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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.

Hazard ratios (95% Confidence Intervals) for the lag analyses by 5 years and 10 years for lung cancer and nut consumption in AARP, by smoking status

Supplementary Tables 3A-C from Use of the Predictive Sugars Biomarker to Evaluate Self-Reported Total Sugars Intake in the Observing Protein and Energy Nutrition (OPEN) Study

Odds ratios and Hazard ratios (95% Confidence Intervals) for lung cancer and nut consumption in EAGLE and AARP, by smoking intensity

Odds ratios and Hazard ratios (95% Confidence Intervals) for lung cancer and nut consumption in EAGLE and AARP, by histology subtypes

Odds ratios and Hazard ratios (95% Confidence Intervals) for lung cancer and nut consumption in EAGLE and AARP, by smoking status

Background: A predictive biomarker for intake of total sugars was recently developed under controlled conditions. We used this biomarker to assess measurement error (ME) structure in self-reported intake of total sugars in free-living individuals.Methods: The Observing Protein and Energy Nutrition (OPEN) study involved 484 participants aged 40 to 69 years. Diet was assessed using two administrations of a food frequency questionnaire (FFQ) and two nonconsecutive 24-hour dietary recalls (24HDR). Two 24-hour urine samples checked for completeness were analyzed on sucrose and fructose. We applied the biomarker calibrated in a feeding study to OPEN data to assess the ME structure and the attenuation factors (AF) for intakes of absolute total sugars and sugars density for the FFQ and 24HDR.Results: The AFs for absolute sugars were similar for a single FFQ and 24HDR, but attenuation decreased with repeated 24HDRs. For sugars density, the AFs for FFQ (men: 0.39; women: 0.33) were greater than for single 24HDR (men: 0.30; women: 0.24), and similar to two 24HDRs (men: 0.41; women: 0.35). The attenuation associated with both instruments was greater in women than in men.Conclusions: Both the FFQ and 24HDR were found to be biased; hence, incorporation of the sugars biomarker in calibration studies within the cohorts may be necessary to more reliably estimate associations of sugars and disease.Impact: In this article, we propose a new dietary reference instrument based on the recently defined class of predictive biomarkers. Using sugars biomarker, we quantify ME in the FFQ- and 24HDR-reported absolute total sugars and total sugars density. Cancer Epidemiol Biomarkers Prev; 20(3); 490–500. ©2011 AACR.

Pearson's correlation and covariance between nuts consumption and nutrients and smoking variables in controls

Hazard ratios (95% Confidence Intervals) for the lag analyses by 5 years and 10 years for lung cancer and nut consumption in AARP, by histologic subtypes

Supplementary Table 1 from Intakes of Red Meat, Processed Meat, and Meat Mutagens Increase Lung Cancer Risk

Supplementary Table 2 from Intakes of Red Meat, Processed Meat, and Meat Mutagens Increase Lung Cancer Risk

Red and processed meat intake may increase lung cancer risk. However, the epidemiologic evidence is inconsistent and few studies have evaluated the role of meat mutagens formed during high cooking temperatures. We investigated the association of red meat, processed meat, and meat mutagen intake with lung cancer risk in Environment And Genetics in Lung cancer Etiology, a population-based case-control study. Primary lung cancer cases (n = 2,101) were recruited from 13 hospitals within the Lombardy region of Italy examining ∼80% of the cases from the area. Noncancer population controls (n = 2,120), matched to cases on gender, residence, and age, were randomly selected from the same catchment area. Diet was assessed in 1,903 cases and 2,073 controls and used in conjunction with a meat mutagen database to estimate intake of heterocyclic amines (HCA) and benzo(a)pyrene (BaP). Multivariable odds ratios (OR) and 95% confidence intervals (95% CI) for sex-specific tertiles of intake were calculated using unconditional logistic regression. Red and processed meat were positively associated with lung cancer risk (highest-versus-lowest tertile: OR, 1.8; 95% CI, 1.5–2.2; P trend < 0.001 and OR, 1.7; 95% CI, 1.4–2.1; P trend < 0.001, respectively); the risks were strongest among never smokers (OR, 2.4; 95% CI, 1.4–4.0; P trend = 0.001 and OR, 2.5; 95% CI, 1.5–4.2; P trend = 0.001, respectively). HCAs and BaP were significantly associated with increased risk of lung cancer. When separated by histology, significant positive associations for both meat groups were restricted to adenocarcinoma and squamous cell carcinoma but not small cell carcinoma of the lung. In summary, red meat, processed meat, and meat mutagens were independently associated with increased risk of lung cancer. [Cancer Res 2009;69(3):932–9]

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.

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 10.6years 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.

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 p 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.

BACKGROUND. The construct and predictive validity of the Healthy Eating Index (HEI) have been demonstrated, but it is unclear how error in reported dietary intake may affect scores. OBJECTIVE. These analyses examined concordance between HEI-2015 scores based on observed versus 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 one 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, 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 if 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 to −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

OBJECTIVES: Providing nutrition feedback and advice to research participants based on reported dietary intake is feasible, but has not traditionally been undertaken in all settings. However, it is unclear how such feedback is received and understood by participants or the general public absent the guidance of a clinician or professional. We conducted usability testing to assess understanding of a report with personalized nutrition information to respondents who provided dietary data via the National Cancer Institute's Automated Self-Administered 24-hour (ASA24) Dietary Assessment tool. METHODS: Two rounds of usability testing (6 participants per round) were conducted in a laboratory equipped with an eye tracker, as well as audio and computer recording. Participants were recruited from the general population and represented a range of race/ethnicities, education levels, and ages. Trained moderators asked participants to freely explore a newly designed Respondent Nutrition Report (RNR) and then guided them through a series of questions designed to elicit information about whether nutrition information was understood and gather feedback on areas of confusion or misunderstanding. In addition to verbal feedback, participant interaction with the RNR was captured via gaze plots and heat maps. RESULTS: Participants requested additional informational text during round 1 of testing, and read text explanations when present in round 2. Participants preferred a narrow vs. wide design such that it would display easily on mobile devices. They focused on numbers enclosed by graphics and lingered on terminology that was unfamiliar or confusing. When large amounts of information were presented in graphs, attention was focused on the first few items presented but then decreased towards the end of rows and the bottom of the page. Highlighted text was not always attended to by participants. Verbal results indicated that choice of words for labels and explanatory text was critical and that participants desired links to more detailed external information when possible. CONCLUSIONS: It is possible to design a personalized nutrition feedback report that is well understood by participants. Care should be taken with wording choices, extraneous information should be limited, and testing within the target population is advisable. FUNDING SOURCES: None.

ObjectiveA 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.ResultsThe 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.ConclusionsThis 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.

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

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.

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 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.

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.

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 ).

OBJECTIVES: To compare self-reported dietary intakes using food frequency questionnaires (FFQs) adjusted for 7-d food checklists against recovery biomarkers (doubly labeled water (DLW) for energy; 24-h urine for protein, sodium, potassium). METHODS: Over 12-months, participants aged 50–74 years, from the Interactive Diet and Activity Tracking (IDATA) Study, were asked to complete 2 FFQs, 2 7-d, 32-item food checklists, 2 24-h urine collections, and 1 DLW administration. Participants who completed at least 1 FFQ, 1 food checklist with ≥5 days, and had DLW and urine results were included (N = 742). FFQ energy, protein, sodium, and potassium intakes and densities were calculated both directly (unadjusted) and adjusted by the ratio of reported intakes, calculated as the average percentage of days with reported intake from the 7-d food checklist divided by the summed FFQ frequency for each food group. The discrepancy between FFQ values and biomarkers were determined by comparing mean unadjusted and checklist-adjusted values to biomarkers. Attenuation factors and validity coefficients were determined for all unadjusted and adjusted FFQ values. RESULTS: Overall, adjusting FFQ values by estimates from the 7-d food checklist resulted in higher values for protein, sodium, potassium, and energy. For both men and women, FFQ energy, protein, sodium and potassium values were lower than biomarker estimates, but all differences were reduced when adjusted for checklist values. The greatest reduction in differences between self-reported values (unadjusted vs. adjusted) and biomarkers were seen for sodium and energy intake in females (−36% to −21% and −29% to −14%, respectively), and total energy intake in males (−29% vs. −14%). , The checklist-adjusted potassium values were higher than the biomarker values, but for men, they were closer to the biomarker. Finally, the adjustment process improved the level of attenuation and validity coefficients for each nutrient and energy. CONCLUSIONS: Though past studies have shown 24-h recalls and food records have higher correlations with recovery biomarkers than FFQs, our results suggest that, simple adjustment of FFQs using food checklists reduces reporting error and may improve the power to detect relationships between dietary exposures from FFQs and health outcomes. FUNDING SOURCES: N/A.

OBJECTIVES: To develop a standardized AICR/WCRF Score that measures adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and provide guidance for its application in research. METHODS: Each of the updated 2018 WCRF/AICR Cancer Prevention Recommendations and the associated goals and statements of advice were examined to inform the definition of a new Score. For each of the weight, physical activity, diet, and breastfeeding-specific recommendations, components and subcomponents were created. Standards for scoring each component were established based on quantitative guidance specified in the recommendations; however, if no specificity was provided, other guidelines (e.g., national guidelines), past research that operationalized 2007 WCRF/AICR recommendations, and expert panel advice were evaluated. RESULTS: The proposed AICR/WCRF Score includes eight of the ten WCRF/AICR 2018 recommendations: 1) Be a healthy weight, 2) Be physically active, 3) Eat a diet rich in whole grains, vegetables, fruits, and beans, 4) Limit consumption of fast foods and other processed foods high in fat, starches, or sugars, 5) Limit consumption of red and processed meats, 6) Limit consumption of sugar-sweetened beverages, 7) Limit alcohol consumption, and, optionally, 8) For mothers: breastfeed your baby, if you can. Each of the components are worth one point: 1, 0.5, and 0 points for fully, partially, and not meeting the recommendations, respectively (total Score: 0–7 or 8 points). Two recommendations were not included in the Score due to uncertain intent of supplement use (Do not use supplements for cancer prevention) and the redundancy of the dependent components in the final recommendation (After a cancer diagnosis: follow our Recommendations, if you can). Additional guidance will stress the importance of taking into account other risk factors, such as smoking, in relevant models using the new Score. CONCLUSIONS: The AICR/WCRF Score is a practical tool operationalizing the 2018 recommendations. Future studies are needed to further examine how adherence to the Score relates to cancer risk and mortality in various populations. FUNDING SOURCES: None.

The authors evaluated the validity of a 152-item semiquantitative food frequency questionnaire (SFFQ) by comparing it with two 7-day dietary records (7DDRs) or up to 4 automated self-administered 24-hour recalls (ASA24s) over a 1-year period in the women's Lifestyle Validation Study (2010-2012), conducted among subgroups of the Nurses' Health Studies. Intakes of energy and 44 nutrients were assessed using the 3 methods among 632 US women. Compared with the 7DDRs, SFFQ responses tended to underestimate sodium intake but overestimate intakes of energy, macronutrients, and several nutrients in fruits and vegetables, such as carotenoids. Spearman correlation coefficients between energy-adjusted intakes from 7DDRs and the SFFQ completed at the end of the data-collection period ranged from 0.36 for lauric acid to 0.77 for alcohol (mean r = 0.53). Correlations of the end-period SFFQ were weaker when ASA24s were used as the comparison method (mean r = 0.43). After adjustment for within-person variation in the comparison method, the correlations of the final SFFQ were similar with 7DDRs (mean r = 0.63) and ASA24s (mean r = 0.62). These data indicate that this SFFQ provided reasonably valid estimates for intakes of a wide variety of dietary variables and that use of multiple 24-hour recalls or 7DDRs as a comparison method provided similar conclusions if day-to-day variation was taken into account.

BACKGROUND: Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE: The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN: We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS: The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10(-15) to 8 × 10(-6)). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS: Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.

Systematic seasonal bias may confound efforts to estimate usual dietary intake and diet quality. Little is known about dietary quality over the winter holiday season.The aims of this study were to test for differences in intakes of energy, percentage of energy from macronutrients, fruits and vegetables, and diet quality measured using the Healthy Eating Index 2010 (HEI-2010) by calendar and winter holiday seasons.Longitudinal cohort design.Data were derived from the Life in All Seasons study. Two cohorts of women aged 40 to 60 years (N=52) from the greater Grand Forks, ND, area were followed for 1 year each between July 2012 and July 2014. Each woman completed an online diet recall using the Automated Self-Administered 24-Hour Recall every 10 days during the year, with a 92% response rate.Effects of calendar and winter holiday seasons on intakes of energy, percent energy from macronutrients, HEI-2010 total and component scores, and grams per day of individual fruits and vegetables were tested using mixed linear models.The mean total HEI-2010 score was 60.1±1.4. There were seasonal differences in some HEI-2010 component scores, but not in total scores. More lettuce or mixed lettuce salad was consumed during summer than during winter (P=0.034), and more fresh tomatoes were consumed during summer and fall compared with winter (P=0.001). More corn, berries, peaches and nectarines, and melons (P0.001) were consumed during summer. There was no seasonal difference in reported intakes of energy (P=0.793). The total HEI-2010 score for dietary intake observed over the winter holiday season was lower than the rest of the year (P0.001). Reported energy intake was not different (P=0.228).In this population, diet quality is significantly lower during the winter holiday period, but mostly consistent by season. Multiple recalls in any season can give a reasonable representation of usual overall diet quality throughout the year.

Background: The Automated Self-Administered 24-hour (ASA24) dietary recall system enhances the feasibility of collecting high-quality intake data in population-based studies. Objective: The aim of this study was to assess the accuracy of portion size reporting in the ASA24 compared with interviewer-administered recalls. Methods: True intake for 3 meals was ascertained in 81 adults aged 20–70 y from the Washington, DC area. Participants were randomly assigned to complete an unannounced ASA24 or an interviewer-administered Automated Multiple-Pass Method (AMPM) recall the following day. An adapted Bland-Altman approach was used to assess agreement between true and reported portion sizes. Linear regression was used to assess log-scale differences between true and reported portion sizes by recall mode. The proportions of reported portion sizes within 10% and 25% of truth were estimated. Analyses were conducted for all foods and drinks and predetermined categories. Results: Mean differences between true and reported portion sizes were 3.7 g for the ASA24 and 11.8 g for the AMPM. According to the Bland-Altman-type plots, between 92% and 100% (depending on food or drink category and recall mode) of observations fell within the limits of agreement. After adjustment for multiple testing, the mean ratio of reported to true portion sizes was significantly >1 for the categories of all foods and drinks, all foods excluding liquids, amorphous or soft foods, and small pieces among AMPM respondents. Misestimation in the AMPM was significantly different from that in the ASA24 for all foods and drinks and for all foods excluding liquids. Small proportions of reported portions fell within 10% (16.2% for the ASA24 and 14.9% for the AMPM) and 25% (37.5% for the ASA24 and 33.2% for the AMPM) of truth. Conclusions: The results raise the possibility that digital images tailored to different types and formats of foods may facilitate improved estimation of amounts eaten but highlight the need for continued work in this aspect of dietary assessment. This trial was registered at clinicaltrials.gov as {"type":"clinical-trial","attrs":{"text":"NCT00978406","term_id":"NCT00978406"}}NCT00978406.

Challenges and complexities associated with assessing dietary intakes are numerous, but not insurmountable. This opinion paper from Canadian researchers draws attention to the importance of building capacity and providing funding opportunities for research in dietary assessment methods in Canada and elsewhere. Such strategies would contribute to a better understanding of the roles played by diet in human health and better translation of this information into the most meaningful and effective dietary guidelines, policies, and interventions.

Background: Although diet is considered one of the major environmental risk factors related to chronic disease, many studies do not evaluate it not only because of practical and logistical issues, but also because of persistent concerns about error in self-reported diet. Web-based tools, however, make it feasible and affordable to collect high-quality, detailed dietary data in large observational studies. The Automated Self-Administered 24-hour recall (ASA24) is an easy-to-use, engaging, self-administered web-based dietary assessment tool. It is freely available from the National Cancer Institute and is available on all mobile devices. Studies can use ASA24 to collect single or multiday 24-hr recalls or food records. ASA24 automatically analyzes reported food, beverage, and dietary supplements intake, generating detailed data files of nutrients and food groups consumed at the person and food/beverage/supplement item level. The ASA24 System includes a researcher website where investigators register to use ASA24, schedule and track participant activities, and obtain data files. Self-reported diet using ASA24 was evaluated against recovery biomarkers (i.e., true intake) in the Interactive Diet and Activity Tracking in AARP (IDATA) Study. Absolute dietary intakes assessed by multiday ASA24 recalls were close to true intakes and outperformed a food frequency questionnaire (FFQ). Aim: To assess the feasibility of using ASA24 (version 2011) in free living adults 50-74 years old. Method: Over a 12-month period, men (n=530) and women (n=545) were contacted by email, every other month, to complete an ASA24-2011 (total, 6 ASA24s/year). If a participant did not complete ASA24 after the first contact, a reminder email was sent on a new randomly selected day. Up to three email notifications were sent to obtain each of six ASA24s. Participants also completed a web-based FFQ at months 1 and 12. Results: Most men (92%) and women (87%) completed at least three ASA24s. 77% of participants completed at least five ASA24s. Completion rate for the 1st FFQ was 81% in men and 73% in women, dropping to 73% and 70%, respectively, for the 2nd FFQ. Most participants (men: 75%; women: 70%) completed ASA24 after the 1st email notification. Another 18% of men and 21% of women completed ASA24 after the 2nd email notification. Median time to complete ASA24-2011 for the 1st administration was 55 minutes in men and 58 minutes in women but declined to about 44 minutes by the 3rd ASA24. Participants 60 years old. A decline in completion time with each subsequent ASA24 did not appear to affect the quality of diet reporting as there were no systematic decreases in reported energy and nutrient intakes across ASA24 administrations. Conclusion: It is feasible to collect high-quality diet data using multiday ASAS24s in longitudinal observational studies. New as well as ongoing epidemiologic studies should consider incorporating a detailed dietary assessment such as ASA24 in future studies. This abstract is also being presented as Poster A35. Citation Format: Yikyung Park, Kevin W. Dodd, Douglas Midthune, Victor Kipnis, Heather Bowles, Amy F. Subar. Feasibility of assessing individual’s diet using a web-based dietary assessment tool, ASA24, in a longitudinal observational study [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr PR04.

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 2012 article, “Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease,” in The Journal of Nutrition by Chiuve and colleagues (1) contributed to a rapidly growing literature on the connections between dietary patterns, as defined by food-based diet quality indexes, and health outcomes. The paper first presented a revised Alternative Healthy Eating Index-2010 (AHEI-2010), with modifications based on the most recent clinical research at that time, and then examined relations between both the Healthy Eating Index-2005 (HEI-2005) and AHEI-2010 with chronic disease risk (including cardiovascular disease, diabetes, cancer, or nontrauma death). The results showed that the HEI-2005 and the AHEI-2010 were associated with 16% and 19% significantly reduced risks of chronic disease comparing the highest with the lowest quintile of diet quality, respectively. These pivotal findings served to advance dietary patterns research and dietary guidance, and highlighted the need for analyses that modeled dietary patterns to reflect the total diet, rather than using only a single marker of intake such as 1 nutrient or dietary constituent.

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.

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.

Recent reports have asserted that, because of energy underreporting, dietary self-report data suffer from measurement error so great that findings that rely on them are of no value. This commentary considers the amassed evidence that shows that self-report dietary intake data can successfully be used to inform dietary guidance and public health policy. Topics discussed include what is known and what can be done about the measurement error inherent in data collected by using self-report dietary assessment instruments and the extent and magnitude of underreporting energy compared with other nutrients and food groups. Also discussed is the overall impact of energy underreporting on dietary surveillance and nutritional epidemiology. In conclusion, 7 specific recommendations for collecting, analyzing, and interpreting self-report dietary data are provided: (1) continue to collect self-report dietary intake data because they contain valuable, rich, and critical information about foods and beverages consumed by populations that can be used to inform nutrition policy and assess diet-disease associations; (2) do not use self-reported energy intake as a measure of true energy intake; (3) do use self-reported energy intake for energy adjustment of other self-reported dietary constituents to improve risk estimation in studies of diet-health associations; (4) acknowledge the limitations of self-report dietary data and analyze and interpret them appropriately; (5) design studies and conduct analyses that allow adjustment for measurement error; (6) design new epidemiologic studies to collect dietary data from both short-term (recalls or food records) and long-term (food-frequency questionnaires) instruments on the entire study population to allow for maximizing the strengths of each instrument; and (7) continue to develop, evaluate, and further expand methods of dietary assessment, including dietary biomarkers and methods using new technologies.

Background Valid methods of diet assessment are important for nutrition research and practice, but can be difficult with children. Objective To validate the 2012 version of the Automated Self-Administered 24-Hour Dietary Recall for Children (ASA24-Kids-2012), a self-administered web-based 24-hour dietary recall (24hDR) instrument, among children aged 9 to 11 years, in two sites. Design Quasiexperimental. Participants/setting In one site, trained staff members observed and recorded foods and drinks consumed by children (n=38) during school lunch. The next day, the observed children completed both ASA24-Kids-2012 and an interviewer-administered 24hDR in a randomized order. Procedures in a second site (n=31) were similar, except observations occurred during dinner in a community location. Statistical analyses Foods were classified as matches (reported and consumed), intrusions (reported, but not consumed), or omissions (not reported, but consumed) for each participant. Rates of matches, intrusions, and omissions were calculated. Rates were compared between each recall method using repeated measures analysis of covariance. For matched foods, the authors determined correlation coefficients between observed and reported serving sizes. Results Match, intrusion, and omission rates between ASA24-Kids-2012 and observed intakes in Site 1 were 37%, 27%, and 35%, respectively. Comparable rates for interviewer-administered 24hDRs were 57%, 20%, and 23%, respectively. In Site 2, match, intrusion, and omission rates between ASA24-Kids-2012 and observed intakes were 53%, 12%, and 36%, respectively, vs 76% matches, 9% intrusions, and 15% omissions for interviewer-administered 24hDRs. The relationship strength between reported and observed serving sizes for matched foods was 0.18 in Site 1 and 0.09 in Site 2 for ASA24-Kids-2012, and 0.46 in Site 1 and 0.11 in Site 2 for interviewer-administered 24hDRs. Conclusions ASA24-Kids-2012 was less accurate than interviewer-administered 24hDRs when compared with observed intakes, but both performed poorly. Additional research should assess the age at which children can complete recalls without the help of a parent or guardian, as well as elucidate under which circumstances recalls can reasonably be used among children.

Dietary pattern analysis considers combinations of food intake and may offer a better measure to assess diet–cancer associations than examining individual foods or nutrients. Although tobacco exposure is the major risk factor for lung cancer, few studies have examined whether dietary patterns, based on preexisting dietary guidelines, influence lung cancer risk. After controlling for smoking, we examined associations between four diet quality indices—Healthy Eating Index–2010 (HEI-2010), Alternate Healthy Eating Index–2010 (AHEI-2010), alternate Mediterranean Diet score (aMED) and Dietary Approaches to Stop Hypertension (DASH)—and lung cancer risk in the NIH–AARP (National Institutes of Health-American Association of Retired Persons) Diet and Health study. Baseline dietary intake was assessed in 460 770 participants. Over a median of 10.5 years of follow-up, 9272 incident lung cancer cases occurred. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and confidence intervals (CIs). Comparing highest to lowest quintiles, HRs (95% CIs) for lung cancer were as follows: HEI-2010=0.83 (0.77–0.89), AHEI-2010=0.86 (0.80–0.92), aMED=0.85 (0.79–0.91) and DASH=0.84 (0.78–0.90). Among the individual components of the dietary indices, higher consumption of whole grains and fruits was significantly inversely associated with lung cancer risk for several of the diet indices. Total index score analyses stratified by smoking status showed inverse associations with lung cancer for former smokers; however, only HEI-2010 was inversely associated in current smokers and no index score was inversely associated among never smokers. Although smoking is the factor most strongly associated with lung cancer, this study adds to a growing body of evidence that diet may have a modest role in reducing lung cancer risk, especially among former smokers.

Twenty-four-hour dietary recalls provide high-quality intake data but have been prohibitively expensive for large epidemiologic studies. This study's goal was to assess whether the web-based Automated Self-Administered 24-Hour Recall (ASA24) performs similarly enough to the standard interviewer-administered, Automated Multiple-Pass Method (AMPM) 24-hour dietary recall to be considered a viable alternative. In 2010–2011, 1,081 adults from 3 integrated health systems in Detroit, Michigan; Marshfield, Wisconsin; and Kaiser-Permanente Northern California participated in a field trial. A quota design ensured a diverse sample by sex, age, and race/ethnicity. Each participant was asked to complete 2 recalls and was randomly assigned to 1 of 4 protocols differing by type of recall and administration order. For energy, the mean intakes were 2,425 versus 2,374 kcal for men and 1,876 versus 1,906 kcal for women by AMPM and ASA24, respectively. Of 20 nutrients/food groups analyzed and controlling for false discovery rate, 87% were judged equivalent at the 20% bound. ASA24 was preferred over AMPM by 70% of the respondents. Attrition was lower in the ASA24/AMPM study group than in the AMPM/ASA24 group, and it was lower in the ASA24/ASA24 group than in the AMPM/AMPM group. ASA24 offers the potential to collect high-quality dietary intake information at low cost with less attrition.

We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.

The Dietary Patterns Methods Project (DPMP) was initiated in 2012 to strengthen research evidence on dietary indices, dietary patterns, and health for upcoming revisions of the Dietary Guidelines for Americans, given that the lack of consistent methodology has impeded development of consistent and reliable conclusions. DPMP investigators developed research questions and a standardized approach to index-based dietary analysis. This article presents a synthesis of findings across the cohorts. Standardized analyses were conducted in the NIH-AARP Diet and Health Study, the Multiethnic Cohort, and the Women’s Health Initiative Observational Study (WHI-OS). Healthy Eating Index 2010, Alternative Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean Diet, and Dietary Approaches to Stop Hypertension (DASH) scores were examined across cohorts for correlations between pairs of indices; concordant classifications into index score quintiles; associations with all-cause, cardiovascular disease (CVD), and cancer mortality with the use of Cox proportional hazards models; and dietary intake of foods and nutrients corresponding to index quintiles. Across all cohorts in women and men, there was a high degree of correlation and consistent classifications between index pairs. Higher diet quality (top quintile) was significantly and consistently associated with an 11–28% reduced risk of death due to all causes, CVD, and cancer compared with the lowest quintile, independent of known confounders. This was true for all diet index–mortality associations, with the exception of AHEI-2010 and cancer mortality in WHI-OS women. In all cohorts, survival benefit was greater with a higher-quality diet, and relatively small intake differences distinguished the index quintiles. The reductions in mortality risk started at relatively lower levels of diet quality. Higher scores on each of the indices, signifying higher diet quality, were associated with marked reductions in mortality. Thus, the DPMP findings suggest that all 4 indices capture the essential components of a healthy diet.

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.

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.

This chapter describes key concepts relevant to the analysis of intake data from recalls and records/diaries, briefly reviews the types of error in data collected using these methods and their potential implications, and discusses analytic strategies for reducing error. It addresses approaches to mitigate error in the analyses of data collected using recalls and records/diaries. There are few reference measures other than recovery biomarkers available to mitigate the effects of measurement error in data from recalls or records. Validation studies using recovery biomarkers indicate that data collected using recalls and records/diaries are prone to substantial random error but less bias than food frequency data. The chapter describes cases in which recalls or records/diaries are the main instrument used to collect dietary intake in a study, as well as those in which recalls or records/diaries are used as the reference instrument to reduce bias in data collected using food frequency questionnaires and screeners.

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.

Poor diet quality is thought to be a leading risk factor for years of life lost. We examined how scores on 4 commonly used diet quality indices—the Healthy Eating Index 2010 (HEI), the Alternative Healthy Eating Index 2010 (AHEI), the Alternate Mediterranean Diet (aMED), and the Dietary Approaches to Stop Hypertension (DASH)—are related to the risks of death from all causes, cardiovascular disease (CVD), and cancer among postmenopausal women. Our prospective cohort study included 63,805 participants in the Women's Health Initiative Observational Study (from 1993–2010) who completed a food frequency questionnaire at enrollment. Cox proportional hazards models were fit using person-years as the underlying time metric. We estimated multivariate hazard ratios and 95% confidence intervals for death associated with increasing quintiles of diet quality index scores. During 12.9 years of follow-up, 5,692 deaths occurred, including 1,483 from CVD and 2,384 from cancer. Across indices and after adjustment for multiple covariates, having better diet quality (as assessed by HEI, AHEI, aMED, and DASH scores) was associated with statistically significant 18%–26% lower all-cause and CVD mortality risk. Higher HEI, aMED, and DASH (but not AHEI) scores were associated with a statistically significant 20%–23% lower risk of cancer death. These results suggest that postmenopausal women consuming a diet in line with a priori diet quality indices have a lower risk of death from chronic disease.

Background: The Automated Self-Administered 24-hour Recall (ASA24), a freely available Web-based tool, was developed to enhance the feasibility of collecting high-quality dietary intake data from large samples. Objective: The purpose of this study was to assess the criterion validity of ASA24 through a feeding study in which the true intake for 3 meals was known. Design: True intake and plate waste from 3 meals were ascertained for 81 adults by inconspicuously weighing foods and beverages offered at a buffet before and after each participant served himor herself. Participants were randomly assigned to complete an ASA24 or an interviewer-administered Automated Multiple-Pass Method (AMPM) recall the following day. With the use of linear and Poisson regression analysis, we examined the associations between recall mode and 1) the proportions of items consumed for which a match was reported and that were excluded, 2) the number of intrusions (items reported but not consumed), and 3) differences between energy, nutrient, food group, and portion size estimates based on true and reported intakes. Results: Respondents completing ASA24 reported 80% of items truly consumed compared with 83% in AMPM (P = 0.07). For both ASA24 and AMPM, additions to or ingredients in multicomponent foods and drinks were more frequently omitted than were main foods or drinks. The number of intrusions was higher in ASA24 (P , 0.01). Little evidence of differences by recall mode was found in the gap between true and reported energy, nutrient, and food group intakes or portion sizes. Conclusions: Although the interviewer-administered AMPM performed somewhat better relative to true intakes for matches, exclusions, and intrusions, ASA24 performed well. Given the substantial cost savings that ASA24 offers, it has the potential to make important contributions to research aimed at describing the diets of populations, assessing the effect of interventions on diet, and elucidating diet and health relations. This trial was registered at clinicaltrials.gov as NCT00978406. Am J Clin Nutr 2014;100:233–40.