Differences in Intakes of Select Nutrients by Urbanization Level in the United States Population 2 Years and Older, NHANES 2013-2018.

BACKGROUND: Differences in nutrient intakes by urbanization level in the Unites States is not well understood. OBJECTIVE: Describe, by urbanization level: 1) intake of protein, fiber, percent of energy from added sugars (AS) and saturated fat (SF), calcium, iron, potassium, sodium, and vitamin D; 2) the percent of the population meeting nutrient recommendations. METHODS: Twenty-four-hour dietary recalls from 23,107 participants aged 2 y and over from the 2013-2018 National Health and Nutrition Examination Surveys were analyzed. Usual intakes were estimated, and linear regression models adjusted for age, sex, race and Hispanic origin, and whether family income met the 130% threshold examined intake differences by urbanization levels-large urban areas (LUA), small to medium metro areas (SMMA), and rural areas (RA). RESULTS: A small percentage of the population met the nutrient recommendations, except for protein (92.8%) and iron (70.5%). A higher percentage of the population met recommendations than SMMA and RA for fiber (11.8% compared with 8.1% and 5.3%, P < 0.001), AS (40.2% compared with 33.4% and 31.3%, P < 0.001), SF (26.8% compared with 18.2% and 20.1%, P < 0.001), and potassium (31.5% compared with 25.5% and 22.0%, P < 0.001). Mean protein intake were also higher in LUA than RA (80.0 g compared with 77.7 g, P = 0.003) and fiber intake higher in LUA than SMMA (16.5 g compared with 15.9 g, P = 0.01) and RA (16.5 g compared with 15.2 g, P = 0.001). In addition, contributions to energy intake were lower in LUA than SMMA for AS (11.3% compared with 12.0%, P < 0.001) and SF (11.5% compared with 11.7%, P < 0.001), and for LUA than RA for AS (11.3% compared with 12.9%, P < 0.001) and SF (11.5% compared with 11.8%, P < 0.001). CONCLUSIONS: RA had some markers of poorer diet quality-lower protein and fiber intake and higher AS intake-compared with LUA, and these differences persisted in adjusted regression models. These results may inform public health efforts to address health disparities by urbanization levels in the Unites States.

Perspective: A Research Roadmap about Ultra-Processed Foods and Human Health for the United States Food System: Proceedings from an Interdisciplinary, Multi-Stakeholder Workshop.

Our objective was to convene interdisciplinary experts from government, academia, and industry to develop a Research Roadmap to identify research priorities about processed food intake and risk for obesity and cardiometabolic diseases (CMD) among United States populations. We convened attendees at various career stages with diverse viewpoints in the field. We held a "Food Processing Primer" to build foundational knowledge of how and why foods are processed, followed by presentations about how processed foods may affect energy intake, obesity, and CMD risk. Breakout groups discussed potential mechanistic and confounding explanations for associations between processed foods and obesity and CMD risk. Facilitators created research questions (RQs) based on key themes from discussions. Different breakout groups convened to discuss what is known and unknown for each RQ and to develop sub-RQs to address gaps. Workshop attendees focused on ultra-processed foods (UPFs; Nova Group 4) because the preponderance of evidence is based on this classification system. Yet, heterogeneity and subjectivity in UPF classification was a challenge for RQ development. The 6 RQs were: 1) What objective methods or measures could further categorize UPFs, considering food processing, formulation, and the interaction of the two? 2) How can exposure assessment of UPF intake be improved? 3) Does UPF intake influence risk for obesity or CMDs, independent of diet quality? 4) What, if any, attributes of UPFs influence ingestive behavior and contribute to excess energy intake? 5) What, if any, attributes of UPFs contribute to clinically meaningful metabolic responses? 6) What, if any, external environmental factors lead people to consume high amounts of UPFs? Uncertainty and complexity around UPF intake warrant further complementary and interdisciplinary causal, mechanistic, and methodological research related to obesity and CMD risk to understand the utility of applying classification by degree of processing to foods in the United States.

Validation of Total Water Intake from the Automated Self-Administered 24-h Recall, 4-d Food Records, and a Food Frequency Questionnaire Using Doubly Labeled Water.

BACKGROUND: Although prior evidence indicates that water intake is important for health, the ability to accurately measure community-dwelling intake is limited. Only a few studies have evaluated self-reported water intake against an objective recovery biomarker. OBJECTIVES: The aim was to compare preformed water intakes (all sources including food) by multiple Automated Self-Administered 24-h recalls (ASA24s), food frequency questionnaires (FFQs), and 4-d food records (4DFRs) against a recovery biomarker, doubly labeled water (DLW), to assess measurement error. METHODS: Over 1 y, 1082 women and men (50%), aged 50 to 74 y, were asked to complete 6 ASA24s, 2 FFQs, 2 unweighted 4DFRs, and an administration of DLW (n = 686). Geometric means of water intake by self-report tools were compared with DLW. Attenuation factors and correlation coefficients between self-reported and the recovery biomarker (DLW) were estimated. RESULTS: Mean water intakes by DLW were 2777 mL/d (interquartile range, 2350 to 3331) in women and 3243 mL/d (interquartile range, 2720 to 3838) in men. Compared with DLW, water intake was underestimated by 18% to 31% on ASA24s and 43% to 44% on 4DFRs. Estimated geometric means from FFQs differed from DLW by -1% to +13%. For a single ASA24, FFQ, and 4DFR, attenuation factors were 0.28, 0.27, and 0.32 and correlation coefficients were 0.46, 0.48, and 0.49, respectively. Repeated use of 6 ASA24s, 2 FFQs, and 2 4DFRs improved attenuation factors to 0.43, 0.32, and 0.39 and correlation coefficients to 0.58, 0.53, and 0.54, respectively. CONCLUSIONS: FFQs may better estimate population means for usual water intake compared with ASA24 and 4DFR. Similar attenuation factors and correlation coefficients across all self-report tools indicate that researchers have 3 feasible options if the goal is understanding intake-disease relationships. The findings are useful for planning future nutrition studies that set policy priorities for populations and to understand the health impact of water. This trial was registered at clinicaltrials.gov as NCT03268577.

Differential associations between television viewing, computer use, and adiposity by age, gender, and race/ethnicity in United States youth: A cross-sectional NHANES analysis.

BACKGROUND: Time spent on screens and adiposity change rapidly from childhood to adolescence, with differences by gender and race/ethnicity. OBJECTIVE: Apply time-varying effect models (TVEMs) to a nationally representative sample of youth to identify the age ranges when the cross-sectional associations between television viewing, computer use, and adiposity are significant. METHODS: Data from 8 to 15-year-olds (n = 3593) from the National Health and Nutrition Examination Survey (2011-2018) were extracted. TVEMs estimated the associations between television viewing, computer use, and fat mass index as dynamic functions of the participants' age, stratified by gender and race/ethnicity. RESULTS: TVEMs revealed age-specific statistically significant associations that differed by gender and race/ethnicity. Notably, computer use was related to higher adiposity in non-Hispanic White females aged 9.3-11.4 years (slope ß-range: 0.1-0.2) and in non-Hispanic Black females older than 14.8 years (ß-range: 0.1-0.5). In males, these age windows were 13.5-15.0 years (non-Hispanic White, ß-range: 0.1-0.2), 11.4-13.0 years (non-Hispanic Black, ß-range: 0.1-0.14), and older than 13.0 years (Hispanic, ß-range: 0.1-0.4). CONCLUSIONS: More research during the specific age ranges in the demographic subgroups identified here could increase our understanding of tailored interventions in youth.

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

BACKGROUND: Objective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health. OBJECTIVES: To identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification. METHODS: In a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m2] 22 ± 11.6) consumed ad libitum a UPF-DP (80% UPF) and an unprocessed DP (UN-DP; 0% UPF) for 2 wk each. Metabolites were measured using liquid chromatography with tandem mass spectrometry in ethylenediaminetetraacetic acid plasma, collected at week 2 and 24-h, and spot urine, collected at weeks 1 and 2, of each DP. Linear mixed models, adjusted for energy intake, were used to identify metabolites that differed between DPs. RESULTS: After multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP. CONCLUSIONS: Consuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Food Processing, According to the Nova Classification System, and Dietary Intake of US Infants and Toddlers.

BACKGROUND: High intake of ultra-processed foods (UPFs) is associated with increased risk of chronic disease; thus, it is important to understand how UPFs influence diet quality early in life. OBJECTIVES: We describe complementary foods and beverages (CFBs) according to the Nova Classification System of Food Processing for infants and toddlers in the United States and estimate how Nova groups and subgroups contribute to energy and select nutrients and food groups. METHODS: We used day 1 24-h recall from infants and toddlers aged 6-23 mo from the cross-sectional, nationally representative 2013-18 National Health and Nutrition Examination Survey (n = 1140). We estimated contributions of Nova groups and subgroups to energy and select nutrients and food groups consumed as CFBs (excluding human milk and formula) using the population ratio with weighted survey commands in SAS. RESULTS: For infants and toddlers in the United States, 42 ± 0.9% (mean ± standard error of the mean) of energy intake from CFBs came from unprocessed/minimally processed foods (U/MPFs) and 45 ± 0.8% from UPFs. U/MPFs contributed most to nutrient intakes (except iron, zinc, and sodium); ≥20% of all selected nutrients was from UPFs. UPFs contributed most to iron (75 ± 1.0%) and zinc (48 ± 1.3%); breakfast cereals were the top source. Most fruit, vegetables, and dairy were from U/MPFs. More than 80% of total grains, whole grains, refined grains, and added sugars were UPFs. CONCLUSIONS: U/MPFs support healthy dietary intake of infants and toddlers in the United States, whereas UPFs contribute meaningfully to nutrients and food groups to be encouraged (iron, zinc, and whole grains), as well as some that should be limited (added sugars and sodium). More research is needed to better understand the utility and sensitivities of using Nova for providing dietary guidance for infants and toddlers in the United States.

Healthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans,2020-2025.

The purpose of this review is to share the process for reviewing, updating, and developing the most recent version of the Healthy Eating Index-2020 (HEI-2020) for ages 2 and older, following the release of the Dietary Guidelines for Americans (DGA), 2020-2025. The overall review process included: 1) gathering information from the updated DGA, experts, and federal stakeholders; 2) considering substantive changes and needs for new development, keeping in mind the HEI's key features and guiding principles, the US Department of Agriculture's Dietary Patterns that serve as the foundation for the HEI, and scoring considerations; and 3) completing evaluation analyses, including the examination of content validity. The review process led to the development of the HEI-2020; a separate HEI-Toddlers-2020 was developed for ages 12 through 23 months. The 13 components and scoring standards of the HEI-2020 fully align with the HEI-2015, although the index was renamed to clarify that it aligned with the most recent 2020-2025 DGA. As the evidence informing the DGA continues to evolve, various aspects of the HEI may need to evolve in the future as well. Further methodological research is encouraged to add to the scientific evidence base on dietary patterns, to examine needs specific to each life stage, and to model optimal trajectories of healthy dietary patterns over the lifespan.

Evaluation of the Healthy Eating Index-Toddlers-2020.

BACKGROUND: With the addition of new guidance for children from birth to 24 months in the Dietary Guidelines for Americans, 2020-2025 (DGA), a Healthy Eating Index (HEI) was developed for toddlers. OBJECTIVE: To evaluate the psychometric properties of the HEI-Toddlers-2020, 5 analyses relevant to construct and concurrent validity and 2 related to reliability were examined. DESIGN: Twenty-four-hour diet recall data from the cross-sectional National Health and Nutrition Examination Survey (2011-2018) were used. In addition, exemplary menus were analyzed. PARTICIPANTS/SETTING: The main analytic sample included toddlers aged 12 through 23 months (n = 838), with additional analyses of toddlers aged 12 through 35 months (n = 1,717) from the United States. Included participants had valid diet recalls and available weight-for-age data. MAIN OUTCOME MEASURES: Outcomes measures included HEI-Toddlers-2020 total and component scores on menus, population distributions, and correlations. STATISTICAL ANALYSES: HEI total and component scores were calculated using menus from the American Academy of Pediatrics and Healthy Eating Research. Score means and distributions were estimated using a Markov Chain Monte Carlo approach with National Health and Nutrition Examination Survey data (2011-2018). Principal component analysis explored dimensions and Pearson correlations examined components, energy, and Cronbach α. In addition, HEI-Toddlers-2020 and HEI-2020 scores were compared for identical intakes at age 24 months. RESULTS: For validity, exemplary menus received high scores with the HEI-Toddlers-2020. The mean ± SE total HEI-Toddlers-2020 score for toddlers aged 12 through 23 months was 62.9 ± 0.78 and ranged from 40.1 to 84.4 (1st to 99th percentile). Correlation between diet quality and diet quantity was low (-0.15); the scree plot revealed multiple factors. In addition, total scores for identical intakes were approximately 1.5 points higher for HEI-Toddlers-2020 compared with HEI-2020 (difference range for component scores, -4.97 to 4.89). For reliability, most of the intercorrelations among components were low to moderate (0 to 0.49), with a few exceptions among related components. Cronbach α was .48. These results indicate that the index is multidimensional, with no single component driving the total score, and no unnecessary components that are highly correlated with another component. CONCLUSIONS: The results demonstrated evidence supportive of validity and reliability. The HEI-Toddlers-2020 can be used to assess alignment with the DGA for toddlers.

Continuity, Considerations, and Future Directions for the Healthy Eating Index-Toddlers-2020.

The Dietary Guidelines for Americans, 2020-2025 includes guidance for infants and toddlers aged birth to 24 months. To assess alignment with this new guidance, the Healthy Eating Index (HEI)-Toddlers-2020 was developed for toddlers 12 through 23 months old. This monograph focuses on the continuity, considerations, and future directions of this new index for toddlers in the context of evolving dietary guidance. There is considerable continuity between the HEI-Toddlers-2020 and previous versions of the HEI. The same process, guiding principles, and features (with caveats) are repeated in the new index. However, there are unique considerations for measurement, analysis, and interpretation for the HEI-Toddlers-2020 that this article addresses, while identifying future directions for the HEI-Toddlers-2020. The continued evolution of dietary guidance for infants, toddlers, and young children will provide additional opportunities for index-based metrics: considering inclusion of multidimensional layers in dietary patterns, defining a healthy eating trajectory, bridging healthy eating across different life stages, and communicating the constructs of balance among dietary constituents.

Development of the Healthy Eating Index-Toddlers-2020.

The Healthy Eating Index-Toddlers-2020 (HEI-Toddlers-2020) is a measure for assessing how well a set of foods aligns with new guidance in the Dietary Guidelines for Americans, 2020-2025 (DGA) for toddlers aged 12 through 23 months. This new tool was developed using consistent features and the guiding principles of the HEI. The HEI-Toddlers-2020, like HEI-2020, has 13 components reflecting all constituents of dietary intake, except for human milk or infant formula. These components include the following: Total Fruits, Whole Fruits, Total Vegetables, Greens and Beans, Whole Grains, Dairy, Total Protein Foods, Seafood and Plant Proteins, Fatty Acids, Refined Grains, Sodium, Added Sugars, and Saturated Fats. Healthy dietary patterns for toddlers have unique considerations reflected in the scoring standards for Added Sugars and Saturated Fats. Toddlers have lower energy intake relative to high nutrient needs and added sugars should be avoided. Another distinctive difference is that there is no recommendation to limit saturated fats to <10% of energy intake in this age group; however, saturated fats cannot be unlimited without displacing the energy available to achieve other food group and subgroup goals. Calculations using the HEI-Toddlers-2020, like the HEI-2020, result in a total score and a set of individual component scores that reveal a dietary pattern. The release of a HEI-Toddlers-2020 will enable assessment of diet quality that aligns with the DGA and support additional methodological research to examine needs specific to each life stage and how to model trajectories of healthy dietary patterns.

Identifying and Estimating Ultraprocessed Food Intake in the US NHANES According to the Nova Classification System of Food Processing.

BACKGROUND: The degree of food processing may be an important dimension of diet in how it relates to health outcomes. A major challenge is standardizing food processing classification systems for commonly used datasets. OBJECTIVES: To standardize and increase transparency in its application, we describe the approach used to classify foods and beverages according to the Nova food processing classification in the 24-h dietary recalls from the 2001-2018 cycles of What We Eat in America (WWEIA), NHANES, and investigate variability and potential for Nova misclassification within WWEIA, NHANES 2017-2018 data via various sensitivity analyses. METHODS: First, we described how the Nova classification system was applied to the 2001-2018 WWEIA, NHANES data using the reference approach. Second, we calculated the percentage energy from Nova groups [1: unprocessed or minimally processed foods, 2: processed culinary ingredients, 3: processed foods, and 4: ultraprocessed foods (UPFs)] for the reference approach using day 1 dietary recall data from non-breastfed participants aged ≥1 y from the 2017-2018 WWEIA, NHANES. We then conducted 4 sensitivity analyses comparing potential alternative approaches (e.g., opting for more vs. less degree of processing for ambiguous items) to the reference approach, to assess how estimates differed. RESULTS: The energy contribution of UPFs using the reference approach was 58.2% ± 0.9% of the total energy; unprocessed or minimally processed foods contributed 27.6% ± 0.7%, processed culinary ingredients contributed 5.2% ± 0.1%, and processed foods contributed 9.0% ± 0.3%. In sensitivity analyses, the dietary energy contribution of UPFs ranged from 53.4% ± 0.8% to 60.1% ± 0.8% across alternative approaches. CONCLUSIONS: We present a reference approach for applying the Nova classification system to WWEIA, NHANES 2001-2018 data to promote standardization and comparability of future research. Alternative approaches are also described, with total energy from UPFs differing by ∼6% between approaches for 2017-2018 WWEIA, NHANES.

A Decade of Dietary Assessment Methodology Research at the National Institutes of Health, 2012-2021.

BACKGROUND: Assessment of individual and population-level dietary intake is critical for public health surveillance, epidemiology, and dietary intervention research. In recognition of that need, the National Insitutes of Health (NIH) has a history of funding research projects designed to support the development, implementation, and refinement of tools to assess dietary intake in humans. OBJECTIVES: This report provides data and information on NIH-funded dietary intake assessment methodological research over the period of 2012-2021. METHODS: Data were extracted from an internal NIH data system using the Research, Condition, and Disease Categorization (RCDC) spending category for Nutrition. Data were then examined to identify research focused on dietary assessment tools or methods to capture or analyze dietary intake. RESULTS: Over the decade of 2012-2021, NIH supported 46 grants and 2 large contracts specific to dietary assessment methods development. The top 6 Institutes and Offices funding dietary assessment methods research were identified. Most projects were limited to adults. Projects ranged from novel methods to capture dietary intake, and refinement of analytical methods, to biomarkers of dietary intake. One key contract supported the automated self-administered 24-h dietary assessment tool (ASA24), a widely used, free tool available to the research community for assessing dietary intake. CONCLUSIONS: NIH's support for dietary assessment methods development over this 10-y period was small but grew over time with an expanding number and variety of methods, data sources, and technological advancements in the assessment of dietary intake. NIH remains committed to supporting research seeking to advance the field of dietary assessment methods research.

Dietary contributions of food outlets by urbanization level in the US population aged 2 years and older-NHANES 2013-2018.

BACKGROUND: Differences in food access, availability, affordability, and dietary intake are influenced by the food environment, which includes outlets where foods are obtained. These differences between food outlets within rural and urban food environments in the United States are not well understood. OBJECTIVES: The aim of this analysis is to describe the contribution of foods and beverages from 6 outlets-grocery stores, convenience stores, full-service restaurants, quick-service restaurants, schools, and other outlets-to the total energy intake and Healthy Eating Index (HEI)-2015 scores in the United States population, by urbanization level (nonmetropolitan statistical areas [MSAs], small-to-medium MSAs, and large MSAs). METHODS: Data from the National Health and Nutrition Examination Survey 2013-2018 were used. Dietary intake from one 24-h dietary recall was analyzed by the outlet where a food or beverage was obtained and by urbanization. Linear regression, adjusted for sex, age, race and Hispanic origin, and family income, was used to predict the contribution of each food outlet to the total energy intake and HEI-2015 total and component scores by urbanization level. RESULTS: During 2013-2018, foods and beverages from grocery stores and quick-service and full-service restaurants contributed to 62.1%, 15.1%, and 8.5% of the energy intake, respectively. The percentage of energy intake from full- and quick-service restaurants increased with increasing urbanization level. HEI-2015 total scores increased with the increasing urbanization level overall (48.1 non-MSAs, 49.2 small-to-medium MSAs, and 51.3 large MSAs) for grocery stores (46.7 non-MSAs, 48.0 small-to-medium MSAs, and 50.6 large MSAs) and for quick-service restaurants (35.8 non-MSAs, 36.3 small-to-medium MSAs, and 37.5 large MSAs). CONCLUSIONS: Grocery stores and restaurants were the largest contributors of energy intake in urban and rural areas. Diet quality improved with increasing urbanization overall and for grocery stores and quick-service restaurants.

Top sources and trends in consumption of total energy and energy from solid fats and added sugars among youth aged 2-18 years: United States 2009-2018.

BACKGROUND: High energy intake from non-nutrient-dense sources correlates with poorer diet quality. OBJECTIVES: The aims were to 1) estimate total energy intake and energy from solid fats and added sugars combined (SoFAS) and identify their top food category sources for ages 2-18 y in 2015-2018 and 2) describe trends over time in 2009-2018. METHODS: Data were from the NHANES. Pairwise differences were examined using univariate t statistics (2015-2018, n = 5038), and trends by age and over time (2009-2018, n = 14,038) were examined using orthogonal polynomials. RESULTS: In 2015-2018, SoFAS contributed a mean (SE) of 30.0% (0.3%) of total energy. Solid fats [16.1% (0.2%)] and added sugars [13.8% (0.2%)] each contributed >10%. The contribution of added sugars increased with age from 11.1% (2-3 y) to 14.4% (14-18 y), and was higher for all other race/Hispanic origins than non-Hispanic Asians. The top 5 sources of energy were sweet bakery products, savory snacks, pizza, other mixed dishes, and unflavored milk, and for SoFAS also included soft drinks, other desserts, candy, and snack bars. Total energy did not change between 2009 and 2018, but energy from SoFAS, and servings of solid fats, and added sugars declined. The contribution of unflavored milk to total energy declined for all ages and most race/Hispanic origins. Fruit drinks (all ages) and soft drinks (9-18 y) remained among top added sugars sources despite declines. The contribution of sweet bakery products to energy from SoFAS increased for most ages and candy and snack bars to energy from added sugars. CONCLUSIONS: In 2015-2018, SoFAS contributed >30% of total energy for ages 2-18 y, which doubled the Dietary Guidelines for Americans' recommended limit of 15%. The top 5 sources of total energy were similar to those of solid fats, and those of SoFAS similar to those of added sugars. These results may inform public health efforts for improving diet quality.

The Accuracy of Portion Size Reporting on Self-Administered Online 24-Hour Dietary Recalls Among Women With Low Incomes.

BACKGROUND: Accurately estimating portion sizes remains a challenge in dietary assessment. Digital images used in online 24-hour dietary recalls may be conducive to accuracy. OBJECTIVE: The current analyses were conducted to examine the accuracy of portion size estimation by women with low incomes who completed 24-hour dietary recalls using the online Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24) in the Food and Eating Assessment Study II. DESIGN: True dietary intake was observed for 3 meals on 1 day through a controlled feeding study conducted from May through July 2016. The following day, participants completed an unannounced 24-hour dietary recall using ASA24, independently or with assistance in a small-group setting. PARTICIPANTS/SETTING: Participants included 302 women aged 18 to 82 years living in the Washington, DC, area who met the income thresholds for the Supplemental Nutrition Assistance Program. MAIN OUTCOME MEASURES: The accuracy of portion size estimation was assessed by comparing the weight truly consumed (observed) and the weight reported for predetermined categories of foods and beverages. STATISTICAL ANALYSES PERFORMED: The differences between observed and reported portions were examined and linear regression tested differences by recall condition. Analyses were conducted by condition and repeated with stratification by racial/ethnic identity, education, and body mass index. RESULTS: On average across foods and beverages, reported portion sizes were 7.4 g (95% CI, 4.3-10.5) and 6.4 g (95% CI, 2.8-10.0) higher than observed portion sizes in the independent and assisted conditions, respectively. Portion sizes were overestimated for small pieces and shaped foods in both conditions, as well as for amorphous/soft foods in the assisted condition and underestimated for single-unit foods in both conditions. Misestimation was fairly consistent by participants' race/ethnicity, education, and body mass index, to varying magnitudes. CONCLUSIONS: Women with low incomes overestimated the amounts of foods and beverages consumed across several categories using online 24-hour dietary recalls with digital images to support portion size estimation. Assistance with ASA24 had little impact on accuracy.

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.

Dietary and Complementary Feeding Practices of US Infants, 6 to 12 Months: A Narrative Review of the Federal Nutrition Monitoring Data.

Complementary foods and beverages (CFBs) are key components of an infant's diet in the second 6 months of life. This article summarizes nutrition and feeding practices examined by the 2020 Dietary Guidelines Advisory Committees during the CFB life stage. Breastfeeding initiation is high (84%), but exclusive breastfeeding at 6 months (26%) is below the Healthy People 2030 goal (42%). Most infants (51%) are introduced to CFBs sometime before 6 months. The primary mode of feeding (ie, human milk fed [HMF]; infant formula or mixed formula and human milk fed [FMF]) at the initiation of CFBs is associated with the timing of introduction and types of CFBs reported. FMF infants (42%) are more likely to be introduced to CFBs before 4 months compared with HMF infants (19%). Different dietary patterns, such as higher prevalence of consumption and mean amounts, were observed, including fruit, grains, dairy, proteins, and solid fats. Compared with HMF infants of the same age, FMF infants consume more total energy (845 vs 631 kcal) and protein (22 vs 12 g) from all sources, and more energy (345 vs 204 kcal) and protein (11 vs 6 g) from CFBs alone. HMF infants have a higher prevalence of risk of inadequate intakes of iron (77% vs 7%), zinc (54% vs <3%), and protein (27% vs <3%). FMF infants are more likely to have an early introduction (<12 months) to fruit juice (45% vs 20%) and cow's milk (36% vs 24%). Registered dietitian nutritionists and nutritional professionals should consider tailoring their advice to caregivers on dietary and complementary feeding practices, taking into account the primary mode of milk feeding during this life stage to support infants' nutrient adequacy. National studies that address the limitations of this analysis, including small sample sizes and imputed breast milk volume, could refine findings from this analysis.

A Standardized Assessment of Processed Red Meat and Processed Poultry Intake in the US Population Aged ≥2 Years Using NHANES.

BACKGROUND: Standardized methods are needed to investigate intake patterns of processed meat subtypes, considering health concerns surrounding processed meat intake. OBJECTIVES: The objectives of this study were to create a standardized method of disaggregating processed meat into processed red meat and processed poultry and describe intake patterns of the US population aged ≥2 y. METHODS: Two researchers independently manually disaggregated processed meat from the Food Patterns Equivalents Database into processed red meat and processed poultry based on available information from the Foods and Nutrient Database for Dietary Studies. We created an SAS program (called Processed Meat Categories) to mimic the manual coding. We used the program to describe intake patterns and trends over time of processed red meat and processed poultry using 24-h recalls from 2007-2008 through 2017-2018 NHANES data with SAS survey-weighted procedures for complex surveys. RESULTS: The SAS program had high agreement with the manual code (Pearson concordance correlation ≥0.95). Of the US population aged ≥2 y, 46.8% (95% CI: 45.3, 48.2%) reported consuming any processed meat, 42.5% (95% CI: 41.0, 43.9%) reported consuming processed red meat, and 11.3% (95% CI: 10.2, 12.4%) reported consuming processed poultry. Most [74.1 ± 0.13% (SEM)] processed meat reported was red meat compared with poultry, and 32.1 ± 0.01% of total red meat and 13.7 ± 0.01% of total poultry reported were processed. Prevalence of processed poultry intake increased from 9.5% (95% CI: 8.9, 10.1%) in 2007-2010 to 11.3% (95% CI: 10.2, 12.4%) in 2015-2018 (P < 0.0001), but mean intake amount did not change. Prevalence of processed red meat intake did not change over time, but mean intake decreased from 0.8 ± 0.03 ounce-equivalents in 2007-2010 to 0.7 ± 0.02 ounce-equivalents (P = 0.0058) in 2015-2018. CONCLUSIONS: The Processed Meat Categories SAS program is a tool available for researchers to standardize estimates of processed meat subtypes for future dietary patterns research. Intake of total processed meat did not change in the United States, but intake amount of processed red meat decreased and the prevalence of processed poultry consumers increased.