Background: Inaccurate self‐report of portion sizes is a major cause of measurement error in dietary assessment. To reduce this error, different portion size estimation aids (PSEAs) have been developed, including food images (image based, IB‐PSE) and textual descriptions of portion sizes (text‐based, TB‐PSE). We assessed the accuracy of portion size estimation by IB‐PSE and TB‐PSE.Methods: True intake of one lunch was ascertained in forty participants. Self‐reported portion sizes were assessed after 2 and 24 hours by means of TB‐PSE and IB‐PSE, in random order. Wilcoxon's tests were used to compare mean true intakes to reported intakes. Moreover, proportions of reported portion sizes within 10% and 25% of true intake were assessed. An adapted Bland‐Altman approach was used to assess agreement between true and reported portion sizes. Analyses were conducted for all foods and drinks combined and for predetermined food types.Results: No significant differences were observed between reported portion sizes at 2 and 24 hours after lunch. Combining median relative errors of all foods items resulted in an overall 0% error rate for TB‐PSE and 6% error rate for IB‐PSE. Comparing reported portion sizes within 10% (31% vs. 13%) and 25% (50% vs. 35%) of the true intake showed a better performance for TB‐PSE compared to IP‐PSE, respectively. Bland‐Altman plots indicated a higher agreement between reported and true intake for TB‐PSE compared to IB‐PSE.Conclusions: Although the use of TB‐PSE still results in measurement error, our results suggest a more accurate dietary intake assessment with TB‐PSE than IB‐PSE.

Conventional dietary assessment methods are affected by measurement errors. We developed a smartphone-based 2-h recall (2hR) methodology to reduce participant burden and memory-related bias. Assessing the validity of the 2hR method against traditional 24-h recalls (24hRs) and objective biomarkers. Dietary intake was assessed in 215 Dutch adults on 6 randomly selected nonconsecutive days (i.e., 3 2hR days and 3 24hRs) during a 4-wk period. Sixty-three participants provided 4 24-h urine samples, to assess urinary nitrogen and potassium concentrations. Intake estimates of energy (2052±503 kcal vs. 1976±483 kcal) and nutrients (e.g., protein: 78±23 g vs. 71±19 g; fat: 84±30 g vs. 79±26 g; carbohydrates: 220±60 g vs. 216±60 g) were slightly higher at 2hR days than at 24hRs. Comparing self-reported protein and potassium intake to urinary nitrogen and potassium concentrations indicated a slightly higher accuracy of 2hR days than 24hRs (protein: -14% vs. -18%; potassium: -11% vs. -16%). Correlation coefficients between methods ranged from 0.41 to 0.75 for energy and macronutrients and from 0.41 to 0.62 for micronutrients. Generally, regularly consumed food groups showed small differences in intake (0.60). Intake of energy, nutrients, and food groups showed similar reproducibility (intraclass correlation coefficient) for 2hR days and 24hRs. Comparing 2hR days with 24hRs showed a relatively similar group-level bias for energy, most nutrients, and food groups. Differences were mostly due to higher intake estimates by 2hR days. Biomarker comparisons showed less underestimation by 2hR days as compared with 24hRs, suggesting that 2hR days are a valid approach to assess the intake of energy, nutrients, and food groups. This trial was registered at the Dutch Central Committee on Research Involving Human Subjects (CCMO) registry as ABR. No. NL69065.081.19.

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and 24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

Prenatal nutrition is a key predictor of early-life development. However, despite mass campaigns to stimulate healthy nutrition during pregnancy, the diet of Dutch pregnant women is often suboptimal. Innovative technologies offer an opportunity to develop tailored tools, which resulted in the release of various apps on healthy nutrition during pregnancy. As midwives act as primary contact for Dutch pregnant women, the goal was to explore the experiences and perspectives of midwives on (1) nutritional counselling during pregnancy, and (2) nutritional mHealth apps to support midwifery care. Analyses of eleven in-depth interviews indicated that nutritional counselling involved the referral to websites, a brochure, and an app developed by the Dutch Nutrition Centre. Midwives were aware of the existence of other nutritional mHealth apps but felt uncertain about their trustworthiness. Nevertheless, midwives were open towards the implementation of new tools providing that these are trustworthy, accessible, user-friendly, personalised, scientifically sound, and contain easy-digestible information. Midwives stressed the need for guidelines for professionals on the implementation of new tools. Involving midwives early-on in the development of future nutritional mHealth apps may facilitate better alignment with the needs and preferences of end-users and professionals, and thus increase the likelihood of successful implementation in midwifery practice.

During recent years, the integration of technology has substantially improved self-reported dietary assessment methods, such as food frequency questionnaires (FFQ), food records, and 24-h recalls. To further reduce measurement error, additional innovations are urgently needed. Memory-related measurement error is one of the aspects that warrants attention, which is where new smartphone technologies and ecological momentary assessment (EMA) approaches provide a unique opportunity. In this article, we describe the DIASS study, which was designed to evaluate an innovative 2-h recall (2hR) smartphone-based methodology, against traditional 24-h recalls, FFQ, and biomarkers, to assess both actual and habitual dietary intake. It is hypothesized that a 2-h reporting window decreases reliance on memory and reporting burden, and increases data accuracy. We included 215 men (28%) and women (72%), with a mean ± SD age of 39 ± 19 years and a mean ± SD BMI of 23.8 ± 4.0. Most participants were highly educated (58%). Response rates for the various dietary assessment methods were >90%. Besides the evaluation of the accuracy, usability, and perceived burden of the 2hR methodology, the study set-up also allows for (further) evaluation of the other administrated dietary assessment tools.

The relationship among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, we compare traditional epidemiologic vs. emerging big data approaches used in obesity research, describing the research questions, needs, and outcomes of three broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project “BigO: Big data against childhood obesity” used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learnings on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.