research about healthy diet

Defining a Healthy Diet: Evidence for The Role of Contemporary Dietary Patterns in Health and Disease

Affiliations.

• 1 Laboratory of Dietetics and Clinical Nutrition, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy.
• 2 Clinical Nutrition and Dietetics Service, Unit of Internal Medicine and Endocrinology, ICS Maugeri IRCCS, 27100 Pavia, Italy.
• 3 Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK.
• 4 NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton SO16 6YD, UK.
• PMID: 32012681
• PMCID: PMC7071223
• DOI: 10.3390/nu12020334

The definition of what constitutes a healthy diet is continually shifting to reflect the evolving understanding of the roles that different foods, essential nutrients, and other food components play in health and disease. A large and growing body of evidence supports that intake of certain types of nutrients, specific food groups, or overarching dietary patterns positively influences health and promotes the prevention of common non-communicable diseases (NCDs). Greater consumption of health-promoting foods and limited intake of unhealthier options are intrinsic to the eating habits of certain regional diets such as the Mediterranean diet or have been constructed as part of dietary patterns designed to reduce disease risk, such as the Dietary Approaches to Stop Hypertension (DASH) or Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets. In comparison with a more traditional Western diet, these healthier alternatives are higher in plant-based foods, including fresh fruits and vegetables, whole grains, legumes, seeds, and nuts and lower in animal-based foods, particularly fatty and processed meats. To better understand the current concept of a "healthy diet," this review describes the features and supporting clinical and epidemiologic data for diets that have been shown to prevent disease and/or positively influence health. In total, evidence from epidemiological studies and clinical trials indicates that these types of dietary patterns reduce risks of NCDs including cardiovascular disease and cancer.

Keywords: healthy dietary patterns; macronutrients; micronutrients; non-communicable diseases; non-essential nutrients; plant-based diets.

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• Diet Surveys
• Diet, Healthy*
• Feeding Behavior*

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The Importance of Healthy Dietary Patterns in Chronic Disease Prevention 1

Marian l neuhouser , phd, rd.

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Correspondence to: Marian L Neuhouser, PhD, RD, Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, M4-B402, Seattle, WA 98109-1024, Tel: 206-667-4797, Fax: 206-667-7850, [email protected]

Issue date 2019 Oct.

The prevalence of chronic diseases in the United States and around the world is very high and not sustainable by most health care systems. While the etiology is complex, many chronic diseases are preventable through life long practices of adhering to healthy dietary patterns, engaging in physical activity and maintaining acceptable weight. Healthy dietary patterns were defined in the 2015 Dietary Guidelines Advisory Committee Scientific Report as diets that are high in fruits, vegetables, whole grains, low and non-fat dairy and lean protein. Other characteristics of healthy dietary patterns are that they are low in saturated fat, trans fat, sodium and added sugars. The preponderance of evidence to date suggests that healthy dietary patterns reduce the risk of the major diet-related chronic diseases, such as diabetes, cardiovascular disease and some cancers. While several methods exist for assessing dietary patterns in population studies, those that characterize dietary patterns using a priori scoring systems of indices, such as the Healthy Eating Index, may be of the most value because they offer a consistent metric that can be applied across multiple studies. It follows that consistency in methods then allows comparisons of results across populations. The nutrition science community can play a major leadership role in national and global health by promoting access to the ability of all population groups to consume a healthy dietary pattern.

Keywords: chronic diseases, dietary patterns, Healthy Eating Index

1. Introduction

Over the past century, the incidence, morbidity and mortality for non-communicable or chronic diseases has surpassed that for infectious or communicable diseases in the United States and much of the globe [ 1 ]. The ten leading causes of death in the United States are chronic diseases and their rank order is heart disease (1), cancer (2), cerebrovascular disease (5), and diabetes mellitus (7). In addition, over two thirds of US adults are overweight or obese [body mass index (BMI) > 30.0 kg/m 2 ] [ 2 ]. Obesity is both a chronic disease itself and it is an antecedent or risk factor for most of the aforementioned major causes of morbidity and mortality. Despite health campaigns to increase public awareness of obesity in recent years (e.g., Let’s Move https://letsmove.obamawhitehouse.archives.gov/ ) , the lack of evidence that the obesity epidemic is decreasing or even plateauing is quite concerning for the future health of the nation. These diet-related health risks are a problem around the globe as well [ 3 – 5 ].

2. Chronic disease incidence

About half of all Americans (~ 117 million individuals) have one or more preventable chronic disease and many millions more have chronic disease risk factors, such as hypertension or dyslipidemia [ 6 , 7 ]. This very high prevalence of chronic disease places tremendous stress on the health care system, decreases economic productivity due to disease-related disability and contributes to poor quality of life for millions of people and their families. The extent of the economic and social burden varies across population sub-groups such as racial/ethnic groups, age, geographic locale and socio-economic status [ 6 – 9 ]. For example, while the prevalence of overweight and obesity are unacceptably high among all Americans, the age-adjusted prevalence is higher among adult Hispanics (78.8%) and Black/African-Americans (76.7%) compared to non-Hispanic whites (68.0%) [ 10 ]. Similarly, diabetes mellitus is also more common in Hispanics (18.0%) and Black/African-Americans (18.0%) compared to non-Hispanic-whites (9.6%) [ 9 ]. Understanding risk factors, population distributions of disease prevalence, and the underlying etiology of chronic disease is one of the first steps to identifying effective programs for prevention, including programs for population subgroups who may be disproportionately represented in incidence and mortality estimates.

Chronic disease etiology is complex and multifactorial. Risk factors include age, family history, genetic predisposition, current and lifetime weight, current and lifetime physical activity, smoking, alcohol, and diet. Of these risk factors, the biggest public health impact will be made with reducing modifiable risk factors, such as diet. The nutrition science community can and should play a major leadership role in reducing the high and unsustainable level of preventable diet-related chronic diseases. In fact, some might suggest that nutrition scientists have a professional and moral obligation to do so. In broad terms, the task before us is to conduct high quality human nutrition research on diet and chronic disease risk to expand the evidence base from which policy decision will be made. Evidence-based clinical practice and policy should be applied to individuals, families, communities, clinicians and our society as a whole with the ultimate goal of improved population health.

3. Reducing diet-related chronic disease and dietary patterns

The first step in the endeavor to reduce diet-related chronic disease risk is to identify a framework for nutrition research. A useful dietary assessment approach that lends itself well to understanding the relationship of diet to chronic disease risk at the population level is dietary patterns [ 11 ]. Dietary patterns were defined by the 2015 Dietary Guidelines Advisory Committee as: “the quantities, proportions, variety or combination of different foods, drinks, and nutrients (when available) in diets and the frequency with which they are consumed” [ 12 ]. Dietary patterns considers the whole diet consumed by individuals and populations day-in and day-out over a period of months and years, as opposed to a reductionist approach that may focus on individual nutrients recorded as consumed on a single day or a few days. This distinction is important because the relationship between diet and chronic disease risk is a long-term exposure, as opposed to an acute exposure on a single day or even over the course of many short term exposures. Moreover, since diet-related chronic diseases have replaced nutrient deficiency diseases as the critical public health nutrition problems, this long-term total diet approach is one that is most suitable for chronic disease research.

Most of the research evidence base examining dietary patterns and chronic disease risk utilizes human prospective cohort studies where usual diet is assessed at baseline (i.e., cohort entry), and for some studies, at various follow-up time points over the course of many years [ 13 – 14 ]. This typically allows reasonable time for the diet (i.e., risk factor) to have a sufficient follow-up period to establish a meaningful and direct biological link to the disease outcome of interest (i.e., diabetes, cardiovascular disease), while also eliminating some of the bias that occurs with case-control and cross-sectional studies where temporality and directionality are not able to be established. Prospective cohorts are further well-suited to investigating diet-chronic disease relationships because the cohort design requires that participants are free of the disease endpoint at the time of enrollment. Disease endpoints or outcomes are accrued over the follow-up time period so the temporal sequence of diet-disease is more clearly and directly established. Additionally, prospective cohort studies collect detailed data on the primary exposures (in this case, diet) as well as variables that could be potential confounders of the relationship between diet and chronic disease outcomes, rendering more reliable and valid analytic models. Some of these confounders are physical activity, smoking and socioeconomic status. Failure to properly measure these confounders and include them in analysis could lead to spurious associations or null findings.

Several approaches have been used to characterize dietary patterns. Most prospective cohorts assess diet with one of many standardized self-reported dietary assessment approaches, such as food frequency questionnaires, 24-hour dietary recalls, or multiple day food records, or in some cases with objective nutritional biomarkers assessed from baseline blood or urine specimens. Dietary patterns are constructed from the self-reported data using 1) indices or a priori scoring systems; 2) patterns reported by the participants themselves; and/or 3) data driven approaches. For indices or scoring systems, investigators typically take the raw data, (i.e. foods and beverages reported as consumed) and apply one of several scoring systems to award points for consumption of food groups thought to be healthful (i.e., fruits, vegetables, whole grains), but no points or reverse scoring for less healthful foods or ingredients (i.e., added sugar, refined grains, red and processed meat). The particular foods or food groups included in these scoring systems or indices are based on empirical evidence for diet disease associations or based on other factors, such as government food policy recommendations. For example, the Healthy Eating Index (HEI) is based on adherence to the U.S Dietary Guidelines for Americans and the Dietary Approaches to Stop Hypertension (DASH) Diet Score is based on adherence to a diet that is similar to that prescribed in the DASH trial [ 15 – 16 ]. Each individual in the cohort is given a score that represents their usual diet or dietary pattern. The cohort members’ dietary pattern scores are then regressed on disease outcomes of interest, such as obesity, cardiovascular disease and cancer. The second approach also uses the dietary self-report of the cohort participants, but instead of a scoring system, patterns are characterized by a description from the participants themselves, such as self-reporting as vegetarian or lacto-ovo vegetarian. The third approach also uses the self-report data, but instead of investigator-driven scoring systems, analytic approaches such as principal components analysis and reduced rank regression are used to derive the patterns. While all three approaches have been used in prospective cohort studies, the broadest applications may be possible for the scoring system type approaches [ 11 , 13 , 14 ]. This is because the scoring systems or indices offer a consistent metric across multiple studies such that data can be compared across cohorts to evaluate consistency of associations. In addition, consistent metrics facilitate pooling of data to examine the totality of associations across the broader population. Further, scoring systems such as the HEI assess adherence to national dietary recommendations (the US Dietary Guidelines for Americans). In this manner, the link between research and policy is direct. In contrast, when a data driven approach is used to specify dietary patterns, tremendous heterogeneity emerges across cohorts since the loading factors often vary tremendously with little sense of consistency between populations studied [ 11 ]. One weakness of all of these approaches is that self-reported diet is used to assess the dietary patterns . Evidence has accumulated over the past 10 or so years that self-report is limited by systematic measurement error [ 17 ]. New and emerging approaches using nutritional biomarkers may have promise for characterizing dietary patterns [ 18 ]. Several studies have shown that the use of biomarkers, such as doubly labeled water for total energy intake and urinary nitrogen for protein intake are more reliable measures of these dietary components because they are objective and less subject to systematic error compared to self-report [ 17 , 18 ]. Research is very active in this area – particularly discovery research that aims to identify groups of biomarkers that could be used to characterize a pattern. For example, the use of metabolomics may uncover groups of metabolites that can characterize groups of foods or a pattern that may be predict chronic disease risk [ 19 ].

4. Communicating healthy dietary patterns

The concept of dietary patterns lends itself well to dissemination and implementation to the public. Diet is complex, consisting of hundreds of components and mixtures of foods spanning over 140 individual nutrients or nutritive compounds. Since the public eats food, not nutrients, recommending foods and food groups that promote health may be more easily adopted than keeping track of a myriad of nutrients. For this reason, the 2015 Scientific Report of the United States Dietary Guidelines Advisory Committee and the subsequent policy, the 2015–2020 Dietary Guidelines for Americans, advocated for use of healthy dietary patterns [ 12 , 20 ]. The Report describes a healthy dietary pattern as one that includes a variety of vegetables from all the subgroups (dark green, red, orange, legumes, starchy and others), fruits (especially whole fruits), grains- half of which should be whole grains, fat-free or low-fat dairy, a variety of protein foods – including seafood, lean meat, poultry, eggs, soy and oils. The Report further specified that a healthy dietary pattern limits saturated fat, trans fat, added sugars and sodium. These recommendations were based on systematic review of peer-reviewed literature. The Dietary Guidelines policy document recommending these healthy dietary patterns is used across US Departments and Agencies for a variety of food, health, consumer and agricultural programs to improve the nutritional status and health of the nation. Scientists, clinicians and policy makers recognize that thorough uptake of healthy dietary patterns across the entire population, including subgroups with multiple risk factors or higher prevalence of diet-related chronic diseases, will require concerted effort on the part of individuals, families, communities, industry and government.

5. Conclusions

This commentary has focused on chronic diseases that are common the United States and how the adoption of healthy dietary has potential for long lasting and sustained improved population health. Importantly, these principles can be applied more broadly to global health. The Global Burden of Disease Collaboration and others [ 3 – 5 ] have documented the rise in lifestyle related diseases around the globe; clearly, this is not a problem isolated to the West. For example, dietary guidelines committees and policies in Mexico have adopted approaches similar to those in the United States [ 21 , 22 ]

A shift towards healthy dietary patterns has the potential to curtail the current unsustainable high level of obesity, cardiovascular disease, diabetes mellitus and cancer in the United States and around the globe. The health and well-being of current and future generations is dependent upon good nutrition as a strong foundation for health.

Acknowledgment

Supported by R01 CA119171, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services

Abbreviations

Dietary Approaches to Stop Hypertension

Healthy Eating Index

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This article was part of the presentations for the 2017 Korean Nutrition Society 50 th Anniversary International Conference, November 2–3, 2017 in Seoul Korea.

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• Published: 06 December 2017

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

• Deborah R. Wahl 1   na1 ,
• Karoline Villinger 1   na1 ,
• Laura M. König   ORCID: orcid.org/0000-0003-3655-8842 1 ,
• Katrin Ziesemer 1 ,
• Harald T. Schupp 1 &
• Britta Renner 1  

Scientific Reports volume  7 , Article number:  17069 ( 2017 ) Cite this article

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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.

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Introduction.

When it comes to eating, researchers, the media, and policy makers mainly focus on negative aspects of eating behaviour, like restricting certain foods, counting calories, and dieting. Likewise, health intervention efforts, including primary prevention campaigns, typically encourage consumers to trade off the expected enjoyment of hedonic and comfort foods against health benefits 1 . However, research has shown that diets and restrained eating are often counterproductive and may even enhance the risk of long-term weight gain and eating disorders 2 , 3 . A promising new perspective entails a shift from food as pure nourishment towards a more positive and well-being centred perspective of human eating behaviour 1 , 4 , 5 . In this context, Block et al . 4 have advocated a paradigm shift from “food as health” to “food as well-being” (p. 848).

Supporting this perspective of “food as well-being”, recent research suggests that “healthy” food choices, such as eating more fruits and vegetables, have not only physical but also mental health benefits 6 , 7 and might be a long-term investment in future well-being 8 . For example, in a nationally representative panel survey of over 12,000 adults from Australia, Mujcic and Oswald 8 showed that fruit and vegetable consumption predicted increases in happiness, life satisfaction, and well-being over two years. Similarly, using lagged analyses, White and colleagues 9 showed that fruit and vegetable consumption predicted improvements in positive affect on the subsequent day but not vice versa. Also, cross-sectional evidence reported by Blanchflower et al . 10 shows that eating fruits and vegetables is positively associated with well-being after adjusting for demographic variables including age, sex, or race 11 . Of note, previous research includes a wide range of time lags between actual eating occasion and well-being assessment, ranging from 24 hours 9 , 12 to 14 days 6 , to 24 months 8 . Thus, the findings support the notion that fruit and vegetable consumption has beneficial effects on different indicators of well-being, such as happiness or general life satisfaction, across a broad range of time spans.

The contention that healthy food choices such as a higher fruit and vegetable consumption is associated with greater happiness and well-being clearly contrasts with the common belief that in particular high-fat, high-sugar, or high-caloric foods taste better and make us happy while we are eating them. When it comes to eating, people usually have a spontaneous “unhealthy = tasty” association 13 and assume that chocolate is a better mood booster than an apple. According to this in-the-moment well-being perspective, consumers have to trade off the expected enjoyment of eating against the health costs of eating unhealthy foods 1 , 4 .

A wealth of research shows that the experience of negative emotions and stress leads to increased consumption in a substantial number of individuals (“emotional eating”) of unhealthy food (“comfort food”) 14 , 15 , 16 , 17 . However, this research stream focuses on emotional eating to “smooth” unpleasant experiences in response to stress or negative mood states, and the mood-boosting effect of eating is typically not assessed 18 . One of the few studies testing the effectiveness of comfort food in improving mood showed that the consumption of “unhealthy” comfort food had a mood boosting effect after a negative mood induction but not to a greater extent than non-comfort or neutral food 19 . Hence, even though people may believe that snacking on “unhealthy” foods like ice cream or chocolate provides greater pleasure and psychological benefits, the consumption of “unhealthy” foods might not actually be more psychologically beneficial than other foods.

However, both streams of research have either focused on a single food category (fruit and vegetable consumption), a single type of meal (snacking), or a single eating occasion (after negative/neutral mood induction). Accordingly, it is unknown whether the boosting effect of eating is specific to certain types of food choices and categories or whether eating has a more general boosting effect that is observable after the consumption of both “healthy” and “unhealthy” foods and across eating occasions. Accordingly, in the present study, we investigated the psychological benefits of eating that varied by food categories and meal types by assessing complete dietary behaviour across eight days in real life.

Furthermore, previous research on the impact of eating on well-being tended to rely on retrospective assessments such as food frequency questionnaires 8 , 10 and written food diaries 9 . Such retrospective self-report methods rely on the challenging task of accurately estimating average intake or remembering individual eating episodes and may lead to under-reporting food intake, particularly unhealthy food choices such as snacks 7 , 20 . To avoid memory and bias problems in the present study we used ecological momentary assessment (EMA) 21 to obtain ecologically valid and comprehensive real life data on eating behaviour and happiness as experienced in-the-moment.

In the present study, we examined the eating happiness and satisfaction experienced in-the-moment, in real time and in real life, using a smartphone based EMA approach. Specifically, healthy participants were asked to record each eating occasion, including main meals and snacks, for eight consecutive days and rate how tasty their meal/snack was, how much they enjoyed it, and how pleased they were with their meal/snack immediately after each eating episode. This intense recording of every eating episode allows assessing eating behaviour on the level of different meal types and food categories to compare experienced eating happiness across meals and categories. Following the two different research streams, we expected on a food category level that not only “unhealthy” foods like sweets would be associated with high experienced eating happiness but also “healthy” food choices such as fruits and vegetables. On a meal type level, we hypothesised that the happiness of meals differs as a function of meal type. According to previous contention, snacking in particular should be accompanied by greater happiness.

Eating episodes

Overall, during the study period, a total of 1,044 completed eating episodes were reported (see also Table  1 ). On average, participants rated their eating happiness with M  = 77.59 which suggests that overall eating occasions were generally positive. However, experienced eating happiness also varied considerably between eating occasions as indicated by a range from 7.00 to 100.00 and a standard deviation of SD  = 16.41.

Food categories and experienced eating happiness

All eating episodes were categorised according to their food category based on the German Nutrient Database (German: Bundeslebensmittelschlüssel), which covers the average nutritional values of approximately 10,000 foods available on the German market and is a validated standard instrument for the assessment of nutritional surveys in Germany. As shown in Table  1 , eating happiness differed significantly across all 14 food categories, F (13, 2131) = 1.78, p  = 0.04. On average, experienced eating happiness varied from 71.82 ( SD  = 18.65) for fish to 83.62 ( SD  = 11.61) for meat substitutes. Post hoc analysis, however, did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.22. Hence, on average, “unhealthy” food choices such as sweets ( M  = 78.93, SD  = 15.27) did not differ in experienced happiness from “healthy” food choices such as fruits ( M  = 78.29, SD  = 16.13) or vegetables ( M  = 77.57, SD  = 17.17). In addition, an intraclass correlation (ICC) of ρ = 0.22 for happiness indicated that less than a quarter of the observed variation in experienced eating happiness was due to differences between food categories, while 78% of the variation was due to differences within food categories.

However, as Figure  1 (left side) depicts, consumption frequency differed greatly across food categories. Frequently consumed food categories encompassed vegetables which were consumed at 38% of all eating occasions ( n  = 400), followed by dairy products with 35% ( n  = 366), and sweets with 34% ( n  = 356). Conversely, rarely consumed food categories included meat substitutes, which were consumed in 2.2% of all eating occasions ( n  = 23), salty extras (1.5%, n  = 16), and pastries (1.3%, n  = 14).

Left side: Average experienced eating happiness (colour intensity: darker colours indicate greater happiness) and consumption frequency (size of the cycle) for the 14 food categories. Right side: Absolute share of the 14 food categories in total experienced eating happiness.

Amount of experienced eating happiness by food category

To account for the frequency of consumption, we calculated and scaled the absolute experienced eating happiness according to the total sum score. As shown in Figure  1 (right side), vegetables contributed the biggest share to the total happiness followed by sweets, dairy products, and bread. Clustering food categories shows that fruits and vegetables accounted for nearly one quarter of total eating happiness score and thus, contributed to a large part of eating related happiness. Grain products such as bread, pasta, and cereals, which are main sources of carbohydrates including starch and fibre, were the second main source for eating happiness. However, “unhealthy” snacks including sweets, salty extras, and pastries represented the third biggest source of eating related happiness.

Experienced eating happiness by meal type

To further elucidate the contribution of snacks to eating happiness, analysis on the meal type level was conducted. Experienced in-the-moment eating happiness significantly varied by meal type consumed, F (4, 1039) = 11.75, p  < 0.001. Frequencies of meal type consumption ranged from snacks being the most frequently logged meal type ( n  = 332; see also Table  1 ) to afternoon tea being the least logged meal type ( n  = 27). Figure  2 illustrates the wide dispersion within as well as between different meal types. Afternoon tea ( M  = 82.41, SD  = 15.26), dinner ( M  = 81.47, SD  = 14.73), and snacks ( M  = 79.45, SD  = 14.94) showed eating happiness values above the grand mean, whereas breakfast ( M  = 74.28, SD  = 16.35) and lunch ( M  = 73.09, SD  = 18.99) were below the eating happiness mean. Comparisons between meal types showed that eating happiness for snacks was significantly higher than for lunch t (533) = −4.44, p  = 0.001, d  = −0.38 and breakfast, t (567) = −3.78, p  = 0.001, d  = −0.33. However, this was also true for dinner, which induced greater eating happiness than lunch t (446) = −5.48, p  < 0.001, d  = −0.50 and breakfast, t (480) = −4.90, p  < 0.001, d  = −0.46. Finally, eating happiness for afternoon tea was greater than for lunch t (228) = −2.83, p  = 0.047, d  = −0.50. All other comparisons did not reach significance, t  ≤ 2.49, p  ≥ 0.093.

Experienced eating happiness per meal type. Small dots represent single eating events, big circles indicate average eating happiness, and the horizontal line indicates the grand mean. Boxes indicate the middle 50% (interquartile range) and median (darker/lighter shade). The whiskers above and below represent 1.5 of the interquartile range.

Control Analyses

In order to test for a potential confounding effect between experienced eating happiness, food categories, and meal type, additional control analyses within meal types were conducted. Comparing experienced eating happiness for dinner and lunch suggested that dinner did not trigger a happiness spill-over effect specific to vegetables since the foods consumed at dinner were generally associated with greater happiness than those consumed at other eating occasions (Supplementary Table  S1 ). Moreover, the relative frequency of vegetables consumed at dinner (73%, n  = 180 out of 245) and at lunch were comparable (69%, n  = 140 out of 203), indicating that the observed happiness-vegetables link does not seem to be mainly a meal type confounding effect.

Since the present study focuses on “food effects” (Level 1) rather than “person effects” (Level 2), we analysed the data at the food item level. However, participants who were generally overall happier with their eating could have inflated the observed happiness scores for certain food categories. In order to account for person-level effects, happiness scores were person-mean centred and thereby adjusted for mean level differences in happiness. The person-mean centred happiness scores ( M cwc ) represent the difference between the individual’s average happiness score (across all single in-the-moment happiness scores per food category) and the single happiness scores of the individual within the respective food category. The centred scores indicate whether the single in-the-moment happiness score was above (indicated by positive values) or below (indicated by negative values) the individual person-mean. As Table  1 depicts, the control analyses with centred values yielded highly similar results. Vegetables were again associated on average with more happiness than other food categories (although people might differ in their general eating happiness). An additional conducted ANOVA with person-centred happiness values as dependent variables and food categories as independent variables provided also a highly similar pattern of results. Replicating the previously reported analysis, eating happiness differed significantly across all 14 food categories, F (13, 2129) = 1.94, p  = 0.023, and post hoc analysis did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.14. Moreover, fruits and vegetables were associated with high happiness values, and “unhealthy” food choices such as sweets did not differ in experienced happiness from “healthy” food choices such as fruits or vegetables. The only difference between the previous and control analysis was that vegetables ( M cwc  = 1.16, SD  = 15.14) gained slightly in importance for eating-related happiness, whereas fruits ( M cwc  = −0.65, SD  = 13.21), salty extras ( M cwc  = −0.07, SD  = 8.01), and pastries ( M cwc  = −2.39, SD  = 18.26) became slightly less important.

This study is the first, to our knowledge, that investigated in-the-moment experienced eating happiness in real time and real life using EMA based self-report and imagery covering the complete diversity of food intake. The present results add to and extend previous findings by suggesting that fruit and vegetable consumption has immediate beneficial psychological effects. Overall, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Thus, in addition to the investment in future well-being indicated by previous research 8 , “healthy” food choices seem to be an investment in the in-the moment well-being.

Importantly, although many cultures convey the belief that eating certain foods has a greater hedonic and mood boosting effect, the present results suggest that this might not reflect actual in-the-moment experiences accurately. Even though people often have a spontaneous “unhealthy = tasty” intuition 13 , thus indicating that a stronger happiness boosting effect of “unhealthy” food is to be expected, the induced eating happiness of sweets did not differ on average from “healthy” food choices such as fruits or vegetables. This was also true for other stereotypically “unhealthy” foods such as pastries and salty extras, which did not show the expected greater boosting effect on happiness. Moreover, analyses on the meal type level support this notion, since snacks, despite their overall positive effect, were not the most psychologically beneficial meal type, i.e., dinner had a comparable “happiness” signature to snacking. Taken together, “healthy choices” seem to be also “happy choices” and at least comparable to or even higher in their hedonic value as compared to stereotypical “unhealthy” food choices.

In general, eating happiness was high, which concurs with previous research from field studies with generally healthy participants. De Castro, Bellisle, and Dalix 22 examined weekly food diaries from 54 French subjects and found that most of the meals were rated as appealing. Also, the observed differences in average eating happiness for the 14 different food categories, albeit statistically significant, were comparable small. One could argue that this simply indicates that participants avoided selecting bad food 22 . Alternatively, this might suggest that the type of food or food categories are less decisive for experienced eating happiness than often assumed. This relates to recent findings in the field of comfort and emotional eating. Many people believe that specific types of food have greater comforting value. Also in research, the foods eaten as response to negative emotional strain, are typically characterised as being high-caloric because such foods are assumed to provide immediate psycho-physical benefits 18 . However, comparing different food types did not provide evidence for the notion that they differed in their provided comfort; rather, eating in general led to significant improvements in mood 19 . This is mirrored in the present findings. Comparing the eating happiness of “healthy” food choices such as fruits and vegetables to that of “unhealthy” food choices such as sweets shows remarkably similar patterns as, on average, they were associated with high eating happiness and their range of experiences ranged from very negative to very positive.

This raises the question of why the idea that we can eat indulgent food to compensate for life’s mishaps is so prevailing. In an innovative experimental study, Adriaanse, Prinsen, de Witt Huberts, de Ridder, and Evers 23 led participants believe that they overate. Those who characterised themselves as emotional eaters falsely attributed their over-consumption to negative emotions, demonstrating a “confabulation”-effect. This indicates that people might have restricted self-knowledge and that recalled eating episodes suffer from systematic recall biases 24 . Moreover, Boelsma, Brink, Stafleu, and Hendriks 25 examined postprandial subjective wellness and objective parameters (e.g., ghrelin, insulin, glucose) after standardised breakfast intakes and did not find direct correlations. This suggests that the impact of different food categories on wellness might not be directly related to biological effects but rather due to conditioning as food is often paired with other positive experienced situations (e.g., social interactions) or to placebo effects 18 . Moreover, experimental and field studies indicate that not only negative, but also positive, emotions trigger eating 15 , 26 . One may speculate that selective attention might contribute to the “myth” of comfort food 19 in that people attend to the consumption effect of “comfort” food in negative situation but neglect the effect in positive ones.

The present data also show that eating behaviour in the real world is a complex behaviour with many different aspects. People make more than 200 food decisions a day 27 which poses a great challenge for the measurement of eating behaviour. Studies often assess specific food categories such as fruit and vegetable consumption using Food Frequency Questionnaires, which has clear advantages in terms of cost-effectiveness. However, focusing on selective aspects of eating and food choices might provide only a selective part of the picture 15 , 17 , 22 . It is important to note that focusing solely on the “unhealthy” food choices such as sweets would have led to the conclusion that they have a high “indulgent” value. To be able to draw conclusions about which foods make people happy, the relation of different food categories needs to be considered. The more comprehensive view, considering the whole dietary behaviour across eating occasions, reveals that “healthy” food choices actually contributed the biggest share to the total experienced eating happiness. Thus, for a more comprehensive understanding of how eating behaviours are regulated, more complete and sensitive measures of the behaviour are necessary. Developments in mobile technologies hold great promise for feasible dietary assessment based on image-assisted methods 28 .

As fruits and vegetables evoked high in-the-moment happiness experiences, one could speculate that these cumulate and have spill-over effects on subsequent general well-being, including life satisfaction across time. Combing in-the-moment measures with longitudinal perspectives might be a promising avenue for future studies for understanding the pathways from eating certain food types to subjective well-being. In the literature different pathways are discussed, including physiological and biochemical aspects of specific food elements or nutrients 7 .

The present EMA based data also revealed that eating happiness varied greatly within the 14 food categories and meal types. As within food category variance represented more than two third of the total observed variance, happiness varied according to nutritional characteristics and meal type; however, a myriad of factors present in the natural environment can affect each and every meal. Thus, widening the “nourishment” perspective by including how much, when, where, how long, and with whom people eat might tell us more about experienced eating happiness. Again, mobile, in-the-moment assessment opens the possibility of assessing the behavioural signature of eating in real life. Moreover, individual factors such as eating motives, habitual eating styles, convenience, and social norms are likely to contribute to eating happiness variance 5 , 29 .

A key strength of this study is that it was the first to examine experienced eating happiness in non-clinical participants using EMA technology and imagery to assess food intake. Despite this strength, there are some limitations to this study that affect the interpretation of the results. In the present study, eating happiness was examined on a food based level. This neglects differences on the individual level and might be examined in future multilevel studies. Furthermore, as a main aim of this study was to assess real life eating behaviour, the “natural” observation level is the meal, the psychological/ecological unit of eating 30 , rather than food categories or nutrients. Therefore, we cannot exclude that specific food categories may have had a comparably higher impact on the experienced happiness of the whole meal. Sample size and therefore Type I and Type II error rates are of concern. Although the total number of observations was higher than in previous studies (see for example, Boushey et al . 28 for a review), the number of participants was small but comparable to previous studies in this field 20 , 31 , 32 , 33 . Small sample sizes can increase error rates because the number of persons is more decisive than the number of nested observations 34 . Specially, nested data can seriously increase Type I error rates, which is rather unlikely to be the case in the present study. Concerning Type II error rates, Aarts et al . 35 illustrated for lower ICCs that adding extra observations per participant also increases power, particularly in the lower observation range. Considering the ICC and the number of observations per participant, one could argue that the power in the present study is likely to be sufficient to render the observed null-differences meaningful. Finally, the predominately white and well-educated sample does limit the degree to which the results can be generalised to the wider community; these results warrant replication with a more representative sample.

Despite these limitations, we think that our study has implications for both theory and practice. The cumulative evidence of psychological benefits from healthy food choices might offer new perspectives for health promotion and public-policy programs 8 . Making people aware of the “healthy = happy” association supported by empirical evidence provides a distinct and novel perspective to the prevailing “unhealthy = tasty” folk intuition and could foster eating choices that increase both in-the-moment happiness and future well-being. Furthermore, the present research lends support to the advocated paradigm shift from “food as health” to “food as well-being” which entails a supporting and encouraging rather constraining and limiting view on eating behaviour.

The study conformed with the Declaration of Helsinki. All study protocols were approved by University of Konstanz’s Institutional Review Board and were conducted in accordance with guidelines and regulations. Upon arrival, all participants signed a written informed consent.

Participants

Thirty-eight participants (28 females: average age = 24.47, SD  = 5.88, range = 18–48 years) from the University of Konstanz assessed their eating behaviour in close to real time and in their natural environment using an event-based ambulatory assessment method (EMA). No participant dropped out or had to be excluded. Thirty-three participants were students, with 52.6% studying psychology. As compensation, participants could choose between taking part in a lottery (4 × 25€) or receiving course credits (2 hours).

Participants were recruited through leaflets distributed at the university and postings on Facebook groups. Prior to participation, all participants gave written informed consent. Participants were invited to the laboratory for individual introductory sessions. During this first session, participants installed the application movisensXS (version 0.8.4203) on their own smartphones and downloaded the study survey (movisensXS Library v4065). In addition, they completed a short baseline questionnaire, including demographic variables like age, gender, education, and eating principles. Participants were instructed to log every eating occasion immediately before eating by using the smartphone to indicate the type of meal, take pictures of the food, and describe its main components using a free input field. Fluid intake was not assessed. Participants were asked to record their food intake on eight consecutive days. After finishing the study, participants were invited back to the laboratory for individual final interviews.

Immediately before eating participants were asked to indicate the type of meal with the following five options: breakfast, lunch, afternoon tea, dinner, snack. In Germany, “afternoon tea” is called “Kaffee & Kuchen” which directly translates as “coffee & cake”. It is similar to the idea of a traditional “afternoon tea” meal in UK. Specifically, in Germany, people have “Kaffee & Kuchen” in the afternoon (between 4–5 pm) and typically coffee (or tea) is served with some cake or cookies. Dinner in Germany is a main meal with mainly savoury food.

After each meal, participants were asked to rate their meal on three dimensions. They rated (1) how much they enjoyed the meal, (2) how pleased they were with their meal, and (3) how tasty their meal was. Ratings were given on a scale of one to 100. For reliability analysis, Cronbach’s Alpha was calculated to assess the internal consistency of the three items. Overall Cronbach’s alpha was calculated with α = 0.87. In addition, the average of the 38 Cronbach’s alpha scores calculated at the person level also yielded a satisfactory value with α = 0.83 ( SD  = 0.24). Thirty-two of 38 participants showed a Cronbach’s alpha value above 0.70 (range = 0.42–0.97). An overall score of experienced happiness of eating was computed using the average of the three questions concerning the meals’ enjoyment, pleasure, and tastiness.

Analytical procedure

The food pictures and descriptions of their main components provided by the participants were subsequently coded by independent and trained raters. Following a standardised manual, additional components displayed in the picture were added to the description by the raters. All consumed foods were categorised into 14 different food categories (see Table  1 ) derived from the food classification system designed by the German Nutrition Society (DGE) and based on the existing food categories of the German Nutrient Database (Max Rubner Institut). Liquid intake and preparation method were not assessed. Therefore, fats and additional recipe ingredients were not included in further analyses, because they do not represent main elements of food intake. Further, salty extras were added to the categorisation.

No participant dropped out or had to be excluded due to high missing rates. Missing values were below 5% for all variables. The compliance rate at the meal level cannot be directly assessed since the numbers of meals and snacks can vary between as well as within persons (between days). As a rough compliance estimate, the numbers of meals that are expected from a “normative” perspective during the eight observation days can be used as a comparison standard (8 x breakfast, 8 × lunch, 8 × dinner = 24 meals). On average, the participants reported M  = 6.3 breakfasts ( SD  = 2.3), M  = 5.3 lunches ( SD  = 1.8), and M  = 6.5 dinners ( SD  = 2.0). In comparison to the “normative” expected 24 meals, these numbers indicate a good compliance (approx. 75%) with a tendency to miss six meals during the study period (approx. 25%). However, the “normative” expected 24 meals for the study period might be too high since participants might also have skipped meals (e.g. breakfast). Also, the present compliance rates are comparable to other studies. For example, Elliston et al . 36 recorded 3.3 meal/snack reports per day in an Australian adult sample and Casperson et al . 37 recorded 2.2 meal reports per day in a sample of adolescents. In the present study, on average, M  = 3.4 ( SD  = 1.35) meals or snacks were reported per day. These data indicate overall a satisfactory compliance rate and did not indicate selective reporting of certain food items.

To graphically visualise data, Tableau (version 10.1) was used and for further statistical analyses, IBM SPSS Statistics (version 24 for Windows).

Data availability

The dataset generated and analysed during the current study is available from the corresponding authors on reasonable request.

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Acknowledgements

This research was supported by the Federal Ministry of Education and Research within the project SmartAct (Grant 01EL1420A, granted to B.R. & H.S.). The funding source had no involvement in the study’s design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit this article for publication. We thank Gudrun Sproesser, Helge Giese, and Angela Whale for their valuable support.

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Deborah R. Wahl and Karoline Villinger contributed equally to this work.

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Department of Psychology, University of Konstanz, Konstanz, Germany

Deborah R. Wahl, Karoline Villinger, Laura M. König, Katrin Ziesemer, Harald T. Schupp & Britta Renner

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B.R. & H.S. developed the study concept. All authors participated in the generation of the study design. D.W., K.V., L.K. & K.Z. conducted the study, including participant recruitment and data collection, under the supervision of B.R. & H.S.; D.W. & K.V. conducted data analyses. D.W. & K.V. prepared the first manuscript draft, and B.R. & H.S. provided critical revisions. All authors approved the final version of the manuscript for submission.

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Wahl, D.R., Villinger, K., König, L.M. et al. Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments. Sci Rep 7 , 17069 (2017). https://doi.org/10.1038/s41598-017-17262-9

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