Skip to main content
Download PDF

Impact of a nudge-based food environment intervention in a hospital convenience store on staff’s food intake and Na/K

BMC Nutrition volume 10, Article number: 113 (2024) Cite this article

Abstract

Background

A food environment intervention using nudge tactics was implemented at a hospital convenience store (CVS) in Tokyo to improve employees’ eating habits. The objective of this study was to evaluate its effects on the urinary sodium-to-potassium ratio (Na/K), food intake, eating attitude, and behavior.

Methods

Using a pre–post design; the intervention incorporated nudge tactics, healthier options, easy-to-pick food placement, and eye-catching information. We also used price incentives. The primary outcomes included changes in Na/K and sodium and potassium excretion assessed using spot urine samples at health checkups. Secondary outcomes were changes in staff food intake, eating attitude, and behavior which were assessed using questionnaire surveys. All outcomes were evaluated statistically. Furthermore, we investigated how the intervention led to outcomes using path analysis.

Results

A total of 140 participant (52men and 88women) were analyzed. Significant changes were observed in Na/K (3.16 to 2.98 in median, p = 0.02) and potassium excretion (43.4 to 45.2 mmol/day in mean, p = 0.03). However, sodium excretion did not change significantly. The intake of fruits and dairy products increased with improved self-efficacy. The most influential factor for lowering Na/K and increasing potassium excretion was information from the CVS; purchasing “balanced meals” to lower Na/K and salads to increase potassium excretion were second.

Conclusions

Food environment intervention using nudge tactics can improve staff’s food intake and lower Na/K.

Trial registration

Registration number: UMIN000049444 (UMIN-CTR).

Date of registration: November. 7. 2022.

Peer Review reports

Background

Protecting the health and well-being of healthcare workers is crucial for providing sustainable healthcare. Nutrition and dietary habits are among the most important factors, and it is of great significance for healthcare organizations to take initiatives to improve the food environment of their facilities.

Recent studies showed the effects of nudge tactics in improving the food environment [1,2,3,4,5,6,7,8,9,10,11,12,13]. Nudge is a strategy based on behavioral economics that uses small cues to influence people's decisions and encourage behavioral change without drastically changing economic incentives or coercing behavior through penalties or rules [14]. Nudges can encourage undesirable decisions, depending on how they are used, therefore it is important to be transparent about how they are implemented [14].

Many studies have reported the effects of nudges such as price incentives and increasing healthier selections at school and worksite cafeterias, restaurants, shops, and vending machines [1,2,3,4,5,6,7,8,9,10,11,12,13]. Among studies in hospital settings [15,16,17,18,19,20], some showed positive effects with increased sales of healthier products, healthier menu selections, and reduced staff energy intake [16,17,18,19,20]. However, none of these studies examined changes in the staff’s food intake.

There are no reports showing the pathways by which nudges stimulate medical staff to change their eating behaviors. Glanz, et al. presented a conceptual model of nutritional environments, indicating that there were two ways in which the environment influences eating patterns [21]. One directly influences eating patterns, and the other is via individual factors, such as knowledge, attitude, belief, and self-efficacy. Environmental factors include access to food, healthy options, placement, price promotions, and access to information. Our hypothesis was that the nudge-based food environment intervention also influences staff eating patterns both directly and indirectly, by changing their attitude and self-efficacy. It is important to clarify the pathways for building the next strategy to improve staff food intake and prevent non-communicable diseases.

Furthermore, most previous studies have focused on weight control [3, 6, 8, 15,16,17,18,19,20]. However, hypertension is the most preventable risk factor for mortality in Japan [22]. The prevalence of hypertension among Japanese adults is 29.5% [23], and the number of people with cardiovascular disease is estimated at 1,732,000 [24]. The salt intake of the population remains high [23], and reducing salt intake is urgently needed to prevent non-communicable diseases [25]. Recent findings indicate that sufficient potassium (K) intake can help decrease blood pressure [26], and that a higher dietary sodium-to-potassium ratio (Na/K) increases cardiovascular mortality risk [27]. Some studies have aimed to reduce salt consumption using nudges in hospitals [28, 29]. However, no study has evaluated the changes in biomarkers, which are more objective than self-reported dietary surveys, for dietary sodium and potassium intake among hospital staff. Thus, in this study, we used nudges to help medical staff, who were not very health-conscious or too busy to put knowledge into practice, make better food choices for themselves. We assessed the effect of food environment interventions in a hospital convenience store on the staff’s urinary Na/K levels and food intake.

Method

Study design, features of the hospital, and staff dietary issues

A pre-post-comparison design was used for this study. The intervention site was the Taito Municipal Hospital convenience store (CVS) in Tokyo. The sales floor is approx. 62.5 m2, and the opening hours are 7:45 AM–7:00 PM during the week and 11:00 AM–6:00 PM during weekends.

According to sales data, the average number of visitors per day was approximately 250, of which 75% were staff, as counted by investigators in September–October 2019. The hospital has no cafeteria and about 30% of its staff use the CVS daily to buy meals at work. Most others bring light meals, such as rice balls or bowl noodles, from home and nearby supermarkets, according to the preliminary survey we conducted in November 2018.

The baseline survey was conducted with all staff (N = 273) from April1 to May31 2018. 222 staff participated (participation rate: 81.3%). 9.5% of the participants had hypertension. The prevalence of hypertension among the staff was lower than that in the national data [22]; however, the survey revealed dietary problems among the staff. Of all participants, 90.1% lacked vegetables (< 350 g/day) and 92.3% lacked fruits (< 200 g/day) in their diet based on the Japanese Food Guide recommendation [30], 97.3% exceeded salt intake limit (males: > 8.0 g, females: > 7.0 g per day) set by the Dietary Reference Intake for Japanese, 2015 [31], and only 15.8% consumed a “balanced meal” consisting of staple food, main dish, and side dish more than twice a day, as recommended by Health Japan 21 (second term) [32].

Procedure

The study procedure and subject selection process are shown in Fig. 1. A baseline survey was conducted in April 2018, and a post-survey was conducted in April 2020 for all employees.

Fig. 1
figure 1

Procedure of the study and the subject selection

Full size image

The preparation period was from April 2018 to March 2019, during which consensus building with hospital managers for conducting research, recruiting project members, coordinating with CVS managers and sales staff, creating display pops, and renovating the inside of CVS were conducted.

The intervention was from April 2019 to March 2020. An additional survey was conducted in April 2021 to obtain the variables for the factorial analysis.

Written informed consent was obtained from all the participants. This study was approved by the Ethics Committee of Japan Association for Development of Community Medicine.

Intervention contents

The contents of the interventions are shown in Table 1. The intervention was based on the Hospital Nutrition Environmental Scan (HNES) by Winston et al. [33] and incorporated one of the nudge frameworks, EAST, advocated by the Behavioural Insight Team (BIT) [34]. The EAST framework focuses on four simple principles to encourage a behavior: making it Easy, Attractive, Social, and Timely [34].

Table 1 Intervention contents
Full size table

HNES is widely used as a hospital food environment evaluation index in the United States [35,36,37]. It is divided into three venue sections: cafeterias, vending machines, and gift shops, each further divided into subsection questions [33]. We designed the intervention by referring to the subsection questions and, dividing them into four categories: healthy options, placement, information, and pricing, each incorporating nudge tactics.

The main contents were 1) offering a “healthy set” (Supplementary File 1) with a discount at grab-and-go; 2) increasing and improving the placement of healthy options (e.g., salads, yogurt, sugar-free drinks, low-salt bowl noodles); 3) providing nutrition information in the CVS and monthly newsletters.

Data collection and outcome measures

Data were collected during the annual staff health checkups in 2018 and 2020. Two types of questionnaire were used in this study. One was Brief-type self-administered diet history questionnaire (BDHQ) developed by Sasaki et al. [38, 39] to evaluate food intake and the other was an original self-administered questionnaire developed for this study to evaluate diet-related attitudes and behaviors (Supplementary Files 2 and 3). They were distributed to staff, with information about their health checkups. During the check-up, the researchers collected and examined the questionnaires and resurveyed or corrected missing or illogical responses. Residual urine after the health checkups (spot urine at fasting) was used in this survey.

Measurements

Urinary Na/K ratio and sodium (Na) and potassium(K) excretion

The primary outcomes were changes in urinary Na/K molar ratio and Na and K excretion. The collected specimens were sent to a laboratory company (BML, Inc.) immediately after the checkup was performed in the morning and analyzed for Na (mEq/L), K (mEq/L), and creatinine (mg/dL). Generally, 24-h urine collection is considered the most reliable method for evaluating Na and K intake [40]. As with other alternatives, it has been reported that six random daytime urine samples collected on different days are sufficient compared to 7-day and 24-h urine collections [41]. However, we decided to use spot urine samples during the health checkups to minimize the burden on the participants.

The estimated 24-h Na and K excretion (E24HNaV and E24HKV, respectively; mmol/day) was calculated using the formula of Tanaka et al. [42], and the Na/K was calculated using the results. The required information and values for calculation, including sex, age, height, and weight (measured in a medical check-up-gown) were collected during the health checkup.

Food group intake and BMI as an energy-intake evaluation index

The secondary outcomes were changes in the intake of five potassium-excretion-related foods: vegetables, fruits, fish, meat, and dairy products [23, 43]. The BDHQ was used for the evaluation [38, 39]. The nutrient and food group intakes obtained from the BDHQ were validated using energy-adjusted values [38, 39].

Additionally, changes in energy intake were evaluated using the body mass index (BMI: kg/m2) calculated using height and weight measured during health checkup.

Diet-related attitudes and behavior

We also evaluated attitudes and behavior toward having a “balanced meal” by asking about its frequency, beliefs, and participants’ self-efficacy to do it. As for awareness, we asked the participants to answer yes or no to each of the following: taking sufficient vegetables, adequate energy intake, reducing salt intake, taking a “balanced meal”, taking sufficient dairy products, and taking fruits every day, using an original self-administered questionnaire (Supplementary File 2).

Factors contributing to outcomes

After evaluating the primary outcomes, we conducted an additional survey to investigate the factors contributing to the results using an original self-administered questionnaire (Supplementary File 3) in April 2021. The variables collected were the frequency of using the CVS, the information provided by the CVS, and purchasing a “healthy set” salad and yogurt at the CVS.

As confounders, occupation, work shift, living alone, and frequency of referring to nutrition labells were also collected using the same self-administered questionnaire.

Analyses

Subjects

The subject selection procedure is shown in Fig. 1. As reported by Sasaki, energy intake values that were likely severely under- or over-reported were excluded from statistical analyses [44]. This included nine participants with an estimated energy intake calculated using the BDHQ less than half the energy requirements for the lowest physical activity level (PAL: I). There were no over-reporters with more than or equal to 1.5 times the energy requirement of the highest physical activity (PAL: III).

We also excluded one subject with missing urine data, resulting in 140 staff members meeting the inclusion criteria.

Statistical analyses

  1. (1)

    Pre-post comparison

    • Preliminary analyses showed no significant difference in the indicators by sex, age group, work shift, or living alone; therefore, we decided to compare them as a whole. Wilcoxon’s signed rank-sum test was used for changes in the Na/K, and paired t-tests were used for changes in E24HNaV, E24HKV, nutrient and food group intake, and BMI.

    • Changes in dietary consciousness were analyzed using the McNemar test, and dietary attitudes, behaviors, and BMI categories were analyzed using the McNemar-Bowker test.

    • All statistical analyses were performed using the Statistical Package for the Social Sciences Statistics (SPSS) 25 made by International Business Machines Corporation (IBM), and the significance level was set at 5%.

  2. (2)

    Path analysis

    • First, we hypothesized that there were two ways to change staff’s food intake following the model of Glanz, et al. [21]. One is that the exposure to the intervention (using CVS, using information from the CVS, frequency of purchasing “healthy set”, salad, and yogurt) directly influenced their food intake, the other is the intervention change their attitude and behavior (awareness of taking "balanced meal", taking sufficient vegetable, taking fruits every day, reducing salt, self-efficacy to take "balanced meal", frequency of taking "balanced meal") which influenced their food intake. Under this hypothesis we performed a multiple regression analysis (stepwise method) to predict the change in Na/K and E24HKV based on the intervention factors, dietary attitudes and behavior as the outputs of the intervention. Age, sex, occupation, and dietary attitude/behavior at baseline were adjusted for confounding factors.

    • Subsequently, we created a causal model based on the results and performed path analysis. The path diagram was laid out, confirming the standardized partial regression coefficient (β) of the multiple regression analysis. The model fit indices the goodness-of-fit index (GFI), adjusted GFI (AGFI), comparative fit index (CFI), root mean square error of approximation (RMSEA), and chi-squared/degree of freedom (CMIN/DF). GFI, AGFI, and CFI values are recommended to be above 0.9 [45, 46] and RMSEA is considered to be perfect fit if it’s less than 0.05 and acceptable if it’s less than 0.08. CMIN/DF is suggested to be 5 or less and desirable not to be significant [46]. Thus, we set these as the cut-off values.

    • SPSS Amos 25 Graphics (IBM) was used for the statistical analyses. The significance level was set at 5%, and variance inflation factor (VIF) values for the level of collinearity were set at 3 [47].

Results

Baseline characteristics of subjects (Table 2)

The age [mean (standard deviation: SD)] was 41.6 (10.9) years; females constituted 62.9%. Occupations were nursing/care workers: 61.4%; technicians (therapists, dietitians, pharmacists): 20.7%; clerical workers: 13.6%; and doctors: 3.6%. Staff who had night shifts comprised 35.0%, and those who lived alone were 23.6%.

Table 2 Subject characteristics at baseline
Full size table

Na/K, E24HNaV, and E24HKV (Table 3)

The Na/K [median (25–75 percentile)] decreased significantly from 3.16 [2.69–3.78] to 2.98 [2.63–3.48] (p = 0.015). E24HNaV (mean (SD) mmol/day) decreased from 137.7 (39.4) to 135.9 (34.3) but was not significant (p = 0.63). E24HKV increased significantly from 43.4 (8.1) to 45.2 (10.2) (p = 0.03).

Table 3 Change of Na/Ka, E24HNaV, E24HKV, and nutrients and food groups intake
Full size table

Food group intake and BMI as energy-intake evaluation indices (Table 3)

Significant increases were shown in fruit and dairy product intake (mean (SD) g/1000 kcal); fruits: 46.5 (40.8) to 51.7 (44.9) (p = 0.002); and dairy products: 72.2 (56.6) to 84.4 (68.7) (p = 0.01). Significant decreases were shown in snacks and beverages; snacks: 29.1(18.2) to 25.5 (17.9) (p = 0.01); beverages, including alcohol: 433.1 (211.9) to 392.2 (221.0) (p = 0.02); and sweetened soft drinks: 39.1 (57.2) to 29.4 (52.2) (p = 0.03). The other parameters showed no significant changes.

BMI increased significantly; however, the proportion of the underweight decreased from 10.0% to 4.3%, and that of normal weight participants increased from 67.9% to 72.9%. 9 of 14 underweight participants at baseline were women, among whom five were classified under normal weight.

Attitude and behavior (Table 4)

The percentage of staff who were conscious of taking a “balanced meal” increased significantly (p = 0.02), and the staff’s SE to do so was also significantly improved (p < 0.001). Other variables showed no significant changes.

Table 4 Change of diet-related attitude and behavior 
Full size table

Factors contributing to changes in Na/K and E24HKV

The results of the multiple regression analysis showed that the frequency of taking a “balanced meal” and using information from the CVS were most influential to the change in the Na/K. Regarding the change in E24HKV, using information from the CVS, the frequency of purchasing salads at the CVS and the awareness of taking sufficient vegetables were the most influential (Table 5).

Table 5 Prediction the change in the Na/K ratio and E24HKV based on the outputs of the intervention by multiple regression analysis
Full size table

The preparatory stages of these variables were investigated. For the change in Na/K, the frequency of taking a “balanced meal” was influenced by self-efficacy to take a “balanced meal” and the awareness of taking sufficient vegetables. Regarding the change in E24HKV, the awareness of taking sufficient vegetables was influenced by the frequency of purchasing salads at the CVS and using information from the CVS, and the frequency of purchasing salads at the CVS was influenced by the frequency of using information from the CVS. All variables were within the intervention period and were independent of sex, age, occupation, baseline awareness, and attitude. We also confirmed that VIF values for all independent variables were lower than 3 (range:1.1–1.6), therefore we considered no multicollinearity existed.

The desirable number of samples for building a reliable model is said to be 10 for 1 independent variable [48]. In this analysis, 140 samples were used for 11 independent variables, therefore we considered the sample size to be adequate.

Based on the results, a path diagram was created (Fig. 2). We confirmed that all path coefficients were significant and the goodness-of-fit indices were GFI = 0.972, AGFI = 0.929, CFI = 0.973, RMSEA = 0.047, and CMIN/DF = 1.3 (p = 0.22). Thus, we considered the model to be a good fit.

Fig. 2
figure 2

Intervention outline and the pass diagram from intervention to outcome

Full size image

Discussion

Changes in Na/K, E24HNaV, and E24HKV

In this study, Na/K decreased from 3.16 to 2.98 and E24HKV increased from 43.4 to 45.2 mmol/day significantly. In previous studies, Vanderlee, et al. reported that improving nutritional information on menus in hospital cafeterias led to reduced salt intake among staff [29]. Some reports have indicated that improving the placement of foods in school and hospital cafeterias nudged children and staff into choosing fruits and vegetables [6, 7, 22, 23]. Sakaguchi, et al. reported that providing low-salt, well-balanced lunches in a company cafeteria lowered employees’ Na/K [49]. The results of this study also support these findings, showing the effects of food environment improvements.

No significant changes were observed in E24HNaV in this study. Considering that meals at home also affect E24HNaV and that the main source of salt intake in Japan is cooking seasonings such as soy sauce and miso [50], it might have been necessary to encourage salt reduction in home cooking, besides food selection at the CVS.

Changes in food intake

Of the five K-increasing food groups examined in this study, the intake of fruits and dairy products increased significantly, but not for other types of food.

For the increase in fruit and dairy products, it could be assumed that increasing attractive fruits and yogurts in the CVS and placing them in a grab-and-go lane with a price-promoted “healthy set” contributed to a certain extent. Previous studies indicated the effectiveness of price promotion and using grab-and-go to improve dietary consumption [11,12,13,14], and this result supported the finding. Also, the sales of these items during the study period increased—plain yogurt: 136%; fruits in yogurt: 340%, compared to average year-on-year sales from April to December 2019 [51].

The BDHQ, a fixed-portion food frequency questionnaire, was a possible reason for the lack of changes in vegetable intake [38, 39]. The intake was estimated from a standardized single-dose intake and frequency; therefore, the change in the amount per meal was not considered.

For the significant decrease in the intake of snacks and sweetened soft drinks, the improvement in planogram allocation in the beverage corner (Easy-to-pick-up, Social normative change) and the energy and salt-amount indication next to the price cards (Easily-recognizable, Attractive-and-Timely indication) might have nudged the staff's purchasing behavior.

Changes in BMI and other lifestyle-related factors

Underweight is a significant issue among young women in Japan [32, 52]. In this study, the proportion of the underweights significantly decreased. This result can be considered a desirable change.

Decreased physical activity during the COVID-19 pandemic [53] may have led to increased BMI. We confirmed this using the results of a standard health checkup questionnaire; however, but no such changes were observed. In addition, 6 of 10 who improved underweight increased their frequency of “balanced meal” intake, and 4 of the 6 were CVS users. Thus, it could be assumed that the food environment improvement in the CVS certainly contributed to the result.

Diet-related attitude and behavior

The percentage of staff conscious of taking a “balanced meal” significantly increased, but no other variables. The message to take a “balanced meal” twice daily with good examples was disseminated through monthly newsletters and the “healthy set” menus more frequently than any other information from the CVS. Repeated exposure to the information may have helped raise awareness.

Furthermore, a recent systematic review revealed that short-term interventions were more effective than those implemented over a longer period [54]. This suggests that the above success was achieved by providing different menus and related information in the short term but repeatedly maintaining a consistent key message of eating a “balanced meal” more than twice a day.

Self-efficacy for taking a “balanced meal” also improved significantly. The environment in which the staff could get “balanced meals” in their workplace at reasonable prices was thought to have contributed to the results.

Many studies showed the effect of providing information along with the targeted food to replace in an effective way [6, 8, 10, 13, 20, 54], and so did this study. This suggests that providing not only information, but also targeted food in appropriate places with price incentives could lead to successful results.

Factors that contributed to changes in Na/K and E24HKV

The frequency of using information from the CVS was the most influential factor for both outcomes. It was widely disseminated to the staff so that even those not using the CVS were exposed to the information, which may have been a reason for the result.

The preparation of attractive salads and a “healthy set” in the CVS was also considered to have contributed to the result since the frequency of taking a “balanced meal” and the frequency of purchasing salads at the CVS were secondly influential.

Vermote, et al. reported that the combination of eat-well posters and an attractive green-heart icon above fruit stands resulted in significant increases in fruit consumption, whereas substitution and social norm messages had limited effects [55]. Almeida, et al. also reported that the placement position and information were the two most effective types of nudges [54]. Consequently, the strategy of presenting specific examples in an appropriate place, combined with related information, was found to be effective in this population.

These results indicated that the environment in which the desirable food, such as price-promoted “healthy set” and attractive salads, prepared in a timely manner at easy-to-pick-up places along with the encouraging information, enhanced awareness and strengthened purchasing behavior, which led to increasing E24HKV and lowering the Na/K.

Strengths and weaknesses

To our knowledge, this is the first study to show positive changes in Na/K and K intake among healthcare workers’ by improving the food environment using nudge tactics in a hospital CVS.

However, this study had some limitations. First, we were unable to set a control in this study. Further controlled trials are required to examine its effectiveness and reliability. Second, this study was conducted at a single hospital. The sample size was also limited, which makes it difficult to generalize. However, the result of examining the statistical power for Na/K, E24HKV and all food groups was 0.9, exceeding the 0.8 recommended by Cohen [56]. Therefore, we believe that our results are adequate. Third, we needed one year to start the food environment intervention after the baseline survey, and the state of the staff immediately before the intervention could not be investigated. Therefore, the possibility that some factors during the preparation period affected the results could not be ruled out. Finally, an additional survey on purchasing behavior before the COVID-19 pandemic, which were used as variables for the multiple regression analysis, was conducted a year after the post-survey; thus, recall bias was inevitable.

Despite these limitations, the method used in this study, which minimized the burden on participants, and led their food intake in a desirable direction, is highly recommended, even for hospitals without cafeterias.

Conclusion

This study showed that nudge-based food environment improvement providing both information and food, as a good example at a hospital CVS, can lead the staff’s food intake toward desirable directions, increasing K intake and lowering the Na/K. Further consideration is needed to reduce salt intake while preserving the K intake.

Availability of data and materials

The datasets generated and analysed during the current study can not to be shared due to ethical restrictions, but are available from the corresponding author on reasonable request.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Na:

Sodium

K:

Potassium

Na/K:

Sodium-to-Potassium ratio

CVS:

Convenience Store

HNES:

Hospital Nutrition Environmental Scan

E24HNaV:

Estimated 24-h Na Value

E24HKV:

Estimated 24-h K Value

BDHQ:

Brief-type self-administered Diet History Questionnaires

BMI:

Body Mass Index

PAL:

Physical Activity Level

SPSS:

Statistical Package for the Social Sciences Statistics

IBM:

International Business Machines Corporation

GFI:

Good Fit Index

AGFI:

Adjusted Good Fit Index

CFI:

Comparative Fit Index

RMSEA:

Root Mean Square Error of Approximation (RMSEA)

CMIN/DF:

Chi-squared/Degree of Freedom

SD:

Standard Deviation

COVID-19:

Coronavirus Disease 2019

References

  1. Arno A, Thomas S. The efficacy of nudge theory strategies in influencing adult dietary behaviour: a systematic review and meta-analysis. BMC Public Health. 2016;16:676. https://doi.org/10.1186/s12889-016-3272-x.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hartmann-Boyce J, Bianchi F, Piernas C, et al. Grocery store interventions to change food purchasing behaviors: a systematic review of randomized controlled trials. Am J Clin Nutr. 2018;107:1004–16. https://doi.org/10.1093/ajcn/nqy045.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Möllenkamp M, Zeppernick M, Schreyögg J. The effectiveness of nudges in improving the self-management of patients with chronic diseases: a systematic literature review. Health Policy. 2019;123:1199–209. https://doi.org/10.1016/j.healthpol.2019.09.008.

    Article  PubMed  Google Scholar 

  4. Harbers MC, Beulens JWJ, Rutters F, et al. The effects of nudges on purchases, food choice, and energy intake or content of purchases in real-life food purchasing environments: a systematic review and evidence synthesis. Nutr J. 2020;19:103. https://doi.org/10.1186/s12937-020-00623-y.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Levy DE, Riis J, Sonnenberg LM, Barraclough SJ, Thorndike AN. Food choices of minority and low-income employees: a cafeteria intervention. Am J Prev Med. 2012;43:240–8. https://doi.org/10.1016/j.amepre.2012.05.004.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Marcano-Olivier M, Pearson R, Ruparell A, et al. A low-cost behavioural nudge and choice architecture intervention targeting school lunches increases children’s consumption of fruit: a cluster randomised trial. Int J Behav Nutr Phys Act. 2019;16:1–9. https://doi.org/10.1186/s12966-019-0773-x.

    Article  Google Scholar 

  7. Hua SV, Kimmel L, Van Emmenes M, et al. Health promotion and healthier products increase vending purchases: a randomized factorial trial. J Acad Nutr Diet. 2017;117:1057–65. https://doi.org/10.1016/j.jand.2016.12.006.

    Article  PubMed  Google Scholar 

  8. Van Gestel LC, Kroese FM, De Ridder DTD. Nudging at the checkout counter–a longitudinal study of the effect of a food repositioning nudge on healthy food choice. Psychol Health. 2018;33:800–9. https://doi.org/10.1080/08870446.2017.1416116.

    Article  PubMed  Google Scholar 

  9. Hoenink JC, Mackenbach JD, Waterlander W, Lakerveld J, van der Laan N, Beulens JWJ. The effects of nudging and pricing on healthy food purchasing behavior in a virtual supermarket setting: a randomized experiment. Int J Behav Nutr Phys Act. 2020;17:98. https://doi.org/10.1186/s12966-020-01005-7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Larson N, Wang Q, Grannon K, et al. A low-cost, grab-and-go breakfast intervention for rural high school students: changes in school breakfast program participation among at-risk students in Minnesota. J Nutr Educ Behav. 2018;50:125–32. https://doi.org/10.1016/j.jneb.2017.08.001.

    Article  PubMed  Google Scholar 

  11. Mehta D, Kumar MHS, Sabikhi L. Development of high protein, high fiber smoothie as a grab-and-go breakfast option using response surface methodology. J Food Sci Technol. 2017;54:3859–66. https://doi.org/10.1007/s13197-017-2841-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Afshin A, Peñalvo JL, Del Gobbo L, et al. The prospective impact of food pricing on improving dietary consumption: a systematic review and meta-analysis. PLoS ONE. 2017;12:e0172277. https://doi.org/10.1371/journal.pone.0172277.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chapman LE, Burstein S, Sadeghzadeh C, et al. Evaluation of a healthy checkout lane “nudge” on grocery and convenience store sales of a price-promoted nutritious food. Health Promot Pract. 2023;24:111–20. https://doi.org/10.1177/15248399211048463.

    Article  PubMed  Google Scholar 

  14. Thaler RH, Sunstein CR. Nudge: The Final Edition. Dublin: Penguin Books; 2022.

    Google Scholar 

  15. Lemon SC, Zapka J, Li W, et al. Step ahead. A worksite obesity prevention trial among hospital employees. Am J Prev Med. 2010; 38:27–38. org/https://doi.org/10.1016/j.amepre.2009.08.028

  16. Patsch AJ, Smith JH, Liebert ML, et al. Improving healthy eating and the bottom line: impact of a price incentive program in 2 hospital cafeterias. Am J Health Promot. 2016;30:425–32. https://doi.org/10.1177/0890117116658237.

    Article  PubMed  Google Scholar 

  17. Allan JL, Powell DJ. Prompting consumers to make healthier food choices in hospitals: a cluster randomised controlled trial. Int J Behav Nutr Phys Act. 2020;17:86. https://doi.org/10.1186/s12966-020-00990-z.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pechey R, Jenkins H, Cartwright E, et al. Altering the availability of healthier vs. less healthy items in UK hospital vending machines: a multiple treatment reversal Design. Int J Behav Nutr Phys Act 2019; 16:114. org/https://doi.org/10.1186/s12966-019-0883-5

  19. Mazza MC, Dynan L, Siegel RM, Tucker AL. Nudging healthier choices in a hospital cafeteria: results from a field study. Health Promot Pract. 2017;19:6. https://doi.org/10.1177/1524839917740119.

    Article  Google Scholar 

  20. Thorndike AN, Sonnenberg LM, Riis J, Barraclough SJ, Levy DE. A 2-phase labeling and choice architecture intervention to improve healthy food and beverage choices. Am J Public Health. 2012;102:527–33. https://doi.org/10.2105/AJPH.2011.300391.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy nutrition environments: concepts and measures. Am J Health Promot. 2005;19:330–3.

    Article  PubMed  Google Scholar 

  22. Nomura S, Sakamoto H, Ghaznavi C, Inoue M. Toward a third term of Health Japan 21 - implications from the rise in non-communicable disease burden and highly preventable risk factors. Lancet Reg Health West Pac. 2022;21:100377. https://doi.org/10.1016/j.lanwpc.2021.100377.

    Article  PubMed  PubMed Central  Google Scholar 

  23. National Institute of Health and Nutrition. The National Health and Nutrition Survey (NHNS) Japan, 2019. https://www.nibiohn.go.jp/eiken/kenkounippon21/download_files/eiyouchousa/2019.pdf . Accessed 20 Nov 2023.

  24. Ministry of Health, Labour and Welfare. Patient Survey 2017, https://www.mhlw.go.jp/toukei/saikin/hw/kanja/17/index.html. [in Japanese]. Accessed 20 June 2024.

  25. GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019; 393:1958–72. https://doi.org/10.1016/S0140-6736(19)30041-8

  26. Stamler J, Chan Q, Daviglus ML, et al. Relation of dietary sodium(salt) to blood pressure and its possible modulation by other dietary factors: the INTERMAP study. Hypertension. 2018;71:631–7. https://doi.org/10.1161/HYPERTENSIONAHA.117.09928.

    Article  CAS  PubMed  Google Scholar 

  27. Okayama A, Okuda N, Miura K, et al. Dietary sodium-to-potassium ratio as a risk factor for stroke, cardiovascular disease, and all-cause mortality in Japan: the NIPPON DATA80 cohort study. BMJ Open. 2016;6:e011632. https://doi.org/10.1136/bmjopen-2016-011632.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Amerson N, Marguerite Nelson M, Radcliffe A, Moody C, Williams L, Miles C. Adoption of sodium reduction strategies in small and rural hospitals, Illinois, 2012. Prev Chronic Dis. 2014;11:E42. https://doi.org/10.5888/pcd11.130261.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Vanderlee L, Hammond D. Does nutrition information on menus impact food choice Comparisons across two hospital cafeterias. Public Health Nutr. 2013;17:1393–402. https://doi.org/10.1017/S136898001300164X.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Yoshiike N, Hayashi F, Takemi Y, Mizoguchi K, Seino F. A new food guide in Japan: the Japanese food guide spinning top. Nutr Rev. 2007;65:149–54. https://doi.org/10.1111/j.1753-4887.2007.tb00294.x.

    Article  PubMed  Google Scholar 

  31. Ministry of Health, Labour and Welfare. Dietary Reference Intake for Japanese, 2015[in Japanese]. https://www.mhlw.go.jp/file/05-Shingikai-10901000-Kenkoukyoku-Soumuka/0000114399.pdf. Accessed 20 Nov 2023.

  32. Ministry of Health, Labour and Welfare. Health Japan 21 (the second term). A basic direction for comprehensive implementation of national health promotion. https://www.mhlw.go.jp/file/06-Seisakujouhou-10900000-Kenkoukyoku/0000047330.pdf . Accessed 20 Nov 2023.

  33. Winston CP, Sallis JF, Swartz MD, et al. Reliability of the hospital nutrition environment scan for cafeterias, vending machines, and gift shops. J Acad Nutr Diet. 2013;113:1069–75. https://doi.org/10.1016/j.jand.2013.04.004.

    Article  PubMed  Google Scholar 

  34. The Behavioural Insight Team: EAST four simple ways to apply behavioural insights. https://www.bi.team/publications/east-four-simple-ways-to-apply-behavioural-insights/. Accessed 20 Nov 2023.

  35. Winston CP, Sallis JF, Swartz MD, et al. Consumer nutrition environments of hospitals: an exploratory analysis using the hospital nutrition environment scan for cafeterias, vending machines, and gift shops, 2012. Prev Chronic Dis. 2013;10:E110. https://doi.org/10.5888/pcd10.120335.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Derrick JW, Bellini SG, Spelman J. Using the hospital nutrition environment scan to evaluate health initiative in hospital cafeterias. J Acad Nutr Diet. 2015;115:1855–60. https://doi.org/10.1016/j.jand.2015.06.378.

    Article  PubMed  Google Scholar 

  37. Dias SEH, Dawson RM, Harris DM, Wirth MD, Abshire DA. Foods and beverages available to nurses in hospital cafeterias, vending machines, and gift shops. Am J Health Promot. 2022;36:1133–41. https://doi.org/10.1177/08901171221089620.

    Article  Google Scholar 

  38. Kobayashi S, Murakami K, Sasaki S, et al. Comparison of relative validity of food group intakes estimated by comprehensive and brief-type self-administered diet history questionnaires against 16 d dietary records in Japanese adults. Public Health Nutr. 2011;14:1200–11. https://doi.org/10.1017/S1368980011000504.

    Article  PubMed  Google Scholar 

  39. Kobayashi S, Honda S, Murakami K, et al. Both comprehensive and brief self-administered diet history questionnaires satisfactorily rank nutrient intakes in Japanese adults. J Epidemiol. 2012;22:151–9. https://doi.org/10.2188/jea.JE20110075.

    Article  PubMed  PubMed Central  Google Scholar 

  40. The Japanese Society of Hypertension, eds. Guidelines for the Management of Hypertension (JSH 2019). Tokyo: Life Science Publishing; 2014. p. 39–42 [in Japanese].

  41. Iwahori T, Ueshima H, Miyagawa N, et al. Six random specimens of daytime casual urine on different days are sufficient to estimate daily sodium/potassium ratio in comparison to 7-day 24-h urine collections. Hypertens Res. 2014;37:765–71. https://doi.org/10.1038/hr.2014.76.

    Article  CAS  PubMed  Google Scholar 

  42. Tanaka T, Okamura T, Miura K, et al. A simple method to estimate populational 24-h urinary sodium and potassium excretion using a casual urine specimen. J Hum Hypertens. 2002;16:97–103. https://doi.org/10.1038/sj.jhh.1001307.

    Article  CAS  PubMed  Google Scholar 

  43. Okuda N, Okayama A, Miura A, et al. Food sources of dietary potassium in the adult Japanese population: the international study of macro-/micronutrients and blood pressure (INTERMAP). Nutrients. 2020;12:787. https://doi.org/10.3390/nu12030787.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sasaki S, Katagiri A, Tsuji T, et al. Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women. Int J Obes Relat Metab Disord. 2003;27:1405–10. https://doi.org/10.1038/sj.ijo.0802425.

    Article  CAS  PubMed  Google Scholar 

  45. Wang K, Xu Y, Wang C, Tan M, Chen P. A Corrected Goodness-of-Fit Index (CGFI) for model evaluation in structural equation modeling. Struct Equ Modeling. 2019. https://doi.org/10.1080/10705511.2019.1695213.

  46. Mamun M, Yahya M, Ibrahim KAR. Covariance Based-Structural Equation Modeling(CB-SEM) Using AMOS in management research. OSR-JBM. 2019;21:2319–7668.

    Google Scholar 

  47. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1:3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x.

    Article  Google Scholar 

  48. Lang TA, Secic M. How to report statistics in medicine. 2nd ed. Philadelphia: American College of Physicians; 2006. p. 82- 7.16.

    Google Scholar 

  49. Sakaguchi K, Takemi Y, Hayashi F, Koiwai K, Nakamura M. Effect of workplace dietary intervention on salt intake and sodium-to-potassium ratio of Japanese employees: a quasi-experimental study. J Occup Health. 2022;63:e12288. https://doi.org/10.1002/1348-9585.12288.

    Article  CAS  Google Scholar 

  50. Asakura K, Uechi K, Masayasu S, et al. Sodium sources in the Japanese diet: difference between generations and sexes. Public Health Nutr. 2015;17:1–13. https://doi.org/10.1017/S1368980015003249.

    Article  Google Scholar 

  51. Kawabata T, Takemi Y, Hayashi F, Nakamura M, Yamada T. Food environment improvement using nudge tactics in a convenient store at a hospital. J Food Syst Res 2021; 4:226–31. [in Japanese] doi.org/https://doi.org/10.5874/jfsr.27.4_226

  52. Takimoto H, Yoshiike N, Kaneda F, Yoshita K. Thinness among young Japanese women. Am J Public Health. 2004;94:1592–5. https://doi.org/10.2105/AJPH.94.9.1592.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Yamada M, Yoshida T, Nakagata T, Nanri H, Miyachi M. Age, sex, and regional differences in the effect of COVID-19 pandemic on objective physical activity in Japan: a 2-year nationwide longitudinal study. J Nutr Health Aging. 2021;25:1032–3. https://doi.org/10.1007/s12603-021-1662-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Almeida C, Azevedo J, Fogel A, Lopes E, Vale C, Padr˜ao P. Effectiveness of nudge interventions to promote fruit and vegetables’ selection, purchase, or consumption: a systematic review. Food Qual Preference. 2024;116:105122.

  55. Vermote M, Nys J, Versele V, et al. The effect of nudges aligned with the renewed Flemish Food Triangle on the purchase of fresh fruits: An on-campus restaurant experiment. Appetite. 2020;144:104479.

  56. Choen J. A power primer. Psychol Bull. 1992;112:155–9.

    Article  Google Scholar 

Download references

Acknowledgements

We thank all staff of Taito Municipal Hospital who participated in this study. We also thank Professor Hiromitsu Ogata (Laboratory of Epidemiology and Biostatistics, Kagawa Nutrition University) for his advice on statistics.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Japan Association for Development of Community Medicine, F15 Todofuken-Kaikan Bldg. 2-6-3 Hirakawa-Chou, Chiyoda-Ward, Tokyo, 102-0093, Japan

    Teruko Kawabata, Masakazu Nakamura & Takashi Yamada

  2. Kagawa Nutrition University Graduate School, Saitama-Pref, 3-9-21 Chiyoda, Sakado-City, 350-0288, Japan

    Yukari Takemi & Fumi Hayashi

  3. Taito Municipal Hospital, 3-20-5 Senzoku, Taito-Ward, Tokyo, 111-0031, Japan

    Takashi Yamada

Authors
  1. Teruko Kawabata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masakazu Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yukari Takemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fumi Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takashi Yamada
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.K, M.N, Y.T, and F.H designed the study. T.K and T.Y conducted the experiment. T.Y contributed to data acquisition. T.K, Y.T, and F.H analysed and interpreted the data. T.K drafted the manuscript. All the authors critically revised and approved the final manuscript.

Corresponding author

Correspondence to Yukari Takemi.

Ethics declarations

Ethics approval and consent to participate

We confirm that all methods in this study were carried out in accordance with the ethical standards of the declaration of Helsinki. Written informed consent was obtained from each participant. This study was approved by the Ethical Committee of the Japan Association for Development of Community Medicine (approval number: 18–032).

Consent for publication

Not applicable.

Competing interests

Teruko Kawabata is a researcher of Japan Association for Development of Community Medicine (JADECOM), which runs Taito Municipal Hospital and was the franchise owner and operator of the convenience store during the study period. 

Masakazu Nakamura is a board member of JADECOM.

Takashi Yamada is the executive vice president of JADECOM, also the administrator of Taito Municipal Hospital.

Other authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawabata, T., Nakamura, M., Takemi, Y. et al. Impact of a nudge-based food environment intervention in a hospital convenience store on staff’s food intake and Na/K. BMC Nutr 10, 113 (2024). https://doi.org/10.1186/s40795-024-00920-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40795-024-00920-3

Keywords

BMC Nutrition

ISSN: 2055-0928

Contact us