FFQs are a popular method of capturing dietary information in epidemiological studies and may be used to derive dietary exposures such as nutrient intake or overall dietary patterns and diet quality. As FFQs can involve large numbers of questions, participants may fail to respond to all questions, leaving researchers to decide how to deal with missing data when deriving intake measures. The aim of the present commentary is to discuss the current practice for dealing with item non-response in FFQs and to propose a research agenda for reporting and handling missing data in FFQs.

Single imputation techniques, such as zero imputation (assuming no consumption of the item) or mean imputation, are commonly used to deal with item non-response in FFQs. However, single imputation methods make strong assumptions about the missing data mechanism and do not reflect the uncertainty created by the missing data. This can lead to incorrect inference about associations between diet and health outcomes. Although the use of multiple imputation methods in epidemiology has increased, these have seldom been used in the field of nutritional epidemiology to address missing data in FFQs. We discuss methods for dealing with item non-response in FFQs, highlighting the assumptions made under each approach.

Researchers analysing FFQs should ensure that missing data are handled appropriately and clearly report how missing data were treated in analyses. Simulation studies are required to enable systematic evaluation of the utility of various methods for handling item non-response in FFQs under different assumptions about the missing data mechanism.

The present study investigated whether parent/child pairs would select more healthful foods when: (i) products were labelled with front-of-package (FOP) nutrition labels relative to packages without labels; (ii) products were labelled with colour-coded Multiple Traffic Light (MTL) FOP labels relative to monochromatic Facts up Front (FuF) FOP labels; and (iii) FOP labels were explained via in-aisle signage v. unexplained.

Participants were randomly assigned to one of five conditions: (i) FuF labels with in-aisle signs explaining the labels; (ii) FuF labels, no signage; (iii) MTL labels with in-aisle signage; (iv) MTL labels, no signage; (v) control group, no labels/signage. Saturated fat, sodium, sugar and energy (calorie) content were compared across conditions.

The study took place in a laboratory grocery aisle.

Parent/child pairs (n 153) completed the study.

Results did not support the hypothesis that MTL labels would lead to more healthful choices than FuF labels. The presence of FOP labels did little to improve the healthfulness of selected foods, with few exceptions (participants with v. without access to FOP labels selected lower-calorie cereals, participants with access to both FOP labels and in-aisle explanatory signage selected products with less saturated fat v. participants without explanatory signage).

Neither MTL nor FuF FOP labels led to food choices with significantly lower saturated fat, sodium or sugar. In-aisle signs explaining the FOP labels were somewhat helpful to consumers in making more healthful dietary decisions. New FOP labelling programmes could benefit from campaigns to increase consumer awareness and understanding of the labels.

To develop and evaluate a Nutrition Transition-FFQ (NT-FFQ) to measure nutrition transition among adolescents in South India.

We developed an interviewer-administered NT-FFQ comprising a 125-item semi-quantitative FFQ and a twenty-seven-item eating behaviour survey. The reproducibility and validity of the NT-FFQ were assessed using Spearman correlations, intra-class correlation coefficients (ICC), and levels of agreement using Bland–Altman and cross-classification over 2 months (NT-FFQ1 and NT-FFQ2). Validity of foods was evaluated against three 24-h dietary recalls (24-HR). Face validity of eating behaviours was evaluated through semi-structured cognitive interviews. The reproducibility of eating behaviours was assessed using weighted kappa (κw) and cross-classification analyses.

Vijayapura, India.

A representative sample of 198 adolescents aged 14–18 years.

Reproducibility of NT-FFQ: Spearman correlations ranged from 0·33 (pulses) to 0·80 (red meat) and ICC from 0·05 (fruits) to 1·00 (tea). On average, concordance (agreement) was 60 % and discordance was 7 % for food groups. For eating behaviours, κw ranged from 0·24 (eating snacks while watching television) to 0·67 (eating lunch at home) with a mean of 0·40. Validity of NT-FFQ: Spearman correlations ranged from 0·11 (fried traditional foods) to 0·70 (tea) and ICC ranged from 0·02 (healthy global foods) to 1·00 (grains). The concordance and discordance were 48 % and 8 %, respectively. Bland–Altman plots showed acceptable agreement between NT-FFQ2 and 24-HR. The eating behaviours had acceptable face validity.

The NT-FFQ has good reproducibility and acceptable validity for food intake and eating behaviours. The NT-FFQ can quantify the nutrition transition among Indian adolescents.

No up-to-date data on the dietary intake of Irish adolescents are available. The aim of the present pilot study was to obtain and compare cross-sectional information on habitual adolescent beverage consumption between four distinct post-primary schools in the Republic of Ireland, in 2014–2015.

A cross-sectional observation study. A beverage consumption questionnaire was used to obtain data on beverage intake and influences on consumption.

Four post-primary mixed-sex schools in Ireland representing the following school classifications were selected for the study: urban fee-paying, urban disadvantaged, rural fee-paying and rural disadvantaged.

Students (n 761) aged 12–18 years.

Data were analysed by Kruskal–Wallis (non-parametric) ANOVA to compare the distribution of beverage consumption across the schools. Water was the most highly consumed beverage among students from all four schools (median 1425 ml/d). Students from urban and rural disadvantaged schools reported a significantly higher volume of carbonated beverage intake than students from fee-paying schools. Students from an urban disadvantaged school also reported a significantly higher volume of carbonated beverage and energy drink intake compared with the other three schools. Students from an urban fee-paying school reported the highest consumption of water, while rural disadvantaged school students were the biggest consumers of tea and milk.

Significant differences in beverage consumption (ml/d) were reported by adolescents from four schools in Ireland. Surveillance on current beverage consumption trends among adolescents is vital to guide policies and interventions, and for appropriate targeting of resources.

As misreporting, mostly under-reporting, of dietary intake is a generally known problem in nutritional research, we aimed to analyse the association between selected determinants and the extent of misreporting by the duplicate portion method (DP), 24 h recall (24hR) and FFQ by linear regression analysis using the biomarker values as unbiased estimates.

For each individual, two DP, two 24hR, two FFQ and two 24 h urinary biomarkers were collected within 1·5 years. Also, for sixty-nine individuals one or two doubly labelled water measurements were obtained. The associations of basic determinants (BMI, gender, age and level of education) with misreporting of energy, protein and K intake of the DP, 24hR and FFQ were evaluated using linear regression analysis. Additionally, associations between other determinants, such as physical activity and smoking habits, and misreporting were investigated.

The Netherlands.

One hundred and ninety-seven individuals aged 20–70 years.

Higher BMI was associated with under-reporting of dietary intake assessed by the different dietary assessment methods for energy, protein and K, except for K by DP. Men tended to under-report protein by the DP, FFQ and 24hR, and persons of older age under-reported K but only by the 24hR and FFQ. When adjusted for the basic determinants, the other determinants did not show a consistent association with misreporting of energy or nutrients and by the different dietary assessment methods.

As BMI was the only consistent determinant of misreporting, we conclude that BMI should always be taken into account when assessing and correcting dietary intake.

Limiting the intake of added sugars in the diet remains a key focus of global dietary recommendations. To date there has been no systematic monitoring of the major types of added sugars used in the Australian food supply. The present study aimed to identify the most common added sugars and non-nutritive sweeteners in the Australian packaged food supply.

Secondary analysis of data from the Australian FoodSwitch database was undertaken. Forty-six added sugars and eight non-nutritive sweetener types were extracted from the ingredient lists of 5744 foods across seventeen food categories.

Australia.

Not applicable.

Added sugar ingredients were found in 61 % of the sample of foods examined and non-nutritive sweetener ingredients were found in 69 %. Only 31 % of foods contained no added sugar or non-nutritive sweetener. Sugar (as an ingredient), glucose syrup, maple syrup, maltodextrin and glucose/dextrose were the most common sugar ingredient types identified. Most Australian packaged food products had at least one added sugar ingredient, the most common being ‘sugar’.

The study provides insight into the most common types of added sugars and non-nutritive sweeteners used in the Australian food supply and is a useful baseline to monitor changes in how added sugars are used in Australian packaged foods over time.

To assess vitamin D status and the influence of risk factors such as skin pigmentation and time spent outdoors on hypovitaminosis D among Guatemalan Kekchi and Garifuna adolescents.

Cross-sectional study, with convenient sampling design. Blood samples, anthropometric and behavioural data were all collected during the dry season. Serum 25-hydroxyvitamin D (25(OH)D) concentrations were measured by RIA.

Communities of Rio Dulce and Livingston, Izabal Province, Caribbean coast of Guatemala, with latitude and longitude of 15°49′N and 88°45′W for Livingston and 15°46′N and 88°49′W for Rio Dulce, respectively.

Eighty-six adolescents, divided evenly by sex and ethnicity, with mean age of 14 years.

Mean (sd) 25(OH)D value was 27·8 (7·2) ng/ml for the total group, with 25·8 (5·9) and 29·8 (7·9) ng/ml, respectively, in Kekchis and Garifunas (P=0·01). Use of vitamin D supplementation, clothing practices and sun protection were not statistically different between groups. Skin area exposed on the day of data collection ranged from 20·0 % minimum to 49·4 % maximum, with mean (sd) exposure of 32·0 (8.5) %. With univariate regression analysis, age (P=0·034), sex (P=0·044), ethnicity (P=0·010), time spent outdoors (P=0·006) and percentage skin area exposed (P=0·001) were predictive. However, multivariate analysis indicated that only sex (P=0·034) and percentage skin area exposed (P=0·044) remained as predictors of 25(OH)D.

Despite residing in an optimal geographic location for sunlight exposure, nearly 65 % of study adolescents were either insufficient or deficient in vitamin D. Correction and long-term prevention of this nutritional problem may be instrumental in avoiding adverse effects in adulthood attributed to low 25(OH)D during adolescence.

To update the Dutch Healthy Diet index, a measure of diet quality, to reflect adherence to the Dutch dietary guidelines 2015 and to evaluate against participants’ characteristics and nutrient intakes with the score based on 24 h recall (24 hR) data and FFQ data.

The Dutch Healthy Diet index 2015 (DHD15-index) consists of fifteen components representing the fifteen food-based Dutch dietary guidelines of 2015. Per component the score ranges between 0 and 10, resulting in a total score between 0 (no adherence) and 150 (complete adherence).

Wageningen area, the Netherlands, 2011–2013.

Data of 885 men and women, aged 20–70 years, participating in the longitudinal NQplus study, who filled out two 24 hR and one FFQ, were used.

Mean (sd) score of the DHD15-index was 68·7 (16·1) for men and 79·4 (16·0) for women. Significant inverse trends were found between the DHD15-index and BMI, smoking, and intakes of energy, total fat and saturated fat. Positive trends were seen across sex-specific quintiles of the DHD15-index score with energy-adjusted micronutrient intakes. Mean DHD15-index score of the FFQ data was 15·5 points higher compared with 24 hR data, with a correlation coefficient of 0·56 between the scores. Observed trends of the DHD15-index based on FFQ with participant characteristics, macronutrient and energy-adjusted micronutrient intakes were similar to those with the DHD15-index based on 24 hR.

The DHD15-index score assesses adherence to the Dutch dietary guidelines 2015 and indicates diet quality. The DHD15-index score can be based on 24 hR data and on FFQ data.

To examine Zn and Fe nutritional status of a healthy population by means of anthropometric, dietary and biochemical measurements and to investigate the relationship of usual Zn and Fe dietary intakes to Zn and Fe status. In addition, to examine the impact of food choices and socio-economic factors on Fe and Zn dietary intakes and status with the aim to identify groups at risk of dietary deficiency and suggest factors that may influence the status of these nutrients.

Food consumption was assessed by 24 h recall questionnaires. Twenty biochemical parameters were measured, of which Hb, haematocrit, erythrocyte count and plasma concentrations of Fe and Zn were directly related to Fe and Zn nutrition. The prevalence of study participants with inadequate micronutrient intakes was calculated using the Estimated Average Requirement cut-point method.

Serbia, Europe.

Apparently healthy adults (25–65 years of age).

Mean daily Zn and Fe intakes were 9·1 mg and 11·6 mg for males and 7·3 mg and 9·4 mg for females, respectively. Five per cent of the study population had inadequate dietary Fe intake and 15–25 % had inadequate Zn intake. Lower Hb concentrations were measured in women with lower Zn intakes. No differences in Fe and Zn intakes and status among various socio-economic groups were observed, except for Fe intake between the low-income and affluent groups.

Regular follow-ups are needed to ensure that potential deficiencies of Zn and Fe do get recognized and addressed in a timely manner.

Access to nutritious foods is key to achieving health promotion goals. While there is evidence that nutritious food access is complex, measures assessing multiple domains of access, including spatial-temporal, economic, social, service delivery and personal, are lacking. The current study evaluates psychometric properties of scales designed to measure perceptions of multiple domains of nutritious food access among low-income populations.

A cross-sectional survey was conducted in 2015. Eighty-one items were selected or developed to represent five domains of nutritious food access for food shopping overall and specific to shopping at farmers’ markets. Evaluation of the items included exploratory factor analysis within each domain and internal consistency reliability for each of the sub-scales.

Data were collected in seventeen urban neighbourhoods in Greater Cleveland, Ohio, USA that have high levels of poverty. All participants had access to at least one farmers’ market within 1·6 km (1 mile) of their home to standardize spatial access to nutritious foods.

Adults (n 304) receiving Supplemental Nutrition Assistance Program benefits.

Each domain included multiple sub-domains: spatial-temporal (four), service delivery (two), economic (two), social (three) and personal (three), for a total of fourteen subdomains. The internal consistency reliability for one of the sub-domains was outstanding (>0·90), seven were excellent (0·80–0·89), five were very good (0·70–0·79) and one scale had poor reliability (0·58).

Multiple sub-domains of nutritious food access can be assessed using short measures that have been tested for internal consistency. These measures are suitable for assessing the complex phenomena of nutritious food access among low-income populations.

To describe: household dietary diversity across four zones in Ethiopia; the relationship between household dietary diversity and consumption of vitamin A-rich foods; and the relationship between household dietary diversity and food security status.

This was a cross-sectional survey. Data were collected using structured questionnaires in the local language. Household dietary diversity scores measured types of foods households consumed, and households were classified by food security status using a modified version of the Household Food Insecurity Access Scale. An ordinal logistics regression model was created to assess the relationship between three tiers of dietary diversity (low, medium and high) and food security while controlling for agricultural zone, educational variables and household characteristics.

Rural households in Tigray, Ethiopia.

Three hundred households in Tigray, Ethiopia, were interviewed.

Of the households, 23, 47 and 30 % had low, medium and high dietary diversity, respectively. Among households with high dietary diversity, eggs and fruit were the most common foods added to the diet. In the fully adjusted model, participants who reported being food secure had 1·8 increased odds of greater dietary diversity (95 % CI 1·0, 3·2) compared with participants who were food insecure.

Food security was positively associated with dietary diversity. In order to enhance health, interventions that improve dietary diversity and vitamin A consumption should remain important areas of focus for health leaders in the region.

To outline key statistical considerations and detailed methodologies for the development and evaluation of a valid and reliable nutrition knowledge questionnaire.

Literature on questionnaire development in a range of fields was reviewed and a set of evidence-based guidelines specific to the creation of a nutrition knowledge questionnaire have been developed. The recommendations describe key qualitative methods and statistical considerations, and include relevant examples from previous papers and existing nutrition knowledge questionnaires. Where details have been omitted for the sake of brevity, the reader has been directed to suitable references.

We recommend an eight-step methodology for nutrition knowledge questionnaire development as follows: (i) definition of the construct and development of a test plan; (ii) generation of the item pool; (iii) choice of the scoring system and response format; (iv) assessment of content validity; (v) assessment of face validity; (vi) purification of the scale using item analysis, including item characteristics, difficulty and discrimination; (vii) evaluation of the scale including its factor structure and internal reliability, or Rasch analysis, including assessment of dimensionality and internal reliability; and (viii) gathering of data to re-examine the questionnaire’s properties, assess temporal stability and confirm construct validity. Several of these methods have previously been overlooked.

The measurement of nutrition knowledge is an important consideration for individuals working in the nutrition field. Improved methods in the development of nutrition knowledge questionnaires, such as the use of factor analysis or Rasch analysis, will enable more confidence in reported measures of nutrition knowledge.

To develop a technology-based method for evaluating the nutritional quality of chain-restaurant menus to increase the efficiency and lower the cost of large-scale data analysis of food items.

Using a Modified Nutrient Profiling Index (MNPI), we assessed chain-restaurant items from the MenuStat database with a process involving three steps: (i) testing ‘extreme’ scores; (ii) crowdsourcing to analyse fruit, nut and vegetable (FNV) amounts; and (iii) analysis of the ambiguous items by a registered dietitian.

In applying the approach to assess 22 422 foods, only 3566 could not be scored automatically based on MenuStat data and required further evaluation to determine healthiness. Items for which there was low agreement between trusted crowd workers, or where the FNV amount was estimated to be >40 %, were sent to a registered dietitian. Crowdsourcing was able to evaluate 3199, leaving only 367 to be reviewed by the registered dietitian. Overall, 7 % of items were categorized as healthy. The healthiest category was soups (26 % healthy), while desserts were the least healthy (2 % healthy).

An algorithm incorporating crowdsourcing and a dietitian can quickly and efficiently analyse restaurant menus, allowing public health researchers to analyse the healthiness of menu items.

To formulate age- and context-specific complementary feeding recommendations (CFR) for infants and young children (IYC) and to compare the potential of filling population-level nutrient gaps using common sets of CFR across age groups.

Linear programming was used to develop CFR using locally available and acceptable foods based on livelihood- and age-group-specific dietary patterns observed through 24 h dietary recalls. Within each livelihood group, the nutrient potential of age-group-specific v. consolidated CFR across the three age groups was tested.

Three food-insecure counties in northern Kenya; namely, settled communities from Isiolo (n 300), pastoralist communities from Marsabit (n 283) and agro-pastoralist communities from Turkana (n 299).

Breast-fed IYC aged 6–23 months (n 882).

Age-specific CFR could achieve adequacy for seven to nine of eleven modelled micronutrients, except among 12–23-month-old children in agro-pastoralist communities. Contribution of Fe, Zn and niacin remained low for most groups, and thiamin, vitamin B6 and folate for some groups. Age-group-consolidated CFR could not reach the same level of nutrient adequacy as age-specific sets among the settled and pastoralist communities.

Context- and age-specific CFR could ensure adequate levels of more modelled nutrients among settled and pastoralist IYC than among agro-pastoralist communities where use of nutrient-dense foods was limited. Adequacy of all eleven modelled micronutrients was not achievable and additional approaches to ensure adequate diets are required. Consolidated messages should be easier to implement as part of a behaviour change strategy; however, they would likely not achieve the same improvements in population-level dietary adequacy as age-specific CFR.

To determine whether exposure to child-targeted fast-food (FF) television (TV) advertising is associated with children’s FF intake in a non-experimental setting.

Cross-sectional survey conducted April–December 2013. Parents reported their pre-school child’s TV viewing time, channels watched and past-week FF consumption. Responses were combined with a list of FF commercials (ads) aired on children’s TV channels during the same period to calculate children’s exposure to child-targeted TV ads for the following chain FF restaurants: McDonald’s, Subway and Wendy’s (MSW).

Paediatric and Women, Infants, and Children (WIC) clinics in New Hampshire, USA.

Parents (n 548) with a child of pre-school age.

Children’s mean age was 4·4 years; 43·2 % ate MSW in the past week. Among the 40·8 % exposed to MSW ads, 23·3 % had low, 34·2 % moderate and 42·5 % high exposure. McDonald’s accounted for over 70 % of children’s MSW ad exposure and consumption. Children’s MSW consumption was significantly associated with their ad exposure, but not overall TV viewing time. After adjusting for demographics, socio-economic status and other screen time, moderate MSW ad exposure was associated with a 31 % (95 % CI 1·12, 1·53) increase and high MSW ad exposure with a 26 % (95 % CI 1·13, 1·41) increase in the likelihood of consuming MSW in the past week. Further adjustment for parent FF consumption did not change the findings substantially.

Exposure to child-targeted FF TV advertising is positively associated with FF consumption among children of pre-school age, highlighting the vulnerability of young children to persuasive advertising and supporting recommendations to limit child-directed FF marketing.

First, to explore item pools developed to measure the physical home environment of pre-school children and assess the psychometric properties of these item pools; second, to explore associations between this environment and vegetable consumption among Norwegian 3–5-year-olds.

Data were collected in three steps: (i) a parental web-based questionnaire assessing the child’s vegetable intake and factors potentially influencing the child’s vegetable consumption; (ii) direct observation of the children’s fruit, berry and vegetable intakes at two meals in one day in the kindergarten; and (iii) a parental web-based 24 h recall.

The target group for this study was pre-school children born in 2010 and 2011, attending public or private kindergartens in the counties of Vestfold and Buskerud, Norway.

A total of 633 children participated.

Principal component analysis on the thirteen-item pool assessing availability/accessibility resulted in two factors labelled ‘availability at home’ and ‘accessibility at home’, while the eight-item pool assessing barriers resulted in two factors labelled ‘serving barriers’ and ‘purchase barriers’. The psychometric properties of these factors were satisfactory. Linear regression of the associations between vegetable intake and the factors showed generally positive associations with ‘availability at home’ and ‘accessibility at home’ and negative associations with ‘serving barriers’.

This age group has so far been understudied and there is a need for comparable studies. Our findings highlight the importance of targeting the physical home environment of pre-school children in future interventions as there are important modifiable factors that both promote and hinder vegetable consumption in this environment.

To examine the comparability of fruit and vegetable (F&V) intake data in the USA from 2001 to 2014 between data acquired from two national data collection programmes.

Cross-sectional analysis. Linear regression models estimated trends in daily per capita intake of total F&V. Pooled differences in intake of individual F&V (n 109) were examined by processing form (fresh, frozen, canned, dried and juice).

What We Eat in America (WWEIA, 2001–2014) and Loss-Adjusted Food Availability data series (LAFA, 2001–2014).

No temporal trends were observed in daily per capita intake of total F&V from 2001 to 2014 using WWEIA and LAFA. Modest differences between WWEIA and LAFA were observed in mean pooled intake of most individual F&V.

WWEIA and LAFA produced similar estimates of F&V intake. However, WWEIA may be best suited for monitoring intake at the national level because it allows for the identification of individual F&V in foods with multiple ingredients, and it is structured for sub-population analysis and covariate control. LAFA does retain advantages for other research protocols, specifically by providing the only nationally representative estimates of food losses at various points in the food system, which makes it useful for examining the adequacy of the food supply at the agricultural, retail and consumer levels.

When planning severe acute malnutrition (SAM) treatment services, estimates of the number of children requiring treatment are needed. Prevalence surveys, used with population estimates, can directly estimate the number of prevalent cases but not the number of subsequent incident cases. Health managers often use a prevalence-to-incidence conversion factor (J) derived from two African cohort studies to estimate incidence and add the expected number of incident cases to prevalent cases to estimate expected SAM caseload for a given period. The present study aimed to estimate J empirically in different contexts.

Observational study, with J estimated by correlating expected numbers of children to be treated, based on prevalence surveys, population estimates and assumed coverage, with the observed numbers of SAM patients treated.

Survey and programme data from six African and Asian countries.

Twenty-four data sets including prevalence surveys and programme admissions data for 5 months following the survey.

A statistically significant relationship between the number of SAM cases admitted to SAM treatment services and the estimated burden of SAM from prevalence surveys was found. Estimate for the slope (intercept forced to be zero) was 2·17 (95 % CI 1·33, 3·79). Estimates for the prevalence-to-incidence conversion factor J varied from 2·81 to 11·21, assuming programme coverage of 100 % and 38 %, respectively.

Estimation of expected caseload from prevalence may require revision of the currently used prevalence-to-incidence conversion factor J of 1·6. Appropriate values for J may vary between different locations.

To prospectively examine the extent to which mealtime habits influences the risk of weight gain and obesity in Mexican adults.

We performed a prospective cohort study. The Mealtime Habits Quality (MHQ) scale was used for assessing participants’ MHQ; the outcomes of interest were gain ≥5 % of body weight, developing overweight/obesity and abdominal obesity, after 7 years of follow-up. In order to estimate the independent effect of MHQ on anthropometric indicators, generalized linear models were computed to obtain adjusted relative risks (95 % CI).

The state of Morelos, Mexico.

Mexican adults (n 837) aged 18–70 years participating in a cohort study.

Compared with participants classified in the higher MHQ category, individuals in the middle and lower MHQ groups had a 4·1 (2·5, 6·7) and 6·2 (3·9, 9·7) fold greater risk of gain ≥5 % of body weight, respectively; 6·6 (2·8, 15·5) and 8·6 (3·7, 19·8) fold greater risk of becoming overweight/obese, respectively; and 3·8 (2·0, 7·3) and 5·3 (2·8, 9·8) fold greater risk of developing abdominal obesity, respectively.

This study provides evidence about the influence of a set of mealtime habits on obesity indicators, showing that greater adherence to unadvisable mealtime habits increases the risk of developing unhealthy anthropometric indicators. Since the meal is one of the most important sources of food intake, and consequently weight status, the MHQ scale can be a useful population tool to predict weight gain and obesity.