research paper for healthy diet

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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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Article Contents

Introduction.

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Young people and healthy eating: a systematic review of research on barriers and facilitators

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J Shepherd, A Harden, R Rees, G Brunton, J Garcia, S Oliver, A Oakley, Young people and healthy eating: a systematic review of research on barriers and facilitators, Health Education Research , Volume 21, Issue 2, 2006, Pages 239–257, https://doi.org/10.1093/her/cyh060

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A systematic review was conducted to examine the barriers to, and facilitators of, healthy eating among young people (11–16 years). The review focused on the wider determinants of health, examining community- and society-level interventions. Seven outcome evaluations and eight studies of young people's views were included. The effectiveness of the interventions was mixed, with improvements in knowledge and increases in healthy eating but differences according to gender. Barriers to healthy eating included poor school meal provision and ease of access to, relative cheapness of and personal taste preferences for fast food. Facilitators included support from family, wider availability of healthy foods, desire to look after one's appearance and will-power. Friends and teachers were generally not a common source of information. Some of the barriers and facilitators identified by young people had been addressed by soundly evaluated effective interventions, but significant gaps were identified where no evaluated interventions appear to have been published (e.g. better labelling of food products), or where there were no methodologically sound evaluations. Rigorous evaluation is required particularly to assess the effectiveness of increasing the availability of affordable healthy food in the public and private spaces occupied by young people.

Healthy eating contributes to an overall sense of well-being, and is a cornerstone in the prevention of a number of conditions, including heart disease, diabetes, high blood pressure, stroke, cancer, dental caries and asthma. For children and young people, healthy eating is particularly important for healthy growth and cognitive development. Eating behaviours adopted during this period are likely to be maintained into adulthood, underscoring the importance of encouraging healthy eating as early as possible [ 1 ]. Guidelines recommend consumption of at least five portions of fruit and vegetables a day, reduced intakes of saturated fat and salt and increased consumption of complex carbohydrates [ 2, 3 ]. Yet average consumption of fruit and vegetables in the UK is only about three portions a day [ 4 ]. A survey of young people aged 11–16 years found that nearly one in five did not eat breakfast before going to school [ 5 ]. Recent figures also show alarming numbers of obese and overweight children and young people [ 6 ]. Discussion about how to tackle the ‘epidemic’ of obesity is currently high on the health policy agenda [ 7 ], and effective health promotion remains a key strategy [ 8–10 ].

Evidence for the effectiveness of interventions is therefore needed to support policy and practice. The aim of this paper is to report a systematic review of the literature on young people and healthy eating. The objectives were

(i) to undertake a ‘systematic mapping’ of research on the barriers to, and facilitators of, healthy eating among young people, especially those from socially excluded groups (e.g. low-income, ethnic minority—in accordance with government health policy);

(ii) to prioritize a subset of studies to systematically review ‘in-depth’;

(iii) to ‘synthesize’ what is known from these studies about the barriers to, and facilitators of, healthy eating with young people, and how these can be addressed and

(iv) to identify gaps in existing research evidence.

General approach

This study followed standard procedures for a systematic review [ 11, 12 ]. It also sought to develop a novel approach in three key areas.

First, it adopted a conceptual framework of ‘barriers’ to and ‘facilitators’ of health. Research findings about the barriers to, and facilitators of, healthy eating among young people can help in the development of potentially effective intervention strategies. Interventions can aim to modify or remove barriers and use or build upon existing facilitators. This framework has been successfully applied in other related systematic reviews in the area of healthy eating in children [ 13 ], physical activity with children [ 14 ] and young people [ 15 ] and mental health with young people [16; S. Oliver, A. Harden, R. Rees, J. Shepherd, G. Brunton and A. Oakley, manuscript in preparation].

Second, the review was carried out in two stages: a systematic search for, and mapping of, literature on healthy eating with young people, followed by an in-depth systematic review of the quality and findings of a subset of these studies. The rationale for a two-stage review to ensure the review was as relevant as possible to users. By mapping a broad area of evidence, the key characteristics of the extant literature can be identified and discussed with review users, with the aim of prioritizing the most relevant research areas for systematic in-depth analysis [ 17, 18 ].

Third, the review utilized a ‘mixed methods’ triangulatory approach. Data from effectiveness studies (‘outcome evaluations’, primarily quantitative data) were combined with data from studies which described young people's views of factors influencing their healthy eating in negative or positive ways (‘views’ studies, primarily qualitative). We also sought data on young people's perceptions of interventions when these had been collected alongside outcomes data in outcome evaluations. However, the main source of young people's views was surveys or interview-based studies that were conducted independently of intervention evaluation (‘non-intervention’ research). The purpose was to enable us to ascertain not just whether interventions are effective, but whether they address issues important to young people, using their views as a marker of appropriateness. Few systematic reviews have attempted to synthesize evidence from both intervention and non-intervention research: most have been restricted to outcome evaluations. This study therefore represents one of the few attempts that have been made to date to integrate different study designs into systematic reviews of effectiveness [ 19–22 ].

Literature searching

A highly sensitive search strategy was developed to locate potentially relevant studies. A wide range of terms for healthy eating (e.g. nutrition, food preferences, feeding behaviour, diets and health food) were combined with health promotion terms or general or specific terms for determinants of health or ill-health (e.g. health promotion, behaviour modification, at-risk-populations, sociocultural factors and poverty) and with terms for young people (e.g. adolescent, teenager, young adult and youth). A number of electronic bibliographic databases were searched, including Medline, EMBASE, The Cochrane Library, PsycINFO, ERIC, Social Science Citation Index, CINAHL, BiblioMap and HealthPromis. The searches covered the full range of publication years available in each database up to 2001 (when the review was completed).

Full reports of potentially relevant studies identified from the literature search were obtained and classified (e.g. in terms of specific topic area, context, characteristics of young people, research design and methodological attributes).

Inclusion screening

Inclusion criteria were developed and applied to each study. The first round of screening was to identify studies to populate the map. To be included, a study had to (i) focus on healthy eating; (ii) include young people aged 11–16 years; (iii) be about the promotion of healthy eating, and/or the barriers to, or facilitators of, healthy eating; (iv) be a relevant study type: (a) an outcome evaluation or (b) a non-intervention study (e.g. cohort or case control studies, or interview studies) conducted in the UK only (to maximize relevance to UK policy and practice) and (v) be published in the English language.

The results of the map, which are reported in greater detail elsewhere [ 23 ], were used to prioritize a subset of policy relevant studies for the in-depth systematic review.

A second round of inclusion screening was performed. As before, all studies had to have healthy eating as their main focus and include young people aged 11–16 years. In addition, outcome evaluations had toFor a non-intervention study to be included it had to

(i) use a comparison or control group; report pre- and post-intervention data and, if a non-randomized trial, equivalent on sociodemographic characteristics and pre-intervention outcome variables (demonstrating their ‘potential soundness’ in advance of further quality assessment);

(ii) report an intervention that aims to make a change at the community or society level and

(iii) measure behavioural and/or physical health status outcomes.

(i) examine young people's attitudes, opinions, beliefs, feelings, understanding or experiences about healthy eating (rather than solely examine health status, behaviour or factual knowledge);

(ii) access views about one or more of the following: young people's definitions of and/or ideas about healthy eating, factors influencing their own or other young people's healthy eating and whether and how young people think healthy eating can be promoted and

(iii) privilege young people's views—presenting views directly as data that are valuable and interesting in themselves, rather than only as a route to generating variables to be tested in a predictive or causal model.

Non-intervention studies published before 1990 were excluded in order to maximize the relevance of the review findings to current policy issues.

Data extraction and quality assessment

All studies meeting inclusion criteria underwent data extraction and quality assessment, using a standardized framework [ 24 ]. Data for each study were entered independently by two researchers into a specialized computer database [ 25 ] (the full and final data extraction and quality assessment judgement for each study in the in-depth systematic review can be viewed on the Internet by visiting http://eppi.ioe.ac.uk ).

Outcome evaluations were considered methodologically ‘sound’ if they reported:Only studies meeting these criteria were used to draw conclusions about effectiveness. The results of the studies which did not meet these quality criteria were judged unclear.

(i) a control or comparison group equivalent to the intervention group on sociodemographic characteristics and pre-intervention outcome variables.

(ii) pre-intervention data for all individuals or groups recruited into the evaluation;

(iii) post-intervention data for all individuals or groups recruited into the evaluation and

(iv) on all outcomes, as described in the aims of the intervention.

Non-intervention studies were assessed according to a total of seven criteria (common to sets of criteria proposed by four research groups for qualitative research [ 26–29 ]):

(i) an explicit account of theoretical framework and/or the inclusion of a literature review which outlined a rationale for the intervention;

(ii) clearly stated aims and objectives;

(iii) a clear description of context which includes detail on factors important for interpreting the results;

(iv) a clear description of the sample;

(v) a clear description of methodology, including systematic data collection methods;

(vi) analysis of the data by more than one researcher and

(vii) the inclusion of sufficient original data to mediate between data and interpretation.

Data synthesis

Three types of analyses were performed: (i) narrative synthesis of outcome evaluations, (ii) narrative synthesis of non-intervention studies and (iii) synthesis of intervention and non-intervention studies together.

For the last of these a matrix was constructed which laid out the barriers and facilitators identified by young people alongside descriptions of the interventions included in the in-depth systematic review of outcome evaluations. The matrix was stratified by four analytical themes to characterize the levels at which the barriers and facilitators appeared to be operating: the school, family and friends, the self and practical and material resources. This methodology is described further elsewhere [ 20, 22, 30 ].

From the matrix it is possible to see:

(i) where barriers have been modified and/or facilitators built upon by soundly evaluated interventions, and ‘promising’ interventions which need further, more rigorous, evaluation (matches) and

(ii) where barriers have not been modified and facilitators not built upon by any evaluated intervention, necessitating the development and rigorous evaluation of new interventions (gaps).

Figure 1 outlines the number of studies included at various stages of the review. Of the total of 7048 reports identified, 135 reports (describing 116 studies) met the first round of screening and were included in the descriptive map. The results of the map are reported in detail in a separate publication—see Shepherd et al. [ 23 ] (the report can be downloaded free of charge via http://eppi.ioe.ac.uk ). A subset of 22 outcome evaluations and 8 studies of young people's views met the criteria for the in-depth systematic review.

The review process.

Outcome evaluations

Of the 22 outcome evaluations, most were conducted in the United States ( n = 16) [ 31–45 ], two in Finland [ 46, 47 ], and one each in the UK [ 48 ], Norway [ 49 ], Denmark [ 50 ] and Australia [ 51 ]. In addition to the main focus on promoting healthy eating, they also addressed other related issues including cardiovascular disease in general, tobacco use, accidents, obesity, alcohol and illicit drug use. Most were based in primary or secondary school settings and were delivered by teachers. Interventions varied considerably in content. While many involved some form of information provision, over half ( n = 13) involved attempts to make structural changes to young people's physical environments; half ( n = 11) trained parents in or about nutrition, seven developed health-screening resources, five provided feedback to young people on biological measures and their behavioural risk status and three aimed to provide social support systems for young people or others in the community. Social learning theory was the most common theoretical framework used to develop these interventions. Only a minority of studies included young people who could be considered socially excluded ( n = 6), primarily young people from ethnic minorities (e.g. African Americans and Hispanics).

Following detailed data extraction and critical appraisal, only seven of the 22 outcome evaluations were judged to be methodologically sound. For the remainder of this section we only report the results of these seven. Four of the seven were from the United States, with one each from the UK, Norway and Finland. The studies varied in the comprehensiveness of their reporting of the characteristics of the young people (e.g. sociodemographic/economic status). Most were White, living in middle class urban areas. All attended secondary schools. Table I details the interventions in these sound studies. Generally, they were multicomponent interventions in which classroom activities were complemented with school-wide initiatives and activities in the home. All but one of the seven sound evaluations included and an integral evaluation of the intervention processes. Some studies report results according to demographic characteristics such as age and gender.

Soundly evaluated outcome evaluations: study characteristics (n = 7)

RCT = Randomized Controlled Trial; CT = controlled trial (no randomization); PE = process evaluation.

Separate evaluations of the same intervention in two populations in New York (the Bronx and Westchester County).

The UK-based intervention was an award scheme (the ‘Wessex Healthy Schools Award’) that sought to make health-promoting changes in school ethos, organizational functioning and curriculum [ 48 ]. Changes made in schools included the introduction of health education curricula, as well as the setting of targets in key health promotion areas (including healthy eating). Knowledge levels, which were high at baseline, changed little over the course of the intervention. Intervention schools performed better in terms of healthy food choices (on audit scores). The impact on measures of healthy eating such as choosing healthy snacks varied according to age and sex. The intervention only appeared possibly to be effective for young women in Year 11 (aged 15–16 years) on these measures (statistical significance not reported).

The ‘Know Your Body’ intervention, a cardiovascular risk reduction programme, was evaluated in two separate studies in two demographically different areas of New York (the Bronx and Westchester County) [ 45 ]. Lasting for 5 years it comprised teacher-led classroom education, parental involvement activities and risk factor examination in elementary and junior high schools. In the Bronx evaluation, statistically significant increases in knowledge were reported, but favourable changes in cholesterol levels and dietary fat were not significant. In the Westchester County evaluation, we judged the effects to be unclear due to shortcomings in methods reported.

A second US-based study, the 3-year ‘Gimme 5’ programme [ 40 ], focused on increasing consumption of fruits and vegetables through a school-wide media campaign, complemented by classroom activities, parental involvement and changes to nutritional content of school meals. The intervention was effective at increasing knowledge (particularly among young women). Effects were measured in terms of changes in knowledge scores between baseline and two follow-up periods. Differences between the intervention and comparison group were significant at both follow-ups. There was a significant increase in consumption of fruit and vegetables in the intervention group, although this was not sustained.

In the third US study, the ‘Slice of Life’ intervention, peer leaders taught 10 sessions covering the benefits of fitness, healthy diets and issues concerning weight control [ 41 ]. School functioning was also addressed by student recommendations to school administrators. For young women, there were statistically significant differences between intervention and comparison groups on healthy eating scores, salt consumption scores, making healthy food choices, knowledge of healthy food, reading food labels for salt and fat content and awareness of healthy eating. However, among young men differences were only significant for salt and knowledge scores. The process evaluation suggested that having peers deliver training was acceptable to students and the peer-trainers themselves.

A Norwegian study evaluated a similar intervention to the ‘Slice of Life’ programme, employing peer educators to lead classroom activities and small group discussions on nutrition [ 49 ]. Students also analysed the availability of healthy food in their social and home environment and used a computer program to analyse the nutritional status of foods. There were significant intervention effects for reported healthy eating behaviour (but not maintained by young men) and for knowledge (not young women).

The second ‘North Karelia Youth Study’ in Finland featured classroom educational activities, a community media campaign, health-screening activities, changes to school meals and a health education initiative in the parents' workplace [ 47 ]. It was judged to be effective for healthy eating behaviour, reducing systolic blood pressure and modifying fat content of school meals, but less so for reducing cholesterol levels and diastolic blood pressure.

The evidence from the well-designed evaluations of the effectiveness of healthy eating initiatives is therefore mixed. Interventions tend to be more effective among young women than young men.

Young people's views

Table II describes the key characteristics of the eight studies of young people's views. The most consistently reported characteristics of the young people were age, gender and social class. Socioeconomic status was mixed, and in the two studies reporting ethnicity, the young people participating were predominantly White. Most studies collected data in mainstream schools and may therefore not be applicable to young people who infrequently or never attend school.

Characteristics of young people's views studies (n = 8)

All eight studies asked young people about their perceptions of, or attitudes towards, healthy eating, while none explicitly asked them what prevents them from eating healthily. Only two studies asked them what they think helps them to eat healthy foods, and only one asked for their ideas about what could or should be done to promote nutrition.

Young people tended to talk about food in terms of what they liked and disliked, rather than what was healthy/unhealthy. Healthy foods were predominantly associated with parents/adults and the home, while ‘fast food’ was associated with pleasure, friendship and social environments. Links were also made between food and appearance, with fast food perceived as having negative consequences on weight and facial appearance (and therefore a rationale for eating healthier foods). Attitudes towards healthy eating were generally positive, and the importance of a healthy diet was acknowledged. However, personal preferences for fast foods on grounds of taste tended to dominate food choice. Young people particularly valued the ability to choose what they eat.

Despite not being explicitly asked about barriers, young people discussed factors inhibiting their ability to eat healthily. These included poor availability of healthy meals at school, healthy foods sometimes being expensive and wide availability of, and personal preferences for, fast foods. Things that young people thought should be done to facilitate healthy eating included reducing the price of healthy snacks and better availability of healthy foods at school, at take-aways and in vending machines. Will-power and encouragement from the family were commonly mentioned support mechanisms for healthy eating, while teachers and peers were the least commonly cited sources of information on nutrition. Ideas for promoting healthy eating included the provision of information on nutritional content of school meals (mentioned by young women particularly) and better food labelling in general.

Table III shows the synthesis matrix which juxtaposes barriers and facilitators alongside results of outcome evaluations. There were some matches but also significant gaps between, on the one hand, what young people say are barriers to healthy eating, what helps them and what could or should be done and, on the other, soundly evaluated interventions that address these issues.

Synthesis matrix

Key to young people's views studies: Y1 , Dennison and Shepherd [ 56 ]; Y2 , Harris [ 57 ]; Y3 , McDougall [ 58 ]; Y4 , Miles and Eid [ 59 ]; Y5 , Roberts et al. [ 60 ]; Y6 , Ross [ 61 ]; Y7 , Watt and Sheiham [ 62 ]; Y8 , Watt and Sheiham [ 63 ]. Key to intervention studies: OE1 , Baranowski et al. [ 31 ]; OE2 , Bush et al. [ 32 ]; OE3 , Coates et al. [ 33 ]; OE4 , Ellison et al. [ 34 ]; OE5 , Flores [ 36 ]; OE6 , Fitzgibbon et al. [ 35 ]; OE7 , Hopper et al. [ 64 ]; OE8 , Holund [ 50 ]; OE9 , Kelder et al. [ 38 ]; OE10 , Klepp and Wilhelmsen [ 49 ]; OE11 , Moon et al. [ 48 ]; OE12 , Nader et al. [ 39 ]; OE13 , Nicklas et al. [ 40 ]; OE14 , Perry et al. [ 41 ]; OE15 , Petchers et al. [ 42 ]; OE16 , Schinke et al. [ 43 ]; OE17 , Wagner et al. [ 44 ]; OE18 , Vandongen et al. [ 51 ]; OE19 , Vartiainen et al. [ 46 ]; OE20 , Vartiainen et al. [ 47 ]; OE21 , Walter I [ 45 ]; OE22 , Walter II [ 45 ]. OE10, OE11, OE13, OE14, OE20, OE21 and OE22 denote a sound outcome evaluation. OE21 and OE22 are separate evaluations of the same intervention. Due to methodological limitations, we have judged the effects of OE22 to be unclear. Y1 and Y2 do not appear in the synthesis matrix as they did not explicitly report barriers or facilitators, and it was not possible for us to infer potential barriers or facilitators. However, these two studies did report what young people understood by healthy eating, their perceptions, and their views and opinions on the importance of eating a healthy diet. OE2, OE12, OE16 and OE17 do not appear in the synthesis matrix as they did not address any of the barriers or facilitators.

In terms of the school environment, most of the barriers identified by young people appear to have been addressed. At least two sound outcome evaluations demonstrated the effectiveness of increasing the availability of healthy foods in the school canteen [ 40, 47 ]. Furthermore, despite the low status of teachers and peers as sources of nutritional information, several soundly evaluated studies showed that they can be employed effectively to deliver nutrition interventions.

Young people associated parents and the home environment with healthy eating, and half of the sound outcome evaluations involved parents in the education of young people about nutrition. However, problems were sometimes experienced in securing parental attendance at intervention activities (e.g. seminar evenings). Why friends were not a common source of information about good nutrition is not clear. However, if peer pressure to eat unhealthy foods is a likely explanation, then it has been addressed by the peer-led interventions in three sound outcome evaluations (generally effectively) [ 41, 47, 49 ] and two outcome evaluations which did not meet the quality criteria (effectiveness unclear) [ 33, 50 ].

The fact that young people choose fast foods on grounds of taste has generally not been addressed by interventions, apart from one soundly evaluated effective intervention which included taste testings of fruit and vegetables [ 40 ]. Young people's concern over their appearance (which could be interpreted as both a barrier and a facilitator) has only been addressed in one of the sound outcome evaluations (which revealed an effective intervention) [ 41 ]. Will-power to eat healthy foods has only been examined in one outcome evaluation in the in-depth systematic review (judged to be sound and effective) (Walter I—Bronx evaluation) [ 45 ]. The need for information on nutrition was addressed by the majority of interventions in the in-depth systematic review. However, no studies were found which evaluated attempts to increase the nutritional content of school meals.

Barriers and facilitators relating to young people's practical and material resources were generally not addressed by interventions, soundly evaluated or otherwise. No studies were found which examined the effectiveness of interventions to lower the price of healthy foods. However, one soundly evaluated intervention was partially effective in increasing the availability of healthy snacks in community youth groups (Walter I—Bronx evaluation) [ 45 ]. At best, interventions have attempted to raise young people's awareness of environmental constraints on eating healthily, or encouraged them to lobby for increased availability of nutritious foods (in the case of the latter without reporting whether any changes have been effected as a result).

This review has systematically identified some of the barriers to, and facilitators of, healthy eating with young people, and illustrated to what extent they have been addressed by soundly evaluated effective interventions.

The evidence for effectiveness is mixed. Increases in knowledge of nutrition (measured in all but one study) were not consistent across studies, and changes in clinical risk factors (measured in two studies) varied, with one study detecting reductions in cholesterol and another detecting no change. Increases in reported healthy eating behaviour were observed, but mostly among young women revealing a distinct gender pattern in the findings. This was the case in four of the seven outcome evaluations (in which analysis was stratified by gender). The authors of one of the studies suggest that emphasis of the intervention on healthy weight management was more likely to appeal to young women. It was proposed that interventions directed at young men should stress the benefits of nutrition on strength, physical endurance and physical activity, particularly to appeal to those who exercise and play sports. Furthermore, age was a significant factor in determining effectiveness in one study [ 48 ]. Impact was greatest on young people in the 15- to 16-year age range (particularly for young women) in comparison with those aged 12–13 years, suggesting that dietary influences may vary with age. Tailoring the intervention to take account of age and gender is therefore crucial to ensure that interventions are as relevant and meaningful as possible.

Other systematic reviews of interventions to promote healthy eating (which included some of the studies with young people fitting the age range of this review) also show mixed results [ 52–55 ]. The findings of these reviews, while not being directly comparable in terms of conceptual framework, methods and age group, seem to offer some support for the findings of this review. The main message is that while there is some evidence to suggest effectiveness, the evidence base is limited. We have identified no comparable systematic reviews in this area.

Unlike other reviews, however, this study adopted a wider perspective through inclusion of studies of young people's views as well as effectiveness studies. A number of barriers to healthy eating were identified, including poor availability of healthy foods at school and in young people's social spaces, teachers and friends not always being a source of information/support for healthy eating, personal preferences for fast foods and healthy foods generally being expensive. Facilitating factors included information about nutritional content of foods/better labelling, parents and family members being supportive; healthy eating to improve or maintain one's personal appearance, will-power and better availability/lower pricing of healthy snacks.

Juxtaposing barriers and facilitators alongside effectiveness studies allowed us to examine the extent to which the needs of young people had been adequately addressed by evaluated interventions. To some extent they had. Most of the barriers and facilitators that related to the school and relationships with family and friends appear to have been taken into account by soundly evaluated interventions, although, as mentioned, their effectiveness varied. Many of the gaps tended to be in relation to young people as individuals (although our prioritization of interventions at the level of the community and society may have resulted in the exclusion of some of these interventions) and the wider determinants of health (‘practical and material resources’). Despite a wide search, we found few evaluations of strategies to improve nutritional labelling on foods particularly in schools or to increase the availability of affordable healthy foods particularly in settings where young people socialize. A number of initiatives are currently in place which may fill these gaps, but their effectiveness does not appear to have been reported yet. It is therefore crucial for any such schemes to be thoroughly evaluated and disseminated, at which point an updated systematic review would be timely.

This review is also constrained by the fact that its conclusions can only be supported by a relatively small proportion of the extant literature. Only seven of the 22 outcome evaluations identified were considered to be methodologically sound. As illustrated in Table III , a number of the remaining 15 interventions appear to modify barriers/build on facilitators but their results can only be judged unclear until more rigorous evaluation of these ‘promising’ interventions has been reported.

Finally, it is important to acknowledge that the majority of the outcome evaluations were conducted in the United States, and by virtue of the inclusion criteria, all the young people's views studies were UK based. The literature therefore might not be generalizable to other countries, where sociocultural values and socioeconomic circumstances may be quite different. Further evidence synthesis is needed on barriers to, and facilitators of, healthy eating and nutrition worldwide, particularly in developing countries.

The aim of this study was to survey what is known about the barriers to, and facilitators of, healthy eating among young people with a view to drawing out the implications for policy and practice. The review has mapped and quality screened the extant research in this area, and brought together the findings from evaluations of interventions aiming to promote healthy eating and studies which have elicited young people's views.

There has been much research activity in this area, yet it is disappointing that so few evaluation studies were methodologically strong enough to enable us to draw conclusions about effectiveness. There is some evidence to suggest that multicomponent school-based interventions can be effective, although effects tended to vary according to age and gender. Tailoring intervention messages accordingly is a promising approach which should therefore be evaluated. A key theme was the value young people place on choice and autonomy in relation to food. Increasing the provision and range of healthy, affordable snacks and meals in schools and social spaces will enable them to exercise their choice of healthier, tasty options.

We have identified that several barriers to, and facilitators of, healthy eating in young people have received little attention in evaluation research. Further work is needed to develop and evaluate interventions which modify or remove these barriers, and build on these facilitators. Further qualitative studies are also needed so that we can continue to listen to the views of young people. This is crucial if we are to develop and test meaningful, appropriate and effective health promotion strategies.

We would like to thank Chris Bonell and Dina Kiwan for undertaking data extraction. We would also like to acknowledge the invaluable help of Amanda Nicholas, James Thomas, Elaine Hogan, Sue Bowdler and Salma Master for support and helpful advice. The Department of Health, England, funds a specific programme of health promotion work at the EPPI-Centre. The views expressed in the report are those of the authors and not necessarily those of the Department of Health.

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Defining a healthy diet: evidence for the role of contemporary dietary patterns in health and disease.

Graphical Abstract

1. Introduction

2. components of a healthy diet and their benefits, 3. common health-promoting dietary patterns, 3.1. mediterranean diet, 3.2. dietary approaches to stop hypertension (dash), 3.3. mediterranean-dash intervention for neurodegenerative delay (mind), 3.4. nordic diet, 3.5. traditional asian diets, 4. additional factors, 5. conclusions, author contributions, acknowledgments, conflicts of interest.

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Cena, H.; Calder, P.C. Defining a Healthy Diet: Evidence for the Role of Contemporary Dietary Patterns in Health and Disease. Nutrients 2020 , 12 , 334. https://doi.org/10.3390/nu12020334

Cena H, Calder PC. Defining a Healthy Diet: Evidence for the Role of Contemporary Dietary Patterns in Health and Disease. Nutrients . 2020; 12(2):334. https://doi.org/10.3390/nu12020334

Cena, Hellas, and Philip C. Calder. 2020. "Defining a Healthy Diet: Evidence for the Role of Contemporary Dietary Patterns in Health and Disease" Nutrients 12, no. 2: 334. https://doi.org/10.3390/nu12020334

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Assessing the Cost of Healthy and Unhealthy Diets: A Systematic Review of Methods

• Public Health Nutrition (KE Charlton, Section Editor)
• Open access
• Published: 09 September 2022
• Volume 11 , pages 600–617, ( 2022 )

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• Cherie Russell   ORCID: orcid.org/0000-0003-1251-4810 1 ,
• Jillian Whelan   ORCID: orcid.org/0000-0001-9434-109X 2 &
• Penelope Love   ORCID: orcid.org/0000-0002-1244-3947 1 , 3  

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Purpose of Review

Poor diets are a leading risk factor for chronic disease globally. Research suggests healthy foods are often harder to access, more expensive, and of a lower quality in rural/remote or low-income/high minority areas. Food pricing studies are frequently undertaken to explore food affordability. We aimed to capture and summarise food environment costing methodologies used in both urban and rural settings.

Recent Findings

Our systematic review of high-income countries between 2006 and 2021 found 100 relevant food pricing studies. Most were conducted in the USA ( n  = 47) and Australia ( n  = 24), predominantly in urban areas ( n  = 74) and cross-sectional in design ( n  = 76). All described a data collection methodology, with just over half ( n  = 57) using a named instrument. The main purpose for studies was to monitor food pricing, predominantly using the ‘food basket’, followed by the Nutrition Environment Measures Survey for Stores (NEMS-S). Comparatively, the Healthy Diets Australian Standardised Affordability and Price (ASAP) instrument supplied data on relative affordability to household incomes.

Future research would benefit from a universal instrument reflecting geographic and socio-cultural context and collecting longitudinal data to inform and evaluate initiatives targeting food affordability, availability, and accessibility.

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Introduction

Poor diets, described as those low in fruits, vegetables, and whole grains, and high in red and processed meats and ultra-processed foods, are a leading risk factor for chronic disease globally [ 1 ]. In most high-income countries (HIC), poor diets disproportionally affect lower socioeconomic populations, Indigenous Peoples, and those living in rural and/or remote areas [ 2 , 3 , 4 , 5 ]. Rather than solely a consequence of individual behaviours, poor diets are critically informed by broad contextual factors, including social, commercial, environmental, and cultural influences [ 6 , 7 ]. Crucially, the consumption of a healthy diet is constrained by the range, affordability, and acceptability of foods available for sale [ 8 ]. Research suggests that healthy foods are often harder to access, more expensive, and often of a lower quality in rural, remote, or low-income/high minority areas, than in metropolitan or high-income areas [ 9 , 10 , 11 , 12 ]. Such food environments contribute to higher rates of diet-related non-communicable diseases and food insecurity [ 13 , 14 ]. In order to improve population diets, all aspects of the food environment must be addressed to ensure healthy foods are affordable, available, and of adequate nutritional quality [ 15 ].

Price is a primary factor impacting food choice, diet quality, and food security, therefore having affordable, acceptable, healthy food should be a political and social priority [ 8 , 15 , 16 ]. Some research suggests that healthy diets are associated with greater total spending [ 17 , 18 , 19 ], while other studies report that adherence to a healthy diet is less expensive than current or ‘unhealthy’ diets [ 9 , 20 , 21 ]. Regardless, the cost of a healthy diet is a proportionately large household expense (> 30% of household income) and may therefore be considered ‘unaffordable’ [ 22 ]. Additionally, public perception that healthy diets are expensive is high, which itself may be a barrier to the purchase of healthy foods [ 23 ]. Therefore, improving the affordability of healthy food could improve population diets, regardless of context [ 24 ].

To address the issue of food affordability and inform appropriate attenuating policy and intervention strategies, food pricing studies are frequently undertaken. Food pricing, however, is not a universal construct and is highly influenced by country and context. Numerous methods have been developed to measure food pricing, with data therefore not always comparable or replicable, and of limited value to inform appropriate policy [ 25 ]. Most studies that collect food pricing data conclude that food prices are rising, making healthy eating unaffordable for many populations. However, few studies to date have used this data to suggest strategies to improve affordability. Our systematic review aims to capture and summarise food environment costing methodologies used in HIC, in both urban and rural settings, between 2006 and 2021. In addressing this aim, we answer the following questions: (i) What is the stated purpose of collecting data on food prices, including whether the data is used to inform or advocate for interventions? (ii) Which instruments are being used to measure food pricing? (iii) What are the strengths and limitations of each instrument as reported by study authors?

To address the research aim, we undertook a systematic review of the literature, following the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 26 ]. We followed four steps: (i) systematic search for relevant literature; (ii) selection of studies, (iii) data extraction, and (iv) analysis and synthesis of results.

Systematic Search Strategy

After consultation with a research liaison librarian, databases used included EBSCOHOST (Academic Search Complete, CINAHL Complete, GlobalHealth, Medline Complete, and PsychINFO) and Informit. We chose these databases for their comprehensiveness and conventional use in the public health nutrition discipline. We identified search terms using a scoping review and key words used in previous food pricing reviews [ 15 , 23 , 27 , 28 ]. We searched both article abstracts and titles using the following search string: ‘food affordability’ OR ‘food cost’ OR ‘food price*’ OR ‘food promotion*’. We completed an initial search for studies published 2016–2021 in October 2021, followed by a search for studies published 2006–2015 in December 2021.

Selection of Studies

Studies were included if they were English, peer-reviewed journal articles presenting original research, monitored food prices in a high-income country/s, and were published between 2006 and 2021. The article by Glanz (2006) [ 15 ] is considered a seminal paper in food pricing research and was therefore chosen as the starting date for our search. Studies prior to this date were considered unlikely to be relevant to the research question and were thus excluded. Review articles, opinion pieces, posters, perspectives, study protocols, viewpoints, editorials, and commentaries were excluded, as well as those assessing middle- or low-income countries.

Study screening involved an initial review of all titles and removal of duplicates by A1 using online Covidence software [ 29 ], followed by abstract screening (A1), and then full text screening of remaining studies (A1). A second reviewer independently screened all articles by abstract and full text to minimise bias (A2 and A3). Disagreements were resolved through discussion between researchers; where no agreement was reached, a third party acted as an arbiter (A2 and A3). Limited hand searching was conducted given the volume of papers identified. Online Resource 1 presents a PRISMA flow chart of the study selection process.

Data Extraction

Included studies were uploaded to an Endnote (V. X9) [ 30 ] library. We systematically extracted details of each study to Microsoft Excel (V. 2112), including the author/s, year published, article title, aim, pricing instrument used (if specified), country and geographical context (e.g. urban or rural), type of data collected, number and type of locations assessed, number and type of food items captured, population (if the study used sales receipts to estimate food prices), time period of study, strengths, limitations, and conclusions.

Data Analysis and Synthesis

The coded data were used to identify major themes that were then synthesised in the results. We used an inductive thematic approach for our analysis, with the results discussed between the research team to limit researcher subjectivity [ 31 ]. We used Microsoft Excel to calculate descriptive statistics and graphical outputs.

Overview of Studies

Database searching identified 2737 studies, with 1882 studies remaining after removal of duplicates. After abstract screening, a total of 287 were identified for full-text screening, with 187 excluded, and a total of 100 studies included in this systematic review (Online Resource 1).

We observed an increasing number of studies each year, with peaks in 2013, 2014, and 2018 (Fig.  1 ).

Frequency of studies published assessing food prices between 2006 and 2021

Most studies measured food prices in the USA ( n  = 47), followed by Australia ( n  = 25). Urban food environments were assessed more frequently ( n  = 74) than rural ( n  = 33). Most studies were cross-sectional ( n  = 77). Most studies included instore price audits ( n  = 59), followed by online price audits (supermarket websites, n  = 13), or electronic point of sale data (consumer receipts, register sales, or electronic scanning of food prices in the home, n  = 12), and a combination of these ( n  = 17). Most studies collected food price data from more than 20 food retail outlets ( n  = 34) (Table 1 ).

Details of all included studies, grouped according to data source used (instore price audits, online price audits, electronic point of sale, and combinations of these), are shown in Tables 2 , 3 , and 4 . Details include instrument used (if applicable), purpose of data collection, country, context, study type (e.g. cross-sectional, longitudinal), healthiness comparisons (between healthy and unhealthy products or diets), study author, and year. The use of a named instrument was captured to identify commonalities in usage of instruments, and not as an indication of study quality. When assessing differentials in ‘healthiness’, studies either presented a comparison of a ‘healthy diet’ with an ‘unhealthy or currently consumed diet’ or a comparison of the cost of ‘healthy’ and ‘unhealthy’ foods or product categories.

Study Purpose for Collecting Data on Food Prices

The studies included in this review had a multitude of aims (Tables 2 , 3 , and 4 ). While most studies were conducted solely to monitor food prices in a specific location/s [ 33 , 39 , 42 , 46 , 47 , 52 , 54 , 56 , 57 , 59 , 64 , 67 , 71 , 75 , 80 , 81 , 88 , 89 , 104 , 106 , 108 , 109 , 114 ], others aimed to monitor food price changes over time [ 53 , 63 , 74 , 83 , 93 , 97 , 111 , 127 ], assess food prices as a function of income, socioeconomic status, or welfare assistance [ 9 , 19 , 20 , 33 , 36 , 37 , 38 , 40 , 41 , 66 , 69 , 70 , 77 , 84 , 85 , 86 , 90 , 91 , 92 , 94 , 100 , 110 , 115 , 116 , 117 , 122 ]; assess food price in relation to geographic distance [ 19 , 77 , 91 , 92 , 94 , 98 ]; compare perceptions of food price with actual food prices [ 68 , 101 , 107 ]; and relate food price with a health outcome [ 34 , 35 , 37 , 40 , 47 , 58 , 70 , 72 , 78 , 105 , 116 , 117 , 124 , 125 ], compare the price of healthy or unhealthy foods/diets [ 9 , 20 , 34 , 43 , 50 , 51 , 55 , 60 , 61 , 62 , 63 , 64 , 65 , 76 , 85 , 86 , 93 , 94 , 95 , 96 , 99 , 102 , 110 , 111 , 112 , 120 , 121 , 123 , 124 , 126 ], assess diet costs for a specific population [ 82 , 118 ], compare food prices between brands [ 79 ], compare approaches for estimating dietary costs [ 32 ], or understand how prices impact consumption [ 44 ]. Only seven studies specifically aimed to collect data to inform policy strategies and/or community interventions to improve population health [ 10 , 11 , 49 , 80 , 87 , 103 , 113 ]. However, 26 studies did discuss their study findings on food price in relation to potential further action to improve food environments [ 9 , 19 , 20 , 33 , 36 , 37 , 40 , 43 , 47 , 49 , 50 , 54 , 55 , 59 , 63 , 64 , 81 , 85 , 86 , 87 , 88 , 103 , 104 , 105 , 110 ]. Specific suggested strategies included those targeting individuals, such as education campaigns to promote healthy and more affordable food choices [ 9 , 36 , 43 , 45 , 49 , 50 , 55 ], and those targeting environmental changes, such as taxes on ‘unhealthy’ foods [ 33 , 49 , 85 , 104 , 110 ], subsidies and exemptions for ‘healthy’ foods [ 9 , 20 , 45 , 62 , 63 , 85 , 104 , 110 ], vouchers for farmer’s markets [ 43 ], establishing more food stores [ 33 , 45 , 48 , 104 ], better public transportation for consumers to access food stores [ 59 ], generating savings at the manufacturer/wholesaler level that can be passed on to customers [ 81 ], establishing community-led food supply options [ 9 ], and increasing welfare support proportionate to food prices and geographic distances to food stores [ 37 , 40 , 50 , 73 , 85 ].

Overview of Instruments Used to Measure Food Prices

Of the 100 included studies, 57 used a named instrument to measure food prices, as described below. The remaining 43 studies did not name a pre-existing data collection instrument; instead, the authors described the data collection methodology used, for example, in store, online, or via electronic sales data.

Food Basket Instruments

The majority ( n  = 30) of studies used a variation of a ‘food basket’ to estimate food prices. Food baskets capture the prices of a pre-defined list of foods, often in quantities representative of the total diet of reference families over a defined timeframe [ 9 ], and is a longstanding methodology used to investigate the availability and affordability of food. Food basket studies were mainly conducted in the USA ( n  = 14) and Australia ( n  = 12) [ 19 , 20 , 80 , 81 , 83 , 87 , 88 , 89 , 90 , 91 , 92 ]. Food basket studies using named instruments were conducted in the USA—using the Thrifty Food Plan Market Basket ( n  = 5), the Fred Hutchinson Cancer Research Center Market Basket ( n  = 3), the University of Washington’s Center for Public Health Nutrition Market Basket ( n  = 3), and the USDA Market Basket ( n  = 2); in Australia—using the Victorian Healthy Food Basket ( n  = 4), the Food Basket informed by the INFORMAS framework ( n  = 2), the Adelaide Healthy Food Basket ( n  = 2), the Illawarra Healthy Food Basket ( n  = 2), the Queensland Healthy Food Access Basket Survey ( n  = 1), and the Northern Territory Market Basket ( n  = 1); and in Canada—using the Ontario Nutritious food basket ( n  = 1), the Revised Northern Food Basket ( n  = 1), and an unspecified market basket ( n  = 1). Food basket studies were conducted in both rural ( n  = 13) [ 19 , 37 , 49 , 50 , 52 , 81 , 83 , 87 , 88 , 90 , 91 , 103 , 110 ] and urban contexts ( n  = 25) [ 19 , 20 , 37 , 38 , 40 , 46 , 49 , 50 , 51 , 52 , 62 , 63 , 64 , 66 , 67 , 70 , 80 , 81 , 83 , 88 , 89 , 92 , 104 , 105 , 111 ].

All but two [ 37 , 40 ] food basket studies collected prices from physical instore locations [ 19 , 20 , 38 , 43 , 46 , 49 , 50 , 51 , 52 , 55 , 62 , 63 , 64 , 66 , 67 , 70 , 73 , 80 , 81 , 83 , 87 , 88 , 89 , 90 , 91 , 92 , 103 , 104 , 105 , 110 ], with four of these studies supplementing the data with online supermarket prices [ 62 , 63 , 64 , 81 ]. Additionally, three instruments compared the cost of a ‘healthy diet’ to either an ‘unhealthy or currently consumed diet’ [ 20 , 88 , 110 ], 13 instruments compared the cost of ‘healthy’ and ‘unhealthy’ individual foods or product categories [ 19 , 38 , 51 , 62 , 63 , 66 , 83 , 87 , 89 , 90 , 103 ], and 14 instruments did not present a comparison [ 37 , 40 , 46 , 49 , 50 , 52 , 64 , 67 , 70 , 80 , 81 , 91 , 92 , 104 , 105 ]. ‘Current’ diets were defined using national survey data [ 20 , 110 ]. Level of healthiness was defined using various benchmarks, namely the NOVA food processing classification system [ 38 ], nutrient composition and energy density [ 38 , 51 , 62 , 63 , 66 , 80 , 83 , 90 ], national Dietary Guidelines [ 19 , 43 , 70 , 87 , 88 , 89 , 90 ], and the Dietary Approaches to Stop Hypertension (DASH) dietary pattern [ 43 ]. Food affordability was benchmarked using household income [ 20 , 49 , 50 , 90 , 91 , 92 , 103 , 105 , 110 ], government subsidies [ 37 , 40 , 87 , 89 , 91 ], and minimum wage [ 38 , 66 , 70 ]; however, most studies ( n  = 13) did not determine relative affordability in their analysis [ 43 , 51 , 52 , 55 , 62 , 63 , 64 , 67 , 73 , 80 , 81 , 83 , 88 ].

Healthy Diets Australian Standardised Affordability and Price (ASAP) Instrument

Following critiques of existing food baskets, the previously described INFORMAS instrument was refined to assess and compare the price and affordability of healthy and current diets in Australia, leading to the development of the Healthy Diets Australian Standardised Affordability and Price (ASAP). This instrument assesses the cost of a ‘recommended’ Australian diet (defined by the Australian Dietary Guidelines and Australian Guide to Healthy Eating) and the cost of the ‘current’ Australian diet (as reported in the 2011–12 Australian Health Survey) using the reference household of two parents and two children (boy aged 14 years; girl aged 8 years) [ 128 ]. Thus, all studies using this instrument present a comparison of the cost of a ‘healthy’ and ‘unhealthy’ diet in their analysis. Intrinsic to the instrument, the relative affordability of a healthy diet is measured against household incomes. The ASAP instrument was used by four studies to collect food price data in physical instore locations [ 9 , 85 , 86 ] or from online supermarkets [ 94 ]. Studies were conducted in both rural ( n  = 2) [ 9 , 85 , 94 ] and urban ( n  = 2) [ 85 , 86 , 94 ] contexts.

Nutrition Environment Measures Survey for Stores (NEMS-S) Instrument

The Nutrition Environment Measures Survey for Stores (NEMS-S) and its variants were also frequently used throughout food pricing studies ( n  = 15). These included NEMS-S-Rev (Nutrition Environment Measures Survey for Stores Revised), TxNEAS (Texas Nutrition Environment Assessment), NEMS-S-NL (Nutrition Environment Measures Survey for Stores Newfoundland and Labrador), and The Bridging the Gap Food Store Observation Form. This instrument was used mostly in the USA ( n  = 11) [ 11 , 33 , 36 , 44 , 47 , 48 , 54 , 57 , 68 , 71 , 107 ]. Studies were conducted in both rural ( n  = 4) [ 10 , 11 , 56 , 106 ] and urban ( n  = 11) [ 33 , 36 , 44 , 47 , 48 , 54 , 57 , 68 , 71 , 107 , 108 ] contexts. Compared to the food basket methodology, the NEMS-S instrument compares products in the same category that are considered ‘healthy’ or ‘unhealthy’ based on American Dietetic Association (ADA) recommended dietary guidelines, focusing on availability, price, and quality. All studies using the NEMS-S instrument collected food price data in physical instore locations. While the instrument itself does not include a calculation of relative affordability, approximately half the NEMS-S studies included this step in their methods [ 33 , 36 , 44 , 47 , 48 , 54 , 57 ], while all others did not [ 10 , 11 , 56 , 68 , 71 , 106 , 107 , 108 ].

Other Instruments

Several other named instruments were identified, used in single studies. These included the Diet and Nutrition Tool for Evaluation (DANTE) [ 101 ], the Flint Store Food Assessment Instrument [ 60 ], the Food Label Trial registry tool [ 76 ], the New Zealand Food Price Index [ 111 ], the USDA Food Store Survey Instrument [ 73 ], USDA Low-cost food plan [ 55 ] and audit forms developed by the Yale Rudd Center [ 39 ], the Hartford Advisory Commission on Food Policy [ 59 ], and the USDA Authorized Food Retailers’ Characteristics and Access Study [ 43 ]. Only three instruments compared healthy and unhealthy products [ 43 , 76 , 111 ] and none analysed the relative affordability of food.

Instrument Strengths and Limitations

The strengths and limitations of instruments commonly used across studies, as identified by study authors, are presented in Online Resource 2 . Commonly cited limitations, regardless of instrument used, included that actual purchasing behaviours were not captured (unless electronic point of sales data was utilised); culturally important and region-specific products were often not captured; tools were cross-sectional in nature, thus seasonality or changes overtime were not considered; and out-shopping, described as food purchases undertaken outside the local residential geography, including internet orders or foods purchased during travel to other communities, could not be accounted for. While some food basket studies and those using the ASAP instrument did contextualise the relative affordability of healthy foods and/or diets, this was not a part of the methodology for NEMS-S. Other limitations specific to NEMS-S included the length of the survey, and a low convergence between NEMS-S results and consumer perceptions of affordability. Specific limitations for food basket studies included results being constrained by the reference family used and the assumption that food is shared equally among household members. Additionally, most instruments did not capture geographical information regarding access to food retail outlets or availability of foods within food retail outlets.

Authors less commonly described instrument strengths. For NEMS-S, cited strengths included the ability to compare food prices between healthy and unhealthy options, that it has strong inter-rater and test-re-test reliability, and that it has been validated in multiple countries. ASAP studies, and some food basket studies, included a comparison between healthy and current (‘unhealthy’) diets (based on actual consumption) and included alcohol in the survey.

Our systematic review details the key purposes, and methodologies used, for measuring food prices in HIC between 2006 and 2021. While most studies were conducted solely to monitor food prices in specific locations, some sought to report price changes over time, and others collected data to assess comparability of food costs to healthier alternatives, average earnings, welfare payments, rurality, and socioeconomic position. Most studies measured food prices in urban areas, using instore food price audits, with an emerging use of online data collection evident. The most frequently used instruments were ‘food baskets’, used predominantly to monitor food prices; the NEMS-S instrument, used to provide data on relative cost and availability; and the ASAP instrument, use to provide data on relative affordability.

Our review differs from previous reviews of food price and affordability instruments [ 23 , 28 ] by taking a broadened focus on food pricing measures used in HIC globally and including new technology that is affording opportunities for electronic food pricing data collection. While a previous review critiqued food pricing measures for relevance specific to a rural context, our review includes both rural and urban contexts [ 28 ]. Another review [ 23 ] also describes the components of individual instruments, such as the identification of differently sized ‘food baskets’, ranging between 30 and 200 food items. Such critique was beyond the scope of our research questions.

Despite emerging options for electronic methodologies, the predominance of in person, instore data collection continues, notwithstanding the time-consuming and resource-intensive nature of this method. Studies indicate that these instore instruments can be targeted and applied within multiple contexts, such as rural [ 9 , 10 , 11 , 12 ], Indigenous [ 129 , 130 ], and low socioeconomic areas [ 85 ]. Perhaps researchers consider instore data collection as providing real-world insights at a community and population health level. Our review identified that food pricing instruments were mostly used to monitor food prices at a single point in time (cross-sectional) rather than changes at different time points (longitudinal). Instruments that enable the comparison of food prices in terms of a healthy diet (as recommended by dietary guidelines) compared with current dietary patterns (as reported through population health surveys) [ 128 ], and relative affordability for families, appear to provide data of greater practice and policy relevance with regard to community strategies, taxes, and subsidies that have potential to enhance food affordability, availability, and accessibility.

Technological innovations are an emerging alternative to in person data collection, facilitating the acquisition of online supermarket prices, a less labour-intensive method for capturing food prices [ 131 ]. To date, this method has been used within major chain-supermarkets, with a recent study reporting similar results when comparing pricing data obtained instore versus online [ 94 ]. This method therefore holds potential where an online supermarket presence exists, which was increasingly the case during the COVID-19 pandemic [ 53 ], providing rapid feedback to inform price promotions. However, for smaller and/or independent food retail outlets, frequently located in rural areas, online data collection does not appear to capture the contextual nuances of instore price promotions.

Our review found an over-representation of food pricing studies within urban areas. This is consistent with multiple studies that reflect inequities experienced within rural environments [ 132 ], and rural food environments are no exception [ 133 ]. The predominance of research within urban areas may also reflect a pragmatic researcher response to the physical proximity of stores (ease of measurement) and larger population reach (potential for greater population impact). Previous research shows significant differences in income-based variables, food environments, and the affordability of healthy food between urban and rural settings [ 134 ]. There is therefore a need for rural-specific food pricing studies, using appropriate instruments, to evaluate and inform rural-specific food environment initiatives [ 28 ].

During the period covered by this review, high level experts from the World Health Organization [ 135 ], the Lancet Commission [ 136 ], and the Food and Agricultural Organisation of the United Nations [ 137 ] have identified the potential benefits that initiatives located within food retail environments can provide in nudging dietary choices towards healthier options through instore food pricing and promotion, with the overall aim of improving population level diets [ 14 ]. Measures of food pricing, and the relative affordability of a healthy diet, are important to both inform and measure the effectiveness of such initiatives. However, few studies in our review explicitly aimed to inform initiatives or strategies, either at the community or policy level. Assessment of author-reported strengths and limitations of food pricing instruments and methodologies also identified a need for a universal instrument that reflects contextual geographic and socio-cultural information; is intended to be used repeatedly over time; and is adaptable to different country/cultural/contextual settings [ 17 , 23 ]. Future research would benefit from linking the purpose of undertaking food pricing data collection more explicitly to potential initiatives. Our review supports this call and suggests that the instrument selected should suit the context and collect longitudinal data to provide greater insights into the design and effectiveness of initiatives that make healthy food not only affordable but also available and accessible.

Strengths and Limitations

This systematic review provides a current and comprehensive overview of international food pricing studies across HIC. We acknowledge that while food prices are an important factor influencing food choice, it is only one component of the food environment; however, analysing instruments that assess food acceptability, availability, and accessibility was beyond the scope of this review. This review focused on HIC and a similar review on food pricing studies in low- and middle-income countries would be informative. This review may have missed additional relevant data as it only included English language studies and did not include grey literature or hand searching of reference lists.

Food security has come under heightened scrutiny given the food supply interruptions experienced worldwide during the COVID-19 pandemic. While studies providing a snapshot of food prices can be useful to identify areas impacted by rising food prices, much of this cross-sectional data is known. This review raises questions regarding the purpose of collecting food price data, and how this data can best be used to inform change through practice and policy strategies. We suggest that longitudinal studies using a consistent methodology, which acknowledges contextual nuances and demonstrates temporal changes in food pricing, are needed to inform and to evaluate community-based or legislative strategies to improve the relative affordability of a healthy diet.

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Open Access funding enabled and organized by CAUL and its Member Institutions CR is supported by an Australian Government Research Training Scholarship. This funder had no involvement in any aspect of the study. JW is funded by a Deakin University Dean’s Postdoctoral Research Fellowship. JW is also supported by the National Health and Medical Research Council (NHMRC) funded Centre of Research Excellence in Food Retail Environments for Health (RE-FRESH) (APP1152968). The opinions, analysis, and conclusions in this paper are those of the authors and should not be attributed to the NHMRC.

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Russell, C., Whelan, J. & Love, P. Assessing the Cost of Healthy and Unhealthy Diets: A Systematic Review of Methods. Curr Nutr Rep 11 , 600–617 (2022). https://doi.org/10.1007/s13668-022-00428-x

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Therapeutic Potential of Various Intermittent Fasting Regimens in Alleviating Type 2 Diabetes Mellitus and Prediabetes: A Narrative Review

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• 1 Department of Human Physiology, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban 4041, South Africa.
• PMID: 39203828
• PMCID: PMC11357349
• DOI: 10.3390/nu16162692

Intermittent fasting has drawn significant interest in the clinical research community due to its potential to address metabolic complications such as obesity and type 2 diabetes mellitus. Various intermittent fasting regimens include alternate-day fasting (24 h of fasting followed by 24 h of eating), time-restricted fasting (fasting for 14 h and eating within a 10 h window), and the 5:2 diet (fasting for two days and eating normally for the other five days). Intermittent fasting is associated with a reduced risk of type 2 diabetes mellitus-related complications and can slow their progression. The increasing global prevalence of type 2 diabetes mellitus highlights the importance of early management. Since prediabetes is a precursor to type 2 diabetes mellitus, understanding its progression is essential. However, the long-term effects of intermittent fasting on prediabetes are not yet well understood. Therefore, this review aims to comprehensively compile existing knowledge on the therapeutic effects of intermittent fasting in managing type 2 diabetes mellitus and prediabetes.

Keywords: HbA1c; glucose tolerance; intermittent fasting; prediabetes; type 2 diabetes mellitus.

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• Mattson M.P., Longo V.D., Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res. Rev. 2017;39:46–58. doi: 10.1016/j.arr.2016.10.005. - DOI - PMC - PubMed
• McDonald R.B., Ramsey J.J. Honoring Clive McCay and 75 Years of Calorie Restriction Research. J. Nutr. 2010;140:1205–1210. doi: 10.3945/jn.110.122804. - DOI - PMC - PubMed
• He S., Wang J., Zhang J., Xu J. Intermittent Versus Continuous Energy Restriction for Weight Loss and Metabolic Improvement: A Meta-Analysis and Systematic Review. Obesity. 2021;29:108–115. doi: 10.1002/oby.23023. - DOI - PubMed
• Cook F., Langdon-Daly J., Serpell L. Compliance of participants undergoing a ‘5-2’ intermittent fasting diet and impact on body weight. Clin. Nutr. ESPEN. 2022;52:257–261. doi: 10.1016/j.clnesp.2022.08.012. - DOI - PubMed
• Taylor R. Calorie restriction for long-term remission of type 2 diabetes. Clin. Med. 2019;19:37–42. doi: 10.7861/clinmedicine.19-1-37. - DOI - PMC - PubMed

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September National Health Observances: Healthy Aging, Sickle Cell Disease, and More

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Optimal Diet Strategies for Weight Loss and Weight Loss Maintenance

Ju young kim.

Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

Obesity has become one of the most important public health problems worldwide, which suggests the need for evidence-based dietary strategies for weight loss and its maintenance. Weight management depends upon complex factors such as amount of food eaten, type of food eaten, and timing of meals. In this review, we identified evidence-based dietary strategies for weight management based on these three components. An energy deficit is the most important factor in weight loss. A low-calorie diet with a low fat or carbohydrate content has been recommended; however, in some cases, a very-low-calorie diet is required for a short period. Some macronutrient composition-based diets, such as the ketogenic diet or high-protein diet, could be considered in some cases, although the potential risks and long-term effectiveness remain unknown. Meal timing is also an important factor in weight management, and higher-calorie breakfasts in combination with overnight fasting may help to prevent obesity. Our review indicated that there is no single best strategy for weight management. Hence, strategies for weight loss and its maintenance should be individualized, and healthcare providers must choose the best strategy based on patient preferences.

INTRODUCTION

More than 650 million adults worldwide suffer from obesity, and the prevalence of this condition has increased rapidly during the past 50 years. 1 Obesity has become one of the most important public health problems globally and is strongly associated with type 2 diabetes mellitus (T2DM); cardiovascular diseases including myocardial infarction and stroke; osteoarthritis; obstructive sleep apnea; depression; and some types of cancer, such as breast, ovarian, prostate, liver, kidney, and colon cancer. 2 , 3

Optimal diets for weight management have been a topic of debate not only among researchers, nutrition experts, and healthcare professionals, but also among the general public. 4 , 5 According to a meta-analysis of several diet programs, calorie restriction was the primary driver of weight loss, followed by macronutrient composition. 6 Another study examined the effects of popular diets without specific calorie targets and showed that the Atkins diet resulted in clinically meaningful weight loss after 6 months. 7 In contrast, another review revealed that the Atkins, Weight Watchers, and Zone diets resulted in modest and similar long-term weight loss after 1 year. 8 Recently, intermittent fasting and time-restricted eating have become popular and seem to be effective for weight loss. 9 However, several questions remain unanswered. Does a high-protein diet aid in weight loss and maintenance? Can a ketogenic diet burn fat? Do carbohydrates increase abdominal fat? Can intermittent fasting help one lose weight? New dietary information has only added to the current confusion due to several controversial dietary regimens, and there is no clear guidance on the optimal diet for weight loss.

Obesogenic environments and biological and psychological factors all contribute to obesity. 10 However, obesogenic environments, including social determinants, cultures, and food supply systems, are challenging to modify. Therefore, dietary interventions remain the cornerstone of weight-management strategies, and pharmacologic and surgical interventions also aim to improve dietary management.

Complex factors shape and influence diets, especially for weight management. However, amount of food eaten, type of food consumed (macronutrient composition), and meal timing of meals are the key components of weight-management strategies. In this review, we discuss several evidence-based dietary interventions for weight loss and weight-loss management based on these components.

AMOUNT OF FOOD INTAKE

Low-calorie diet vs. very-low-calorie diet.

The key component of diets for weight loss and weight-loss maintenance is an energy deficit. Under the “calories-in, calories-out” model, dietary management has focused on the concept of “eat less, move more,” and patients have been advised to consider and calculate their calorie balance whenever they eat. However, energy intake and energy expenditure are dynamic processes influenced by body weight and influence each other. 11 Thus, interventions aimed at creating an energy deficit through the diet are countered by physiological adaptations that resist weight loss.

A low-calorie diet involves consumption of 1,000–1,500 calories per day; deficits of 500–750 calories per day have been used for weight loss and are recommended by many obesity societies and guidelines. 12 - 15 Low-calorie diets typically restrict fats or carbohydrates, neither of which has been determined to be more important for weight loss if only a calorie deficit occurs. The 2018 the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study found no significant differences in weight loss between low-fat and low-carbohydrate diets. 16 However, meal planning and preparation take effort, and weight-loss maintenance requires a sustained low-calorie diet. Moreover, metabolic adaptations to decrease energy expenditure can lead to a plateau with this type of diet, which individuals may misinterpret as “failure” due to “lack of willpower.”

Traditionally, a very-low-calorie diet (VLCD), which provides <800 kcal a day, is not recommended for routine weight management and should only be used in limited circumstances along with medical monitoring according to obesity guidelines. 12 However, a recent review suggested that a VLCD used in combination with behavioral programs can provide greater long-term weight loss than behavioral programs alone, and that it is tolerable and has few adverse effects. 17 Additionally, a VLCD with meal replacement is effective for achieving diabetes remission in individuals with obesity lasting for at least 2 years. 18 , 19 Another form of the VLCD—the very-low-calorie ketogenic diet (VLCKD)—has been proposed as a promising option for significant weight loss in a short duration of time and stability for 2 years. 20 The VLCKD consists of very-low-calorie (<700–800 kcal/day) and low-carbohydrate (<30–50 g/day) intake along with adequate protein consumption (equivalent to 0.8–1.2 g/day/kg of ideal body weight) for a short period, followed by a gradual switch to a low-calorie diet. The VLCKD program is recommended by the Italian Society of Endocrinology in cases of severe obesity, sarcopenic obesity, obesity associated with T2DM, hypertriglyceridemia, and hypertension. 21 However, this program is contraindicated in pregnant women; those with type 1 diabetes mellitus (T1DM), kidney failure, or cardiac arrhythmia; and older patients with frailty.

Meal replacements

Meal replacements include not only products marketed as soups, shakes, and bars, but also portion-controlled, ready-made meals. Meal replacements are used instead of “normal” food for one or more meals to reduce the daily calorie intake. Meal replacements can be useful for calorie control because people tend to overestimate or underestimate the amount of calories in food. 22 A systematic review showed the usefulness of meal replacement in weight loss, demonstrating a mean difference of –2.22 to –6.13 kg compared with other diets involving support alone. 23 Despite their convenience and affordability, meal replacements are typically not successful for maintaining weight loss over a long duration.

In summary, evidence shows that an energy deficit is the most important factor for weight loss, but metabolic adaptations to decrease energy intake can also lead to reduced energy expenditure. Therefore, long-term strategies for inducing an energy deficit are needed. If traditional low-calorie diet programs do not work or when there is a need for significant weight loss, a VLCD and meal replacement diets can be useful options.

TYPES OF FOOD EATEN

Low-fat diet.

The strategy of reducing total fat intake is widely used for weight loss because a single gram of fat contains more calories than a gram of carbohydrates or protein. A low-fat diet usually consists of a dietary composition of fat ranging from very low (≤10% of calories from fat) to more moderate (≤30% of calories from fat and <7%–10% from saturated fatty acids). 24 However, randomized trials have failed to demonstrate better weight-loss maintenance by reducing energy intake from fat than other dietary interventions. 25 The results of a meta-analysis did not support use of low-fat diets over other dietary interventions for long-term weight loss. 26

In another study, although low-density lipoprotein cholesterol (LDL-C) level was reduced among individuals with obesity who followed a low-fat diet, triglyceride level increased and high-density lipoprotein cholesterol levels decreased. 27 Another review revealed that diets high in fat and saturated fatty acids could have unfavorable effects on the gut microbiota and are associated with an unhealthy metabolic state. 28 Consuming large amounts of energy-dense foods with a high saturated fatty acid content can cause dysbiosis in the gut and is associated with obesity and low-grade chronic inflammation. 29 Thus, diets low in saturated fatty acids, as well as those supplemented with good-quality fat and fibers, are a reliable and healthy strategy for people with obesity to achieve weight management and to prevent some types of cancer (including colorectal cancer and breast cancer)30,31 when combined with total calorie restriction.

Low-carbohydrate diet

Although an energy deficit is the most important way to lose weight, weight regain after successful weight loss is very common and may seem inevitable. Thus, alternative dietary approaches for weight loss and its maintenance have become an area of interest among researchers and healthcare professionals.

Low-carbohydrate (low-carb) diets have been widely used not only for weight reduction, but also to manage T2DM; many randomized controlled trials have been conducted. 32 , 33 A low-carb diet is defined as a carbohydrate intake below the lower boundary of the macronutrient distribution range for healthy adults (45%–65% of total daily energy)34 and encompasses a range of carbohydrate intake from 50–130 g/day or 10%–45% total energy from carbohydrates. 35 , 36 With carbohydrate intake <10% (or <20–50 g/day), nutritional ketosis can occur; this type of diet is called a ketogenic diet. In this situation, daily protein intake is usually 0.8–1.5 g/kg of ideal body weight to preserve lean body mass. 37

A systematic review and network meta-analysis that compared 14 dietary macronutrient patterns showed that most macronutrient diets resulted in modest weight loss over 6 months, but weight reduction and improvements in cardiometabolic factors largely disappeared after 12 months. 38 A review of macronutrient pattern-based diets without specific calorie targets suggested that the Atkins diet, which involves low carbohydrate intake and high protein intake, was effective for clinically meaningful weight loss at 6 and 12 months post-initiation. 7 Moreover, a recent review showed that the Mediterranean diet provided the strongest and most consistent benefits for both weight loss and improvement in cardiometabolic parameters. 39 It should be noted that adequate protein intake is extremely important in calorie restriction for preservation of muscle mass 40 regardless of diet.

Ketogenic diet

Ketogenic diet is characterized by an extreme reduction in carbohydrate intake (<50 g/day) and a relative increase in the proportions of protein and fat. 41 Ketogenic diets may decrease appetite and increase lipolysis, which may result in greater metabolic efficiency for fat consumption and can provide the same thermic effects as proteins. 41 There are several types of carbohydrate-restricted diets, some of which limit carbohydrates to certain levels without restricting dietary protein and fat (such as the Atkins diet), whereas others allow moderate carbohydrate intake as well as moderate protein and fat intake. 37

In one study, ketogenic diet showed mixed effects on LDL-C level and was not superior to other dietary interventions for weight loss. 37 Ketogenic diet can suppress hunger during calorie restriction and may have some therapeutic effects on T2DM, polycystic ovary syndrome, and cardiovascular and neurological diseases. However, more evidence is required to confirm its effectiveness and safety. 37 , 41 , 42 Similar to VLCKDs, ketogenic diet is contraindicated in pregnant women; those with T1DM, kidney failure, or cardiac arrhythmia; and in older patients with frailty.

High-protein diet

High-protein diet has been popularized as a promising tool for weight loss because it improves satiety and decreases fat mass. 43 Dietary guidelines for adults recommend protein intake of 46–56 g or 0.8 g/kg of ideal body weight per day. 44 Thus, if dietary protein consumption exceeds 0.8 g/kg/day, it is considered a high-protein diet. Usually, a high-protein diet refers to an increased protein intake to 30% of the total daily calories or 1–1.2 g/kg of the ideal body weight per day. The Atkins diet has gained popularity as a non-energy-restricting, low-carbohydrate, high-protein, and high-fat diet. 45 In addition, diets high in protein with normal amounts of carbohydrates have been used to improve metabolic parameters. 46 Diets with higher protein intake can provide significant benefits to prevent weight regain. 47 A satiating effect is most significant with high-protein diets, and this effect helps decrease energy intake and maintain successful weight loss.

The thermic effect of food, which is called diet-induced thermogenesis, is increased energy expenditure that results from nutrient processing; these values are highest for protein. 48 Secretion of gut neuropeptides that induce satiation, such as glucagon-like peptide-1 or cholecystokinin, is increased in high-protein diets. 49 These types of diets also may help preserve lean body mass during weight loss. 50 A meta-analysis showed that protein supplementation can help preserve lean body mass in adults and older adults, although the effects on muscle strength and synthesis were less clear. 51

Some studies have indicated that high intakes of protein and fat can increase the risk of T2DM. 52 In addition, diets high in protein pose a potential risk to the kidneys due to their associated protein-induced acid loads, such as the sulfuric acid produced from oxidation of methionine and cysteine. 53 High-protein diets do not adversely influence kidney function in healthy adults, though they are associated with increases in serum urea level and urinary calcium excretion, which might be related to a higher risk of kidney stone formation. 54 Protein from red meat consumption may increase the risk of chronic kidney disease; in contrast, low-fat dairy proteins, fish, and seafood do not have such an effect; proteins from fruits and vegetables actually might be renal protective. 55 Considering that obesity is associated with chronic kidney disease and a high prevalence of subclinical chronic kidney disease, 56 long-term high-protein intake, especially from animal sources, should be closely monitored in patients with obesity. 57

Mediterranean diet

The Mediterranean diet involves high intake of fruits and vegetables, poultry, and fish and dairy products, and little to no consumption of red meat. 58 The effectiveness of the Mediterranean diet for weight loss and preventing cardiovascular disease is supported by sufficient evidence. 59 , 60 Its benefits may extend to the reduction in cancer risk and significant reduction in digestive cancer risk. 61 Additionally, adherence to a Mediterranean diet may improve cognitive function and decrease the risk of dementia, although the evidence supporting this association is weak to moderate. 62 One systematic review of the Mediterranean diet for long-term weight loss reported similar results to other diets despite greater weight loss than with a low-fat diet after 12 months. 63 Despite this finding, a recent review reported that the Mediterranean diet showed the strongest evidence for weight loss and improvements in cardiometabolic parameters ( Fig. 1 ). 39 The Mediterranean diet is food-based, nutrient adequate, and focused on vegetables, healthy fats, and fish; thus, it is a good strategy for maintaining long-term weight reduction.

Summary of the results reported by a meta-analysis of randomized controlled trials in adults according to dietary interventions. Green: evidence of a beneficial effect (i.e., a decrease in all outcomes except HDL-C); grey: no effect; red: evidence of a detrimental effect (i.e., an increase in all outcomes except HDL-C). The size of the circles reflects the number of unique meta-analyses available. BMI, body mass index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; HbA1c, glycated hemoglobin; BP, blood pressure; GI, glycemic index; GL, glycemic load; ER, energy restriction; DASH, Dietary Approaches to Stop Hypertension. Adapted from Dinu M, et al. Adv Nutr 2020;11:815-33, with permission from Elsevier. 39

Low-fat and low-carbohydrate diets are good options for initial weight loss; in some cases, a ketogenic diet could be a viable alternative. High-protein diets may be effective in weight loss maintenance, and the Mediterranean diet not only helps with weight loss maintenance, but also aids in improving cardiovascular risk factors, cognitive functions, and mood.

OTHER DIETARY STRATEGIES

Paleolithic.

The Paleolithic (Paleo) diet is also known as the hunter-gatherer diet, caveman diet, primal diet, or Stone Age diet; all these diets suggest that our bodies have not evolved to handle highly processed foods. 64 This diet follows the nutritional patterns of early humans who lived in the Paleolithic era, which began more than 2 million years ago and continued until about 10,000 years ago, when humans started to cultivate plants and domesticate animals. Estimates are that our ancestors took in 35% of their calories from fat, 35% as carbohydrates (mostly fruits and vegetables), and 30% from protein. 65 This diet advises consuming lean meat, fish, vegetables, fruits, and nuts while avoiding grains, dairy products, processed foods, and added sugar and salt. A review regarding the Paleo diet and its impact on cardiovascular risk factors suggested that it has favorable effects on lipid profile, blood pressure, and circulating C-reactive protein concentrations, but the evidence is not yet conclusive. 66 The Paleo diet emphasizes vegetables and unprocessed foods, but it is also high in saturated fats, which might increase the risk of cardiovascular disease.

Low glycemic index/glycemic load diet

The glycemic index (GI) is a measurement system that ranks foods according to effect on blood glucose level; the rates at which different foods raise blood glucose level are ranked in comparison with absorption of 50 g of pure glucose as a reference (GI=100). 67 A low-GI diet emphasizes exchanging high-GI foods for low-GI alternatives. Nothing is strictly forbidden with the low-GI diet, but high-GI foods such as white bread, bagels, cereals, mashed potatoes, pasta, and noodles should be replaced by low-GI foods. The low-GI diet offers benefits in managing T2DM and decreasing body weight. 68 However, one study found no differences in weight among low-GI diets and a diet based on healthy nutritional recommendations among overweight adolescent girls. 69 The low-GI diet also does not provide a complete nutritional picture and does not include recommendations for daily intake of fat, protein, or fiber.

Nordic diet

The new Nordic diet is based on unprocessed whole grains, high-fiber vegetables, fish, low-fat dairy foods, lean meat of all types (beef, pork, lamb), beans and lentils, fruit, dense breads, tofu, and skinless poultry. 70 This diet recommends more calories from plant foods and fewer from meat and more foods from the sea, lakes, and the wild countryside. It is based on whole and minimally processed foods and is high in both fiber and omega-3 fats. A systemic review showed that adherence to the Nordic diet significantly improved body weight. 71 However, these types of food may be not easily accessible or affordable for everyone and may make the diet difficult to maintain.

Vegetarian diet

There are many reasons to adopt a vegetarian diet for health. These diets can lower the risk of ischemic heart disease, T2DM, and cancer. 72 Vegetarian diets can reduce blood pressure, 73 lipid profiles, 74 and inflammatory biomarkers 75 and improve glycemic control 76 and other cardiometabolic risk factors. 77 This diet excludes meat, fish, and poultry, but there are many variations of the diet, including lactovegetarians and lacto-ovo-vegetarians. Dietary guidelines recommend vegetarian-patterned diets. 78 A systemic review suggested that vegetarian diets reduce mean body weight, but the studies are few and of variable quality. 79 Since fish and seafood are excluded, this diet is low in omega-3 fats.

Dietary approaches to stop hypertension

The Dietary Approaches to Stop Hypertension (DASH) diet was originally developed to lower blood pressure without medication, but it is now considered one of the healthiest eating patterns. 80 The DASH plan includes many vegetables, fruits, and grains with an emphasis on whole grains. Low-fat or non-fat dairy foods, pulses, nuts, seeds, lean meats, poultry, and seafood are also allowed. The diet limits sodium intake to 2,300 mg/day and can reduce the risk of cancer, 81 cardiovascular risk factors, 82 and both all-cause and cause-specific mortality. 83 The DASH diet also aids in losing weight, but the differences were relatively small. 84

The Portfolio diet is a vegan plan that emphasizes a “portfolio” of foods or food components that lower cholesterol. 85 When these foods are eaten together as part of a healthy diet, they presumably lower LDL-C better than any one of the portfolio foods could alone. To include a portfolio of cholesterol-lowering foods, the diet recommends daily consumption of 2 g of plant sterols, 50 g of nuts, 10–25 g of soluble fibers from plant foods, and 50 g of soy protein; meat, poultry, seafood, dairy, and eggs are not allowed. This diet also helps reduce LDL-C, 86 but its effects on weight loss were small (–0.8 kg to –1.2 kg). 87

TIMING OF MEAL CONSUMPTION

Intermittent fasting.

Recently, fasting has received interest not only from medical experts, but also from members of the general public with an interest in health. Intermittent fasting involves regular periods with no or very limited calorie intake. The three most widely used regimens are alternate-day fasting, 5:2 intermittent fasting (fasting or consuming 900–1,000 calories for 2 days each week), and daily time-restricted feeding (fasting for 16–18 hours a day). 88 The benefits of intermittent fasting come not only from reduction in calorie intake, but also from its effects on metabolic switching to reverse insulin resistance, strengthen the immune system, and enhance physical and cognitive functions. 88 Recent reviews on intermittent fasting have suggested that, as a weight-loss strategy, it could benefit patients with obesity and has effects comparable to daily calorie restriction. 89 However, little is known about the long-term sustainability and health effects of this type of fasting.

Intermittent fasting focuses on the time window of eating instead of calorie calculations or macronutrient composition, helping people to restrict food intake without having to count calories and to avoid late-night snacking. With this diet, careful attention should be paid to patients being treated with hypoglycemic agents because fasting might cause dizziness, general weakness, halitosis, headache, chills, and lack of concentration, although no serious adverse events have been reported. 89

Meal timing

Recently, much interest has focused on “when to eat.” Meal timing and the circadian rhythm have raised a novel issue in weight management. 90 Alterations in circadian rhythms produce biochemical, physiological, and behavioral circadian rhythm disruptions, which can be caused by the lack of change between day/night synchronization (such as being exposed to artificial light at night), eating at night, or a shift in time due to jet lag or shift work. 90 Eating late can cause circadian disruption, resulting in production of free cortisol, changes in daily rhythms of body temperature, decreased resting energy expenditure, and decreased glucose tolerance. 91 , 92 Thus, timing of meals could have serious implications not only for weight management, but also for development of cardiovascular disease. A recent review confirmed that skipping breakfast increased the risk of overweight and obesity. 93 Additionally, late-night eating was associated with obesity as well as metabolic syndrome. 94

The American Heart Association recommends distributing calories over a defined period of the day, consuming a greater share of the total calorie intake earlier in the day, and maintaining consistent overnight fasting periods. 95 Eating a high-calorie breakfast and overnight fasting could have positive effects on prevention of obesity, while intermittent fasting may help control calorie intake in people with obesity.

There is no single best strategy for weight management, although some evidence-based methods have been suggested ( Table 1 ). Reducing daily calorie intake is the most important factor for weight loss. Low-calorie recipes, especially those for low-fat or low-carbohydrate diets, have been suggested as the first dietary strategy, although in some cases, a VLCD is required for a short period. Except for energy deficit, there seems to be no significant difference between macronutrient composition-based diets. Improvement in cardiometabolic factors strongly depends on degree of weight loss. However, as in the Mediterranean diet, increasing consumption of fruits and vegetables and intake of healthy fats (including monounsaturated as well as polyunsaturated fats) can be a healthy strategy for weight loss and maintenance. Additionally, increased protein intake can help with weight loss maintenance.

Dietary strategies for weight loss

Eating breakfast and avoiding late-night eating should be considered important dietary strategies not only for weight loss, but also for metabolic health and are based on the physiologic clock. Time-restricted eating or intermittent fasting can be considered other options for weight loss and its maintenance. Maintenance of a low-calorie intake should be continued throughout an individual’s lifespan. Thus, the best diet for weight management is one that can be maintained in the long term. Healthcare providers should consult with patients before choosing the optimal diet strategy because successful weight loss and its maintenance depend on the patient’s choices, preferences, and long-term adherence to the diet plan.

CONFLICTS OF INTEREST

Ju Young Kim is the Editorial Board member of the Journal of Obesity & Metabolic Syndrome. However, she is not involved in the peer reviewer selection, evaluation, or decision process of this article. Otherwise, no other potential conflicts of interest relevant to this article were reported.

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Writing Seminar in Global English: Global Health (Fall 2024 ): Researching the White Paper

• Getting started
• News and Opinion Sites
• Academic Sources
• Grey Literature
• Substantive News Sources
• What to Do When You Are Stuck
• Understanding a citation
• Examples of Quotation
• Examples of Paraphrase
• Chicago Manual of Style: Citing Images
• Researching the Op-Ed
• Researching Prospective Employers
• Resume Resources
• Cover Letter Resources

Research the White Paper

Researching the white paper:.

The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper.  First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain. 

Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of  providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author.     
 The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it.  You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources. 

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