Caroline T. M. van Rossum

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

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 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.

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 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.

The aim of this study was to provide recent data on the prevalence of undernutrition based on screening and diagnosis in Dutch community-dwelling older adults. The data from the 2021 to 2022 examination wave from the Longitudinal Aging Study Amsterdam (n = 1138) and the Dutch National Food Consumption Survey 2019–2021 (n = 607) on community-dwelling men and women aged 65 years and older were used. The prevalence of undernutrition was based on a positive score on the Short Nutritional Assessment Questionnaire 65+ (SNAQ⁶⁵⁺) screening tool, a positive diagnosis using the Global Leadership Initiative on Malnutrition (GLIM) criteria and their combination. Of the combined sample (n = 1745), the mean age was 74 (SD 6) years, where 16.7% were aged 80 years or older, 50.5% was female, 56.9% had a high education level, and 30.3% lived alone. The prevalence of undernutrition based on the SNAQ⁶⁵⁺ screening in the combined sample was 8.5% (95% CI 7.3–9.9%). In the subgroup of LASA participants with complete data on all GLIM criteria (n = 700), the prevalence of undernutrition was 5.4% based on SNAQ⁶⁵⁺ and 7.1% based on GLIM. A positive SNAQ⁶⁵⁺ screening followed by a positive GLIM diagnosis resulted in a lower prevalence (3.1%). Being female, older, living alone, receiving formal home care, and having poor self-rated health, poor appetite, or mobility limitations, they were all associated with a higher prevalence, with more than two-fold higher prevalence rates in some subgroups. The results show that currently one out of twelve community-dwelling adults aged 65 years and older is undernourished based on the SNAQ65+ screening, and one out of fourteen is undernourished based on the GLIM diagnosis criteria. Awareness is needed to increase early recognition and treatment in community and primary care, especially among the more vulnerable groups.

This ‘cohort profile’ aims to provide a description of the study design, methodology, and baseline characteristics of the participants in the Corona Behavioral Unit cohort. This cohort was established in response to the COVID-19 pandemic by the Dutch National Institute for Public Health and the Environment (RIVM) and the regional public health services. The aim was to investigate adherence of and support for COVID-19 prevention measures, psychosocial determinants of COVID-19 behaviors, well-being, COVID-19 vaccination, and media use. The cohort also examined specific motivations and beliefs, such as for vaccination, which were collected through either closed-ended items or open text responses. In April 2020, 89,943 participants aged 16 years and older were recruited from existing nation-wide panels. Between May 2020 and September 2022, 99,676 additional participants were recruited through online social media platforms and mailing lists of higher education organizations. Participants who consented were initially invited every three weeks (5 rounds), then every six weeks (13 rounds), and since the summer of 2022 every 12 weeks (3 rounds). To date, 66% of participants were female, 30% were 39 years and younger, and 54% completed two or more questionnaires, with an average of 9.2 (SD = 5.7) questionnaires. The Corona Behavioral Unit COVID-19 cohort has published detailed insights into longitudinal patterns of COVID-19 related behaviors, support of COVID-19 preventive measures, as well as peoples’ mental wellbeing in relation to the stringency of these measures. The results have informed COVID-19 policy making and pandemic communication in the Netherlands throughout the COVID-19 pandemic. The cohort data will continuously be used to examine COVID-19 related outcomes for scientific analyses, as well as to inform future pandemic preparedness plans.

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 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.

Screening of children’s lifestyle, including nutrition, may contribute to the prevention of lifestyle-related conditions in childhood and later in life. Screening tools can evaluate a wide variety of lifestyle factors, resulting in different (risk) scores and prospects of action. This systematic review aimed to summarise the design, psychometric properties and implementation of lifestyle screening tools for children in community settings. We searched the electronic databases of Embase, Medline (PubMed) and CINAHL to identify articles published between 2004 and July 2020 addressing lifestyle screening tools for children aged 0–18 years in the community setting. Independent screening and selection by two reviewers was followed by data extraction and the qualitative analysis of findings. We identified 41 unique lifestyle screening tools, with the majority addressing dietary and/or lifestyle behaviours and habits related to overweight and obesity. The domains mostly covered were nutrition, physical activity and sedentary behaviour/screen time. Tool validation was limited, and deliberate implementation features, such as the availability of clear prospects of actions following tool outcomes, were lacking. Despite the multitude of existing lifestyle screening tools for children in the community setting, there is a need for a validated easy-to-administer tool that enables risk classification and offers specific prospects of action to prevent children from adverse health outcomes.

Improving dietary habits at a young age could prevent adverse health outcomes. The aim was to gain insight into the adequacy of the dietary intake of Dutch toddlers, which may provide valuable information for preventive measures. Data obtained from the Dutch National Food Consumption Survey 2012–2016 were used, which included 672 children aged one to three years. Habitual intakes of nutrients were evaluated according to recommendations set by the Dutch Health Council. Specific food groups were evaluated according to the Dutch food-based dietary guidelines. For most nutrients, intakes were estimated to be adequate. High intakes were found for saturated fatty acids, retinol, iodine, copper, zinc, and sodium. No statement could be provided on the adequacy of intakes of alpha-linoleic acids, N-3 fish fatty acids, fiber, and iron. 74% of the toddlers used dietary supplements, and 59% used vitamin D supplements specifically. Total median intakes of vegetables, bread, and milk products were sufficient. Consumption of bread, potatoes and cereals, milk products, fats, and drinks consisted largely of unhealthy products. Consumption of unfavorable products may have been the cause of the observed high and low intakes of several nutrients. Shifting towards a healthier diet that is more in line with the guidelines may positively affect the dietary intake of Dutch toddlers and prevent negative health impacts, also later in life.

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.

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.