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OBJECTIVES: Ethnic minorities are often not included in studies of diet and health because of a lack of validated instruments to assess their habitual diets. Given the increased ethnic diversity in many high-income countries, insight into the diets of ethnic minorities is needed for the development of nutritional policies and interventions. In this paper, we describe the development of ethnic-specific food frequency questionnaires (FFQs) to study the diets of Surinamese (African and South Asian), Turkish, Moroccan and ethnic Dutch residents of The Netherlands. METHODS: An existing Dutch FFQ was adapted and formed the basis for three new FFQs. Information on food intake was obtained from single 24 h recalls. Food items were selected according to their percentage contribution to and variance in absolute nutrient intake of the respective ethnic groups. A nutrient database for each FFQ was constructed, consisting of data from the Dutch Food Composition table; data on ethnic foods were based on new chemical analyses and available international data. RESULTS: We developed four ethnic-specific FFQs using a standardised approach that included ~200 food items each and that covered more than 90% of the intake of the main nutrients of interest. CONCLUSIONS: The developed FFQs will enable standardised and comparable assessment of the diet of five different ethnic groups and provide insight into the role of diet in differences in health between ethnic groups. The methodology described in this paper and the choices made during the development phase may be useful in developing similar FFQs in other settings. European Journal of Clinical Nutrition (2015) 69, 579-584; doi: 10.1038/ejcn.2014.180; published online 17 September 2014, INTRODUCTION Assessing habitual dietary intake is one of the greatest challenges in dietary research, especially in ethnic minority groups. To date, ethnic minorities have been underrepresented in studies on diet [...]

Background: Nutrition apps seem to be promising tools for supporting consumers toward healthier eating habits. There is a wide variety of nutrition apps available; however, users often discontinue app use at an early stage before a permanent change in dietary behavior can be achieved., Objective: The main objective of this study was to identify, from both a user and nonuser perspective, which functionalities should be included in nutrition apps to increase intentions to start and maintain use of these apps. A secondary objective was to gain insight into reasons to quit using nutrition apps at an early stage., Methods: This study used a mixed methods approach and included a qualitative and a quantitative study. The qualitative study (n=40) consisted of a home-use test with 6 commercially available nutrition apps, followed by 6 focus group discussions (FGDs) to investigate user experiences. The quantitative study was a large-scale survey (n=1420), which was performed in a representative sample of the Dutch population to quantify the FGDs' results. In the survey, several app functionalities were rated on 7-point Likert scales ranging from 1 (very unimportant) to 7 (very important)., Results: A total of 3 different phases of app use, subdivided into 10 user-centric app aspects and 46 associated app functionalities, were identified as relevant nutrition app elements in the FGDs. Relevance was confirmed in the survey, as all user-centric aspects and almost all app functionalities were rated as important to include in a nutrition app. In the starting phase, a clear introduction (mean 5.45, SD 1.32), purpose (mean 5.40, SD 1.40), and flexible food tracking options (mean 5.33, SD 1.45) were the most important functionalities. In the use phase, a complete and reliable food product database (mean 5.58, SD 1.41), easy navigation (mean 5.56, SD 1.36), and limited advertisements (mean 5.53, SD 1.51) were the most important functionalities. In the end phase, the possibility of setting realistic goals (mean 5.23, SD 1.44), new personal goals (mean 5.13, SD 1.45), and continuously offering new information (mean 4.88, SD 1.44) were the most important functionalities. No large differences between users, former users, and nonusers were found. The main reason for quitting a nutrition app in the survey was the high time investment (14/38, 37%). This was also identified as a barrier in the FGDs., Conclusions: Nutrition apps should be supportive in all 3 phases of use (start, use, and end) to increase consumers' intentions to start and maintain the use of these apps and achieve a change in dietary behavior. Each phase includes several key app functionalities that require specific attention from app developers. High time investment is an important reason to quit nutrition app use at an early stage., (©Sandra van der Haar, Ireen Raaijmakers, Muriel C D Verain, Saskia Meijboom. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 20.06.2023.)

Objective: In the Netherlands, various FFQs have been administered in large cohort studies, which hampers comparison and pooling of dietary data. The present study aimed to describe the development of a standardized Dutch FFQ, FFQ-NL1.0, and assess its compatibility with existing Dutch FFQs., Design: Dutch FFQTOOLTM was used to develop the FFQ-NL1.0 by selecting food items with the largest contributions to total intake and explained variance in intake of energy and thirty-nine nutrients in adults aged 25-69 years from the Dutch National Food Consumption Survey (DNFCS) 2007-2010. Compatibility with the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ was assessed by comparing the number of food items, the covered energy and nutrient intake, and the covered variance in intake., Results: FFQ-NL1.0 comprised 160 food items, v. 253, 183 and 154 food items for the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ, respectively. FFQ-NL1.0 covered ≥85 % of energy and all nutrients reported in the DNFCS. Covered variance in intake ranged from 57 to 99 % for energy and macronutrients, and from 45 to 93 % for micronutrients. Differences between FFQ-NL1.0 and the other FFQs in covered nutrient intake and covered variance in intake were <5 % for energy and all macronutrients. For micronutrients, differences between FFQ-NL and other FFQs in covered level of intake were <15 %, but differences in covered variance were much larger, the maximum difference being 36 %., Conclusions: The FFQ-NL1.0 was compatible with other FFQs regarding energy and macronutrient intake. However, compatibility for covered variance of intake was limited for some of the micronutrients. If implemented in existing cohorts, it is advised to administer the old and the new FFQ in combination to derive calibration factors.

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 B 1 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.

Objective: 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., Design: 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)., Setting: Wageningen area, the Netherlands, 2011-2013., Subjects: 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., Results: 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., Conclusions: 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.

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.

Background: Invalid information on dietary intake may lead to false diet-disease associations. This study was conducted to examine the relative validity of the food frequency questionnaire (FFQ) used to assess dietary intake in the Leiden Longevity Study., Methods: A total of 128 men and women participating in the Leiden Longevity Study were included in the present validation study. The performance of the FFQ was evaluated using the mean of three 24-hour recalls as the reference method. Evaluation in estimating dietary intake at the group level was done by paired t-tests. The relative validity of the individual energy adjusted level of intake was assessed with correlation analyses (Pearson's), with correction for measurement error., Results: On group level, the FFQ overestimated as well as underestimated absolute intake of various nutrients and foods. The Bland and Altman plot for total energy intake showed that the agreement between the FFQ and the 24-hour recalls was dependent of intake level. Pearson correlation coefficients ranged from 0.21 (alpha linolenic acid) to 0.78 (ethanol) for nutrients and from -0.02 (legumes, non-significant) to 0.78 (alcoholic beverages) for foods. Adjustment for energy intake slightly lowered the correlation coefficients for nutrients (mean coefficient: 0.48 versus 0.50), while adjustment for within-subject variation in the 24-h recalls resulted in higher correlation coefficients for both nutrients and foods (mean coefficient: 0.69 for nutrients and 0.65 for foods)., Conclusions: For most nutrients and foods, the ability of the FFQ to rank subjects was acceptable to good.