Using Short-Term Dietary Intake Data to Address Research Questions Related to Usual Dietary Intake among Populations and Subpopulations: Assumptions, Statistical Techniques, and Considerations.

Many research questions focused on characterizing usual, or long-term average, dietary intake of populations and subpopulations rely on short-term intake data. The objective of this paper is to review key assumptions, statistical techniques, and considerations underpinning the use of short-term dietary intake data to make inference about usual dietary intake. The focus is on measurement error and strategies to mitigate its effects on estimated characteristics of population-level usual intake, with attention to relevant analytic issues such as accounting for survey design. Key assumptions are that short-term assessments are subject to random error only (i.e., unbiased for individual usual intake) and that some aspects of the error structure apply to all respondents, allowing estimation of this error structure in data sets with only a few repeat measures per person. Under these assumptions, a single 24-hour dietary recall per person can be used to estimate group mean intake; and with as little as one repeat on a subsample and with more complex statistical techniques, other characteristics of distributions of usual intake, such as percentiles, can be estimated. Related considerations include the number of days of data available, skewness of intake distributions, whether the dietary components of interest are consumed nearly daily by nearly everyone or episodically, the number of correlated dietary components of interest, time-varying nuisance effects related to day of week and season, and variance estimation and inference. Appropriate application of assumptions and recommended statistical techniques allows researchers to address a range of research questions, though it is imperative to acknowledge systematic error (bias) in short-term data and its implications for conclusions.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error.

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

Healthy Eating Index-2015 Scores Among Adults Based on Observed vs Recalled Dietary Intake.

BACKGROUND: The construct and predictive validity of the Healthy Eating Index (HEI) have been demonstrated, but how error in reported dietary intake may affect scores is unclear. OBJECTIVE: These analyses examined concordance between HEI-2015 scores based on observed vs reported intake among adults. DESIGN: Data were from two feeding studies (Food and Eating Assessment STudy, or FEAST, I and II) in which true intake was observed for three meals on 1 day. The following day, participants completed an unannounced 24-hour dietary recall. PARTICIPANTS/SETTING: FEAST I (2012) included 81 men and women, aged 20 to 70 years, living in the Washington, DC, area. FEAST II (2016) included 302 women, aged 18 years or older, with low household incomes and living in the Washington, DC, area. In FEAST I, recalls were completed independently using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24-2011) or interviewer-administered using the Automated Multiple-Pass Method. In FEAST II, recalls were completed using ASA24-2016, independently or in a small group setting with assistance. MAIN OUTCOME MEASURES: HEI-2015 scores were calculated using the population ratio method. STATISTICAL ANALYSES PERFORMED: T-tests determined whether differences between scores based on observed and reported intake were different from zero. FEAST I data were stratified by sex, and in FEAST II, analyses were repeated by education and body mass index (BMI). RESULTS: Differences in total HEI-2015 scores between observed and reported intake ranged from -1.3 to 5.8 points among those completing ASA24 independently in both studies, compared with -2.5 points in the small group setting. For interviewer-administered recalls, the differences were -1.1 for men and 2.3 for women. In FEAST II, total HEI-2015 scores derived from observed intake were lower than scores derived from reported intake among those who had completed high school or less (-3.2, SE 1.1, P<0.01) and those with BMI ≥ 30 (-2.8, SE 1.1, P = 0.01). CONCLUSIONS: HEI-2015 scores based on 24-hour dietary recall data are generally well estimated.

Examining the association between meal context and diet quality: an observational study of meal context in older adults.

BACKGROUND: Though a healthy diet is widely associated with reduced risks for chronic disease and mortality, older adults in the U.S. on average do not meet dietary recommendations. Given that few studies have examined the association between meal context on older adult diet quality, the aims of this study were (1) to compare the dietary quality of foods consumed in different meal contexts, as measured by the Healthy Eating Index 2015 (HEI-2015): meal location, the presence of others, and the use of electronic screens; and (2) to examine which components of the HEI-2015 drove differences in HEI-2015 total scores by meal context. METHODS: Interactive Diet and Activity Tracking in AARP study participants (50-74 years) completed the Automated Self-Administered 24-h Dietary Assessment tool (ASA24, version 2011) that included foods and beverages consumed and three meal contexts: "at home" versus "away from home," "alone" versus "with company," and "with screen time" versus "without screen time." A population ratio approach was used to estimate HEI-2015 total and component scores for all food items consumed by meal context. Mean HEI-2015 scores (range: 0-100) for the three meal context variables were compared using t-tests. Where there were significant differences in total scores, additional t-tests were used to explore which HEI-2015 components were the primary drivers. All tests were stratified by sex and adjusted for multiple comparisons. RESULTS: HEI-2015 scores were lower for meals consumed away vs. at home (mean difference (SE), males: - 8.23 (1.02); females: - 7.29 (0.93); both p < 0.0001) and for meals eaten with vs. without company (mean difference (SE), males: - 6.61 (1.06); females: - 7.34 (1.18); both p < 0.0001). There was no difference comparing with vs. without screen time. When HEI-2015 component scores were examined, fewer total fruits, whole grains, and dairy were consumed away from home or with company; more total vegetables and greens and beans, and less added sugars were consumed with company. CONCLUSIONS: Our findings suggest an association between the behavior cues of meal location and companions and dietary choices among older adults. Future studies can explore the individual and interactive effects of meal context on diet quality and subsequent health outcomes.

Performance and Feasibility of Recalls Completed Using the Automated Self-Administered 24-Hour Dietary Assessment Tool in Relation to Other Self-Report Tools and Biomarkers in the Interactive Diet and Activity Tracking in AARP (IDATA) Study.

BACKGROUND: Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a self-administered web-based tool designed to collect detailed dietary data at low cost in observational studies. OBJECTIVE: The objectives of this study were to describe, overall and by demographic groups, the performance and feasibility of ASA24-2011 recalls and compare Healthy Eating Index-2015 (HEI-2015) total and component scores to 4-day food records (4DFRs) and food frequency questionnaires (FFQs). DESIGN: Over 12 months, participants completed up to 6 ASA24 recalls, 2 web-based FFQs, and 2 unweighed paper-and-pencil 4DFRs. Up to 3 attempts were made to obtain each ASA24 recall. Participants were administered doubly-labeled water to provide a measure of total energy expenditure and collected two 24-hour urine samples to assess concentrations of nitrogen, sodium, and potassium. PARTICIPANTS/SETTING: From January through September 2012, 1,110 adult members of AARP, 50 to 74 years of age, were recruited from the Pittsburgh, PA, area to participate in the Interactive Diet and Activity Tracking in AARP (IDATA) study. After excluding 33 participants who had not completed any dietary assessments, 531 men and 546 women remained. MAIN OUTCOME MEASURES: Response rates, nutrient intakes compared to recovery biomarkers across each ASA24 administration day, and HEI-2015 total and component scores were measured. STATISTICAL ANALYSES PERFORMED: Means, medians, standard deviations, interquartile ranges, and HEI-2015 total and component scores computed using a multivariate measurement error model are presented. RESULTS: Ninety-one percent of men and 86% of women completed 3 ASA24 recalls. Approximately three-quarters completed 5 or more, higher than the completion rates for 2 4DFRs and 2 FFQs. Approximately, three-quarters of men and 70% of women completed ASA24 on the first attempt; 1 in 5 completed it on the second. Completion rates varied slightly by age and body mass index. Median time to complete ASA24-2011 (current version: ASA24-2020) declined with subsequent recalls from 55 to 41 minutes in men and from 58 to 42 minutes in women and was lowest in those younger than 60 years. Mean nutrient intakes were similar across recalls. For each recording day, energy intakes estimated by ASA24 were lower than energy expenditure. Reported intakes for protein, potassium, and sodium were closer to recovery biomarkers for women, but not for men. Geometric means of reported intakes of these nutrients did not systematically vary across ASA24 administrations, but differences between reported intakes and biomarkers differed by nutrient. Of 100 possible points, HEI-2015 total scores were nearly identical for 4DFRs and ASA24 recalls and higher for FFQs (men: 61, 60, and 68; women: 64, 64, and 72, respectively). CONCLUSIONS: ASA24, a freely available dietary assessment tool for use in large-scale nutrition research, was found to be highly feasible. Similar to previously reported data for nutrient intakes, HEI-2015 total and component scores for ASA24 recalls were comparable to those for 4DFRs, but not FFQs. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03268577 (http://www.clinicaltrials.gov).

Best Practices for Conducting and Interpreting Studies to Validate Self-Report Dietary Assessment Methods.

Careful consideration of the validity and reliability of methods intended to assess dietary intake is central to the robustness of nutrition research. A dietary assessment method with high validity is capable of providing useful measurement for a given purpose and context. More specifically, a method with high validity is well grounded in theory; its performance is consistent with that theory; and it is precise, dependable, and accurate within specified performance standards. Assessing the extent to which dietary assessment methods possess these characteristics can be difficult due to the complexity of dietary intake, as well as difficulties capturing true intake. We identified challenges and best practices related to the validation of self-report dietary assessment methods. The term validation is used to encompass various dimensions that must be assessed and considered to determine whether a given method is suitable for a specific purpose. Evidence on the varied concepts of validity and reliability should be interpreted in combination to inform judgments about the suitability of a method for a specified purpose. Self-report methods are the focus because they are used in most studies seeking to measure dietary intake. Biomarkers are important reference measures to validate self-report methods and are also discussed. A checklist is proposed to contribute to strengthening the literature on the validation of dietary assessment methods and ultimately, the nutrition literature more broadly.

The application of six dietary scores to a Middle Eastern population: a comparative analysis of mortality in a prospective study.

Background The associations between dietary indices and mortality have not been evaluated in populations from the Middle East, which have different dietary patterns compared to the US and Europe. In this study, we evaluated the association between six dietary indices and mortality in the Golestan Cohort Study (GCS) in Iran, which is the largest prospective study in the Middle East with 50,045 participants. Methods The six dietary indices, namely the Healthy Eating Index (HEI-2015), Alternative Healthy Eating Index (AHEI-2010), Alternative Mediterranean Diet (AMED), Dietary Approach to Stop Hypertension created by Fung (DASH-Fung) and Mellen (DASH-Mellen), and the World Cancer Research Fund (WCRF/AICR) index, were applied to data from a food frequency questionnaire, computed and divided into quintiles. Adjusted Cox models were used to estimate hazards ratio (HR) and 95% confidence intervals (CI) for overall and cause-specific mortality, using the lowest quintile as a reference group. Results Among 42,373 participants included in the current analyses, 4424 subjects died during 10.6 years of follow-up. Participants with the highest quintile dietary scores, compared with the lowest quintile dietary scores, had significantly decreased overall mortality in the AHEI-2010, AMED, DASH-Fung, and WCRF/AICR indices (HR 0.88, 95% CI = 0.80-0.97; 0.80, 0.70-0.91; 0.77, 0.70-0.86; and 0.79, 0.70-0.90, respectively). A reduced cardiovascular mortality was found for high AHEI-2010 and DASH-Fung scores (17% and 23%, respectively), and a reduced cancer mortality for high HEI-2015, AMED, and DASH-Fung scores (21, 37 and 25%, respectively). Conclusion Various indices of dietary quality are inversely associated with overall mortality, and selectively with cancer and cardiovascular mortality in the GCS, which contribute to the generalizability and validity of dietary guidelines.

Dietary assessment toolkits: an overview.

OBJECTIVE: A wide variety of methods are available to assess dietary intake, each one with different strengths and weaknesses. Researchers face multiple challenges when diet and nutrition need to be accurately assessed, particularly in the selection of the most appropriate dietary assessment method for their study. The goal of the current collaborative work is to present a collection of available resources for dietary assessment implementation.Design/Setting/ParticipantsAs a follow-up to the 9th International Conference on Diet and Physical Activity Methods held in 2015, developers of dietary assessment toolkits agreed to collaborate in the preparation of the present paper, which provides an overview of each toolkit. The toolkits presented include: the Diet, Anthropometry and Physical Activity Measurement Toolkit (DAPA; UK); the National Cancer Institute's (NCI) Dietary Assessment Primer (USA); the Nutritools website (UK); the Australasian Child and Adolescent Obesity Research Network (ACAORN) method selector (Australia); and the Danone Dietary Assessment Toolkit (DanoneDAT; France). An at-a-glance summary of features and comparison of the toolkits is provided. RESULTS: The present review contains general background on dietary assessment, along with a summary of each of the included toolkits, a feature comparison table and direct links to each toolkit, all of which are freely available online. CONCLUSIONS: This overview of dietary assessment toolkits provides comprehensive information to aid users in the selection and implementation of the most appropriate dietary assessment method, or combination of methods, with the goal of collecting the highest-quality dietary data possible.

Establishing Validity and Cross-Context Equivalence of Measures and Indicators.

Quantitative research depends on using measures to collect data that are valid (ie, reflect well the phenomena of interest) and perform equivalently across contexts. Demonstrating validity and cross-context equivalence requires specifically designed studies, but many such studies have problems that have limited their usefulness. This article explains validity and cross-context equivalence of measures (and important related concepts) and clarifies how to establish them. Validation is the process of determining whether a measure or indicator is suitable for providing useful analytical measurement for a given purpose and context. Cross-context equivalence means that a measure performs comparably across contexts. Four types of equivalence are construct, item, measurement, and scalar. Establishing validity and cross-context equivalence requires representing mathematically the errors (ie, imprecision, undependability, and inaccuracy) of a measure and using appropriate statistical methods to quantify these errors. Studies aiming to provide evidence about the validity of a measure need to clarify the purpose and context for use of that measure. Choose one of the two conceptual systems for validation; obtain data to establish the extent to which the measure is well constructed, reliable, and accurate; and use analytic methods beyond simple correlations to provide a basis for making reasoned judgment about whether the measure provides useful analytic measurement for the particular purpose(s) and context. Establishing accuracy of a measure requires having available other measures known to be accurate as comparators; in the case that no other measure understood to be more accurate is available, then the study will be able to establish agreement rather than validity.

Update of the Healthy Eating Index: HEI-2015.

The Healthy Eating Index (HEI) is a measure for assessing whether a set of foods aligns with the Dietary Guidelines for Americans (DGA). An updated HEI is released to correspond to each new edition of the DGA, and this article introduces the latest version, which reflects the 2015-2020 DGA. The HEI-2015 components are the same as in the HEI-2010, except Saturated Fat and Added Sugars replace Empty Calories, with the result being 13 components. The 2015-2020 DGA include explicit recommendations to limit intakes of both Added Sugars and Saturated Fats to <10% of energy. HEI-2015 does not account for excessive energy from alcohol within a separate component, but continues to account for all energy from alcohol within total energy (the denominator for most components). All other components remain the same as for HEI-2010, except for a change in the allocation of legumes. Previous versions of the HEI accounted for legumes in either the two vegetable or the two protein foods components, whereas HEI-2015 counts legumes toward all four components. Weighting approaches are similar to those of previous versions, and scoring standards were maintained, refined, or developed to increase consistency across components; better ensure face validity; follow precedent; cover a range of intakes; and, when applicable, ensure the DGA level corresponds to a score >7 out of 10. HEI-2015 component scores can be examined collectively using radar graphs to reveal a pattern of diet quality and summed to represent overall diet quality.

Applications of the Healthy Eating Index for Surveillance, Epidemiology, and Intervention Research: Considerations and Caveats.

The Healthy Eating Index (HEI) is a measure of diet quality that can be used to examine alignment of dietary patterns with the Dietary Guidelines for Americans. The HEI is made up of multiple adequacy and moderation components, most of which are expressed relative to energy intake (ie, as densities) for the purpose of calculating scores. Due to these characteristics and the complexity of dietary intake data more broadly, calculating and using HEI scores can involve unique statistical considerations and, depending on the particular application, intensive computational methods. The objective of this article is to review potential applications of the HEI, including those relevant to surveillance, epidemiology, and intervention research, and to summarize available guidance for appropriate analysis and interpretation. Steps in calculating HEI scores are reviewed and statistical methods described. Consideration of salient issues in the calculation and interpretation of scores can help researchers avoid common pitfalls and reviewers ensure that articles reporting on the use of the HEI include sufficient details such that the work is comprehensible and replicable, with the overall goal of contributing to knowledge on dietary patterns and health among Americans.

Evaluation of the Healthy Eating Index-2015.

BACKGROUND: The Healthy Eating Index (HEI), a diet quality index that measures alignment with the Dietary Guidelines for Americans, was updated with the 2015-2020 Dietary Guidelines for Americans. OBJECTIVE AND DESIGN: To evaluate the psychometric properties of the HEI-2015, eight questions were examined: five relevant to construct validity, two related to reliability, and one to assess criterion validity. DATA SOURCES: Three data sources were used: exemplary menus (n=4), National Health and Nutrition Examination Survey 2011-2012 (N=7,935), and the National Institutes of Health-AARP (formally known as the American Association of Retired Persons) Diet and Health Study (N=422,928). STATISTICAL ANALYSES: Exemplary menus: Scores were calculated using the population ratio method. National Health and Nutrition Examination Survey 2011-2012: Means and standard errors were estimated using the Markov Chain Monte Carlo approach. Analyses were stratified to compare groups (with t tests and analysis of variance). Principal components analysis examined the number of dimensions. Pearson correlations were estimated between components, energy, and Cronbach's coefficient alpha. National Institutes of Health-AARP Diet and Health Study: Adjusted Cox proportional hazards models were used to examine scores and mortality outcomes. RESULTS: For construct validity, the HEI-2015 yielded high scores for exemplary menus as four menus received high scores (87.8 to 100). The mean score for National Health and Nutrition Examination Survey was 56.6, and the first to 99th percentile were 32.6 to 81.2, respectively, supporting sufficient variation. Among smokers, the mean score was significantly lower than among nonsmokers (53.3 and 59.7, respectively) (P<0.01), demonstrating differentiation between groups. The correlation between diet quality and diet quantity was low (all <0.25) supporting these elements being independent. The components demonstrated multidimensionality when examined with a scree plot (at least four dimensions). For reliability, most of the intercorrelations among the components were low to moderate (0.01 to 0.49) with a few exceptions, and the standardized Cronbach's alpha was .67. For criterion validity, the highest vs the lowest quintile of HEI-2015 scores were associated with a 13% to 23% decreased risk of all-cause, cancer, and cardiovascular disease mortality. CONCLUSIONS: The results demonstrated evidence supportive of construct validity, reliability, and criterion validity. The HEI-2015 can be used to examine diet quality relative to the 2015-2020 Dietary Guidelines for Americans.

Combining a Food Frequency Questionnaire With 24-Hour Recalls to Increase the Precision of Estimation of Usual Dietary Intakes-Evidence From the Validation Studies Pooling Project.

Improving estimates of individuals' dietary intakes is key to obtaining more reliable evidence for diet-health relationships from nutritional cohort studies. One approach to improvement is combining information from different self-report instruments. Previous work evaluated the gains obtained from combining information from a food frequency questionnaire (FFQ) and multiple 24-hour recalls (24HRs), based on assuming that 24HRs provide unbiased measures of individual intakes. Here we evaluate the same approach of combining instruments but base it on the better assumption that recovery biomarkers provide unbiased measures of individual intakes. Our analysis uses data from the 5 large validation studies included in the Validation Studies Pooling Project: the Observing Protein and Energy Nutrition Study (1999-2000), the Automated Multiple-Pass Method validation study (2002-2004), the Energetics Study (2006-2009), the Nutrition Biomarker Study (2004-2005), and the Nutrition and Physical Activity Assessment Study (2007-2009). The data included intakes of energy, protein, potassium, and sodium. Under a time-varying usual-intake model analysis, the combination of an FFQ with 4 24HRs improved correlations with true intake for predicted protein density, potassium density, and sodium density (range, 0.39-0.61) in comparison with use of a single FFQ (range, 0.34-0.50). Absolute increases in correlation ranged from 0.02 to 0.26, depending on nutrient and sex, with an average increase of 0.14. Based on unbiased recovery biomarker evaluation for these nutrients, we confirm that combining an FFQ with multiple 24HRs modestly improves the accuracy of estimates of individual intakes.

Comparing Reported Dietary Supplement Intakes between Two 24-Hour Recall Methods: The Automated Self-Administered 24-Hour Dietary Assessment Tool and the Interview-Administered Automated Multiple Pass Method.

BACKGROUND: The Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24) includes a highly standardized multipass web-based recall that, like the Automated Multiple Pass Method (AMPM), captures detailed information about dietary intake using multiple probes and reminders to enhance recall of intakes. The primary distinction between ASA24 and AMPM is that the ASA24 user interface guides participants, thus removing the need for interviewers. OBJECTIVE: The objective of this study was to compare dietary supplement use reported on self-administered (ASA24-2011) vs interviewer-administered (AMPM) 24-hour recalls. DESIGN: The Food Reporting Comparison Study was an evaluation study designed to compare self-reported intakes captured using the self-administered ASA24 vs data collected via interviewer-administered AMPM recalls. Between 2010 and 2011, 1081 women and men were enrolled from three integrated health care systems that belong to the National Cancer Institute-funded Cancer Research Network: Security Health Plan Marshfield Clinic, Wisconsin; Henry Ford Health System, Michigan; and Kaiser Permanente Northern California, California. Quota sampling was used to ensure a balance of age, sex, and race/ethnicity. Participants were randomly assigned to four groups, and each group was asked to complete two dietary recalls: group 1, two ASA24s; group 2, two AMPMs; group 3, ASA24 first and AMPM second; and group 4, AMPM first and ASA24 second. Dietary supplements were coded using the 2007-2008 National Health and Nutrition Examination Survey Dietary Supplement Database. Analyses used the two one-sided tests, known as TOST, to assess equivalence of reported supplement use between methods. RESULTS: Complete 24-hour dietary recalls that included both dietary and supplement intake data were available for 1076 participants (507 men and 569 women). The proportions reporting supplement use via ASA24 and AMPM were 46% and 43%, respectively. These proportions were equivalent, with a small effect size of less than 20%. There were two exceptions in subgroup analyses: reported use among those 40 to 59 years of age and reported use by non-Hispanic black subjects were higher for ASA24 than AMPM. CONCLUSIONS: This study provides evidence that there is little difference in reported supplement use by mode of administration (ie, interview-administered vs self-administered recall).

Extending Methods in Dietary Patterns Research.

The National Cancer Institute (NCI) and the National Institutes of Health (NIH) Office of Disease Prevention held a workshop titled, “Extending Methods in Dietary Patterns Research”, in May of 2016. The workshop’s goal was to articulate, refine, and prioritize methodological questions to advance the science of dietary patterns in epidemiological research. Although the focus was on how to improve methods for assessing the relationship between dietary patterns and cancer risk, many, if not all, of the discussions and conclusions are relevant for other health outcomes as well. Recognizing that dietary intake is both multidimensional (i.e., it is a complex, multi-layered exposure and behavior) and dynamic (i.e., it varies over time and the life course), workshop presenters and participants discussed methodological advances required to include these concepts in dietary patterns research. This commentary highlights key needs that were identified to extend methods in dietary patterns research by integrating multidimensionality and dynamism into how dietary patterns are measured and defined, and how relationships with dietary patterns and health outcomes are modeled.

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

Background: A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers. Objective: The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting. Design: Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s (2011 version), 2 unweighed 4DFRs, 2 FFQs, two 24-h urine collections (biomarkers for protein, potassium, and sodium intakes), and 1 administration of doubly labeled water (biomarker for energy intake). Absolute and density-based energy-adjusted nutrient intakes were calculated. The prevalence of under- and overreporting of self-report against biomarkers was estimated. Results: Ninety-two percent of men and 87% of women completed ≥3 ASA24s (mean ASA24s completed: 5.4 and 5.1 for men and women, respectively). Absolute intakes of energy, protein, potassium, and sodium assessed by all self-reported instruments were systematically lower than those from recovery biomarkers, with underreporting greater for energy than for other nutrients. On average, compared with the energy biomarker, intake was underestimated by 15-17% on ASA24s, 18-21% on 4DFRs, and 29-34% on FFQs. Underreporting was more prevalent on FFQs than on ASA24s and 4DFRs and among obese individuals. Mean protein and sodium densities on ASA24s, 4DFRs, and FFQs were similar to biomarker values, but potassium density on FFQs was 26-40% higher, leading to a substantial increase in the prevalence of overreporting compared with absolute potassium intake. Conclusions: Although misreporting is present in all self-report dietary assessment tools, multiple ASA24s and a 4DFR provided the best estimates of absolute dietary intakes for these few nutrients and outperformed FFQs. Energy adjustment improved estimates from FFQs for protein and sodium but not for potassium. The ASA24, which now can be used to collect both recalls and records, is a feasible means to collect dietary data for nutrition research.

Comparison of recruitment and retention among demographic subgroups in a large diverse population study of diet.

OBJECTIVE: We examined the feasibility of conducting a longitudinal study of diet among diverse populations by comparing rates of response throughout recruitment and retention phases by demographic and other characteristics. METHODS: Using quota sampling, participants were recruited from 3 geographically and demographically diverse integrated health systems in the United States. Overall, 12,860 adults, ages 20-70, were invited to participate via mail. Participation first required accessing the study's website and later meeting eligibility criteria via telephone interview. Enrollees were asked to provide two 24-hour dietary recalls, either interviewer-administered or self-administered on the web, over 6 weeks. Stepped monetary incentives were provided. RESULTS: Rates for accessing the study website ranged from 6% to 23% (9% overall) across sites. Site differences may reflect differences in recruitment strategy or target samples. Of those accessing the website, enrollment was high (≥ 87%). Of the 1185 enrollees, 42% were non-Hispanic white, 34% were non-Hispanic black, and 24% were Hispanic. Men and minorities had lower enrollment rates than women and non-Hispanic whites, partially due to less successful telephone contact for eligibility screening. Once enrolled, 90% provided 1 recall and 80% provided both. Women had higher retention rates than men, as did older compared to younger participants. Retention rates were similar across race/ethnicity groups. CONCLUSIONS: While study recruitment remains challenging, once recruited most participants, regardless of race/ethnicity, completed two 24-hour dietary recalls, both interviewer-administered and self-administered on the web. This study demonstrates the feasibility of collecting multiple 24-hour recalls including less expensive automated self-administered recalls among diverse populations.

Dietary Flavonoid Intake Reduces the Risk of Head and Neck but Not Esophageal or Gastric Cancer in US Men and Women.

Background: Flavonoids are bioactive polyphenolic compounds found in fruits, vegetables, and beverages of plant origin. Previous studies have shown that flavonoid intake reduces the risk of certain cancers; however, few studies to date have examined associations of flavonoids with upper gastrointestinal cancers or used prospective cohorts.Objective: Our study examined the association between intake of flavonoids (anthocyanidins, flavan-3-ols, flavanones, flavones, flavonols, and isoflavones) and risk of head and neck, esophageal, and gastric cancers.Methods: The NIH-AARP Diet and Health Study is a prospective cohort study that consists of 469,008 participants. Over a mean 12-y follow-up, 2453 head and neck (including 1078 oral cavity, 424 pharyngeal, and 817 laryngeal), 1165 esophageal (890 adenocarcinoma and 275 squamous cell carcinoma), and 1297 gastric (625 cardia and 672 noncardia) cancer cases were identified. We used Cox proportional hazards regression models to estimate HRs and CIs for the associations between flavonoid intake assessed at study baseline and cancer outcomes. For 56 hypotheses examined, P-trend values were adjusted using the Benjamini-Hochberg (BH) procedure for false discovery rate control.Results: The highest quintile of total flavonoid intake was associated with a 24% lower risk of head and neck cancer (HR: 0.76; 95% CI: 0.66, 0.86; BH-adjusted 95% CI: 0.63, 0.91; P-trend = 0.02) compared with the lowest quintile. Notably, anthocyanidins were associated with a 28% lower risk of head and neck cancer (HR: 0.72; 95% CI: 0.62, 0.82; BH-adjusted 95% CI: 0.59, 0.87; P-trend = 0.0005), and flavanones were associated with a 22% lower risk of head and neck cancer (HR: 0.78; 95% CI: 0.68, 0.89; BH-adjusted 95% CI: 0.64, 0.94; P-trend: 0.02). No associations between flavonoid intake and risk of esophageal or gastric cancers were found.Conclusions: Our results indicate that flavonoid intake is associated with lower head and neck cancer risk. These associations suggest a protective effect of dietary flavonoids on head and neck cancer risk, and thus potential as a risk reduction strategy.