Memorable meals: The memory-experience gap in day-to-day experiences

How Similar Are Students' Aggregated State Emotions to Their Self-Reported Trait Emotions? Results from a Measurement Burst Design Across Three School Years

Students' emotions in the classrom are highly dynamic and thus typically strongly vary from one moment to the next. Methodologies like experience sampling and daily diaries have been increasingly used to capture these momentary emotional states and its fluctuations. A recurring question is to what extent aggregated state ratings of emotions over a longer period of time are similar to self-reported traits of emotions. Thus, this study aims to investigate the extent of similarity between students' aggregated emotional states and self-reported traits over a two-week period in three consecutive school years (N T1 = 149, average ageT1 = 15.64 years). Six discrete emotions (enjoyment, anger, pride, anxiety, shame, and boredom) were assessed in German, English, French, and mathematics classes. We investigated similarity in terms of convergence, mean-level differences, long-term stability, and incremental predictive validity of aggregated states and self-reported traits. Results indicated substantial convergence between aggregated states and self-reported traits, with both showing similar long-term stability. However, aggregated states did not demonstrate superior predictive validity compared to self-reported traits for academic outcomes, while momentary assessments offer insights into short-term emotional fluctuations, on a person-aggregated level aggregated states and self-reported traits behave rather similarly. This suggests that both can be used interchangeably to study students' trait-related research questions, like interindividual differences or long-term emotional processes in educational settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10648-025-09995-1.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

The Use of Mobile-Based Ecological Momentary Assessment (mEMA) Methodology to Assess Dietary Intake, Food Consumption Behaviours and Context in Young People: A Systematic Review

Mobile-based ecological momentary assessment (mEMA) offers a novel method for dietary assessment and may reduce recall bias and participant burden. This review evaluated mEMA methodology and the feasibility, acceptability and validity as a dietary assessment method in young people. Five databases were searched from January 2008 to September 2021 for studies including healthy young people aged 16–30 years and used mEMA for obtaining dietary intake data, food consumption behaviours and/or contextual factors. Data on the method used to administer mEMA, compliance with recording and validation were extracted. A total of 46 articles from 39 independent studies were included, demonstrating a wide variation in mEMA methods. Signal-contingent prompting (timed notification to record throughout the day) was used in 26 studies, 9 used event-contingent (food consumption triggered recordings), while 4 used both. Monitoring periods varied and most studies reported a compliance rate of 80% or more. Two studies found mEMA to be burdensome and six reported mEMA as easy to use. Most studies (31/39) reported using previously validated questions. mEMA appears to be a feasible and acceptable methodology to assess dietary intake and food consumption in near real time.

Investigating the Relationship between Perceived Meal Colour Variety and Food Intake across Meal Types in a Smartphone-Based Ecological Momentary Assessment

Although most people are aware of the health benefits of consuming sufficient amounts of fruit and vegetables, many do not adhere to current dietary recommendations. Recent studies have suggested meal colour variety as an intuitive cue for healthy and enjoyable lunch meal choices. The present study extends this research by testing the “colourful = healthy” association across meal types. Using smartphone-based Ecological Momentary Assessment, 110 participants recorded 2818 eating occasions over a period of eight days. For each eating occasion, a picture, a short written description of the meal, the meal type (breakfast, lunch, afternoon tea, dinner, snack) and the perceived meal colour variety were recorded. Foods were classified into seven food groups based on the pictures and descriptions. Data were analysed using multilevel modelling. For all meal types except afternoon tea which did not include vegetables, perceived that meal colour variety was positively related to vegetable consumption (bs ≥ 0.001, ts ≥ 3.27, ps ≤ 0.002, quasi-R²s ≥ 0.06). Moreover, perceived meal colour variety was negatively associated with sweets consumption for breakfast, dinner and snacks (bs ≤ −0.001, ts ≤ −2.82, ps ≤ 0.006, quasi-R²s ≥ 0.01). The “colourful = healthy” association can be generalized across meal types and thus may be a promising strategy to promote a healthier diet.