Perspective: Nutrition Health Disparities Framework: A Model to Advance Health Equity.

Disparities in nutrition, such as poor diet quality and inadequate nutrient intake, arise from multiple factors and are related to adverse health outcomes such as obesity, diabetes, cardiovascular disease, and some cancers. The aim of the current perspective is to present a nutrition-centric socioecological framework that delineates determinants and factors that contribute to diet and nutrition-related disparities among disadvantaged populations. The Nutrition Health Disparities Framework (NHDF) describes the domains (biological, behavioral, physical/built environment, sociocultural environment, and healthcare system) that influence nutrition-related health disparities through the lens of each level of influence (that is, individual, interpersonal, community, and societal). On the basis of the scientific literature, the authors engaged in consensus decision making in selecting nutrition-related determinants of health within each domain and socioecological level when creating the NHDF. The framework identifies how neighborhood food availability and access (individual/built environment) intersect with cultural norms and practices (interpersonal/sociocultural environment) to influence dietary behaviors, exposures, and risk of diet-related diseases. In addition, the NHDF shows how factors such as genetic predisposition (individual/biology), family dietary practices (interpersonal/behavioral), and food marketing policies (societal) may impact the consumption of unhealthy foods and beverages and increase chronic disease risk. Family and peer norms (interpersonal/behavior) related to breastfeeding and early childhood nutrition interact with resource-poor environments such as lack of access to preventive healthcare settings (societal/healthcare system) and low usage of federal nutrition programs (societal/behavioral), which may increase risk of poor nutrition during childhood and food insecurity. The NHDF describes the synergistic interrelationships among factors at different levels of the socioecological model that influence nutrition-related outcomes and exacerbate health disparities. The framework is a useful resource for nutrition researchers, practitioners, food industry leaders, and policymakers interested in improving diet-related health outcomes and promoting health equity in diverse populations.

Methodological examination of the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Score.

The objective of this study was to examine the impact of methodological changes to the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Score on associations with risk for all-cause mortality, cancer mortality, and cancer risk jointly among older adults in the NIH-AARP Diet and Health Study. Weights were incorporated for each Score component; a continuous point scale was developed in place of the Score's fully discrete cut-points; and cut-point values were changed for physical activity and red meat based on evidence-based recommendations. Exploratory aims also examined the impact of separating components with more than one sub-component and whether all components were necessary to retain within this population utilizing a penalized scoring approach. Findings suggested weighting the original 2018 WCRF/AICR Score improved the score's predictive performance in association with all-cause mortality and provided more precise estimates in relation to cancer risk and mortality outcomes. The importance of healthy weight, physically activity, and plant-based foods in relation to cancer and overall mortality risk were highlighted in this population of older adults. Further studies are needed to better understand the consistency and generalizability of these findings across other populations.

Conceptualization and Assessment of 24-H Timing of Eating and Energy Intake: A Methodological Systematic Review of the Chronic Disease Literature.

Timing of eating (TOE) and energy intake (TOEI) has important implications for chronic disease risk beyond diet quality. The 2020 Dietary Guidelines Advisory Committee recommended developing consistent terminology to address the lack of TOE/TOEI standardization. The primary objective of this methodological systematic review was to characterize the conceptualization and assessment of TOE/TOEI within the chronic disease literature (International Prospective Register of Systematic Reviews registration number: CRD42021236621). Literature searches in Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, Embase, PubMed, and Scopus were limited to English language publications from 2000 to August 2022. Eligible studies reported the association between TOE/TOEI and obesity, cardiovascular disease, type 2 diabetes mellitus, cancer, or a related clinical risk factor among adults (≥19 y) in observational and intervention studies. A qualitative synthesis described and compared TOE/TOEI conceptualization, definitions, and assessment methods across studies. Of the 7579 unique publications identified, 259 studies (observational [51.4 %], intervention [47.5 %], or both [1.2 %]) were eligible for inclusion. Key findings indicated that most studies (49.6 %) were conducted in the context of obesity and body weight. TOE/TOEI variables or assigned conditions conceptualized interrelated aspects of time and eating or energy intake in varying ways. Common TOE/TOEI conceptualizations included the following: 1) timepoint (specific time to represent when intake occurs, such as time of breakfast [74.8 %]); 2) duration (length of time or interval when intake does/does not occur, such as "eating window" [56.5 %]); 3) distribution (proportion of daily intake at a given time interval, such as "percentage of energy before noon" [29.8 %]); and 4) cluster (grouping individuals based on temporal ingestive characteristics [5.0 %]). Assessment, definition, and operationalization of 24-h TOE/TOEI variables varied widely across studies. Observational studies most often used surveys or questionnaires (28.9 %), whereas interventions used virtual or in-person meetings (23.8 %) to assess TOE/TOEI adherence. Overall, the diversity of terminology and methods solidifies the need for standardization to guide future research in chrononutrition and to facilitate inter-study comparisons.

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.

Time-Restricted Eating in Community-Dwelling Adults: Correlates of Adherence and Discontinuation in a Cross-Sectional Online Survey Study.

BACKGROUND: Time-restricted eating (TRE), a type of intermittent fasting in which all daily calories are consumed within a window of ≤12 hours, is hypothesized to promote long-term weight management because of its relative simplicity. OBJECTIVE: This study reports correlates of adherence among community-dwelling adults currently or formerly following a TRE dietary strategy. DESIGN: A 25-minute cross-sectional online survey was developed, including questions about TRE perceptions, behaviors, motivators and drivers, and demographics. The survey was administered in February 2021 via Prolific, an online platform for sample recruitment and survey dissemination. PARTICIPANTS: Eligibility criteria included US adult ages 18+ who currently or formerly (past 3 months) followed TRE (ie, consumed all daily calories within a window of ≤12 hours) for a minimum of 1 week. STATISTICAL ANALYSES: χ2 tests and analysis of covariance (ANCOVA; adjusting for sex and age) compared responses between current and former followers. RESULTS: Current followers (n = 296, mean [SD]: 34.2 ± 12.2y) were older than former followers (n = 295, mean [SD]: 31.1 ± 10.9 y) and practiced TRE for longer (median: 395 vs 90 days, P < 0.001). Current followers reported more success with meeting TRE goals (P ≤ 0.015), were less likely to report TRE concerns (P < 0.001), and more likely to report TRE satisfaction (P < 0.001). Four TRE motivators were more important among current (vs former) followers: weight maintenance, health (not weight), improved sleep, and preventing disease (P ≤ 0.017); weight loss was more important among former (vs current) followers (P = 0.003). Among adherence drivers, ability to work from home and the impact of COVID-19 were reported as more helpful for TRE adherence among current compared with former followers (P ≤ 0.028). CONCLUSIONS: TRE motivators and drivers differed between current and former followers; interventions tailored to individuals' preferences and circumstances may benefit TRE adherence.

Development of the Canadian Eating Practices Screener to assess eating practices based on 2019 Canada's Food Guide recommendations.

In 2019, Health Canada released a new iteration of Canada's Food Guide (2019-CFG), which, for the first time, highlighted recommendations regarding eating practices, i.e., guidance on where, when, why, and how to eat. The objective of this study was to develop a brief self-administered screener to assess eating practices recommended in the 2019-CFG among adults aged 18-65 years. Development of the screener items was informed by a review of existing tools and mapping of items onto 2019-CFG recommendations. Face and content validity were assessed with experts in public health nutrition and/or dietary assessment (n = 16) and individuals from Government of Canada (n = 14). Cognitive interviews were conducted with English-speaking (n = 16) and French-speaking (n = 16) adults living in Canada to assess face validity and understanding of the screener items. While some modifications were identified to improve relevance or clarity, overall, the screener items were found to be relevant, well-constructed, and clearly worded. This comprehensive process resulted in the Canadian Eating Practices Screener/Questionnaire court canadien sur les pratiques alimentaires, which includes 21 items that assess eating practices recommended in the 2019-CFG. This screener can facilitate monitoring and surveillance efforts of the 2019-CFG eating practices as well as research exploring how these practices are associated with various health outcomes.

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.

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.

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.

The 2018 World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) score and diabetes risk in the Diabetes Prevention Program Outcomes Study (DPPOS).

BACKGROUND: The 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) 3rd expert report highlights up-to-date Cancer Prevention Recommendations that may reduce burdens of many chronic diseases, including diabetes. This study examined if following a lifestyle that aligns with the recommendations - assessed via the 2018 WCRF/AICR Score - was associated with lower risk of type 2 diabetes in high-risk adults participating in the Diabetes Prevention Program Outcomes Study (DPPOS). METHODS: The Diabetes Prevention Program (DPP) randomized adults at high risk for diabetes to receive a lifestyle intervention (ILS), metformin (MET) or a placebo (PLB) (mean: 3.2 years), with additional follow-up in DPPOS for 11 years (mean: 15 years total). 2018 WCRF/AICR Scores included seven components: body weight, physical activity, plant-based foods, fast foods, red and processed meat, sugar-sweetened beverages, and alcohol; the optional breastfeeding component was excluded. Scores ranged 0-7 points (with greater scores indicating greater alignment with the recommendations) and were estimated at years 0, 1, 5, 6, 9, and 15 (N=3,147). Fasting glucose and HbA1c were measured every six months and oral glucose tolerance tests were performed annually. Adjusted Cox proportional hazard ratios (HRs) and 95% confidence intervals (CIs) were used to examine the association of both Score changes from years 0-1 and time-dependent Score changes on diabetes risk through DPP and year 15. RESULTS: Scores improved within all groups over 15 years (p<0.001); ILS Scores improved more than MET or PLB Scores after 1 year (p<0.001). For every 1-unit improvement from years 0-1, there was a 31% and 15% lower diabetes risk in ILS (95% CI: 0.56-0.84) and PLB (95% CI: 0.72-0.97) through DPP, and no significant association in MET. Associations were greatest among American Indian participants, followed by non-Hispanic White and Hispanic participants. Score changes from years 0-1 and time-dependent Score changes in ILS and PLB remained associated with lower risk through year 15. CONCLUSIONS: Score improvements were associated with long-term, lower diabetes risk among high-risk adults randomized to ILS and PLB, but not MET. Future research should explore impact of the Score on cancer risk. TRIAL REGISTRATION: Diabetes Prevention Program: NCT00004992 ; Diabetes Prevention Program Outcomes Study: NCT00038727.

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.

A qualitative exploration of facilitators and barriers of adherence to time-restricted eating.

Time-restricted eating (TRE), a dietary strategy that involves limiting daily energy intake to a window of ≤12 h is appealing for weight management and metabolic health due to its relative simplicity and the ability to consume ad libitum diet during eating windows. Despite the potential utility of TRE for improving health and reducing disease, the feasibility of adherence depends upon a variety of multilevel factors which are largely unexplored. The primary aim of our study was to explore facilitators and barriers of adherence to TRE among community-dwelling individuals. Semi-structured qualitative interviews were conducted among 24 individuals (50% male; M age: 34, range: 18-57; 58% overweight/obese) who currently or formerly practiced TRE. Thematic analysis identified facilitators of and barriers to TRE adherence at multiple levels of influence (i.e., biological, behavioral, psychosocial, environmental). Key facilitators of adherence included improvements in physical health and energy levels, alignment with other aspects of diet, exercise and sleep patterns, self-monitoring and positive psychological impacts, social support, and busy or regular schedules. Key barriers included negative physical health effects, feelings of hunger and sluggishness, difficulty in skipping valued baseline eating routines or inadequate diet quality during the eating window, misalignment of TRE with 24-h activity behaviors, difficulties with self-monitoring, the need to mitigate negative feelings, social situations that discourage TRE, and irregular or idle schedules. Results illustrate that key drivers of adherence differ across individuals and their unique settings and that multiple drivers of behavior should be considered in the successful implementation of TRE. Findings may inform interventions seeking to tailor TRE schedules to fit individuals' diverse behavioral patterns and preferences, thereby optimizing adherence.

The 2018 World Cancer Research Fund/American Institute for Cancer Research Score and Cancer Risk: A Longitudinal Analysis in the NIH-AARP Diet and Health Study.

BACKGROUND: We examined associations between adherence to the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Cancer Prevention Recommendations using the standardized 2018 WCRF/AICR Score and cancer risk among older U.S. adults. METHODS: Participants included 215,102 adults in the NIH-AARP Diet and Health Study followed between 2004 and 2011 (mean 7.0 person-years). Scores (range: 0-7 points) were calculated from self-reported weight, physical activity, and diet and alcohol intake measures. Outcomes included 17 cancers reviewed by WCRF/AICR (cases: male n = 11,066; female n = 8,865) and top three U.S. cancers in males (total n = 4,658; lung n = 2,211; prostate n = 920; colorectal n = 1,527) and females (total n = 5,957; lung n = 1,475; post-menopausal breast n = 3,546; colorectal n = 936). Cox proportional hazard ratios (HRs) were estimated for score and cancer risk associations, stratifying by sex and smoking status. RESULTS: Each one-point score increase was associated with 6% to 13% reduced cancer risk across combined outcomes, except for male never smokers' risk for top three cancers and male current smokers' risk for both combined cancer outcomes. Higher scores were associated with decreased lung cancer risk only among male former smokers (HR, 0.84; 95% CI, 0.79-0.89) and female current smokers (HR, 0.89; 95% CI, 0.82-0.96). Higher scores were associated with 7% to 19% decreased breast cancer risk across smoking strata and 10% to 14% decreased colorectal cancer risk among male and female never and former smokers. CONCLUSIONS: Greater recommendations adherence was associated with reduced cancer risk. IMPACT: Findings emphasize the importance of considering combined contributions of multiple lifestyle factors for cancer prevention among older adults and the potential modifying role of smoking history.