An Investigation of Meat Eating in Samples from Australia and Germany: The Role of Justifications, Perceptions, and Empathy.

Despite concerns about animal welfare, and health and environmental issues associated with eating meat, meat consumption has continued to increase worldwide, including in Australia. One exception to this is Germany, with 2021 meat consumption levels being the lowest in the last 30 years. This pre-registered study investigated socio-cultural variables associated with meat consumption in Germany (n = 399) and Australia (n = 399) in a cross-sectional online survey. Participants reported levels of current and intended meat consumption, and they completed measures of speciesism, motivations to eat meat, empathy, animal farming perceptions, perceived behavioural control (PBC) over meat eating, and avoidance and dissociation regarding the animal origins of meat. In both Australia and Germany, enjoying the taste of meat positively predicted consumption and empathy towards farmed animals negatively predicted consumption. PBC was a strong positive predictor of intentions to reduce meat consumption in both countries. Empathy and liking the taste of meat were among the best predictors of red meat and poultry consumption, suggesting that interventions to reduce meat consumption may work best by targeting these factors while also increasing people's sense of control over their food choices.

Effect of nutrient, processing and hedonic claims on food-related perceptions: An experimental online study in Brazil and Germany.

Prior studies on perceived healthiness of foods have often compared nutrient and hedonic claims, neglecting comparisons to a control condition. The effect of food claims focusing on the food processing level has received considerably less research attention, although food processing has been included in dietary guidelines in Brazil. Thus, the present study aimed to explore the effect of a control and processing claim, additionally to the effects of a nutrient. and hedonic claim, on perceptions related to a food item typically considered "less healthy" (chocolate cake). We further compared these effects between Brazil and Germany, a country where food processing is currently not included in dietary guidelines. A total of 634 lay adults were recruited in a cross-sectional online study and randomised to see the photo of a piece of cake with one of the four different claim conditions and to report their health-related perceptions of the cake. The main analyses included two-way ANCOVAs (4 claims x 2 countries) for each dependent variable controlled for gender, age and level of hunger; followed by post hoc tests. Overall, results revealed that in both countries, the claims highlighting nutrients or processing aspects rendered the perceptions of the cake healthier compared to the control claim. These effects were more pronounced among Brazilian than among German participants. Food-related perceptions mostly did not differ between the hedonic and control claim and did not differ at all between the nutrient and processing claims. In conclusion, it is noteworthy that, even for an inherently considered "less healthy food item" (chocolate cake) nutrient and processing claims increase healthiness perceptions. While our findings may imply that in both countries lay people are highly susceptible to nutrient claims, food processing aspects seem to be similarly relevant.

Food healthiness judgements among Brazilian and German lay adults.

This study investigated which foods are most saliently judged as healthy and unhealthy in Brazil and Germany and the reasons for these judgements. Dietary guidelines in the two countries differ in that those in Brazil are based on a food processing classification rather than nutrient profiling, whereas dietary guidelines in Germany do not include the processing level of food. In an online study with 355 lay adults (Brazil n = 205, Germany n = 150), we explored which foods are listed as healthy and unhealthy using a free-listing method. The main reasons for these healthiness judgements were then identified with a one or two-word phrase and compared between countries. Saliency analysis was conducted to identify the 15 most salient healthy and unhealthy foods in each country. Principles of content analysis were used to assess the reasons why these 15 items were listed as most salient by the participants. Results showed that both Brazilians and Germans listed mostly natural or minimally processed food (e.g. fruits, vegetables, grains, fish and milk) as healthy, whereas types of convenience and fast food, sweets and other ultra-processed foods (e.g. chocolate, soda, French fries, pizza and hamburger) were the most salient unhealthy items listed in both countries. Differences in culturally relevant items listed in each country are discussed. Further, in both countries, despite differences in their dietary guidelines, food healthiness judgements for the most salient items listed relied heavily on the nutritional content of food, reinforced the 'good/healthy' and 'bad/unhealthy' dichotomy, and were centred on benefits or harms to the body (e.g. prevention or cause of diseases and weight control). The similarity of food healthiness judgements between the two countries, together with their agreement with conventional health claims and dietary guidelines, suggest that lay Brazilian and German adults are knowledgeable about the general concepts of 'healthy' and 'unhealthy' food. Finally, these findings suggest that rather than just providing more nutritional information, policymakers and health professionals need to take into account the multiple psychosocial and environmental determinants of eating in these countries.

Do nutrition knowledge, food preferences, and habit strength moderate the association between preference for intuition and deliberation in eating decision-making and dietary intake?

An intuitive style in eating decision-making, for example, basing decisions on one's gut feeling, has been related to a less healthy diet, whereas deliberately deciding what to eat, such as making plans about eating behavior, has been related to a healthier diet. The present study investigated whether nutrition knowledge, food preferences, and habit strength for healthy and unhealthy eating moderate these relationships. In total, 1245 participants took part in a preregistered cross-sectional online survey. Results revealed that neither nutrition knowledge, nor liking of healthy or unhealthy foods, nor habit strength for healthy or unhealthy eating interacted with the preference for intuition or deliberation in eating decision-making in affecting dietary intake (ßs ≤ |.06|; ts ≤ |2.11|; ps ≥ .035). Instead, including the potential moderating variables in analyses rendered the effect of a preference for intuition largely non-significant. In contrast, the positive effect of a preference for deliberation was largely stable even when including the potential moderating variables. Thus, the present study confirms the general health-promoting effect of a preference for deliberation in eating decision-making. In contrast, results speak in favor of a generally minor role of a preference for intuition for healthy or unhealthy eating.

Traditional and modern eating in older adults: a comparison between an urban and rural sample from Gujarat, Western India.

Background: The present study aimed to investigate how often and to what degree older adults living in an area of Gujarat, Western India, enact traditional and modern eating behaviors. Specifically, we aimed to determine which facets of traditional eating are enacted rarely and which facets of modern eating are enacted often. Moreover, we hypothesized that urban older adults show a higher level of modern eating behaviors than rural older adults. Furthermore, we examined which traditional eating behaviors are more prevalent in rural older adults, and which are more prevalent in urban older adults. Methods: A trained research assistant administered a questionnaire in a face-to-face situation with 120 older adults in a rural and an urban area of Gujarat, Western India. Participants were asked how often and to what degree they perform 57 traditional and modern eating behaviors. Results: Overall, our sample of older Gujaratis reported a high level of traditional eating behaviors and a low level of modern eating behaviors. However, we also found, for example, a low level of the traditional eating facet of men getting preferential treatment and a high level of the modern eating facet of food being readily available. Moreover, most modern eating facets were more pronounced in the urban than in the rural sample. This was also the case for half of all traditional eating facets. Conclusion: Our sample of older adults living in an area of Gujarat displayed more modern eating behaviors in urban than in rural areas. At the same time, however, the urban sample showed also more traditional eating behaviors, such as eating more fruits, possibly because of better food availability. Altogether, results might hint at some signs of modernization among older adults in this area of Gujarat with regard to changing gender roles and better food availability.

Similar or different? Comparing food cultures with regard to traditional and modern eating across ten countries.

Food cultures can play a role in health and well-being. This raises the questions of whether nation boundaries unite the food cultures of different regions and ethnic groups, what characterises food cultures from very different parts of the world, and what similarities and differences exist. The present study aimed to investigate these questions with regard to eating traditions and modern eating practices. In this cross-sectional study, we recruited 3722 participants from ten countries - Brazil, China, France, Germany, Ghana, India, Japan, Mexico, Turkey, and the USA. Participants represented 25 regional and ethnic groups. They were queried about 86 traditional and modern facets of their food cultures in interviews, paper-pencil and online questionnaires. First, hierarchical cluster analysis suggested nine distinct clusters of food cultures - the food cultures of the Brazilian, Chinese, Ghanaian, Indian, Japanese, Mexican, Turkish, African and Latin US American samples, and of European descendants. Interestingly, for seven of the ten investigated countries, nation boundaries united food cultures. Second, each of the nine food culture clusters was characterised by a unique pattern of traditional and modern eating practices. Third, the nine food culture clusters varied more in their traditional eating practices than their modern eating practices. These results might promote a better understanding of the link between food cultures and health and well-being that goes beyond nutrients. For instance, food cultures might be linked to well-being via strengthening people's sense of cultural identity. Moreover, the present results contribute to a better understanding of the complex interplay between food and culture, and could help in developing culturally competent interventions to improve diet and reduce the risk of eating-related diseases.

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

OBJECTIVES: Two distinct lines of research separately regard either deliberately regulated eating or intuitive eating as most beneficial for health. The present research aims to integrate these seemingly contradictory findings by investigating the relationships between Preference for Intuition and Deliberation in Eating Decision-making (E-PID) and subjective and objective health-related parameters using a newly developed scale. DESIGN: Study 1: online survey; Study 2: cross-sectional community cohort sample. METHODS: Study 1 (N = 699) assessed E-PID, intuitive, restrained, and external eating, motivation to eat healthily, domain-unspecific Preference for Intuition and Deliberation, and general self-control. Study 2 (N = 1,212) assessed E-PID, positive eating, intention to eat healthily, eating behaviour, and objective health risk factors (BMI, waist circumference, blood pressure, fasting serum glucose, HDL cholesterol, triglycerides). RESULTS: Study 1 confirmed a two-factor structure and provided evidence for construct validity: A higher preference for intuition was positively related to intuitive eating, while a higher preference for deliberation was associated with increased dietary restraint. In Study 2, preference for deliberation was positively related to the intention to eat healthy, healthy eating behaviour, and health parameters. Preference for intuition was generally unrelated to health parameters, although a higher preference for intuition was related to a somewhat unhealthier eating pattern. CONCLUSIONS: Preference for intuition and deliberation when making food choices are two distinct decision-making styles: The former focuses on internal cues, while the latter focuses on cognitive regulation of food intake. Despite these differences, neither decision-making style was shown to be detrimental.

The Relationship Between Healthy Eating Motivation and Protein Intake in Community-Dwelling Older Adults With Varying Functional Status.

In older adults, the relationship between healthy eating motivation (HEM) and protein intake as key component of a healthy diet is poorly understood. Therefore, we investigate the association of HEM with (1) total protein intake and (2) intake of different protein sources in older adults with varying functional status. In this cross-sectional study including 250 adults (≥70 years), we assessed HEM with "The Eating Motivation Survey" and protein intake by 7-day food records. In addition, gender, age, Body Mass Index (BMI), energy intake and functional status were considered. Regression analyses revealed that HEM was neither related to total (ß = -0.02; p = 0.723) nor to relative protein intake (ß = 0.04; p = 0.370). Notwithstanding this, participants with stronger HEM showed lower intake of protein from meat and meat products (ß = -0.14; p = 0.018), higher intake of overall plant-based protein (ß = 0.11; p = 0.032), protein from fruit and vegetables (ß = 0.20.; p = 0.002) and from pulses, nuts an seeds (ß = 0.16; p = 0.016). Our findings suggest HEM as a valuable indicator for intake of distinct protein sources. However, since HEM is not related to total protein intake, the importance of sufficient protein consumption should be emphasized by promoting healthy eating, regardless of self-perceived HEM.

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.

Understanding traditional and modern eating: the TEP10 framework.

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.

The Eating Motivation Survey in Brazil: Results From a Sample of the General Adult Population.

Brazil is undergoing a rapid nutrition transition with unfavorable effects on dietary healthiness. To counteract this trend, knowledge about individual drivers of consumption is needed that go beyond environmental factors. The Eating Motivation Survey represents a comprehensive measure of such individual drivers, assessing 15 eating motives, such as choosing food for health reasons or eating because of a good taste. The aim of this study was to examine the psychometric properties and validity of a Brazilian Portuguese version of The Eating Motivation Survey. In total, 442 participants from the general adult population living in the state of São Paulo, Brazil filled in an online survey. Results showed that the model, including 15 motive factors and 45 items, had a reasonable model fit. Moreover, factor loadings and corrected item-scale correlations were generally good. Fourteen out of the 15 motive scales had a reliability above 0.60. Results also confirmed convergent validity. These results demonstrate that the Brazilian Portuguese version of The Eating Motivation Survey is generally reliable and valid to assess individual drivers of eating behavior in Brazil.

Measuring eating motives in older adults with and without functional impairments with The Eating Motivation Survey (TEMS).

Gaining a better understanding which motives play a role in daily eating is crucial in order to develop personalized interventions promoting adequate diet. The Eating Motivation Survey (TEMS) is a comprehensive tool to assess manifold reasons for eating but has not been specifically applied in an older sample including very old persons and persons differing in functional status. Therefore, the first step of the study was to investigate whether the basic motives of TEMS are consistent in an older sample and comparable across older adults with and without functional impairments. Second, the study aimed at describing which motives underlie eating behavior in older age most often as well as to analyze the relationship between eating motives and functional status. 376 community-dwelling older persons aged 70 years and older filled in the brief survey version (15 motives à 3 items) of TEMS. The sample comprises 149 unimpaired and 227 impaired participants in terms of physical function. Confirmatory factor analysis yielded good model fit with RMSEA 0.036 and SRMR 0.048. Furthermore, multi-group analysis revealed a generally invariant factor structure across participants with and without functional impairments. Liking was rated to underlie eating behavior most often, followed by Health and Natural Concerns. Considering gender, age and BMI, significant associations between functional status and the motive Sociability emerged. Since the results suggest that the fifteen basic eating motives are generalizable in older age and across different functional states, TEMS might be a valuable tool in nutrition-related health promotion to developing individualized approaches considering health aspects as well as hedonistic factors.

Positive Self-perceptions of Aging Promote Healthy Eating Behavior Across the Life Span via Social-Cognitive Processes.

OBJECTIVES: Negative self-perceptions of aging (SPA) have been shown to result in lower levels of preventive behavior, health, and longevity. This study focuses on the understudied SPA effects on healthy eating across the life span. Moreover, it aims to provide longitudinal evidence of the psychological mechanisms behind this relationship. METHOD: We investigated whether SPA (T1) can predict changes in eating behavior (T3) over 1 year in 1,321 participants (T1), aged 18-92 years. The explanatory role of social-cognitive processes (T1, T2) was tested via a two-step mediation analysis with multigroup modeling for different age and education levels. RESULTS: Baseline positive SPA predicted more healthy eating at T3 (b = 0.68, SE = 0.24, p = .01), controlling for baseline eating (T1), age, education, gender, BMI, and illnesses. Self-efficacy (T1) and intention to eat healthily (T2) serially mediated this effect, indirect effect: b = 0.04, p = .02, 95% CI (0.02, 0.08). SPA had stronger effects in older and less educated participants. DISCUSSION: This study provides important insights into the mechanisms behind positive SPA fueling successful health behavior change dynamics. Fostering more positive SPA through interventions might be especially important for vulnerable groups. Addressing SPA already in younger ages might help establish health-promoting life-span dynamics.

Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach.

BACKGROUND: Although mobile technologies such as smartphone apps are promising means for motivating people to adopt a healthier lifestyle (mHealth apps), previous studies have shown low adoption and continued use rates. Developing the means to address this issue requires further understanding of mHealth app nonusers and adoption processes. This study utilized a stage model approach based on the Precaution Adoption Process Model (PAPM), which proposes that people pass through qualitatively different motivational stages when adopting a behavior. OBJECTIVE: To establish a better understanding of between-stage transitions during app adoption, this study aimed to investigate the adoption process of nutrition and fitness app usage, and the sociodemographic and behavioral characteristics and decision-making style preferences of people at different adoption stages. METHODS: Participants (N=1236) were recruited onsite within the cohort study Konstanz Life Study. Use of mobile devices and nutrition and fitness apps, 5 behavior adoption stages of using nutrition and fitness apps, preference for intuition and deliberation in eating decision-making (E-PID), healthy eating style, sociodemographic variables, and body mass index (BMI) were assessed. RESULTS: Analysis of the 5 behavior adoption stages showed that stage 1 ("unengaged") was the most prevalent motivational stage for both nutrition and fitness app use, with half of the participants stating that they had never thought about using a nutrition app (52.41%, 533/1017), whereas less than one-third stated they had never thought about using a fitness app (29.25%, 301/1029). "Unengaged" nonusers (stage 1) showed a higher preference for an intuitive decision-making style when making eating decisions, whereas those who were already "acting" (stage 4) showed a greater preference for a deliberative decision-making style (F4,1012=21.83, P<.001). Furthermore, participants differed widely in their readiness to adopt nutrition and fitness apps, ranging from having "decided to" but not yet begun to act (stage 2; nutrition: 6.88%, 70/1017; fitness: 9.23%, 95/1029) to being "disengaged" following previous adoption (stage 5; nutrition: 13.77%, 140/1017; fitness: 15.06%, 155/1029). CONCLUSIONS: Using a behavior stage model approach to describe the process of adopting nutrition and fitness apps revealed motivational stage differences between nonusers (being "unengaged," having "decided not to act," having "decided to act," and being "disengaged"), which might contribute to a better understanding of the process of adopting mHealth apps and thus inform the future development of digital interventions. This study highlights that new user groups might be better reached by apps designed to address a more intuitive decision-making style.

What Constitutes Traditional and Modern Eating? The Case of Japan.

Traditional Japanese dietary culture might be a factor contributing to the high life expectancy in Japan. As little is known about what constitutes traditional and modern eating in Japan, the aims of the current study were to (1) comprehensively compile and systematize the various facets of traditional and modern eating; and (2) investigate whether these facets also apply to traditional and modern eating in Japan. In Study 1, an extensive international literature review was performed. Forty-five facets of traditional and modern eating were compiled and systematized into the dimensions of what and how people eat, and into eleven separate subdimensions. In Study 2, 340 adults from Japan answered a questionnaire. Results showed that traditional and modern eating in Japan is reflected in both what and how people eat. Within these two dimensions, ten subdimensions were found: the ingredients, processing, temporal origin, spatial origin, and variety of consumed foods, as well as temporal, spatial, and social aspects, appreciation, and concerns when eating. This study provides a broad compilation of facets of traditional and modern eating in Japan. Future research should investigate how these facets are related to life expectancy and health.

The Eating Motivation Survey: results from the USA, India and Germany.

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.

The positive eating scale: relationship with objective health parameters and validity in Germany, the USA and India.

OBJECTIVE: The prevailing focus regarding eating behaviour is on restriction, concern, worry and pathology. In contrast, the purpose of the present studies was to focus on a positive relationship with eating in non-clinical samples from Germany, the USA and India. DESIGN: In Study 1, the Positive Eating Scale (PES) was tested and validated in a large longitudinal sample (T1: N = 772; T2: N = 510). In Study 2, the PES was tested in online samples from the USA, India and Germany (total N = 749). MAIN OUTCOME MEASURES: Health risk status was measured in Study 1 with objective health parameters (fasting serum glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, waist circumference, BMI). RESULTS: Study 1 revealed acceptable psychometric properties of the PES, internal consistency (α = .87), as well as test-retest reliability after six months (r = .67). Importantly, a positive relationship with eating was associated with decreased health risk factors six months later. In Study 2, the structure of the PES was confirmed for German, Indian and US-American adults, suggesting validity across remarkably different eating environments. CONCLUSION: A positive relationship with eating might be a fruitful starting point for prevention and intervention programmes promoting physical and psychological health.

Self-Other Differences in Perceiving Why People Eat What They Eat.

People often view themselves more favorably than others, displaying unrealistic optimism. In the present study, we investigated whether people perceive their reasons for eating as better than those of others. Furthermore, we investigated which mechanisms of inaccuracy might underlie a possible bias when perceiving why people eat what they eat. In Study 1, 117 participants rated the social desirability of eating motives. In Study 2, 772 participants provided information on their own and others' motives for eating behavior. In Study 1, particularly desirable motives were eating because of hunger, health reasons, and liking. Particularly undesirable motives were eating to make a good impression, to comply with social norms, and to regulate negative affect. Study 2 revealed that for socially desirable motives, participants perceived their own motives to be stronger; for undesirable motives, the opposite pattern emerged, with others being attributed stronger motives. Moreover, the perception of others' emotional and social motives varied with participants' own healthy eating behavior. Since the perception of eating motives of others should be independent of one's own behavior, this pattern of results indicates a relative inaccuracy in the perception of others' eating motives. In conclusion, there is evidence for unrealistic optimism in eating motives. For social and emotional motives, this self-favoring view seems to be driven by a relatively inaccurate perception of others.

I Eat Healthier Than You: Differences in Healthy and Unhealthy Food Choices for Oneself and for Others.

The present study investigated self-other biases in actual eating behavior based on the observation of three different eating situations. To capture the complexity of real life food choices within a well-controlled setting, an ecologically valid fake food buffet with 72 different foods was employed. Sixty participants chose a healthy, a typical, and an unhealthy meal for themselves and for an average peer. We found that the typical meal for the self was more similar to the healthy than to the unhealthy meal in terms of energy content: The mean difference between the typical and healthy meals was MΔ = 1368 kJ (327 kcal) as compared to a mean difference between the typical and unhealthy meals of MΔ = 3075 kJ (735 kcal). Moreover, there was evidence that people apply asymmetrical standards for themselves and others: Participants chose more energy for a peer than for themselves (M = 4983 kJ or 1191 kcal on average for the peers' meals vs. M = 3929 kJ or 939 kcal on average for the own meals) and more high-caloric food items for a typical meal, indicating a self-other bias. This comparatively positive self-view is in stark contrast to epidemiological data indicating overall unhealthy eating habits and demands further examination of its consequences for behavior change.

Being and feeling liked by others: how social inclusion impacts health.

This study examined the effects of perceived and actual social inclusion on health across and within individuals from a network perspective. During the first semester, 75 freshmen students provided bi-weekly ratings on their perceived social inclusion and health. To capture actual social inclusion, each student nominated liked and disliked fellow students. Perceived social inclusion mediated the effect of actual social inclusion on health. Specifically, students with more 'likes' perceived more social inclusion and those with higher perceived inclusion reported a better health status (between-person effect). In addition, at time points, when students received more 'likes' they also perceived more social inclusion. They reported better health at times when they felt more included (within-person effect). Thus, the perception of social inclusion is rooted in reality and actual social inclusion has an impact on health when passing the filter of perception.