Healthy Eating Index Component Scores Research Articles

Diet Quality and Eating Frequency Were Associated with Insulin-Taking Status among Adults.

This pilot cross-sectional study explored differences in dietary intake and eating behaviors between healthy adults and a group of adults taking insulin to manage diabetes. A characteristic questionnaire and up to four Automated Self-Administered 24-Hour dietary recalls were collected from 152 adults aged 18-65 years (96 healthy and 56 adults taking insulin) from Indiana and across the U.S. from 2022 to 2023. The macronutrient intake, diet quality via the Healthy Eating Index (HEI)-2015, eating frequency, and consistency of timing of eating were calculated and compared between the two groups using adjusted linear or logistic regression models. The total mean HEI scores were very low, at 56 out of 100 and 49 out of 100 for the healthy and insulin-taking groups, respectively. Insulin-taking adults had significantly lower HEI total (p = 0.003) and component scores compared to the healthy group for greens and beans (2.0 vs. 3.0, p = 0.02), whole fruit (2.1 vs. 2.9, p = 0.05), seafood and plant proteins (2.1 vs. 3.3, p = 0.004), and saturated fats (3.7 vs. 5.4, p = 0.05). Eating frequency was significantly lower in the insulin-taking group than in the healthy group (3.0 vs. 3.4 eating occasions/day, p = 0.05). Evidence of the low diet quality and eating frequency of insulin takers may help inform and justify nutrition education to control and manage diabetes.

Evaluation of the Healthy Eating Index-Toddlers-2020.

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. 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. 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. 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. Outcomes measures included HEI-Toddlers-2020 total and component scores on menus, population distributions, and correlations. 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. 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. The results demonstrated evidence supportive of validity and reliability. The HEI-Toddlers-2020 can be used to assess alignment with the DGA for toddlers.

Dietary per- and polyfluoroalkyl substance (PFAS) exposure in adolescents: The HOME study

BackgroundDiet is the primary exposure pathway for per- and polyfluoroalkyl substances (PFAS) in non-occupationally exposed populations. Few studies have examined associations of dietary quality and macronutrient intake with PFAS exposure among US adolescents. ObjectiveTo assess relationships of self-reported dietary quality and macronutrient intake with serum PFAS concentrations in adolescents. MethodsWe used cross-sectional data from 193 Cincinnati, Ohio area adolescents (median age 12.3 years) collected from 2016 to 2019. Using 24-h food recalls completed by adolescents on three separate days, we derived Healthy Eating Index (HEI) scores, HEI components, and macronutrient intake. We measured perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA) concentrations in fasting serum samples. Using linear regression, we estimated covariate-adjusted associations between dietary variables and serum PFAS concentrations. ResultsThe median HEI score was 44 and median serum PFOA, PFOS, PFHxS, and PFNA concentrations were 1.3, 2.4, 0.7, and 0.3 ng/mL respectively. In adjusted models, higher total HEI scores, whole fruit and total fruit HEI component scores, and total dietary fiber intake were associated with lower concentrations of all four PFAS. For example, serum PFOA concentrations were 7% lower (95% CI: −15, 2) per standard deviation increase in total HEI score and 9% lower (95% CI: −18, 1) per standard deviation increase in dietary fiber. SignificanceGiven adverse health effects associated with PFAS exposure, it is crucial to understand modifiable exposure pathways. Findings from this study may inform future policy decisions aiming to limit human exposure to PFAS.

Abstract P126: Sociodemographic Predictors of Diet Quality During Adolescence

Introduction: Optimal nutrition is critical for near-term health, during periods of growth in childhood, and long-term health in adulthood (e.g., heart disease). The Healthy Eating Index (HEI) is an indicator of diet quality that is aligned with the Dietary Guidelines for Americans, which declines in U.S. children aged 2-18 years. Effective nutrition interventions are needed to address this decline in diet quality. Understanding sociodemographic determinants of diet quality in adolescents will help to inform such interventions. We aimed to describe changes in diet quality and to determine if sociodemographic factors associated with diet quality in adolescence. Methods: Participants of the Sleep and Growth Study (S-Grow) completed three 24-hour diet recalls in 8 th and 9 th grade. The diet data were processed using Nutrient Data System for Research software to calculate HEI-2015 score (range: 0-100, with a higher score indicating greater diet quality) using 13 food group components. Linear mixed effect models were used to assess: 1) changes in HEI-2015 score (and individual components) from 8 th to 9 th grade, 2) sociodemographic associations with HEI-2015 score (sex, race, body mass index [BMI], and household income), and 3) sociodemographic factor-by-grade interactions to determine if any associations differed in 8 th and 9 th grades. Results: The HEI-2015 score was stable from 8 th (44.6) to 9 th grade (45.3). Similarly, HEI component scores were stable from 8 th to 9 th grade, except for an increase in green vegetables (beta=0.37, P<0.05) and a decline in added sugars (beta=-0.50, P<0.05). Black (compared to White) adolescents and those living in households with <$40K (compared to >$99K) annual income were associated with lower overall diet quality (beta=-3.28, P<0.05; beta=-4.90, P<0.05; respectively). Male (compared to female) sex was associated with higher total dairy intake (beta=1.74, P<0.001); those with obesity (compared to normal weight) were more likely to have lower seafood intake (beta=-1.00, P<0.001) and higher refined grains intake (beta=1.26, P<0.05). The interaction analyses revealed that some household income and BMI associations with changes in HEI outcomes from 8 th and 9 th grades were distinct. For example, total fruit intake declined from 8 th to 9 th grade, but specifically among adolescents living in households with incomes of $40-69K (beta=-0.77, P=0.02) and for adolescents with obesity (beta=-1.03, P=0.003). Conclusion: Diet quality was consistently suboptimal in 8 th and 9 th grades (i.e., 45/100). Further, we detected sex, race, and household income disparities in diet quality. Overall, these data will help to inform future intervention efforts to improve and prevent reductions in diet quality in adolescents.

The impact of exercise and cumulative physical activity on energy intake and diet quality in adults enrolled in the Midwest Exercise Trial for the Prevention of Weight Regain.

The purpose of this study was to assess impact of different volumes of exercise as well as cumulative moderate to vigorous physical activity (MVPA) on energy intake (EI) and diet quality, as assessed by the Healthy Eating Index-2010(HEI-2010), across a 12-month weight maintenance intervention. Participants were asked to attend group behavioural sessions, eat a diet designed for weight maintenance and exercise either 150, 225 or 300 min/week. Dietary intake was assessed by 3-d food records, and MVPA was assessed by accelerometry. Two hundred and twenty-four participants (42·5 years of age, 82 % female) provided valid dietary data for at least one time point. There was no evidence of group differences in EI, total HEI-2010 score or any of the HEI-2010 component scores (all P > 0·05). After adjusting for age, sex, time, group and group-by-time interactions, there was an effect of cumulative MVPA on EI (1·08, P = 0·04), total HEI-2010 scores (-0·02, P = 0·003), Na (-0·006, P = 0·002) and empty energy scores (-0·007, P = 0·004. There was evidence of a small relationship between cumulative daily EI and weight (β: 0·00187, 95 % CI 0·001, P = 0·003). However, there was no evidence for a relationship between HEI total score (β: -0·006, 95 % CI 0·07, 0·06) or component scores (all P > 0·05) and change in weight across time. The results of this study suggest that increased cumulative MVPA is associated with clinically insignificant increases in EI and decreases in HEI.

Diet Quality of Frequent Fast-Food Consumers on a Non-Fast Food Intake Day Is Similar to a Day with Fast Food, What We Eat in America, NHANES 2013-2016

BackgroundFrequent fast food (FF) consumers may make more healthful food choices at eating occasions without FF. However, it is not clear if poor diet quality of frequent FF consumers is a function of FF consumption or less healthful food choices overall. ObjectiveThe objective of this study was to compare diet quality, energy, and nutrient intakes of infrequent FF consumers (INFREQ) with that of frequent FF consumers on an intake day with FF (FREQ-FF) or without FF (FREQ_NO FF). DesignThis study is a cross-sectional analysis of 1 day dietary intake data from What We Eat in America, National Health and Nutrition Examination Survey 2013-2016, the dietary intake component of National Health and Nutrition Examination Survey. Participants/settingParticipants included adults aged 20 years and older (N = 4,012), who, during the previous 7 days, reported no consumption of FF or pizza (n = 2,142 INFREQ) or reported FF or pizza three or more times and either had FF/pizza (n = 1,455 FREQ_FF) or did not have FF/pizza (n = 415 FREQ_NO FF) on the intake day. Main outcome measuresMain outcomes were energy, nutrient density (nutrient intake per 1,000 kcal), and diet quality evaluated using Healthy Eating Index (HEI) 2015. Statistical analyses performedGroup comparisons were made using t tests. Differences were considered significant at P < 0.001. ResultsCompared with INFREQ consumers, FREQ_FF consumers had higher intakes of energy and fat per 1,000 kcal, lower scores for total HEI and most components, and lower nutrient densities of most micronutrients (P < 0.001). HEI component scores of INFREQ consumers for vegetables, fruit, whole grains, and added sugars were higher than FREQ_FF consumers (P < 0.001). Energy intake, most nutrient densities, and total HEI and component scores of FREQ_NO FF consumers were not significantly different from FREQ_FF consumers with the exception of fruit but were different from INFREQ consumers for some nutrients and HEI components. ConclusionsResults suggest diet quality and nutrient intake of frequent FF consumers on a non-FF intake day is not markedly different from a day with FF.

A High-Fiber Diet Intervention Improves Diet Quality and Is Related to Blood Pressure and Bacteriome Composition in Caregiver-Child Dyads

ObjectivesHigh-fiber dietary patterns are associated with cardiovascular health and intestinal microbiome composition. Caregivers influence diets of children, affecting dyadic health and microbiomes. The objectives of this study were to evaluate the relationship between changes in caregiver diet quality and caregiver and child: 1) systolic blood pressure (SBP) and 2) fecal bacteriome composition. MethodsCaregiver-child (age 8–9 years) dyads were randomized to either a 10-week high-fiber diet intervention (fruits, vegetables, and whole grains) or control. Caregiver diet quality (30-day FFQs and Healthy Eating Index (HEI)), and caregiver and child SBP and fecal samples were collected pre- and post-intervention. Fecal bacteriomes were analyzed via 16S rRNA gene amplicon sequencing. HEI and SBP were tested via 2-way ANOVA. Bacteriome differentials (i.e., ratio of amplicon sequence variants (ASVs) with related relative abundances) associated with HEI were identified via Songbird and Qurro. Caregiver HEI was used to guide differential assembly in caregiver bacteriomes and as a proxy for child diet quality to guide differential assembly in child bacteriomes. Pearson correlations assessed inter-data relationships. ResultsComplete data were collected from 19 caregivers and 16 children. Intervention caregivers increased total (T) HEI (+4.4 vs – 1.7 points, P = 0.047) and whole grain (WG) HEI component scores (+1.7 vs 0 points, P = 0.014) compared to control. WG-HEI was correlated with caregiver SBP (r = –0.37, P = 0.046). T-HEI was correlated with caregiver and child T-HEI bacteriome differentials (r = 0.36, P = 0.027 and r = 0.57, P = 0.001). WG-HEI correlated with caregiver and child WG-HEI bacteriome differentials (r = 0.45, P = 0.013 and r = 0.60, P < 0.001). Child WG-HEI differential correlated with child SBP (r = –0.46, P = 0.01). Five ASVs were shared between caregiver and child T-HEI bacteriome differentials, implying shared bacterial responses to intervention. ConclusionsA high-fiber dietary intervention positively impacts diet quality and is related to changes in SBP and bacteriome composition in caregiver-child dyads. Metabolites derived from bacterial metabolism of dietary fiber should be investigated for their role in blood pressure. Funding SourcesUSDA NIFA, Nationwide Children’s Hospital.

Genetic and environmental influences on covariation in reproducible diet–metabolite associations

Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet-metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. This research aims to examine the genetic and environmental factors influencing covariation in diet-metabolite associations that are reproducible over time in healthy twins. The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15-0.49). For 8 diet-metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (β: 0.40-0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (β: -0.50, P<0.001) and β-hydroxybutyrate (β: -0.46, P<0.001), as well as intakes of saturated fat and acetoacetate (β: 0.47, P<0.001) and β-hydroxybutyrate (β: 0.52, P<0.001). For these diet-metabolite associations a common shared environmental factor explained 66-69% of variance in the metabolites. This study shows that diet-metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.

Do measures of healthy eating differ in survivors of early adversity?

Early life adversity has been linked to poor health, including obesity. Understanding the role of unhealthy food intake, may elucidate the importance of self-soothing behaviors in explaining the association between early life adversity and poor health in adulthood. The purpose of this study was to assess the association between early life adversity and dietary quality in a sample of adults from the Lifestyle Influences of Family Environment study. Early life adversity, demographic, and dietary data were obtained for 145 participants using formal interviews and two days of interviewer-administered 24-h recalls. Dietary quality was measured using the 2015 Healthy Eating Index (HEI) scoring algorithm to compute total and component scores. The association between early life adversity and dietary quality was assessed through linear regression and in models adjusted for age and sex. The mean ± SD HEI score for all participants was 54.6 ± 12.8. Individuals with early life adversity had a 4.51 lower overall HEI score when compared to those without early life adversity, 95% CI (0.35, 8.68). After adjusting for age and sex, early life adversity was associated with a 4.6 lower HEI score, 95% CI (0.45, 8.73). HEI component scores indicated that individuals with early life adversity were significantly more likely to have lower whole grain (0.7 versus 2.4) and total dairy (4.3 versus 6.1) scores compared to those without early life adversity. ELA was associated with lower measures of dietary quality. Results warrant future research on dietary and behavioral factors that underly the association between early life adversity and poor health outcomes.

Fruit and Vegetable Healthy Eating Index Component Scores of Distributed Food Bags Were Positively Associated with Client Diet Scores in a Sample of Rural, Midwestern Food Pantries

BackgroundFood pantries have the potential to improve the quality of clients’ diets. ObjectiveThis study evaluated the relationship between the quality of the mix of foods in pantry inventories and client food bags (separately), as assessed by the Healthy Eating Index-2010 (HEI-2010), with client diet quality and how these relationships varied by food security status. DesignThis cross-sectional, secondary analysis used baseline data from the Voices for Food intervention study (Clinical Trial Registry: NCT03566095). A demographic questionnaire, the US Household Food Security Survey Module, and up to three 24-hour dietary recalls on nonconsecutive days, including weekdays and weekends, were collected. Foods available in pantry inventories and distributed in client food bags were recorded at one time point during baseline data collection. Participants and settingA convenience sample of adult food pantry clients (N = 575) from 24 rural, food pantries in the US Midwest was recruited from August to November 2014. Main outcome measuresPantry inventories, client food bags, and client diets were scored using the HEI-2010. Main outcomes were client HEI-2010 scores. Statistical analyses performedLinear regression models estimated associations between HEI-2010 total and component scores for pantry inventories and client food bags (in separate models) and the corresponding scores for client dietary intake. The interaction of client food security status, and potential pantry- and client-level confounders, was considered. ResultsClient food bag HEI-2010 scores were positively associated with client diet scores for total vegetables, greens and beans, and total fruit components, whereas pantry inventory HEI-2010 scores were negatively associated with client diet scores for total fruit, total protein foods, and seafood and plant proteins components. Client food bag whole-grains scores were more strongly associated with very low food secure compared with food secure client diet scores (all P values < 0.05). ConclusionsThe quality of client food bags, but not of pantry inventories, was positively associated with client diet quality in a rural sample in the US Midwest.

Disparities in the Healthfulness of School Food Environments and the Nutritional Quality of School Lunches.

The Healthy, Hunger-Free Kids Act (HHFKA), a public law in the United States passed in 2010, sought to improve the healthfulness of the school food environment by requiring updated nutrition standards for school meals and competitive foods. Studies conducted since the passage of the HHFKA indicate improvements in the food environment overall, but few studies have examined whether these improvements varied by the socioeconomic and racial/ethnic composition of students in schools. To better understand the extent of disparities in the school food environment after HHFKA, this paper examined differences in the healthfulness of school food environments and the nutritional quality of school lunches by the school poverty level and racial/ethnic composition of students using data from the School Nutrition and Meal Cost Study. Results from chi-square analyses showed lower proportions of high poverty, majority black, and majority Hispanic schools had access to competitive foods, while higher proportions of these schools had a school wellness policy in addition to a district wellness policy. The overall nutritional quality of school lunches, as measured by total Healthy Eating Index (HEI)-2010 scores, did not vary significantly across school types, although some HEI component scores did. From these findings, we concluded that there were disparities in the school food environment based on the socioeconomic and racial/ethnic composition of students in schools, but no significant disparities in the overall nutritional quality of school lunches were found.

Assessing Diet Quality in a Racially and Ethnically Diverse Cohort of Low-income Toddlers.

Low-income racially and ethnically diverse children are at higher risk for obesity compared with their counterparts; yet, few studies have assessed their diet quality. The aim of the study was to evaluate the diet quality of a racially and ethnically diverse cohort of 2-year-olds using the Healthy Eating Index (HEI)-2010. We used 24-hour dietary recall data from caregivers of toddlers (24-34 months) at 4 pediatric resident clinics that participated in the Greenlight Study to calculate compliance with the Dietary Guidelines for Americans (DGA) using total HEI score (range 0-100) and 12 component scores. Participants (n = 231) were mostly Hispanic (57%) or non-Hispanic black (27%) and from low-income families. Mean HEI-2010 score was 62.8 (standard deviation [SD] 10.5). Though not significant, Hispanics had the highest HEI score. Toddlers of caregivers without obesity, older than 35 years and born outside the United States had higher HEI scores. Most had high HEI component scores for dairy, fruit, and protein foods, but few achieved maximum scores, particularly for whole grains (13%), vegetables (10%), and fatty acid ratio (7%). Despite scores reflective of DGA recommendations for fruit, dairy and protein foods, toddlers in this diverse sample had low quality diets as measured by the HEI, driven largely by low component scores for whole grains, vegetables, and ratio of unsaturated to saturated fatty acids.

Significant Relationships Exist between Perceived and Objective Diet Quality in Young Adults

BackgroundThere is a disproportionate burden of low diet quality among young adults compared with other adult subpopulations that is not understood. Perceived and objective diet qualities are studied to understand possible barriers to change. ObjectiveTo explore the association between perceived diet quality and objective diet quality in young adults in the United States. DesignThis cross-sectional study used data from the 2013-2014 National Health and Nutrition Examination Survey to calculate total Healthy Eating Index-2010 (HEI-2010) score and component scores and study their relationships with perceived diet quality scores. Participants/settingThe sample consisted of 1,261 young adults, aged 18 to 30, with 24-hour recall data. Main outcome measuresPerceived and objective diet quality were evaluated. Statistical analyses performedMultiple regression analysis was used to test the ability to predict total HEI-2010 scores from perceived diet quality scores. Ordered logit was used to test whether HEI-2010 component scores predicted the odds of having higher perceived diet quality. ResultsExcellent (b=8.442, P<0.001), very good (b=9.733, P<0.001), and good (b=5.527, P<0.001) perceived diet quality were significant predictors of total HEI-2010 score, compared with the referent category of poor perceived diet quality. The full regression model predicted 17.0% of the variance in total HEI-2010 score. Whole fruit (odds ratio [OR]=1.098, P=.017), whole grains (OR=1.046, P=.023), and empty calories (OR=1.054, P<0.001) were each significantly associated with the odds of having a higher rating of perceived diet quality (fair, good, very good, excellent) instead of a poor rating of diet quality; the difference in odds was low across categories. ConclusionsThere are significant relationships between perceived and objective diet quality, but other variables play a substantial role in explaining these two types of diet quality.

P10 Evaluating Diet Quality of College Students Using the Healthy Eating Index

Background College students face a number of challenges regarding food choices and eating behaviors and frequently consume a diet low in fruits and vegetables, and high in fat and added sugars. Objective The purpose of this study was to evaluate the dietary quality of college students using the Healthy Eating Index (HEI) to shed light on the importance of nutrition education interventions for young adults to meet the dietary guidelines. Study Design, Settings, Participants Six hundred and sixty nine college students participated in this cross-sectional survey. Participants were categorized into three groups based on percentage of body fat (under-fat, normal, and over-fat/obese) using a Tanita bioelectrical impedance analysis (BIA) scale. The National Institutes of Health Diet History Questionnaire (DHQ) was used to obtain data on dietary intake. Measurable Outcome/Analysis The DHQ data was recoded to obtain Healthy Eating Index (HEI) scores based on HEI-2015. HEI scores range from 0-100, with >80 good, 51-80 needs improvement, and >51 being poor. Mean differences in HEI scores by gender and weight status were assessed using analysis of variance (ANOVA). Results The average HEI score was 63.37 ± 9.0, and female students had a significantly higher HEI score than male students (P = .005, 63.82 ± 9.0 and 61.24 ± 8.7, respectively). In addition, female students had higher HEI component scores than males for total fruits, total vegetables, greens and beans, and whole grains. However, males had a higher HEI component score for total protein foods. Obese students had lower HEI component scores for added sugars and for seafood and plant proteins compared to normal weight students; however, there was no significant differences in total HEI scores among the three body fat category groups. Conclusion These results may contribute to the development of dietary interventions for young adults in order to prevent or postpone onset of chronic diseases later in life. Funding None.

Comparing Diet Quality of Racial Minority Children from Low-Income Households During the Summer versus School Year: Results from the Project SWEAT Study (P04-102-19)

ObjectivesCompare the diet quality of racial minority school-age children from low-income households during the summer versus school year. MethodsTwo elementary schools in low-income urban neighborhoods of Columbus, OH were recruited. Families with children at these schools were invited to participate. Caregivers completed a demographic survey at baseline (t0). Child diet was assessed using three 24-hr dietary recalls (2 weekdays and 1 weekend day) at 3 time points: 1) baseline/beginning of summer (t0); 2) middle of summer (t1); and 3) beginning of subsequent school year (t2). Demographic data were summarized. Healthy Eating Index (HEI) 2015 was calculated: total [100] and component [Adequacy: total fruit (5), whole fruit (5), total vegetable (5), greens and beans (5), whole grains (10), dairy (10), total protein (5), seafood and plant protein (5), fatty acids (10); Moderation: refined grains (10), sodium (10), added sugars (10), saturated fats (10)]. Paired t-tests were conducted to determine differences in diet quality between time points (t0: t1, t1: t2, t0: t2). Results62 children (39 families) enrolled. Mean age was 7.08 ±0.34 yr, 80.67% were African American, and 69.35% low-income. Participant retention from t0: t1 and t0: t2 was 95.16% and 75.81%. Mean total HEI was 47.37 ± 1.57 (t0), 47.51 ± 1.22 (t1), and 51.13 ± 1.46 (t2) with significant differences between summer and school year time points (t0: t2, P = 0.02; t1: t2, P = 0.01). Significant differences in adequacy and moderation HEI component scores between the two summer time points and school year time point were observed such that child diet quality was better during the school year. Adequacy: whole fruit (t1: t2 P = 0.02), total vegetable (t0: t2 P < 0.01; t1: t2 P < 0.01), greens beans (t0: t1 P < 0.01; t1: t2 P < 0.01), and seafood and plant protein (t0: t2 P = 0.06; t1: t2 P = 0.08). Moderation: sodium (t0: t2 P = 0.07) and added sugar (t0: t2 P = 0.06; t1: t2 P = 0.02). The HEI component score for whole grains was significantly better during the summer compared to the school year (t0: t2 P < 0.01; t1: t2 P < 0.01). ConclusionsExcept for whole grains, child diet quality is better during the school year versus summer. Findings from this study provide the first insight into potential determinants of unhealthy weight gain observed among economically disadvantaged, school-age children during the summer months. Funding SourcesUSDA North Central Nutrition Education for Excellence.

Nutrition Environment Scores of Local Food Retailers and Proximity to Study Participants’ Residence in Rural South (P04-157-19)

ObjectivesAn 18-month, two-arm, randomized, controlled trial designed to evaluate the comparative impact of two home visiting programs on gestational weight gain, postpartum weight loss, and diet was conducted with rural, Southern, African American women. Although nutrition education was an experimental treatment component, the intervention was not effective at improving participants’ poor diets. To better understand the dietary choices made by these women, an examination of the food environment was conducted in the towns in which they resided. MethodsFood retailers were measured with the Nutritional Environment Measures Surveys (NEMS). ArcGIS was used to geocode study participants’ residence and food retailers and to compute driving distances between participants’ residence and food retailers. Participants’ diet quality was measured with Healthy Eating Index (HEI). Associations among food retailers’ type and NEMS score, driving distances between residences and food retailers, and HEI total and component scores were examined. ResultsSignificant differences between types of food retailers were found for % of maximum NEMS scores: grocery stores (60%), full service restaurants (42%), fast food restaurants (36%), and convenience stores (26%). Participants were closer to convenience stores (mean = .4 miles) and fast food restaurants (mean = .5 miles) than to full service restaurants (mean = 1.1 miles) and grocery stores (mean = 1.6 miles). Three-fourths of participants lived within ½ mile of a convenience store and two-thirds lived within ½ mile of a fast food restaurant, while 6% lived within ½ mile of a grocery store. Only 19% shopped at their closest grocery store while two-thirds traveled a greater distance to shop at a larger national chain grocery store. The mean distance to the closest grocery store was 1.6 miles as compared to 4.2 miles for the store where most of the food was purchased. The refined grains HEI score was negatively correlated with distances to the closest convenience store, fast food restaurant, and full service restaurant. ConclusionsThe food environment of these rural, Southern, African American women provided numerous, convenient opportunities for unhealthy eating. Funding SourcesUS Department of Agriculture, Agricultural Research Service.

Healthy eating index versus alternate healthy index in relation to diabetes status and health markers in U.S. adults: NHANES 2007\u20132010

BackgroundIt remains to be determined whether the Alternate Healthy Eating Index 2010 (AHEI-2010) or the Healthy Eating Index 2010 (HEI-2010) is preferably recommended as means to assess dietary quality in people with type 2 diabetes (T2DM).MethodsThe purpose of this study was to determine whether the AHEI-2010 provides a more accurate assessment of dietary quality than the HEI-2010 in relation to diabetes status, while controlling for health markers, sociodemographic and lifestyle factors. The 2007–2010 National Health and Nutrition Examination Survey (NHANES) was used as a representative sample of U.S. adults age 20+ years (n = 4097). HEI-2010 and the AHEI-2010 scores were used as measures of dietary quality and were calculated using data from the first 24-h dietary recall. Health markers evaluated include anthropometrics, blood pressure, lipid and inflammatory markers, and presence of co-morbid diseases. Least Squares Means were computed to determine differences across diabetes status (nondiabetes, prediabetes, T2DM) for total and sub-component HEI-2010 and AHEI-2010 scores, and to determine differences across total HEI-2010 and AHEI-2010 quartiles for health markers. Covariate-adjusted logistic regression was used to examine the association between total HEI-2010 and AHEI-2010 scores and diabetes status.ResultsAdults with T2DM showed higher HEI-2010 and AHEI-2010 scores compared to adults with prediabetes and nondiabetes but did not have better health markers. For HEI-2010 component scores, adults with T2DM had highest consumption (highest score) of total protein foods and lowest consumption (highest score) for empty calories (p < 0.01). For AHEI-2010 component scores, adults with T2DM had the lowest consumption (highest score) for sugar-sweetened beverages and fruit juice, sodium, and alcohol (lowest score). In addition, adults with T2DM had the highest consumption (lowest score) for red and/or processed meats (p < 0.01). However, neither total HEI-2010 nor AHEI-2010 scores were significantly associated with diabetes status (p > 0.05). Results suggest that neither index was clearly superior to the other in terms of its predictive ability in relation to T2DM.ConclusionNeither total HEI-2010 nor AHEI-2010 scores performed better in terms of their relationship with diabetes status. However, the significant relationships between 1) diabetes status and health markers and 2) between HEI-2010 and AHEI-2010 scores and health markers suggest that diet has some influence on T2DM.

Nutrition Counseling for Hypertension Within a Grocery Store: An Example of the Patient-Centered Medical Neighborhood Model

ObjectiveTo evaluate the effectiveness of nutrition counseling for patients with hypertension, provided in a grocery store setting. DesignSingle-arm pretest–posttest design implementing a 12-week dietary intervention. SettingGrocery store. ParticipantsThirty adults with hypertension recruited from a primary care practice. InterventionRegistered dietitian nutritionists provided counseling based on the Dietary Approaches to Stop Hypertension diet. Main Outcome MeasuresDietary intake patterns and Healthy Eating Index-2010 (HEI-2010) scores measured via food-frequency questionnaire. Change in systolic blood pressure (SBP) was a secondary outcome. AnalysisPaired t tests were used to test for differences between HEI-2010 scores, intake of key food pattern components, and SBP at baseline compared with follow-up. Statistical significance was established at P ≤ .05. ResultsEight HEI-2010 component scores increased significantly from baseline to follow-up (a change toward a more desirable eating pattern): total fruit, whole fruit, greens and beans, whole grains, fatty acids, refined grains, and empty calories. Sodium (P < .001), saturated fat (P < .001), discretionary solid fat (P < .001), added sugars (P = .01), and total fat (P < .001) all decreased significantly. The change in SBP was not significant. Conclusions and ImplicationsGrocery store–based counseling for patients with hypertension may be an effective strategy to provide lifestyle counseling that is not typically available within primary care.

Where do U.S. households purchase healthy foods? An analysis of food-at-home purchases across different types of retailers in a nationally representative dataset

Food shopping decisions are pathways between food environment, diet and health outcomes, including chronic diseases such as diabetes and obesity. The choices of where to shop and what to buy are interrelated, though a better understanding of this dynamic is needed. The U.S. Department of Agriculture's nationally representative Food Acquisitions and Purchase Survey food-at-home dataset was joined with other databases of retailer characteristics and Healthy Eating Index-2010 (HEI) of purchases. We used linear regression models with general estimating equations to assess relationships between trip, store, and shopper characteristics with trip HEI scores. We examined HEI component scores for conventional supermarkets and discount/limited assortment retailers with descriptive statistics. Overall, 4962 shoppers made 11,472 shopping trips over one-week periods, 2012–2013. Trips to conventional supermarkets were the most common (53.6%), followed by supercenters (18.6%). Compared to conventional supermarkets, purchases at natural/gourmet stores had significantly higher HEI scores (β = 6.48, 95% CI = [4.45, 8.51], while those from “other” retailers (including corner and convenience stores) were significantly lower (−3.89, [−5.87, −1.92]). Older participants (versus younger) and women (versus men) made significantly healthier purchases (1.19, [0.29, 2.10]). Shoppers with less than some college education made significantly less-healthy purchases, versus shoppers with more education, as did households participating in SNAP, versus those with incomes above 185% of the Federal Poverty Level. Individual, trip, and store characteristics influenced the healthfulness of foods purchased. Interventions to encourage healthy purchasing should reflect these dynamics in terms of how, where, and for whom they are implemented.

Sugar-Sweetened Beverage and Water Intake in Relation to Diet Quality in U.S. Children

Sugar-sweetened beverages (SSBs) are a major contributor to children's added sugar consumption. This study examines whether children's SSB and water intakes are associated with diet quality and total energy intake. Using data on children aged 2-18 years from the 2009-2014 National Health and Nutrition Examination Survey, linear regression models were used to analyze SSB and water intake in relation to Healthy Eating Index 2010 (HEI-2010) scores and total energy intake. Generalized linear models were used to analyze SSB and water intake in relation to the HEI-2010 scores. Analyses were conducted including and excluding caloric contributions from SSBs and were conducted in 2016-2017. SSB intake was inversely associated with the HEI-2010 total scores (9.5-point lower score comparing more than two servings/day with zero servings/day, p-trend<0.0001) and positively associated with total energy intake (394 kcal higher comparing more than two servings/day with zero servings/day, p-trend<0.0001). The associations between SSB and HEI-2010 total scores were similar when SSBs were excluded from HEI-2010 calculations. Water intake was positively associated with HEI-2010 total scores, but not associated with total energy intake. SSB intake was inversely associated with several HEI-2010 component scores, notably vegetables, total fruit, whole fruit, greens and beans, whole grains, dairy, seafood and plant proteins, and empty calories. Water intake was positively associated with most of the same HEI-2010 component scores. Children who consume SSBs have poorer diet quality and higher total energy intake than children who do not consume SSBs. Interventions for obesity and chronic disease should focus on replacing SSBs with water and improving other aspects of diet quality that correlate with SSB consumption.