reducing intake of dietary fat. Greater proportions of studies employing such strategies reported significant findings, and the magnitude of the change in dietary fat (using the differences-in-deltas approach) was notably higher among these studies. Although studies that involved families in the interventions and that used interactive food-related activities were more likely to report significant decreases in fat intake, the magnitude of the decrease was not higher than in studies that did not incorporate these special features. Finally, although very few studies were designed to be culturally or ethnically specific (to the study sample), our results suggest that such studies report greater decreases in dietary fat (although we did not have a sufficient number of articles to explore the magnitude of this decrease).
In this evidence report, we posed three key questions about the efficacy of behavioral dietary interventions in reducing cancer risk. The background and the overall analytic framework for this work, with attention to specific concerns of the National Cancer Institute, were presented in Chapters 1 and 2. The specific questions were the following:
Question 1 -- Is there evidence that one type of intervention or combination of interventions, using a broad typology of behavior interventions and including emerging technologies and approaches, is more effective than another for helping individuals or groups modify their diet to consume more fruits and vegetables and less fat?
Question 2 -- What is the evidence by subgroup (e.g., African American, Hispanic, Asian American, Native American) and for males and females within these groups?
Question 3 -- What conclusions (if any) can be reached about the cost-effectiveness of these types of interventions?
Because of the similarity of dietary recommendations for prevention of the major chronic diseases, we chose to include in our review articles addressing primary and, to a lesser extent, secondary prevention of cancer; coronary heart (cardiovascular) disease; and non-insulin-dependent (type 2) diabetes mellitus. Also included were studies using dietary intake as the final outcome, with no stated disease outcome. We excluded studies that tested therapeutic diets for specific health conditions, such as hypertension, type 1 diabetes, or obesity.
Our evidence review lends support to the notion that a wide variety of dietary interventions delivered in many different settings to individuals of different ages, ethnicities, and genders can have a positive impact on dietary behaviors associated with cancer risk reduction. The large proportion of studies showing favorable outcomes in various situations suggests an overall positive effect, although the potential for publication bias may have influenced the likelihood of identifying positive effects of interventions. The restriction of the literature search to papers published in English also provides a potential source of bias.
The lack of similarity across studies in outcome measures, study design, analysis strategy, and intervention technique makes it impossible to draw broad conclusions about the most efficacious behavioral dietary interventions. Nevertheless, our findings offer insight into intervention components that may hold promise for future research efforts. At the same time, our work did not necessarily support the efficacy of some commonly accepted intervention techniques and underpinnings. As we describe below, we urge caution in overinterpreting these findings until substantial additional research is conducted (see Chapter 5).
The number of studies available to address the second key question -- regarding evidence for the efficacy of dietary interventions by subgroup -- was very limited. This was particularly true for lower-income and minority subgroups among whom the burden of suffering due to cancer and other life-threatening or chronic diseases is greatest. More information was available to determine relative intervention effectiveness between other subgroups such as high-risk versus general-risk intervention participants and adults versus children.
Even fewer studies address the cost-effectiveness of dietary interventions. Some investigations that did include cost estimates did not meet our inclusion criteria for study design and measures. Among those studies meeting our criteria for inclusion, cost data were rare, and reporting of cost relative to the effectiveness of the intervention was almost nonexistent.
As described in Chapters 2 and 3, we used multiple analytic strategies (including meta-analysis, differences-in-deltas, and analysis of the significance of the intervention effect) in an attempt to clarify the existing knowledge base and offer directions for future research. The diversity of study populations, interventions, study designs, and approaches to data analysis necessitated groupings and comparisons that varied according to the analytic strategy being employed.
Our most rigorous approach, meta-analysis, could be applied to the studies assessing change in dietary fat but not to change in fruit and vegetable intake, for which the number of studies was much smaller. The differences-in-deltas approach, which is based on a percentage magnitude of change, allowed us to accommodate a wider variety of outcome measures reported for fruits and vegetables as well as dietary fat, and thus to include a larger number of studies. The final approach, analysis of the significance of the intervention effect, although statistically the weakest, was also the most inclusive, allowing us to examine the broadest diversity of outcomes and approaches to statistical reporting.
Incorporating as many studies as possible in our efforts to synthesize and understand the dietary change literature is desirable, but the drawbacks to more inclusive analytic strategies must be recognized. The more inclusive the method, the closer one comes to the proverbial situation of comparing apples and oranges. Sometimes this problem arises from incomplete reporting of study methods or results. Other times, however, study outcomes may be strikingly different-for example, the percentage of calories from saturated fat versus the frequency with which skin is removed from chicken before consumption. Both outcomes indeed capture similar changes in dietary behavior, but the vast difference in their units and measurement approaches can make for unstable comparisons.
Casting ever-wider nets (through our three analysis strategies) also means that the results may not be comparable with each other. For example, we found that the differences-in-deltas approach suggested larger effect sizes in fruit and vegetable intake among interventions employing food-related activities, but the analysis of the significance of intervention effects approach suggested the reverse. The first method included 12 studies. By contrast, the second method comprised 22 studies, along with a much wider variety of "acceptable" outcome measures. When comparing other intervention characteristics, such as cultural/ethnic specificity, social support, or inclusion of a family component, the numbers of studies in each cell often become quite small, causing instability in the estimates. The establishment of a minimum number of studies as a prerequisite for conducting any of the secondary analysis strategies we employed, while preventing the generation of extremely unstable estimates, also prevented the exploration of several theoretically significant population and intervention characteristics (such as intervention intensity and setting).
Our highest level of confidence is in the meta-analytic approach. Unfortunately, as discussed in Chapter 3, we were unable to conduct a statistical meta-analysis or pooling for the fruit and vegetable intake outcomes because of the small number of studies and the unacceptable diversity of outcomes.
We were able to make some comparisons of the effectiveness of dietary behavioral interventions on total dietary fat intake across study settings, populations, and lengths of follow-up. Of the 80 studies reporting results for relevant (e.g., fat-related) outcomes, 28 were ultimately included in this meta-analysis. Although this approach did not provide a single overall estimate of the efficacy of behavioral interventions on dietary change, the results of the meta-analysis indicate the potential of these interventions for producing reductions in fat intake across a variety of different settings and study populations.
The differences-in-deltas approach allowed us to calculate median difference in percentage change in dietary outcomes of interest between intervention and control groups for selected outcome measures for all eligible articles (17/39 for fruits and vegetables, 34/80 for dietary fat). The median difference in percentage change in fruit and vegetable intake was +16.6 favoring the intervention group. This translates into a mean change in fruit and vegetable consumption among intervention groups of approximately 0.6 servings. For total fat as a percentage of energy intake, the median difference was a -15.7 percent change (i.e., a better result for the intervention groups). The estimate of mean change in total fat intake is a 7.3 percent reduction in the percentage of calories from fat. Most clinicians would consider this a moderate but clinically significant improvement in diet.
Results from the second and third analytic approaches used to compare different populations, settings, and intervention characteristics are best viewed in combination to assess overall trends. The problem with individualized comparisons is the wide variety of studies and the appreciable difference in the numbers of studies in many of the cells. Conclusions about these results (along with meta-analytic findings) are discussed under each key clinical question below.
Several dietary intervention components appear to be promising in modifying dietary change. These factors include social support, goal setting, small groups, food-related activities, and the incorporation of family components. Interventions that included "interactions with food," such as cooking or taste testing, seemed particularly promising in increasing fruit and vegetable intake and reducing fat intake. As an example, based on our meta-analysis, one study with a nutritionist-delivered intervention in type 2 diabetes patients that also included social support, a family component, and goal setting reported a mean difference in change of 6.0 percent. 76 By contrast, another nutritionist-led intervention study augmented only with goal setting achieved a 3.0 percent mean difference in change. 57 While these findings are not surprising, they do support efforts by nutritionists and other health professionals to continue refining and evaluating the most effective intervention components with an eye toward achieving greater efficiency, less participant burden, and increased cost-effectiveness.
Another intervention component that appeared to have promise for dietary change (specifically, fat intake) was cultural or ethnic specificity. The number of studies that either included individual tailoring or specifically mentioned culturally sensitive interventions was so small that the estimates must be considered very unstable. In some cases, studies testing tailored interventions have compared different tailoring approaches without a usual-care control group. 121 Thus, the overall impact of tailoring on dietary change cannot be determined.
We had speculated that nutrition intervention studies that also included other lifestyle modification emphases might detract from the diet-related focus. Our review neither supported nor refuted this proposition. The meta-analysis for dietary fat did suggest that in one study among men the addition of a physical activity intervention seemed to bolster the dietary change outcomes. More research is needed to determine whether multiple lifestyle intervention strategies have a synergistic or a distracting effect on dietary change.
In our initial analyses with the non-meta-analytic techniques, we found limited support for a positive association between the use of a theoretical base for intervention design and positive dietary outcomes. From reviewing the literature, however, as well as recognizing that changes are occurring in federal specifications for "Requests for [Research] Applications" and in the evaluation criteria that grant review panels apply, we are aware that the use of behavior theory for intervention design has received much more emphasis in the past 5 years than previously. Therefore, we re-analyzed the data including only studies from 1995 onward. With this step, we found a suggestion of substantial positive association between theory application and positive dietary change. We had hoped to be able to classify the degree to which interventions were "theory driven" versus "theory informed," but few articles provided adequate detail to make this distinction.
We had hoped to determine whether interventions that include a maintenance component are more successful in helping participants sustain positive changes made as part of the initial intervention. Unfortunately, very few studies reported anything that could be clearly distinguished as maintenance. Even when articles reported longer-term follow-up, they usually did not provide information sufficient to determine if the intervention either included continued intervention elements or involved a specific approach designed to prevent relapse or maintain existing positive change. Not surprisingly, our meta-analytic results for dietary fat did suggest that continuing reinforcement of interventions may be associated with the maintenance of changes in behavior. However, few studies address the question of how long and at what intensity a maintenance intervention must be sustained to prevent relapse in the long run.
Dietary intervention studies involving health outcomes in which specific biological markers are associated with elevated risk (such as cholesterol for coronary heart disease) often include such outcome measures to corroborate assessment of dietary change. As we report inChapter 3, positive findings with respect to self-reported dietary change scores accompanied by small or no difference with respect to measured changes in biological markers, such as blood lipids, are not uncommon in clinical trials of dietary interventions. This discrepancy may be attributable in part to social desirability bias on the part of participants -- that is, they know how they should be eating, and they are inclined to answer dietary assessment questions in a way that reflects this knowledge. Participants in the intervention groups of these studies may well make more appropriate dietary changes than do usual-care participants, but these changes were not fully reflected by the observed reductions in total and low-density lipoprotein (LDL) cholesterol.
Not surprisingly, interventions among higher-risk individuals or those already diagnosed with cancer, diabetes, or cardiovascular disease (CVD) seemed to have a greater impact than among those of general-risk status. These findings held true across all three levels of analysis. Some of the larger effects were associated with individuals who had already sustained a cardiovascular event and who participated in a very intensive and comprehensive lifestyle modification program.48 This fact may mean that such studies have only limited generalizability to the public at large. Indeed, subject willingness to participate in extremely intensive interventions may be closely tied to risk status. The potential confounding between subject motivation (and participation) and the intensity of intervention should be kept in mind when interpreting these results.
Interestingly, individuals diagnosed with type 2 diabetes showed much smaller effects than those with either cancer or CVD. We speculate that relative to individuals who have been diagnosed with cancer or who have had a cardiovascular event, diabetes may be perceived more as a chronic condition than as a life-threatening illness, and this factor may in turn be associated with the lower efficacy of interventions in this population. However, we offer this interpretation with considerable caution because the interventions for diabetic patients were not as intense as those for individuals with cancer or CVD. Moreover, heavy reliance on diabetes medications (oral agents and insulin) may distract both patient and provider from adequate attention to dietary change.
A limited number of studies reported results separately for males and females. As described above for the meta-analysis, we grouped some studies on the basis of the gender of participants, but these groupings did not allow direct comparisons of similar interventions by gender. Thus, we cannot explain through these methods the extent to which difference in the efficacy of the interventions may be attributable to gender.
Of particular interest in our review was the effort to assess the degree to which dietary interventions are effective in populations distinguished by ethnicity or income. Minority and low-income populations are at increased risk for many cancers as well as other chronic diseases associated with a high intake of fat and low consumption of fruits and vegetables. Of the 92 studies reported in our Evidence Tables, only a fraction either focused on a low-income population exclusively or made mention of including low-income individuals. Few of the latter studies conducted subanalyses to determine relative intervention effectiveness among different income groups. As reported for the meta-analyses of dietary fat, changes in the range of 6 percentage points were achieved in two community-based studies with low-income women (one with African Americans and one with Hispanics) who participated in a series of weekly education sessions over 12 weeks.107,112 Successful dietary changes in a handful of other studies either including or limited to low-income individuals add support to the notion that these interventions can be effective across socioeconomic strata. However, we cannot say anything definitive about relative levels of success or about what type of interventions are most effective among individuals for whom conventional interventions may be a poor fit with their needs.
Many of the studies we reviewed included an ethnically diverse population, but few included an adequate number of minorities for comparison purposes, and even fewer reported any analysis of effect size by minority subgroup. As with the income variable, we are not able to comment on relative levels of success of different interventions or types of intervention among specific minority groups. We can say, however, that our review supports the conclusion that many different kinds of interventions have shown at least modest success for ethnically diverse participants.
Our analysis of intervention components or characteristics did suggest potentially greater effectiveness among those studies reporting efforts to design culturally sensitive interventions. However, it was difficult to determine the extent of cultural sensitivity. Quite possibly, interventions could be culturally sensitive without being described as such, while interventions that are described as "culturally sensitive" may include only token efforts to be so. The limited level of intervention description in most manuscripts (attributable largely to page limitations by journals) makes it impossible to conduct a thorough assessment of the degree to which any intervention is "culturally sensitive."
Of the three key questions, our ability to answer the third was the most limited. Of those studies qualifying for inclusion in our Evidence Tables, none made more than a passing mention of cost associated with the intervention. Although some studies have assessed the cost or cost-effectiveness of dietary interventions,124-125 they have generally focused on aspects of nutrition and health beyond the scope of this review or have not included the study design and analysis required for inclusion in our body of evidence.
None of the dietary intervention studies we reviewed specifically evaluated the question of whether the intervention could or did result in harm to participants. Such an outcome is relatively unlikely with the types of dietary modifications implemented, but case studies have appeared in the literature regarding failure to thrive among infants and children whose parents were overly strident in restricting dietary fat. With any dietary intervention in adolescent girls, taking precautions against the possibility of eating disorders is prudent.
More recently, another potential harm related to interventions designed to reduce dietary fat intake has become apparent. Rising obesity levels in the face of declines in self-reported fat intake have raised the possibility that reduction in fat as a percentage of total intake has been accompanied by an increase in overall caloric intake. Presumably this is because people are eating larger quantities of low-fat foods, which are often quite high in simple carbohydrate sources of calories.
Two important objectives of this evidence-based review are (1) to assess the state of the science in the area of behavioral dietary interventions to reduce cancer risk and (2) to make recommendations regarding directions for future research. In this chapter, we address two main topics related to this objective. First, we identify intervention approaches and target populations that appear to be understudied. Second, we offer recommendations about study design and measurement strategies that will facilitate future evidence-based reviews. We suggest guidelines for the description of methods and results in manuscripts that we believe will improve the reporting of research findings and enhance our ability to synthesize the literature.
Using our key questions as a guide, much more research is available to answer Key Question 1 (evidence that one type of intervention or combination of interventions is more effective than another) than to address either Key Question 2 (evidence for efficacy of dietary interventions by subgroup, particularly related to ethnicity and gender) or Key Question 3 (cost-effectiveness of interventions).
We began the literature review process using a slight modification of the Rimer typology of behavioral interventions, which has been described by Rimer and others as a guide to the categories of interventions we anticipated finding.16-18 This typology classifies interventions into eight categories: individual-directed (including school, community, worksite, and health care settings), system- and physician-directed interventions, access-enhancing interventions, policy-level interventions, media campaigns (including broadcast and print media, and point-of-purchase interventions) community-based interventions multistrategy interventions, and tailored interventions or interventions using emerging technologies.
Recognizing overlap among a number of categories, the vast majority of the studies in this evidence report looked at individual-directed interventions, including those in the three subsections of school, community, and health care settings. Although we identified studies in most of the categories listed, our inclusion criteria specifying pre- and postintervention measures of individual dietary intake meant that many of these studies were not eligible. For example, we found several supermarket point-of-purchase intervention studies. Generally, the outcomes for these studies included such things as changes in food purchasing behaviors, changes in knowledge or awareness, and sometimes shelf disappearance data. We also found a few studies reporting on media campaigns and policy-level interventions, such as the 5-A-Day program in California, but, again, measures did not include individual level of dietary change.
Thus, more research is needed that uses dietary intervention categories within the Rimer typology but also includes assessment of dietary change at the individual level. Including this level of measurement will facilitate comparisons across widely different intervention approaches.
A series of papers presented at a recent meeting, "Maintenance of Behavior Change in Cardiorespiratory Risk Reduction" (sponsored by the National Heart, Lung, and Blood Institute [NHLBI]) and published in Health Psychology, offers recommendations for new models of population health-behavior change and maintenance that integrate individual-level with broader environmental- and societal-level policy influences.126-128 Using McKinlay's Population-Based Health Promotion Model,129-130 they describe dietary interventions as downstream (individual-level interventions for those at risk), midstream (population-level prevention strategies targeting defined groups), and upstream (macro-level state and national public policy and environmental interventions). Our review clearly suggests that, to date, the majority of work has been done in the "downstream" category. As researchers move "upstream," they will face new challenges in designing innovative interventions and evaluations to promote change and measure the impact in a way that will help identify highly effective strategies.
An area of considerable interest to the National Cancer Institute is emerging technologies in health communications. These technologies often include sophisticated computer tailoring systems providing participant feedback and education based on individual assessment data. Innovative uses of lower-level technologies (such as telephone and mail) are also being tested. Several studies published in this area include data on individual dietary change, but no "critical mass" of this literature exists yet that would allow comparisons with other intervention approaches.
These gaps suggest that one important area of future research will be to assess the degree to which new health communication technologies, either alone or in combination with more traditional intervention approaches, can improve diet-related behavior-change outcomes. Also important will be the evaluation of Internet-based intervention approaches that reach beyond the standard one-on-one and group counseling approaches to both increase efficiency and reach a broader audience, particularly those who are not reached by traditional intervention approaches.
As described earlier, we found very scant information about Key Question 3 concerning what conclusions (if any) can be reached about the cost-effectiveness of these types of interventions. Cost-effectiveness data should be collected as a part of intervention evaluation research whenever possible. This will be particularly important as part of evaluating emerging technological approaches (e.g., Web-based interventions), which may require a very large "front end" investment but then potentially allow for reaching a broad audience using fewer personnel resources. Investigators should not confuse cost-benefit analyses, which are far more complex and often arbitrary, with a more focused cost-effectiveness approach. Although research teams can collect cost-related data retrospectively, planning the necessary data collection instruments and procedures from the initiation of their projects will greatly facilitate their inclusion and the accurate conduct of economic analyses.
Unfortunately, we were unable to determine whether greater intervention intensity was associated with more significant changes in dietary outcome measures. Given the limited detail often included in intervention descriptions, assessment of intensity level was a rough estimate at best, and we did not have sufficient numbers of studies that were clearly of "high" or "low" intensity. Nonetheless, critical to the development of cost-effective interventions is the ability to determine the optimal "dose" or duration needed to effect significant change at reasonable cost. This question could be addressed more directly with research efforts that compare several levels of intervention intensity within the same study.
Our review also reveals that few studies follow participants for more than a year from the beginning of the intervention and that few include a clearly defined maintenance intervention designed to prevent relapse over time. At the same time, there is strong epidemiologic evidence for the association between long-term dietary practices and chronic disease outcomes. Thus, a critical area for research is the development and evaluation of innovative approaches to cost-effective, long-term intervention strategies that facilitate maintenance of positive dietary change. Intervention trials ought to include an adequate length of follow-up to determine whether intervention effects are sustained after the formal intervention period has ended. Presenters at the NHLBI conference described above called for more theory-based and interdisciplinary research on the maintenance process and for interventions that address more than one behavioral risk at a time.126
Much more additional research is needed to answer more effectively Key Question 2: What is the evidence by subgroup (focus on ethnicity) and for males and females within these groups? We reviewed numerous studies that included only men or only women. However, the studies were vastly different in terms of intervention design and outcomes measurement, so comparison was not possible. A limited number of studies that include men and women in the same intervention reported effect sizes by gender. Perhaps of greatest interest is not whether men are more successful than women (or vice versa) in changing their diets, but rather which intervention components or mediating variables are most important for each sex. For example, is support group attendance associated with greater positive changes in one sex or the other? Do women achieve better dietary change when they have a concomitant increase in self-efficacy? Do men with higher levels of perceived health threat show greater changes in diet? If we can gain a greater understanding of some of these associations, we will be better able to design interventions tailored to the needs of the audience.
Thanks, in part, to funding priorities of federal and local agencies, more studies in recent years have included and/or emphasized underserved and minority subgroups. However, much additional work is needed in this area. As mentioned previously in this report, few studies either targeted or included subgroup analysis for low-income or minority populations. Given that these groups are generally at highest risk for cancer and other chronic diseases,131-132 the need for intervention research is great.
This research should not simply test the same interventions that have been applied in nonminority, middle- to upper-income populations; rather, it should represent innovative strategies to reach traditionally hard-to-reach groups. Intervention studies including adequate numbers of ethnic subgroups should include subgroup analysis to determine effectiveness across groups.
As with research specific to male-female differences in intervention acceptability and impact described above, such studies will need to collect good process and mediator data in order to understand the impact of specific components of the intervention as well as the intervening variables that are necessary to effect dietary change.
Too little is known about why people eat what they do. More research specific to the cause of specific dietary behaviors will ultimately help us improve the design and evaluation of interventions. Pilot studies should be designed to detect changes in mediating variables, initial field studies to assess individual-level dietary change, and second-level field studies to detect changes in subgroups.
Any researchers who have embarked on the daunting task of an evidence-based review quickly become aware of the inconsistencies and deficiencies in the currently published literature. Our recommendations here closely parallel any general advice to researchers about study design and measurement. Investigators should be aware that failure to meet many of these criteria will mean that their research may be excluded from evidence-based reviews. In other cases, our recommendations are aimed at improving the ability of future evidence reviewers to answer important emerging questions about the efficacy of behavioral dietary interventions.
Attention to a number of basic study design and measurement issues would greatly improve the available literature in the field of behavioral dietary change that serves as the basis for evidence reviews. A certain amount of flexibility in these areas is important to facilitate inclusion of a broader variety of intervention strategies, but most of these recommendations should be achievable with nearly any intervention approach.
All intervention evaluation studies should include a control or at least a comparison group. When possible, group assignment should be random, and both baseline and follow-up data should be collected for both groups. A legitimate concern among intervention researchers is the need to keep control group participants adequately invested in the study so that they are willing to complete follow-up data collection. For individual-level studies, this can be accomplished by close communication with participants and incentives to stay involved. For community-level intervention research, a delayed intervention approach, or "interim" nondiet interventions during the study period, are often helpful in maintaining involvement of the control group. Because we were interested in the issue of intensity of intervention experience by control groups, we conducted a set of analyses looking at whether the differences in change in dietary behavior were larger with studies that used either strict "usual care" or a very minimal intervention for the control group. Our results did not support an association between intervention success and intensity of the control group (data not shown).
Ideally, all dietary assessment measures and lengths of follow-up should be standardized to facilitate comparisons across studies. Practically, studies addressing dietary interventions to reduce cancer risk should measure fruit and vegetable intake based on total servings per day and assess dietary fat intake based on grams of total fat and saturated fat; they should also evaluate both types of intake as a percentage of total calories. Recognizing the need for cultural adaptation of dietary assessment instruments and the biases inherent in self-report measures, it is critical that investigators ensure the validity of their instruments and methods relative to the study population. Other dietary measures that may facilitate the intervention or provide more behaviorally oriented data (such as food preparation practices) are encouraged, but they should be accompanied by more standardized measures to facilitate broader comparisons. The timing of follow-up measures is largely dependent on the intervention length and design. We strongly encourage that one data collection point follow immediately after the intervention period. For cross-study comparison purposes, standardized follow-up periods such as 3 months, 6 months, 1 year, and 2 years should be encouraged.
Even a very well-designed and -conducted study will be excluded from an evidence review if a few basic data reporting guidelines are not followed. At a minimum, all manuscripts should include the statistics needed for meta-analysis. This includes the mean and standard error or standard deviation for all outcome measures as well as actual p values (not just significance cut-points) for both significant and nonsignificant findings. Journal editors could facilitate this process by setting similar criteria for review. Other seemingly basic information that is often omitted from manuscripts includes complete information about sample size and loss to follow-up at each measurement period.
Finally, for the efficacy of an intervention to be evaluated, it must be adequately described. Intervention researchers face the challenge of condensing the description of very complex and multifaceted programs into the tight page limitations of most journals. One solution is to describe the intervention more comprehensively in a baseline or methods paper, but publishing this kind of paper can be difficult. Based on our sense of the literature and on the requirements for a rigorous evidence review process, the intervention elements that we believe are critical to include are information needed to assess generalizability (e.g., the recruitment pool), response rates for individuals and sites (e.g., schools, worksites), the elements of intervention intensity (number of contacts or exposures, delivery channels, length of active intervention period, environmental exposures or manipulations), title and training of individuals involved with intervention delivery, the specific behavioral theories used and how they are applied to the intervention, and the existence and extent of ongoing reinforcement or maintenance interventions.
An evidence review provides an excellent opportunity to clarify the existing data on a particular topic and to identify areas of need for future research. Recognizing the increased role of rigorous evidence-based reviews in synthesizing the literature on any particular topic, we believe that researchers engaged in this process should also offer recommendations for research methodology and reporting that can facilitate and enhance future such reviews. Behavioral dietary interventions for cancer risk reduction are highly diverse in terms of intervention approach, study design, data collection, analysis, and reporting. Randomized controlled trials evaluating theory-based interventions are relatively new to this literature. The availability of biologic markers that can be used to validate self-reported dietary change, particularly in the area of cancer, is likely to increase rapidly in the next several years. Therefore, we anticipate that future evidence-based reviews and updates in the area of behavioral dietary change will continue to offer significant insight into this rapidly developing area of research.
This study was supported by Contract No. 290-97-0011 from the Agency for Healthcare Research and Quality (AHRQ, formerly Agency for Health Care Policy and Research). This evidence report was produced in response to an RFA from AHCPR (now AHRQ) made available for bidding to all EPCs: RFP AHCPR-97-0001. We acknowledge the assistance of Jacqueline Besteman, JD, MA, the AHRQ Task Order Officer for the Evidence-based Practice Center Program, and Kate Rickard, MPA, the AHRQ Task Order Officer for this project.
The Technical Expert Advisory Group (TEAG) played an integral and active role in shaping and producing this evidence report.
In addition, the RTI/UNC EPC wishes to thank its Scientific Advisers, Lenore Arab, PhD, Marci Campbell, PhD, RD, and Thomas Keyserling, MD, MPH, for providing their expertise throughout the project.
The investigators appreciate the time and assistance of the clinical and methods data abstractors. The clinical data abstractors were Semra Aytur, MPH, Kerry-Ann daCosta, PhD, Denise D. Dickinson, MPH, Alyssa Ghiradelli, MPH, RD, Christine S. Hardy, RD, and Hugh C. Law, RD. The methods abstractor team were Tracy L. Bouchard-Cyr, MSPH, Nancy A. Davis, MSHE, MPH, Ho-Jui Tung, MPH, Kimberly Truesdale, MSPH, and Carole Toselli, MD. The expertise of Lynn Whitener, DrPH, MSLS, and Susan Tolleson-Rinehart, PhD, as well as the administrative assistance of Donna Curasi and Joan Kavanaugh, were also fundamental to the data collection and dissemination phases of the project.
We thank the following individuals from the University of North Carolina at Chapel Hill: Gordon DeFriese, PhD, Co-Director of the RTI-UNC Evidence-based Practice Center, Timothy S. Carey, MD, MPH, Scientific Adviser to the RTI-UNC Evidence-based Practice Center, and the RTI/UNC EPC Executive Scientists, Steven Zeisel, MD, PhD and Michael S. O'Malley, PhD.
We thank Russell Glasgow, PhD, and Ralph Coates, PhD, for providing additional data needed for the meta-analysis; in turn, we appreciate the time and effort that Norma Gavin, PhD, Christopher Wiesen, PhD, and Victor Hasselblad, PhD, spent designing the meta-analysis.
Finally, we are grateful for the guidance and assistance we received from Linda Lux, MPA, Nancy Berkman, PhD, Anjolie Idicula, BA, and Suzanne L. West, PhD, in preparing this report, Linda Fonville for her outstanding wordprocessing support, and Nicole Walker, Richard Strowd, JD, and Tim Weinzapfel, CACM, for their exceptional contracting support.
We gratefully acknowledge the substantial involvement of and assistance from the technical expert advisory group (TEAG). TEAG members are listed at the end of this Appendix. The TEAG served many roles in this project. Its reason for existence was, in essence, to (1) advance AHRQ's broader goals of forging and sustaining partnerships in the health sector as well as the consumer and private sectors, and (2) meet the needs of an expansive array of potential customers and users of its products. In sum, the TEAG was both a substantive resource and a "sounding board" throughout the study. It was also the body from which expertise was formally sought at several junctures.
The TEAG was composed of experts in a field relating to nutrition or behavior with respect to cancer risk. They included (1) technical/clinical experts, (2) an educated representative of the population at large, and (3) a technical expert from an organization whose mission concerns the interest and perspectives of patients and consumers. In sum, the eight members of the TEAG were one clinician, six technical experts, one representative of an organizational perspective, and one representative of potential user groups. The final decision about TEAG membership was based on candidates' availability for scheduled conference calls, availability to provide input, willingness to review materials, and ability to give advice and assistance within a short turnaround time. All members had to be approved by the AHRQ Task Order Officer.
The RTI-UNC EPC team solicited the views of TEAG members from the beginning of the project. Among other issues, TEAG members provided insights into and reactions to key clinical questions, input to the literature review process by ensuring that we included all known published research meeting our inclusion criteria, review of our data abstraction forms, and comments on our approach to the meta-analysis.
In keeping with AHRQ's standards for employing a multidisciplinary approach to the development of evidence reports, we called on our TEAG at key points during this task. First, the group was asked to comment on the literature synthesis and to give us feedback on our overall plans at that stage of the analysis, which included approaches to developing evidence tables. Second, the TEAG was consulted on the feasibility of performing a meta-analysis on fruit and vegetable literature. Last, the TEAG provided input on analysis strategies as well as input on the dietary fat meta-analysis.
Cheryl L. Achterberg, PhD
Dean, The Schreyer Honors College 214 Willard Building The Pennsylvania State University University Park, PA 16802 (814) 863-2635 fax (814) 863-8688 agy@psu.edu
Karen Glanz, PhD, MPH
Professor, Cancer Prevention and Control Program University of Hawaii Cancer Research Center of Hawaii 1236 Lauhala Street, Suite 406 Honolulu, HI 96813 (808) 586-3076 fax (808) 586-3077 kglanz@hawaii.edu
Tom Baranowski, PhD
Professor of Behavioral Nutrition Children's Nutrition Research Center Department of Pediatrics Baylor College of Medicine 1100 Bates Houston, TX 77030 (713) 798-6636 tbaranow@bcm.tmc.edu
Susan J. Curry, PhD
Group Health Cooperative of Puget Sound Director, Center for Health Studies 1730 Minor Avenue, Suite 1600 Seattle, WA 98101 (206) 287-2873 fax (206) 287-2871 Curry.s@ghc.org
Russell E. Glasgow, PhD
AMC Cancer Research Center 11716 98th Place SW Vashon, WA 98070 (206) 567-5915 (303) 239-3341 fax (303) 239-3500 glasgowr@amc.org
Shiriki Kumanyika, PhD, MPH
Associate Dean for Health Promotion and Disease Prevention Professor of Epidemiology University of Pennsylvania School of Medicine Center for Clinical Epidemiology and Biostatistics 8th floor Blockley Hall 423 Guardian Drive Philadelphia, PA 19104-6021 (215) 898-2629 fax (215) 573-5315 skumanyi@cceb.med.upenn.edu
Rev. Joseph C. Paige, EdM, MDiv, EdD
Professor, Interim Provost Shaw Divinity School PO Box 27924 Raleigh, NC 27611 (919) 833-4809
An important first step in the identification of potential peer reviewers was to determine the appropriate constituencies from which our reviewers should be approached. The categories that were determined and the number of reviewers who accepted our invitation to serve as peer reviewers for this report are (1) scientific experts (including three to four peer reviewers) and (2) experts from health-related organizations or societies (including six peer reviewers).
We believe that these two categories represent the full range of health care experts, users, and patient groups that should be involved in reviewing this evidence report on the efficacy of behavioral dietary intervention. The specific peer reviewers are listed at the end of this appendix.
Our peer reviewer panel also includes the eight members of the TEAG because they played a major role throughout the project in conceptualizing the work and reviewing materials; moreover, because they are active professionals in the field, the RTI/UNC EPC believed that their comments at this stage would be very valuable. The peer reviewers who were not members of the TEAG were identified by issuing an invitation to the organization's executive officer/director (e.g., president, CEO) or to a public sector agency head asking them to nominate a peer reviewer and by soliciting nominations from the TEAG. A preliminary (and longer) list of organizations, agencies, or individuals was submitted to the AHRQ Task Order Officer for this project for review, comment, and approval. We then contacted all potential peer reviewers to determine their willingness to serve as peer reviewers, alerting them to the fact that this service would require them to prepare formal written reviews according to the checklist developed for this evidence report. Their comments and suggestions formed the basis of our revisions to the evidence report.
Sharon K. Davis, MEd, MPA, PhD
Associate Professor and Director Morehouse School of Medicine Ethnicity, Policy, and Social Epidemiology Research Program MRC-248 Atlanta, GA 30310-1495 (404) 752-1627 fax (404) 752-1154 skdavis@msm.edu
C. Tracey Orleans, PhD
Senior Scientist Robert Wood Johnson Foundation College Road East PO Box 2316 Princeton, NJ 08543 (609) 243-5962 fax (609) 987-8746 cto@rwjf.org
Kathleen Fairfield, MD, MS
Instructor in Medicine Harvard Medical School Lilly 330, Division of General Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue Boston, MA 02215 (617) 667-3454 fax (617) 667-2751 kfairfie@caregroup.harvard.edu
Colleen Doyle, MS, RD
Director, Nutrition and Physical Activity American Cancer Society 1599 Clifton Road, NE Atlanta, GA 30329 (404) 329-7575 fax (404) 248-1780 Cdoyle@Cancer.org
Leslie Lytle, PhD, RD
American Dietetic Association Associate Professor of Epidemiology University of Minnesota Epidemiology, WBOB 1300 South 2nd Street Minneapolis, MN 55454 (612) 624-1818 fax (612) 624-0315 lalytle@tc.umn.edu
Cheryl Rock, PhD
Society of Behavioral Medicine Associate Professor, Department of Family and Preventive Medicine Cancer Prevention and Control Program University of California San Diego 9500 Gilman Drive, Department 0901 La Jolla, CA 92093-0901 (858) 822-1126 fax (858) 822-1497 clrock@ucsd.edu
Arthur Hadley, MD
American College of Preventive Medicine 11777 Katy Freeway Houston, TX 77079 (281) 597-1010 fax (281) 597-0015 ahadley@wt.net
| A. Study Identification |
|---|
| 1. Review (circle one): [a] abstractor rating [b] reconciled rating |
| 2a. Abstraction date: __ __/__ __ /1999 |
| 2b. Reconciliation date: __ __/ __ __/1999 |
| 3a. Article ID no.: __ __ __ __ |
| 3b. Abstractor initials: __ __ __ |
| 4a. Authors(last names of first three authors): |
| 4b. Year of article: 19 __ __ |
| 5. Name for the study (if provided):(e.g., PRAISE, Women's Health Initiative) |
| 6. Provide citations (last name of first three authors and year) for any supporting articles (i.e., multiplearticles from the same study/author that are necessary to report pieces of information missing from the current article): |
| Items requiring resolution: (please list the item number and a brief description of any problematic or unclear items that require further attention by senior project staff) |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| B. | Screening Questions | |
| All articles will need to be screened for potential exclusion. Several articles are likely to be excluded because they do not meet our criteria. Examine the article for the following exclusion criteria, and circle "yes" for any of the following that apply:[here and in several questions below, the reader is told to circle "yes" but that's not possible. Does it mean to circle "the number in the "yes" column"? Or what? (And what does the number in the "yes" column mean?] | Yes | |
| 7. | No nutrition intervention (e.g., epidemiologic study with no intervention)Note: any dietary counseling or nutrition advice counts as an intervention | 1 |
| 8. | Review article without primary data | 1 |
| 9. | No measures of dietary fat intake or fruit and/or vegetable intake (see guidelines) | 1 |
| If "yes" was circled for #9, does the article include: | ||
| a. Only behavioral mediators? (e.g., dietary knowledge, attitudes, beliefs, or intentions) | 1 | |
| b. Only biochemical indicators of diet? (e.g., serum/blood lipids [cholesterol], ascorbic acid, carotenoids) | 1 | |
| 10. | Baseline data study only (see guidelines) | 1 |
| 11. | Limited applicability | |
| a. Institutionalized population (Include studies on military personnel. Exclude studies on prisoners, nursing home residents, and other non-free-living individuals.) | 1 | |
| b. Studies where diet was externally controlled (Exclude the study if meals were provided/prescribed for subjects.) | 1 | |
| c. Study outside North America, Australia, or Europe | 1 | |
| d. Study was on infants or reproductive health | 1 | |
| e. Other (specify): | 1 | |
| 12. | The study did not include a control/comparison group (see guidelines) | 1 |
| 13. | The total sample size at follow-up was fewer than 40 subjects | 1 |
| If the article is excluded for any of these reasons, do not continue | ||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| C. | Intervention Characteristics (see guidelines) | Yes |
|---|---|---|
| 14. | Theoretical frameworks used for intervention(s) | |
| (Circle "yes" for all that apply): | ||
| a. Fishbein/Ajzen (theory of reasoned action)........................... | 1 | |
| b. Health belief model................................................................. | 1 | |
| c. Social cognitive theory/social learning theory....................... | 1 | |
| d. Stages of change (transtheoretical theory).......................... | 1 | |
| e. Enabling/Access Enhancing (PRECEDE model).................. | 1 | |
| f. Other theory (specify):........................................................... | 1 | |
| g. No theory reported................................................................. | 8 | |
| 15. | Type of intervention (see guidelines)(Circle "yes" for all types of interventions that apply to the study. If more than one intervention group is included, circle intervention characteristics for each group separately.) | Yes |
| a1. Individual-directed intervention | 1 | |
| a2. Family-directed intervention | 1 | |
| If the intervention is an individual or family intervention, circle all settings that apply | ||
| a1. Healthcare.................................................................................. | ||
| a2. Worksite............................................................................. | 1 | |
| a3. School................................................................................... | 1 | |
| a4. Point-of-purchase............................................................................. | 1 | |
| a5. Community............................................................................ | 1 | |
| a6 Home..................................................................................... | 1 | |
| a7. Other (briefly describe):....................................................................... | 1 | |
| b. System/physician directed | 1 | |
| c. Access-enhancing intervention | 1 | |
| d. Policy intervention | 1 | |
| e. Environmental intervention | 1 | |
| f. Media campaign | 1 | |
| If the intervention is a media intervention, circle all forms[not clear what is to be circled-the form itself or the "yes" column number?]that apply | ||
| f1. Television......................................................................................... | 1 | |
| f2. Radio................................................................................................ | 1 | |
| f3. Newspaper...................................................................................... | 1 | |
| f4. Other (specify:)................................................................................. | 1 | |
| g. Community intervention | 1 | |
| h. Tailored computerized intervention or emerging technologies | 1 | |
| 16. | List the primary setting of the intervention (from #15 above, e.g., healthcare setting, school setting, environmental, media)(see guidelines) | |
| 17. | Describe the components of the intervention received by the intervention group(s) and control group (e.g., classes, types of instructional materials, one-on-one counseling, reducing prices of nutrition foods, offering low-fat choices in a cafeteria, exercise groups, stress management therapy)(see guidelines) | |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| Group | Nutrition components | Collateral interventions |
|---|---|---|
| Intervention group: | ||
| Intervention group 2: (if applicable) | ||
| Control group: |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| 18. | Who delivered the intervention received by the intervention group(s) and control group (e.g., dietician, physician, regular classroom teacher, clinical psychologist)(see guidelines) | |
| Intervention group: | ||
| Intervention group 2(if applicable): | ||
| Control group(if the control group received any intervention): | ||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| 19. | Briefly describe the main nutrition message of the intervention (e.g., increase fruit and vegetable intake, decrease fat intake, follow a vegetarian diet)(see guidelines) | |
| Intervention group: | ||
| Intervention group 2(if applicable): | ||
| Control group(if the control group received any intervention): | ||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| C. Intervention Characteristics (continued) | |||
| 20. | Special features of the intervention | Yes | No |
| a. Did the intervention include a social support component (such as support groups either during the intervention or long-term)?If yes, briefly describe: | 1 | 0 | |
| b. Did the intervention include a family intervention component (i.e., including the family/spouse/parent of the primary target of the intervention)?If yes, briefly describe: | 1 | 0 | |
| c. Was the intervention specifically designed to provide individually tailored guidance/messages? (see guidelines)If yes, briefly describe: | 1 | 0 | |
| d. Was the intervention designed to be culturally/ethnically specific? If yes, briefly describe: | 1 | 0 | |
| e. Does the article report data on the cost of the intervention? | 1 | 0 | |
| 21. | Describe the duration of intervention Include length, number of exposures, and any other information related to intensity).(see guidelines) | ||
| 22. | Did the study report that a maintenance component was included in the intervention? (see guidelines) | Yes | No |
| 1 | 0 | ||
| If yes, briefly describe the format(e.g., support group, phone call reminders), theintensity(i.e., number of exposures), and thedurationof the maintenance component: | |||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| D. | Population Characteristics(Indicate whether the study included, was restricted to, orconducted separate analysis for the following subgroups) | Included | Restricted to | Separate Analyses for |
| 23. | Sex: | |||
| a. Males.................................................................... | 1 | 2 | 3 | |
| b. Females................................................................. | 1 | 2 | 3 | |
| c. Sex not reported .................................................... | 8 | 8 | 8 | |
| 24. | Age of participants | |||
| a. (write in age range or other age descriptor included in the study): ______________________________ | 1 | 2 | 3 | |
| b. Age not reported.................................................. | 8 | 8 | 8 | |
| 25. | Racial/ethnic characteristics (circle for all ethnicities included) | |||
| a. Black/African American....................................... | 1 | 2 | 3 | |
| b. Hispanic or Latino................................................. | 1 | 2 | 3 | |
| c. Non-Hispanic Whites........................................... | 1 | 2 | 3 | |
| d. Asian/Asian Americans........................................ | 1 | 2 | 3 | |
| e. Native Americans................................................. | 1 | 2 | 3 | |
| f. Other (specify)..................................................... | 1 | 2 | 3 | |
| g. Race/ethnicity not reported.................................. | 8 | 8 | 8 | |
| 26. | Low-income populations | |||
| a. (Circle if the study included, was restricted to, or conducted separate analyses for low-income populations, and briefly describe the population (e.g., Food Stamp participants, blue collar workers)_________________________________ | 1 | 2 | 3 | |
| b. Income not reported............................................ | 8 | 8 | 8 | |
| 27. | Country | |||
| a. United States........................................................ | 1 | 2 | 3 | |
| b. Canada................................................................. | 1 | 2 | 3 | |
| c. Caribbean (specify country)................................ | 1 | 2 | 3 | |
| d. Mexico.................................................................. | 1 | 2 | 3 | |
| e. Europe (specify country)..................................... | 1 | 2 | 3 | |
| f. Australia or New Zealand..................................... | 1 | 2 | 3 | |
| g. Other (specify).................................................... | 1 | 2 | 3 | |
| h. Country not reported............................................. | 8 | 8 | 8 | |
| Note: Remember that studies conducted outside North America, Australia, and Europe are excluded. | ||||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| D. | Population Characteristics (continued)(Indicate whether the study included, was restricted to, orconducted separate analysis for the following subgroups) | Included | Restricted to | Separate Analyses for |
| 28. | Risk status (circle for all risk groups included in the study)(see guidelines) | |||
| a. General population......................................................... | 1 | 2 | 3 | |
| b. High risk (e.g., undergoing treatment , family history of disease) | 1 | 2 | 3 | |
| b1. If high risk, specify type of condition: | ||||
| a. Cancer............................................................. | 1 | 2 | 3 | |
| b. CVD................................................................. | 1 | 2 | 3 | |
| c. NIDDM............................................................. | 1 | 2 | 3 | |
| d. Other (specify)..................................................... | 1 | 2 | 3 | |
| b2. If high risk, how was risk determined? | ||||
| a. Family history of the condition | 1 | 2 | 3 | |
| b. EARLY risk factor for disease identified specify risk factor: _________________________ | 1 | 2 | 3 | |
| c. Subjects currently have condition | 1 | 2 | 3 | |
| d. Subjects previously had condition | 1 | 2 | 3 | |
| e. Other (briefly describe):________________________________ | 1 | 2 | 3 | |
| f. Not reported | 8 | 8 | 8 | |
| c. Risk status not reported........................................... | 8 | 8 | 8 |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| E. Design and Analysis Characteristics | ||
| 29. | Research design (see guidelines)(circle all that apply) | Yes |
| a. Random assignment of individuals into intervention or control......................... | 1 | |
| b. Random assignment of units (e.g., clinics, schools, communities) into intervention or control........................................................................................ | 1 | |
| c. Other randomized design (briefly describe):............................................................. | 1 | |
| d. ["d" not aligned] Non-equivalent comparison group design (briefly describe):.............................. | 1 | |
| e. Not reported...................................................................................................... | 8 | |
| 30. | Unit of analysis in the study (circle all that apply) | Yes |
| a. Individuals.......................................................................................................... | 1 | |
| b. Units (e.g., schools, worksites) (briefly describe).............................................. | 1 | |
| 31. | Length of time from baseline until: (e.g., immediate, ____days/weeks/months, other, NR) | |
| a. Follow-up 1: | ||
| b. Follow-up 2 (if applicable): | ||
| c. Follow-up 3 (if applicable): | ||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| 32. | Recruitment and retention rates (Note: If information not provided, write "NR" in the appropriate cell. If information not applicable, write "n/a" in the appropriate cell.)(see guidelines) | Intv. Group | Intv. Group 2 (if appl) | Control Group |
| a. Participation rate (% of subjects originally assigned to groups who completed participation in the intervention) | ||||
| b. Provide the % (or number) of subjects who were retained in the study at each follow-up period | ||||
| b1. Number of subjects at baseline | ||||
| b2. Number (or %) of subjects at follow-up 1 | ||||
| b3. Number (or %) of subjects at follow-up 2 | ||||
| b4. Number (or %) of subjects at follow-up 3 |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| 33. | Describe any weaknesses in the study design or population characteristics that you noticed (i.e., factors that limit generalizability, significant differences between the intervention and control groups at baseline, other problems, etc.) |
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| F. Study Outcomes (see guidelines) | ||||
| 34. | Summary of measurement findings: Follow priorities listed in guidelines and indicate whether significant effects were found for the following outcomes. Detailed results for outcomes a-d will be abstracted in detail in the following sections. The results for outcomes e- f will only be summarized in this table. | Sig. | n.s. | |
| a. | Fruit and/or vegetable intake | |||
| Measure 1 (briefly list outcome): | 1 | 2 | ||
| b. | Fat intake | |||
| Measure 1 (briefly list outcome): | 1 | 2 | ||
| Measure 2 (briefly list outcome): | 1 | 2 | ||
| Measure 3 (briefly list outcome): | 1 | 2 | ||
| c. | Biochemical indicators | |||
| Measure 1 (briefly list outcome):[not aligned] | 1 | 2 | ||
| Measure 2 (briefly list outcome): | 1 | 2 | ||
| Measure 3 (briefly list outcome): | 1 | 2 | ||
| d. | Other dietary outcomes | |||
| Measure 1 (briefly list outcome): | 1 | 2 | ||
| Measure 2 (briefly list outcome): | 1 | 2 | ||
| Measure 3 (briefly list outcome): | 1 | 2 | ||
| e. | Behavioral mediators | |||
| Dietary knowledge (briefly list outcome): | 1 | 2 | ||
| Dietary attitudes (briefly list outcome): | 1 | 2 | ||
| Self-efficacy related to diet (briefly list outcome): | 1 | 2 | ||
| Stages of change related to diet (briefly list outcome): | 1 | 2 | ||
| Other behavioral mediators (briefly list outcome): | 1 | 2 | ||
| f. | Physical activity (briefly list outcome): | 1 | 2 | |
| g. | Other physiologic outcomes (associated with CVD or cancer) explored | |||
| Measure 1 (briefly list outcome): | 1 | 2 | ||
| Measure 2 (briefly list outcome): | 1 | 2 | ||
| Measure 3 (briefly list outcome): | 1 | 2 | ||
| Measure 4 (briefly list outcome): | 1 | 2 | ||
| Measure 5 (briefly list outcome): | 1 | 2 | ||
Instructions for abstracting statistical results: The following pages elicit information about the actual statistics used to determine the efficacy of the intervention at changing dietary outcomes. Outcomes a-d (from the table above) will be abstracted in detail. Up to three measures may be abstracted for each outcome. Only the following measures should be abstracted (if reported).
Fruits and vegetables: Only total intake of fruits and vegetables
Dietary fat: Only total fat intake, saturated fat intake, index of fat consumption, index of fat-related behaviors, individual fat-related behaviors, or individual foods used as proxy indicators of fat intake. Only abstract up to three of these measures.
Biochemical indicators: Only total cholesterol, low-density lipoprotein (LDL) cholesterol, or total carotenoids
Other dietary outcomes: Only calcium intake (or dairy intake) or fiber intake (or whole foods used as proxy indicators of fiber)
| F. Study Outcomes (continued) | ||||||||||||||||||
| 35a. | Fruit and vegetable intake: | 0 Not Reported 1 Reported (briefly list outcome): | ||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||
| 2. | Did the fruit and vegetable outcome include potatoes? (specify if only some forms of potatoes were included)
| |||||||||||||||||
| 3. | Name of instrument used to collect fruit and vegetable information: | |||||||||||||||||
| 4. | Type of instrument: (check all that apply)
| |||||||||||||||||
| 5. | Validity of dietary instrument
| |||||||||||||||||
| 6. | Reliability (or internal consistency) of dietary instrument
| |||||||||||||||||
| 7. | Are the statistical results presented for: | Yes | ||||||||||||||||
| a. The entire sample (or intervention vs. control) | 1 | |||||||||||||||||
| b. A subsample (e.g., males and females separately) | 1 | |||||||||||||||||
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||||||||||||||||||
| Findings for Fruit and Vegetable Intake | ||||||||||||||||||
| 8. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||||||||||||||||||
| 9. Statistics (see guidelines for instructions) | ||||||||||||||||||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in F&V intake at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1[here and later, what does superscript 1 refer to?] | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fruit and vegetable intake at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fruit and vegetable intake at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 10. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||||||||
| 36a. | Fat intake or fat-related behavior -- Measure #1 | 0 Not Reported 1 Reported (briefly list outcome): | ||||||||||||||||||||||
| 1. | Units for outcome
(circle one,
and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||||
| 2. | Name of instrument used to collect dietary fat information: | |||||||||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument:
| |||||||||||||||||||||||
| Findings for Fat Intake -- Measure 1 | ||
| 6. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 7. | Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | |
| 8. | Statistics (see guidelines for instruction) | |
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2(if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||||||||
| 36b. | Fat Intake or Fat-Related Behavior -- Measure 2 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||||
| Note: Complete items 2-5 only if different from Dietary Fat Measure 1 | ||||||||||||||||||||||||
| 2. | Name of instrument used to collect dietary fat information: | |||||||||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument:
| |||||||||||||||||||||||
| Findings for Fat Intake -- Measure 2 | ||
| 6. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 7. | Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | |
| 8. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at baseline. Provide the sample size, statistic reported, and p value.Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 1. Provide the sample size, statistic reported, and p value.Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 2. Provide the sample size, statistic reported, and p value.Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes | ||||||||||||||||||||||||
| 36c. | Fat Intake or Fat-Related Behavior -- Measure 3 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||||
| Note: Complete items 2-5 only if different from Dietary Fat Measure 1 | ||||||||||||||||||||||||
| 2. | Name of instrument used to collect dietary fat information: | |||||||||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument:
| |||||||||||||||||||||||
| Findings for Fat Intake -- Measure 3 | ||
| 6. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 7. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 8. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in fat intake at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||||||
| 37a. | Biochemical Indicators of Dietary Intake -- Measure 1 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||
| 2. | Name of test or brief description of method used to measure outcome: | |||||||||||||||||||||
| 3. | Validity of biochemical procedure
| |||||||||||||||||||||
| 4. | a. Relationship to fruit and vegetable outcome
| |||||||||||||||||||||
| Findings for Biochemical Indicator -- Measure 1 | ||
| 5. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 6. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 7. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 8. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||||||
| 37b. | Biochemical Indicators of Dietary Intake -- Measure 2 | 0 Not Reported
1 Reported(briefly list outcome): | ||||||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||
| 2. | Name of test or brief description of method used to measure outcome: | |||||||||||||||||||||
| 3. | Validity of biochemical procedure
| |||||||||||||||||||||
| 4. | a. Relationship to fruit and vegetable outcome
| |||||||||||||||||||||
| Findings for Biochemical Indicator -- Measure 2 | ||
| 5. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 6. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 7. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Intervention Group(s) | Control Group | |||||
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 8. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 37c. | Biochemical Indicators of Dietary Intake -- Measure 3 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||||||
| 2. | Name of test or brief description of method used to measure outcome: | |||||||||||||||||||||
| 3. | Validity of biochemical procedure
| |||||||||||||||||||||
| 4. | a. Relationship to fruit and vegetable outcome
| |||||||||||||||||||||
| Findings for Biochemical Indicator -- Measure 3 | ||
| 5. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 6. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 7. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 8. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38a. | Other Dietary Outcome -- Measure 1 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||
| 2. | Name of instrument used to collect dietary information: | |||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument
| |||||||||||||||||
| Findings for Other Dietary Outcome -- Measure 1 | ||
|---|---|---|
| 6. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 7. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 8. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38b. | Other Dietary Outcome -- Measure 2 | 0 Not Reported 1 Reported(Briefly list outcome): | ||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||
| Note: Complete items 2-5 only if different from Other Dietary Outcome -- Measure 1 | ||||||||||||||||||
| 2. | Name of instrument used to collect dietary information: | |||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument
| |||||||||||||||||
| Findings for Other Dietary Outcome -- Measure 2 | ||||||
|---|---|---|---|---|---|---|
| 6. | Are the statistical results presented for: | Yes | ||||
| a. The entire sample (or intervention vs. control) | 1 | |||||
| b. A subsample (e.g., males and females separately) | 1 | |||||
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||||||
| 7. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||||||
| 8. Statistics (see guidelines for instruction) | ||||||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1 _____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported) _____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| F. Study Outcomes (continued) | ||||||||||||||||||
| 38c. | Other Dietary Outcome -- Measure 3 | 0 Not Reported 1 Reported(briefly list outcome): | ||||||||||||||||
| 1. | Units for outcome (circle one, and indicate if the variable was logged/back-transformed):
| |||||||||||||||||
| Note: Complete items 2-5 only if different from Other Dietary Outcome -- Measure 1 | ||||||||||||||||||
| 2. | Name of instrument used to collect dietary information: | |||||||||||||||||
| 3. | Type of instrument (check all that apply):
| |||||||||||||||||
| 4. | Validity of dietary instrument
| |||||||||||||||||
| 5. | Reliability (or internal consistency) of dietary instrument:
| |||||||||||||||||
| Findings for Other Dietary Outcome -- Measure 3 | ||
|---|---|---|
| 6. | Are the statistical results presented for: | Yes |
| a. The entire sample (or intervention vs. control) | 1 | |
| b. A subsample (e.g., males and females separately) | 1 | |
| If results are presented for a subsample, briefly describe, and obtain additional forms to report the results for another subgroup if necessary: | ||
| 7. Briefly describe the statistical procedure used to determine whether the intervention had a significant effect on dietary intake (e.g., compared means using t-tests, compared proportions using chi-square, used a "treatment" by "time" interaction term in a regression model) | ||
| 8. Statistics (see guidelines for instruction) | ||
| Intervention Group(s) | Control Group | |||||
|---|---|---|---|---|---|---|
| Baseline differences b/w groups: | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at baseline. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., s.e, %) and the numerical value. If you present means, include s.d. or s.e. (specify which). If you present odds ratios, include 95% CI. | ||||||
| Differences at follow-up 1_____weeks after intervention1 | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 1. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| Differences at follow-up 2 (if reported)_____weeks after intervention | n | statistic | p | n | statistic | p |
| In the boxes to the right, report any statistics used to determine whether the intervention group(s) and control group differed in the measure at follow-up 2. Provide the sample size, statistic reported, and p value. Write the name of the statistic (i.e., mean, s.d., OR) and the numerical value. | ||||||
| 9. Other findings (see guidelines) (e.g., a third follow-up period was considered) or notes: | ||||||
| Category I. Description of the study: (30 points) | Points (circle 1 for each item) | |
| 1. | Was the description of the intervention setting, components, delivery, duration, and intensity | |
| (a) Low quality (unclear, many details missing) | 0 | |
| (b) Medium quality (pretty clear, most details provided) | 5 | |
| (c) High quality (very clear, all essential details provided) | 10 | |
| 2. | Was the description of the study population (i.e., gender, ethnicity, income), recruitment strategy, and inclusion/exclusion criteria | |
| (a) Low quality (unclear, many details missing) | 0 | |
| (b) Medium quality (pretty clear, most details provided) | 5 | |
| (c) High quality (very clear, all essential details provided) | 10 | |
| 3. | Was the description of the variable measurement and statistical analysis procedure | |
| (a) Low quality (unclear, many details missing) | 0 | |
| (b) Medium quality (pretty clear, most details provided) | 5 | |
| (c) High quality (very clear, all essential details provided) | 10 | |
| Subtotal (Category I): | ||
| Category II. Quality of the study design and methodology (70 points) | Points (circle 1 for each item) | |
| 4. | Was the intervention theoretically based | |
| (a) No (include not reported) | 0 | |
| (b) Yes | 5 | |
| 5. | Was the research design | |
| (a) Non-equivalent comparison group or not reported | 0 | |
| (b) Random allocation of individuals or units to intervention vs. control group | 10 | |
| 6. | Was the sample size of the intervention group at baseline | |
| (a) Less than 50 (or not reported) | 0 | |
| (b) 50-100 | 5 | |
| (c) More than 100 | 10 | |
| 7. | Was the total duration of follow-up | |
| (a) Less than 3 months | 0 | |
| (b) 3 to 6 months | 3 | |
| (c) 6 months to 1 year | 6 | |
| (d) More than 1 year | 10 | |
| 8. | Was the loss at follow-up 1 in both the intervention and control groups | |
| (a) More than 30% (or not reported)? | 0 | |
| (b) 20-30% | 5 | |
| (c) Less than 20% | 10 | |
| Quality Rating Form (continued) | Points (circle 1 for each item) | |
| 9. | Was the dietary assessment tool clearly described and the validity of the tool specified or referenced? | |
| (a) Neither clearly described nor validity addressed (include not reported) | 0 | |
| (b) Tool was clearly described, but validity was not mentioned | 3 | |
| (c) Tool was clearly described and validity was specified/referenced | 5 | |
| 10. | Were changes in biochemical outcomes explored in the study | |
| (a) No (include not reported) | 0 | |
| (b) Yes | 5 | |
| 11. | Were analysts blind to the assignment of intervention and control groups? | |
| (a) No (include not reported) | 0 | |
| (b) Yes | 5 | |
| 12. | Is the generalizability (i.e., applicability to the general population) of the results | |
| (a) Low (study sample not representative of general population, or intervention extremely unrealistic/expensive) | 0 | |
| (b) Medium (study sample fairly representative) | 5 | |
| (c) High (study sample representative of general population) | 10 | |
| Subtotal (Category II): | ||
| Total Quality Points: | ||
The Evidence Tables are organized by intervention setting and are presented in four sections: school, health care (this section includes interventions conducted in a health care setting and interventions for which subjects were recruited from healthcare settings), worksite, and community/other (this section includes interventions conducted in homes, churches, grocery stores, and communities). Abbreviations used in the Evidence Tables, and in this description of the Evidence Tables, are defined in the Glossary that follows this description.
This column provides the RTI/UNC EPC identification number for each article, the first three authors' last names, and the year of publication. In the case of multiple articles reporting the results from the same study, the results are presented in one entry, with all supporting articles cited in the "author/year" column. If applicable, the name of the study/intervention is also provided in this column.
The following information is provided for all articles in the tables:
Setting:
a description of the setting of the intervention (with the site/subject recruitment strategy provided in parentheses)
Gender:
a description of whether the study included males, females, or both
Age:
a description of the age range of subjects (if provided) or the mean age of subjects (if the range is not provided) at baseline
Race:
a description of all racial/ethnic groups included in the study
Risk:
an indication of whether the sample was at risk for (or diagnosed with) a particular condition (e.g., "CVD risk") or whether the subjects were derived from the general population
A brief description of the study design is provided at the top of the column (e.g., "RCT"). Then a longer description of how sites/subjects were assigned to treatment conditions, the number of arms of the study, and other relevant design characteristics (e.g., matching, use of subsamples) are provided.
This column includes the following information for all articles:
Participation rate:
the percentage of eligible subjects who either participated in the study or provided baseline data
Baseline sample size:
the number of subjects who provided baseline data (which is not necessarily the sample size used in the statistical analyses)
Follow-up sample size:
the number of subjects who provided data at each follow-up point of the study (which is not necessarily the sample size used in the statistical analyses)
Retention rate:
the percentage of subjects who provided baseline data and also provided follow-up data
Comparison of drop-outs:
statistically significant differences between subjects lost to follow-up (or not completing the intervention) and subjects who remained in the study
This column describes the intervention received by the intervention group and the conditions experienced by the control group. If more then one intervention group was included in the study, this is indicated in parentheses next to "Intervention group," and the differences in the interventions received by the intervention groups are noted under the "intervention components" subheading. The following information is included for the intervention group(s):
Intervention components:
a brief description of the major features of the intervention (e.g., counseling, lectures, activities, exercise classes), including non-dietary components, reported in the article
Intervention delivery:
a list of all persons involved in the delivery of the intervention (e.g., RD, trained lay educators)
Special features:
an indication of whether the intervention included small-group learning, social-support components, family components (e.g., inviting spouses of the intervention participants to take part in activities), cultural/ethnic specificity, and individually tailored components
Nutritional message:
a brief summary of the main nutrition message taught to the participants in the intervention (e.g., decrease dietary fat, increase fruits and vegetables [F&V])
This column includes all information on the duration of the intervention reported in the article (e.g., number of contacts, frequency of contacts, length of contacts, time between contacts), broken down (when reported) into intervention components. If the intervention included a maintenance component (or follow-up activities other than data collection), a brief description of this element is provided.
The length of time until follow-up data collection (usually from baseline data collection) is provided in this column. The duration of follow-up is reported for each follow-up data collection point.
The following information is included in this column:
Variables:
a brief list of the fruit and vegetable (F&V) outcomes (and units, when reported) is included in the Evidence Tables; results for up to three relevant outcomes are reported for F&V
Other variables analyzed:
a list of any other F&V outcomes for which results are reported in the full article but are not included in the Evidence Tables
Instrument:
a description of the methodology used to measure the F&V outcomes (e.g., dietary recalls, food frequency questionnaires [FFQs]), including the number of days (if reported) for dietary recalls and food records, and the number of items (if reported) for FFQs or other questionnaires
Statistics:
a description of the statistical analysis procedures used to determine the significance of the intervention (wording closely follows the description in the article)
This column presents the statistical results for up to three relevant F&V outcomes. The outcome is in boldface type, and the statistics reported in the column are described in parentheses next to the boldfaced outcome (e.g., "mean and sd"). The notation "sd/se NR" indicates that the standard deviation (sd) or standard error (se) was not reported (NR) in the article. Relevant statistics are presented for each reported data collection point for each treatment group. The sample sizes used in the analyses are presented in parentheses or brackets for each treatment group.
Significant effects:
a brief summary of all reported p values (if the actual p value is not reported in the article, the upper limit is provided [e.g., "p < 0.05"]) for all analyses reported in the article
Intention to treat:
a brief description of the manner in which the authors addressed the "intention to treat" principle in their analyses (if reported)
The last names of the first three authors and the year of publication are reported in this column.
The following information is included in this column:
Variables:
a brief list of the dietary fat outcomes (and units, when reported) included in the Evidence Tables; results for up to three relevant outcomes are reported for fat, including total fat (either as percentage of energy from fat or in grams), saturated fat (either as percentage of energy from fat or in grams), foods used as proxy measures of fat (e.g., fried foods), and/or fat-related behaviors (e.g., trimming fat off meat, drinking skim milk)
Other variables analyzed:
a list of any other dietary fat outcomes for which results are reported in the full article but are not included in the Evidence Tables
Instrument:
a description of the methodology used to measure the fat outcomes (e.g., dietary recalls, FFQ), including the number of days (if reported) for dietary recalls and food records, and the number of items (if reported) for FFQs or other questionnaires
Statistics:
a description of the statistical analysis procedures used to determine the significance of the intervention (wording closely follows the description in the article)
This column presents the statistical results for up to three relevant dietary fat outcomes. The outcome is in boldface type, and the statistics reported in the column are described in parentheses next to the boldfaced outcome (e.g., "mean and sd"). The notation "sd/se NR" indicates that the standard deviation (se) or standard error (se) was not reported (NR) in the article. Relevant statistics are presented for each reported data collection point for each treatment group. The sample sizes used in the analyses are presented in parentheses or brackets for each treatment group.
Significant effects:
a brief summary of all reported p values (if the actual p value is not reported in the article, the upper limit is provided [e.g., "p < 0.05"]) for all analyses reported in the article
Intention to treat:
a brief description of the manner in which authors addressed the "intention to treat" principle in their analyses (if reported)
This column indicates whether a significant intervention effect was found for outcomes related to either fiber (including actual fiber intake, grain intake, wheat intake, or intake of individual foods such as breads or cereals) or calcium (including actual calcium intake, dairy intake, or intake of individual foods such as milk or cheese). The outcome is listed, and a brief indication of whether an intervention effect was reported in the article is provided.
The following information is included in this column:
Variables:
a brief list of the biochemical outcomes (and units, when reported) included in the Evidence Tables; results for up to three relevant outcomes are reported, including total cholesterol, LDL-C, and carotenoids
Other variables analyzed:
a list of any other biochemical outcomes for which results are reported in the full article but are not included in the Evidence Tables
Instrument:
a description of the methodology used to measure the biochemical outcomes, including whether the sample was obtained with the subject in the fasting state (if reported), how the sample was analyzed (if reported), and information about laboratory quality control procedures (if reported)
Statistics:
a description of the statistical analysis procedures used to determine the significance of the intervention (wording closely follows the description in the article)
This column presents the statistical results for up to three relevant biochemical outcomes. The outcome is in boldface type, and the statistics reported in the column are described in parentheses next to the boldfaced outcome (e.g., "mean and sd"). The notation "sd/se NR" indicates that the standard deviation (sd) or standard error (se) was not reported (NR) in the article. Relevant statistics are presented for each reported data collection point for each treatment group. The sample sizes used in the analyses are presented in parentheses or brackets for each treatment group.
Significant effects:
a brief summary of all reported p values (if the actual p value is not reported in the article, the upper limit is provided [e.g., " p < 0.05"]) for all analyses reported in the article
Intention to treat:
a brief description of the manner in which authors addressed the "intention to treat" principle in their analyses (if reported)
This column indicates whether a significant intervention effect was found for behavioral mediators (or antecedents) related to dietary intake (including attitudes, self-efficacy, knowledge, intentions, stage of change, perceived social support, and other relevant outcomes). The outcome is listed, and a brief indication of whether an intervention effect was reported in the article is provided.
The quality score assigned to each article is a numerical value ranging from 0 to 100. This score is based on the following factors:
the quality of the description of the intervention (including relevant details about the setting, components, delivery, duration, and intensity of the intervention)
the quality of the description of the study population, recruitment strategy, and inclusion/exclusion criteria
the quality of the description of the variable measurement and statistical analysis procedure
whether the intervention was theoretically based
whether the research design was random allocation of individuals/units to treatment groups
the sample size
the duration of follow-up
the retention rates
the description and validity of the dietary assessment tool
whether changes in biochemical outcomes were explored
whether analysts were blind to the assignment of treatment groups
the generalizability of the results (based on the representativeness of the sample and the practicality of the intervention)
~: approximately
% energy: percentage of energy (total kcals or MJ)
A: Asian American
adj: adjusted (adjusted for)
ANCOVA: analysis of covariance
ANOVA: analysis of variance
B: Black
b/w: between
base: baseline
beh: behavior
carb: carbohydrates
chol: cholesterol
chng: change
comp: comparison
con: control
CVD: cardiovascular disease
d: day
dep: dependent
diff: difference
ed: education
exerc: exercise
EFNEP: Expanded Food and Nutrition Education Program
F: female
FFQ: food frequency questionnaire
F&V: fruits and vegetables
f/u: follow-up
fam: family
g: gram
grp: group
H: Hispanic
hr (hrs): hour (hours)
IDDM: insulin-dependent diabetes mellitus
indiv: individual
info: information
intent. to treat: intention to treat
intv: intervention
LDL-C: low-density lipoprotein cholesterol
M: male
MANCOVA: multiple analysis of covariance
MANOVA: multiple analysis of variance
med: medication
mg/dL: milligrams per deciliter
MI: myocardial infarction
mo (mos): month (months)
mmol/L: millimoles per liter
µmol/L: micromoles per liter
N: Native American
NIDDM: non-insulin-dependent diabetes mellitus
n.s.: not significant
NR: not reported
nutr: nutrition
O: other race
p:s: polyunsaturated to saturated fat ratio
part: participation
pop: population
prot: protein
pts: patients
RCT: randomized controlled/clinical trial
RD: registered dietician
reg: regression
ret: retention
RN: registered nurse
sat: saturated
sd: standard deviation
se: standard error
sig: significant/significance
soc: social
TC: total cholesterol
tot tx: total treatment
unsat: unsaturated
var: variable
veg: vegetable
W: White
w/i: within
wk (wks): week (weeks)
yr (yrs): year (years)
* See the "Glossary of Abbreviations" (following the Appendices) for abbreviations used in the Evidence Tables.
Free Full text in PMC]Free Full text in PMC]Free Full text in PMC]Free Full text in PMC]Free Full text in PMC]Free Full text in PMC] [PubMed]Free Full text in PMC]Free Full text in PMC]Free Full text in PMC]
