The Mediterranean-Dietary Approach to Stop Hypertension (DASH) Intervention for Neurodegenerative Delay (MIND) diet is a dietary pattern designed to prevent cognitive decline. Dietary adherence is assessed with the MIND diet scoring system, which is currently based on the American diet and serving sizes. It is known that serving sizes and consumed food products differ between countries. Existing literature lacks reporting on food products included within the MIND diet and weight or volume equivalents corresponding to MIND diet servings, impeding accurate comparisons across studies. This study sought to overcome these limitations by evaluating MIND food products consumed in the Dutch context and developing a scoring system based on consumed quantities in weight or volume amounts rather than in standard serving amounts. The third objective was to modify an existing Dutch brief FFQ (Eetscore-FFQ) to evaluate adherence to the MIND diet. We translated nine of the fifteen MIND food groups directly to grams and volumes using the United States Department of Agriculture measurement conversion table. For the remaining food groups, we employed indirect translation to align them as closely as possible to the original MIND diet. These translated quantities in weight and volumes amounts were subsequently rounded to the nearest Dutch household measures, resulting in the culturally adapted MIND-NL diet scoring. The development of the MIND-NL-Eetscore-FFQ, comprising seventy-two food items (forty-one questions), is described. Our adaption approach is reproducible and can be used to customize the MIND diet scoring system to other cultures.

Ethiopia announced its first food-based dietary guidelines (FBDGs) on 15 March 2022. The present study aims to develop and evaluate the Ethiopian Healthy Eating Index (Et-HEI) based on the FBDG. Data were collected from 494 Ethiopian women of reproductive age sampled from households in five different regions. The Et-HEI consists of eleven components, and each component was scored between 0 and 10 points, the total score ranging from 0 to 110, with maximum adherence to the FBDG. The Et-HEI score was evaluated against the Minimum Dietary Diversity for Women (MDD-W) and the probability of nutrient adequacy. The average Et-HEI score for women of reproductive age was 49 out of 110. Adherence to the recommendations for grains, vegetables, legumes, fat and oils, salt, sugar and alcohol contributed the most to this score. Most women had low scores for fruits, nuts and seeds, and animal-sourced foods, indicating low intake. The Cronbach's alpha coefficient, indicating the reliability of the Et-HEI to assess its diet quality, was 0⋅53. The low mean Et-HEI score agreed with a low mean score of the MDD-W (3⋅5 out of 10). Also, low nutrient adequacies confirmed poor adherence to nutrient-dense components of the FBDG. The Et-HEI was not associated with the intake of vitamin B12, vitamin C and calcium in this study population. Women who completed secondary school and above had relatively lower Et-HEI scores. The newly developed Et-HEI is able to estimate nutrient adequacy while also assessing adherence to the Ethiopian FBDG though there is room for improvement.

The Eetscore FFQ was developed to score the Dutch Healthy Diet index 2015 (DHD2015-index) representing the Dutch food-based dietary guidelines of 2015. This paper describes the development of the Eetscore FFQ, a short screener assessing diet quality, examines associations between diet quality and participants’ characteristics, and evaluates the relative validity and reproducibility of the Eetscore FFQ in a cross-sectional study with Dutch adults. The study sample consisted of 751 participants, aged 19–91 years, recruited from the EetMeetWeet research panel. The mean DHD2015-index score based on the Eetscore FFQ of the total sample was 111 (sd 17·5) out of a maximum score of 160 points and was significantly higher in women than in men, positively associated with age and education level, and inversely associated with BMI. The Kendall’s tau-b coefficient of the DHD2015-index between the Eetscore FFQ and the full-length FFQ (on average 1·7-month interval, n 565) was 0·51 (95 % CI 0·47, 0·55), indicating an acceptable ranking ability. The intraclass correlation coefficient between DHD2015-index scores derived from two repeated Eetscore FFQ (on average 3·8-month interval, n 343) was 0·91 (95 % CI 0·89, 0·93) suggesting a very good reproducibility. In conclusion, the Eetscore FFQ was considered acceptable in ranking participants according to their diet quality compared with the full-length FFQ and showed good to excellent reproducibility.

Taste is a key driver of food choice and intake. Taste preferences are widely studied, unlike the diet’s taste profile. This study assessed dietary taste patterns in the Netherlands by sex, BMI, age and education. A taste database, containing 476 foods’ taste values, was combined with 2-d 24-h recalls in two study populations. The percentage of energy intake from six taste clusters was assessed in the Dutch National Food Consumption Survey (DNFCS 2007–2010; n 1351) and in an independent observational study: the Nutrition Questionnaires plus (NQplus) study (2011–2013; n 944). Dietary taste patterns were similar across study populations. Men consumed relatively more energy from ‘salt, umami and fat’ (DNFCS; 24 % energy, NQplus study; 23 %)- and ‘bitter’ (7 %)-tasting foods compared with women (21 %, P<0·001, 22 %, P=0·005; 3 %, P<0·001, 4 %, P<0·001, respectively). Women consumed more % energy from ‘sweet and fat’ (15 %)- and ‘sweet and sour’ (13 %, 12 %, respectively)-tasting foods compared with men (12 %, P<0·001, 13 %, P=0·001; 10 %, P<0·001). Obese individuals consumed more % energy from ‘salt, umami and fat’- and less from ‘sweet and fat’-tasting foods than normal-weight individuals (‘salt, umami and fat’, men; obese both studies 26 %, normal-weight DNFCS 23 %, P=0·037, NQplus 22 %, P=0·001, women; obese 23 %, 24 %, normal weight 20 %, P=0·004, P=0·011, respectively, ‘sweet and fat’, men; obese 11 %, 10 %, normal weight 13 %, P<0·05, 14 %, P<0·01, women; obese 14 %, 15 %, normal weight 16 %, P=0·12, P=0·99). In conclusion, our taste database can be used to deepen our understanding of the role of taste in dietary intake in the Netherlands by sex, BMI, age and education.

This study aimed to evaluate the effects of an intervention including nutritional telemonitoring, nutrition education, and follow-up by a nurse on nutritional status, diet quality, appetite, physical functioning and quality of life of Dutch community-dwelling elderly. We used a parallel arm pre-test post-test design with 214 older adults (average age 80 years) who were allocated to the intervention group (n 97) or control group (n 107), based on the municipality. The intervention group received a 6-month intervention including telemonitoring measurements, nutrition education and follow-up by a nurse. Effect measurements took place at baseline, after 4·5 months, and at the end of the study. The intervention improved nutritional status of participants at risk of undernutrition (β (T1)=2·55; 95 % CI 1·41, 3·68; β (T2)=1·77; 95 % CI 0·60, 2·94) and scores for compliance with Dutch guidelines for the intake of vegetables (β=1·27; 95 % CI 0·49, 2·05), fruit (β=1·24; 95 % CI 0·60, 1·88), dietary fibre (β=1·13; 95 % CI 0·70, 1·57), protein (β=1·20; 95 % CI 0·15, 2·24) and physical activity (β=2·13; 95 % CI 0·98, 3·29). The intervention did not have an effect on body weight, appetite, physical functioning and quality of life. In conclusion, this intervention leads to improved nutritional status in older adults at risk of undernutrition, and to improved diet quality and physical activity levels of community-dwelling elderly. Future studies with a longer duration should focus on older adults at higher risk of undernutrition than this study population to investigate whether the impact of the intervention on nutritional and functional outcomes can be improved.

Self-administered web-based 24-h dietary recalls (24 hR) may save a lot of time and money as compared with interviewer-administered telephone-based 24 hR interviews and may therefore be useful in large-scale studies. Within the Nutrition Questionnaires plus (NQplus) study, the web-based 24 hR tool Compl-eat™ was developed to assess Dutch participants’ dietary intake. The aim of the present study was to evaluate the performance of this tool against the interviewer-administered telephone-based 24 hR method. A subgroup of participants of the NQplus study (20–70 years, n 514) completed three self-administered web-based 24 hR and three telephone 24 hR interviews administered by a dietitian over a 1-year period. Compl-eat™ as well as the dietitians guided the participants to report all foods consumed the previous day. Compl-eat™ on average underestimated the intake of energy by 8 %, of macronutrients by 10 % and of micronutrients by 13 % as compared with telephone recalls. The agreement between both methods, estimated using Lin's concordance coefficients (LCC), ranged from 0·15 for vitamin B1 to 0·70 for alcohol intake (mean LCC 0·38). The lower estimations by Compl-eat™ can be explained by a lower number of total reported foods and lower estimated intakes of the food groups, fats, oils and savoury sauces, sugar and confectionery, dairy and cheese. The performance of the tool may be improved by, for example, adding an option to automatically select frequently used foods and including more recall cues. We conclude that Compl-eat™ may be a useful tool in large-scale Dutch studies after suggested improvements have been implemented and evaluated.

A standardised, national, 160-item FFQ, the FFQ-NL 1.0, was recently developed for Dutch epidemiological studies. The objective was to validate the FFQ-NL 1.0 against multiple 24-h recalls (24hR) and recovery and concentration biomarkers. The FFQ-NL 1.0 was filled out by 383 participants (25–69 years) from the Nutrition Questionnaires plus study. For each participant, one to two urinary and blood samples and one to five (mean 2·7) telephone-based 24hR were available. Group-level bias, correlation coefficients, attenuation factors, de-attenuated correlation coefficients and ranking agreement were assessed. Compared with the 24hR, the FFQ-NL 1.0 estimated the intake of energy and macronutrients well. However, it underestimated intakes of SFA and trans-fatty acids and alcohol and overestimated intakes of most vitamins by >5 %. The median correlation coefficient was 0·39 for energy and macronutrients, 0·30 for micronutrients and 0·30 for food groups. The FFQ underestimated protein intake by an average of 16 % and K by 5 %, relative to their urinary recovery biomarkers. Attenuation factors were 0·44 and 0·46 for protein and K, respectively. Correlation coefficients were 0·43–0·47 between (fatty) fish intake and plasma EPA and DHA and 0·24–0·43 between fruit and vegetable intakes and plasma carotenoids. In conclusion, the overall validity of the newly developed FFQ-NL 1.0 was acceptable to good. The FFQ-NL 1.0 is well suited for future use within Dutch cohort studies among adults.

Generally, there is a need for short questionnaires to estimate diet quality in the Netherlands. We developed a thirty-four-item FFQ – the Dutch Healthy Diet FFQ (DHD-FFQ) – to estimate adherence to the most recent Dutch guidelines for a healthy diet of 2006 using the DHD-index. The objectives of the present study were to evaluate the DHD-index derived from the DHD-FFQ by comparing it with the index based on a reference method and to examine associations with participant characteristics, nutrient intakes and levels of cardiometabolic risk factors. Data of 1235 Dutch men and women, aged between 20 and 70 years, participating in the Nutrition Questionnaires plus study were used. The DHD-index was calculated from the DHD-FFQ and from a reference method consisting of a 180-item FFQ combined with a 24-h urinary Na excretion value. Ranking was studied using Spearman’s correlations, and absolute agreement was studied using a Bland–Altman plot. Nutrient intakes derived from the 180-item FFQ were studied according to quintiles of the DHD-index using DHD-FFQ data. The correlation between the DHD-index derived from the DHD-FFQ and the reference method was 0·56 (95 % CI 0·52, 0·60). The Bland–Altman plot showed a small mean overestimation of the DHD-index derived from the DHD-FFQ compared with the reference method. The DHD-index score was in the favourable direction associated with most macronutrient and micronutrient intakes when adjusted for energy intake. No associations between the DHD-index score and cardiometabolic risk factors were observed. In conclusion, the DHD-index derived from the DHD-FFQ was considered acceptable in ranking but relatively poor in individual assessment of diet quality.

As FFQ are subject to measurement error, associations between self-reported intake by FFQ and outcome measures should be adjusted by correction factors obtained from a validation study. Whether the correction is adequate depends on the characteristics of the reference method used in the validation study. Preferably, reference methods should (1) be unbiased and (2) have uncorrelated errors with those in the FFQ. The aim of the present study was to assess the validity of the duplicate portion (DP) technique as a reference method and compare its validity with that of a commonly used reference method, the 24 h recall (24hR), for protein, K and Na using urinary markers as the unbiased reference method. For 198 subjects, two DP, two FFQ, two urinary biomarkers and between one and fifteen 24hR (web based and/or telephone based) were collected within 1·5 years. Multivariate measurement error models were used to estimate bias, error correlations between FFQ and DP or 24hR, and attenuation factors of these methods. The DP was less influenced by proportional scaling bias (0·58 for protein, 0·72 for K and 0·52 for Na), and correlated errors between DP and FFQ were lowest (protein 0·28, K 0·17 and Na 0·19) compared with the 24hR. Attenuation factors (protein 0·74, K 0·54 and Na 0·43) also indicated that the DP performed better than the 24hR. Therefore, the DP is probably the best available reference method for FFQ validation for nutrients that currently have no generally accepted recovery biomarker.

Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose–response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258·0 g, the correlation between observed and predicted intake was 0·78 and the mean difference between observed and predicted intake was − 1·7 g (limits of agreement: − 466·3, 462·8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201·1 g, the correlation was 0·65 and the mean bias was 2·4 g (limits of agreement: − 368·2, 373·0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.

The Dutch Healthy Diet index (DHD-index) was developed using data from two 24 h recalls (24hR) and appeared useful to evaluate diet quality in Dutch adults. As many epidemiologic studies use FFQ, we now estimated the DHD-index score using FFQ data. We compared whether this score showed similar associations with participants' characteristics, micronutrient intakes, and biomarkers of intake and metabolism compared with the DHD-index using 24hR data. Data of 121 Dutch participants of the European Food Consumption Validation study were used. Dietary intake was assessed by two 24hR and a 180-item FFQ. Biomarkers measured were serum total cholesterol and carotenoids, EPA + DHA in plasma phospholipids and 24 h urinary Na. A correlation of 0·48 (95 % CI 0·33, 0·61) was observed between the DHD-index score based on 24hR data and on FFQ data. Classification of participants into the same tertiles of the DHD-index was achieved for 57 %. Women showed higher DHD-index scores. Energy intake was inversely associated with both DHD-index scores. Furthermore, age and intakes of folate, Fe, Mg, K, vitamin B6 and vitamin C were positively associated with both DHD-index scores. DHD-index scores showed acceptable correlations with the four combined biomarkers taking energy intake into account (r24hR 0.55; rFFQ 0.51). In conclusion, the DHD-index score based on FFQ data shows similar associations with participants' characteristics, energy intake, micronutrient intake and biomarkers compared with the score based on 24hR data. Furthermore, ranking of participants was acceptable for both methods. FFQ data may therefore be used to assess diet quality using the DHD-index in Dutch populations.

It is generally assumed that a FFQ is not suitable to estimate the absolute levels of individual energy intake. However, in epidemiological studies, reported nutrients by FFQ are often corrected for this intake. The objective of the present study was to assess how accurately participants report their energy intakes by FFQ. We compared reported energy intake with actual energy intake needed to maintain stable body weights during eleven controlled dietary trials. FFQ were developed to capture at least 90 % of energy intake. Participants, 342 women and 174 men, with a mean BMI of 22·8 (sd 3·1) kg/m2 filled out the FFQ just before the trials. Energy intakes during the trials were calculated from provided foods and reported free-food items, representing 90 and 10 % of energy intake, respectively. Mean reported energy intake was 97·5 (sd 12·7) % of actual energy intake during the trials; it was 98·9 (sd 15·2) % for women and 94·7 (sd 16·3) % for men (P = 0·004 for difference between sexes). Correlation coefficients between reported and actual energy intakes were 0·82 for all participants, 0·74 for women and 0·80 for men. Individual reported energy intake as a percentage of actual intake ranged from 56·3 to 159·6 % in women and from 43·8 to 151·0 % in men. In conclusion, the FFQ appeared to be accurate for estimating the mean level of energy intakes of these participants and for ranking them according to their intake. However, the large differences found on the individual level may affect the results of epidemiological studies in an unknown direction if nutrients are corrected for energy intakes reported by FFQ.

The use of two non-consecutive 24 h recalls using EPIC-Soft for standardised dietary monitoring in European countries has previously been proposed in the European Food Consumption Survey Method consortium. Whether this methodology is sufficiently valid to assess nutrient intake in a comparable way, among populations with different food patterns in Europe, is the subject of study in the European Food Consumption Validation consortium. The objective of the study was to compare the validity of usual protein and K intake estimated from two non-consecutive standardised 24 h recalls using EPIC-Soft between five selected centres in Europe. A total of 600 adults, aged 45–65 years, were recruited in Belgium, the Czech Republic, France, The Netherlands and Norway. From each participant, two 24 h recalls and two 24 h urines were collected. The mean and distribution of usual protein and K intake, as well as the ranking of intake, were compared with protein and K excretions within and between centres. Underestimation of protein (range 2–13 %) and K (range 4–17 %) intake was seen in all centres, except in the Czech Republic. We found a fair agreement between prevalences estimated based on the intake and excretion data at the lower end of the usual intake distribution ( < 10 % difference), but larger differences at other points. Protein and K intake was moderately correlated with excretion within the centres (ranges = 0·39–0·67 and 0·37–0·69, respectively). These were comparable across centres. In conclusion, two standardised 24 h recalls (EPIC-Soft) appear to be sufficiently valid for assessing and comparing the mean and distribution of protein and K intake across five centres in Europe as well as for ranking individuals.

The authors automated the selection of foods in a computer system that compiles and processes tailored FFQ. For the selection of food items, several methods are available. The aim of the present study was to compare food lists made by MOM2, which identifies food items with highest between-person variance in intake of the nutrients of interest without taking other items into account, with food lists made by forward regression. The name MOM2 refers to the variance, which is the second moment of the nutrient intake distribution. Food items were selected for the nutrients of interest from 2 d of recorded intake in 3524 adults aged 25–65 years. Food lists by 80 % MOM2 were compared to those by 80 % explained variance for regression on differences between the number and type of food items, and were evaluated on (1) the percentage of explained variance and (2) percentage contribution to population intake computed for the selected items on the food list. MOM2 selected the same food items for Ca, a few more for fat and vitamin C, and a few less for carbohydrates and dietary fibre than forward regression. Food lists by MOM2 based on 80 % of variance in intake covered 75–87 % of explained variance for different nutrients by regression and contributed 53–75 % to total population intake. Concluding, for developing food lists of FFQ, it appears sufficient to select food items based on the contribution to variance in nutrient intake without taking covariance into account.

In order to assess nutritional adequacy, valid estimates of nutrient intake are required. One of the main errors in dietary assessment is misreporting. The objective was to review the extent, nature and determinants of misreporting in dietary assessment, how this affects reported intakes of micronutrients and how this is identified and measured, and to identify the best ways of dealing with misreporting when interpreting results. A systematic literature search was conducted for studies of misreporting of dietary intake in adults by 24 hour recalls or by estimated or weighed food records, published up to March 2008. Thirty-seven relevant studies were identified. Possible causes of misreporting were identified. Methods most used to identify misreporting were the Goldberg cut-off (46 % studies) and the doubly labelled water technique (24 % studies). The magnitude of misreporting of energy intake was similar in all three dietary assessment methods. The percentage of under-reporters was about 30 % and energy intake was underestimated by approximately 15 %. Seven papers presented usable data for micronutrient intake. Absolute intakes of Fe, Ca and vitamin C (the three micronutrients addressed in all papers) were on average 30 % lower in low-energy reporters (LER) than that in non-LER and, although results were not consistent, there was a tendency for micronutrient density to be higher in LER. Excluding underreporters or using energy adjustment methods for micronutrient intakes is discussed. Residual method of energy adjustment seems to be a good tool for practice to decrease an influence of misreporting when interpreting results of studies based on food records and 24 hour recalls.