To develop a healthy diet for Ethiopian women closely resembling their current diet and taking fasting periods into account while tracking the cost difference.

Linear goal programming models were built for three scenarios (non-fasting, continuous fasting and intermittent fasting). Each model minimised a function of deviations from nutrient reference values for eleven nutrients (protein, Ca, Fe, Zn, folate, and the vitamins A, B1, B2, B3, B6, and B12). The energy intake in optimised diets could only deviate 5 % from the current diet.

Five regions are included in the urban and rural areas of Ethiopia.

Two non-consecutive 24-h dietary recalls (24HDR) were collected from 494 Ethiopian women of reproductive age from November to December 2019.

Women’s mean energy intake was well above 2000 kcal across all socio-demographic subgroups. Compared to the current diet, the estimated intake of several food groups was considerably higher in the optimised modelled diets, that is, milk and dairy foods (396 v. 30 g/d), nuts and seeds (20 v. 1 g/d) and fruits (200 v. 7 g/d). Except for Ca and vitamin B12 intake in the continuous fasting diet, the proposed diets provide an adequate intake of the targeted micronutrients. The proposed diets had a maximum cost of 120 Ethiopian birrs ($3·5) per d, twice the current diet’s cost.

The modelled diets may be feasible for women of reproductive age as they are close to their current diets and fulfil their energy and nutrient demands. However, the costs may be a barrier to implementation.

To determine the relative validity and reproducibility of the Eetscore FFQ, a short screener for assessing diet quality, in patients with (severe) obesity before and after bariatric surgery (BS).

The Eetscore FFQ was evaluated against 3-d food records (3d-FR) before (T0) and 6 months after BS (T6) by comparing index scores of the Dutch Healthy Diet index 2015 (DHD2015-index). Relative validity was assessed using paired t tests, Kendall’s tau-b correlation coefficients (τb), cross-classification by tertiles, weighted kappa values (kw) and Bland–Altman plots. Reproducibility of the Eetscore FFQ was assessed using intraclass correlation coefficients (ICC).

Regional hospital, the Netherlands.

Hundred and forty participants with obesity who were scheduled for BS.

At T0, mean total DHD2015-index score derived from the Eetscore FFQ was 10·2 points higher than the food record-derived score (P < 0·001) and showed an acceptable correlation (τb = 0·42, 95 % CI: 0·27, 0·55). There was a fair agreement with a correct classification of 50 % (kw = 0·37, 95 % CI: 0·25, 0·49). Correlation coefficients of the individual DHD components varied from 0·01–0·54. Similar results were observed at T6 (τb = 0·31, 95 % CI: 0·12, 0·48, correct classification of 43·7 %; kw = 0·25, 95 % CI: 0·11, 0·40). Reproducibility of the Eetscore FFQ was good (ICC = 0·78, 95 % CI: 0·69, 0·84).

The Eetscore FFQ showed to be acceptably correlated with the DHD2015-index derived from 3d-FR, but absolute agreement was poor. Considering the need for dietary assessment methods that reduce the burden for patients, practitioners and researchers, the Eetscore FFQ can be used for ranking according to diet quality and for monitoring changes over time.

FFQ assess habitual dietary intake and are relatively inexpensive to process, but may take up to 60 min to complete. This article describes the validation of the Flower-FFQ, which consists of four short FFQ measuring the intake of energy and macronutrients or specific (micro)nutrients/foods that can be merged into one complete daily assessment using predefined algorithms.

Participants completed the Flower-FFQ and validated regular-FFQ (n 401). Urinary N (n 242) and K excretions (n 361) were measured. We evaluated: (1) group-level bias, (2) correlations and (3) cross-classification.

Observational study.

Dutch adults, 54 ± 11 (mean ± SD) years.

Flower-FFQ1, Flower-FFQ2, Flower-FFQ3 and Flower-FFQ4 were completed in ±24, 9, 8 and 9 min (±50 min total), respectively. The regular-FFQ was completed in ±43 min. Mean energy (flower v. regular: 7953 v. 8718 kJ/d) and macronutrient intakes (carbohydrates: 204 v. 222 g/d; protein: 75 v. 76 g/d; fat: 74 v. 83 g/d; ethanol: 8 v. 12 g/d) were comparatively similar. Spearman correlations between Flower-FFQ and regular-FFQ ranged from 0·60 to 0·80 for macronutrients and from 0·40 to 0·80 for micronutrients and foods. For all micronutrients and foods, ≥ 78 % of the participants classified in the same/adjacent quartile. The Flower-FFQ underestimated urinary N and K excretions by 24 and 18 %; 75 and 73 % of the participants ranked in the same/adjacent quartile.

Completing the Flower-FFQ required 50 min with a maximum of 25 min per short FFQ. The Flower-FFQ has a moderate to good ranking ability for most nutrients and foods and performs sufficiently to study diet–disease associations.

Alcohol consumption may be wrongly estimated because of inaccurate information on actual portion sizes. We compared portion sizes of wine, fortified wine and straight spirits poured at home with the Dutch standard drink sizes.

Participants measured portion sizes of wine, fortified wine and straight spirits at home up to a maximum of three times and reported these via an online survey. Average portion sizes (in millilitres) were compared with the Dutch standard drink sizes. Portion sizes were compared between subgroups of gender, age, BMI and level of education, and for different glass types.

Wageningen and surroundings, the Netherlands.

Adults (N 201) living in the Netherlands and consuming wine and/or straight spirits at home at least once per week.

Participants poured on average 129·4 ml white wine and 131·7 ml red wine, which is significantly more than the standard of 100 ml. For fortified wine, the average poured amount was 94·0 ml, significantly more than the standard of 50 ml; also for straight spirits the poured amount was significantly more than the standard (47·0 v. 35 ml).

Participants’ portion sizes of wine, fortified wine and straight spirits poured at home were on average larger than the Dutch standard drink sizes. This suggests that at-home alcohol consumption in the Netherlands is underestimated.

The present study aimed to conduct a process evaluation of a multicomponent nutritional telemonitoring intervention implemented among Dutch community-dwelling older adults.

A mixed-methods approach was employed, guided by the process evaluation framework of the Medical Research Council and the Unified Theory of Acceptance and Use of Technology. The process indicators reach, dose, fidelity and acceptability were measured at several time points within the 6-month intervention among participants and/or nurses.

The intervention was implemented in the context of two care organisations in the Netherlands.

In total, ninety-seven participants (average age 78 years) participated in the intervention and eight nurses were involved in implementation.

About 80 % of participants completed the intervention. Dropouts were significantly older, had worse cognitive and physical functioning, and were more care-dependent. The intervention was largely implemented as intended and received well by participants (satisfaction score 4·1, scale 1–5), but less well by nurses (satisfaction score 3·5, scale 1–5). Participants adhered better to weight telemonitoring than to telemonitoring by means of questionnaires, for which half the participants needed help. Intention to use the intervention was predicted by performance expectancy (β=0·40; 95 % CI 0·13, 0·67) and social influence (β=0·17; 95 % CI 0·00, 0·34). No association between process indicators and intervention outcomes was found.

This process evaluation showed that nutritional telemonitoring among older adults is feasible and accepted by older adults, but nurses’ satisfaction should be improved. The study provided relevant insights for future development and implementation of eHealth interventions among older adults.

To compare the performance of the commonly used 24 h recall (24hR) with the more distinct duplicate portion (DP) as reference method for validation of fatty acid intake estimated with an FFQ.

Intakes of SFA, MUFA, n-3 fatty acids and linoleic acid (LA) were estimated by chemical analysis of two DP and by on average five 24hR and two FFQ. Plasma n-3 fatty acids and LA were used to objectively compare ranking of individuals based on DP and 24hR. Multivariate measurement error models were used to estimate validity coefficients and attenuation factors for the FFQ with the DP and 24hR as reference methods.

Wageningen, the Netherlands.

Ninety-two men and 106 women (aged 20–70 years).

Validity coefficients for the fatty acid estimates by the FFQ tended to be lower when using the DP as reference method compared with the 24hR. Attenuation factors for the FFQ tended to be slightly higher based on the DP than those based on the 24hR as reference method. Furthermore, when using plasma fatty acids as reference, the DP showed comparable to slightly better ranking of participants according to their intake of n-3 fatty acids (0·33) and n-3:LA (0·34) than the 24hR (0·22 and 0·24, respectively).

The 24hR gives only slightly different results compared with the distinctive but less feasible DP, therefore use of the 24hR seems appropriate as the reference method for FFQ validation of fatty acid intake.

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.

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.

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 %.

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.

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.

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.

Insight into the role of acculturation in dietary patterns is important to inform the development of nutrition programmes that target ethnic minority groups. Therefore, the present study aimed to investigate how the adherence to dietary patterns within an ethnic minority population in the Netherlands varies by acculturation level compared with the host population.

Cross-sectional study using data of the HELIUS study. Dietary patterns were assessed with an ethnic-specific FFQ. Acculturation was operationalized using unidimensional proxies (residence duration, age at migration and generation status) as well as on the basis of the bidimensional perspective, defined by four distinct acculturation strategies: assimilation, integration, separation and marginalization.

Amsterdam, the Netherlands.

Participants of Dutch (n 1370) and Surinamese (n 1727) origin.

Three dietary patterns were identified: (i) ‘noodle/rice dishes and white meat’ (traditional Surinamese pattern); (ii) ‘red meat, snacks and sweets’; and (iii) ‘vegetables, fruit and nuts’. Surinamese-origin respondents adhered more to the traditional Surinamese pattern than the other dietary patterns. Neither the unidimensional proxies nor the bidimensional acculturation strategies demonstrated consistent associations with dietary patterns.

The lack of consistent association between acculturation and dietary patterns in the present study indicates that dietary patterns are quite robust. Understanding the continued adherence to traditional dietary patterns when developing dietary interventions in ethnic minority groups is warranted.

The most accurate method to estimate Na and K intakes is to determine 24 h urinary excretions of these minerals. However, collecting 24 h urine is burdensome. Therefore it was studied whether spot urine could be used to replace 24 h urine samples.

Participants collected 24 h urine and kept one voiding sample separate. Na, K and creatinine concentrations were analysed in both 24 h and spot urine samples. Also 24 h excretions of Na and K were predicted from spot urine concentrations using the Tanaka and Danish methods.

In 2011 and 2012, urine samples were collected and brought to the study centre at Wageningen University, the Netherlands.

Women (n 147) aged 19–26 years.

According to p-aminobenzoic acid excretions, 127 urine collections were complete. Correlations of Na:creatinine, K:creatinine and Na:K between spot urine and 24 h urine were 0·68, 0·57 and 0·64, respectively. Mean 24 h Na excretion predicted with the Tanaka method was higher (difference 21·2 mmol/d, P<0·001) than the measured excretion of 131·6 mmol/d and mean 24 h Na excretion predicted with the Danish method was similar (difference 3·2 mmol/d, P=0·417) to the measured excretion. The mean 24 h K excretion predicted with the Tanaka method was higher (difference 13·6 mmol/d, P<0·001) than the measured excretion of 66·8 mmol/d. Bland–Altman plots showed large individual differences between predicted and measured 24 h Na and K excretions.

The ratios of Na:creatinine and K:creatinine in spot urine were reasonably well associated with their respective ratios in 24 h urine and appear to predict mean 24 h Na excretion of these young, Caucasian women.

To illustrate the impact of intake-related bias in FFQ and 24 h recall (24hR), and correlated errors between these methods, on intake–health associations.

Dietary intake was assessed by a 180-item semi-quantitative FFQ and two 24hR. Urinary N and urinary K were estimated from two 24 h urine samples. We compared four scenarios to correct associations for errors in an FFQ estimating protein and K intakes.

Wageningen, The Netherlands.

Fifty-nine men and fifty-eight women aged 45–65 years.

For this FFQ, measurement error weakened a true relative risk of 2·0 to 1·4 for protein and 1·5 for K. As compared with calibration to duplicate recovery biomarkers (i.e. the preferred scenario 1), estimating a validity coefficient using this duplicate biomarker resulted in overcorrected associations, caused by intake-related bias in the FFQ (scenario 2). The correction factor based on a triad using biomarkers and 24hR was hampered by this intake-related bias and by correlated errors between FFQ and 24hR, and in this population resulted in a nearly perfect correction for protein but an overcorrection for K (scenario 3). When the 24hR was used for calibration, only a small correction was done, due to correlated errors between the methods and intake-related bias in the 24hR (scenario 4).

Calibration to a gold standard reference method is the preferred approach to correct intake–health associations for FFQ measurement error. If it is not possible to do so, using the 24hR as reference method only partly removes the errors, but may result in improved intake–health associations.

To support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.

Selection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.

The food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.

The results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

To evaluate the impact of different modes of administration (face-to-face v. telephone), recall days (first v. second), days of the week (weekday v. weekend) and interview days (1 d later v. 2 d later) on bias in protein and K intakes collected with 24 h dietary recalls (24-HDR).

Two non-consecutive 24-HDR (collected with standardised EPIC-Soft software) were used to estimate protein and K intakes by a face-to-face interview at the research centres and a telephone interview, and included all days of the week. Two 24 h urine collections were used to determine biomarkers of protein and K intake. The bias in intake was defined as the ratio between the 24-HDR estimate and the biomarker.

Five centres in Belgium, Czech Republic, France, the Netherlands and Norway in the European Food Consumption Validation (EFCOVAL) study.

About 120 adults (aged 45–65 years) per centre.

The bias in protein intake in the Czech Republic and Norway was smaller for telephone than face-to-face interviews (P = 0·01). The second 24-HDR estimates of protein intake in France and K intake in Belgium had a larger bias than the first 24-HDR (P = 0·01 and 0·04, respectively). In the Czech Republic, protein intake estimated during weekends and K intake estimated during weekdays had a larger bias than during other days of the week (P = 0·01). In addition, K intake collected 2 d later in the Czech Republic was likely to be overestimated.

The biases in protein and K intakes were comparable between modes of administration, recall days, days of the week and interview days in some, but not all, study centres.

To investigate the impact of intensive group education on the Mediterranean diet on dietary intake and serum total cholesterol after 16 and 52 weeks, compared to a posted leaflet with the Dutch nutritional guidelines, in the context of primary prevention of cardiovascular disease (CVD).

Controlled comparison study of an intervention group given intensive group education about the Mediterranean diet and a control group of hypercholesterolaemic persons given usual care by general practitioners (GPs).

A socioeconomically deprived area in the Netherlands with an elevated coronary heart disease (CHD) mortality ratio.

Two hundred and sixty-six hypercholesterolaemic persons with at least two other CVD risk factors.

After 52 weeks, the intervention group decreased total and saturated fat intake more than the control group (net differences were 1.8 en% (95%CI 0.2–3.4) and 1.1 en% (95%CI 0.4–1.9), respectively). According to the Mediterranean diet guidelines the intake of fish, fruit, poultry and bread increased in the intervention group, more than in the control group. Within the intervention group, intake of fish (+100%), poultry (+28%) and bread (+6%) was significantly increased after 1 year (P < 0.05). The intensive programme on dietary education did not significantly lower serum cholesterol level more (−3%) than the posted leaflet (−2%) (net difference 0.06 mmol l−1, 95%CI −0.10 to 0.22). Initially, the body mass index (BMI) decreased more in the intervention group, but after 1 year the intervention and control group gained weight equally (+1%).

Despite beneficial changes in dietary habits in the intervention group compared with the control group, after 1 year BMI increased and total fat and saturated fat intake were still too high.