Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment

Abstract

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.

Preference for Intuition and Deliberation in Eating Decision‐making: Scale validation and associations with eating behaviour and health

“…However, if, for instance, internal satiety cues cannot be clearly perceived because the individual is distracted, for example, by watching television (Hetherington, Anderson, Norton, & Newson, 2006), an intuitive approach could also lead to less favourable outcomes. Thus, whether a particular decision‐making style leads to healthy food choices might strongly depend on the context (Wahl et al ., 2020), and interactions between decision‐making styles and contextual factors should be explored in future research. In addition, future research may explore the relationship between Preference for Intuition and Deliberation in Eating Decision‐making and the concept of flexible control (Westenhoefer, Stunkard, & Pudel, 1999).…”

Occurrence of and Reasons for “Missing Events” in Mobile Dietary Assessments: Results From Three Event-Based Ecological Momentary Assessment Studies

“…Other than a few amendments, the procedure was identical to that of study 1. The participants were provided with a study smartphone (ASUS Padfone Infinity, Android 5.0.2), and the MIDA was realized with the SMARTFOOD app, which was developed as part of the research project SMARTACT [ 2 , 18 , 46 - 48 ] and included a feature to set a reminder in the morning to record food intake. The participants were asked to set the reminder at the beginning of the study during the introduction session.…”

“…Specifically, taking images of eating events has been proposed as a method for reducing the burden on participants in dietary studies. Various models based on image technology using mobile apps have been developed in recent years (eg, Technology Assisted Dietary Assessment, My Meal Mate, and SMARTFOOD; for more details, see study by Boushey et al [ 5 ], Eldridge et al [ 7 ], Villinger et al [ 18 ], and Wahl et al [ 2 ]), and they are increasingly being used to assess and change eating behavior and food intake in different populations, including patients and generally healthy adults or adolescents [ 19 , 20 ]. As with any dietary assessment method, the more eating events are captured, the more thorough the analysis, and in an ideal study, the user would capture all eating events including an image before and, if applicable, after consumption.…”

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

“…The present study used an event-based Ecological Momentary Assessment (EMA, [32][33][34][35][36][37][38][39][40] to investigate the memory-experience gap in recurring day-to-day experiences in a real-life ecological setting. The participants were asked to report their food intake and rate their eating happiness (i.e.…”