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.

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

BACKGROUND: Establishing a methodology for assessing nutritional behavior comprehensively and accurately poses a great challenge. Mobile technologies such as mobile image-based food recording apps enable eating events to be assessed in the moment in real time, thereby reducing memory biases inherent in retrospective food records. However, users might find it challenging to take images of the food they consume at every eating event over an extended period, which might lead to incomplete records of eating events (missing events). OBJECTIVE: Analyzing data from 3 studies that used mobile image-based food recording apps and varied in their technical enrichment, this study aims to assess how often eating events (meals and snacks) were missed over a period of 8 days in a naturalistic setting by comparing the number of recorded events with the number of normative expected events, over time, and with recollections of missing events. METHODS: Participants in 3 event-based Ecological Momentary Assessment (EMA) studies using mobile image-based dietary assessments were asked to record all eating events (study 1, N=38, 1070 eating events; study 2, N=35, 934 eating events; study 3, N=110, 3469 eating events). Study 1 used a basic app; study 2 included 1 fixed reminder and the possibility to add meals after the actual eating events occurred instead of in the moment (addendum); and study 3 included 2 fixed reminders, an addendum feature, and the option to record skipped meals. The number of recalled missed events and their reasons were assessed by semistructured interviews after the EMA period (studies 1 and 2) and daily questionnaires (study 3). RESULTS: Overall, 183 participants reported 5473 eating events. Although the momentary adherence rate as indexed by a comparison with normative expected events was generally high across all 3 studies, a differential pattern of results emerged with a higher rate of logged meals in the more technically intensive study 3. Multilevel models for the logging trajectories of reported meals in all 3 studies showed a significant, albeit small, decline over time (b=-.11 to -.14, Ps<.001, pseudo-R²=0.04-0.06), mainly because of a drop in reported snacks between days 1 and 2. Intraclass coefficients indicated that 38% or less of the observed variance was because of individual differences. The most common reasons for missing events were competing activities and technical issues, whereas situational barriers were less important. CONCLUSIONS: Three different indicators (normative, time stability, and recalled missing events) consistently indicated missing events. However, given the intensive nature of diet EMA protocols, the effect sizes were rather small and the logging trajectories over time were remarkably stable. Moreover, the individual's actual state and context seemed to exert a greater influence on adherence rates than stable individual differences, which emphasizes the need for a more nuanced understanding of the factors that affect momentary adherence.

Do We Know What We Enjoy? Accuracy of Forecasted Eating Happiness.

Forecasting how we will react in the future is important in every area of our lives. However, people often demonstrate an "impact bias" which leads them to inaccurately forecast their affective reactions to distinct and outstanding future events. The present study examined forecasting accuracy for a day-to-day repetitive experience for which people have a wealth of past experiences (eating happiness), along with dispositional expectations toward eating ("foodiness"). Seventy-three participants (67.12% women, M age = 41.85 years) used a smartphone-based ecological momentary assessment to assess their food intake and eating happiness over 14 days. Eating happiness experienced in-the-moment showed considerable inter-and intra-individual variation, ICC = 0.47. Comparing forecasted and in-the-moment eating happiness revealed a significant discrepancy whose magnitude was affected by dispositional expectations and the variability of the experience. The results demonstrate that biased forecasts are a general phenomenon prevalent both in outstanding and well-known experiences, while also emphasizing the importance of inter-individual differences for a detailed understanding of affective forecasting.

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

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.

Quantifying Actual and Perceived Inaccuracy When Estimating the Sugar, Energy Content and Portion Size of Foods.

In order to adhere to dietary guidelines and manage health risks, consumers need to be able to estimate with some accuracy the sugar and energy content of foods. The present study compared how well participants could estimate the sugar and energy content of foods, the weight of foods, and approximate portion size (using a hand measure estimation aid). The study had three aims. First, it aimed to investigate differences in accuracy across the four measures. Second, it aimed to examine whether these differences in accuracy between estimation measures were accurately perceived by the participants. Third, it aimed to test if estimation accuracy was related to food journaling experience, body-mass index or gender. One hundred and ninety-seven participants took part in an estimation task and filled in a questionnaire. While the participants were inaccurate when using all four estimation measures, inaccuracy was most pronounced for sugar content (ds ≥ 0.39), which was consistently overestimated by between 62.1% and 98.5% of the sample. None of the other measures showed a consistent pattern of under- or overestimation. Participants' perceived accuracy did not match their actual accuracy (rs ≤ |0.20|, ps ≥ 0.005). Actual accuracy showed only marginal covariation with food journaling experience (ts ≤ 2.01, ps ≥ 0.049, ds ≤ 0.14), body-mass index (rs ≤ |0.15|, ps ≥ 0.041) or gender (ts ≤ 3.17, ps ≥ 0.002, ds ≤ 0.46). It is particularly challenging for consumers to estimate the sugar content of food, which might have negative consequences for health and well-being. Thus, more education about sugar content and misperceptions is needed to support consumers so that they can make healthy food choices.

Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments.

Research suggests that "healthy" food choices such as eating fruits and vegetables have not only physical but also mental health benefits and might be a long-term investment in future well-being. This view contrasts with the belief that high-caloric foods taste better, make us happy, and alleviate a negative mood. To provide a more comprehensive assessment of food choice and well-being, we investigated in-the-moment eating happiness by assessing complete, real life dietary behaviour across eight days using smartphone-based ecological momentary assessment. Three main findings emerged: First, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Second, sweets on average provided comparable induced eating happiness to "healthy" food choices such as fruits or vegetables. Third, dinner elicited comparable eating happiness to snacking. These findings are discussed within the "food as health" and "food as well-being" perspectives on eating behaviour.

Relative validation of a food frequency questionnaire to estimate food intake in an adult population.

Background: Scientifically valid descriptions of dietary intake at population level are crucial for investigating diet effects on health and disease. Food frequency questionnaires (FFQs) are the most common dietary tools used in large epidemiological studies. Objective: To examine the relative validity of a newly developed FFQ to be used as dietary assessment tool in epidemiological studies. Design: Validity was evaluated by comparing the FFQ and a 4-day weighed food record (4-d FR) at nutrient and food group levels, Spearman's correlations, Bland-Altman analysis and Wilcoxon rank sum tests were used. Fifty-six participants completed a paper format FFQ and a 4-d FR within 4 weeks. Results: Corrected correlations between the two instruments ranged from 0.27 (carbohydrates) to 0.55 (protein), and at food group level from 0.09 (soup) to 0.92 (alcohol). Nine out of 25 food groups showed correlations > 0.5, indicating moderate validity. More than half the food groups were overestimated in the FFQ, especially vegetables (82.8%) and fruits (56.3%). Water, tea and coffee were underestimated (-14.0%). Conclusions: The FFQ showed moderate relative validity for protein and the food groups fruits, egg, meat, sausage, nuts, salty snacks and beverages. This study supports the use of the FFQ as an acceptable tool for assessing nutrition as a health determinant in large epidemiological studies.