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OBJECTIVE: Ultraprocessed food (UPF) intake is associated with BMI, but effects on regional adipose depots or related to minimally processed food (MPF) intake are unknown. METHODS: Data included 12,297 adults in the National Health and Nutrition Examination Survey (NHANES), 2011 to 2016. This study analyzed associations between usual percentage of kilocalories from UPFs and MPFs and three adiposity indicators: supine sagittal abdominal diameter to height ratio (SADHtR, estimates visceral adiposity); waist circumference to height ratio (WHtR, estimates abdominal adiposity); and BMI, using linear and multinomial logistic regression. RESULTS: Standardized β coefficients per 10% increase in UPF intake were 0.0926, 0.0846, and 0.0791 for SADHtR, WHtR, and BMI, respectively (all p < 0.001; p > 0.26 for pairwise differences). For MPF intake, the β coefficients were −0.0901, −0.0806, and −0.0688 (all p < 0.001; p > 0.18 pairwise). Adjusted odds ratios (95% CI) for adiposity tertile 3 versus tertile 1 (comparing UPF intake quartiles 2, 3, and 4 to quartile 1) were 1.33 (1.22-1.45), 1.67 (1.43-1.95), and 2.24 (1.76-2.86), respectively, for SADHtR; 1.31 (1.19-1.44), 1.62 (1.37-1.91), and 2.13 (1.63-2.78), respectively, for WHtR; and 1.27 (1.16-1.39), 1.53 (1.31-1.79), and 1.96 (1.53-2.51), respectively, for BMI. MPF intake showed inverse associations with similar trends in association strength. CONCLUSIONS: Among US adults, abdominal and visceral adiposity indictors were positively associated with UPFs and inversely associated with MPFs.

Objective:To explore whether individuals who consume higher amounts of ultra-processed food (UPF) have more adverse mental health symptoms.Design:Using a cross-sectional design, we measured the consumption of UPF as a percentage of total energy intake in kilo-calories using the NOVA food classification system. We explored whether individuals who consume higher amounts of UPF were more likely to report mild depression, more mentally unhealthy days and more anxious days per month using multivariable analyses adjusting for potential confounding variables.Setting:Representative sample from the United States National Health and Nutrition Examination Survey between 2007 and 2012.Participants:10 359 adults aged 18+ without a history of cocaine, methamphetamine or heroin use.Results:After adjusting for covariates, individuals with the highest level of UPF consumption were significantly more likely to report at least mild depression (OR: 1·81; 95 % CI1·09, 3·02), more mentally unhealthy (risk ratio (RR): 1·22; 95 % CI 1·18, 1·25) and more anxious days per month (RR: 1·19; 95 % CI 1·16, 1·23). They were also significantly less likely to report zero mentally unhealthy (OR: 0·60; 95 % CI 0·41, 0·88) or anxious days (OR: 0·65; 95 % CI 0·47, 0·90).Conclusions:Individuals reporting higher intakes of UPF were significantly more likely to report mild depression, more mentally unhealthy and more anxious days and less likely to report zero mentally unhealthy or anxious days. These data add important information to a growing body of evidence concerning the potential adverse effects of UPF consumption on mental health.

BACKGROUND: Ultra-processed foods contribute to risks of obesity and cardiometabolic disease, and higher intakes have been observed in low-income populations in the United States. Consumption of ultra-processed foods may be particularly higher among individuals experiencing food insecurity and participating in the Supplemental Nutrition Assistance Program (SNAP). OBJECTIVES: Using data from the 2007–2016 NHANES, we examined the associations between food insecurity, SNAP participation, and ultra-processed food consumption. METHODS: The study population comprised 9190 adults, aged 20–65 y, with incomes ≤300% of the federal poverty level (FPL). Food insecurity was assessed using the Household Food Security Survey Module and SNAP participation over the past 12 mo was self-reported. Dietary intake was measured from two 24-h dietary recalls. Ultra-processed food consumption (percentage of total energy intake) was defined using the NOVA food classification system. Linear regression models were used to examine the associations between food insecurity, SNAP participation, and ultra-processed food consumption, adjusting for sociodemographic and health characteristics. RESULTS: More severe food insecurity was associated with higher intakes of ultra-processed foods (P-trend = 0.003). The adjusted means of ultra-processed food intake ranged from 52.6% for adults with high food security to 55.7% for adults with very low food security. SNAP participation was also associated with higher intakes of ultra-processed foods (adjusted mean: 54.7%), compared with income-eligible participants (adjusted mean: 53.0%). Furthermore, the association between food insecurity and ultra-processed foods was modified by SNAP participation (P-interaction = 0.02). Among income-eligible nonparticipants and income-ineligible nonparticipants, more severe food insecurity was associated with higher consumption of ultra-processed foods. Among SNAP participants, the association between food insecurity and consumption of ultra-processed foods was nonsignificant. CONCLUSION: In a nationally representative sample of adults, food insecurity and SNAP participation were both associated with higher levels of ultra-processed food consumption.

Introduction: The Supplemental Nutrition Assistance Program (SNAP) plays an important role in shaping the diet of low-income individuals and families. Trends in ultra-processed food intake (UPF) among SNAP participants and non-participants has not been studied. Hypothesis: Compared to non-participants, SNAP participants would have a higher UPF intake; purchases would be larger for foods from SNAP-eligible versus ineligible venues. Methods: Data from NHANES survey cycles 2003-2004 through 2017-2018 were used. Dietary recalls from individuals ≥1 year of age were categorized according to Nova classification. Venues from which individuals reported buying food were grouped as SNAP-eligible (ex. grocery/supermarkets, convenience stores) or SNAP-ineligible venues (ex. restaurant/cafeteria). SNAP participation status was self-reported and categorized as SNAP participants; income-eligible nonparticipants (no SNAP + family income ≤130% of federal poverty line, FPL); lower-income, ineligible non-participants (no SNAP + family income >130% - ≤300% of FPL); higher-income ineligible non-participants (no SNAP + family income >300% FPL). UPF (% of total daily kcal) across eight survey cycles was examined using multivariable linear regression, stratified by SNAP participation status. Heterogeneous trends across SNAP participation status were tested by Wald F models. Trends were examined for UPF purchased from different venues. Analyses were conducted in youths (ages 1-19) and adults (≥ 20), separately. Results: The sample included 54979 participants from 2003-2004 through 2017-2018. The % of energy from UPF increased over time in both youth (Δ 2018-2003 , 4.3%; 95% CI, 2.9% - 5.8%; P linear trend < .001) and adults (Δ 2018-2003 , 2.5%; 95% CI, 0.2% - 4.9%; P linear trend = .002). There were no differences between SNAP participants, income-eligible non-participants, lower-income ineligible and higher-income non-participants. The mean contribution of UPF from SNAP-eligible venues decreased in both youth (Δ 2018-2003 , -2.6%; 95% CI, -6.1% - 0.9%; P linear trend = .001) and adults (Δ 2018-2003 , -2.3%; 95% CI, -5.0% - 0.8%; P cubic trend < .001) with no differences by SNAP participation. UPF purchases from fast-food restaurants increased in youth (Δ 2018-2003 , 2.2%; 95% CI, -0.4% - 4.8%; P quadratic trend = .036) and adults (Δ 2018-2003 , 2.9%; 95% CI, 0.6% - 5.1%; P cubic trend = .007), while those from schools remained unchanged. There were no differences by SNAP participation status. Conclusions: Total UPF consumption increased between 2003-2004 and 2017-2018 and were seen among both participants of SNAP and among income-eligible and ineligible non-participants. UPF were increasingly sourced from SNAP ineligible venues. SNAP benefit allotments should account for secular changes in dietary patterns by enabling the purchase of healthier processed and prepared foods.

OBJECTIVE To study the association between ultra-processed food consumption and carbon and water footprints of the Brazilian diet. METHODS Cross-sectional analysis on data collected in 2008–2009 on a probabilistic sample of the Brazilian population aged ≥ 10 years (n = 32,886). Individual food intake was assessed using two 24-hour food records, on non-consecutive days. The environmental impact of individual diets was calculated by multiplying the amount of each food by coefficients that quantify the atmospheric emissions of greenhouse gases in grams of carbon dioxide equivalent (carbon footprint) and freshwater use in liters (water footprint), both per gram or milliliter of food. The two coefficients consider the food life cycle ‘from farm to fork.’ Crude and adjusted linear regression models and tests for linear trends assessed the association between the ultra-processed food contribution to total energy intake (quintiles) and the diet carbon and water footprints. Potential confounders included age, sex, education, income, and region. Total energy intake was assessed as a potential mediation variable. RESULTS In the crude models, the dietary contribution of ultra-processed foods was linearly associated with the carbon and water footprints of the Brazilian diet. After adjustment for potential confounders, the association remained significant only regarding the diet water footprint, which increased by 10.1% between the lowest and highest quintile of the contribution of ultra-processed foods. Additional adjustment for total energy intake eliminated this association indicating that the dietary contribution of ultra-processed foods increases the diet water footprint by increasing energy intake. CONCLUSIONS The negative impact of ultra-processed foods on the diet water footprint, shown for the first time in this study, adds to the negative impacts of these foods, already demonstrated regarding dietary nutrient profiles and the risk for several chronic non-communicable diseases. This reinforces the recommendation to avoid ultra-processed foods made in the official Brazilian Dietary Guidelines and increasingly in dietary guidelines of other countries.

Purpose This study evaluated the association between ultra-processed food (UPF) consumption and cognitive performance among older US adults. Methods This cross-sectional study assessed 3632 participants aged 60+ years from the National Health and Nutrition Examination Survey (NHANES) 2011–14. Cognitive performance was assessed using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Word Learning test, Animal Fluency test, and the Digit Symbol Substitution test (DSST). Dietary intake was assessed using two 24-h diet recalls. Food items were classified according to the NOVA system, a classification based on the nature, extent, and purpose of industrial food processing. Linear regression models were used to evaluate the association of dietary share of UPF (% of daily energy intake) (categorized as tertiles) and cognitive test scores, adjusting for socio-demographic variables, physical activity, smoking status, and chronic diseases (cardiovascular diseases, diabetes, and depression). Models excluding participants with pre-existing diseases were carried out to address potential reverse causality. Results On average, UPF accounted for 53% of total energy intake, ranging from 33 to 70% across extreme tertiles. Inverted U-shape association between UPF consumption and Animal fluency and DSST was observed. No significant associations were observed between the UPF intake tertiles and the cognitive test results. Nonetheless, UPF consumption was significantly associated with worse performance in Animal Fluency in older adults without pre-existing diseases (P Conclusion UPF consumption was associated with worse performance in Animal Fluency among older people without pre-existing diseases. Decreasing UPF consumption may be a way to improve impaired cognition among older adults.

OBJECTIVE: To estimate the carbon footprint of the Brazilian diet and of sociodemographic strata of this population. METHODS: Carbon footprint of the diet was estimated based on data from two 24-hour diet records, obtained in 2008 and 2009, from a probabilistic sample of the Brazilian population aged 10 years and over (n = 34,003) and on environmental impact coefficients of food and culinary preparations consumed in Brazil (gCO2e/kg). Means with 95% confidence intervals of food consumption (kcal/person/day) and the carbon footprint of the diet (gCO2e/person/day and in gCO2e/2,000kcal) were calculated for the population as a whole and for strata according to sex, age, income, education, macro-regions and Federative Unit. Linear regression models were used to identify significant differences (p < 0.05) in the dietary carbon footprint of different sociodemographic strata. RESULTS: The average carbon footprint of the Brazilian diet was 4,489gCO2e/person/day. It was higher for males, for the age group from 20 to 49 years and for the North and Midwest regions, and tended to increase with income and education. The pattern of association of footprint with sociodemographic variables did not change substantially with adjustment for differences in the amount of food consumed, except for a reduction in the relative excess of the footprint among males and an increase in the relative excess of the footprint in the Midwest region. CONCLUSION: The carbon footprint of the Brazilian diet exceeds by about 30% the footprint of the human diet, which could simultaneously meet the nutritional requirements of a healthy diet and the global goal of containing the increase in the planet's average temperature. The pattern of association of this footprint with sociodemographic variables can help identify priority targets for public actions aimed at reducing the environmental impacts of food consumption in Brazil. RESUMO OBJETIVO: Estimar a pegada de carbono da dieta brasileira e de estratos sociodemográficos dessa população. MÉTODOS: A pegada de carbono da dieta foi estimada com base nos dados de dois registros alimentares de 24 horas, obtidos em 2008 e 2009, de uma amostra probabilística da população brasileira com 10 ou mais anos de idade (n = 34.003) e em coeficientes de impacto ambiental de alimentos e preparações culinárias consumidos no Brasil (gCO2e/kg). Médias com intervalos de confiança de 95% do consumo alimentar (kcal/pessoa/dia) e da pegada de carbono da dieta (gCO2e/pessoa/dia e em gCO2e/2.000kcal) foram calculadas para o conjunto da população e para estratos segundo sexo, idade, renda, escolaridade, macrorregiões e Unidade Federativa. Modelos de regressão linear foram utilizados para identificar diferenças significativas (p < 0,05) na pegada de carbono da dieta de diferentes estratos sociodemográficos. RESULTADOS: A pegada média de carbono da dieta brasileira foi de 4.489gCO2e/pessoa/dia. Foi maior para o sexo masculino, para a faixa etária de 20 a 49 anos e para as regiões Norte e Centro-Oeste, e tendeu a aumentar com a renda e a escolaridade. O padrão de associação da pegada a variáveis sociodemográficas não se alterou substancialmente com o ajuste para diferenças na quantidade consumida de alimentos, exceto por uma redução no excesso relativo da pegada entre homens e pelo aumento no excesso relativo da pegada na região Centro-Oeste. CONCLUSÃO: A pegada de carbono da dieta brasileira excede em cerca de 30% a pegada da dieta humana que poderia atender, simultaneamente, os requisitos nutricionais de uma dieta saudável e a meta global de contenção do aumento da temperatura média do planeta. O padrão de associação dessa pegada às variáveis sociodemográficas pode auxiliar na identificação de alvos prioritários para ações públicas que visem a reduzir os impactos ambientais do consumo alimentar no Brasil.

OBJETIVO: Estimar o consumo de carne bovina e a sua influência nas pegadas de carbono e na pegada hídrica, bem como mesurar a qualidade nutricional da dieta no Brasil. MÉTODOS: A quantidade consumida de carne bovina e dos demais alimentos foi avaliada por dois registros alimentares de 24 horas em amostra representativa da população brasileira ≥ 10 anos de idade (n = 32.853) entre 2008 e 2009. O impacto ambiental da dieta considerou os coeficientes da pegada de carbono (gCO2e/kg) e da pegada hídrica (litros/kg) dos alimentos, bem como sua qualidade nutricional considerando a composição de cada alimento em nutrientes associados à prevenção de deficiências nutricionais ou ao aumento/diminuição do risco de doenças crônicas. Modelos de regressão linear e logística, brutos e ajustados para sexo, idade, escolaridade, renda, região e área, foram utilizados para estudar, respectivamente, a associação de quintos da contribuição calórica de carne bovina com os impactos ambientais da dieta e com a ingestão inadequada de nutrientes. RESULTADOS: As pegadas de carbono e hídrica e os teores de proteína, ferro, zinco, vitamina B12, gordura saturada e sódio foram maiores na fração da dieta composta por carnes bovinas, enquanto o teor de fibra e de açúcar de adição foram maiores na fração composta pelos demais alimentos. A contribuição dietética de carne bovina mostrou-se associada diretamente com as pegadas de carbono e hídrica da dieta e com o risco de ingestão excessiva de gordura saturada e de sódio, além de ingestão insuficiente de fibra, associando-se inversamente com o risco de ingestão insuficiente de proteína, ferro, zinco e vitamina B12. CONCLUSÃO: A redução no consumo de carne bovina no Brasil diminuiria as pegadas de carbono e hídrica da dieta, assim como o risco de doenças crônicas relacionadas à alimentação. Portanto, para não aumentar o risco de deficiências nutricionais, é sugerido o acompanhamento do aumento da ingestão de outros alimentos fontes de proteína, ferro, zinco e vitamina B12.

OBJECTIVES: Ultra-processed food have been associated with multiple chronic diseases, yet recent data regarding its consumption in the U.S. and potential differences in intake across population groups is lacking. We determined the intake of ultra-processed food across diverse socioeconomic strata in the U.S. adult population. METHODS: We performed cross-sectional analysis of dietary intake among adults (>20y, N = 9759) in the National Health and Nutrition Examination Survey (NHANES) 2015–2018. Data on dietary intake was collected by 24h dietary recall. Foods were classified as ultra-processed/non ultra-processed according to the NOVA classification. We determined intake of ultra-processed food (%kcal) in the overall sample, and stratified by education (

The present commentary contains a clear and simple guide designed to identify ultra-processed foods. It responds to the growing interest in ultra-processed foods among policy makers, academic researchers, health professionals, journalists and consumers concerned to devise policies, investigate dietary patterns, advise people, prepare media coverage, and when buying food and checking labels in shops or at home. Ultra-processed foods are defined within the NOVA classification system, which groups foods according to the extent and purpose of industrial processing. Processes enabling the manufacture of ultra-processed foods include the fractioning of whole foods into substances, chemical modifications of these substances, assembly of unmodified and modified food substances, frequent use of cosmetic additives and sophisticated packaging. Processes and ingredients used to manufacture ultra-processed foods are designed to create highly profitable (low-cost ingredients, long shelf-life, emphatic branding), convenient (ready-to-consume), hyper-palatable products liable to displace all other NOVA food groups, notably unprocessed or minimally processed foods. A practical way to identify an ultra-processed product is to check to see if its list of ingredients contains at least one item characteristic of the NOVA ultra-processed food group, which is to say, either food substances never or rarely used in kitchens (such as high-fructose corn syrup, hydrogenated or interesterified oils, and hydrolysed proteins), or classes of additives designed to make the final product palatable or more appealing (such as flavours, flavour enhancers, colours, emulsifiers, emulsifying salts, sweeteners, thickeners, and anti-foaming, bulking, carbonating, foaming, gelling and glazing agents).

Accumulating evidence links ultra-processed foods to poor diet quality and chronic diseases. Understanding dietary trends is essential to inform priorities and policies to improve diet quality and prevent diet-related chronic diseases. Data are lacking, however, for trends in ultra-processed food intake.We examined US secular trends in food consumption according to processing level from 2001 to 2018.We analyzed dietary data collected by 24-h recalls from adult participants (aged19 y; N = 40,937) in 9 cross-sectional waves of the NHANES (2001-2002 to 2017-2018). We calculated participants' intake of minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods as the relative contribution to daily energy intake (%kcal) using the NOVA framework. Trends analyses were performed using linear regression, testing for linear trends by modeling the 9 surveys as an ordinal independent variable. Models were adjusted for age, sex, race/ethnicity, education level, and income. Consumption trends were reported for the full sample and stratified by sex, age groups, race/ethnicity, education level, and income level.Adjusting for changes in population characteristics, the consumption of ultra-processed foods increased among all US adults from 2001-2002 to 2017-2018 (from 53.5 to 57.0 %kcal; P-trend 0.001). The trend was consistent among all sociodemographic subgroups, except Hispanics, in stratified analyses. In contrast, the consumption of minimally processed foods decreased significantly over the study period (from 32.7 to 27.4 %kcal; P-trend 0.001) and across all sociodemographic strata. The consumption of processed culinary ingredients increased from 3.9 to 5.4 %kcal (P-trend 0.001), whereas the intake of processed foods remained stable at ∼10 %kcal throughout the study period (P-trend = 0.052).The current findings highlight the high consumption of ultra-processed foods in all parts of the US population and demonstrate that intake has continuously increased in the majority of the population in the past 2 decades.

Background Few low-burden indicators of diet quality exist to track trends over time at low cost and with low technical expertise requirements. Objective The aim was to develop and validate a suite of low-burden indicators to reflect adherence to global dietary recommendations. Methods Using nationally representative, cross-sectional, quantitative dietary intake datasets from Brazil and the United States, we tested the association of food-group scores with quantitative consumption aligned with 11 global dietary recommendations. We updated the Healthy Diet Indicator (HDI) to include current quantifiable recommendations of the WHO (HDI-2020). We developed 3 food-group-based scores-an overall Global Dietary Recommendations (GDR) score as an indicator of all 11 recommendations composed of 2 subcomponents: GDR-Healthy, an indicator of the recommendations on "healthy" foods, and GDR-Limit, an indicator of the recommendations on dietary components to limit. We tested associations between these scores and the HDI-2020 and its respective subcomponents. We developed 9 dichotomous food-group-based indicators to reflect adherence to the global recommendations for fruits and vegetables, dietary fiber, free sugars, saturated fat, total fat, legumes, nuts and seeds, whole grains, and processed meats. We conducted receiver operating characteristic and sensitivity-specificity analyses to determine whether the dichotomous indicators were valid to predict adherence to the recommendations in both countries. Results The GDR score and its subcomponents were moderately to strongly associated with the HDI-2020 and its respective subcomponents (absolute values of rank correlation coefficients ranged from 0.55 to 0.66). Of the 9 dichotomous indicators, 8 largely met the criteria for predicting individual global dietary recommendations in both countries; 1 indicator (total fat) did not perform satisfactorily. Conclusions Food-group consumption data can be used to indicate adherence to quantitative global dietary recommendations at population level. These indicators may be used to track progress of countries and populations toward meeting WHO guidance on healthy diets.

Objective:The majority of groceries purchased by US households are industrially processed, yet it is unclear how processing level influences diet quality. We sought to determine if processing level is associated with diet quality of grocery purchases.Design:We analysed grocery purchasing data from the National Household Food Acquisition and Purchase Survey 2012–2013. Household grocery purchases were categorized by the NOVA framework as minimally processed, processed culinary ingredients, processed foods or ultra-processed foods. The energy share of each processing level (percentage of energy; %E) and Healthy Eating Index-2015 (HEI-2015) component and total scores were calculated for each household’s purchases. The association between %E from processed foods and ultra-processed foods, respectively, and HEI-2015 total score was determined by multivariable linear regression. Foods purchased by households with the highest v. lowest ultra-processed food purchases and HEI-2015 total score v. ≥60 were compared using linear regression.Setting:USA.Participants:Nationally representative sample of 3961 households.Results:Processed foods and ultra-processed foods provided 9·2 (se 0·3) % and 55·8 (se 0·6) % of purchased energy, respectively. Mean HEI-2015 score was 54·7 (se 0·4). Substituting 10 %E from minimally processed foods and processed culinary ingredients for ultra-processed foods decreased total HEI-2015 score by 1·8 points (β = −1·8; 95 % CI −2·0, −1·5). Processed food purchases were not associated with diet quality. Among households with high ultra-processed food purchases, those with HEI-2015 score Conclusions:Increasing purchases of minimally processed foods, decreasing purchases of ultra-processed foods and selecting healthier foods at each processing level may improve diet quality.

BACKGROUND There is cumulative recognition that increases in the dietary share of ultra-processed foods result in deterioration of the nutritional quality of the overall diet and adverse health outcomes. OBJECTIVE The purpose of this study was to assess the consumption of ultra-processed foods and to examine its association with added sugar content in the diet of US children aged 2 to 19 years. METHODS We classified all food items according to the NOVA food classification system and looked at the consumption of ultra-processed foods, measured as percentage of total energy intake. We assessed dietary added sugar content by estimating its contribution to total energy intake and the proportion of individuals with diets exceeding the recommended limit of 10% of total energy intake from added sugars. We also examined the proportion of those who surpassed twice that limit. Gaussian and Poisson regressions estimated the association between consumption of ultra-processed foods and estimates of added sugar content. RESULTS Ultra-processed foods contributed to 65% of total energy intake and to 92% of energy from added sugars in the diet of US children. The mean percent energy provided by added sugars in the observed period was 14.3%. Most individuals (70.9%) had diets exceeding the recommended limit of added sugar (

OBJECTIVES: The objective of this research was to develop and test a low-burden method to gather diet quality data that is comparable across countries, consistently implemented, and easily understood by respondents. A common method for collecting dietary diversity data consists of open-ended food group questions, e.g., Yesterday, did you eat any porridge, bread, rice, pastaor otherfoods made from grains? Our hypotheses were (1) the majority of consumption of each food group could be represented by a few foods in each country setting (sentinel foods); (2) respondents who did not eat the listed examples might misclassify other foods they ate as belonging to the same food group. We sought to refine the method by modifying each question to be closed-ended. METHODS: We developed a 26-item diet quality questionnaire (DQ-Q), where each yes/no question asks about consumption of a distinct food group in the previous day or night. We tested the first hypothesis using 24-hour nationally representative dietary intake data from Brazil (Individual Food Intake Survey 2008–2009) and the United States (NHANES 2009–2014). We categorized each food and beverage item into the 26 food groups of the DQ-Q, and identified the most commonly consumed foods in each. Individuals were categorized according to whether they had consumed at least one item in each food group (1) or not (0). We tested the second hypothesis through 82 cognitive interviews in five languages São Paulo and New York City, in which we compared responses to closed-ended sentinel food questions to open-ended food group questions. RESULTS: On average, 1–7 sentinel foods captured 96–97% of people who consumed each food group (range 85–100%). Respondents in both countries sometimes miscategorized foods when asked open-ended food group questions, and open-ended questions presented an additional cognitive burden. The DQ-Q took 3–5 minutes to administer. CONCLUSIONS: The DQ-Q is a rapid low-burden method to collect diet quality data. Closed-ended questions using sentinel foods capture the vast majority of consumption and are better understood by respondents than open-ended list-based methods, for measuring dietary diversity and other aspects of diet related to NCD risk. FUNDING SOURCES: Funding for this work was provided by the Global Alliance for Improved Nutrition (GAIN) and the Swiss Agency for Development and Cooperation (SDC).