research about healthy and unhealthy food

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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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• Human behaviour

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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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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Availability of healthier vs. less healthy food and food choice: an online experiment

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Our environments shape our behaviour, but little research has addressed whether healthier cues have a similar impact to less healthy ones. This online study examined the impact on food choices of the number of (i) healthier and (ii) less healthy snack foods available, and possible moderation by cognitive load and socioeconomic status.

UK adults ( n  = 1509) were randomly allocated to one of six groups (two cognitive load x three availability conditions). Participants memorised a 7-digit number (7777777: low cognitive load; 8529713: high cognitive load). While remembering this number, participants chose the food they would most like to eat from: (a) two healthier and two less healthy foods, (b) six healthier and two less healthy foods, or (c) two healthier and six less healthy foods.

Compared to being offered two healthier and two less healthy options, the odds of choosing a healthier option were twice as high (Odds Ratio (OR): 2.0, 95%CI: 1.6, 2.6) with four additional healthier options, while the odds of choosing a less healthy option were four times higher (OR: 4.3, 95%CI: 3.1, 6.0) with four additional less healthy options. There were no significant main effects or interactions with cognitive load or socioeconomic status.

Conclusions

This study provides a novel test of the impact of healthier vs. less healthy food cues on food choice, suggesting that less healthy food cues have a larger effect than healthier ones. Consequently, removing less healthy as opposed to adding healthier food options could have greater impact on healthier choices. Studies are now needed in which choices are made between physically-present foods.

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Non-communicable diseases (NCDs), including diabetes, cardiovascular disease and cancer, now cause the majority of premature preventable deaths worldwide [ 1 , 2 ]. Patterns of unhealthy behaviour, including excessive energy intake, are key contributors to these NCDs, and are socially patterned, i.e. less healthy behaviours are generally more common amongst the poorest, contributing in turn to the substantial socioeconomic inequalities in life expectancy and years lived in good health.

One strategy that may be effective in targeting these behavioural risk factors is to target the physical micro-environment, addressing the multiple cues – aspects of our environments that can influence behaviour – which act detrimentally by limiting healthier options or promoting less healthy ones [ 3 ]. This approach (sometimes termed ‘choice architecture’ or ‘nudging’) [ 4 , 5 , 6 ] is based on dual process models of behaviour [ 7 , 8 ]. It has been hypothesised that interventions targeting non-conscious processes regulating behaviour are more effective than more information-based interventions, as they do not necessarily rely on individuals’ cognitive resources [ 3 , 9 ]. One such environmental cue is the availability (including both the number and range) of healthier vs. less healthy foods, which represents one of the top three interventions suggested in the McKinsey Global Institute report on obesity [ 10 ] as having the highest likely impact across the population. While the mechanisms underlying the effects of altering availability have not been explored to our knowledge, increasing the availability of product(s) may influence consumption by increasing the visibility or salience of these products to consumers, and/or increased options may lead to these appealing to a wider range of people. Evidence is beginning to accumulate to support the effectiveness of targeting product availability to change behaviour [ 11 , 12 , 14 ].

One choice when designing interventions to alter availability is whether to increase healthier foods, decrease less healthy foods or both simultaneously. Thus far, there is a paucity of evidence on this, although observational data suggests that the availability of less healthy foods but not fruit and vegetables is associated with body mass index (BMI) [ 15 ]. Establishing if there is a difference in response to healthier vs. less healthy food cues could help prioritise interventions that are likely to be most effective to change behaviour.

Looking at food cues beyond product availability, evidence comparing responses to healthier vs. less healthy food cues remains limited. There are a small number of observational studies demonstrating that individuals may be more responsive to price promotions on less healthy rather than healthier products [ 16 ], and that consumers may be more responsive to price discounts on less healthy foods and price increases on healthier foods [ 17 ]. Experimental studies looking at changing the proximity of foods have altered both healthier and less healthy foods, and have not suggested any differences by food healthiness [ 18 , 19 ] – however, these have focused on altering just one example of a healthier and less healthy food. No experimental studies to our knowledge have set out to isolate responses to altering a range of healthier vs. less healthy foods, which is likely to better reflect many food environments.

This distinction between healthier and less healthy food cues may also have implications for socioeconomic inequalities. Living in differentially ‘obesogenic’ environments may drive some of the socioeconomic differences in diet-related behaviours, e.g. those who are more deprived may have less exposure to healthier environmental cues, such as the presence of healthier food outlets, and greater exposure to less healthy environmental cues, such as unhealthy food outlets [ 20 , 21 , 22 ]. How people respond to the same environmental cues may additionally contribute to inequalities: response inhibition (a core element of executive function that includes being able to resist impulsive behaviour [ 23 ]) is associated with socioeconomic status (SES) [ 24 , 25 ], and predicts obesity and food-related behaviour [ 26 , 27 , 28 , 29 ]. The healthiness of the food involved may play a role, however, with response inhibition having a more limited (if any) impact on consumption of healthier foods [ 30 , 31 , 32 ]. As such, the choice of targeting healthier or less healthy food cues may have implications for the effectiveness of an intervention across socioeconomic groups, and any differential responsiveness is essential to establish in order to select interventions for implementation that will not inadvertently increase inequalities.

Given the association between socioeconomic status and response inhibition [ 24 , 25 ], it is interesting to investigate people’s responses to food cues when their response inhibition has been lowered. One means of targeting response inhibition is increasing cognitive load, which can be used to temporarily deplete an individual’s cognitive resources (including response inhibition) [ 33 , 34 , 35 ]. The effect of increasing cognitive load is also worthy of exploration in the context of making changes to environmental cues, such as product availability, given that it has been hypothesised the changes to the physical micro-environment may impact on behaviour without relying on individuals’ cognitive resources [ 3 , 9 ]. Moreover the effects of increasing cognitive load when exploring cues targeting healthier vs. less healthy foods have not been explored to our knowledge, and may vary given the different associations between response inhibition and healthier/ less healthy food choices.

It is worth noting that any effects of response inhibition may also be moderated by food appeal – with those with strong appeal towards less healthy foods and lower response inhibition being more likely to make less healthy choices and to gain the most weight [ 36 , 37 , 38 , 39 ]. This may also have further implications for socioeconomic inequalities, as some healthier foods have higher appeal for less deprived individuals [ 40 ]. As such, food appeal may act alongside response inhibition to mediate some of the socioeconomic patterning seen in diet-related behaviour, and may contribute to any differences in responses to healthier and less healthy food cues.

To address some of the gaps in the extant literature, the current study aims to examine: (a) the impact of increasing the range of (i) healthier (i.e. lower energy) snack foods vs. (ii) less healthy (i.e. higher energy) snack foods on food selection in an online task; and the potential moderation of responses to these cues by (b) cognitive load and (c) by socioeconomic status. In addition, response inhibition and food appeal will be investigated as potential mediators of any influence of socioeconomic status on food choice. Snack foods (operationalized as single-serve pre-packaged foods, including confectionery, potato chips and cereal bars) were chosen as an initial category to investigate this hypothesis, given they are more likely to be selected and consumed within a short interval, potentially making them more susceptible to fluctuations in response inhibition than meals. The specific hypotheses tested are set out below.

Primary hypothesis

Increasing the number of less healthy food items has a larger effect on the healthiness of food choices than increasing the number of healthier food items

Secondary hypotheses

Cognitive load: Participants under high (vs low) cognitive load:

will show no differences in their likelihood of selecting healthier foods after seeing a greater number of healthier food options

will be more likely to select less healthy foods after seeing a greater number of less healthy food options

Socioeconomic status: Participants with higher (vs lower) socioeconomic status:

will be more likely to select healthier foods after seeing a greater number of healthier food options

will be less likely to choose less healthy foods after seeing a greater number of less healthy food options

Response inhibition and food appeal both partially mediate the impact of socioeconomic status on food choice

Participants were randomly allocated to one of six groups in a between-subjects design (three availability conditions x two cognitive load conditions). Randomisation was conducted online using the Qualtrics randomiser element, and was performed separately for each of three socioeconomic groups (defined by occupational group), to achieve similar numbers of participants of each socioeconomic status in each study group. As such, neither the recruiter nor researcher were aware of participants’ group assignment prior to participation.

Availability conditions

Participants were asked to select an item from an array of snack foods that they would most like to eat right now. The composition of this array differed between participants depending on their assignment to one of three conditions: (1) two healthier and two less healthy food items (reference); (2) two healthier and six less healthy food items (increased less healthy); (3) six healthier and two less healthy food items (increased healthier). As such, comparing condition 2 to condition 1 involved changing the number of less healthy items while keeping the number of healthier items constant (and vice versa comparing condition 3 to condition 1). The intervention also involved changing the proportion of healthier to less healthy and the overall number of options, but this was mirrored across the two conditions where options were increased (conditions 2 and 3).

Cognitive load conditions

Participants were asked to memorise a 7-digit number as part of the study. They were randomised to either a complex string (e.g. 8529713; high load) or simple string (e.g. 7777777; low load).

The study was pre-registered on the Open Science Framework ( https://osf.io/nxt4s/ ), and ethical approval was obtained from the Cambridge Psychology Research Ethics Committee (Pre.2017.016).

The sample of 1509 UK adults was recruited from an online market research company panel (Research Now). No specific inclusion criteria were used, but the sample was selected to be representative of the UK in terms of age and gender, with quotas set for socioeconomic status (evenly divided between occupational status groups A&B: Higher and intermediate managerial, administrative and professional occupations; C1&C2: Supervisory, clerical and junior managerial, administrative and professional occupations; D&E: Semi-skilled and unskilled manual occupations). Participants are paid in vouchers for their time spent completing surveys for the market research company, with participation in this study being paid at the usual rate.

The planned sample size was determined using G*Power (version 3.1.9.2), for a logistic regression, with power of 0.8 and alpha =0.025, to detect a small effect size (odds ratio 1.5) using a binomial predictor variable, with balanced groups. The effect size was based on the impact of availability on food choice in pilot work and the r-squared accounted for by control variables was taken to be medium-sized (0.25). This gives a sample estimate of 1257, for a 2-group comparison (i.e. 629 per group, which was rounded to 630 to give a slight over-recruitment). For the 3 availability conditions × 2 cognitive load conditions, this gave a total sample size of 3780.

However, due to issues with recruitment from the online panel, the total sample size could not be achieved. When this became apparent, recruitment was paused and a post-hoc internal pilot was conducted to determine whether additional data should be sought from an alternative source. The data obtained thus far was given to a statistician (who was not responsible for the study analyses), who conducted an updated sample size estimate, based on the actual effect size. This revised estimate suggested that a total sample size of 579 would allow a test the impact of availability on food choice with a power of 0.8. Given this sample had already been achieved, recruitment was halted.

Anyone completing the survey in less than 30% of the median time was excluded (no participants met this exclusion criteria). In addition, participants had to correctly answer a quality control question as part of the study to ensure that they are paying attention to the questions (“How many times have you visited the planet Mars?”). Anyone answering incorrectly (i.e. any answer other than “Never”) was screened out and was not counted towards the study quotas ( n  = 321).

Outcome: Food choice (healthier or less healthy)

Participants’ choice of a healthier or less healthy snack food was the main study outcome. Participants were asked which of an array of items they would most like to eat right now, with the array differing depending on their assigned availability condition.

Healthier vs. less healthy foods

The study focused on pre-packaged snack food, with the relative healthiness of snack foods defined by kcal per pack. While this does not encompass the full picture with regard to healthier diets, reducing the energy consumed from discretionary foods like snacks – which tend to have limited nutritional value [ 41 ] – is a relevant public health target, given adults on average consume 200 kcal per day over their recommended energy intake in the UK [ 42 ].

Healthier snack foods: 100 kcal or less per pack. The 100 kcal limit was chosen based on Change4Life’s recommendation of a 400 kcal per day allowance for snacks and drinks [ 43 ], dividing this into a 100 kcal allowance for two snacks and two drinks.

Less healthy snack foods: 200 kcal or more per pack. This would then mean that consuming two of these snacks daily would exceed Change4Life’s recommended allowance, without considering drinks.

Piloting of healthy and less healthy food choices

A pilot survey was conducted to choose the snack foods to use in the main study. One hundred UK adults were recruited by the same market research company, with equal quotas by the same three occupational groups. Participants were presented with pictures of food items (front-of-pack only), and asked to rate these on familiarity, appeal, serving size and healthiness.

This pilot work identified a selection of six healthier and six less healthy snack foods whereby:

Healthier items all had higher mean perceived healthiness scores than any of the less healthy items;

Healthier and less healthy foods were matched in terms of perceived familiarity;

All packages were perceived as single-serve.

The six healthier options were Alpen Light Chocolate and Fudge bar (19 g), Special K Red Berry Cereal bar (21.5 g), Nakd Banana Bread bar (30 g), Walkers Pops Original (19 g), Sunbites Lightly Salted Popcorn (20 g) and Kettle Bites Maple Barbeque Waves (22 g). The six less healthy options were: Reese’s Snack Mix (56 g), Dairy Milk Big Taste Toffee Whole Nut bar (43 g), Niknaks Nice ‘N’ Spicy (50 g), Kettle Chips Crispy Bacon and Maple Syrup (40 g), Lindt Lindor Milk Chocolate Orange bar (38 g), Walkers Max Paprika (50 g).

Food items were not matched on appeal, given that food appeal may vary between healthier and less healthy foods, and may mediate some of the pathway between socioeconomic status and food choice.

• Socioeconomic status

This was assessed via four indicators: (1) occupational group; (2) highest educational qualification, (3) total annual household income, and (4) Index of Multiple Deprivation scores.

Participants’ occupational group was provided by the market research company. In addition, participants were asked to indicate their highest educational qualification and total annual household income (see Table  1 for the categorisations used). Index of Multiple Deprivation scores were derived from participants’ postcodes (using adjusted indices to account for participants being from different parts of the UK [ 44 ]).

Food appeal

Participants were presented with pictures of snack foods, including those used in the food choice task, and rated “How enjoyable is eating this food?” using a seven-point scale from Unenjoyable – Enjoyable (e.g. [ 45 ]). The order in which pictures were presented was randomised.

Response inhibition

The Short-form UPPS-P Impulsive Behavior Scale (SUPPS-P [ 46 ]) was used as a trait measure of impulsivity. The order in which items were presented was randomised.

Members of the market research panel were sent a link to the study website, where the study was described as investigating the appeal of snack food. After consenting to the study, they rated pictures of snack foods for enjoyability (food appeal) and completed the SUPPS-P (response inhibition). The quality control question was embedded within the picture rating section; participants answering this incorrectly were screened out of the survey. Following this, participants were randomised to one of six groups (two cognitive load conditions x three availability conditions). All participants were asked to memorise a 7-digit number (either a complex string for high cognitive load or a simple string for low cognitive load). Participants needed to press two keys (‘Q’ and ‘P’) simultaneously to reveal the number (to discourage cheating on this task, given it was conducted online), which was displayed for 10 s (using Inquisit Web). Participants were then shown an array of food items on screen, and asked to select the item that they would most like to eat right now. The image for each item displayed the front-of-pack only. Each participant saw a single array, from which they were able to select one item. The number of healthier and less healthy foods in the array differed depending on their assigned availability condition. The food items offered to each participant were selected at random from the pool of healthier and less healthy items, and their positions in the array were also randomly determined (operationalised in Inquisit by setting the selection mode to random). Following the food choice, participants were asked to recall the 7-digit number that they had memorised. Finally participants completed a set of demographic questions, including socioeconomic status measures, and hunger ratings (using a 7-pt rating scale from Very hungry – Very full).

Changes from study pre-registration

Two changes were made after pre-registration of this study ( https://osf.io/nxt4s/ ):

Firstly, the sample size was reduced, as outlined above, due to problems with recruitment

Secondly, due to concerns about the length of the survey, the planned implicit measures of food appeal and response inhibition were moved to a secondary study session, and as such are not reported here. We used the explicit measures of food appeal and response inhibition described here to explore our hypotheses with regard to these variables.

Hypothesis 1 ( Increasing the number of less healthy food items has a larger effect than increasing the number of healthier food items ):

This was analysed via logistic regression (using Stata SE version 12.1) predicting choice of a healthier food option, with dummy variables indicating the availability and cognitive load conditions as the key predictors. For availability, the two healthier & two less healthy choices condition was the reference group, with two dummy variables for the other availability conditions indicating (1) increase in healthier options and (2) increase in less healthy options. For cognitive load, a dummy variable indicating high load was used. Control variables included socioeconomic status, gender, age and hunger.

Hypotheses 2a & 2b: Cognitive load ( Participants under high cognitive load will not be significantly more or significantly less likely to choose healthier foods after seeing a greater number of healthier food options than those under low cognitive load; Participants under high cognitive load are more likely to choose less healthy foods after seeing a greater number of less healthy food options than those under low cognitive load ):

Interactions between availability condition and cognitive load were added to the model used for hypothesis 1. That is, dummy variables indicating (1) high_cognitive_load* increase_in_healthier_options; (2) high_cognitive_load* increase_in_less_healthy_options.

Hypotheses 3a & 3b: Socioeconomic status ( Participants with higher socioeconomic status are more likely to choose healthier foods after seeing a greater number of healthier food options than those with lower socioeconomic status; Participants with higher socioeconomic status are less likely to choose less healthy foods after seeing a greater number of less healthy food options than those with lower socioeconomic status ):

Interactions between availability condition and socioeconomic status (separately for each of the four indicators) were added to the model used for hypothesis 1. Socioeconomic patterning was examined for each different measure, given that these indices are thought to be conceptually distinct, and have different pathways of influence. Each socioeconomic indicator was modelled as a set of dummy variables, using the categorisations shown in Table 1 .

For example, for the occupational group socioeconomic status indicator, with occupational group A&B as the reference, these interactions were dummy variables indicating (1) occupational_group_C1&C2* increase_in_healthier_options; (2) occupational_group_D&E* increase_in_healthier_options; (3) occupational_group_C1&C2* increase_in_less_healthy_options; (4) occupational_group_D&E* increase_in_less_healthy_options.

Hypothesis 4 ( Response inhibition and food appeal both partially mediate the impact of socioeconomic status on food choice ):

If the analyses in (1) and (3) suggested a relationship between socioeconomic status and food choice, separate mediation analyses were planned to investigate the extent to which (a) response inhibition variables and (b) food appeal variables mediate any relationship between socioeconomic status (each indicator separately) and food choice.

For our primary hypothesis (hypothesis 1), we used p  < 0.05 (two-tailed) to infer if there was a statistically significant effect. For the remaining analyses (secondary hypotheses regarding interactions and mediators), we used a p -value < 0.0027 (two-tailed), using a Bonferroni adjustment to account for the different hypotheses tested and analyses by different SES indicators ( p  = 0.05/18).

Table 1 shows the study group allocation and characteristics of the 1509 study participants. Their mean age was 49.6 (s.d. 15.4; range 18–92), and 46.6% identified as female (the remainder identifying as male).

Figure  1 shows the percentage of participants choosing a healthier option, broken down by cognitive load condition (see Additional file  1 : Table S1 for numbers in each group). The pattern of results here suggests a strong effect of availability (55% choosing a healthier item with increased healthier options vs. 38% with equal options vs. 12% with increased less healthy options), with limited impact apparent by cognitive load condition.

Percentage choosing healthier option by cognitive load condition

Hypothesis 1

Figure  2 presents the results of logistic regressions used to test the impact of availability for healthier vs. less healthy food options (Hypothesis 1; see Additional file 1 : Table S2 for full results). The odds of choosing a healthier option are twice as high (OR: 2.0, 95%CI: 1.6, 2.6) when offered six healthier options and two less healthy, than when offered two healthier and two less healthy options. The odds of choosing a less healthy option (i.e. reversing the outcome measure to obtain comparable odds ratios) are four times higher (OR: 4.3, 95%CI: 3.1, 6.0) when offered two healthier options and six less healthy, than when offered two healthier and two less healthy options. Comparing these two odds ratios (using Stata’s ‘contrast’ command), the odds of making a less healthy choice after seeing an increased number of less healthy options are 2.16 times higher (95%CI: 1.8, 2.5) than the odds of a healthier choice after seeing an increased number of healthier options.

Effects of increasing healthier vs. less healthy options on food choice: Odds ratios (and 95% CIs) of making a healthier (less healthy) choice with increased numbers of healthier (less healthy) options, relative to having equal numbers of healthier and less healthy options

Hypothesis 2

No significant main effects of cognitive load, or interactions between cognitive load and availability condition, were shown in analyses of food choice (see Additional file 1 : Table S3).

Hypothesis 3

Figure  3 shows the percentage choosing healthier options by occupational group. Regression analyses found no significant differences in choosing a healthier option across socioeconomic status using any of the four measures examined. Similarly, no interactions between any measure of socioeconomic status and availability condition were shown in analyses (see Additional file 1 : Tables S4a-d).

Percentage choosing healthier options by occupational group 1 . 1 A&B: Higher and intermediate managerial, administrative and professional occupations; C1&C2: Supervisory, clerical and junior managerial, administrative and professional occupations; D&E: Semi-skilled and unskilled manual occupations

Hypothesis 4

Given the expected socioeconomic patterning in food choice was not found, the planned mediation analysis for hypothesis 4 was not applicable. Exploratory regression analyses (using p -value < 0.002, to adjust for the additional comparisons) instead examined the two halves of the pathway in the proposed mediation, i.e. (1) whether socioeconomic status predicted (i) food appeal and (ii) response inhibition; and (2) whether (i) food appeal and (ii) response inhibition predicted food choice.

Analyses showed no significant differences by any measure of socioeconomic status for either food appeal or response inhibition (see Additional file 1 : Table S5).

Both enjoyment ratings (food appeal) but not SUPPS-P scores (response inhibition) predicted food choice (see Additional file 1 : Table S6), with higher odds of participants choosing a healthier option if they had less liking for less healthy snacks (OR: 0.41, 95%CI: 0.35, 0.49) or greater liking for healthier snacks (OR: 2.17, 95%CI: 1.84, 2.57)).

The results of this study suggest that altering the availability of less healthy food may have more impact on the healthiness of food choices than altering the availability of healthier food, supporting Hypothesis 1. Indeed, the odds of making a less healthy choice after seeing an increased number of less healthy options were twice as high as the odds of a healthier choice after seeing an increased number of healthier options. This is one of the first experimental studies to explore the relative effectiveness of healthier vs. less healthy food cues at influencing behaviour.

These results tie in with previous observational research looking at both food availability and price [ 15 , 16 , 17 ], which suggested people may be more responsive to cues encouraging less healthy food choices. This may in part reflect differential appeal of healthier and less healthy items (less healthy items were rated as more enjoyable to eat in the current study), with people perhaps being more responsive to cues for foods they find more appealing. That said, in the exploratory analyses the effects of availability did not change when enjoyment ratings for healthier and less healthy snack foods were included in models. While previous experimental studies examining the effect of proximity of healthier and less healthy foods have not suggested differential responsiveness [ 18 , 19 ], the current study set-up involves both a wider range of food items and an explicit choice (rather than being able to select both), which may allow an effect of food healthiness to be more readily observed.

The study also explored additional hypotheses relating to the potential for cognitive load or socioeconomic status to modify the impact of these different food cues. However, the results suggested no significant main effects on food choice, or interactions with availability condition for either cognitive load or socioeconomic status. As such, the other hypothesised relationships were not supported by these analyses.

In terms of cognitive load, the two main explanations for the effects found here are that the manipulation we used was not effective, or that it was effective but the choices made were not affected by cognitive load. It cannot be discounted that the manipulation may not have impacted on cognitive load as strongly as expected, as it was not possible to include a manipulation check due to concerns about survey length. Given that Shiv and Fedorikhin found that the effect of cognitive load in their study was only apparent when actual foods were presented [ 33 ], repeating this element of the study with choices between physically-present foods would be valuable. Nevertheless, the lack of effect may indicate that cognitive load did not influence people’s choice of food, as has previously been demonstrated in studies of food proximity [ 47 ]. This could suggest that altering the availability of healthier and less healthy food impacts behaviour without requiring cognitive resource, such as response inhibition, as has been hypothesised for interventions targeting physical micro-environments [ 3 , 9 ]. If so, then this could mean that this intervention is likely to be effective regardless of people’s current cognitive resources, which would be promising in terms of the potential for such interventions to change behaviour across socioeconomic groups.

While socioeconomic patterning in diet is well documented [ 48 , 49 , 50 ], this does vary by food type [ 51 ]. The results here suggest that the snack foods used in the current study may represent a set of food for which appeal and choice does not differ across socioeconomic groups. As such, if there is any differential response to certain types of food cue by socioeconomic status, driven in part by differential response inhibition or food appeal, this would not be picked up in the current study. On the other hand, if the lack of social patterning seen here in responses to the intervention does prove consistent across food types, this would suggest that interventions targeting food availability would be unlikely to widen health inequalities.

In terms of the potential for response inhibition and food appeal to mediate differences in food choice by socioeconomic group, exploratory analyses suggested that food appeal (but not response inhibition) predicted food choice in the current investigation. While these results support food appeal as a potential driver of diet-related behaviour, the lack of effect of response inhibition is in contrast to those seen in previous studies [ 26 , 27 , 28 , 29 ]. However, given that the SUPPS-P is a trait-level measure, this may reflect that while measures such as the SUPPS-P might predict aggregated food choices over time, they may not be discriminatory for a one-off task.

Strengths and limitations

This study offers a novel test of the relative impact of increasing healthier vs. less healthy food cues, matching healthier and less healthy food items on familiarity and controlling for the number of each. The study was conducted using a large sample, broadly representative of the UK in terms of age and gender, and with quotas ensuring equal representation by occupational status. It should be noted, however, that this did not equate to the sample being representative across all socioeconomic indicators, with the sample being more highly educated than the UK as a whole. Nonetheless, this study provides some of the most robust evidence to date that there may be a stronger impact of reducing less healthy food cues than increasing – by an equivalent number – healthier food cues.

However, several limitations to the study should be noted. Firstly, as this was an online study, the food choice task did not include selection with physically present foods or consumption. This can be addressed in subsequent studies using a food choice task in which participants receive the food item in question. Secondly, it was not possible to include a manipulation check for the cognitive load manipulation due to concerns over study length. While other studies have used a similar task to manipulate cognitive load [ 33 , 34 , 35 ], uncertainty remains in the current context. Thirdly, while focusing on only a small number of food items was necessary for this online food choice task, this limited the potential to examine socioeconomic patterning, seen when looking across diets. Indeed, investigating a wider range of foods, including items that have a healthier nutritional profile overall – rather than focusing only on lower energy items – would be a valuable extension to this study. Finally, including implicit (state rather than trait) measures of food appeal and response inhibition concurrent with the food choice task would strengthen testing of these potential pathways, in particular, for response inhibition.

Implications for research and policy

These results require replication, in particular, in real world settings used by those who are more and less socially deprived, and altering availability in additional ways such as changing the number but not the range of options. If replicated, the greater impact of less healthy food cues compared to healthier food cues would prioritise removing less healthy cues over adding healthier cues in policies for healthier eating. Further research could also explore the potential for differential effects by socioeconomic status through examining a broader range of foods or food types for which consumption is known to be socially patterned. Establishing which cues are most influential on behaviour, and in particular which have the greatest impact on more socially deprived groups, could help in designing more effective public health interventions to reduce both the substantial burden of non-communicable diseases and their contribution to health inequalities.

This study provides a novel test of the relative impact of healthier vs. less healthy food cues on food choice, suggesting that less healthy food cues may be more influential. Further work is required to try to replicate these findings in experiments requiring participants to make choices between physically-present food items, and when using different ways of altering availability, as well as to explore the potential for differential effects by socioeconomic status using other food options. If replicated, the greater impact of less healthy food cues than healthier food cues should prioritise a healthier eating policy focus on reducing less healthy food cues rather than increasing healthier cues.

Abbreviations

Body mass index

Non-communicable diseases

Short-form Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency, Impulsive Behavior Scale

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Acknowledgements

We would like to thank Mark Pilling for statistical advice.

RP is supported by a Wellcome Research Fellowship in Society and Ethics [106679/Z/14/Z] and the Behaviour and Health Research Unit is funded by the Department of Health Policy Research Programme (Policy Research Unit in Behaviour and Health [PR-UN-0409-10109]). The funders had no role in the design of the study, in collection, analysis, and interpretation of data, or in writing the manuscript.

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The dataset generated during the current study is available in the Open Science Framework https://osf.io/54rfg/ (data dictionary: https://osf.io/hw3gc/ ).

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RP designed the study, collected data, performed the statistical analysis, and drafted the manuscript. TMM participated in study design and critical revision of the manuscript. Both authors have read and approved the final manuscript.

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Additional file

Additional file 1:.

Table S1. Percentage (n) choosing healthier option. Table S2. Results of logistic regression predicting healthier food choice (less healthy food choice for alternative outcome row) from availability conditions, controlling for age, gender, hunger, social class. Table S3. Results of logistic regression predicting healthier food choice from availability conditions, with interactions by cognitive load. Tables S4a-d. Results of logistic regressions predicting healthier food choice from availability conditions, with interactions by socioeconomic status. Table S5. Regression coefficients predicting (i) enjoyment of healthier snack options, (ii) enjoyment of less healthy snack options and (iii) SUPPS-P total scores from socioeconomic status, controlling for availability condition, cognitive load condition, gender, age and hunger. Table S6. Results of logistic regression predicting healthier food choice from availability conditions, with enjoyment ratings and impulsivity (SUPPS-P) as predictors. (DOCX 42 kb)

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Pechey, R., Marteau, T.M. Availability of healthier vs. less healthy food and food choice: an online experiment. BMC Public Health 18 , 1296 (2018). https://doi.org/10.1186/s12889-018-6112-3

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Perceiving food as tasty is important. It’s not good enough simply to tell people what is healthy if they don’t think those foods are also tasty. Suzanna Forwood

The solution to the obesity epidemic is simple: eat less, move more. But take a deep breath before you type these four words into a search engine. The results exceed 9 million. Of the top four results, two websites argue against the statement and two for it. Below, arguments about eating and exercise rage fast and furious with dozens of assertions backed by equations, flowcharts, promises of slimming success, and lists of the latest superfoods.

“Despite all we know about food, we know remarkably little about the process of food choice,” says Dr Suzanna Forwood, until recently Research Associate at the Behaviour and Health Research Unit (Cambridge University) and now Lecturer in Psychology at Anglia Ruskin University. “In a supermarket we’re bombarded with the thousands of products on the shelves and but most of the time we happily make relatively quick decisions about what to buy. So what’s going on in our minds when we reach out for our favourite breakfast cereal?”

When it comes to eating, we’re all experts. We’re secure in our own opinions (and prejudices) and have no shortage of advice for everyone else. The truth is that, in common with many human activities, our relationship with food is complex and deeply embedded in culture. Forwood says: “Whenever I give a talk, even to an academic audience, people will listen to me talk about the big picture and then come up to me afterwards to tell me about their personal experiences – typically what they spotted in other people’s trolleys the day before.”

We might broadly agree that eating less (and better) and moving more, a message endorsed by the NHS, makes sense – but do we act accordingly?  We don’t. Finding out exactly what people eat is hard, finding out why they make those choices is harder – and changing those eating patterns is harder still. “Most of the data we have – and we have lots of it – is observational rather than experimental,” says Forwood. “There have been relatively few experiments looking at food choice – and those that have been carried out tend to have a low number of participants.”

In the late 1980s government began to realise that it was facing an obesity epidemic on a scale that demanded intervention. Levels of obesity in the UK have tripled since 1980: almost 25% of the adult population is now obese with the UK topping the tables for Western Europe. These worrying figures led to nationwide initiatives to promote healthy living – and increased efforts to understand food choice behaviour.

Research has shown that obesity is linked to deprivation and low levels of education – as well as to a whole range of life-threatening conditions. Top of the list of ‘avoidable diseases’ associated with obesity is type 2 diabetes (treatment of type 2 diabetes costs the NHS an estimated £8.8 bn each year), followed by cancer, high blood pressure and heart disease. “In the past, weight status has long been regarded as a matter of personal choice,” says Forwood. “And this is reflected by the government’s desire for non-regulatory interventions.” The preference for a light touch approach is exemplified by the establishment of the so-called Nudge Unit (Behavioural Insight Team).

In 2009 the government launched its Change4Life campaign as a ‘movement’ to improve the nation’s health. Change4Life’s online advice for adults makes a series of suggestions for ‘swaps’ and ‘nudges’. Swap a large plate for a smaller one, swap fast eating for slow eating, and swap food high in fat or sugar for healthy fruit and vegetables. Look closely at labelling and make healthy choices based on a comparison of calories and nutritional information.

The current focus is on reducing intake of sugar – not the sugar that occurs naturally in fruit, or even the sugar we sprinkle on our cereal, but the hidden sugar that sweetens so many processed foods and flavours so many popular drinks. In the case of sugar, what is proposed is a financial nudge in the form of a ‘sugar tax’. “Taxes have been shown to be effective but they have to be carefully designed,” says Forwood. “Sugar taxes, for example, need to avoid raising the price of fruit juices which are high in sugar.”

Do other strands of swaps and nudges work? Research suggests that people are remarkably resilient in their food choices. Taste emerges as the most important factor. Forwood’s work shows that healthy foods (such as fruit and vegetables) are not perceived as tasty, particularly by groups who are reluctant to choose healthy foods. She says: “That might seem tautological but there is strong observational data to suggest that perceiving food as tasty is important. It’s not good enough simply to tell people what is healthy if they don’t think those foods are also tasty.”

The perception of healthy foods as less tasty than unhealthy foods prompts the question: could product labelling, promoting the tastiness of healthy foods, nudge consumers into making ‘better’ choices when they’re shopping. In research published last year, Forwood and colleagues looked at the ‘nudging power’ of labelling to increase the percentage of people who might say ‘no’ to a chocolate bar and ‘yes’ to an apple as part of a notional meal deal.

In the online study, around half of a representative sample of people expressed a preference for an apple when given the choice of apple or chocolate bar. Participants were divided into five groups and given the same choice (apple or chocolate bar) with the apple labelled in five different ways: ‘apple’, ‘healthy apple’, ‘succulent apple’, ‘healthy and succulent apple’, ‘succulent and healthy apple’. Labels combining both health and taste descriptors significantly increased the rate of apple selection – to 65.9% in the case of ‘healthy and succulent’ and 62.4% for ‘succulent and healthy’.

Another study , also published last year, looked at the potential for food swaps – often used as part of social media campaigns – as a means for reducing dietary levels of energy, fat, sugar or salt. Using the model of an online supermarket, built as a testing platform, participants were asked to complete a 12-item shopping task. In the course of the purchasing process, they were offered alternatives with lower energy densities (ED). For each item, lower ED alternatives were offered or imposed, either at the point of selection or at the checkout.

“Our study showed that within-category swaps did not reduce the ED of food purchases. Only a minority of swaps were accepted by the consumer and the notional benefits to swaps were slight. It was striking that more than 47% of the participants offered alternatives did not accept any of the swaps they were offered,” says Forwood. “Female participants and better-off participants were more likely to accept swaps. This was predictable in that these are the people who we know from other research typically make healthier choices anyway.”

It has been argued that omnipresence of food imagery in the modern built environment, and via all kinds of media, contributes to rising rates of obesity with adverts for less healthier foods identified as a driver for consumption of such foods. A study in Australia showed that people who watched commercial television channels (which carry advertising for fast foods) were, perhaps not surprisingly, more likely to purchase TV dinners .

“What we’re talking about here is, of course, observational data,” says Forwood, “It may, for example, be that people who consume TV dinners are more attracted to certain television programmes that are on commercial channels. Remember that huge sums of money are spent targeting TV adverts in order to make sure that the right population sees them. But this raises the question: could advertising represent an opportunity for policy makers looking to promote consumption of healthier choices?” 

‘Priming’ is described as a psychological effect in which exposure to a stimulus – such as advertising – modifies behaviour. When Forwood and colleagues    tested the effectiveness of priming by asking volunteers to look at an advertisement for healthy food (such as fruit) and then choose between healthy and unhealthy, they found that the priming had little difference. The observations were different, however, when the participants were hungry, in which case the preference for the energy dense foods rose. However, when the hungry volunteers were shown an advertisement for fruit in advance of their choice, the ‘hungry factor’ was offset by the priming.

The initial experiment was carried out in Cambridge where the participants were predominantly female, well-educated and older – and likely to be in favour of healthy eating.  When the experiment was carried out with a more nationally representative sample, the results showed that priming was ineffective in socially disadvantaged groups. “These people are hard to reach and represent a real challenge to policy-makers,” says Forwood. “Research tells us that 89% of people want to make dietary changes to improve their health. We need to identify the levers that can support them.”

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Frontiers for Young Minds

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The Impacts of Junk Food on Health

Energy-dense, nutrient-poor foods, otherwise known as junk foods, have never been more accessible and available. Young people are bombarded with unhealthy junk-food choices daily, and this can lead to life-long dietary habits that are difficult to undo. In this article, we explore the scientific evidence behind both the short-term and long-term impacts of junk food consumption on our health.

Introduction

The world is currently facing an obesity epidemic, which puts people at risk for chronic diseases like heart disease and diabetes. Junk food can contribute to obesity and yet it is becoming a part of our everyday lives because of our fast-paced lifestyles. Life can be jam-packed when you are juggling school, sport, and hanging with friends and family! Junk food companies make food convenient, tasty, and affordable, so it has largely replaced preparing and eating healthy homemade meals. Junk foods include foods like burgers, fried chicken, and pizza from fast-food restaurants, as well as packaged foods like chips, biscuits, and ice-cream, sugar-sweetened beverages like soda, fatty meats like bacon, sugary cereals, and frozen ready meals like lasagne. These are typically highly processed foods , meaning several steps were involved in making the food, with a focus on making them tasty and thus easy to overeat. Unfortunately, junk foods provide lots of calories and energy, but little of the vital nutrients our bodies need to grow and be healthy, like proteins, vitamins, minerals, and fiber. Australian teenagers aged 14–18 years get more than 40% of their daily energy from these types of foods, which is concerning [ 1 ]. Junk foods are also known as discretionary foods , which means they are “not needed to meet nutrient requirements and do not belong to the five food groups” [ 2 ]. According to the dietary guidelines of Australian and many other countries, these five food groups are grains and cereals, vegetables and legumes, fruits, dairy and dairy alternatives, and meat and meat alternatives.

Young people are often the targets of sneaky advertising tactics by junk food companies, which show our heroes and icons promoting junk foods. In Australia, cricket, one of our favorite sports, is sponsored by a big fast-food brand. Elite athletes like cricket players are not fuelling their bodies with fried chicken, burgers, and fries! A study showed that adolescents aged 12–17 years view over 14.4 million food advertisements in a single year on popular websites, with cakes, cookies, and ice cream being the most frequently advertised products [ 3 ]. Another study examining YouTube videos popular amongst children reported that 38% of all ads involved a food or beverage and 56% of those food ads were for junk foods [ 4 ].

What Happens to Our Bodies Shortly After We Eat Junk Foods?

Food is made up of three major nutrients: carbohydrates, proteins, and fats. There are also vitamins and minerals in food that support good health, growth, and development. Getting the proper nutrition is very important during our teenage years. However, when we eat junk foods, we are consuming high amounts of carbohydrates, proteins, and fats, which are quickly absorbed by the body.

Let us take the example of eating a hamburger. A burger typically contains carbohydrates from the bun, proteins and fats from the beef patty, and fats from the cheese and sauce. On average, a burger from a fast-food chain contains 36–40% of your daily energy needs and this does not account for any chips or drinks consumed with it ( Figure 1 ). This is a large amount of food for the body to digest—not good if you are about to hit the cricket pitch!

• Figure 1 - The nutritional composition of a popular burger from a famous fast-food restaurant, detailing the average quantity per serving and per 100 g.
• The carbohydrates of a burger are mainly from the bun, while the protein comes from the beef patty. Large amounts of fat come from the cheese and sauce. Based on the Australian dietary guidelines, just one burger can be 36% of the recommended daily energy intake for teenage boys aged 12–15 years and 40% of the recommendations for teenage girls 12–15 years.

A few hours to a few days after eating rich, heavy foods such as a burger, unpleasant symptoms like tiredness, poor sleep, and even hunger can result ( Figure 2 ). Rather than providing an energy boost, junk foods can lead to a lack of energy. For a short time, sugar (a type of carbohydrate) makes people feel energized, happy, and upbeat as it is used by the body for energy. However, refined sugar , which is the type of sugar commonly found in junk foods, leads to a quick drop in blood sugar levels because it is digested quickly by the body. This can lead tiredness and cravings [ 5 ].

• Figure 2 - The short- and long-term impacts of junk food consumption.
• In the short-term, junk foods can make you feel tired, bloated, and unable to concentrate. Long-term, junk foods can lead to tooth decay and poor bowel habits. Junk foods can also lead to obesity and associated diseases such as heart disease. When junk foods are regularly consumed over long periods of time, the damages and complications to health are increasingly costly.

Fiber is a good carbohydrate commonly found in vegetables, fruits, barley, legumes, nuts, and seeds—foods from the five food groups. Fiber not only keeps the digestive system healthy, but also slows the stomach’s emptying process, keeping us feeling full for longer. Junk foods tend to lack fiber, so when we eat them, we notice decreasing energy and increasing hunger sooner.

Foods such as walnuts, berries, tuna, and green veggies can boost concentration levels. This is particularly important for young minds who are doing lots of schoolwork. These foods are what most elite athletes are eating! On the other hand, eating junk foods can lead to poor concentration. Eating junk foods can lead to swelling in the part of the brain that has a major role in memory. A study performed in humans showed that eating an unhealthy breakfast high in fat and sugar for 4 days in a row caused disruptions to the learning and memory parts of the brain [ 6 ].

Long-Term Impacts of Junk Foods

If we eat mostly junk foods over many weeks, months, or years, there can be several long-term impacts on health ( Figure 2 ). For example, high saturated fat intake is strongly linked with high levels of bad cholesterol in the blood, which can be a sign of heart disease. Respected research studies found that young people who eat only small amounts of saturated fat have lower total cholesterol levels [ 7 ].

Frequent consumption of junk foods can also increase the risk of diseases such as hypertension and stroke. Hypertension is also known as high blood pressure and a stroke is damage to the brain from reduced blood supply, which prevents the brain from receiving the oxygen and nutrients it needs to survive. Hypertension and stroke can occur because of the high amounts of cholesterol and salt in junk foods.

Furthermore, junk foods can trigger the “happy hormone,” dopamine , to be released in the brain, making us feel good when we eat these foods. This can lead us to wanting more junk food to get that same happy feeling again [ 8 ]. Other long-term effects of eating too much junk food include tooth decay and constipation. Soft drinks, for instance, can cause tooth decay due to high amounts of sugar and acid that can wear down the protective tooth enamel. Junk foods are typically low in fiber too, which has negative consequences for gut health in the long term. Fiber forms the bulk of our poop and without it, it can be hard to poop!

Tips for Being Healthy

One way to figure out whether a food is a junk food is to think about how processed it is. When we think of foods in their whole and original forms, like a fresh tomato, a grain of rice, or milk squeezed from a cow, we can then start to imagine how many steps are involved to transform that whole food into something that is ready-to-eat, tasty, convenient, and has a long shelf life.

For teenagers 13–14 years old, the recommended daily energy intake is 8,200–9,900 kJ/day or 1,960 kcal-2,370 kcal/day for boys and 7,400–8,200 kJ/day or 1,770–1,960 kcal for girls, according to the Australian dietary guidelines. Of course, the more physically active you are, the higher your energy needs. Remember that junk foods are okay to eat occasionally, but they should not make up more than 10% of your daily energy intake. In a day, this may be a simple treat such as a small muffin or a few squares of chocolate. On a weekly basis, this might mean no more than two fast-food meals per week. The remaining 90% of food eaten should be from the five food groups.

In conclusion, we know that junk foods are tasty, affordable, and convenient. This makes it hard to limit the amount of junk food we eat. However, if junk foods become a staple of our diets, there can be negative impacts on our health. We should aim for high-fiber foods such as whole grains, vegetables, and fruits; meals that have moderate amounts of sugar and salt; and calcium-rich and iron-rich foods. Healthy foods help to build strong bodies and brains. Limiting junk food intake can happen on an individual level, based on our food choices, or through government policies and health-promotion strategies. We need governments to stop junk food companies from advertising to young people, and we need their help to replace junk food restaurants with more healthy options. Researchers can focus on education and health promotion around healthy food options and can work with young people to develop solutions. If we all work together, we can help young people across the world to make food choices that will improve their short and long-term health.

Obesity : ↑ A disorder where too much body fat increases the risk of health problems.

Processed Food : ↑ A raw agricultural food that has undergone processes to be washed, ground, cleaned and/or cooked further.

Discretionary Food : ↑ Foods and drinks not necessary to provide the nutrients the body needs but that may add variety to a person’s diet (according to the Australian dietary guidelines).

Refined Sugar : ↑ Sugar that has been processed from raw sources such as sugar cane, sugar beets or corn.

Saturated Fat : ↑ A type of fat commonly eaten from animal sources such as beef, chicken and pork, which typically promotes the production of “bad” cholesterol in the body.

Dopamine : ↑ A hormone that is released when the brain is expecting a reward and is associated with activities that generate pleasure, such as eating or shopping.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

[1] ↑ Australian Bureau of Statistics. 2013. 4324.0.55.002 - Microdata: Australian Health Survey: Nutrition and Physical Activity, 2011-12 . Australian Bureau of Statistics. Available online at: http://bit.ly/2jkRRZO (accessed December 13, 2019).

[2] ↑ National Health and Medical Research Council. 2013. Australian Dietary Guidelines Summary . Canberra, ACT: National Health and Medical Research Council.

[3] ↑ Potvin Kent, M., and Pauzé, E. 2018. The frequency and healthfulness of food and beverages advertised on adolescents’ preferred web sites in Canada. J. Adolesc. Health. 63:102–7. doi: 10.1016/j.jadohealth.2018.01.007

[4] ↑ Tan, L., Ng, S. H., Omar, A., and Karupaiah, T. 2018. What’s on YouTube? A case study on food and beverage advertising in videos targeted at children on social media. Child Obes. 14:280–90. doi: 10.1089/chi.2018.0037

[5] ↑ Gómez-Pinilla, F. 2008. Brain foods: the effects of nutrients on brain function. Nat. Rev. Neurosci. 9, 568–78. doi: 10.1038/nrn2421

[6] ↑ Attuquayefio, T., Stevenson, R. J., Oaten, M. J., and Francis, H. M. 2017. A four-day western-style dietary intervention causes reductions in hippocampal-dependent learning and memory and interoceptive sensitivity. PLoS ONE . 12:e0172645. doi: 10.1371/journal.pone.0172645

[7] ↑ Te Morenga, L., and Montez, J. 2017. Health effects of saturated and trans-fatty acid intake in children and adolescents: systematic review and meta-analysis. PLoS ONE. 12:e0186672. doi: 10.1371/journal.pone.0186672

[8] ↑ Reichelt, A. C. 2016. Adolescent maturational transitions in the prefrontal cortex and dopamine signaling as a risk factor for the development of obesity and high fat/high sugar diet induced cognitive deficits. Front. Behav. Neurosci. 10. doi: 10.3389/fnbeh.2016.00189

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Home / Nutrition & Fitness / Mayo Clinic Minute: The relationship between food and disease

Mayo Clinic Minute: The relationship between food and disease

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The phrase “you are what you eat” is commonly used in conversations about health and the connection between food and the body. Eating an unhealthy diet can have serious consequences and can increase someone’s risk of dying from heart disease, stroke and Type 2 diabetes.

In this Mayo Clinic Minute, Dr. Stephen Kopecky, a preventive cardiologist at Mayo Clinic, discusses the relationship between food and disease.

Things like smoking and genetics put us at risk for developing different diseases, but neither are the biggest risk factor.

“Nutrition is now the No. 1 cause of early death, and early disease in our country and the world,” says Dr. Kopecky.

Dr. Kopecky says having genes for disease will increase your risk by 30% to 40%, but having a bad lifestyle for disease will increase your risk by 300% to 400%.

“About 57% of the calories we consume every day in this country are ultraprocessed foods,” says Dr. Kopecky.

While ultraprocessed foods tend to be convenient and cost-effective, they are inflammatory and can cause a host of health issues over time.

“It bothers our tissues. It bothers our heart. It bothers our arteries, our brains, our pancreas, our liver and our lungs. And that leads to disease,” says Dr. Kopecky. “It could be in the brain with Alzheimer’s, the heart with coronary artery disease, or cancers elsewhere.”

The good news is it’s never too late to change your eating habits, and no change is too small.

“It’s been shown if you take one bite of say a processed meat or ultraprocessed food, replace that with some unprocessed food or a healthier choice ― you know vegetables and black beans ― after a year or two, that will actually lower your risk of heart attack and stroke.”

Of the four levels of food processing, the most processed are termed ultraprocessed foods. These foods have many added ingredients, such as sugar; salt; fat; and artificial colors, preservatives or stabilizers. The ingredient list sometimes has words that sound like chemicals. Examples are obvious foods like soft drinks, hot dogs, cold cuts, fast food, packaged snacks and cookies, but can also include canned baked beans, low-fat fruit yogurt, packaged bread, ready-made pasta sauces and breakfast cereals.

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Perceived Availability of Healthy and Unhealthy Foods in the Community, Work, and Higher Education Settings across Five Countries: Findings from the International Food Policy Study 2018

Affiliations.

• 1 Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico.
• 2 National Council for Science and Technology, Mexico City, Mexico.
• 3 École de Nutrition, Centre Nutrition, santé et société (Centre NUTRISS), and Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, Canada.
• 4 Global Obesity Centre (GLOBE), Institute for Health Transformation, Deakin University, Burwood Victoria, Geelong, Australia.
• 5 Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
• 6 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
• 7 School of Public Health and Sciences, University of Waterloo, Waterloo, Canada.
• PMID: 35544236
• PMCID: PMC9188857
• DOI: 10.1093/jn/nxac070

Background: Food environments play a key role in dietary behavior and vary due to different contexts, regulations, and policies.

Objectives: This study aimed to characterize the perceived availability of healthy and unhealthy foods in 3 different settings in 5 countries.

Methods: We analyzed data from the 2018 International Food Policy Study, a cross-sectional survey of adults (18-100 y, n = 22,824) from Australia, Canada, Mexico, the United Kingdom (UK), and the USA. Perceived availability of unhealthy (junk food and sugary drinks) and healthy foods (fruit or vegetables, healthy snacks, and water) in the community, workplace, and university settings were measured (i.e. not available, available for purchase, or available for free). Differences in perceived availability across countries were tested using adjusted multinomial logistic regression models.

Results: Across countries, unhealthy foods were perceived as highly available in all settings; in university and work settings unhealthy foods were perceived as more available than healthy foods. Australia and Canada had the highest perceived availability of unhealthy foods (range 87.5-90.6% between categories), and the UK had the highest perceived availability of fruits and vegetables for purchase (89.3%) in the community. In university and work settings, Mexico had the highest perceived availability for purchase of unhealthy foods (range 69.9-84.9%). The USA and the UK had the highest perceived availability of fruits and vegetables for purchase (65.3-66.3%) or for free (21.2-22.8%) in the university. In the workplace, the UK had high perceived availability of fruits and vegetables for purchase (40.2%) or for free (18.5%), and the USA had the highest perceived availability of junk food for free (17.3%).

Conclusions: Across countries, unhealthy foods were perceived as highly available in all settings. Variability between countries may reflect differences in policies and regulations. Results underscore the need for the continuation and improvement of policy efforts to generate healthier food environments.

Keywords: community; food environment; food policy; perceived availability; university; work environment.

© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.

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“Healthy”/“Unhealthy” Food Brands Influence Health, Calorie, and Price Ratings of Food

• Travis D. Masterson, PhD Travis D. Masterson Correspondence Address for correspondence: Travis D. Masterson, PhD, Department of Epidemiology, Geisel School of Medicine, Dartmouth College, 833 Rubin Bldg, One Medical Center Dr, Lebanon, NH 03756 Contact Affiliations Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH Search for articles by this author
• Caterina Florissi, BA Caterina Florissi Affiliations Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH Search for articles by this author
• Kimberly R. Clark, PhD Kimberly R. Clark Affiliations Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH Search for articles by this author
• Diane Gilbert-Diamond, ScD Diane Gilbert-Diamond Affiliations Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH Search for articles by this author

Conclusions and Implications

• food branding
• health perception
• calorie estimation
• price estimation

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Does healthy eating cost more.

Decisions regarding food choices are based on a variety of factors including cost, taste, convenience, and availability. Many people feel that nutritious foods cost more than foods high in calories and low in important nutrients (Carlson & Frazao, 2012). In an effort to save money, people may select less nutritious foods when shopping resulting in less healthy meals and snacks. This is a problem because diets rich in fruits, vegetables, whole grains, beans, and healthy fats have been found to reduce the risk of chronic diseases such as obesity, diabetes, and heart disease among others. In this article we discuss the cost of healthy eating and offer strategies to make healthy eating more affordable.

Cost of Eating Nutritious Foods

The MyPlateDietary Guidelines for Americans 2015-2020 provide the following recommendations for healthy eating:

• Fill half your plate with a wide variety of fruit and vegetables
• Make half your grains whole grains
• Eat fat free or low fat dairy products
• Eat healthy fats such as a variety of vegetable oils
• Eat lean meats, legumes, nuts, seeds, and soy
• Limit the amount of added sugars, salt, and saturated fats (United States Department of Agriculture, 2015)

Unfortunately, these recommendations are easier said than done. In fact, very few Americans are meeting the Dietary Guidelines for Americans and many argue it is too difficult to eat healthy foods on a limited food budget. The most common reason people report not eating more nutritious foods is the belief that healthy foods cost more than highly processed foods that are typically less nutritious (Carlson & Frazao, 2012). So the questions is, does healthy eating actually cost more?

The answer to that question is complicated. A recent study found that following the MyPlate Dietary Guidelines would cost a family of four between $1,000-$1,200 a month ($12,000.00-$14,400 annually) depending on the age of the family members and the percentage of fruits and vegetables that were fresh, frozen, and canned (Mulik & Haynes-Maslow, 2017). For a comparison, the average middle income family in the United States spends roughly $6,224 on food each year with the average low income family spending even less at roughly $3,862 per year (USDA, 2017). With this information in mind, following these recommendations may not be feasible for the typical family.

Other studies argue that whether healthy foods such as fruits and vegetables cost more at the checkout counter is a matter of how you calculate cost (Drewnowski, 2013, Drewnowski & Rehm, 2013; Calrson & Frazao, 2012). For example, if you look at food costs per calorie, unhealthy food costs less, but if you look at food costs per typical portion, many healthy foods are less expensive than unhealthy foods (Carlson & Frazoa, 2012). Further, if you are looking to improve your health, you get more for your money when you consider cost per nutrient value of your food choices. Sweet potatoes, tomato juices and soups, white potatoes, dark green leafy vegetables, pumpkin, and dry beans provide the most nutrition (i.e., protein, fiber, Vitamins A and C, among other nutrients) for the least cost (Drewnowski et al., 2013). When looking at food cost from this perspective, there is a wide variety of nutritious foods, specifically fruits and vegetables that can be incorporated into a diet at a lower cost.

Cost of Not Eating Nutritious Foods

When discussing the cost of healthy eating, it is important to consider the cost of not incorporating nutritious foods into meals on a regular basis. Unhealthy dietary patterns that consist of high amounts of sugar, saturated fat, sodium, and calories, are linked to higher rates of chronic diseases such as overweight and obesity, heart disease, high blood pressure, and type 2 diabetes, among many others. Not only is the risk of chronic disease greater, but the financial cost of treating the diseases listed are expensive. For example, individuals who are obese have medical costs that are nearly $1,500 more per year on average than normal weight individuals (Finkelstein, Trogdon, Cohen, & Dietz, 2009). As the number of chronic diseases an individual has increases, the annual health care costs for that individual also increases (Cohen, 2015). For example, a person with three to four chronic diseases will spend $25,000 annually on health care expenses while individuals without any chronic diseases will spend $6,000 annually (Cohen, 2015). These statistics indicate that the cost of regularly incorporating nutritious foods into one’s diet is much less expensive than the cost of treating chronic diseases later on.

Potential Policies for Reducing the Cost of Healthy Foods

In order to improve the dietary intake of Americans and reduce the risk of chronic diseases, new approaches focused on changing the cost of food, are being considered. For instance, there have been polices proposed to add a tax to certain snack foods and sugar-sweetened beverages. The theory is, by increasing the cost of less nutritious foods, people would purchase less of it and would instead buy healthier alternatives. Another option is reducing the cost of nutritious foods like fruits and vegetables through government programs. One study found that people ate 25% more fruits and vegetables when their cost had been reduced by 50%  (Thow, Downs, Jan, 2014). Although there are several policies that have been considered to make healthy foods more affordable to Americans, more research needs to be done to determine which policy would result in the biggest impact on individual food choices.

Strategies for Healthy Eating on a Budget

Here are a few tips for making the most out of your grocery budget while shopping for nutritious foods.

• Bell, D., Corsten, D., & Knox, G. (2010). Unplanned buying on shopping trips. Marketing Science Institute Working Paper Series 2010, 10-109. Retrieved from https://www.researchgate.net/profile/George_ Knox/publication/242551690_Unplanned_Buying_on_Shopping_Trips/links/0f317530b47fb1ab38000000.pdf
• Buzby, J., & Hyman, J. (2012). Total and per capita value of food loss in the United States. Food Policy, 37, 561-570. doi.org/10.1016/j.foodpol.2012.06.002
• Carlson, A., & Frazao, E. (2012). Are healthy foods really more expensive? It depends on how you measure the price. Retrieved from https://www.ers.usda.gov/publications/pub-details/?pubid=44679
• Cohen, S. (2015). The concentration and persistence in the level of health expenditures over time: Estimates for the U.S. population, 2012-2013. Retrieved from https://meps.ahrq.gov/data_files/publications/st481/stat481.pdf
• Consumer Reports. (2012). Store-brand vs. name-brand taste-off. Retrieved from https://www.consumerreports.org/cro/magazine/2012/10/store-brand-vs-name-brand-taste-off/index.htm
• Drewnowski, A., & Rehm C. (2013) Vegetable cost metrics show that potatoes and beans provide most nutrients per penny. PLOS ONE, 8(5). doi.org/10.1371/journal.pone.0063277
• Drewnowski, A. (2013) New metrics of affordable nutrition: Which vegetables provide most nutrients for least cost? Journal of Academy of Nutrition and Dietetics, 113(9). doi: 10.1016/j.jand.2013.03.015
• Finkelstein, E., Trogdon, J., Cohen, J., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer- and service-specific estimates. Health Affairs, 28(5), w822-31.doi.org/10.1377/hlthaff.28.5.w822
• Mulik, K. & Haynes-Maslow, L. The affordability of MyPlate: An Analysis of SNAP benefits and the actual cost of eating according to the Dietary Guidelines. Journal of Nutrition Education and Behavior, 49(8), 623-631. doi: 10.1016/j.jneb.2017.06.005.
• Plattner, K., Perez, A., & Thornsbury, S. (2014). Evolving U.S. fruit markets and seasonal grower price patterns, Economic Research Service. Retrieved from http://usda.mannlib.cornell.edu/usda/ers/FTS/2010s /2014/FTS-09-29-2014.pdf
• Thow, M., Downs, S., & Jan, S. (2014). A systematic review of the effectiveness of food taxes and subsidies to improve diets: Understanding the recent evidence. Nutrition Reviews, 72(9),551-565. doi: 10.1111/nure.12123
• Tiwari, A., Aggarwal, A., Tang, W., & Drewnowski, A. (2017). Cooking at home: A strategy to comply with U.S. Dietary Guidelines at no extra cost. American Journal of Preventive Medicine, 52(5),616-624. doi: 10.1016/j.amepre. 2017.01.017
• United States Department of Agriculture. (2015). Dietary Guidelines for Americans 2015-2020 eighth edition. Retrieved from https://health.gov/dietaryguidelines/2015/guidelines/
• United States Department of Agriculture. (2017). Food prices and spending. Retrieved from https://www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/food-prices-and-spending/

Mateja R. Savoie-Roskos PhD, MPH, RD; Mary Ann Jorgensen Dietetics Student; Carrie Durward PhD, RD

Carrie Durward

Nutrition Specialist

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More Evidence Links Ultraprocessed Foods to Dementia

Recent research, including a new study on processed meat, has suggested these foods can affect brain health. Experts are trying to understand why.

By Dana G. Smith and Alice Callahan

People who regularly eat processed red meat, like hot dogs, bacon, sausage, salami and bologna, have a greater risk of developing dementia later in life. That was the conclusion of preliminary research presented this week at the Alzheimer’s Association International Conference.

The study tracked more than 130,000 adults in the United States for up to 43 years. During that period, 11,173 people developed dementia. Those who consumed about two servings of processed red meat per week had a 14 percent greater risk of developing dementia compared to those who ate fewer than three servings per month.

Eating unprocessed red meat, like steak or pork chops, did not significantly increase the risk for dementia, though people who ate it every day were more likely to report that they felt their cognition had declined than those who ate red meat less often. (The results of the study have not yet been published in a journal.)

The vast majority of processed meats are classified as “ ultraprocessed foods ” — products made with ingredients that you wouldn’t find in a home kitchen, like soy protein isolate, high fructose corn syrup, modified starches, flavorings or color additives. Many of these foods also have high levels of sugar, fat or sodium, which have long been known to adversely affect health.

Ultraprocessed foods, which also include items like sodas, flavored yogurts, instant soups and most breakfast cereals, make up a huge part of the American diet. They account for about 58 percent of the calories consumed by both children and adults, on average. In the last decade, researchers have linked these foods to health conditions including heart disease, Type 2 diabetes, obesity and some types of cancer and gastrointestinal diseases.

Now scientists are examining the connection between these foods and brain health.

What does the research suggest?

Several studies published in the past few years have found an association between eating more ultraprocessed foods and cognitive decline. In one study of more than 10,000 middle-aged adults in Brazil , for example, people who consumed 20 percent or more of their daily calories from ultraprocessed foods experienced more rapid cognitive decline, particularly on tests of executive functioning, over the course of eight years.

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Honey added to yogurt supports probiotic cultures for digestive health

by Marianne Stein, College of Agricultural, Consumer and Environmental Sciences at the University of Illinois Urbana-Champaign

If you enjoy a bowl of plain yogurt in the morning, adding a spoonful of honey is a delicious way to sweeten your favorite breakfast food. It also supports the probiotic cultures in the popular fermented dairy product, according to two new studies from the University of Illinois Urbana-Champaign.

"We were interested in the culinary pairing of yogurt and honey , which is common in the Mediterranean diet, and how it impacts the gastrointestinal microbiome," said Hannah Holscher, associate professor in the Department of Food Science and Human Nutrition, part of the College of Agricultural, Consumer and Environmental Sciences at Illinois. She is a co-author of the two studies, which are both published in The Journal of Nutrition .

Greek yogurt and other yogurts contain probiotic strains such as Bifidobacterium animalis in addition to conventional yogurt starter cultures. Consumption of certain probiotics can promote digestive health and regular bowel movements, and it can have a positive effect on mood and cognition.

"The enzymes in our mouth, stomach, and intestines help with digestion and facilitate nutrient absorption, but they also reduce the viability of microbes. That's great when it's pathogens but not necessarily when it comes to beneficial bacteria," Holscher said. "We wanted to see if honey could help probiotic bacteria survive in the gut."

In the first study , "Honey Varietals Differentially Impact Bifidobacterium animalis ssp. lactis Survivability in Yogurt through Simulated In Vitro Digestion," the researchers conducted a laboratory experiment where they tested the effect of four different kinds of honey (alfalfa, buckwheat, clover, and orange blossom) on the viability of B. animalis in yogurt through simulated digestion processes. They grew microbes in petri dishes with solutions that mimicked the composition of saliva, stomach acid, intestinal bile, and enzymes.

For the saliva and stomach fluids, there were no differences in B. animalis survival between any of the honey varietals and control treatments of yogurt mixed with sugar or water. However, yogurt with honey—particularly the clover varietal—helped support the survival of probiotics in the intestinal phase of digestion.

Next, the researchers wanted to test their findings in a clinical study titled "Honey Added to Yogurt with Bifidobacterium animalis subsp. lactis DN-173 010/CNCM I-2494 Supports Probiotic Enrichment but Does Not Reduce Intestinal Transit Time in Healthy Adults: A Randomized, Controlled, Crossover Trial."

They recruited 66 healthy adults and asked them to consume two different items for two weeks each—yogurt with clover honey and pasteurized, heat-treated yogurt. The participants provided stool samples and information about their bowel movements. They also filled out questionnaires and completed tasks evaluating their mood, cognition, and overall well-being.

"Our findings showed that pairing honey with yogurt supported the survival of the yogurt's probiotic bacteria in the gut, so the lab study results did translate to real-world application in humans," Holscher stated.

However, there were no changes in intestinal transit time, bowel movement frequency, or any of the mood and cognition measures. Holscher said this is likely because the participants already were healthy adults with regular bowel movements, so there wasn't a lot of room for improvement.

The researchers also conducted a smaller follow-up study with 36 participants who consumed a third food item, yogurt with sugar. When the researchers compared the results of all three conditions, the combination of yogurt with honey preserved the most probiotics, but there were no effects on the health measures.

"We found that one tablespoon of honey in a serving of yogurt helps support probiotic survival. However, we have to keep in mind that honey is an added sugar , and most Americans need to be cognizant of the amount of sugar in their diet to maintain a healthy body weight," she stated. "But adding a little bit of honey to unsweetened yogurt is a nice culinary pairing to incorporate into your menu rotation."

You can also add toppings to make a yogurt parfait and support gut health and the microbiome by getting more fiber in your diet. For example, you can add berries and seeds, or nuts, and drizzle a bit of honey on top, Holscher suggested.

Annemarie R Mysonhimer et al, Honey Added to Yogurt with Bifidobacterium animalis subsp. lactis DN-173 010/CNCM I-2494 Supports Probiotic Enrichment but Does Not Reduce Intestinal Transit Time in Healthy Adults: A Randomized, Controlled, Crossover Trial, The Journal of Nutrition (2024). DOI: 10.1016/j.tjnut.2024.05.028

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NIH Launches Community-Led Research Program to Advance Health Equity

Awards to community organizations will enable examination of structural drivers of health

September 28, 2023 • Press Release

The National Institutes of Health is funding a first-of-its-kind community-led research program to study ways to address the underlying structural factors within communities that affect health, such as access to safe spaces, healthy food, employment opportunities, transportation, and quality health care. Through the NIH Common Fund Community Partnerships to Advance Science for Society (ComPASS) program, NIH made 26 awards to community organizations and a coordinating center, totaling approximately $171 million over five years, pending the availability of funds. Through these awards, ComPASS will enable research into sustainable solutions that promote health equity to create lasting change in communities across the nation.

NIH is directly funding research projects led by community organizations. Leaders from the organizations will work in collaboration with their research partners at academic institutions and other organizations in all phases of the research process. ComPASS projects study social determinants of health — the social, physical, and economic conditions where people are born, grow, live, work, age, and play — that contribute to health inequities.

"The ComPASS research model harnesses diverse perspectives and expertise to examine systemic factors that impact the health of individuals, communities, and populations," said NIH Acting Director Lawrence Tabak, D.D.S., Ph.D. “We are excited to see how results from these awards exemplify the transformative power of community-driven research."

The projects  will examine underlying conditions and environments that influence health outcomes by enabling the development, implementation, and assessment of structural interventions. Structural interventions are meant to alter social determinants of health by changing factors that create differences in opportunities to achieve optimal health.

Each award will foster the design of strategies to improve health outcomes through innovative structural interventions to address community concerns, such as economic development, social and community context, neighborhood characteristics, health care access and quality, and nutrition and food environment. Community organizations and their research partners will work together to develop a structural intervention, launch it within their communities, and then assess whether the intervention improves health outcomes. Several examples of ComPASS-supported research projects, which focus on populations that experience health disparities  , include:

• Supporting access to healthy food in underserved rural communities through the delivery of food boxes to local stores and individuals, and facilitating local food harvesting, processing, and distribution in the community. The project will measure whether these interventions reduce hunger, improve diet quality, promote healthy weight, and protect people against chronic diseases such as diabetes and cardiovascular disease.
• Assessing whether early childcare strategies improve mental health for children and their parents and guardians. This project will develop and examine community strategies that increase access to public early childcare, education, and programming to support young children and families in areas with limited access to childcare.
• Enhancing access to health care through individualized travel information and resources along with a transportation stipend for health care and related trips. The project will assess whether improved transportation access can reduce emergency department readmissions and secondary infections, decrease hospital costs, and improve disease management.
• Improving access to quality health care for older adults from sexual and gender minority populations by creating culturally appropriate and inclusive protocols in the local health system. The project will measure how these changes in the local health system affect overall physical and mental health.
• Assessing whether enhancing telehealth models in rural communities can improve preventative screening and disease management for cancer, depression, diabetes, high blood pressure, and other chronic diseases among agricultural workers. The project will improve telehealth by transforming the workers' access to affordable, reliable high-speed broadband internet.

NIH will gain valuable experience and insight into how to support successful future community-led health research. Each project will also contribute valuable data to a growing body of knowledge about social determinants of health and structural inequities.

The ComPASS program is funded by the NIH Common Fund and managed collaboratively by NIH staff from the Common Fund; National Cancer Institute; National Institute of Mental Health; National Institute on Minority Health and Health Disparities; National Institute of Nursing Research; National Heart, Lung, and Blood Institute; and NIH Office of Research on Women's Health, with many of the NIH Institutes Centers and Offices providing input and participating in program development and management. More information is available on the ComPASS program website: https://commonfund.nih.gov/compass  .

To learn more about ComPASS, watch this brief video   :

About the NIH Common Fund: The NIH Common Fund encourages collaboration and supports a series of exceptionally high-impact, NIH-wide programs. Common Fund programs are managed by the Office of Strategic Coordination in the Division of Program Coordination, Planning, and Strategic Initiatives in the NIH Office of the Director in partnership with the NIH Institutes, Centers, and Offices. More information is available at the Common Fund website: https://commonfund.nih.gov  .

About the National Institute of Mental Health (NIMH): The mission of the NIMH  is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery and cure. For more information, visit the NIMH website .

About the National Institutes of Health (NIH) : NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH  and its programs, visit the NIH website  .

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Perceived Availability of Healthy and Unhealthy Foods in the Community, Work, and Higher Education Settings across Five Countries: Findings from the International Food Policy Study 2018

Alejandra contreras-manzano.

Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico

Claudia Nieto

Alejandra jáuregui, carolina pérez ferrer.

National Council for Science and Technology, Mexico City, Mexico

Lana Vanderlee

École de Nutrition, Centre Nutrition, santé et société (Centre NUTRISS), and Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, Canada

Simón Barquera

Global Obesity Centre (GLOBE), Institute for Health Transformation, Deakin University, Burwood Victoria, Geelong, Australia

Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

James F Thrasher

Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

David Hammond

School of Public Health and Sciences, University of Waterloo, Waterloo, Canada

Associated Data

Food environments play a key role in dietary behavior and vary due to different contexts, regulations, and policies.

This study aimed to characterize the perceived availability of healthy and unhealthy foods in 3 different settings in 5 countries.

We analyzed data from the 2018 International Food Policy Study, a cross-sectional survey of adults (18–100 y, n  = 22,824) from Australia, Canada, Mexico, the United Kingdom (UK), and the USA. Perceived availability of unhealthy (junk food and sugary drinks) and healthy foods (fruit or vegetables, healthy snacks, and water) in the community, workplace, and university settings were measured (i.e. not available, available for purchase, or available for free). Differences in perceived availability across countries were tested using adjusted multinomial logistic regression models.

Across countries, unhealthy foods were perceived as highly available in all settings; in university and work settings unhealthy foods were perceived as more available than healthy foods. Australia and Canada had the highest perceived availability of unhealthy foods (range 87.5–90.6% between categories), and the UK had the highest perceived availability of fruits and vegetables for purchase (89.3%) in the community. In university and work settings, Mexico had the highest perceived availability for purchase of unhealthy foods (range 69.9–84.9%). The USA and the UK had the highest perceived availability of fruits and vegetables for purchase (65.3–66.3%) or for free (21.2–22.8%) in the university. In the workplace, the UK had high perceived availability of fruits and vegetables for purchase (40.2%) or for free (18.5%), and the USA had the highest perceived availability of junk food for free (17.3%).

Conclusions

Across countries, unhealthy foods were perceived as highly available in all settings. Variability between countries may reflect differences in policies and regulations. Results underscore the need for the continuation and improvement of policy efforts to generate healthier food environments.

Introduction

There is a growing epidemic of obesity and noncommunicable diseases related to diet ( 1 ). A key driver of this epidemic is the availability and accessibility of foods in the environments where people live, study, and work ( 2 , 3 ). Over the last several years, the global demand and supply of unhealthy foods with salt, sugars, saturated fats, and trans fats has increased, often at the expense of diversity and displacing local and healthier diets ( 4 ). Few studies have compared food environments across countries due to a lack of comparable data. Identifying similarities and differences in food environments between countries has the potential to highlight drivers of country-level variability in dietary patterns, evaluate differences in policy approach, and identify opportunities for new policy interventions.

Food environments vary across countries due to different contexts, regulations, and policies implemented with various degrees of enforcement ( 4 , 5 ) ( Supplemental Table 1 ) ( 6–35 ). Food environment interventions in education centers are among the most common strategies used to limit access to unhealthy foods or increase the availability of healthier foods ( 36–39 ). However, these regulations are not always adopted in higher education settings. In some cases, universities have guidelines that focus on specific aspects of the food environment, which may include removing sugar-sweetened beverages, reducing portion sizes, pricing strategies, and increasing the accessibility to healthy choices ( 6 , 40 , 41 ).

Worksite food access policies are even more heterogeneous as they often respond to occupational health policies of public and private sector organizations, with few governmental programs in place ( 12 ). Guidelines for making healthier choices more available in these settings have been used in several countries ( 13 , 16 , 21 , 42–44 ). However, guidelines are generally voluntary, except for a few cases where nutrition guidelines are mandatory (i.e. in UK central government contexts) ( 45 ).

The community food environment (e.g. location and accessibility of food outlets outside the home) is less regulated than schools or workplaces, with few interventions conducted at the local governmental level ( 15 ). The evidence around “what works” to foster healthy food environments at the community level is still developing; however, some options include zoning ordinances and land-use plans which can influence placement and access to food outlets, as well as in-store policies aiming to improve access to healthy foods ( 46 , 47 ).

Although most studies investigating the food environment have used objective measures, perceived measures may play an important and distinct role in influencing diet, since they take into account the experience and reality for consumers ( 3 , 48 ). Perceived measures of the food environment are correlated with objective measures and become relevant since they are a critical mediating factor with respect to consumer behaviors ( 47 ). Studies suggest that personal perceptions might be stronger determinants of food acquisition, diets, and health, than other objective measures like proximity ( 49 ).

To our knowledge, no previous study has examined the perceived availability of healthy and unhealthy foods in different settings across countries. This study aimed to describe the perceived availability of healthy and unhealthy foods in the community, workplace, and university settings across 5 countries. Based on global trends in food supply as well as differences in available efforts to regulate food environments across settings ( 4 ), we hypothesized that the perceived availability of unhealthy foods is higher than for healthy foods in all the countries analyzed, but may vary across community, workplace, and university settings and between countries.

We analyzed data from the 2018 International Food Policy Study (IFPS), a cross-sectional survey of adults aged 18–100 y ( n  = 22,824) from 5 countries; Australia, Canada, Mexico, the UK, and the USA. The selection of countries was based on broad similarities in the food environment, language, and culture. In the case of Mexico, geographic proximity and sociocultural similarities to key US subpopulations were also a consideration. The IFPS countries also differ in major national-level nutrition policies that have been implemented, including marketing restrictions, food labeling, and fiscal policies.

Data were collected via self-completed web-based surveys conducted in November/December 2018. The study sample was recruited via Nielsen Consumer Insights Global Panel and their partners’ panels. Nielsen drew random samples from online panels in each country, stratified for age and sex utilizing quotas that approximated the known proportions for males and females in 4 age groups: 18–25, 30–44, 45–64, and 65–100 y, according to national census estimates. E-mail invitations with unique survey access links were sent to a random sample of panelists within each country after targeting for demographics; panelists known to be ineligible were not invited. Potential respondents were screened for eligibility and quota requirements using age, sex, and minimum device screen size (to restrict respondents from completing the survey on a smartphone). Surveys were conducted in English in Australia and the UK; Spanish in Mexico; English or French in Canada; and English or Spanish in the USA (based on the panelist's known language preference).

All potential participants were provided with information about the study and were asked to provide informed consent before completing the online survey. Participants received compensation by their panel's usual incentive structure (e.g. points-based or monetary rewards, chances to win prizes) after completing the survey. The study was reviewed by and received ethics clearance through a University of Waterloo Research Ethics Committee (ORE# 30,828). A full description of the study methods and the questionnaires can be found in the IFPS: Technical Report – 2018 Survey (Wave 2. Available at: at https://foodpolicystudy.com/methods/ ).

Perceived availability of foods and beverages

The current study analyzed survey questions related to the perceived availability of healthy and unhealthy foods in the community, university, and/or work. Perceived food availability in the community ( n  = 21,369) was measured with the question: “Are the following foods or drinks sold in stores you can get to WITHIN 5 MINUTES FROM YOUR HOME , using your usual mode of transportation (e.g. walk, drive, or public transit)?” for the following foods or drink categories: junk food; fresh fruit or vegetables; other healthy snacks; sugary drinks; and clean drinking water. Response options were: not available to buy; available to buy; don't know; or refuse to answer. Perceived food availability in universities was examined among adults who reported attending an education center with the question ( n  = 3253), “Are the following foods or drinks available at your SCHOOL ? Do not include items you bring from home,” with the same food and drink categories. Response options were: not available, available to buy, available for free, don't know, or refuse to answer. A similar question was used to assess perceived food availability in workplaces among those who reported working at a paid job or business ( n  = 11,233) with the same response options. Responses were recategorized as follows: 0 = Not available (Not available to buy or not available), 1 = Available for purchase, and 2 = Available for free. Participants answering don't know or refuse to answer, as well as those with discrepant responses (e.g. the participant indicated not available to buy and available to buy) were treated as missing data.

Demographic information was assessed using survey measures taken directly or adapted from population-level surveys within each country ( 50–55 ). Variables were recoded and harmonized for comparison across countries, and included sex at birth (male; female), age (continuous), education level was categorized as “low” (i.e. completed secondary school or less), “medium” (i.e. some postsecondary qualifications), or “high” (i.e. university degree or higher) according to country-specific criteria related to the highest level of formal education completed. Race or ethnicity was categorized as “majority” if participants only identified themselves as “white” in Canada, the UK, and the USA, solely English-speaking in Australia, or non-Indigenous in Mexico. Income adequacy was measured with the question: “Thinking about your total monthly income, how difficult or easy is it for you to make ends meet?” with responses collapsed into very difficult or difficult (“very difficult” and “difficult”), neither easy nor difficult, and easy or very easy (“Easy” and “Very easy”). Self-reported nutrition knowledge was assessed with the question “How would you rate your nutrition knowledge?” with responses collapsed into not knowledgeable (“not at all knowledgeable” and “a little knowledgeable”), somewhat knowledgeable, and knowledgeable (“very knowledgeable” and “extremely knowledgeable”).

Data analysis

A total of 22,824 adults completed the survey. After removing participants who did not provide information about their perceived availability of foods and beverages at any of the settings (i.e. not answering or answering don't know, refuse to answer, or having discrepant responses for the university, work, or community) ( n  = 572), and those with invalid or missing responses for covariates ( n  = 486), a total of 21,766 were retained in the analyses (Canada: n  = 4156; Australia: n  = 3941; UK: n  = 5181; USA: n  = 4474; and Mexico: n  = 4014). All analyses were weighted with poststratification sample weights constructed using a ranking algorithm with population estimates from the census in each country, based on age group, sex at birth, region, ethnicity (except in Canada), and education (except in Mexico).

To determine differences by sociodemographic characteristics and ethnicity, linear regression and Pearson χ2 tests were calculated. To compare the perceived availability of foods and beverages in the university, community, and work settings across countries, we fitted multinomial logistic regression models with the perceived availability as the outcome measure (0 = No availability, 1 = Available for free, 2 = Available for purchase) and the country as the independent variable. Separate models were fitted for each setting and food category. All models were adjusted for age, sex, race or ethnicity, education level, income adequacy, and self-reported nutritional knowledge. Comparisons between all countries and availability categories were examined by introducing each country or category as the reference category. Adjusted percentages and 99% CIs derived from multinomial logistic regression models were estimated and graphically presented. Analyses were conducted using Stata v14.

All sample characteristics, except sex, were significantly different among countries ( P  <0.05) ( Table 1 ). In all settings, the perceived availability of sugary drinks and junk food across countries was greater than the perceived availability of fruits and vegetables and other healthy foods ( Figures 1 – 3 ). Differences in the availability of each food category were observed across countries in the university ( Supplemental Table 2 ), workplace ( Supplemental Table 3 ), and the community ( Supplemental Table 4 ). These differences are discussed in the following sections and, for ease of interpretation, in some cases relative risk ratios are presented using a different reference category (country or availability category) than the one presented in the Supplementary Tables .

Adjusted percentage of participants reporting that different food and beverages are or are not available for purchase or for free in the university setting in all countries (IFPS 2018, n  = 3295). All percentages were adjusted by age, sex, education level, race or ethnicity, income adequacy, and nutritional knowledge throughout multinomial logistic regression models. 99% CI. Panel A: not available, B: available for free, and C: available for purchase. International Food Policy StudyS.

Adjusted percentage of participants reporting that different food and beverages are available for purchase in the community setting in all countries (IFPS 2018, n  = 21,791). All percentages were adjusted by age, sex, education level, race or ethnicity, income adequacy, and nutritional knowledge throughout multinomial logistic regression models. 99% CI. International Food Policy StudyS.

Sociodemographic characteristics of participants of the study (IFPS 2018, n  = 21,766) 1

International Food Policy Study.

University setting

In university settings, sugary drinks and junk food were reported to be available for purchase by 67–85% of participants ( Figure 1C ), whereas fruits and vegetables and other healthy snacks were reported to be available for purchase by 56–73% of participants. Water was reported to be available for free by 32.8–51.4% of participants ( Figure 1B ).

Across countries, participants in Canada (72.4%) and the UK (67.4%), were less likely than those in Mexico (81.6%) to report that junk food was available for purchase (range of RRRs = 0.48–0.56) ( Figure 1C ). Those in Australia (78.1%) and the UK (74.0%) were less likely to report that sugary drinks were available for purchase in universities compared with participants in Mexico (84.9%) (range of RRRs: 0.45–0.47). There were no between-country differences in the availability of junk food or sugary drinks for free in this setting.

Regarding healthy foods, participants from all countries (61.0–67.0%) were more likely to report the availability of fruits and vegetables for purchase than Mexican participants (56.6%) (range of RRRs = 1.89–3.32). Those in the UK (65.3%) and the USA (67%) were more likely to report the availability of fruits and vegetables for purchase in the university setting than those in Australia (63.8%) (range of RRRs: 1.71–1.75) and Canada (61.0%) (range of RRRs: 1.70–1.75) ( Figure 1C ). The pattern of findings was the same for fruits and vegetables available for free, with the exception that there was no difference between Canada (20.1%), the USA (21.2%), and the UK (22.8%) ( Figure 1B ). Participants in Mexico (21.3%) were more likely to report that other healthy snacks were not available in this setting compared with the rest of the countries (12.9–16.0%) (range of RRRs: 2.11–2.91) ( Figure 1A ). Mexican participants (62.3%) were more likely to report that water was available for purchase in the university setting compared with Canadian participants (40.5%) (RRR: 2.54; 99% CI: 1.23–5.23) ( Figure 1C ). There were no between-country differences in the availability of water for free in the university setting.

Across countries, around 50% of participants reported that junk food and sugary drinks were available for purchase; meanwhile ∼30–40% perceived that fruits and vegetables and other healthy snacks were available for purchase ( Figure 2C ). Most reported that water was available for free ( Figure 2B ).

Adjusted percentage of participants reporting that different food and beverages are or are not available for purchase or for free in the workplace in all countries (IFPS 2018, n  = 11,247). All percentages were adjusted by age, sex, education level, race or ethnicity, income adequacy, and nutritional knowledge throughout multinomial logistic regression models. 99% CI. Panel A: not available, B: available for free, and C: available for purchase. International Food Policy Study.

Some differences across countries were observed ( Supplemental Table 3 ). Junk food was perceived as more available for purchase in the workplace in Mexico (69.9%) compared with the rest of the countries (45.6–53.5%) (range of RRRs: 1.57–2.79). Participants in the USA (53.5%) were more likely than those in Australia (50.8%), Canada (49.0%), and the UK (45.6%) to report that these foods were available for purchase (range of RRRs: 1.37–1.77); whereas those in Australia (RRR: 1.28, 99% CI: 1.06–1.56) reported higher availability than participants in the UK ( Figure 2C ). US participants (17.3%) were more likely to report the availability of junk food for free in the workplace compared with those in the rest of the countries (5.8–11.0%) (range of RRRs: 2.07–2.54) ( Figure 2B ). A similar pattern as the one observed for the availability of junk food for purchase was observed for sugary drinks for purchase. Participants in Australia (32.7%), Canada (34.9%), and the UK (37.4%) were more likely to report that sugary drinks were not available in the workplace than those in Mexico (17.3%) (range of RRRs:1.66–1.91) and the USA (26.6%) (range of RRRs: 1.76–2.02) ( Figure 2A ).

Regarding healthy foods, UK (40.2%) participants were more likely to report the availability of fruits and vegetables for purchase in the workplace than the rest of the countries (34.2–38.0%) (range of RRRs: 1.24–1.52). Fruits and vegetables were more available for free in this setting in Australia (20.2%) and the UK (18.5%) than in Canada (12.3%) (range of RRRs: 1.95–1.97) and Mexico (9.6%) (range of RRRs: 2.44–2.47); and in the USA compared with Canada (RRR: 1.56; 99% CI: 1.16–2.09) and Mexico (RRR: 1.96, 99% CI: 1.48–2.59) ( Figure 2B ). Mexican (47.1%) and US (45.6%) participants were more likely to report that other healthy snacks were available for purchase in the workplace than those in Australia (42.0%) (range of RRRs: 1.23–1.30) and Canada (41.4%) (range of RRRs: 1.27–1.34). US (15.0%) participants were more likely to report that other healthy snacks were available for free in the workplace compared with the rest of the countries (≈10%) (range of RRRs:1.55–1.75). Mexican (48.1%) participants were more likely to report that water was available for purchase in the workplace compared with the rest of the countries (25.8–32.5%) (range of RRR: 2.63–3.40). Participants in the UK (10.8%) and the USA (10.0%) were more likely than Mexican participants (5.4%) to report that water was not available in this setting (range of RRRs: 1.50–1.53) ( Figure 2A ).

Most participants (>80%) in all countries perceived most categories to be available for purchase in the community setting ( Figure 3 ).

Small but significant differences in the perceived availability of foods for purchase in the community were observed across countries ( Supplemental Table 2 ). Overall, UK (85.1%) participants were less likely than those from Australia (87.5%), Canada (88.8%), and the USA (88.8%) to report that junk food was available for purchase in their community (range of RRRs: 0.71–0.81). Participants in Mexico (87.7%) were less likely to report that sugary drinks were available for purchase in the community than those in Australia (90.3%), Canada (90.6%), and the UK (90.1%) (range of RRRs: 0.71–0.78).

Concerning healthy foods, UK (89.3%) and Australian (87.7%) participants were more likely to report fruit and vegetable availability for purchase than those in the rest of the countries (83.8–85.3%, range of RRRs: 1.16–1.61). Participants in all countries were more likely (85.8–86.3) to report the availability of other healthy snacks for purchase in the community compared with Mexico (74.7%) (range of RRRs: 2.04–2.13). Participants in the USA (92.3%), Mexico (93.0%), and Canada (92.3%) were more likely to report the availability of water for purchase in the community than those in the UK (90.0%) (range of RRRs: 1.26–1.47).

Perceived availability of food and beverages varied across countries and settings. In the community, healthy and unhealthy foods were perceived as highly available across countries; in university and workplace settings sugary drinks and junk food were highly perceived as available across countries, with lower availability for fruits and vegetables and other healthy foods. In addition, few participants reported that water was not available across settings and countries. These findings are in line with trends in the global demand and supply of food suggesting an increase of unhealthy foods which may have displaced healthier options ( 4 ). The fact that unhealthy foods are highly available is important since studies support a relation between perceived food availability and dietary intake and diet-related outcomes ( 56–60 ). This becomes even more relevant for spaces where people spend most of their time and have access to a significant part of the food they eat, such as university and workplace settings.

Overall, the results of this study are somewhat in line with an 11-country study comparing the implementation of recommended food environment policies ( 56 ). Among the countries included in that study, UK had the highest proportion of policies rated at “high” implementation, most policies were rated as “low” or “medium” implementation in Australia and Canada, whereas Mexico had the highest proportion of policies rated at “very low if any” implementation. In a broader sense, our results are also in line with the higher availability of food environment policies addressing the school, workplace, and university settings in the UK compared with the rest of the countries ( Supplemental Table 1 ).

Our study showed that the UK had the highest availability of free fruits and vegetables and other healthy snacks, and the lowest availability of junk food at the university setting. Although no specific regulations targeted at higher education centers exist in the UK, the high perceived availability of healthy foods in these settings may be explained by different strategies aiming to improve the healthiness of foods offered by catering services, or spillover effects of existing standards for school food or programs aimed to increase the intake of fruit and vegetables in school settings ( 29 , 30 , 61 ). The USA also tended to have high perceived availability for purchase or for free fruits and vegetables and other healthy snacks in the university setting, which is in line with voluntary regulations for food and beverage sales in the campuses of some US universities. For example, the Healthier Campus Initiative through the Partnership for a Healthier America, aiming to make nutritious foods and opportunities for physical activity both accessible and built into the campus culture ( 18 , 19 ). However, our results also indicate high availability of junk food and sugary beverages for free in these settings, which may be explained by the voluntary nature of existing regulations or give-away events. In contrast, Mexico had the highest perceived availability of unhealthy foods and beverages for purchase in universities. These findings may be explained by the fact that in Mexico no regulations exist for the promotion or expenditure of foods in these settings. Overall, the findings underscore the importance of implementing and enforcing mandatory programs aimed at providing healthy food environments in universities. Several examples exist on how to develop and implement policies in these settings to ensure access to healthier foods ( 19 ).

Results of our study also showed that the UK had the lowest perceived availability of junk food and sugary drinks in the workplace, which is in line with available regulations and guidelines to provide healthy foods and beverages at these settings ( 30 , 61 ). Similarly, the USA had the highest reported availability of free healthy snacks in the workplace, which may be due to healthy food procurement policies requiring that the food in specific settings (e.g. school, work, or community) is healthy ( 62 ). However, junk food and sugary drinks were also highly available for free in US work settings which may also be explained by the voluntary nature of the initiatives and guidelines ( 20 , 21 ). Junk food and sugary drinks were also highly available at Mexican workplaces, along with the low perceived availability of free healthy foods in this setting. In this country, no mandatory regulations exist regarding the procurement of healthy foods in this setting. The only available regulation addresses the nutritional standards for voluntary food assistance programs (i.e. meals, food stamps, or food baskets) for Mexican workers, which may not be offered by all employers ( 16 ). These results underscore the need for a better implementation of the existing food environment regulations in the workplace. Effective interventions to promote dietary changes may include increasing access to healthy food or reducing prices of healthy snacks in vending machines ( 63 ). Investments in healthy food environments in these settings have shown their potential to reduce healthcare costs as well as overall absenteeism ( 64 ).

Our results suggest that the community setting had the largest perceived availability of healthy and unhealthy foods across the studied settings. This finding is in line with studies documenting a high density of grocery stores and an increasing number of convenience stores and supermarkets in some of the countries studied ( 65 , 66 ). However, variations across countries were observed for specific foods, which may be explained by the nature of implemented food policies. For example, Australia and Canada were the countries with the highest perceived availability of junk food and sugary drinks in this setting. Although both countries have implemented policies with potential impacts on the availability of foods and beverages at the community level (e.g. subsidies on healthy foods, strategies to increase access to healthy foods in remote or underserved communities) ( 8–11 ), to the authors’ knowledge no zoning or land-use regulations addressing the placing and access to healthy and unhealthy food outlets have been implemented in these countries. This may explain the observed high availability of unhealthy foods in these settings, in line with previous studies ( 67 , 68 ). In contrast, the role of planning in promoting healthy communities has been recognized in policy documents and regulations in the UK, where according to our results a higher perceived availability of healthy foods was reported ( 34 ). One of the most significant differences across countries was the low perceived availability of fruits and vegetables and other healthy snacks in Mexican communities. Despite the fact that local production of fruits and vegetables is sufficient for the Mexican population, calls have been made to develop policies to improve access to healthy foods and guarantee their equitable distribution ( 69 ). Differences in the prevalence of different types of stores (e.g. public markets or small food stores compared with chain supermarkets or convenience stores), pedestrian access to these outlets, as well as differences in transportation modes across countries, may also help explain some of the above-mentioned differences, warranting further studies across different cultural settings ( 70 , 71 ). However, results underscore the need for strategies aiming to increase the availability of healthy foods in the community setting as well as information campaigns to help people identify healthy foods and healthier retail food environments. Interventions focusing on the in-store food environment, placing fruits and vegetables at the end-aisle to make them more visible and appealing to the consumer, and implementing promotions like 2-for-1 sales for healthy food options, may also be desirable ( 35 ).

Findings of this study build on previous findings suggesting the high availability of unhealthy foods across settings ( 4 ) and contribute to filling the gap in the literature regarding perceived availability among countries. Future studies should explore other components of the food environment, such as food accessibility and affordability ( 3 ), a broader variety of countries of low- and middle-income, and specific questions related to individual policies. Further, results of this study should be interpreted within the context of several limitations. Participants were recruited using nonprobability-based sampling; therefore, findings do not provide nationally representative estimates. The instrument used to measure the food environment has not been validated against objective measures. Respondents were not provided with examples or definitions for the included food groups, making interpretations susceptible to subjectivity and cultural differences across countries. Adjusted models may have partially addressed this issue by considering covariates that could be related to such subjectivity (i.e. nutrition knowledge, age, ethnicity, education level, or income adequacy). Analyses did not consider the type of setting (public or private), which could have been useful to further investigate differences in the perceived availability of foods across settings. Finally, results do not allow identifying the level of difference in access these results represent (e.g. regularly available versus occasionally available).

In conclusion, across countries, there was high perceived availability of unhealthy foods in all settings, and in school and work settings they were more available than healthy foods. Some variability between countries was documented which may reflect differences in policies and regulations targeting the food environment, as well as their degree of enforcement. Our results underscore the need for the continuation and improvement of policy efforts to generate healthier food environments.

Supplementary Material

Nxac070_supplemental_file, acknowledgements.

We thank Christine White for her ongoing project/data management support for the IFPS study. The authors’ responsibilities were as follows—DH and LV: designed and conducted the research; AJ: conceived the analyses; ACM: analyzed data; ACM, AJ, CF, and CN: wrote the manuscript; JA, JFT, GS, and SB: revised the manuscript for intellectual content; DH: had primary responsibility for the final content; and all authors reviewed and provided critical feedback, and read and approved the final manuscript.

This supplement was supported by funding from a Project Grant from the Canadian Institutes of Health Research (PJT-162167). The views expressed herein are solely the responsibility of the authors and do not necessarily represent the official views of the Canadian Institutes for Health Research or other sources of funding. Funding for the International Food Policy Study was provided by a Canadian Institutes of Health Research (CIHR) Project Grant, with additional support from an International Health Grant, the Public Health Agency of Canada (PHAC), and a CIHR – PHAC Applied Public Health Chair (DH). JA is supported by the Medical Research Council (grant number MC_UU_00006/7). Funders had no role in designing the study, collecting, analyzing, and interpreting the data, drafting the manuscript nor the decision to publish findings. The supporting sources had no involvement or restrictions regarding publication.

Author disclosures: AC-M works in a civil society organization funded by Bloomberg Philanthropies. CN was awarded the healthy food policy fellowship from Vital Strategies. DH has served as a paid expert witness on behalf of public health authorities in the legal challenge to San Francisco's health warning ordinance for sugar-sweetened beverages. All other authors report no conflicts of interest.

Publication costs for this supplement were defrayed in part by the payment of page charges. The opinions expressed in this publication are those of the authors and are not attributable to the sponsors or the publisher, Editor, or Editorial Board of The Journal of Nutrition .

Supplemental Tables 1–4 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/ .

Contributor Information

Alejandra Contreras-Manzano, Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico.

Claudia Nieto, Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico.

Alejandra Jáuregui, Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico.

Carolina Pérez Ferrer, Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico. National Council for Science and Technology, Mexico City, Mexico.

Lana Vanderlee, École de Nutrition, Centre Nutrition, santé et société (Centre NUTRISS), and Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, Canada.

Simón Barquera, Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico.

Gary Sacks, Global Obesity Centre (GLOBE), Institute for Health Transformation, Deakin University, Burwood Victoria, Geelong, Australia.

Jean Adams, Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.

James F Thrasher, Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.

David Hammond, School of Public Health and Sciences, University of Waterloo, Waterloo, Canada.