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Who chooses “healthy” meals? An analysis of lunchtime meal quality in a workplace cafeteria

BMC Public Health volume 24, Article number: 921 (2024) Cite this article

Abstract

Background

The workplace can play an important role in shaping the eating behaviors of U.S. adults. Unfortunately, foods obtained in the workplace tend to be low in nutritional quality. Questions remain about the best way to approach the promotion of healthy food purchases among employees and to what extent health promotion activities should be tailored to the demographic characteristics of the employees. The purpose of this study was to (1) assess the nutritional quality of lunchtime meal purchases by employees in cafeterias of a large organization, (2) examine associations between lunchtime meal quality selection and the demographic characteristics of employees, and (3) determine the healthfulness of foods and beverages offered in the cafeterias of this organization.

Methods

A cross-sectional analysis was conducted using secondary data from a food labeling study implemented in three worksite cafeterias. Demographic data was collected via surveys and meal data was collected using a photo capture system for 378 participants. The Healthy Eating Index 2015 (HEI-2015) was used to determine meal quality and a total score for the menu of options available in the cafeterias during the study period. Summary statistics were generated, and the analysis of variance (ANOVA) was used to compare the HEI-2015 scores between groups.

Results

The mean HEI-2015 total score for the menu items offered (n = 1,229) in the cafeteria during the study period was 63.1 (SD = 1.83). The mean HEI-2015 score for individual lunchtime meal observations (n = 378) was 47.1 (SD = 6.8). In general, HEI-2015 total scores were higher for non-smokers, individuals who self-identified as Asian, had higher physical activity levels, scored higher on numeracy and literacy assessments, and reported higher education levels, incomes, and health status.

Conclusions

The overall HEI-2015 scores indicate that the menu of options offered in the cafeterias and individual meal selections did not align with the Dietary Guidelines for Americans, and there were significant associations between average lunchtime meal quality scores and several demographic characteristics. These results suggest that healthy eating promotion activities in workplaces may need to be tailored to the demographic characteristics of the employees, and efforts to improve the food environment in the workplace could improve meal quality for all employees.

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Background

Unhealthy diets contribute to the development of non-communicable diseases, including cardiovascular disease, type 2 diabetes, and some types of cancer [1, 2], and may also contribute to worse health outcomes from COVID-19 [3]. While there have been some improvements in the dietary quality of Americans over the last several decades, many Americans have suboptimal diets, and they are not meeting the Dietary Guidelines for Americans [4, 5]. For example, the overall average dietary quality score for working-age Americans (19–59 years), as measured by Healthy Eating Index (HEI-2015), was 58 out of 100, with 100 indicating complete alignment with the 2015–2020 United States (U.S.) Dietary Guidelines for Americans [6]. In addition, according to data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS), approximately 12% of adults in the U.S. met the 2020–2025 Dietary Guidelines for fruit intake, and 10% meet the guidelines for vegetables [7].

The workplace may play an important role in shaping the eating behaviors of U.S. adults, as many spend most of their day in the workplace environment. However, surveys of working adults in the U.S. have found that foods and beverages obtained in the workplace (either purchased or for free) tend to be low in nutritional quality and energy dense [8, 9]. In addition, workplace health promotion programs and healthier foods policies are limited, especially among worksites with smaller numbers of employees [10].

Unhealthy diets and associated chronic conditions have been linked to lost productivity, absenteeism, and higher healthcare costs for employers [11, 12]. As a result, employers have an interest in promoting healthy eating practices at the workplace and beyond to support the health of their workforce. The Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have also identified the workplace as an important location for health promotion [13, 14], and the CDC recommends that guidelines for foods and beverages served at worksites align with the most recent version of the U.S. Dietary Guidelines for Americans [15].

Several factors have been identified that may influence eating behaviors in the workplace, including the availability and cost of food, time available for eating, workplace stress or pressure, provision of nutrition information, and social norms or the influence of work colleagues [16,17,18,19]. More broadly, taste, healthfulness, convenience, and price have been found to be important determinants of meal choice across different populations [20, 21], but the relative importance of these factors has been found to differ by demographic characteristics [18, 21, 22]. For example, data from a study of U.S. working adults indicate that older workers view convenience and health as the most important factors influencing their meal choices, but younger workers reported convenience and taste as the most important factors [18]. Results from a survey of an Irish working population indicate food choice motives vary by sex, with females reporting to a greater extent than males that price, sensory appeal, health, and convenience were more important to their food choice [22].

While studies examining how factors influencing reported food choice in worksites vary by demographic characteristics are important, they are limited in the fact that they do not assess actual purchases. As a result, questions remain about the best way to approach the promotion of healthy food purchases among employees and to what extent health promotion activities should be tailored to the demographic characteristics of the employees at particular worksites.

The purpose of this study was to (1) assess the nutritional quality of lunchtime meal purchases by employees in cafeterias of a large organization, (2) examine associations between lunchtime meal quality selection and the demographic characteristics of employees, and (3) determine the healthfulness of foods and beverages offered in the cafeterias of this organization during data collection. Results from this study can be used to inform future interventions to promote healthy eating in workplaces.

Methods

Design

This cross-sectional study is a secondary analysis of data from the Effects of Physical Activity Calorie Expenditure (PACE) food labeling study implemented in three worksite cafeterias that are part of one large not-for-profit health insurance organization located in the Southeastern United States. Details about the design and results of the PACE study were published elsewhere [23, 24]. Briefly, PACE was a three-year quasi-experimental study (2015–2017) that compared the effects of PACE labels to calorie-only labels in worksite cafeterias on calories purchased before and after the label intervention. Results of the main study showed that the PACE labeling and calorie-only labeling interventions both resulted in a modest decrease in lunchtime calories purchased, but there were no significant differences between the two types of labels [24].

Participants

There were 414 employees who participated in the original PACE study. Data from a subset of the original participants were included in this secondary analysis (n = 378). Participants were included if there was complete data for at least one meal purchased in one of the cafeterias during the original three-year study. No new participants were recruited for this analysis, and all data were collected between 2015 and 2017. The characteristics of the subsample (n = 378) are provided in Table 1 and were similar to those of the original sample (n = 414) and full employee population at the time of the study.

Data collection

Demographic information and medical and dietary history were collected at baseline on an electronic tablet [23]. Participants self-reported whether they had a history of high blood pressure, high cholesterol, and/or diabetes. Participants also completed a health literacy assessment (Newest Vital Sign) [25] and a three-item numeracy assessment [26] at baseline. Adequate numeracy was defined as at least two out of three items correct, and health literacy was defined as at least five out of six items correct. Biometric data, including height, weight, and body mass index (BMI), were also collected [23]. To capture data on physical activity, participants completed a physical activity assessment form [27, 28], and were asked to wear an accelerometer (Actigraph wGT3X-BT) at two time points during the study (baseline and during the intervention year).

Data on meals purchased by all participants were collected using a photo capture system and notes from onsite study coordinators [23, 24]. Study coordinators helped participants place their food on a shelf and took a picture of the entire meal they had purchased. Meal photos were taken over 2-week periods every three months during the study. A list of menu items served across the three worksite cafeterias and serving sizes for these items were provided by the cafeteria staff. When serving sizes were not provided, they were estimated using a food atlas developed for the study [23]. Nutrient information for the menu items was compiled using U.S. Department of Agriculture nutrient databases, and the process is explained in detail elsewhere [29].

Meal and menu quality

The nutritional quality of meals selected by participants and the menu items offered in the cafeterias were assessed using the Healthy Eating Index 2015 (HEI-2015). The HEI-2015 is an index designed to determine how well a set of foods aligns with the 2015–2020 U.S. Dietary Guidelines for Americans [6]. The total overall score for HEI-2015 ranges from 0 to 100, with a higher score indicating better alignment with the Dietary Guidelines [30]. The total score is based on 13 subscores that range from 0 to a maximum score of either 5 or 10 [30]. The 13 subscores measure the extent to which the Dietary Guidelines for Americans for whole grains, total vegetables, greens and beans, fruits (total and whole), dairy, total protein foods, seafood and plant proteins, and fatty acids are adequately met (adequacy components 1 through 9) and the extent to which refined grains, sodium, added sugars, and saturated fats are included in moderation (moderation components 10 through 13) [30]. A graded approach has been used to interpret HEI-2015 scores [30]. Overall scores of 90–100 represent an “A” grade, 80–89 a “B”, 70–79 a “C”, 60–69 a “D”, and 0–59 an “F.” Component or subscores can be interpreted in a similar way. For example, 90–100% of the maximum component score is an “A.”

An HEI-2015 total score and component scores were calculated for the entire menu of options available in all the cafeterias during the study period. The HEI-2015 total score and component scores for the menu items were generated using a SAS macro for the Population Ratio Method [31].

The HEI-2015 meal quality scores were generated by combining foods and beverages purchased by participants into “meals” using SAS software (version 9.43) [32]. A SAS macro (HEI-2015 scoring macro) provided by the National Cancer Institute was used to generate HEI-2015 component scores and an HEI-2015 total score for each of the meals purchased by each participant [31]. Additional details about the meal quality scores are described elsewhere [29]. The mean of the HEI-2015 total scores and component scores were then calculated for each participant.

Data analysis

Summary statistics, such as means, standard deviation, range, and percentiles, were generated for the meal index. The analysis of variance (ANOVA) was used to compare the HEI-2015 scores between groups of a categorical variable. An equal variance assumption was assumed. However, if the assumption was rejected by Levene’s test, we used Welch’s robust test to test the difference between group means.

Table 1 Demographic characteristics of participants in the Effects of Physical Activity Calorie Expenditure (PACE) Food Labeling research study (2015–2017) (n = 378)
Full size table

Results

Of the original 414 PACE participants, 378 had data on at least one meal and complete demographic information and were included in the analyses. The number of meals per participant ranged from 1 to 67 (mean = 21.3, Standard Deviation (SD) = 15.9). The mean Healthy Eating Index-2015 (HEI-2015) score for lunch observations across participants was 47.1 (SD = 6.8) with a range of 15.0-74.7.

Table 2 provides an overview of the associations between the characteristics of participants and their average HEI-2015 total score (across all meals). Associations between participant characteristics and the HEI-2015 subcomponent scores are provided in an additional file [see Additional file 1]. The associations between HEI-2015 total meal scores and age, sex (self-disclosed), body mass index (BMI), and history of high blood pressure, high cholesterol, or diabetes were not statistically significant. Significant associations were found between meal quality and race, education level, current smoker status, numeracy level, health literacy level, self-reported health status, total yearly household income, occupation description, and physical activity level. By race, the average HEI-2015 scores were significantly higher for individuals who self-identified as Asian, followed by those who self-identified as other, White, and Black. A dose-response relationship was present for education – as education level increased, meal scores also increased. Current smokers had lower meal quality scores than participants who were not current smokers. Individuals with adequate scores on numeracy and individuals with adequate scores on health literacy had higher meal quality scores than those who did not have adequate scores. Meal quality scores increased as household income increased and as physical activity increased. Finally, individuals who worked in management or financial/technical jobs had higher meal quality scores than those who worked in administration/clerical or customer service/sales jobs.

Table 2 Associations between various demographic characteristics of participants in the Effects of Physical Activity Calorie Expenditure (PACE) Food Labeling research study (2015–2017) and their average HEI-2015 meal score (n = 378)
Full size table

The total mean HEI-2015 score for the menu items offered in the cafeteria during the study period was 63.1. Table 3 contains the mean component scores for the menu items.

Table 3 Cafeteria-level Healthy Eating Index-2015 and component scores computed from the 1,229 menu items offered during the data collection period (2015–2017) in the worksite cafeterias
Full size table

Discussion

In this study, we found that the average nutritional quality of meals selected by study participants in the worksite cafeterias was 47.1 out of 100 – a failing grade on the grade scale that has been used to interpret HEI-2015 [30]. The highest score for an individual meal was 89.6. In addition, certain HEI-2015 meal subcomponent scores were below 50% of the maximum subcomponent score (greens and beans, total fruit, whole fruits, whole grains, dairy, seafood and plant protein, and sodium). Studies examining the nutritional quality of items obtained in the workplace are limited, but our findings are consistent with another study that assessed the nutritional quality of items obtained from the work environment [8]. Onufrak et al. [8] used the 2010 Healthy Eating Index (HEI-2010) to assess the dietary quality of foods that were purchased or obtained for free in the workplace and estimated an average score of 48.2 out of 100 and noted that items were especially low in total fruits and whole grains.

There are several possible reasons for the low meal quality scores found in this study. The foods and beverages available in the cafeteria and the prices of these items could have influenced the selection of items by participants. Price has been shown to influence food selection [16,17,18, 20, 21] and might be a particularly important factor in food choices made by lower-income consumers [17, 20]. The HEI-2015 score for all the menu items offered during the study period indicates that healthy options were available to participants, but several less healthy items were also available for purchase. The HEI-2015 score for all of the menu items in the cafeterias was approximately 63, and the component scores for total fruit, dairy, and items to moderate were between 38% and 68% of the maximum subcomponent scores, indicating that the menu items were not in alignment with the Dietary Guidelines for Americans. However, the HEI-2015 component scores for total vegetables, beans and greens, whole fruits, total protein foods, seafood and plant proteins, and fatty acids (the ratio of poly- and monounsaturated fat to saturated fat) were between 76% and 100% of the maximum subcomponent scores. It is important to note that the full menu of items analyzed for this study was not available at every cafeteria and on every observation day. If the healthier entrée items available on particular days did not look appealing to participants, it’s possible they may have selected less healthy quick service items. The availability of unhealthy items may also reflect a response to consumer demand, and/or the purchase of these items could have been due to price differences between healthy and less healthy options. Participant preferences and price differences are important to consider for future research.

Another reason for the lower average meal scores could have been the result of using HEI-2015 as the index for measuring meal quality. The HEI-2015 was designed to assess overall diet quality and the nutritional quality of food supplies [33, 34]. To measure overall diet quality, HEI-2015 scores are often based on multiple eating occasions over the course of a day or several days. For this study, HEI-2015 scores were generated from meal observations, which may not reflect participants’ overall dietary patterns. It is possible that individuals were consuming various food groups at different eating occasions and thus did not select these components at lunch. As a result, HEI-2015 meal scores should not be expected to be “perfect” or reach the maximum score possible.

Nevertheless, it is also possible that the meal scores may be associated with or reflect the overall dietary patterns of participants. Results from a study of the U.S. workforce that compared the nutritional quality of items obtained by study participants at workplaces to overall HEI-2015 dietary quality scores indicate that participants who purchase or obtain healthier items at work tended to have higher overall dietary quality scores [9]. Individuals with healthier purchases at work also tended to have a lower prevalence of cardiometabolic risk factors (obesity, hypertension, prediabetes/diabetes, and/or hyperlipidemia) compared to individuals with the least healthy purchases [9]. In addition, according to 2017–2018 NHANES data, working-age Americans (19–59) have an average overall HEI-2015 total score of 58 and lower HEI-2015 subcomponent scores for fruit and whole grains [6]. While this overall diet quality score is higher than the average score for meals in this study, it still indicates that the diet and meal quality of working-age Americans needs to improve.

In this study, we also found significant associations between average meal quality scores and race, education level, smoking status, income, physical activity levels, occupation type, and health literacy and numeracy scores. Studies examining associations between demographic characteristics and worksite meal quality or worksite food purchases are limited. However, findings from this study were consistent with some findings from a study that assessed demographic differences in meal quality from full-service and fast-food restaurants among NHANES participants [35]. For that study, researchers used the American Heart Association (AHA) diet score as a measure of meal quality and found, similar to this study, that meal quality from full-service restaurants tended to be lower among non-Hispanic Blacks and individuals with lower education levels. Unlike this study, meal scores from fast food and full-service restaurants tended to also be lower among adults with overweight or obesity [35].

Some similar trends have also been observed in studies that have examined associations between demographic characteristics and scores from indices that measure overall diet quality. Similar to this study, diet quality scores have been found to be higher among non-Hispanic Asians compared to non-Hispanic Whites and non-Hispanic African Americans [36, 37]. In addition, studies have found that individuals who have higher dietary quality scores are more likely to have higher incomes [37,38,39] and higher education levels [37, 39,40,41,42]. Smoking status has also been linked to overall diet quality. Individuals who have never smoked tend to have a better diet quality than former smokers and current smokers [39, 42, 43]. Furthermore, individuals with higher dietary quality scores also tend to be more physically active than those with lower scores [39, 42, 44].

We did not find significant differences in meal scores by age, sex, self-reported health status, body mass index, or history of high blood pressure, high cholesterol, or diabetes status. However, significant differences in meal and diet quality scores have been reported in previous studies by sex, with women tending to have higher scores than men [35, 39, 41]. Older populations have also been found to have higher meal and dietary quality scores [35, 41, 42]. Studies have also found an inverse relationship between obesity and dietary indices [45].

The demographic differences found in meal quality selection in this study and in studies of overall diet quality warrant further investigation, as do potential interventions to address possible disparities in knowledge about nutrition or access and availability of healthier foods that may exist. For example, efforts to address dietary disparities by education level could be addressed through interventions that focus on increasing consumers’ knowledge about nutrition through tailored communication or providing more information at the point of purchase about the healthfulness of items [46,47,48]. Pricing strategies could also be employed to decrease the cost of healthier food and make it more accessible to people with lower incomes [49, 50]. However, while there were significant differences in meal quality scores between various demographic groups in this study, overall, these differences were not large. The results indicate that there is considerable room for improvement in healthy meal selection among most participants.

As mentioned previously, workplace interventions may be an important area of focus for improving health behaviors overall and for decreasing disparities in diet and meal quality among various populations in the U.S. For example, multicomponent workplace interventions that focus on improving food quality, reducing portion sizes, increasing employee’s knowledge of and/or motivation for purchasing healthy food, reducing the price of healthy foods, and/or targeting food choice at the point of purchase (e.g. labeling or signage for healthier options) have been found to be effective at improving eating behaviors [49, 50] and in some cases body weight and cardiometabolic risk factors [50]. In addition, many studies focused on changing the food environment to promote healthy eating in workplaces have resulted in a decrease in the number of calories purchased, an increase in sales of healthier options, and/or an improvement in fruit and vegetable consumption [51, 52]. Implementing behavioral design strategies, sometimes referred to as choice architecture, may help point people toward healthier options and be effective in changing behaviors when it comes to making food choices [53,54,55]. Finally, implementing food guidelines or policies like the U.S. Federal Food Service Guidelines (FFSG) in government and private workplaces may help increase healthy options for employees, improve health outcomes, and decrease healthcare costs [56].

Limitations

The purpose of this study was to examine possible differences in meal quality by demographic characteristics and not necessarily the factors contributing to differences in the selection of food in the workplace environment. Given the observational study design, we cannot conclude that there is a causal relationship between demographic characteristics included in this study and food selection. There may be other factors, beyond the ones described here, influencing food choice that could be addressed in future workplace food environment interventions. For example, the price and placement of healthy and less healthy items may have driven the selection of items in the cafeteria. It is possible that less healthy items were available at lower prices than healthier items, and less healthy items were more prominently displayed in the cafeterias. The prices of the menu items and menu item placement within the cafeteria were not collected as part of this study.

It is also important to note that the demographic categorizations used for this study are imprecise measures and likely do not capture the heterogeneity of individuals in these groups. For example, race is a social construction that does not have a biological basis [57,58,59], and the definitions of races have changed over time [60]. Race, however, has been described as a proxy measure for historic and ongoing discrimination and systematic racism experienced by marginalized racial groups, and racism can impact health behaviors and result in health disparities [57, 61, 62]. Along these lines, one potential mechanism that could explain some of the differences in food selection by race is exposure to food marketing. Studies have shown that African Americans are disproportionately targeted and exposed to advertisements for energy-dense and low-nutritional quality foods and beverages [63,64,65,66,67], which may influence food preferences and consumption [65, 68].Another potential limitation, as previously mentioned, is the use of HEI-2015 as the index for measuring meal quality may have contributed to the lower average meal scores. As far as we are aware, a gold standard index for measuring meal quality in the U.S. population does not exist. Despite this, scores for individual meals using HEI-2015 may provide valuable information for consumers, public health and nutrition professionals, and researchers. HEI-2015 and tools like the USDA’s MyPlate may serve as starting points for creating an index designed to assess meal quality. For example, the 2020–2025 Dietary Guidelines suggest adding fruit to meals may be a good way to achieve the recommended amount for the day, and MyPlate provides a visual for what a single meal should contain to meet the dietary guidelines [5].

Conclusion

While we found significant associations between average lunchtime meal quality scores and several important demographic characteristics, we also found low meal scores across most study participants. In addition, the overall HEI-2015 score for menu items offered in the three cafeterias during the data collection period was moderate, but healthier options were available. These results suggest that healthy eating promotion activities in workplaces may need to be tailored to the demographic characteristics of the employees, and efforts to improve the food environment and promote health in the workplace could improve meal quality for all employees.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.

References

  1. Afshin A, Sur PJ, Fay KA, Cornaby L, Ferrara G, Salama JS, Mullany EC, Abate KH, Abbafati C, Abebe Z, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2019;393(10184):1958–72.

    Article  Google Scholar 

  2. Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States. JAMA: J Am Med Association. 2017;317(9):912–24.

    Article  Google Scholar 

  3. Division of Nutrition PA, Obesity. National center for chronic disease prevention and health promotion, obesity, ethnicity, and COVID-19. 2022. https://www.cdc.gov/obesity/data/obesity-and-covid-19.html. Accessed 6 June 2022.

  4. Shan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, Mozaffarian D, Zhang FF, Bhupathiraju SN. Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999–2016. JAMA: J Am Med Association. 2019;322(12):1178–87.

    Article  CAS  Google Scholar 

  5. U.S. Department of Agriculture, U.S. Department of health and human services. Dietary guidelines for Americans, 2020–2025. 2020. https://www.dietaryguidelines.gov/sites/default/files/2020-12/Dietary_Guidelines_for_Americans_2020-2025.pdf. Accessed 6 Feb 2021.

  6. U.S. Department of Agriculture, Food and Nutrition Service, Center for Nutrition Policy and Promotion. Average healthy eating index-2015 scores for all Americans by age groups. What We Eat in America, NHANES 2017–2018. 2022. https://www.fns.usda.gov/hei-scores-americans#:~:text=An%20ideal%20overall%20HEI%20score,is%2058%20out%20of%20100. Accessed 19 July 2022.

  7. Lee SH, Moore LV, Park S, Harris DM, Blanck HM. Adults meeting fruit and vegetable intake recommendations—United States, 2019. Morb Mortal Wkly Rep. 2022;71(1):1.

    Article  CAS  Google Scholar 

  8. Onufrak SJ, Zaganjor H, Pan L, Lee-Kwan SH, Park S, Harris DM. Foods and beverages obtained at worksites in the United States. J Acad Nutr Diet. 2019;119(6):999–1008.

    Article  PubMed  PubMed Central  Google Scholar 

  9. McCurley JL, Levy DE, Rimm EB, Gelsomin ED, Anderson EM, Sanford JM, Thorndike AN. Association of worksite food purchases and employees’ overall dietary quality and health. Am J Prev Med. 2019;57(1):87–94.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Linnan LA, Cluff L, Lang JE, Penne M, Leff MS. Results of the Workplace Health in America Survey. Am J Health Promotion. 2019;33(5):652–65.

    Article  Google Scholar 

  11. Schultz AB, Chen CY, Edington DW. The cost and impact of health conditions on presenteeism to employers: A review of the literature. PharmacoEconomics. 2009;27(5):365–78.

    Article  PubMed  Google Scholar 

  12. Burton WN, Conti DJ, Chen CY, Schultz AB, Edington DW. The role of health risk factors and disease on worker productivity. J Occup Environ Med. 1999;41(10):863–77.

    Article  CAS  PubMed  Google Scholar 

  13. The Centers for Disease Control and Prevention. Workplace Health Promotion. 2019. https://www.cdc.gov/workplacehealthpromotion/index.html. Accessed 3 Feb 2023.

  14. The World Health Organization. Occupational Health. 2023. https://www.who.int/health-topics/occupational-health. Accessed 3 Feb 2023.

  15. The Centers for Disease Control and Prevention. Food Service Guidelines. 2021. https://www.cdc.gov/nutrition/food-service-guidelines/index.html. Accessed 20 June 2023.

  16. Clohessy S, Walasek L, Meyer C. Factors influencing employees’ eating behaviours in the office-based workplace: A systematic review. Obes Rev. 2019;20(12):1771–80.

    Article  PubMed  Google Scholar 

  17. Larson N, Story M. A review of environmental influences on food choices. Ann Behav med. 2009;38(11):s56–73.

    Article  PubMed  Google Scholar 

  18. Blanck HM, Yaroch AL, Atienza AA, Yi SL, Zhang J, Mâsse LC. Factors influencing lunchtime food choices among working Americans. Health Educ Behav. 2009;36(2):289–301.

    Article  PubMed  Google Scholar 

  19. Levy DE, Pachucki MC, O’Malley AJ, Porneala B, Awesta Y, Thorndike AN. Social connections and the healthfulness of food choices in an employee population. Nat Hum Behav. 2021;5(10):1349–57.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kershaw KN, Klikuszowian E, Schrader L, Siddique J, Van Horn L, Womack VY, Zenk SN. Assessment of the influence of food attributes on meal choice selection by socioeconomic status and race/ethnicity among women living in Chicago, USA: A discrete choice experiment. Appetite. 2019;139:19–25.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Glanz K, Basil M, Maibach E, Goldberg J, Snyder DAN. Why Americans eat what they do: Taste, nutrition, cost, convenience, and weight control concerns as influences on food consumption. J Am Diet Assoc. 1998;98(10):1118–26.

    Article  CAS  PubMed  Google Scholar 

  22. Schliemann D, Woodside JV, Geaney F, Cardwell C, McKinley MC, Perry I. Do socio-demographic and anthropometric characteristics predict food choice motives in an Irish working population? Br J Nutr. 2019;122(1):111–9.

    Article  CAS  PubMed  Google Scholar 

  23. Viera AJ, Tuttle L, Olsson E, Gras-Najjar J, Gizlice Z, Hales D, Linnan L, Lin FC, Noar SM, Ammerman A. Effects of physical activity calorie expenditure (PACE) labeling: Study design and baseline sample characteristics. BMC Public Health. 2017;17(1):702.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Viera AJ, Gizlice Z, Tuttle L, Olsson E, Gras-Najjar J, Hales D, Linnan L, Lin F-C, Noar SM, Ammerman A. Effect of calories-only vs physical activity calorie expenditure labeling on lunch calories purchased in worksite cafeterias. BMC Public Health. 2019;19(1):107–107.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Weiss BD, Mays MZ, Martz W, Castro KM, DeWalt DA, Pignone MP, Mockbee J, Hale FA. Quick assessment of literacy in primary care: The newest vital sign. Ann Fam Med. 2005;3(6):514–22.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Schwartz LM, Woloshin S, Black WC, Welch HG. The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med. 1997;127(11):966–72.

    Article  CAS  PubMed  Google Scholar 

  27. Hekler EB, Buman MP, Haskell WL, Conway TL, Cain KL, Sallis JF, Saelens BE, Frank LD, Kerr J, King AC. Reliability and validity of CHAMPS self-reported sedentary-to-vigorous intensity physical activity in older adults. J Phys Act Health. 2012;9(2):225–36.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Resnicow K, McCarty F, Blissett D, Wang T, Heitzler C, Lee RE. Validity of a modified CHAMPS physical activity questionnaire among African-Americans. Med Sci Sports Exerc. 2003;35(9):1537–45.

    Article  PubMed  Google Scholar 

  29. Bullock SL, Miller HM, Ammerman AS, Viera AJ. Comparisons of four diet quality indexes to define single meal healthfulness. J Acad Nutr Dietetics. 2022;122(1):149–58.

    Article  Google Scholar 

  30. Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, Wilson MM, Reedy J. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–602.

    Article  PubMed  PubMed Central  Google Scholar 

  31. National Cancer Institute, Division of Cancer Control & Population Sciences. Healthy Eating Index SAS Code. 2020. https://epi.grants.cancer.gov/hei/sas-code.html. Accessed 12 June 2019.

  32. SAS Institute Inc. SAS for, Windows. Version 9.4. In. Cary, NC: SAS Institute Inc.; 2013.

  33. Kirkpatrick SI, Reedy J, Krebs-Smith SM, Pannucci TE, Subar AF, Wilson MM, Lerman JL, Tooze JA. Applications of the healthy eating index for surveillance, epidemiology, and intervention research: Considerations and caveats. J Acad Nutr Diet. 2018;118(9):1603–21.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kirkpatrick SI, Reedy J, Kahle LL, Harris JL, Ohri-Vachaspati P, Krebs-Smith SM. Fast-food menu offerings vary in dietary quality, but are consistently poor. Public Health Nutr. 2014;17(4):924–31.

    Article  PubMed  Google Scholar 

  35. Liu J, Rehm CD, Micha R, Mozaffarian D. Quality of meals consumed by US adults at full-service and fast-food restaurants, 2003–2016: Persistent low quality and widening disparities. J Nutr. 2020;150(4):873–83.

    Article  PubMed  PubMed Central  Google Scholar 

  36. U.S. Department of agriculture, food and nutrition service, center for nutrition policy and promotion. Average healthy eating index-2015 scores for Americans by race/ethnicity, ages 2 years and older, WWEIA, NHANES 2017–2018. 2022. https://www.fns.usda.gov/hei-scores-americans#:~:text=An%20ideal%20overall%20HEI%20score,is%2058%20out%20of%20100. Accessed 20 July 2022.

  37. Rodriguez LA, Jin Y, Talegawkar SA, Otto MCO, Kandula NR, Herrington DM, Kanaya AM. Differences in diet quality among multiple US racial/ethnic groups from the mediators of atherosclerosis in South Asians living in America (MASALA) study and the Multi-Ethnic Study of Atherosclerosis (MESA). J Nutr. 2020;150(6):1509–15.

    Article  PubMed  PubMed Central  Google Scholar 

  38. U.S. Department of Agriculture, Food and Nutrition Service, Center for Nutrition Policy and Promotion. Average healthy eating index-2015 scores for Americans by poverty income ratio (PIR), WWEIA, NHANES 2017–2018. 2022. https://www.fns.usda.gov/hei-scores-americans#:~:text=An%20ideal%20overall%20HEI%20score,is%2058%20out%20of%20100. Accessed 20 July 2022.

  39. Hu EA, Steffen LM, Coresh J, Appel LJ, Rebholz CM. Adherence to the healthy eating index–2015 and other dietary patterns may reduce risk of cardiovascular disease, cardiovascular mortality, and all-cause mortality. J Nutr. 2019;150(2):312–21.

    Article  PubMed Central  Google Scholar 

  40. Hiza HA, Casavale KO, Guenther PM, Davis CA. Diet quality of Americans differs by age, sex, race/ethnicity, income, and education level. J Acad Nutr Diet. 2013;113(2):297–306.

    Article  PubMed  Google Scholar 

  41. Chen AY, Sturm R. Diet quality in the United States improved during the great recession and deteriorated during economic recovery. J Acad Nutr Diet. 2022;122(5):974–80.

    Article  PubMed  Google Scholar 

  42. Panizza CE, Shvetsov YB, Harmon BE, Wilkens LR, Le Marchand L, Haiman C, Reedy J, Boushey CJ. Testing the predictive validity of the Healthy Eating Index-2015 in the multiethnic cohort: Is the score associated with a reduced risk of all-cause and cause-specific mortality? Nutrients. 2018; 10(4).

  43. MacLean RR, Cowan A, Vernarelli JA. More to gain: Dietary energy density is related to smoking status in US adults. BMC Public Health. 2018;18(1):365.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Feig EH, Levy DE, McCurley JL, Rimm EB, Anderson EM, Gelsomin ED, Thorndike AN. Association of work-related and leisure-time physical activity with workplace food purchases, dietary quality, and health of hospital employees. BMC Public Health. 2019;19(1):1583.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Asghari G, Mirmiran P, Yuzbashian E, Azizi F. A systematic review of diet quality indices in relation to obesity. Br J Nutr. 2017;117(8):1055–65.

    Article  CAS  PubMed  Google Scholar 

  46. Thomson JL, Goodman MH, Landry AS, Donoghue A, Chandler A, Bilderback R. Feasibility of online nutrition education in the workplace: Working toward healthy lifestyles. J Nutr Educ Behav. 2018;50(9):868–75.

    Article  PubMed  Google Scholar 

  47. Hassani B, Amani R, Haghighizadeh MH, Araban M. A priority oriented nutrition education program to improve nutritional and cardiometabolic status in the workplace: A randomized field trial. J Occup Med Toxicol. 2020;15(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  48. al-Tamimi K, van Herwerden L, Abdul M, Utter J. Hospital-based food environment interventions to improve workforce dietary behaviour: a systematic literature review. Am J Lifestyle Med. 2023; 15598276231184813.

  49. Naicker A, Shrestha A, Joshi C, Willett W, Spiegelman D. Workplace cafeteria and other multicomponent interventions to promote healthy eating among adults: A systematic review. Prev Med Rep. 2021; 22(101333.

  50. Peñalvo JL, Sagastume D, Mertens E, Uzhova I, Smith J, Wu JHY, Bishop E, Onopa J, Shi P, Micha R, et al. Effectiveness of workplace wellness programmes for dietary habits, overweight, and cardiometabolic health: A systematic review and meta-analysis. Lancet Public Health. 2021;6(9):e648–60.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Allan J, Querstret D, Banas K, de Bruin M. Environmental interventions for altering eating behaviours of employees in the workplace: A systematic review. Obes Rev. 2017;18(2):214–26.

    Article  CAS  PubMed  Google Scholar 

  52. Schliemann D, Woodside JV. The effectiveness of dietary workplace interventions: A systematic review of systematic reviews. Public Health Nutr. 2019;22(5):942–55.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Mertens S, Herberz M, Hahnel UJJ, Brosch T. The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proc Natl Acad Sci. 2022;119(1):e2107346118.

    Article  CAS  PubMed  Google Scholar 

  54. Skov LR, Lourenço S, Hansen GL, Mikkelsen BE, Schofield C. Choice architecture as a means to change eating behaviour in self-service settings: A systematic review. Obes Rev. 2013;14(3):187–96.

    Article  CAS  PubMed  Google Scholar 

  55. Division of Nutrition PA, and, Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Apply Behavioral Design Strategies. 2023. https://www.cdc.gov/nutrition/food-service-guidelines/strategize-and-act/applied-strategies.html. Accessed 17 Feb 2024.

  56. Abrahams-Gessel S, Wilde P, Zhang FF, Lizewski L, Sy S, Liu J, Ruan M, Lee Y, Mozaffarian D, Micha R, et al. Implementing federal food service guidelines in federal and private worksite cafeterias in the United States leads to improved health outcomes and is cost saving. J Public Health Policy. 2022;43(2):266–80.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Williams DR, Lawrence JA, Davis BA. Racism and health: Evidence and needed research. Annu Rev Public Health. 2019;40:105–25.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Goodman AH. Why genes don’t count (for racial differences in health). Am J Public Health. 2000;90(11):1699–702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Krieger N. Refiguring race: Epidemiology, racialized biology, and biological expressions of race relations. Int J Health Serv. 2000;30(1):211–6.

    Article  CAS  PubMed  Google Scholar 

  60. Lewis C, Cohen PR, Bahl D, Levine EM, Khaliq W. Race and ethnic categories: A brief review of global terms and nomenclature. Cureus. 2023;15(7):e41253.

    PubMed  PubMed Central  Google Scholar 

  61. Lewis TT, Cogburn CD, Williams DR. Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues. Annu Rev Clin Psychol. 2015;11:407–40.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Pascoe EA, Smart Richman L. Perceived discrimination and health: A meta-analytic review. Psychol Bull. 2009;135(4):531–54.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Grier SA, Kumanyika SK. The context for choice: Health implications of targeted food and beverage marketing to African Americans. Am J Public Health. 2008;98(9):1616–29.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Harris J, Shehan C, Gross R, Kumanyika S, Lassiter V, Ramirez A, Gallion K. Food advertising targeted to Hispanic and Black youth: Contributing to health disparities. 2015. https://uconnruddcenter.org/research/food-marketing/targetedmarketing/. Accessed 17 Feb 2024.

  65. Bragg MA, Miller AN, Kalkstein DA, Elbel B, Roberto CA. Evaluating the influence of racially targeted food and beverage advertisements on black and white adolescents’ perceptions and preferences. Appetite. 2019;140:41–9.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Henderson VR, Kelly B. Food Advertising in the age of obesity: Content analysis of food advertising on general market and African American television. J Nutr Educ Behav. 2005;37(4):191–6.

    Article  PubMed  Google Scholar 

  67. Powell LM, Wada R, Kumanyika SK. Racial/ethnic and income disparities in child and adolescent exposure to food and beverage television ads across the U.S. media markets. Health Place. 2014;29:124–31.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Folkvord F, Hermans RCJ. Food marketing in an obesogenic environment: A narrative overview of the potential of healthy food promotion to children and adults. Curr Addict Rep. 2020;7(4):431–6.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the study participants and study team members for their contributions to this study.

Funding

The PACE Study was funded by R01 CA184473-01A1 from the National Cancer Institute, National Institutes of Health. The funding body played no role in study design, data collection, analysis, interpretation of data, or writing the manuscript.

Author information

Authors and Affiliations

  1. Davidson College, PO Box 5000, 209 Ridge Road, Davidson, NC, 28035, USA

    Sally L. Bullock

  2. Duke University School of Medicine, 2200 W Main St, Durham, NC, 27705, USA

    Hilary M. Winthrop

  3. Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill, 1700 MLK Jr. Blvd, CB#7426,, Chapel Hill, NC, 27599, USA

    Derek Hales

  4. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, 3101 McGavran-Greenberg Hall, CB #7420,, Chapel Hill, NC, 27599, USA

    Feng-Chang Lin & Yumei Yang

  5. Department of Nutrition, Gillings School of Global Public Health, Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill, CB# 7426, 1700 MLK Jr. Blvd, Room 239,, Chapel Hill, 27599, USA

    Alice S. Ammerman

  6. Department of Family Medicine and Community Health, Duke University School of Medicine, 2200 W Main St, Suite 400, Durham, NC, 27705, USA

    Anthony J. Viera

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Contributions

Conceptualization, SLB, HMW, DH, ASA, and AJV; methodology, SLB, HMW, and AJV; formal analysis, Y.Y., F.L., S.L.B.; writing—original draft preparation, S.L.B.; writing—review and editing, all authors; supervision, AJV; funding acquisition, AJV, ASA. All authors have read and agreed to the published version of this article.

Corresponding author

Correspondence to Sally L. Bullock.

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Ethics approval and consent to participate

The study was approved by the Institutional Review Board of the Office of Human Research Ethics at the University of North Carolina at Chapel Hill and written informed consent for participation was obtained from participants.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Bullock, S.L., Winthrop, H.M., Hales, D. et al. Who chooses “healthy” meals? An analysis of lunchtime meal quality in a workplace cafeteria. BMC Public Health 24, 921 (2024). https://doi.org/10.1186/s12889-024-18284-5

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ISSN: 1471-2458

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