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Personal, behavioural and socio-environmental correlates of emerging adults’ sustainable food consumption in a cross-sectional analysis

Published online by Cambridge University Press:  04 April 2023

Elizabeth Ludwig-Borycz*
Affiliation:
Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA
Dianne Neumark-Sztainer
Affiliation:
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
Nicole Larson
Affiliation:
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
Ana Baylin
Affiliation:
Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
Andrew D Jones
Affiliation:
Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA
Allison Webster
Affiliation:
International Food Information Council Foundation, Washington, DC, USA
Katherine W Bauer
Affiliation:
Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA
*
*Corresponding author: Email lizzer@umich.edu
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Abstract

Objective:

Describe how dietary intake patterns of US young adults align with the EAT-Lancet Planetary Health Diet (PHD) sustainable diet goals and identify personal, behavioural, and socio-environmental correlates of sustainable intake.

Design:

Data on past-year dietary intake were captured using a FFQ. The PHD was applied to specific food groups, and a total PHD score was calculated. Linear regression models were used to identify associations between personal, behavioural and socio-environmental factors and PHD scores.

Setting:

This cross-sectional analysis uses data from the second wave of EAT 2010–2018 (Eating and Activity over Time), a population-based longitudinal study recruited in Minnesota.

Participants:

Ethnically/racially diverse group of participants (n 1308) with a mean age of 22·1 (sd 2·0) years.

Results:

The mean PHD score was 4·1 (sd 1·4) on a scale of 0–14, with 14 representing the most sustainable. On average, participants consumed fewer whole grains, fish, legumes, soya, and nuts than ideal for a sustainable diet, and an excess of eggs, added sugar, and meat. The PHD score was higher for participants with higher socio-economic status (SES) and greater educational attainment. Higher home availability of healthy food (β = 0·24, P < 0·001) and less frequent fast-food consumption (β = –0·26, P < 0·001) were the strongest correlates of PHD scores.

Conclusions:

Results suggest that a high percentage of participants may not be achieving the sustainable diet goals defined by the PHD. Reductions in meat consumption and increases in plant-based foods are necessary to increase the sustainability of US young adults’ diets.

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Nutrition Society

In 2015, the Paris Agreement set the goal to limit the global temperature increase to less than 2°C to mitigate the devastating effects of climate change(1). Sustainable food systems are essential to meeting the goal of the Paris Agreement because agriculture is responsible for about 25 % of greenhouse gas emission globally, more than 70 % of freshwater use(Reference Willett, Rockström and Loken2), 80 % of deforestation(Reference Kissinger, Herold and DeSy3), and is the single largest contributor to biodiversity loss(Reference Tilman, Clark and Williams4). A sustainable food system involves diets that provide for both the flourishing of human and environmental health and are affordable, equitable, safe, and culturally appropriate(5). Individuals can support sustainable food systems by consuming a diet comprised of foods that arise from sustainable practices.

As the prevalence of overweight and obesity has increased beyond 2 billion globally(Reference Swinburn, Sacks and Hall6), another 2 billion individuals remain micronutrient-deficient(Reference Tulchinsky7) and 821 million individuals are undernourished (habitual insufficient energetic intake)(8). Identifying ways to optimise human health that fit within safe planetary boundaries is imperative both to combat climate change and meet nutritional needs(Reference Rockström, Steffen and Noone9). Globally, nations are working to mitigate climate change and maximise human nutrition by incorporating sustainability into their dietary recommendations. The EAT-Lancet Commission brought together experts in the fields of human health, agriculture, political science and environmental sustainability to help meet the Sustainable Development Goals and Paris Agreement, which allows for feeding an estimated 10 billion people globally by 2050(Reference Willett, Rockström and Loken2). The EAT-Lancet Planetary Health Diet (PHD)(Reference Willett, Rockström and Loken2) was developed by the EAT-Lancet Commission in 2019 as one approach to establish an adaptable metric with which to assess diet sustainability in a manner that simultaneously recognises the environmental and health impacts of consumption of various food groups. The PHD was designed to be healthy for humans and the environment with regard to greenhouse gas emission, nitrogen and phosphorus application, agricultural water use, biodiversity loss, and cropland use(Reference Garnett10) and relies predominantly on plant-based foods which is consistent with a recent literature review(Reference Reinhardt, Boehm and Blackstone11).

In 2019, Wang et al. estimated that 25 % of premature deaths could be prevented if the US populations consumed diets that aligned with the PHD(Reference Wang, Li and Afshin12). Notwithstanding, some shortcomings of the diet have been noted, particularly in relation to the affordability of the PHD. Calculated as costing an average of US$2·65 per d in 2011, the PHD is affordable for the vast majority of US population groups(Reference Hirvonen, Bai and Headey13) even so, many Americans may find the PHD challenging to adopt as it differs from current US dietary patterns(Reference Perera, Russo and Takata14) and affordability does not necessarily translate to accessibility.

However, few studies have assessed the extent to which national dietary recommendations and current intake patterns of US populations align with the PHD goals. This assessment is important as the Dietary Guidelines for Americans (DGA) are used to inform many federal nutrition programmes and public health strategies targeting health promotion and disease prevention. Blackstone and Conrad(Reference Blackstone and Conrad15) identified that the 2015–2020 DGA fall below recommendations for sustainable dietary intake based on the PHD, and a recent analysis of US school lunches served at elementary, middle and high schools found that these meals were particularly low in whole grains and vegetables, while high in meat and dairy products, when compared with the PHD(Reference Poole, Musicus and Kenney16). These findings suggest that US nutrition programmes and actual dietary intake may likely be substandard with respect to diet sustainability, particularly when measured by the PHD.

Further, the factors that support consumption of sustainable diets have not been rigorously examined. A small number of large population-based studies conducted among adults in the USA, France and Poland have identified that individuals who consume more sustainable diets have a lower BMI(Reference Seconda, Baudry and Pointereau17), engage in more physical activity(Reference Górnicka, Drywień and Zielinska18), consume less fast food and alcohol(Reference Seconda, Baudry and Pointereau17,Reference Larson, Laska and Neumark-sztainer19) , and overall, have better diet quality(Reference Seconda, Baudry and Pointereau17,Reference Larson, Laska and Neumark-sztainer19,Reference Rose, Heller and Willits-smith20) . Additionally, studies among adults in Denmark and Belgium show that sustainable diets are more common among higher socio-economic status (SES) groups including those with higher educational attainment, higher income and food security(Reference Smed, Tetens and Lund21,Reference Vanhonacker, van Loo and Gellynck22) . None of these studies used the PHD as a measure of diet sustainability, highlighting the need for a standardised measure to assess sustainable dietary intake.

The objective of the current study is to assess diet sustainability among a large, racially/ethnically diverse population-based sample of young adults recruited from a large metropolitan area of Minnesota by comparing their dietary intake to the targets of the PHD. Young adults hold particular importance since they are at a life stage of increasing independence and are developing habits that may persist throughout their adult lives(Reference VanKim, Larson and Laska23). Additionally, we identify personal, behavioural and socio-environmental correlates of young adults’ sustainable dietary intake assessed via the PHD. One study found that less than 35 % of young adults value sustainable diet practices and that greater value for sustainable diet practices was associated with higher diet quality, greater intake of vegetables and less fast food consumption(Reference Larson, Laska and Neumark-sztainer19). Therefore, we hypothesise that most young adult participants have substandard sustainable dietary intake based on the PHD and that sustainable dietary intake will correlate with other health-promoting behaviours (e.g. physical activity, sleep and low fast-food consumption). The knowledge obtained from this study will provide the first benchmark regarding sustainable dietary intake using the PHD among a young adult US sample from Minnesota and suggest intervention targets that could reduce barriers to and promote sustainable food consumption across diverse communities.

Methods

Study population

The current cross-sectional analysis uses data from the second wave of EAT 2010–2018 (Eating and Activity over Time), a population-based study designed to understand weight-related health across the life course. EAT 2010 was conducted within the Minneapolis and St. Paul school districts of Minnesota, USA(Reference Larson, Laska and Neumark-Sztainer24). Consideration was given to involvement in other research studies and enrolling an ethnically/racially diverse sample of adolescents when identifying schools for participation in the EAT 2010 study. Two urban school districts, which served a large number of schools and diverse students, were invited to participate and twenty schools within these districts were recruited after the study was approved by the school district research boards. Survey dates were scheduled with teachers at each school, and EAT staff visited school classrooms at least 10 d prior to survey administration in order to distribute parent consent forms. Adolescents in health, physical education and science classes were given the opportunity to assent just prior to survey administration only if their parent/guardian did not return a signed consent form indicating their refusal to have their child participate. Among adolescents who were at school on the days of survey administration, 96·3 % had parental consent and chose to participate. The enrolled student sample (n 2793) was similar in terms of ethnic/racial composition to the overall student population within each district in 2010 based on data maintained by the Minnesota Department of Education. Students received a $10 Target gift card as compensation for their participation in the study. The mean age of participants was 14·4 years (sd = 2·0)(Reference Larson, Laska and Neumark-Sztainer24). In 2017–2018, a follow-up study was conducted, and EAT 2010 participants were invited to complete another survey and FFQ. There were 2383 EAT 2010 participants that were invited to take part in the study (410 were lost to follow-up) and 1568 responded by completing a survey online or by mail(Reference Larson, Laska and Neumark-Sztainer24). To account for missing data due to attrition, inverse probability weighting was used(Reference Seaman and White25). The current analysis included only the participants who completed both the survey and FFQ, excluding those who reported biologically implausible energetic intake (consuming < 400 or > 7000 kcal/d) (n 175). Participants with missing values for covariates (age, gender, income, education, race and total energetic intake) were also excluded to ensure comparability among models, resulting in a final sample of 1308 young adults; see Supplementary Figure 1 for a flow diagram of the analytic sample. The sample was more diverse than the overall population in Minneapolis–St. Paul, Minnesota with 20·8 % White, 20·6 % Asian American, 17·1 % Hispanic, 26·5 % African American or Black and 11·5 % mixed or other.

Assessment of personal, behavioural and socio-environmental variables

The EAT surveys were developed to integrate an ecological perspective with Social Cognitive Theory. Personal, behavioural and socio-environmental variables (see Table 1) for this analysis were identified based on Social Cognitive Theory and on our existing understanding of predictors of sustainable diet intake within each of the Social Cognitive Theory domains(Reference Bandura33). Understanding the personal, behavioural and socio-environmental correlates of the PHD would identify subgroups of individuals that are consuming more sustainable diets and could suggest policy-based, environmental, and educational levers with the potential to move other groups towards more sustainable intake. To promote ease of interpretation, all variables were standardised to a mean of 0 and sd of 1.

Table 1 Assessment of personal, behavioural and socio-environmental factors

Assessment of diet

A semi-quantitative 149-item validated FFQ was administered at the same time as the EAT survey to assess usual dietary intake in the past year(Reference Yuan, Spiegelman and Rimm34). To compare intake to the PHD criteria, the scoring method developed by Hanley-Cook et al.(Reference Hanley-cook, Argaw and De Kok35) with minimum intake values was applied. Participants’ intake was categorised into one of the fourteen PHD food groups (Supplemental Table 1), and conversion factors reported by Blackstone et al.(Reference Blackstone and Conrad15) were used to translate from servings per d to grams per d (1 serving fruit = 182 g; 1 serving dark green vegetables = 118 g; 1 serving red and orange vegetables = 114 g; 1 serving starchy vegetables = 134 g; 1 serving other vegetables = 140 g; 1 serving whole grains = 51 g; 1 serving dairy products = 149 g; 1 serving meat = 31 g; 1 serving poultry = 29 g, 1 serving eggs = 50 g; 1 serving fish = 29 g; 1 serving nuts and seeds = 15 g; 1 serving soya = 24 g, and 1 serving legumes = 44 g). In accordance with Hanley-Cook et al.(Reference Hanley-cook, Argaw and De Kok35), a score of 1 was given for each food group when average daily intake fell within the following ranges: whole grains (232·0–464·0 g/d), tubers (50·0–100·0 g/d), dairy products (250·0–500·0 g/d), beef, lamb and pork (14·0–28·0 g/d), chicken and other poultry (29·0–58·0 g/d), eggs (13·0–25·0 g/d), fish (28·0–100·0 g/d), dry beans, lentils, peas (50·0–100·0 g/d), soya (25·0–50·0 g/d), peanuts or tree nuts (25·0–100·0 g/d), added fat (20·0–91·8 g/d), and added sugar (0·0–31·0 g/d). A score of 0 was given to those who were outside (both below and above) the PHD intake range(Reference Hanley-cook, Argaw and De Kok35). An exception was made for vegetables and fruits, which only had a minimum intake without a maximum intake in accordance with Knuppel et al.(Reference Knuppel, Papier and Key36) so as to not penalise high consumption of fruits and vegetables. For vegetables and fruits, a score of 1 was given to those who met or exceeded the minimum intake (≥ 200 g/d) and (≥ 100/d), respectively, while a score of 0 was given to those who fell short of the PHD(Reference Knuppel, Papier and Key36).

The PHD was developed to align with daily energy intake of 2500 kcal/d. To standardise the application of the PHD to the total energetic intake of participants, their intake in grams was scaled to 2500 kcal/d. In contrast to this method, a sensitivity analysis was conducted by weighting the PHD to align with a 1500 kcal/d intake and 2000 kcal/d intake creating ideal intake goals for three ranges: < 1500 kcal/d, 1500–2500 kcal/d and > 2500 kcal/d. The results of the sensitivity analysis (online Supplementary Tables 24) were similar to the analysis based on energy intake of 2500 kcal/d when participants’ individual intake in grams was scaled based on energy intake, demonstrating the robustness of the findings.

PHD score

The primary outcome, overall PHD score, was created in accordance with Hanley-Cook et al.(Reference Knuppel, Papier and Key36) by summing points for achieving optimal intake in each of fourteen food categories derived from the FFQ, resulting in an index with possible scores ranging from 0 to 14, with 0 being the least sustainable and 14 being the most sustainable. Furthermore, percent difference of participant intake from the PHD for each of the food categories was calculated by subtracting the midpoint of the suggested PHD energetic range from the observed participant intake weighted by that participant’s ideal intake range(Reference Willett, Rockström and Loken2).

Sociodemographic characteristics

Ethnicity/race was determined by asking ‘Do you think of yourself as White, Black or African American, Hispanic or Latino, Asian American, American Indian or Native American, or Other’. Socio-economic status was classified using participants’ highest level of parental education along with eligibility for public assistance, free or reduced-price school lunches, and parental employment status. Gender, educational attainment, birth year and student status were self-reported(Reference Larson, Laska and Neumark-Sztainer24).

Statistical analysis

Descriptive statistics were used to examine PHD scores (overall and for each food group) across participant characteristics, including age, gender, ethnicity/race, educational attainment, SES, student status and total energy intake. The authors calculated means and standard deviations of PHD scores, the percent of participants achieving the PHD goals, percent below the PHD goal and percent exceeding the PHD goal. The differences in mean PHD composite score across sociodemographic groups (gender, ethnicity/race, educational attainment and SES) were compared using ANOVA. Linear regression models were then constructed to allow for separately examining each personal, behavioural and socio-environmental factor of interest as a predictor of PHD composite score. Model assumptions were checked prior to running the models. Crude models were first constructed and then further adjusted for potential confounders in alignment with previous studies, including ethnicity/race, educational attainment, gender, age, SES and total energy intake(Reference Larson, Laska and Neumark-sztainer19,Reference Smed, Tetens and Lund21,Reference Vanhonacker, van Loo and Gellynck22) . A P-value of < 0·05 was used to indicate statistical significance. Statistical analyses were carried out in SAS version 9.4.

Results

The weighted descriptive characteristics of the study sample in 2018 are presented in Table 2. The mean age of study participants was 22·1 (sd = 2·0), and just under half (41·8 %) were enrolled in college. Over half of participants (59·8 %) were of low- or low-middle SES.

Table 2 Sociodemographic characteristics of Project EAT 2018 participants (n 1349)

Participants’ overall PHD score was 4·1 on average (sd = 1·4), on a scale of 0 to 14 possible, with 14 being the most sustainable (Table 3). Participants of low socio-economic status had significantly lower overall PHD scores (4·1 (sd = 1·4)) than those of high SES (4·5 (sd = 1·2)). Likewise, those with lower educational attainment, only some high school education, had lower overall PHD scores (3·9 (sd = 1·5)) than those with greater educational attainment, an associate, vocational, technical, trade, bachelor’s, graduate or professional degree (4·3 (sd = 1·4)).

Table 3 Planetary Health Diet scores by sociodemographic characteristics

Note: Means with common superscript letters do not differ at P < 0·05.

Figure 1 shows the percent difference between the average intake of participants for each food group compared with the ideal PHD intake. Overall, participants were close to meeting PHD recommendations for potatoes (3·9 %), dairy products (7·7 %) and poultry (8·6 %). However, on average, participants over-consumed meat (148·5 %), eggs (70·0 %), and added sugar (83·2 %), and under-consumed whole grains (–54·8 %), fish (–94·7 %), legumes (–121·5 %), soya (–146·0 %) and nuts (–175·2 %). The mean scaled intake of meat is high at 47·4 (sd = 32·6) g/d with more than 71 % of participants consuming above the PHD recommendations. In comparison, the mean scaled intake of fish was 10·0 (sd = 12·8) g/d, and mean scaled intakes of plant-based proteins were 12·2 (sd = 20·4) g/d for legumes, 3·9 (sd = 11·9) g/d for soya and 3·3 (sd = 7·2) g/d for nuts, with more than 90 % of participants having intakes that were below PHD recommendations across all four categories (Table 4).

Fig. 1 Difference of Project EAT 2018 Participant Intake from Planetary Health Diet Targets

Table 4 Planetary Health Diet for Project EAT 2018 Participants

Note: As min intake ranges were used in this analysis in alignment with Hanley-cook et al.(Reference Framson, Kristal and Schenk29), neither % below nor % above the PHD is considered ideal.

Participants’ overall adjusted PHD scores were most strongly associated with standardised (mean = 0, sd = 1) scores indicating higher availability of healthy food at home (β = 0·24, P value < 0·001) and less frequent fast-food consumption (β = –0·26, P value < 0·001) (Table 5). Other personal characteristics associated with the PHD score were greater self-efficacy for cooking (β = 0·16, P value < 0·001), self-esteem (β = 0·10, P value = 0·009) and overall body satisfaction (β = 0·12, P value = 0·008). Increased hours of physical activity per week (β = 0·15, P value = 0·0002) and number of lifestyle weight management behaviours performed last year (β = 0·11, P value < 0·0001) were behavioural characteristics associated with more sustainable dietary intake. Meanwhile, less frequently eating at a restaurant (β = –0·25, P value < 0·0001) and fewer hours of screen time (β = –0·16, P value < 0·0001) were associated with sustainable dietary intake. Finally, participants reporting greater parental encouragement of healthy eating (β = 0·15, P value = 0·0002) experienced higher overall PHD scores on average, while participants experiencing food insecurity had moderately lower PHD scores (β = –0·09, P value = 0·02). The remaining personal (BMI, depressive symptoms and unmanaged stress), behavioural (mindful eating, alcohol consumption, hours of sleep per d and number of unhealthy weight control behaviours performed last year) and socio-environmental characteristics (support for healthy eating and physical activity at work) were not associated with the PHD score.

Table 5 Associations between personal, behavioural and socio-environmental characteristics* and Planetary Health Diet score

* Personal, behavioural and socio-environmental predictors have been standardised to mean = 0, sd = 1 to allow for comparison of estimates across models.

Models adjusted for ethnicity/race, educational attainment, gender, age, socio-economic status (SES) and total energy intake.

Discussion

The objective of the current study was to assess intake of a sustainable dietary pattern among a large, socio-economically and ethnically/racially diverse sample of US young adults by comparing it to the targets of the PHD. Additionally, we identified personal, behavioural and socio-environmental correlates of young adults’ sustainable dietary intake assessed via the PHD. Overall, as hypothesised, young adults participating in EAT 2018 were not consuming diets that aligned with PHD recommendations. While most young adults met the PHD recommended intakes for fruits, vegetables and added fats, the majority under-consumed whole grains, plant-based proteins, and fish, and overconsumed meat and added sugar. Young adults of high SES and those with higher educational attainment consumed diets more aligned with PHD recommendations than their peers. Furthermore, the strongest correlates of meeting the PHD recommendations were greater healthy food availability at home and less frequently consuming food from fast-food restaurants.

Study findings are consistent with dietary patterns observed in other high-income countries (HIC) and contrast with patterns observed in low-to-middle-income countries with regard to meat and whole-grain consumption. For example, prior research using the cross-sectional nationally representative National School Lunch Program data found that the average amount of food prepared for by elementary, middle and high school cafeterias exceeded the PHD for dairy products, fruit, refined grains, red meat, and starchy vegetables and was insufficient for whole grains, legumes, vegetables and nuts(Reference Poole, Musicus and Kenney16). An additional study in the UK has shown relatively few individuals meet the PHD recommendations for whole grains (36·1 %) and most met (66·6 %) or exceeded (33·4 %) the recommendations for meat(Reference Knuppel, Papier and Key36). In India, consumption expenditures for urban and rural populations, respectively, show that the PHD recommendations were exceeded for whole grains 1029 kcal/d and 1275 kcal/d and fell short of meeting recommendations for meat 3 kcal/d and 5 kcal/d, fish 8 kcal/d and 9 kcal/d, and eggs 6 kcal/d and 10 kcal/d(Reference Sharma, Kishore and Roy37). A primary difference between the study conducted in India and the studies in the USA and UK are the discrepancies in animal-source food consumption and whole grains. In the USA and UK, the PHD recommendations are widely met or exceeded for animal-sourced foods, while in India they fall short of meeting them. Conversely, in India, the PHD recommendation is exceeded for whole grains, while in the USA and UK they fall short of meeting it(Reference Knuppel, Papier and Key36,Reference Sharma, Kishore and Roy37) . These patterns mirror common dietary patterns among low-to-middle-income countries and HIC globally, which necessitates a shift in consumption in order to meet sustainability goals(Reference Adesogan, Havelaar and McKune38). In low-to-middle-income countries, meeting the dual planetary and human health sustainability goals requires a higher intake of animal-based protein to replace some of the energy content they are getting from whole grains (especially to meet the nutritional needs of women and children in low-to-middle-income countries)(Reference Hanley-cook, Argaw and De Kok35), while HIC need to reduce meat consumption and supplement it with a greater intake of whole grains and plant-based protein.

In HIC like the USA, reducing meat consumption and increasing intake of plant-based sources of protein provide a clear path for making gains in the sustainability of dietary intake. Such a change would likely also be economically advantageous for consumers, although not all scholars agree, and exceptions can be found. A 2021 Global Modelling Study found that in HIC vegetarian and vegan diets were on average more affordable than current dietary patterns by up to 34 %(Reference Springmann, Clark and Rayner39). In the current study, young adults with the lowest SES consumed the most meat (beef, lamb and pork) in comparison with higher SES groups. This pattern is often observed within HIC(Reference Clonan, Roberts and Holdsworth40). One reason that individuals from lower SES households may consume more meat, and thus have lower overall PHD scores, is more frequent fast-food consumption (e.g. burgers). Among young adults in the EAT 2010–2018 study, fast-food consumption was one of the strongest correlates of lower diet sustainability. A recent study demonstrated the positive association between income and processed meat consumption; furthermore, it showed an additive interaction between income, neighbourhood density of fast-food outlets and the outcome of interest, processed meat consumption(Reference Burgoine, Sarkar and Webster41). One innovative intervention strategy to improve the sustainability of low SES individuals’ diets is encouraging fast-food restaurants to showcase plant-based proteins, particularly ones that keep costs low. In 2021, seven fast-food restaurants (Burger King, Chipotle, Starbucks, KFC, Panera Bread, Pizza Hut and Taco Bell) were recognised for leading the way in plant-based protein alternatives in alignment with their corporate commitments to reducing meat consumption(Reference Kiggins42). However, proximity to fast food is only one structural barrier that may contribute to the increased meat consumption among those in lower SES households; other potential structural barriers are food access, time constraints, perceived cost, cooking knowledge, taste and cultural preferences. Poole et al.(Reference Poole, Musicus and Kenney16) examined the perceived cost barrier and found that school lunches meeting the PHD recommendations in the USA were less expensive than those that did not. Another study in Baltimore City examined taste as a barrier and found that a shift to eating PHD meals was well accepted by low-income families on the basis of taste, appearance and healthfulness of meals(Reference Semba, Ramsing and Rahman43).

Beyond shifts towards plant-based protein in the fast-food industry, fiscal policies known to alter the healthfulness of diets would likely also positively impact consumers’ PHD score(Reference Sassi44). For example, during the COVID-19 pandemic, the USA increased benefits for the Program for Women, Infants, and Children (WIC) from $9 per child and $11 per adult to $35 per person, and an evaluation found that participating children increased their fruit and vegetable intake after the benefit bump occurred(45). Continuing this programme’s expanded benefits into the future may help improve the accessibility of healthful and sustainable diets to low-income families in the USA. Additionally, the USA could adopt other fiscal policies such as a sugar-sweetened beverages tax. The WHO recommends at least a 20 % tax on sugar-sweetened beverages and other unhealthy foods to be coupled with comparable subsidies on nutrient-dense foods like fruit, vegetables, whole grains, legumes and nuts as a method to shift consumption patterns, especially among low-income groups(46). A case study can be found in Mexico, back in 2013 the government levied a 10 % sugar-sweetened beverages tax that reduced consumption by almost 10 %(Reference Arantxa Cochero, Rivera-Dommarco and Popkin47). In contrast to this approach, the USA currently subsidises commodity crops that are frequently used to produce unhealthy foods many of which are a source of added sugar.

Another important component to help people in the USA consume more sustainable diets is ensuring that the DGA consider the shared goals of improving physical and environmental health. This is particularly important as a growing number of people are turning to the DGA for nutritional advice(48), and the current DGA have similar or poorer environmental sustainability compared with current US dietary intake(Reference Reinhardt, Boehm and Blackstone11). Notably, the 2015–2020 Dietary Guidelines Advisory Committee recommended that sustainability be considered as part of the DGA, but this recommendation was removed from the final guidelines as it was deemed beyond the scope of the Committee’s charge(Reference Levy49). The most recent iteration of the DGA, 2020–2025, did not revisit the topic, and currently, the DGA allow for a much higher consumption of meat, refined grains and discretionary energy content than does the PHD(Reference Blackstone and Conrad15). The DGA also inform many federal nutrition programmes that supplement the diets of low SES individuals. As our study found that lower-income people had lower diet sustainability, bringing the DGA closer in alignment with the PHD could bolster the diet sustainability of lower SES individuals.

While this study had multiple strengths including a large population-based sample in Minnesota and socio-economically and racially/ethnically diverse participants, an important limitation was the brief assessment of plant-based proteins on the FFQ. This may have led to an underestimation of participants’ soya intake, resulting in lower overall PHD scores. Future research focused on assessing sustainable diets should ensure that their measures of dietary intake more comprehensively capture plant-based protein consumption. Participants were drawn from only one area in the USA, thereby limiting the generalisability of study findings to other young adult populations outside of the Minneapolis/St. Paul area of Minnesota. Additionally, as this study was cross-sectional, causality cannot be determined. Participants may have also over-reported behaviours or characteristics they perceived as socially acceptable and under-reported behaviours or characteristics they perceived as socially unacceptable due to social desirability. This would have the effect of attenuating the correlations of personal, behavioural and socio-environmental characteristics with the PHD.

The majority of young adults participating in the EAT 2010–2018 study had substandard sustainable dietary intake based on the PHD. This was particularly true for individuals of lower SES and educational attainment. Most young adults consumed high amounts of meat, a dietary behaviour that is especially harmful to the environment. Reducing meat consumption, especially by substituting plant-based proteins, is an important target for intervention among US young adults. Policy and environmental changes known to improve diet healthfulness such as taxing sugar-sweetened beverage and other unhealthy foods, subsidising nutrient-dense foods, fast-food restaurants committing to reducing meat consumption, and including sustainability into the DGA hold promising potential for shifting diets towards more environmentally sustainable choices.

Acknowledgements

Financial support: This study was supported by Grant Numbers R01HL127077 and R35HL139853 from the National Heart, Lung and Blood Institute (PI: Dianne Neumark-Sztainer). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health. Authorship: E.L.B. wrote the manuscript and conducted the analysis. D.N.S. developed the study design and supervised data collection. N.L. assisted with the study design and data collection. K.B., A.B., A.J., A.W., N.L. and D.N.S. critically revised the manuscript. Ethics of human subject participation:This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the University of Minnesota’s Institutional Review Board. Written informed consent was obtained from all subjects/patients.

Conflict of interest:

There are no conflicts of interest.

Supplementary material

For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S1368980023000654

References

United Nation (2015) Paris Agreement, pp. 127. Paris: United Nations.Google Scholar
Willett, W, Rockström, J, Loken, B et al. (2019) The Lancet Commissions Food in the Anthropocene: the EAT – Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447492.CrossRefGoogle Scholar
Kissinger, G, Herold, M, DeSy, V et al. (2012) Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. Vancouver, Canada: Lexeme Consulting. August 2012.Google Scholar
Tilman, D, Clark, M, Williams, DR et al. (2017) Future threats to biodiversity and pathways to their prevention. Nature 546, 7381.CrossRefGoogle ScholarPubMed
The Food and Agriculture Organization of the United Nations (2019) World Health Organization. Sustainable Healthy Diets Guiding Principles. https://www.who.int/publications/i/item/9789241516648 (accessed May 2022).Google Scholar
Swinburn, BA, Sacks, G, Hall, KD et al. (2011) The global obesity pandemic: shaped by global drivers and local environments. Lancet 378, 804814.CrossRefGoogle ScholarPubMed
Tulchinsky, TH (2010) Micronutrient deficiency conditions: global health issues. Public Health Rev 32, 243255.CrossRefGoogle Scholar
FAO, IFAD, UNICEF et al. (2018) The State of Food Security and Nutrition in the World 2018. Building Climate Resilience for Food Security and Nutrition. The State of Food Security and Nutrition in the World 2019. Rome: FAO.Google Scholar
Rockström, J, Steffen, W, Noone, K et al. (2009) Planetary boundaries: exploring the safe operating space for humanity. Ecol Soc 14, 133.CrossRefGoogle Scholar
Garnett, T (2016) Plating up solutions. Am Assoc Adv Sci 353, 12021204.Google ScholarPubMed
Reinhardt, SL, Boehm, R, Blackstone, NT et al. (2020) Systematic review of dietary patterns and sustainability in the United States. Adv Nutr An Int Rev J 11, 10161031.CrossRefGoogle ScholarPubMed
Wang, DD, Li, Y, Afshin, A et al. (2019) Global improvement in dietary quality could lead to substantial reduction in premature death. J Nutr 149, 10651074.CrossRefGoogle ScholarPubMed
Hirvonen, K, Bai, Y, Headey, D et al. (2020) Affordability of the EAT–Lancet reference diet: a global analysis. Lancet Glob Heal 8, e5966.CrossRefGoogle Scholar
Perera, T, Russo, C, Takata, Y et al. (2020) Legume consumption patterns in US adults: National Health and Nutrition Examination Survey (NHANES) 2011–2014 and Beans, Lentils, Peas (BLP) 2017 survey. Nutrients 12, 1237.CrossRefGoogle ScholarPubMed
Blackstone, NT & Conrad, Z (2020) Comparing the recommended eating patterns of the EAT-Lancet Commission and Dietary Guidelines for Americans: implications for sustainable nutrition. Curr Dev Nutr 4, nzaa015.CrossRefGoogle ScholarPubMed
Poole, MK, Musicus, AA & Kenney, EL (2020) Alignment of US school lunches with the EAT-Lancet healthy reference diet’s standards for planetary health. Health Aff 39, 21442152.CrossRefGoogle ScholarPubMed
Seconda, L, Baudry, J, Pointereau, P et al. (2019) Development and validation of an individual sustainable diet index in the NutriNet-Santé study cohort. Br J Nutr 121, 11661177.CrossRefGoogle ScholarPubMed
Górnicka, M, Drywień, ME, Zielinska, MA et al. (2020) Dietary and lifestyle changes during covid-19 and the subsequent lockdowns among polish adults: a cross-sectional online survey PLifeCOVID-19 study. Nutrients 12, 123.CrossRefGoogle ScholarPubMed
Larson, N, Laska, MN & Neumark-sztainer, D (2019) Do young adults value sustainable diet practices? Continuity in values from adolescence to adulthood and linkages to dietary behaviour. Public Health Nutr 22, 25982608.CrossRefGoogle Scholar
Rose, D, Heller, MC, Willits-smith, AM, et al. (2019) Carbon footprint of self-selected US diets: nutritional, demographic, and behavioral correlates. Am J Clin Nutr 109, 526534.CrossRefGoogle ScholarPubMed
Smed, S, Tetens, I, Lund, TB et al. (2017) The consequences of unemployment on diet composition and purchase behaviour: a longitudinal study from Denmark. Public Health Nutr 21, 580592.CrossRefGoogle Scholar
Vanhonacker, F, van Loo, EJ, Gellynck, X et al. (2013) Flemish consumer attitudes towards more sustainable food choices. Appetite 62, 716.10.1016/j.appet.2012.11.003CrossRefGoogle ScholarPubMed
VanKim, N, Larson, N & Laska, M (2012) Emerging adulthood: a critical age for preventing excess weight gain? Adolesc Med State Art Rev 23, 571588.Google ScholarPubMed
Larson, N, Laska, MN & Neumark-Sztainer, D (2020) Food insecurity, diet quality, home food availability, and health risk behaviors among emerging adults: findings from the EAT 2010–2018 study. AJPH 110, 14221428.CrossRefGoogle ScholarPubMed
Seaman, SR & White, IR (2013) Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res 22, 278295.CrossRefGoogle ScholarPubMed
Condrasky, MD, Williams, JE, Catalano, PM et al. (2011) Development of psychosocial scales for evaluating the impact of a culinary nutrition education program on cooking and healthful eating. J Nutr Educ Behav 43, 511516.CrossRefGoogle ScholarPubMed
Boynton Health Service (2011) 2011 College Student Health Survey Report: Health and Health-Related Behaviors. Minneapolis. https://www.metrostate.edu/sites/default/files/2018-06/Boynton%202011.pdf (accessed January 2022).Google Scholar
Pingitore, R, Spring, B & Garfield, D (1997) Gender differences in body satisfaction. Obes Res 5, 402409.CrossRefGoogle ScholarPubMed
Framson, C, Kristal, AR, Schenk, JM et al. (2009) Development and validation of the mindful Eating Questionnaire. J Am Diet Assoc 109, 14391444.CrossRefGoogle ScholarPubMed
Neumark-Sztainer, D, Story, M, Hannan, PJ et al. (2002) Weight-related concerns and behaviors among overweight and nonoverweight adolescents: implications for preventing weight-related disorders. Arch Pediatr Adolesc Med 156, 171178.CrossRefGoogle ScholarPubMed
Bauer, KW, Laska, MN, Fulkerson, JA et al. (2011) Longitudinal and secular trends in parental encouragement for healthful eating, physical activity, and dieting throughout the adolescent years. J Adolesc Health 49, 130184.CrossRefGoogle ScholarPubMed
Davison, KK (2004) Activity-related support from parents, peers, and siblings and adolescents’ physical activity: are there gender differences? J Phys Act Health 1, 363376.CrossRefGoogle Scholar
Bandura, A (1989) Social Cognitive Theory. Annals of Child Development. Vol 6. Six Theories of Child Development. Greenwish, CT: JAI Press. pp. 160.Google Scholar
Yuan, C, Spiegelman, D, Rimm, EB et al. (2017) Validity of a dietary Questionnaire assessed by comparison with multiple weighed dietary records or 24-hour recalls. Am J Epidemiol 185, 570584.CrossRefGoogle ScholarPubMed
Hanley-cook, GT, Argaw, AA, De Kok, BP et al. (2020) EAT – Lancet diet score requires minimum intake values to predict higher micronutrient adequacy of diets in rural women of reproductive age from five low- and middle-income countries. Br J Nutr 126, 92100.CrossRefGoogle ScholarPubMed
Knuppel, A, Papier, K, Key, TJ et al. (2019) EAT-Lancet score and major health outcomes: the EPIC-Oxford study. Lancet 394, 213214.CrossRefGoogle ScholarPubMed
Sharma, M, Kishore, A, Roy, D et al. (2020) A comparison of the Indian diet with the EAT-Lancet reference diet. BMC Public Health 20, 812.CrossRefGoogle ScholarPubMed
Adesogan, AT, Havelaar, AH, McKune, SL et al. (2020) Animal source foods: sustainability problem or malnutrition and sustainability solution? Perspective matters. Glob Food Sec 25, 100325.CrossRefGoogle Scholar
Springmann, M, Clark, MA, Rayner, M et al. (2021) The global and regional costs of healthy and sustainable dietary patterns: a modelling study. Lancet Planet Health 5, e797807.CrossRefGoogle ScholarPubMed
Clonan, A, Roberts, KE & Holdsworth, M (2016) Socioeconomic and demographic drivers of red and processed meat consumption: implications for health and environmental sustainability. Proc Nutr Soc 75, 367373.CrossRefGoogle ScholarPubMed
Burgoine, T, Sarkar, C, Webster, CJ et al. (2018) Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants. Int J Behav Nutr Phys Act 15, 112.Google ScholarPubMed
Kiggins, S (2021) Burger King, Chipotle, Starbucks get Top Marks at World Vegan Day 2021 (Internet). USA Today. https://www.usatoday.com/story/money/food/2021/10/31/burger-king-chipotle-starbucks-world-vegan-day/8576524002/ (accessed May 2022).Google Scholar
Semba, R, Ramsing, R, Rahman, N et al. (2020) Providing planetary health diet meals to low-income families in Baltimore City during the COVID-19 pandemic. J Agric Food Syst Community Dev 10, 19.CrossRefGoogle Scholar
Sassi, F (2010) Obesity and the Economics of Prevention: Fit not Fat. Paris: OECD Publishing.CrossRefGoogle Scholar
National WIC Association. (2022) The State Of WIC: Investing in the Next Generation. https://thewichub.org/the-state-of-wic-investing-in-the-next-generation/ (accessed May 2022).Google Scholar
WHO (2021) Implementing Fiscal and Pricing Policies to Promote Healthy Diets: A Review of Contextual Factors. Geneva: WHO.Google Scholar
Arantxa Cochero, M, Rivera-Dommarco, J, Popkin, BM et al. (2017) In Mexico, evidence of sustained consumer response two years after implementing a sugar-sweetened beverage tax. Health Aff 36, 564571.Google Scholar
International Food Information Council (2020) Food & Health Survey 2020. https://foodinsight.org/2020-food-and-health-survey/ (accessed January 2022).Google Scholar
Levy, S (2015) Highlights of the 2015–2020 Dietary Guidelines for Americans – DGA News – Today’s Dietitian Magazine (Internet). Today’s Dietitian. https://www.todaysdietitian.com/enewsletter/enews_0116_02.shtml (accessed May 2022).Google Scholar
Figure 0

Table 1 Assessment of personal, behavioural and socio-environmental factors

Figure 1

Table 2 Sociodemographic characteristics of Project EAT 2018 participants (n 1349)

Figure 2

Table 3 Planetary Health Diet scores by sociodemographic characteristics

Figure 3

Fig. 1 Difference of Project EAT 2018 Participant Intake from Planetary Health Diet Targets

Figure 4

Table 4 Planetary Health Diet for Project EAT 2018 Participants

Figure 5

Table 5 Associations between personal, behavioural and socio-environmental characteristics* and Planetary Health Diet score†

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