Participant Characteristics Associated with High Responsiveness to Personalized Healthy Food Incentives: a Secondary Analysis of the Randomized Controlled Crossover Smart Cart Study.

BACKGROUND: Personalized dietary behavioral interventions could be enhanced by understanding factors accounting for individual variability in dietary decisions. OBJECTIVE: This study was a secondary analysis of the Smart Cart randomized controlled trial to determine whether participant characteristics predicted high responsiveness to personalized grocery incentives for purchasing healthy food. METHODS: This secondary analysis of a 9-mo crossover study included 192 regular shoppers (86%) from a Rhode Island supermarket. To analyze whether health, behavioral, and/or sociodemographic characteristics predicted responsiveness to a personalized grocery incentive intervention, participants were divided into 3 categories [high (n = 47), moderate (n = 50), and unresponsive (n = 95)] based on percentage changes in their Grocery Purchase Quality Index scores during the intervention versus control period calculated from sales data. We tested whether participant characteristics, including individual, household, and intervention-related factors, differed across responsiveness groups using ANOVA and whether they predicted the likelihood of being high responsive versus unresponsive or moderate responsive using logistic regression. RESULTS: Participants had a mean (SD) age of 56.0 (13.8) y and were 89% female. Education, self-reported BMI, income, diet-related medical condition, food insecurity, cooking adequacy, and value consciousness differed across responsiveness categories (P < 0.1). High versus moderate and unresponsive participants increased their percentage of spending on targeted foods (P < 0.0001) and purchased fewer unique items (P = 0.01). In multinomial adjusted models, the odds of being high versus unresponsive or moderate responsive were lower for participants with a BMI (in kg/m2) <25 versus ≥25 (OR: 0.41; 95% CI: 0.19, 0.90) and higher with a diet-related medical condition present (OR: 3.75; 95% CI: 1.20, 11.8). Other characteristics were not associated with responsiveness. CONCLUSIONS: Findings demonstrated that a BMI ≥25 and having a diet-related medical condition within the household predicted high responsiveness to a personalized grocery purchasing intervention, suggesting that personalized dietary interventions may be particularly effective for households with higher health risk. This trial is registered at www.clinicaltrials.gov as NCT03748056.

The relationship between maternal prenatal and postnatal vegetable intake and repeated measures of infant vegetable intake frequency in a national U.S. sample.

Experimental research suggests that passive flavor transfer from maternal diet to the infant via amniotic fluid and breastmilk may improve infant vegetable intake. This secondary analysis examined associations between maternal (prenatal and postnatal) and infant vegetable intake in 696 mothers with eligible dietary data from the U.S. longitudinal Infant Feeding Practices Study II. Adjusted mixed models examined associations between 4 levels of maternal vegetable intake (mean splits of high/low on prenatal and postnatal food frequency questionnaires) and repeated measures of infant vegetable intake frequency (times/day, from monthly surveys). Mothers were on average 29.5 years old, mostly non-Hispanic White (86.2%) and educated (84.0% ≥some college). In base models, mothers with consistently high vegetable intake (vs. consistently low) reported more frequent infant vegetable intake. In multivariable models, infant vegetable intake was significantly more frequent amongst mothers with consistently high prenatal/high postnatal intake (0.9 times/day) versus consistently low intake (0.8 times/day). In this sample, maternal vegetable consumption was associated with frequency of infant vegetable consumption; consistently high vegetable intake across prenatal and postnatal periods was most strongly associated with infant intake. While infant vegetable intake is multifactorial, maternal prenatal and postnatal vegetable intake appeared to have a small but significant influence.

Contributions of Food Environments to Dietary Quality and Cardiovascular Disease Risk.

PURPOSE OF REVIEW: To evaluate the multidimensional influence of food environments on food choice, dietary quality, and diet-related health and identify critical gaps necessary to develop effective population interventions that influence food choice. RECENT FINDINGS: Multicomponent interventions that interact with multiple layers of the food environment show limited but consistent effects on dietary behaviors and may have wider and substantive population-level reach with greater incorporation of validated, holistic measurement tools. Opportunities to use smartphone technology to measure multiple components of the food environment will facilitate future interventions, particularly as food environments expand into online settings and interact with consumers in novel ways to shape food choice. While studies suggest that all dimensions of the food environment influence diet and health outcomes, robust and consistent measurements of food environments that integrate objective and subjective components are essential for developing stronger evidence needed to shift public policies.

Socio-economic and racial prenatal diet quality disparities in a national US sample.

OBJECTIVE: To examine differences in prenatal diet quality by socio-economic status (SES) and race/ethnicity. DESIGN: A secondary, cross-sectional analysis. Race and SES were self-reported prenatally; SES was categorized into four groups: high-income, middle-income and low-income WIC (Special Supplemental Nutrition Program for Women, Infants, and Children) participant/non-participant. The Alternative Healthy Eating Index for Pregnancy (AHEI-P) measured diet quality, including four moderation and nine adequacy components (higher scores = healthier diet). Generalized linear models adjusted for covariates and post hoc testing with Tukey adjustment compared AHEI-P scores between groups, using a threshold of P < 0·05. SETTING: Infant Feeding Practices Study II, conducted in a national US convenience cohort. PARTICIPANTS: Women in their third trimester (n 1322) with dietary history. RESULTS: Participants were of 28·9 (se 5·6) years on average and predominantly non-Hispanic White (84 %); approximately one-third participated in WIC and 17 % were high-income. The mean AHEI-P score was 61·7 (se 10·8) of 130 points. High-income women had higher total (62·4 (se 1·0)) and moderation component AHEI-P scores than middle-income (60·1 (se 0·8), P = 0·02), low-income WIC participants (58·3 (se 0·8), P < 0·0001) and non-participants (58·9 (se 0·9), P = 0·001). Non-Hispanic Black participants had lower total (57·8 (se 1·4)) and adequacy scores than Other races (i.e. neither non-Hispanic Black nor White, 62·1 (se 0·9), P = 0·02). CONCLUSIONS: Disparities in prenatal diet quality were observed, with non-Hispanic Black women, low-/middle-income and WIC participants having lower diet quality. However, interventions are needed to improve prenatal diet quality broadly among US women.

Modified-Weight Healthy Eating Index-2015 Scores Are More Strongly Associated With Mortality Risk Than Standard Scores.

BACKGROUND: Various diet quality scores are consistently and similarly associated with mortality risk. Emerging evidence suggests that individual diet quality components are differentially associated with mortality risk, but it is unclear whether or not modified weights reflective of relative component differences would strengthen mortality associations. OBJECTIVE: This study examined whether Healthy Eating Index 2015 (HEI-2015) scores with modified (vs standard) component weights are differentially associated with mortality risk. DESIGN: This was a longitudinal analysis of the National Health and Nutrition Examination Survey III (1988-94) with 23 years of mortality follow-up. The HEI-2015 and modified-weight scores were calculated from one 24-hour recall. The a priori Key Facets HEI was a subset score equivalently weighting fruits, vegetables, whole grains, and seafood and plant proteins. In the least absolute shrinkage and selection operator regression-weighted HEI, components were assigned weights reflecting relative mortality risk associations. PARTICIPANTS/SETTING: Analyses included 10,789 US adults (aged 20 years and older) who were not pregnant and without prior diabetes, cancer, cardiovascular disease, or chronic kidney disease diagnoses. MAIN OUTCOME MEASURES: All-cause and cardiovascular disease mortality risk were the primary outcomes examined. STATISTICAL ANALYSES PERFORMED: Three energy-adjusted HEI scores were assigned to quintiles; covariate-adjusted sex-stratified Cox models with age as the timescale assessed associations between and trends across quintiles of HEI scores and all-cause and cardiovascular disease mortality risk. RESULTS: Modified-weight HEI scores were associated with 23% to 38% reduced all-cause mortality risk in the highest vs lowest quintile, whereas comparisons were only significant for women (Key Facets P = 0.02 and least absolute shrinkage and selection operator regression-weighted P = 0.001; for men P = 0.06 on both scores), trends across quintiles of modified-weight scores were significant for men and women. The HEI-2015 was not significantly associated with all-cause mortality risk, and none of the scores were associated with cardiovascular disease mortality risk. CONCLUSIONS: Only modified-weight HEI scores were associated with reduced all-cause mortality risk. Findings suggest modified diet quality weighting schemes warrant further examination to determine their replicability.

Allostatic Load and Mortality: A Systematic Review and Meta-Analysis.

INTRODUCTION: Allostatic load, a measure of stress-related physiologic dysregulation, is associated with numerous mortality risk factors. This systematic review and meta-analysis examines the relationship between high allostatic load (i.e., increased dysregulation versus low dysregulation) and mortality (cardiovascular disease and all-cause mortality). METHODS: Systematic searches of 2 databases conducted in May 2021 yielded 336 unique records; 17 eligible studies (2001-2020) were included. RESULTS: High allostatic load was associated with increased risk of all-cause mortality across all the 17 individual studies (hazard ratio=1.08-2.75) and in 6 of 8 studies examining cardiovascular disease mortality (hazard ratio=1.19-3.06). Meta-analyses indicated that high allostatic load was associated with increased risk of all-cause mortality, overall (hazard ratio=1.22, 95% CI=1.14, 1.30, n=10) and across subgroups (hazard ratio=1.11-1.41), and similarly for cardiovascular disease mortality (hazard ratio=1.31, 95% CI=1.10, 1.57, n=6). Although studies were generally of good quality (n=13), heterogeneity was high in most pooled estimates (I2>90%). DISCUSSION: In this review of relatively good-quality studies, high allostatic load was associated with an increased mortality risk of 22% for all-cause mortality and 31% for cardiovascular disease mortality. Thus, allostatic load is an emerging and potent modifiable risk factor for all-cause and cardiovascular disease mortality that shows promise as a prognostic indicator for mortality. The heterogeneity in allostatic load assessment across studies highlights the need for standardized measurement. The findings underscore the importance of allostatic load's dynamic nature, which may be especially relevant for mitigating mortality risk in younger adults. Because older adults are oversampled, future allostatic load research should prioritize younger adults and longitudinal monitoring and specific cardiovascular disease mortality risk associations and individualize behavioral and lifestyle targets for reducing allostatic load.

Effect of Personalized Incentives on Dietary Quality of Groceries Purchased: A Randomized Crossover Trial.

Importance: Many factors are associated with food choice. Personalized interventions could help improve dietary intake by using individual purchasing preferences to promote healthier grocery purchases. Objective: To test whether a healthy food incentive intervention using an algorithm incorporating customer preferences, purchase history, and baseline diet quality improves grocery purchase dietary quality and spending on healthy foods. Design, Setting, and Participants: This was a 9-month randomized clinical crossover trial (AB-BA) with a 2- to 4-week washout period between 3-month intervention periods. Participants included 224 loyalty program members at an independent Rhode Island supermarket who completed baseline questionnaires and were randomized from July to September 2018 to group 1 (AB) or group 2 (BA). Data analysis was performed from September 2019 to May 2020. Intervention: Participants received personalized weekly coupons with nutrition education during the intervention period (A) and occasional generic coupons with nutrition education during the control period (B). An automated study algorithm used customer data to allocate personalized healthy food incentives to participant loyalty cards. All participants received a 5% grocery discount. Main Outcomes and Measures: Grocery Purchase Quality Index-2016 (GPQI-16) scores (range, 0-75, with higher scores denoting healthier purchases) and percentage spending on targeted foods were calculated from cumulative purchasing data. Participants in the top and bottom 1% of spending were excluded. Paired t tests examined between-group differences. Results: The analytical sample included 209 participants (104 in group 1 and 105 in group 2), with a mean (SD) age of 55.4 (14.0) years. They were predominantly non-Hispanic White (193 of 206 participants [94.1%]) and female (187 of 207 participants [90.3%]). Of 161 participants with income data, 81 (50.3%) had annual household incomes greater than or equal to $100 000. Paired t tests showed that the intervention increased GPQI-16 scores (between-group difference, 1.06; 95% CI, 0.27-1.86; P = .01) and percentage spending on targeted foods (between-group difference, 1.38%; 95% CI, 0.08%-2.69%; P = .04). During the initial intervention period, group 1 (AB) and group 2 (BA) had similar mean (SD) GPQI-16 scores (41.2 [6.6] vs 41.0 [7.5]) and mean (SD) percentage spending on targeted healthy foods (32.0% [10.8%] vs 31.0% [10.5%]). During the crossover intervention period, group 2 had a higher mean (SD) GPQI-16 score than group 1 (42.9 [7.7] vs 41.0 [6.8]) and mean (SD) percentage spending on targeted foods (34.0% [12.1%] vs 32.0% [13.1%]). Conclusions and Relevance: This pilot trial demonstrated preliminary evidence for the effectiveness of a novel personalized healthy food incentive algorithm to improve grocery purchase dietary quality. Trial Registration: ClinicalTrials.gov Identifier: NCT03748056.

Sociodemographic Differences in the Dietary Quality of Food-at-Home Acquisitions and Purchases among Participants in the U.S. Nationally Representative Food Acquisition and Purchase Survey (FoodAPS).

Insufficient research has explored whether sociodemographic differences in self-reported, individual-level diet quality are similarly reflected by grocery purchase quality. This cross-sectional analysis of n = 3961 U.S. households from the nationally representative Food Acquisition and Purchase Survey (FoodAPS) compared Healthy Eating Index (HEI)-2015 scores from 1 week of food-at-home acquisitions across self-reported demographic factors (race/ethnicity, Supplemental Nutrition Assistance Program (SNAP) participation, food security, and household-level obesity status). Multivariable-adjusted, survey-weighted regression models compared household HEI-2015 scores across sociodemographic groups. Respondents were primarily White and female, with a mean age of 50.6 years, 14.0% were food insecure, and 12.7% were SNAP-participating. Mean HEI-2015 scores were 54.7; scores differed across all sociodemographic exposures (p < 0.05). Interactions (p < 0.1) were detected between SNAP participation and (1) food insecurity and (2) household-level obesity, and race/ethnicity and (1) household-level obesity. HEI-2015 scores were higher among food secure, non-SNAP households than among food insecure, SNAP-participating households (53.9 ± 0.5 vs. 50.3 ± 0.7, p = 0.007); non-SNAP households without obesity had significantly higher HEI-2015 scores than other households. Household-level obesity was associated with lower HEI-2015 scores in White (50.8 ± 0.5 vs. 52.5 ± 0.7, p = 0.046) and Black (48.8 ± 1.5 vs. 53.1 ± 1.4, p = 0.018) but not Hispanic households (54.4 ± 1.0 vs. 52.2 ± 1.2, p = 0.21). Sociodemographic disparities in household HEI-2015 scores were consistent with previous research on individual-level diet quality.

Evaluating the effect of individually-targeted food incentives on grocery purchases: The smart cart study protocol for a randomized controlled cross-over trial.

BACKGROUND/AIMS: Health stakeholders are interested in the promise of healthy food incentives to improve dietary quality. The Smart Cart Study tested whether targeting healthful food incentives based on customer preferences and purchase history was effective for improving grocery purchase quality. DESIGN: Randomized controlled crossover design of 224 adults who shopped at an independent supermarket for ≥50% of their groceries, participated in the store's loyalty program, and completed validated diet and sociodemographic/behavioral questionnaires. Participants were randomized using 1:1 blocked randomization; all participants received a 5% discount on their purchases with their loyalty card. For the first 13-weeks, the intervention group received individually-targeted weekly coupons (valued up to $10) with brief nutrition education to improve grocery purchase quality. The study team developed healthy food coupons, and the study algorithm allocated targeted coupons to participants' loyalty cards using purchase history, dietary preferences/allergies, and baseline diet quality. Control participants received weekly untargeted nutrition education and occasional generic coupons. Following a 2-4 week washout period, the two groups crossed over. The primary study outcomes were purchases of targeted products and grocery purchase quality measured using the validated Grocery Purchase Quality Index-2016; the study was powered to detect a minimum 3% difference in purchase quality. CONCLUSIONS: The Smart Cart Study tested a novel application of automated individually-targeted marketing using customer purchase history, dietary quality, and preferences to identify and deliver targeted incentives to improve grocery purchase quality. Future research could scale this program through collaboration between multiple stakeholders, including supermarkets, workplace wellness initiatives and insurance companies.

Diet quality of vegetarian diets compared with nonvegetarian diets: a systematic review.

Objective: Vegetarian diets are consistently associated with improved health outcomes, and higher diet quality may contribute to improved health outcomes. This systematic review aims to qualitatively compare the a priori diet quality of vegetarian and nonvegetarian diets. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 2 online databases (Web of Science and PubMed) were searched for English language studies comparing diet quality among vegetarian and nonvegetarian adults using an a priori diet quality index. Two reviewers assessed study eligibility. Comparisons were made between total and component (when available) diet quality scores among the 12 studies meeting inclusion criteria. Conclusions: Lacto-ovo vegetarians or vegans had higher overall diet quality (4.5-16.4 points higher on the Healthy Eating Index 2010 [HEI-2010]) compared with nonvegetarians in 9 of 12 studies. Higher HEI-2010 scores for vegetarians were driven by closer adherence to recommendations for total fruit, whole grains, seafood and plant protein, and sodium. However, nonvegetarians had closer adherence to recommendations for refined grains and total protein foods. Higher diet quality in vegetarian diets may partially explain improvements in health outcomes compared with nonvegetarians; however, more research controlling for known confounders like health consciousness is needed.

Associations between pre-pregnancy BMI, gestational weight gain, and prenatal diet quality in a national sample.

This secondary analysis explored the association between gestational weight gain, pre-pregnancy body mass index (BMI), and prenatal diet quality in a United States national sample. The sample comprised 1322 pregnant women in the longitudinal Infant Feeding Practices Study II with Diet History Questionnaire data. Diet quality in the third trimester was assessed using the Alternative Healthy Eating Index for Pregnancy. Self-reported pre-pregnancy BMI (categorized as underweight<18.5, normal weight 18.5-24.9, overweight 25.0-29.9, and obese≥30.0) and total gestational weight gain were used to categorize adherence to the Institute of Medicine's recommendations as inadequate, adequate, or excessive weight gain. Diet quality in pre-pregnancy BMI and gestational weight gain groups were compared using Tukey-adjusted generalized linear models adjusted for sociodemographic factors, Women, Infants, and Children participation, parity, and energy intake. Due to missing gestational weight gain data, sensitivity analyses with multiply imputed data were conducted. Women were on average 28.9 years old and of higher socioeconomic status (40% college graduates) and mostly non-Hispanic White (84%), and the mean Alternative Healthy Eating Index for Pregnancy score was 61.2 (of 130). Both pre-pregnancy BMI and gestational weight gain were inversely associated with diet quality scores (p<0.01). The interaction between pre-pregnancy BMI and gestational weight gain was significant (p = 0.04), therefore gestational weight gain models were stratified by BMI group. In stratified adjusted models, gestational weight gain was differently associated with diet quality scores (p<0.05) among women with underweight, normal weight, overweight, and obesity. The relationship between gestational weight gain and prenatal diet quality depended on pre-pregnancy BMI. For example, within women with normal weight, higher diet quality was observed in the adequate gestational weight gain group. Interventions to broadly improve prenatal diet quality are needed, however, resources can be used to target women with higher pre-pregnancy BMIs and women with inadequate or excessive gestational weight gain.

Geographic Differences in the Dietary Quality of Food Purchases among Participants in the Nationally Representative Food Acquisition and Purchase Survey (FoodAPS).

Objective grocery transactions may reflect diet, but it is unclear whether the diet quality of grocery purchases mirrors geographic and racial/ethnic disparities in diet-related diseases. This cross-sectional analysis of 3961 households in the nationally representative Food Acquisition and Purchase Survey evaluated geographic and racial/ethnic disparities in grocery purchase quality. Respondents self-reported demographics and recorded purchases over 7 days; the Healthy Eating Index (HEI) 2015 assessed diet quality. Survey-weighted multivariable-adjusted regression determined whether there were geographic and racial/ethnic differences in HEI-15 scores. Respondents were, on average, 50.6 years, non-Hispanic white (NHW) (70.3%), female (70.2%), and had attended some college (57.8%). HEI-15 scores differed across geographic region (p < 0.05), with the highest scores in the West (57.0 ± 0.8) and lowest scores in the South (53.1 ± 0.8), and there was effect modification by race/ethnicity (p-interaction = 0.02). Regionally, there were diet disparities among NHW and non-Hispanic black (NHB) households; NHWs in the South had HEI-15 scores 3.2 points lower than NHWs in the West (p = 0.003). Southern NHB households had HEI-15 scores 8.1 points lower than Western NHB households (p = 0.013). Racial/ethnic disparities in total HEI-15 by region existed in the Midwest and South, where Hispanic households in the Midwest and South had significantly lower diet quality than NHW households. Heterogeneous disparities in the diet quality of grocery purchases by region and race/ethnicity necessitate tailored approaches to reduce diet-related disease.