Across the world, there has been a movement from traditional to modern eating, including a movement of traditional eating patterns from their origin culture to new cultures, and the emergence of new foods and eating behaviors. This trend toward modern eating is of particular significance because traditional eating has been related to positive health outcomes and sustainability. Yet, there is no consensus on what constitutes traditional and modern eating. The present study provides a comprehensive compilation of the various facets that seem to make up traditional and modern eating. Specifically, 106 facets were mentioned in the previous literature and expert discussions, combining international and interdisciplinary perspectives. The present study provides a framework (the TEP10 framework) systematizing these 106 facets into two major dimensions, what and how people eat, and 12 subdimensions. Hence, focusing only on single facets of traditional and modern eating is an oversimplification of this complex phenomenon. Instead, the multidimensionality and interplay between different facets should be considered to gain a comprehensive understanding of the trends, consequences, and underlying factors of traditional and modern eating.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background: Why do we eat? Our motives for eating are diverse, ranging from hunger and liking to social norms and affect regulation. Although eating motives can vary from eating event to eating event, which implies substantial moment-to-moment differences, current ways of measuring eating motives rely on single timepoint questionnaires that assess eating motives as situation-stable dispositions (traits). However, mobile technologies including smartphones allow eating events and motives to be captured in real time and real life, thus capturing experienced eating motives in-the-moment (states).Objective: This study aimed to examine differences between why people think they eat (trait motives) and why they eat in the moment of consumption (state motives) by comparing a dispositional (trait) and an in-the-moment (state) assessment of eating motives.Methods: A total of 15 basic eating motives included in The Eating Motivation Survey (ie, liking, habit, need and hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image) were assessed in 35 participants using 2 methodological approaches: (1) a single timepoint dispositional assessment and (2) a smartphone-based ecological momentary assessment (EMA) across 8 days (N=888 meals) capturing eating motives in the moment of eating. Similarities between dispositional and in-the-moment eating motive profiles were assessed according to 4 different indices of profile similarity, that is, overall fit, shape, scatter, and elevation. Moreover, a visualized person × motive data matrix was created to visualize and analyze between- and within-person differences in trait and state eating motives.Results: Similarity analyses yielded a good overall fit between the trait and state eating motive profiles across participants, indicated by a double-entry intraclass correlation of 0.52 (P<.001). However, although trait and state motives revealed a comparable rank order (r=0.65; P<.001), trait motives overestimated 12 of 15 state motives (P<.001; d=1.97). Specifically, the participants assumed that 6 motives (need and hunger, price, habit, sociability, traditional eating, and natural concerns) are more essential for eating than they actually were in the moment (d>0.8). Furthermore, the visualized person × motive data matrix revealed substantial interindividual differences in intraindividual motive profiles.Conclusions: For a comprehensive understanding of why we eat what we eat, dispositional assessments need to be extended by in-the-moment assessments of eating motives. Smartphone-based EMAs reveal considerable intra- and interindividual differences in eating motives, which are not captured by single timepoint dispositional assessments. Targeting these differences between why people think they eat what they eat and why they actually eat in the moment may hold great promise for tailored mobile health interventions facilitating behavior changes.

Objective: Research has shown that there is a large variety of different motives underlying why people eat what they eat, which can be assessed with The Eating Motivation Survey (TEMS). The present study investigates the consistency and measurement invariance of the fifteen basic motives included in TEMS in countries with greatly differing eating environments. Design: The fifteen-factor structure of TEMS (brief version: forty-six items) was tested in confirmatory factor analyses. Setting: An online survey was conducted. Subjects: US-American, Indian and German adults (total N 749) took part. Results: Despite the complexity of the model, fit indices indicated a reasonable model fit (for the total sample: χ ²/df=4·03; standardized root-mean-squared residual (SRMR)=0·063; root-mean-square error of approximation (RMSEA)=0·064 (95 % CI 0·062, 0·066)). Only the comparative fit index (CFI) was below the recommended threshold (for the total sample: CFI=0·84). Altogether, 181 out of 184 item loadings were above the recommended threshold of 0·30. Furthermore, the factorial structure of TEMS was invariant across countries with respect to factor configuration and factor loadings (configural v. metric invariance model: ΔCFI=0·009; ΔRMSEA=0·001; ΔSRMR=0·001). Moreover, forty-three out of forty-six items showed invariant intercepts across countries. Conclusions: The fifteen-factor structure of TEMS was, in general, confirmed across countries despite marked differences in eating environments. Moreover, latent means of fourteen out of fifteen motive factors can be compared across countries in future studies. This is a first step towards determining generalizability of the fifteen basic eating motives of TEMS across eating environments.