Update of the Healthy Eating Index: HEI-2015 Krebs-Smith, Susan M.; Pannucci, TusaRebecca E.; Subar, Amy F. ...
Journal of the Academy of Nutrition and Dietetics,
September 2018, 2018-09-00, 20180901, Letnik:
118, Številka:
9
Journal Article
Recenzirano
Odprti dostop
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.
Evaluation of the Healthy Eating Index-2015 Reedy, Jill; Lerman, Jennifer L.; Krebs-Smith, Susan M. ...
Journal of the Academy of Nutrition and Dietetics,
September 2018, 2018-09-00, 20180901, Letnik:
118, Številka:
9
Journal Article
Recenzirano
Odprti dostop
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.
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.
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).
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.
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.
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.
Abstract This monograph describes the National Cancer Institute’s Dietary Assessment Primer, a web resource developed to help researchers choose the best available dietary assessment approach to ...achieve their research objective. All self-report instruments have error, but understanding the nature of that error can lead to better assessment, analysis, and interpretation of results. The Primer includes profiles of the major self-report dietary assessment instruments, including guidance on the best uses of each instrument; discussion of validation and measurement error generally and with respect to each instrument; guidance for choosing a dietary assessment approach for different research questions; and additional resources, such as a glossary, references, and overviews of specific/important issues in the field. This monograph also describes some future research needs in the field of dietary assessment.
ABSTRACT
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.
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.
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.
We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires ...(FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.
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 him- or 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.
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