Table Correction: A Smartphone Food Record App Developed for the Dutch National Food Consumption Survey: Relative Validity Study.

[This corrects the article DOI: 10.2196/50196.].

Procedure to identify fortified foods in the Dutch branded food database.

Introduction: Information on fortified foods is needed for multiple purposes, including food consumption research and dietary advice. Branded food databases are a valuable source of food label data. European labeling legislation prescribes that food fortification should be indicated in the ingredient list, and nutrient values should be declared under certain conditions. This creates the potential to identify fortified foods in branded food databases, though it is not straightforward and labor-intensive. The aim of our study was to develop an automated approach to identify fortified foods in the Dutch branded food database called LEDA. Methods: An automated procedure, based on a stepwise approach conforming with European labeling legislation, using a list of rules and search terms, was developed to identify fortified foods. Fortification with calcium, folic acid, vitamin B12, and zinc was studied as an example. The results of a random stratified sample with fortified and not-fortified foods were validated by two experts. Results: The automated approach resulted in identifying 1,817 foods fortified with one or more of the selected nutrients in the LEDA dataset (0.94%). The proportions of fortified foods per nutrient were below 0.7%. The classification of fortified/non-fortified foods matched manual validation by experts for the majority of the foods in the sample, i.e., sensitivity and specificity indicating the probability of correctly identifying fortified and non-fortified foods was high (>94.0%). Conclusion: The automated approach is capable of easily and quickly identifying fortified foods in the Dutch branded food database with high accuracy, although some improvements to the automated procedure could be made. In addition, the completeness, correctness, and consistency of the LEDA database can be improved. To fully benefit from this automated approach, it needs to be expanded to cover all micronutrients that may be added to foods.

A Smartphone Food Record App Developed for the Dutch National Food Consumption Survey: Relative Validity Study.

BACKGROUND: In the Dutch National Food Consumption Survey, dietary intake has been assessed since 2003 through 24-hour dietary recalls using the GloboDiet software. A new self-administered smartphone food record app called DitEetIk! was developed for potential use in future surveys. OBJECTIVE: This study aims to evaluate the data collected using the DitEetIk! app and its relative validity for food group, energy, and nutrient intake compared with the previous dietary assessment method (GloboDiet 24-hour dietary recalls). METHODS: A total of 300 participants aged 18 to 79 years were recruited from a consumer panel. Participants were asked to keep a record of their consumption using the DitEetIk! app on 3 nonconsecutive days. Trained dietitians conducted a 24-hour dietary recall interview by telephone using the GloboDiet software (International Agency for Research on Cancer) regarding 1 of 3 DitEetIk! recording days. Nutrient intake was calculated using the NEVO database (version 2021/7.0). Relative validity was studied by comparing data from GloboDiet 24-hour dietary recalls and the DitEetIk app for the same day. Participants with implausible records, defined as days with energy intake of <0.6 or >3.0 basal metabolic rate, were excluded from the analyses. For 19 food groups and 29 nutrients, differences in median intake were assessed using the Wilcoxon signed rank test, and Spearman correlation coefficients were calculated. Bland-Altman plots with mean differences and 95% limits of agreement were created for energy intake and the contribution to energy intake from fat, carbohydrates, and protein. RESULTS: A total of 227 participants completed a combination of a DitEetIk! app recording day and a 24-hour dietary recall interview for the same day. Of this group, 211 participants (n=104, 49.3% men and n=107, 50.7% women) had plausible recording days. Of all recorded food items, 12.8% (114/894) were entered via food barcode scanning, and 18.9% (169/894) were searched at the brand level. For 31% (5/16) of the food groups, the median intake assessed using the DitEetIk! app was >10% lower than that assessed using 24-hour dietary recalls; this was the case for fruit (P=.005), added fats (P=.001), milk and milk products (P=.02), cereal products (P=.01), and sauces (P<.001). This was also the case for 14% (4/29) of the nutrients (all P<.001). Regarding mean intake, differences were generally smaller. Regarding energy intake, the mean difference and 95% limits of agreement were 14 kcal (-1096 to 1124). Spearman correlation coefficients between intake assessed using the DitEetIk! app and 24-hour dietary recalls ranged from 0.48 to 0.88 (median 0.78) for food groups and from 0.58 to 0.90 (median 0.72) for nutrients. CONCLUSIONS: Compared with GloboDiet 24-hour dietary recalls, the DitEetIk! app assessed similar mean energy intake levels but somewhat lower median intake levels for several food groups and nutrients.

Transitioning towards more plant-based diets: sharing expert knowledge through a system lens.

Transitioning towards more plant-based protein diets is essential for public and planetary health. Current research about consumption practices of protein sources provides limited insight in the multidisciplinary nature and interconnectivity of the food environment. This study aimed to collect mental models of review authors by synthesizing both their implicit and explicit system views into an overarching system view. Published reviews were used to select participants and identify variables that explain the protein transition in relation to the food environment. To overcome differences in disciplines and scale levels (e.g. individual, interpersonal, environmental), variables were organized according to the Determinants of Nutrition and Eating Framework. Eight review authors shared their mental models in an interview. Participants were asked to construct a causal loop diagram (CLD), a tool proven valuable in making one's ontology explicit to others. Implicit system views in narrative were converted into CLDs using a coding framework. The overarching system view suggests that a multitude of feedback loops sustain current consumption patterns of protein sources, for example by reinforcement through habit, availability and peer support. Several aspects require further research, such as variable relationships that were subject to disagreement and the lack of reciprocity between the physical and social elements of the food environment. In addition, knowledge gaps were exposed, including long-term behaviour and interaction of multiple variables. As a boundary object, the overarching system view can facilitate the direction of future research. The findings underscore the interconnected nature of many disparate elements within the food environment, stressing the need for holistic methods like systems thinking. These are essential in developing a systemic understanding and facilitating the transition towards more plant-based diets.

A randomized crossover trial assessing time of day snack consumption and resulting postprandial glycemic response in a real-life setting among healthy adults.

The postprandial glycemic response is an important metabolic health factor, which, from laboratory studies, is known to change from low to high over the course of the day, and from which negative health outcomes have been linked to nightly eating. We applied interstitial continuous glucose monitoring to examine the glycemic response to a standardized carbohydrate-rich snack (198 kcal) across the day in a real-life setting. Twenty-four healthy participants (12 men, 12 women, 27-61 y old) consumed the snack nine times during 6 d in a crossover design, altering the time of consumption between morning, afternoon and evening. The snack was consumed in the participant's own environment with a preceding fast of at least 2.5 h between their customary main meals and practices. Linear mixed models were used with fixed effect of timing, and participant as random effect, to assess incremental area under the curve, peak value and time-to-peak of the glycemic response. Overall, the highest glycemic excursions were observed in the morning, while a more dampened but prolonged response was observed in the evening. These findings do not concur with previously published laboratory studies. This implies that results obtained under controlled experimental conditions in laboratories cannot be generalized directly to predict chrononutritional effects on the glycemic response in healthy individuals and their daily routines.

The protein gap-increasing protein intake in the diet of community-dwelling older adults: a simulation study.

OBJECTIVE: Approximately 50 % of Dutch community-dwelling older adults does not meet protein recommendations. This study assesses the effect of replacing low protein foods with protein-rich alternatives on the protein intake of Dutch community-dwelling older adults. DESIGN: The Dutch National Food Consumption Survey-Older Adults 2010-2012 (DNFCS-OA) was used for scenario modelling. Dietary intake was estimated based on two 24-h recalls. Commonly consumed products were replaced by comparable products rich in protein (scenario 1), foods enriched in protein (scenario 2) and a combination of both (scenario 3). Replacement scenarios were confined to participants whose dietary protein intake was < 1·0 g/kg BW/d (n 391). Habitual protein intake of all older adults was estimated, adjusting for effects of within-person variation in the 2-d intake data. SETTING: A simulation study based on the DNFCS-OA. PARTICIPANTS: 727 Dutch community-dwelling older adults aged 70+. RESULTS: Mean protein intake of the total population increased from 1·0 to 1·2 g/kg BW/d (scenarios 1 and 2) and to 1·3 g/kg BW/d (scenario 3). The percentage of participants with intakes of ≥ 1·0 g/kg BW/d increased from 47·1 % to 91·4 %, 90·2 % and 94·6 %, respectively, in scenarios 1, 2 and 3. The largest increases in protein intake were due to replacements in food groups: yoghurt, cream desserts and pudding, potatoes, vegetables and legumes and non-alcoholic beverages and milk in scenario 1 and bread; yoghurt, cream desserts and pudding and soups in scenario 2. CONCLUSIONS: This simulation model shows that replacing low protein foods with comparable alternatives rich in protein can increase the protein intake of Dutch community-dwelling older adults considerably. Results can be used as a basis for nutritional counselling.

A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps.

Mobile dietary record apps have been increasingly validated by studies with various study designs. This review aims to evaluate the overall accuracy of dietary record apps in measuring the intake of energy, macro- and micronutrients, and food groups in real-life settings and the designs of validation studies. We systematically searched mobile dietary record validation studies published during the period from 2013 to 2019. We identified 14 studies for the systematic review, of which 11 studies were suitable for meta-analyses on energy intake and 8 studies on macronutrient intake. Mean differences and SDs of nutrient estimations between the app and the reference method from studies were pooled using a random-effects model. All apps underestimated energy intake when compared with their reference methods, with a pooled effect of -202 kcal/d (95% CI: -319, -85 kcal/d); the heterogeneity of studies was 72%. After stratification, studies that used the same food-composition table for both the app and the reference method had a lower level of heterogeneity (0%) and a pooled effect of -57 kcal/d (95% CI: -116, 2 kcal/d). The heterogeneity of studies in the differences in carbohydrate, fat, and protein intake was 54%, 73%, and 80%, with the pooled effect of -18.8 g/d, -12.7 g/d, and -12.2 g/d, respectively, after excluding outliers. The intakes of micronutrients and food groups were statistically nonsignificantly underestimated by the apps in most cases. In conclusion, dietary record apps underestimated food consumption compared with traditional dietary assessment methods. Moreover, varying study designs have been found across studies. Recommended practices for conducting validation studies were formulated including considering biomarkers as the reference, testing in a larger and more representative study population for a longer period, avoiding the learning effect of each method, and comparing food group or food item consumption in addition to comparing energy and nutrient intakes.

Dietary Changes among Adults in The Netherlands in the Period 2007-2010 and 2012-2016. Results from Two Cross-Sectional National Food Consumption Surveys.

Insight into dietary trends is important for the development and evaluation of dietary policies. The aim of this study is to describe changes in dietary intakes of Dutch adults and to evaluate these changes by age, gender, and education. In 2007-2010 and 2012-2016, two national food consumption surveys were conducted including 2106 and 1540 adults, respectively. Data collection included two non-consecutive 24 h dietary recalls. Mean habitual intakes of foods and nutrients relevant for a healthy diet of both surveys were estimated. Between the two periods the mean consumption of red or processed meat, dairy, sodium and alcohol and the ratio of whole-grain to cereal products decreased by 4-30% and the consumption of fibre and unsaturated fatty acids increased by about 3% and 6%, respectively. For most food groups, changes in consumption were comparable for both sexes and in all age groups. A healthier consumption pattern and several favorable changes were observed among higher-educated people. Most, but not all, changes in food consumption are favorable from a public health point of view. However, there is still a large potential for further improvements. A healthier consumption pattern was observed in adults with a higher educational level which calls for attention to social disparities when developing dietary policies.

Can Healthy and Sustainable Dietary Patterns That Fit within Current Dutch Food Habits Be Identified?

This study investigated major healthy and sustainable dietary patterns in the Dutch population. Two 24-hour dietary recalls were collected in 2078 participants aged 19-79 years in the Dutch National Food Consumption Survey 2012-2016. Dietary patterns were identified using reduced rank regression. Predictor variables were food groups and response variables were Dutch Healthy Diet index 2015 (DHD15-index) score, greenhouse gas emissions (GHGE), and blue water use. Three patterns were discovered, including a "high fruit and vegetable dietary pattern", a "low meat dietary pattern", and a "high dairy, low fruit juices dietary pattern". Diets in the highest quartile of these patterns had higher DHD15-index score than the average population. However, diets of the "high fruit and vegetable dietary pattern" were associated with higher dietary GHGE (14%) and blue water use (69.2%) compared to the average population. Diets of the "low meat dietary pattern" were associated with lower GHGE (19.6%) and higher blue water use (7.7%). Concluding, the "low meat dietary pattern" was the most healthy and sustainable dietary pattern in this population. The addition of blue water use as an environmental impact indicator shows the difficulty of finding existing dietary patterns that have low environmental impact in all determinants.

Relative Validity of MijnEetmeter: A Food Diary App for Self-Monitoring of Dietary Intake.

This study aimed to evaluate the relative validity of intake of energy, nutrients and food groups assessed with MijnEetmeter food diary as compared to 24-h dietary recalls, and if this differed between experienced and new users. One hundred men and women aged 18-70 y participated, of whom 47 had prior experience with the tool. Participants kept MijnEetmeter on three days. Trained dietitians called them three times for a 24-h dietary recall interview, once recalling food consumption on the same day as the food recording in MijnEetmeter. Systematic differences and correlations were assessed, and Bland-Altman plots were created; both for 3-day mean intakes and for intakes on the same day. Relative to 24-h dietary recalls, MijnEetmeter underestimated consumption of drinks, added fat, cereal products, and potatoes. Relative underestimation was observed for energy intake (6%) and about half of the nutrients. Experienced MijnEetmeter users underestimated intake the least. For intake of energy and six key nutrients, correlations between 3-day mean intakes were above 0.7 except for sodium intake. In conclusion, MijnEetmeter moderately underestimates intakes of energy and some nutrients and food groups. To improve the self-monitoring of dietary intake, it is recommended that the users record food consumption for several days and that the apps probes for easily forgotten foods and drinks.

A comparison of complementary measures of vitamin B6 status, function, and metabolism in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

BACKGROUND: Vitamin B6 insufficiency has been linked to increased risk of cancer and other chronic diseases. The circulating concentration of pyridoxal 5'-phosphate (PLP) is a commonly used measure of vitamin B6 status. Ratios of substrates indicating PLP coenzymatic function and metabolism may be useful complementary measures to further explore the role of vitamin B6 in health. OBJECTIVES: We explored the sensitivity of 5 outcomes, namely PLP concentration, homocysteine:cysteine (Hcy:Cys), cystathionine:cysteine (Cysta:Cys), the 3´-hydroxykynurenine ratio (HKr), and the 4-pyridoxic acid ratio (PAr) to vitamin B6 intake as well as personal and lifestyle characteristics. MEDTHODS: Dietary intake and biomarker data were collected from participants from 3 nested case-control studies within the European Prospective Investigation into Cancer and Nutrition (EPIC). Bayesian regression models assessed the associations of the 5 biomarker outcomes with vitamin B6 intake and personal and lifestyle covariates. Analogous models examined the relations of Hcy:Cys, Cysta:Cys, and HKr with PLP. RESULTS: In total, 4608 participants were included in the analyses. Vitamin B6 intake was most strongly associated with PLP, moderately associated with Hcy:Cys, Cysta:Cys, and HKr, and not associated with PAr (fold change in marker given a doubling of vitamin B6 intake: PLP 1.60 [95% credible interval (CrI): 1.50, 1.71]; Hcy:Cys 0.87 [95% CrI: 0.84, 0.90]; Cysta:Cys 0.89 [95% CrI: 0.84, 0.94]; HKr 0.88 [95% CrI: 0.85, 0.91]; PAr 1.00 [95% CrI: 0.95, 1.05]). PAr was most sensitive to age, and HKr was least sensitive to BMI and alcohol intake. Sex and menopause status were strongly associated with all 5 markers. CONCLUSIONS: We found that 5 different markers, capturing different aspects of vitamin B6-related biological processes, varied in their associations with vitamin B6 intake and personal and lifestyle predictors.

Protein intake pattern over the day and its association with low total protein intake in Dutch community-dwelling older adults.

OBJECTIVE: Investigate protein intake patterns over the day and their association with total protein intake in older adults. DESIGN: Cross-sectional study utilising the dietary data collected through two non-consecutive, dietary record-assisted 24-h recalls. Days with low protein intake (n 290) were defined using the RDA (<0·8 g protein/kg adjusted BW/d). For each day, the amount and proportion of protein ingested at every hour of the day and during morning, mid-day and evening hours was calculated. Amounts and proportions were compared between low and high protein intake days and related to total protein intake and risk of low protein intake. SETTING: Community. PARTICIPANTS: 739 Dutch community-dwelling adults ≥70 years. RESULTS: The mean protein intake was 76·3 (sd 0·7) g/d. At each hour of the day, the amount of protein ingested was higher on days with a high protein intake than on days with a low protein intake and associated with a higher total protein intake. The proportion of protein ingested during morning hours was higher (22 v. 17 %, P < 0·0001) on days with a low protein intake, and a higher proportion of protein ingested during morning hours was associated with a lower total protein intake (P < 0·0001) and a higher odds of low protein intake (OR 1·04, 95 % CI 1·03, 1·06). For the proportion of protein intake during mid-day or evening hours, opposite but weaker associations were found. CONCLUSIONS: In this sample, timing of protein intake was associated with total protein intake. Additional studies need to clarify the importance of these findings to optimise protein intake.

Prevalence of protein intake below recommended in community-dwelling older adults: a meta-analysis across cohorts from the PROMISS consortium.

BACKGROUND: Lower protein intake in older adults is associated with loss of muscle mass and strength. The present study aimed to provide a pooled estimate of the overall prevalence of protein intake below recommended (according to different cut-off values) among community-dwelling older adults, both within the general older population and within specific subgroups. METHODS: As part of the PRevention Of Malnutrition In Senior Subjects in the EU (PROMISS) project, a meta-analysis was performed using data from four cohorts (from the Netherlands, UK, Canada, and USA) and four national surveys [from the Netherlands, Finland (two), and Italy]. Within those studies, data on protein and energy intake of community-dwelling men and women aged ≥55 years were obtained by either a food frequency questionnaire, 24 h recalls administered on 2 or 3 days, or food diaries administered on 3 days. Protein intake below recommended was based on the recommended dietary allowance of 0.8 g/kg body weight (BW)/d, by using adjusted BW (aBW) instead of actual BW. Cut-off values of 1.0 and 1.2 were applied in additional analyses. Prevalences were also examined for subgroups according to sex, age, body mass index (BMI), education level, appetite, living status, and recent weight loss. RESULTS: The study sample comprised 8107 older persons. Mean ± standard deviation protein intake ranged from 64.3 ± 22.3 (UK) to 80.6 ± 23.7 g/d [the Netherlands (cohort)] or from 0.94 ± 0.38 (USA) to 1.17z ± 0.30 g/kg aBW/d (Italy) when related to BW. The overall pooled prevalence of protein intake below recommended was 21.5% (95% confidence interval: 14.0-30.1), 46.7% (38.3-55.3), and 70.8% (65.1-76.3) using the 0.8, 1.0, and 1.2 cut-off value, respectively. A higher prevalence was observed among women, individuals with higher BMI, and individuals with poor appetite. The prevalence differed only marginally by age, education level, living status, and recent weight loss. CONCLUSIONS: In community-dwelling older adults, the prevalence of protein intake below the current recommendation of 0.8 g/kg aBW/d is substantial (14-30%) and increases to 65-76% according to a cut-off value of 1.2 g/kg aBW/d. To what extent the protein intakes are below the requirements of these older people warrants further investigation.

Educational differences in healthy, environmentally sustainable and safe food consumption among adults in the Netherlands.

OBJECTIVE: To assess the differences in healthy, environmentally sustainable and safe food consumption by education levels among adults aged 19-69 in the Netherlands. DESIGN: This study used data from the Dutch National Food Consumption Survey 2007-10. Food consumption data were obtained via two 24-h recalls. Food consumption data were linked to data on food composition, greenhouse gas emissions (GHGe) and concentrations of contaminants. The Dutch dietary guidelines (2015), dietary GHGe and dietary exposure to contaminants were used as indicators for healthy, environmentally sustainable and safe food consumption, respectively. SETTING: The Netherlands. PARTICIPANTS: 2106 adults aged 19-69 years. RESULTS: High education groups consumed significantly more fruit (+28 g), vegetables (men +22 g; women +27 g) and fish (men +6 g; women +7 g), and significantly less meat (men -33 g; women -14 g) compared with low education groups. Overall, no educational differences were found in total GHGe, although its food sources differed. Exposure to contaminants showed some differences between education groups. CONCLUSIONS: The consumption patterns differed by education groups, resulting in a more healthy diet, but equally environmentally sustainable diet among high compared with low education groups. Exposure to food contaminants differed between education groups, but was not above safe levels, except for acrylamide and aflatoxin B1. For these substances, a health risk could not be excluded for all education groups. These insights may be used in policy measures focusing on the improvement of a healthy diet for all.

How does a simplified recipe collection procedure in dietary assessment tools affect the food group and nutrient intake distributions of the population.

Technology advancements have driven the use of self-administered dietary assessment methods in large-scale dietary surveys. Interviewer-assisted methods generally have a complicated recipe recording procedure enabling the adjustment from a standard recipe. In order to decide if this functionality can be omitted for self-administered dietary assessment, this study aimed to assess the extent of standard recipe modifications in the Dutch National Food Consumption Survey and measure the impact on the food group and nutrient intake distributions of the population when the modifications were disregarded. A two-scenario simulation analysis was conducted. Firstly, the individual recipe scenario omitted the full modifications to the standard recipes made by people who knew their recipes. Secondly, the modified recipe scenario omitted the modifications made by those who partially modified the standard recipe due to their limited knowledge. The weighted percentage differences for the nutrient and food group intake distributions between the scenarios and the original data set were calculated. The highest percentage of energy consumed through mixed dishes was 10 % for females aged 19-79 years. Comparing the combined scenario and the original data set, the average of the absolute percentage difference for the population mean intakes was 1·6 % across all food groups and 0·6 % for nutrients. The soup group (-6·6 %) and DHA (-2·3 %) showed the largest percentage difference. The recipe simplification caused a slight underestimation of the consumed amount of both foods (-0·2 %) and nutrients (-0·4 %). These results are promising for developing self-administered 24-hour recalls or food diary applications without complex recipe function.

Mycotoxin exposure assessments in a multi-center European validation study by 24-hour dietary recall and biological fluid sampling.

The European Food Consumption Validation (EFCOVAL) project includes 600 men and women from Belgium, the Czech Republic, France, the Netherlands, and Norway, who had given serum and 24-hour urine samples, and completed 24-hour dietary recall (24-HDR) interviews. Consumption, according to 24-HDR, was matched against the European Food Safety Authority (EFSA) databases of mycotoxin contaminations, via the FoodEx1 standard classifications, producing an indirect external estimate of dietary mycotoxin exposure. Direct, internal measurements of dietary mycotoxin exposure were made in serum and urine by ultra-performance liquid chromatography coupled to tandem mass spectrometry. For the first time, mycotoxin exposures were thoroughly compared between two 24-HDRs, and two 24-hour urine samples collected during the same days covered by the 24-HDRs. These measurements were compared to a single-time point serum measurement to investigate evidence of chronic mycotoxin exposure. According to 24-HDR data, all 600 individuals were exposed to between 4 and 34 mycotoxins, whereof 10 found to exceed the tolerable daily intake. Correlations were observed between two time points, and significant correlations were observed between concentrations in serum and urine. However, only acetyldeoxynivalenol, ochratoxin A, and sterigmatocystin were found to have significant positive correlations between 24-HDR exposures and serum, while aflatoxin G1 and G2, HT-2 toxin, and deoxynivalenol were associated between concurrent 24-HDR and 24-hour urine. Substantial agreements on quantitative levels between serum and urine were observed for the groups Type B Trichothecenes and Zearalenone. Further research is required to bridge the interpretation of external and internal exposure estimates of the individual on a time scale of hours. Additionally, metabolomic profiling of dietary mycotoxin exposures could help with a comprehensive assessment of single time-point exposures, but also with the identification of chronic exposure biomarkers. Such detailed characterization informs population exposure assessments, and aids in the interpretation of epidemiological health outcomes related to multi-mycotoxin exposure.

Development of healthy and sustainable food-based dietary guidelines for the Netherlands.

OBJECTIVE: To derive healthy and sustainable food-based dietary guidelines (FBDG) for different target groups in the Netherlands and describe the process. DESIGN: Optimised dietary patterns for children, adolescents, adults and the elderly were calculated using an optimisation model. Foods high in saturated and trans-fatty acids, salt and sugar, and low in dietary fibre, were excluded. The dietary patterns resembled the current food consumption as closely as possible, while simultaneously meeting recommendations for food groups, nutrients, maximum limits for foods with a high environmental impact, and within 85 % of the energy requirement. Recommended daily amounts of food groups were based on the optimised dietary patterns and expert judgement. SETTING: The Netherlands. PARTICIPANTS: FBDG were derived for Dutch people with different ages, genders, activity levels and food preferences. RESULTS: For most target groups the optimisation model provided dietary patterns that complied with all requirements. For some food groups, the optimised amounts varied largely between target groups. For consistent messages to consumers, the optimised dietary patterns were adjusted to uniform recommendations per target group. Recommendations were visualised in the Wheel of Five. The advice is to eat the recommended amounts of foods according to the Wheel of Five and limit consumption of other foods. CONCLUSIONS: Based on an optimisation model, scientific evidence, information on dietary patterns and expert knowledge, we derived FBDG for different target groups. The Wheel of Five is a key food-counselling model that can help Dutch consumers to make their diets healthier and more environmentally sustainable.

Importance of details in food descriptions in estimating population nutrient intake distributions.

BACKGROUND: National food consumption surveys are important policy instruments that could monitor food consumption of a certain population. To be used for multiple purposes, this type of survey usually collects comprehensive food information using dietary assessment methods like 24-h dietary recalls (24HRs). However, the collection and handling of such detailed information require tremendous efforts. We aimed to improve the efficiency of data collection and handling in 24HRs, by identifying less important characteristics of food descriptions (facets) and assessing the impact of disregarding them on energy and nutrient intake distributions. METHODS: In the Dutch National Food Consumption Survey 2007-2010, food consumption data were collected through interviewer-administered 24HRs using GloboDiet software in 3819 persons. Interviewers asked participants about the characteristics of each food item according to applicable facets. Food consumption data were subsequently linked to the food composition database. The importance of facets for predicting energy and each of the 33 nutrients was estimated using the random forest algorithm. Then a simulation study was performed to determine the influence of deleting less important facets on population nutrient intake distributions. RESULTS: We identified 35% facets as unimportant and deleted them from the total food consumption database. The majority (79.4%) of the percent difference between percentile estimates of the population nutrient intake distributions before and after facet deletion ranged from 0 to 1%, while 20% cases ranged from 1 to 5% and 0.6% cases more than 10%. CONCLUSION: We concluded that our procedure was successful in identifying less important food descriptions in estimating population nutrient intake distributions. The reduction in food descriptions has the potential to reduce the time needed for conducting interviews and data handling while maintaining the data quality of the survey.

The Maastricht FFQ: Development and validation of a comprehensive food frequency questionnaire for the Maastricht study.

OBJECTIVE: The aim of this study was to develop and validate a comprehensive food frequency questionnaire (FFQ) for The Maastricht Study, a population-based prospective cohort study in Maastricht, The Netherlands. METHODS: Item selection for the FFQ was based on explained variation and contribution to intake of energy and 24 nutrients. For validation, the FFQ was completed by 135 participants (25-70 y of age) of the Nutrition Questionnaires plus study. Per person, on average 2.8 (range 1-5) telephone-based 24-h dietary recalls (24HRs), two 24-h urinary samples, and one blood sample were available. Validity of 54 nutrients and 22 food groups was assessed by ranking agreement, correlation coefficients, attenuation factors, and ultimately deattenuated correlation coefficients (validity coefficients). RESULTS: Median correlation coefficients for energy and macronutrients, micronutrients, and food groups were 0.45, 0.36, and 0.38, respectively. Median deattenuated correlation coefficients were 0.53 for energy and macronutrients, 0.45 for micronutrients, and 0.64 for food groups, being >0.50 for 18 of 22 macronutrients, 16 of 30 micronutrients and >0.50 for 17 of 22 food groups. The FFQ underestimated protein and potassium intake compared with 24-h urinary nitrogen and potassium excretion by -18% and -2%, respectively. Correlation coefficients ranged from 0.50 and 0.55 for (fatty) fish intake and plasma eicosapentaenoic acid and docosahexaenoic acid, and from 0.26 to 0.42 between fruit and vegetable intake and plasma carotenoids. CONCLUSION: Overall, the validity of the 253-item Maastricht FFQ was satisfactory. The comprehensiveness of this FFQ make it well suited for use in The Maastricht Study and similar populations.