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Healthy Eating Learning Opportunities and Nutrition Education

Healthy eating learning opportunities includes nutrition education and other activities integrated into the school day that can give children knowledge and skills to help choose and consume healthy foods and beverages. 1 Nutrition education is a vital part of a comprehensive health education program and empowers children with knowledge and skills to make healthy food and beverage choices. 2-8

US students receive less than 8 hours of required nutrition education each school year, 9 far below the 40 to 50 hours that are needed to affect behavior change. 10,11 Additionally, the percentage of schools providing required instruction on nutrition and dietary behaviors decreased from 84.6% to 74.1% between 2000 and 2014. 9

Given the important role that diet plays in preventing chronic diseases and supporting good health, schools would ideally provide students with more hours of nutrition education instruction and engage teachers and parents in nutrition education activities. 5, 12 Research shows that nutrition education can teach students to recognize how healthy diet influences emotional well-being and how emotions may influence eating habits. However, because schools face many demands, school staff can consider ways to add nutrition education into the existing schedule. 11

Nutrition education can be incorporated throughout the school day and in various locations within a school. This provides flexibility allowing schools to use strategies that work with their settings, daily schedule, and resources.

In the Classroom

Nutrition education can take place in the classroom, either through a stand-alone health education class or combined into other subjects including 2,5 :

Counting with pictures of fruits and vegetables.
Learning fractions by measuring ingredients for a recipe.
Examining how plants grow.
Learning about cultural food traditions.

Nutrition education should align with the National Health Education Standards and incorporate the characteristics of an effective health education curriculum .

Farm to School

Farm-to-school programs vary in each school or district, but often include one or more of the following strategies:

Purchasing and serving local or regionally produced foods in the school meal programs.
Educating students about agriculture, food, health, and nutrition.
Engaging students in hands-on learning opportunities through gardening, cooking lessons, or farm field trips.

Students who participate in farm-to-school activities have increased knowledge about nutrition and agriculture, are more willing to try new foods, and consume more fruits and vegetables. 14-17

School Gardens

School garden programs can increase students’ nutrition knowledge, willingness to try fruit and vegetables, and positive attitudes about fruits and vegetables. 18-22 School gardens vary in size and purpose. Schools may have window sill gardens, raised beds, greenhouses, or planted fields.

Students can prepare the soil for the garden, plant seeds, harvest the fruits and vegetables, and taste the food from the garden. Produce from school gardens can be incorporated into school meals or taste tests. Classroom teachers can teach lessons in math, science, history, and language arts using the school garden.

In the Cafeteria

Cafeterias are learning labs where students are exposed to new foods through the school meal program, see what balanced meals look like, and may be encouraged to try new foods through verbal prompts from school nutrition staff, 23 or taste tests. 24-25 Cafeterias may also be decorated with nutrition promotion posters or student artwork promoting healthy eating. 24

Other Opportunities

Schools can add messages about nutrition and healthy eating into the following:

Morning announcements.
School assemblies.
Materials sent home to parents and guardians. 24
Staff meetings.
Parent-teacher group meetings.

These strategies can help reinforce messages about good nutrition and help ensure that students see and hear consistent information about healthy eating across the school campus and at home. 2

Shared use agreements can extend healthy eating learning opportunities. As an example, an after-school STEM club could gain access to school gardens as learning labs.

CDC Parents for Healthy Schools: Ideas for Parents

Nutrition: Gardening Interventions | The Community Guide

Dietary Guidelines for Americans, 2020–2025

Introduction to School Gardens

Learning Through the Garden

National Farm-to-School Network

National Farm to School Network Resource Database

National Health Education Standards

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USDA Farm to School

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Joint Committee on National Health Education Standards. National Health Education Standards: Achieving Excellence. 2nd ed. Atlanta, GA: American Cancer Society; 2007.
Centers for Disease Control and Prevention. Health Education Curriculum Analysis Tool, 2012, Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2012. Available at http://www.cdc.gov/healthyyouth/hecat/index.htm. Accessed April 9, 2019.
Price C, Cohen D, Pribis P, Cerami J. Nutrition education and body mass index in grades K–12: a systematic review. J Sch Health. 2017;87:715–720.
Meiklejohn S, Ryan L, Palermo C. A systematic review of the impact of multi-strategy nutrition education programs on health and nutrition of adolescents. J Nutr Educ Behav . 2016;48:631–646.
Silveira JA, Taddei JA, Guerra PH, Nobre MR. The effect of participation in school-based nutrition education interventions on body mass index: A meta-analysis of randomized controlled community trials. Prev Med . 2013;56:237–243.
County Health Rankings and Roadmaps. School-based Nutrition Education Programs website. http://www.countyhealthrankings.org/take-action-to-improve-health/what-works-for-health/policies/school-based-nutrition-education-programs . Accessed on April 9, 2019.
Results from the School Health Policies and Practices Study 2014 . Atlanta, GA: Centers for Disease Control and Prevention; 2014.
Connell DB, Turner RR, Mason EF. Results of the school health education evaluation: health promotion effectiveness, implementation, and costs . J Sch Health . 1985;55(8):316–321.
Institute of Medicine. Nutrition Education in the K–12 Curriculum: The Role of National Standards: Workshop Summary. Washington, DC: The National Academies Press; 2014.
Murimi MW, Moyeda-Carabaza AF, Nguyen B, Saha S, Amin R, Njike V. Factors that contribute to effective nutrition education interventions in children: a systematic review. Nutr Rev . 2018;76(8):553–580.
Hayes D, Contento IR, Weekly C. Position of the American Dietetic Association, School Nutrition Association, and Society for Nutrition Education: comprehensive school nutrition services. J Acad Nutr Diet . 2018; 118:913–919.
Joshi A, Misako Azuma A, Feenstra G. Do farm-to-school programs make a difference? Findings and future research needs . J Hunger Environ Nutr . 2008;3:229–246.
Moss A, Smith S, Null D, Long Roth S, Tragoudas U. Farm to school and nutrition education: Positively affecting elementary school-aged children’s nutrition knowledge and consumption behavior. Child Obes . 2013;9(1):51–6.
Bontrager Yoder AB, Liebhart JL, McCarty DJ, Meinen A, Schoeller D, Vargas C, LaRowe T. Farm to elementary school programming increases access to fruits and vegetables and increases their consumption among those with low intake . J Nutr Educ Behav . 2014;46(5):341–9.
The National Farm to School Network. The Benefits of Farm to School website. http://www.farmtoschool.org/Resources/BenefitsFactSheet.pdf . Accessed on June 14, 2019.
Berezowitz CK, Bontrager Yoder AB, Schoeller DA. School gardens enhance academic performance and dietary outcomes in children. J Sch Health . 2015;85:508–518.
Davis JN, Spaniol MR, Somerset S. Sustenance and sustainability: maximizing the impact of school gardens on health outcomes. Public Health Nutr . 2014;18(13):2358–2367.
Langellotto GA, Gupta A. Gardening increases vegetable consumption in school-aged children: A meta-analytical synthesis. Horttechnology . 2012;22(4):430–445.
Community Preventative Services Task Force. Nutrition: Gardening Interventions to Increase Fruit and Vegetable Consumption Among Children. Finding and Rationale Statement .. https://www.thecommunityguide.org/sites/default/files/assets/Nutrition-Gardening-Fruit-Vegetable-Consumption-Children-508.pdf . Accessed on May 16, 2019.
Savoie-Roskos MR, Wengreen H, Durward C. Increasing Fruit and Vegetable Intake among Children and Youth through Gardening-Based Interventions: A Systematic Review. Journal of the Academy of Nutrition and Dietetics 2017;11(2):240–50.
Schwartz M. The influence of a verbal prompt on school lunch fruit consumption: a pilot study. Int J Behav Nutr Phys Act. 2007;4:6.
Fulkerson JA, French SA, Story M, Nelson H, Hannan PJ. Promotions to increase lower-fat food choices among students in secondary schools: description and outcomes of TACOS (Trying Alternative Cafeteria Options in Schools). Public Health Nutr. 2003 ;7(5):665–674.
Action for Healthy Kids. Tips for Hosting a Successful Taste Test website. http://www.actionforhealthykids.org/tools-for-schools/find-challenges/classroom-challenges/701-tips-for-hosting-a-successful-taste-test . Accessed on May 19, 2019.

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Original Communication
Published: 28 August 2003

Nutrition education in schools: experiences and challenges

C Pérez-Rodrigo 1 &
J Aranceta 1

European Journal of Clinical Nutrition volume 57 , pages S82–S85 ( 2003 ) Cite this article

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Health promotion from the early stages in life by fostering healthy eating practices and regular physical activity has the potential for a major impact on health and well-being during childhood and later stages in life.

School-based nutrition education should consider the needs and interests of students, teachers and the school. Educational strategies include efforts to increase health awareness, communication and skill building.

Previous literature reviews identified educational strategies directly relevant to a behavioural focus and theory-driven strategies among the elements conducive to successful programmes. Other features that contribute to gain effectiveness are the provision of adequate time and intensity for the intervention, involvement of families, particularly for younger children, and incorporation of self-assessment and feedback in interventions for older children. School meals provide a valuable opportunity for nutrition education. The emphasis on environmental and behavioural factors in successful school-based physical activity and nutrition interventions highlights the importance of involving parents and other community members.

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Pérez-Rodrigo, C., Aranceta, J. Nutrition education in schools: experiences and challenges. Eur J Clin Nutr 57 (Suppl 1), S82–S85 (2003). https://doi.org/10.1038/sj.ejcn.1601824

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Nutrition Education: Linking Research, Theory, and Practice

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School health and nutrition

Good health and nutrition are foundations for learning and a crucial investment for more sustainable, inclusive and peaceful futures – they can improve education outcomes, empower learners to thrive and promote inclusion and equity in education and health.

What is the state of school health and nutrition around the world?

The good news is that:

9 in 10 countries globally invest in school health and nutrition programmes.
More than 100 countries have school vaccination programmes.
One in two primary school children receives school meals
Almost every country includes education for health and well-being in its curriculum.

And yet many children, in particular girls, are missing out especially in the poorest countries.

73 million of the most marginalized children are not reached by school feeding, undermining their ability to benefit from education.
Over 246 million learners experience violence in and around school every year.
1 in 3 schools do not have basic drinking water and adequate sanitation.

Developed by UNESCO and five UN partners (UNICEF, WFP, FAO, GPE, and WHO), in collaboration with the World Bank, the Research Consortium for School Health and Nutrition and the UN-Nutrition Secretariat, Ready to learn and thrive takes stock of countries’ policies and programmes around health and nutrition, and underscores school health and nutrition as an effective and affordable way to ensure learners learn and thrive throughout their education pathway and beyond.

What does health and nutrition mean for learners and schools?

School health and nutrition is about investing both in learners’ education and their health, with benefits extending to homes and communities. Ensuring the health and well-being of learners is one of the most transformative ways to improve education outcomes, promote inclusion and equity and to rebuild the education system, especially following the COVID-19 pandemic.

The report shows that healthy, well-nourished and happy children and adolescents learn better and are more likely to lead healthy and fulfilling lives. For example, learners are 50% less likely to skip school when the learning environment is free from violence; absenteeism is reduced in low-income countries when promoting handwashing in particular for girls during menstruation when water, sanitation and hygiene is improved, and enrolment rates increase when school meals are provided to learners.

What are some of the key challenges?

Despite significant progress on school health and nutrition, more work must be done to ensure that the programmes in place are comprehensive, meet the needs of all learners and can be sustained. Many children are still missing out, especially in the poorest countries and most marginalized communities.

While the multisectoral nature of school health and nutrition is a strength, it can also lead to diffused action and scattered interventions. More attention needs to be paid to the quality of progammes, the synergies with existing efforts and the monitoring and evaluating of actions’ delivery and impact.

As the world is facing a global food crisis and struggling with the devastating effects of the COVID-19 pandemic, school health and nutrition must be integral to the daily mission of education systems across the globe.

What can we do about it?

To transform education and the lives of children and adolescents, this publication urges governments and development partners to put learners’ health and well-being at the core of the education agenda and to improve the quality and reach of school health and nutrition programmes.

We need comprehensive policies and programmes that address all learners’ needs holistically, are relevant and responsive to contexts and evolving needs, coordinated across sectors and sustained by increased policy and financial commitments.

There are many ways in which schools can promote physical and mental health and well-being. This starts by including health and well-being in curriculum, providing nutritious school meals and ensuring access to health services. It also means ensuring that school environments are free from violence and conducive to good health, nutrition, development and learning. Greater efforts to engage learners and communities and to ensure school staff and teachers have the necessary knowledge, tools and support are also needed.

School health and nutrition actions are a cost-effective investment. They can help reach marginalized learners and advance inclusion and equity, while benefitting multiple sectors including education, health, social protection and agriculture.

How does UNESCO work to advance school health and nutrition?

At UNESCO, school health and nutrition are core parts of its education mandate. We know that children and youth learn better when they are happy, healthy and thriving in school. This means that their learning environment must feel safe, offer healthy meals and promote physical and mental health.

Guided by its Strategy on education for health and well-being , UNESCO offers technical advice and resources, and fosters resilient and health-promoting education systems. The Global Standards for Health-Promoting Schools by UNESCO and WHO, for example, supports countries to adopt and institutionalize a holistic approach that promotes the physical and mental health and well-being of all learners.

The COVID-19 pandemic has demonstrated the interlinkages between education and health and the urgent need to work together across sectors. This is especially the case around the mental health of learners post-COVID. In Chile for example, UNESCO provided technical advice to the Seamos Comunidad programme which addresses the effects of the pandemic through a focus on improved relations and infrastructures, and better mental health and learning in school.

Through its work with governments, partners and civil society, UNESCO seeks to create and support education and school systems that foster a safe and healthy learning environment, enabling learners to thrive and get the most benefits out of their education. A series of guidance and tools were produced by UNESCO to help countries respond to school violence and bullying , school-related gender-based violence , and other forms of violence in and around school.

School health and nutrition for every learner

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Download the global report and the highlights from the report
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Press release: Educational achievement is hampered by lack of investment in health and nutrition
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On the interplay between educational attainment and nutrition: a spatially-aware perspective

Narges Azizi Fard 1 ,
Gianmarco De Francisci Morales 2 ,
Yelena Mejova 2 &
Rossano Schifanella ORCID: orcid.org/0000-0002-3745-5792 1 , 2

EPJ Data Science volume 10 , Article number: 18 ( 2021 ) Cite this article

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Food choices are an integral part of wellbeing and longevity, yet poor nutrition is responsible for millions of deaths every year. Among the complex mosaic of determinants of food choices are demographic, socioeconomic, physiological, and also cultural. In this work, we explore the connection between educational attainment, as a proxy for cultural capital, and food purchases, as a proxy for food consumption. Unlike existing studies, which use diaries and surveys, we use a large-scale dataset of food-related products purchased from a major retailer in London over the course of one year. By using this high-resolution dataset, we are able to explore the spatial dependence of the various factors impacting food choices, and estimate their direct and indirect spatial effects. We characterize food consumption across two complementary dimensions of (1) diet composition, and (2) diet variety. By building spatial auto-regressive models on these variables, we obtain an improved fit compared to traditional regression, and illustrate the importance of spillover effects. Our results consistently confirm the association between a higher educational attainment and a healthier diet, even when controlling for spatial correlation. First, a low educational level is connected to diets high in carbohydrates and low in fibers. Second, it is also associated with higher consumption of sweets and red meats, while high educational level is linked to a greater consumption of fruits, vegetables, and fish. Third, highly-educated areas show an increased nutritional diversity, together with a lower caloric intake. Finally, we show the presence of spillover effects within the neighboring communities, which would need to be taken in consideration when designing public health policies and interventions.

1 Introduction

Globally, in 2017 approximately 11 million deaths and a loss of 255 million disability-adjusted life-years were attributable to poor diet [ 1 ]. In the UK, 63% of the adult population are overweight and 27% are obese. Footnote 1 Given that nutrition is a key determinant for health and wellbeing, understanding food choices is of paramount importance. The process that leads to food choices is a complex one, which has been studied from several points of view, ranging from the biological [ 2 , 3 ], to the demographic [ 4 – 6 ] and socioeconomic [ 7 ], focusing predominantly on developed Western countries. However, only recently has the cultural aspect started to garner attention [ 8 – 10 ]. Culture can be seen as a sort of collective memory that influences individual behaviors [ 11 ]. Food choices can be reinforced via the existence of “lifestyle enclaves” [ 12 ], where small preferences get amplified by elective affinities. For instance, occupation might influence food intake through work-related social circles [ 13 , 14 ].

In this work, we take inspiration from the theory of the French sociologist Pierre Bourdieu, which links health and lifestyle to social class identity [ 15 ]. This identity is then differentiated through “taste” in music, art, and—in our context—culinary preferences. The theory connects taste to the pursuing of cultural capital, a non-material resource that accumulates throughout the life course [ 16 ]. Bourdieu introduced three forms of cultural capital [ 17 ]: incorporated , e.g., skills, competencies, personal effort, and time investment, objectified , e.g., possession of books, dictionaries, instruments related to artistic expressions, and institutionalized , e.g., educational attainment at the level of individual or family.

We focus on the third form of cultural capital, operationalized as the level of educational attainment. In particular, we show how education can explain inequities in food choices that go beyond socioeconomic determination and the consequent barriers to food access due to cost. To this extent, there is general agreement among researchers [ 4 , 18 , 19 ] that education and income are distinct concepts that are likely to make separate and unique contributions to health outcomes [ 20 ]. Education has been widely studied in connection to health and food choices for several reasons. First, it may provide the tools to access and comprehend dietary information and its impact on health. Second, social diffusion theory suggests that highly educated people generally take up innovations sooner than less-educated [ 21 ], which might affect the success of health-related interventions. Third, is can affect time use and thus the opportunities to allocate time to food acquisition and preparation.

Existing studies around cultural capital and nutrition use small-scale, traditional methodologies [ 10 , 22 – 25 ] such as food diaries, questionnaires [ 26 , 27 ], and surveys [ 21 , 28 ]. As such, existing datasets have not allowed the modeling of spatial clustering, which is an important confounder for both culture and health, as it has been shown in other fields [ 29 ]. The presence of this confounder may invalidate the associations discovered by conventional methods such as OLS regression, and produce estimates that are biased and inconsistent. This confounder occurs when individual data points are not independent and identically distributed, rather they are spatially correlated. Observed variation in the dependent variable may thus arise from latent influences related to culture, infrastructure, recreational amenities, and a host of other factors not present in the data. Modeling these latent influences is important especially because many health interventions work at a community level, such as “farm to school” programs [ 30 ], food literacy initiatives [ 31 ], and community gardens [ 32 ].

In this work, we use a unique fine-grained dataset that allows us to reveal the role of space and geography in modeling the relationship between food consumption and culture. Unlike traditional approaches, we use a large-scale log of the food-related purchases of 1.6M customers of a major retail chain in London across the time span of a year [ 33 ]. This resource allows us to observe—in an indirect way—the daily food choices at an unprecedented granularity, with the assumption that supermarket purchases largely represent the dietary intake of a household. Footnote 2 This granularity enables the exploration of two complementary dimensions that capture multiple facets of food consumption: (1) diet composition, along the dimensions of macronutrients and product categories, and (2) diet diversity.

Through the use of spatially-aware regression models, which include multiple environmental features of an areal unit, we illustrate the effects of spatial clustering in the emergence of localized communities with homogeneous behavior, and the advantages over standard regression approaches on fitting performance and biases in the estimates. We further show the presence of spillover effects within neighboring communities, all of which need to be considered when designing policies and interventions, or informing decision support systems for public health.

2 Related work

A large body of literature explores the interplay between socioeconomic status and food consumption, showing how inequities in the access to resources, privilege, or power, play a role in shaping people’s dietary habits. The dimension of wealth, often modeled with the average household income or employment rate, has been connected to the ability to afford certain categories of food products. Cost has been mentioned as one of the main obstacles to a widespread consumption of fruits, vegetables [ 34 , 35 ], or lean meats [ 36 ], and connected to the lack of a healthy diet [ 37 ]. The individual and household disadvantages are compounded by residential segregation. For example, disadvantaged communities often face spatio-structural barriers to access to food [ 38 ], as well as to physical activities, fitness clubs, and weight loss programs [ 36 ], all of which have effects on health outcomes. As a result, disadvantaged communities have a higher incidence of obesity than wealthy ones [ 39 ]. In this direction, a vast body of literature studies the relationship between food environment and diet to capture the degree of food access using both respondent-based perceived measures and quantitative approaches that leverage Geographic Information System (GIS) technology [ 40 ]. The latter commonly use store density (using buffer distances), or proximity to the nearest food store to operationalize food access [ 41 ]. Another common method involves store audits, in which researchers estimate the shelf-space occupied by certain foods in a store, or assess product variety and food prices within stores using measures such as the Nutrition Environment Measure Survey (NEMS) [ 42 ]. Several food environment conceptualizations have been proposed, mainly divided into community food environment and consumer food environment [ 43 ] that draw attention to the distribution of food sources within a community or within a local retailer, respectively. It has been suggested that a largely accepted food access conceptualization involves 5 dimensions relevant in the healthcare setting: availability, accessibility, affordability, acceptability, and accommodation [ 41 ]. Refer to [ 44 ] for a literature review. In addition, spatial modeling and GIS have been adopted to characterize additional dimensions of the food ecosystem. For example, Khushi et al. [ 45 ] investigated spatial inequality in food consumption, nutrient consumption, and production-consumption gaps at the sub-national levels in the Punjab province of Pakistan. Dohyeong et al. [ 46 ] characterized the spatial patterns of unhealthy food consumption in South Korea and they modeled the presence of areas with constrained access to fresh and nutritious foods, providing guidelines for targeted nutrition and public health programs. Moreover, methods for mapping provincial spatial food consumption data by accounting for spatial variability in population structure (age and gender) have been proposed in [ 47 ] with the intent to inform policy makers interested in promoting the consumption of locally produced food, as assessing localized nutritional demand. However, in contrast with our analysis, all these studies are based on corse geographical units such as administrative districts or regions, making it hard to address the variability in consumption within localized communities, e.g., neighborhoods, that often show a pronounced diversity especially in multi-cultural and multi-ethnic megacities.

Along with the socioeconomic dimensions, it is broadly acknowledged how the cultural group to which one belongs is of great importance when it comes to food preferences [ 48 ]. In the 1980s, the French sociologist Bourdieu [ 15 ] proposed a theory on the relationship between material and non-material capital to explain social inequalities, stratification and the distribution of power. Bourdieu connected taste, a multidimensional concept involving attributes, such as musical, artistic and culinary preferences, to the pursuing of cultural capital, a non-material resource that accumulates throughout one’s life course [ 15 ].

Even though several studies attempted to quantitatively characterize the different forms of cultural capital and their relation to food choices [ 10 , 22 – 25 ], they were mainly based on interviews and questionnaires on small samples of the population and potentially affected by common biases [ 49 ] related to the way a question is designed or administered. Kamphuis et al. [ 16 ] performed a systematic review of cultural capital indicators; they identify several indicators of family institutionalized (e.g. parents’ education completed) and objectivized (e.g. possession of books, art) or incorporated cultural capital (e.g. cultural participation, skills). After designing a questionnaire to capture these dimensions along with food habits of the participants, they found evidence of a connection between cultural capital and healthy food choices. The link between healthy diet and cultural capital has been observed in several studies. For instance, in a study of a cohort of adolescents in Norway, Fismen et al. [ 50 ] identified cultural capital as a stronger predictor than material capital of disparities in consumption of fruit and vegetables (positively correlated), and it was the only significant predictor of consumption of sweets and sugared soft drinks (negatively correlated).

Institutionalized cultural capital in the form of educational attainment has been widely adopted by the studies that focused on modeling cultural inequities and food choices, since level of education arguably affects what type of social milieu people inhabit, and consequently it affects what type of food one is exposed to [ 15 ]. Moreover, the availability of aggregated data at a fined-grained geographical scale, usually from the census, is another driving reason of this choice, one that we also embrace in this work. A low educational level has been connected to diets higher in fat density [ 51 – 53 ], ultra-processed and ready made foods [ 54 ], sugar-rich [ 55 ] products, meat products (especially red meat) [ 21 ], and to a lower food group variety [ 8 , 21 ]. On the contrary, highly educated people tend to consume more fruits and vegetables [ 55 ], fish [ 56 ], and to follow a more diverse diet. At last, social diffusion theory suggests that highly educated people generally take up innovations sooner than less-educated people. For example, in the UK, foods and diets low in saturated fat were adopted by the tertiary-educated before others [ 57 ]. Social epidemiologists also suggest that education enables people to rise up the social class hierarchy, thus allowing them greater power over outcomes in their lives, for example through higher incomes. In this study, we aim to re-examine some of these trends using a new high-granularity, large dataset of nutritional behavior.

In this work, we characterize food consumption by using the Tesco Grocery 1.0 dataset [ 33 ] that contains an anonymized record of 420M food items purchased by 1.6M fidelity card owners who shopped in one of the 411 Tesco stores in Greater London during 2015. Tesco is the largest food retailer in UK with around 30% market share and a solid geographical coverage in the area of study. The dataset contains aggregated and privacy-preserving data views that combine individual purchases at different spatial granularities by using the home location field from the loyalty card application as the way to geolocate customers. The fine-grained geographical information included in Tesco Grocery 1.0 is the key to link food consumption data to any attribute that can be measured at the level of statistical census areas, e.g., demographic, socioeconomic, and health determinants. In [ 33 ], the authors provide an analysis of the representativeness of the Tesco consumers base by comparing the number of unique customers to the general population, and report a solid match. Moreover, they prove the ecological validity of the dataset by comparing the grocery purchases with metabolic syndrome conditions that are strongly linked to food consumption habits. An in-depth discussion on sample bias is provided in the Limitations section of the current work.

To model food consumption, we focus on three groups of variables of interest: macronutrients , product categories , as a proxy for diet composition, and diet variety . The first captures the nutritional properties of a food product and it is connected to the concept of energy intake that we measure in calories. In fact, a food item contains different types of nutrients in different proportions, which are transformed by the human body into energy and structural material for its growth and maintenance. We consider the following nutrients that have been connected to diet and culture in the literature: fats , carbohydrates , proteins , and fibers . A few studies distinguish between different types of fats, e.g., saturated fats that are fat molecules that have no double bonds between carbon molecules because they are saturated with hydrogen molecules. However, in our work, we model the lipids intake within a single macro category, this is justified also by the high degree of correlation observed between the variables fats and saturated fats in the Tesco dataset ( $\rho _{\mathrm{fats}}^{\mathrm{saturated}\ \mathrm{fats}}=0.8$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ). A similar observation holds for sugar that loosely refers to a number of carbohydrates, such as monosaccharides, disaccharides, or oligosaccharides, as its excessive consumption has been implicated in the onset of obesity, diabetes, cardiovascular diseases, dementia, and tooth decay [ 58 – 60 ]. Accordingly, we do not consider sugar separately in the experimental setting due to the strong correlation with the macronutrient carbohydrates ( $\rho _{\mathrm{carbohydrates}}^{\mathrm{sugar}}=0.85$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ).

The second group of variables is related to a classification of the food products into categories. Even though several food taxonomies have been proposed in the literature [ 61 ], there is no consensus on how to group foods [ 62 ]. In this work, we adopt the classification in [ 33 ] which focuses on the following categories: oils , fish , produce , red meats , readymade , and sweets . Among the food categories, we observe two pairs of highly correlated variables, poultry and red meats ( $\rho _{\mathrm{poultry}}^{\mathrm{red}\ \mathrm{meats}}=0.77$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ), and sweets and grains ( $\rho _{\mathrm{sweets}}^{\mathrm{grains}}=0.79$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ). As such, in the rest of the experiments, we use red meats to characterize food products containing animal flesh, and sweets to include baked products using grain flours and, for example, candies or chocolate. The produce category includes fresh vegetables and fruits, while readymade contains pre-cooked meals that are usually available in a specific area of the store and that need to be open, often warmed-up, and eaten. At last, to test the hypothesis that eating a wide variety of foods improves dietary adequacy, we adopt the normalized entropy of the distributions of nutrients ( h_nutrients ), and food groups ( h_products ) as proxies for variety. Moreover, we consider the weight , and the calories intake ( energy ) of the average product sold in an area as a measure of quantity. At this stage, we have for each spatial unit a characterization of the diet quality and variety in that area that could be potentially linked to census variables. It is worth noting that we represent the nutritional features of the hypothetical average product consumed in an area, since we cannot characterize individual or group behaviors.

We characterize the educational attainment variables by using the 2011 Census data and, in particular, the table Highest level of qualification by sex Footnote 3 in the Local Characteristics series. The highest level of qualification is derived from the question asking people to indicate the types of qualifications held. The following levels are available: no qualifications , level 1 , level 2 , apprenticeship , level 3 , level 4 and above , and other qualifications . In this work, we restrict our analysis to a representative class for the low-educated (level 1) and high-educated (level 4+) population strata. This design choice implies a linear approximation of the effect of education, which might hide more complex effects of the distribution of education levels. However, we think that the interpretability of the model, and its applicability, offer a good tradeoff for this simplifying assumption. To account for confounding variables that could influence both education and than food choices, we explore the dimensions of gender, age, and income, which have been extensively linked to dietary habits in previous work. Gender and age, in the form of average age, are extracted from the census, while economic status is quantified via a model-based estimate of the equivalized net income per household after the deduction of housing costs. Footnote 4 All the variables are standardized with zero mean and unit variance. A summary of all the variables used in this study can be found in Table 1 , and a characterization of the spatial distribution and cross-correlation is presented in the Additional file 1 .

Data is aggregated at the geographical level of administrative areas in UK, thus implementing a privacy-preserving methodology. In particular, we adopt as a reference the spatial units of Middle Super Output Areas (MSOA), that have an average population of 7200 [ 63 ] and an average surface of 1.6 square kilometers within the Greater London region. In contrast with previous work, MSOA provide a considerably finer granularity that enables community-level observations. We refer to the variable representativeness ( $\mathrm{mean}=0.37$ , $\sigma =0.16$ ) defined in [ 8 ] as the min-max normalized ratio between the number of unique customers and the number of residents to characterize how much the user base captures the census statistics. We limit our analysis to the 774 out of 983 geographical units that have a $\mathtt{representativeness} \geq 0.15$ (see Fig. 1 for more details on the spatial coverage). The hierarchical structure and the shapefiles of the various census units are provided by the Open Geography Portal Footnote 5 of the Office for National Statistics (ONS). Footnote 6

Representativeness of the Tesco customers base. Gray areas are filtered out from the experimental setting due to low significance

3.2 Spatial modeling

Since a large extent of socioeconomic and cultural phenomena are driven by spatially-aware data generation processes, and the modeling of food consumption has been rarely addressed in space, we approach our problem with spatial econometrics tools. To formalize the neighboring relation between areal units we refer to spatial weights [ 64 ] and we adopt a contiguity approach based on the binary queen criterion where $w_{h,k}=1$ if the areas h and k share at least one vertex, 0 otherwise. To test the sensitivity of the results to the choice of a different spatial arrangements, we explore alternative weights structures based on distance, in particular the k-nearest neighbors (knn) , where each area has a fixed number of k closest neighbors, and approaches based on kernel functions with adaptive bandwidth. To diagnose the presence of spatial dependence in the outcome variables or in the residuals of the regressors, we rely on the global measure of spatial autocorrelation Moran’s I [ 65 ] that tests how a variable in space differs significantly from the expected value under the null hypothesis of spatial randomness.

In this work, we start from the following vector notation of the general linear spatial econometric model for cross-sectional data:

Y denotes an $N\times 1$ vector with the observations of the dependent variable for every spatial unit in the sample ( $i=1,\ldots, N$ ), $\iota _{N}$ is a $N\times 1$ vector of ones associated with the constant parameter α , X contains a $N\times K$ matrix of exogenous explanatory variables, with the associated parameters β represented in a $K\times 1$ vector, and $\varepsilon =(\varepsilon _{1},\ldots ,\varepsilon _{N})^{T}$ is a vector of disturbance terms, where the $\varepsilon _{i}$ are independently and identically distributed error terms with zero mean and variance $\delta ^{2}$ . W denotes an $N\times N$ nonnegative matrix describing the spatial arrangement of the units in the sample. The model specifies three main terms: (1) an endogenous interaction effect $\rho WY$ , (2) an exogenous interaction effect $WX\theta $ , and (3) an interaction effect amongst error terms $\lambda Wu$ . They model, respectively, the interplay between the value of the dependent, independent, and error terms in a spatial unit i and the values of the other spatial units. From the general nested model, the configuration of the parameters ρ , θ , λ leads to different spatial model specifications. For example, $\lambda =0$ , while removing the lagged errors, leads to the definition of the Spatial Durbin Model [ 66 , 67 ] (SDM), that resolves in a Spatial Lag Model [ 68 ] (SLM) when $\theta =0$ . In a similar way, nullifying the lagged dependent variable component ( $\rho =0$ ) defines the Spatial Error Durbin Model [ 69 ] (SEDM) and the simpler Spatial Error Model [ 68 ] (SEM) when also $\theta =0$ (the spatial dependence is modeled via the error term alone). When all the ρ , θ , λ parameters are null, the specification traces back to a standard linear regression. In this settings, the interpretation of results involves two main components: a direct impact that links the characteristics of spatial unit with the value of the dependent variable in the same unit, and an indirect impact that models the spillover effects. The spillover effects can be further categorized in local or global . In local spillovers ( $\rho =0$ ) the indirect effects are measurable only in the neighboring units, e.g., areas where $W_{ij}\neq 0$ . This leads to the adoption of a SEDM or SEM specifications. In contrast, in global spillover effects ( $\rho \neq 0$ ) the indirect influence of a spatial unit falls on the entire set of locations, producing high-order effects observable even in spatial units that are not directly connected. This is compatible with the SDM or the SLM specifications.

In the literature, different approaches have been adopted to find the most appropriate spatial econometric model specification given an empirical use case. In this work, following the discussion in [ 70 ], we use a theoretical rather than data-driven approach to guide the decision. In the case of food choices, it is difficult to form a reasonable argument to include endogenous interaction effects even though they are found statistically. Including endogenous interaction effects would imply that the consumption of a particular food in an area has effects on the entire city, which is difficult to justify. The scenario points to a local spillover specification and, accordingly, we focus our analysis on the SDEM and SEM models. For completeness, we also run the Lagrange Multiplier [ 71 ] (LM) diagnostics based on the OLS residuals in their standard and robust forms that are at the core of the methodology described in [ 72 ].

For the experiments we use the functions errorsarlm and lagsarlm of the spatialreg Footnote 7 package in R.

To assess the presence of spatial dependence in the outcome, we run a permutation test for the Moran’s I statistic in the case of the educational attainment variables level1 and level4 with the number of random permutations $n=10\text{,}000$ . We observe a strong positive autocorrelation in both cases $I_{\mathrm{level}1}=0.7202$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ and $I_{\mathrm{level}4}=0.684$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ . The presence of clusters of areas with similar behavior is confirmed by the visual inspection of the spatial distribution of values shown in Fig. 2 and from the Moran I scatterplot in Fig. 3 . It is worth noting how the two variables show complementary spatial patterns: in fact, the areas with a high prevalence of a low-educated population (dark areas in the left map) correspond to the areas with a low presence of high-educated population (yellow areas in the right map) and vice versa. In this direction, we mainly focus on modeling the high education outcome ( level4 ), and we show how the complementary variable for low education ( level1 ) performs when space allows. To test if the observed spatial autocorrelation could be explained by the spatial structure of the covariates alone, we first run a linear regression analysis and we check for the presence of autocorrelation in the residuals [ 72 ]. Fitting performance is evaluated with the Akaike Information Criterion [ 73 ] (AIC), the Bayesian Information Criterion [ 74 ] (BIC), and the Nagelkerke’s pseudo R 2 [ 75 ] when appropriate.

Spatial distribution of the educational attainment variables

Moran scatter plot: the slope of the linear fit equals Moran’s I statistics. It shows how neighboring areas behave similarly

We organize the rest of this section in two main parts that follow the same methodological pipeline and that capture the complementary dimensions of food consumption: (1) diet composition, along the dimensions of macronutrients and product categories, and (2) diet diversity.

4.1 Diet composition

Macronutrients.

In this section, we explore the interplay between educational attainment and the consumption of different macronutrients. We first compute a linear regression model observing the presence of significant spatial autocorrelation in the residuals ( $I_{\mathrm{level}4}=0.4409$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ) (refer to Additional file 2 for more details). To account for local spillover effects, we estimate the SEM and SDEM models. The spatial specification choice is coherent with the results of the robust Lagrange Multiplier tests, $\mathrm{LM}^{\mathrm{error}}_{\mathrm{level}4}=154.32$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ and $\mathrm{LM}^{\mathrm{lag}}_{\mathrm{level}4}=39.549$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ that suggests the adoption of a lagged error specification due to the higher value of the corresponding statistics. Figure 4 summarizes the fitting performance of the alternatives tested, thus identifying SDEM as the best performing model across measures ( $\mathrm{AIC}=490$ ). A likelihood ratio test using the function LR.sarlm in the spatialreg package confirms the goodness of the choice of SDEM over SEM ( $\mathrm{LR}=61.077$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ ).

Summary of the fitting performance of the different model specifications in the case of low (level1) and high (level4) educational levels (the lower, the better)

In Fig. 5 (a) we present the SEDM regression results for the target variables level1 and level4 . In both cases, the parameter λ is highly significant ( $\lambda _{\mathrm{level}1}=0.67$ and $\lambda _{\mathrm{level}4}=0.71$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ), thus confirming the presence of a strong spatial error lag in the empirical data. The group of variables on the left side defines the direct impact while the variables on the right side estimate the indirect effect of the neighboring spatial units as defined in Sect. 3 . Moreover, we test for the presence of spatial autocorrelation in the residuals, observing respectively a Moran’s $I_{\mathrm{level}1}=-0.02$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.8$ and $I_{\mathrm{level}4}=-0.024$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.83$ that show how the SEDM, unlike a standard linear regressor, produces an uncorrelated spatial structure in the residual in accordance with the hypothesis of independence. Finally, we observe the presence of heteroscedasticity via a studentized Breusch–Pagan [ 76 ] test ( $\mathrm{BP}_{\mathrm{level}4}=69.6$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ). While heteroscedasticity does not affect the estimation of the coefficients, it biases the estimation of the significance; however, since the observed p-values have, in the most cases, values below 10 −3 , the net effect is not substantial.

Model parameters (variable weights) of the SDEM for the nutrients, categories, and diversity scenarios

Product categories

We explore the dimension of the food categories following the same pipeline described in the previous section. The residuals of a linear regressor shows a significant spatial autocorrelation ( $I_{\mathrm{level}4}=0.41$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ), and the LM tests are coherent with the current choice of spatial specification ( $\mathrm{LM}^{\mathrm{error}}_{\mathrm{level}4}=112.5$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ and $\mathrm{LM}^{\mathrm{lag}}_{\mathrm{level}4}=60.7$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ). SDEM shows the best fitting performance also for the case of food categories (see Fig. 4 ), in accordance with a likelihood ratio test ( $\mathrm{LR}=59.7$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ ). Figure 5 (b) summarizes the direct and indirect impacts for the fitted model, confirming significant spatial effects in the error terms ( $\lambda _{\mathrm{level}1}=0.69$ and $\lambda _{\mathrm{level}4}=0.7$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ). The application of SDEM produces residuals free from spatial autocorrelation (Moran’s $I_{\mathrm{level}1}=-0.015$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.71$ and $I_{\mathrm{level}4}=-0.02$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.77$ ). At last, we observe heteroscedasticity ( $\mathrm{BP}_{\mathrm{level}4}=58$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ) similarly to the macronutrients case.

4.2 Diet variety

Spatial patterns are observed in the residuals of a linear regressor (Moran’s $I_{\mathrm{level}4}=0.53$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ), and the LM tests are coherent with the current choice of spatial specification ( $\mathrm{LM}^{\mathrm{error}}_{\mathrm{level}4}=197.4$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ and $\mathrm{LM}^{\mathrm{lag}}_{\mathrm{level}4}=42.3$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ ). Figure 5 (c) summarizes the direct and indirect effects in the case of the best performing model SEDM (see Fig. 4 in accordance with the likelihood ratio test $\mathrm{LR}=52.4$ , $\mathtt{p}\mbox{-}\mathtt{value} <0.0001$ ). The parameters $\lambda _{\mathrm{level}1}=0.78$ and $\lambda _{\mathrm{level}4}=0.78$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.0001$ confirm the presence of significant spatial patterns. We acknowledge the presence of heteroscedasticity ( $\mathrm{BP}_{\mathrm{level}4}=30.05$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.008$ ) and missing spatial autocorrelation in the residual of SEDM (Moran’s $I_{\mathrm{level}1}=-0.03$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.88$ and $I_{\mathrm{level}4}=-0.03$ , $\mathtt{p}\mbox{-}\mathtt{value}=0.93$ ).

4.3 Sensitivity analysis

In this section, we explore the extent to which the observed results are sensitive to changes in the experimental design. First, we focus on a comparative analysis between the baseline model with only the socioeconomic confounds and the complete model including the food choices variables. The interplay between educational attainment and socioeconomic determinants has been extensively studied in previous work and, consistently, we observe how they play a primary role in the predictive framework. However, as shown in Fig. 4 , adding the food consumption dimensions does provide a significant improvement in the fitting performance. We measure a 30%, 21%, and 18% reduction of the AIC for the nutrients, food categories, and diet variety cases ( level1 ) and, specularly, a 48%, 35%, and 28% reduction in the case of the high education outcome ( level4 ). A consistent behavior is observed in relation to alternative performance metrics, e.g., BIC and Nagelkerke $R^{2}$ as summarized in Additional file 2 . Moreover, it is worth noting that the spatial-aware models consistently outperform the standard linear regression framework by a large extent (on average we observe an improvements in AIC greater or equal to 75%) underscoring the benefits of taking into account the geographical structure of the determinants.

Second, we focus on the choice of the weighting scheme that has a central role in a spatial econometric framework [ 64 ]. We extend the initial experimental settings based on a contiguity approach with different distance-based spatial arrangements methods: the k -nearest neighbors ( k-nn ), in which each spatial unit is connected to a fixed number of k closest neighbors, and a class of kernel functions with adaptive bandwidth (gaussian, quadratic, triangular, quartic, and uniform). For simplicity, we present the nutrients and level4 case, similar results apply to the other scenarios. In the case of the nearest neighbors approach, we explore the range $k \in [3,15]$ obtaining the best performing model with $k=6$ (see Fig. 6 (A)) and an overall performance that is slightly lower than the contiguity case ( $\mathrm{AIC}=278$ ). We present the full results for the best performing model with $k=6$ in Additional file 2 showing how the learned relations are stable and change only partially in strength. Switching to the kernel weights approach, we study the behavior of different classes of kernel functions exploring a bandwidth size within the same range of the k-nn scenario. Figure 6 (B)–(F) summarize the observed performance curves showing similar results across methods. An extensive comparison between kernel functions is out of the scope of the paper, however, the best performing kernel settings is the triangular function with bandwidth size equals to 9 ( $\mathrm{AIC}=257$ ) which reaches a very similar output to the contiguity case ( $\mathrm{AIC}=260$ ). These results confirm the stability of the learned relations across a wide range of spatial arrangements.

AIC fitting performance under different weighting schemes

5 Discussion

The first dimension of food choices that we explore is related to nutrients consumption. First, we focus on the direct impacts that model the effect of the intrinsic characteristics of a spatial unit on the educational attainment variable. As shown in Fig. 5 (a), we observe that a low educational level is connected to diets high in carbohydrates [ 55 ], including sugar. Conversely, areas with a predominance of highly educated residents show a higher consumption of fibers, which provides a range of important health benefits, particularly in preventing heart and cardiovascular diseases, stroke, hypertension, diabetes, obesity, and some gastrointestinal pathologies [ 77 – 79 ]. Diets higher in fat density have been associated to lower education in several studies [ 51 – 53 ], which is consistent with the empirical measure of rank correlation observed in our scenario ( $\rho _{\mathrm{level}4}^{\mathrm{fats}}=-0.36$ and $\rho _{\mathrm{level}1}^{\mathrm{fats}}=0.37$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ). However, in a multivariate settings and discounting for the presence of the other predictors, the fat variable—due to its strong interplay with the carbohydrates variable ( $\rho _{\mathrm{fats}}^{\mathrm{carbs}}=0.57$ )—appears to have a weak positive effect on the high education outcome that could be due to the presence of multicollinearity or misspecification of the model [ 80 ]. At last, we observe a non statistically significant direct association with protein consumption, this could be potentially related to the diverse set of sources of proteins and that have been associated in literature with different heath outcomes, socioeconomic factors, as well as impact on the environment. In fact, protein supply comes from both plant, e.g., legumes, soya, nuts and seeds, and animal sources, e.g., fish and seafood, poultry, pork and beef, and derivatives of milk such as dairy products. These products have been associated with low and high educational levels depending on their relation with a healthy diet; in this scenario, a definitive association is hard to pinpoint. When focusing on the spatial spillover effects, we do not observe significant neighboring effects for fiber and fat, while carbohydrates shows also in this case the stronger effect, that underlines how the neighboring units affect the model’s decision in the same direction as the observed direct effects. For the case of protein, the results show an indirect negative impact on the level4 variable. We remark that the spatial autoregressive model does not necessarily capture a causal model, i.e., we do not assume that the food choices have a causal effect on the education attainment. Therefore, the interpretation of the indirect effect needs some care, as they represent a spatial spillover of the covariates and the dependent variable, rather than actual influence of the neighboring units. The direction of these spillover effects is in almost all cases in accordance with the direct effects, which is a confirmation of the robustness of the results. These spillovers may be caused by several modeling factors, including the granularity of the spatial discretization, the specific choice of neighborhood function, and the arbitrariness of the spatial borders.

Switching now to food categories, there is a vast literature discussing the interplay between food choices and socioeconomic factors, and to a lesser extent, cultural capital. A high intake of fruit and vegetables is one of the cornerstones of a healthy diet, and has been recommended to the general public to reduce the risk of cardiovascular, coronary hearth diseases, and stroke [ 81 ]. Consistently with previous work, we observe a positive association between high educational attainment and consumption of vegetables and fruits [ 50 , 55 , 82 , 83 ]. The prevalence of sweets and, in general, products high in density of sugar, is evident in communities with a lower educational level [ 50 ], an effect that, similarly to the case of carbohydrates, is the strongest in intensity in our experimental scenario. Focusing on animal-based products, it is interesting to note the different behavior between the variables fish and red meats . While high-educated people are less likely to be regular consumers of several meat products [ 21 ] ( $\beta =-0.114$ ), in particular for the case of processed meats, they tend to consume more fish [ 56 ] ( $\beta =0.076$ ), including seafood. Moreover, fish [ 84 ] and seafood [ 85 ] are an excellent source of protein and provide a range of benefits for major health outcomes among adults, even when taking into account that the presence of contaminants such as mercury in polluted natural environment could pose potential risks [ 84 ]. We observe non significant relations for the readymade and oils categories. In the first case, consumption of readymade and ultra-processed food products have been associated to the lower educational strata [ 54 ], however, even if the pairwise correlation follows this tendency ( $\rho _{\mathrm{level}4}^{\mathrm{readymade}}=-0.31$ and $\rho _{\mathrm{level}1}^{\mathrm{readymade}}=0.45$ , $\mathtt{p}\mbox{-}\mathtt{value}<0.001$ ), when adjusting for confounders the relation becomes not significant. Moreover, the tendency of creating healthy versions of readymade food products to target singles, small households, or professionals that tend to have a higher level of education is a factor to take into in consideration when exploring this dimension in its full extent. Consistently with the difficulty to characterize fats consumption in the case of nutrients, the heterogeneity of the oils category does not allow to draw a significant picture. In the case of level4 , the contribution of the spatial spillover effects are relevant for the dimensions of sweets , red meats , and produce , thus indicating the importance to consider the influence of neighboring areas and the community effects. Indirect impacts are moderately significant for the readymade category in the expected direction.

Education is a valuable variable to consider as a proxy for consumers’ dietary knowledge and ability to process nutritional information. As a consequence, a solid body of research associates more educated subjects to the awareness and the importance of a balanced diet [ 21 , 86 , 87 ]. Consistently, we observe that the variety of nutrients h_nutrients is positively ( $\beta _{\mathrm{level}4}=0.2$ ) associated to a higher level of educational attainment. It is worth noting that the variety of products h_items that reflects the number of unique purchased products follows an opposite trend ( $\beta _{\mathrm{level}4}=-0.13$ ). This result means that, even though it seems that low-educated customers select amongst a wider range of products, the nutritional variety is limited. Moreover, high-educated people show the tendency to have a smaller caloric footprint ( $\beta _{\mathrm{level}4}=-0.08$ ) and to consume a smaller quantity of food products in weight ( $\beta _{\mathrm{level}4}=-0.07$ ) that is consistent with previous studies linking this observation to a lower incidence to obesity and a lower average Body Index Mass (BMI) [ 88 ]. The variables energy and weight show a significant or moderately significant spatial spillovers.

Further exploring the causal pathways of the relationships will help in designing effective interventions for improving health outcomes and dietary behavior in particular. For instance, Chandola et al. [ 89 ] examined six hypothetical pathways linking education and health via structural equation modeling. Since they found a combination of mechanisms being involved, they conclude that “improvements in population educational attainment may not automatically lead to improvements in population health”. Such pathways may also involve access to outside resources including nutritional and health services. When examining the connection between education and health outcomes of an older Japanese cohort, Oshio [ 90 ] finds regular health check-ups to be one of the primary mediators. Further, previous studies have shown [ 91 , 92 ] that health literacy in particular may be an important factor mediating the relationship between educational attainment and many health behaviors, such as being physically inactive, making diet choices, and being obese. Ongoing policy efforts are already attempting to incorporate health education and related services into educational environments, such as the “Whole School, Whole Community, Whole Child” (WSCC) approach developed by the U.S. Centers for Disease Control and Prevention (CDC) [ 93 ] and the “Skilled for Health” initiative lead by U.K.’s National Health Service (NHS) [ 94 ]. The long-term impact of such interventions within communities will be made clearer by using data-driven, anonymous monitoring of nutritional behaviors of large cohorts, such as one presented in this work.

Limitations

There are few limitations and open points that should be mentioned:

Our study is based upon the dataset provided in [ 33 ] that aggregates the purchasing history of customers of a specific retailer and who have opted for a loyalty card. Even though the authors provide a representativeness score and some empirical evaluation of the biases introduced in their study, the user base is not exempt from sample bias. It might occur that the average customer is more likely to represent a specific level of educational attainment, and, by reflection, specific age and income profiles. For example, a student that is in the process of obtaining a degree, might be less willing to sign in for a loyalty card and, therefore, be accounted for in the study. This could lead to a biased estimation of the interplay between education and food choices; however, the large-scale nature of the dataset and the extensive adoption of the Tesco loyalty program [ 95 ] by its customers might reduce this effect. Moreover, food choices are aggregated at the level of administrative units that does not enable the characterization of the dietary habits of individuals, and models the average behavior in a geographical area instead.

There is no consensus in food and nutrition research on how to group food products in coherent categories [ 62 ] leaving the choice to the specific use case. However, the heterogeneity of food products in a group can be very high, smoothing out the intrinsic differences in health outcomes, affordability, or sociodemographic adoption determinants such as education or gender. For example, proteins are not all the same: there is a wide variety of foods that provide a protein intake that have a very different source, organoleptic properties, connection with health outcomes, impact of the environment and sustainability, or ethical concerns. Not being able to control the aggregation schema limits the applicability of an hypothesis-driven approach where the groups formation is guided by the research question under study.

Education attainment is only one aspect of the broader concept of cultural capital (institutionalized cultural capital) and, in general, of the cultural substrate that has been arguably identified as crucial when it comes to model food choices. For example, to better capture demographic variations, other measures should be considered. For instance, Rohit et al. [ 96 ] show that, for older African Americans, reading level may be a better predictor of baseline neurocognitive status than the years of schooling (possibly due to different quality of schooling available to students of different races). Taking into consideration other measures of cultural capital in general, and cognitive development in particular, may reduce observed racial disparities [ 97 ].

The tight interplay between food variables in characterizing dietary habits and the complex construct of socioeconomic determinants give rise to multicollinearity effects in the explanatory variables. Even though some researchers seem to assume that different socioeconomic indicators reflect the same underlying information and can therefore be used interchangeably [ 18 ] (typically, correlations between education, occupation, and income are weak to moderate with magnitude in the range 0.3–0.6 in developed countries [ 98 ]), we embrace the evidence from several previous studies that shows the unique contribution of each indicator. Moreover, the relevance of a specific indicator might differ between subgroups of the population, such as between adults and adolescents.

As pointed out in previous work, the process that underlies food choices is multifaceted and it involves a broad range of dimensions that are not fully captured in our study. These omitted variables are left out partially due to the unavailability of location-aware data with compatible spatial and temporal scales. Moreover, we aim at testing a specific set of hypotheses rather than exploring a wider spectrum of determinants. It is worth noting that these unobserved confounding factors could potentially explain away the link between education and diet; however, we rely on the extensive literature that explores the extent of this relation to corroborate our findings. We speculate that ethnicity, religion, or the dimensions being part of the Index of Multiple Deprivation Footnote 8 (IMD) could play a role in this direction.

6 Conclusion

The interplay between educational attainment and food choices has been the subject of a wide body of literature especially for the important ramifications in the public health domain. In this work, we explored the interplay between institutionalized cultural capital, a form of cultural capital theorized by the sociologist Pierre Bordieu, and dietary choices showing how education plays an important role beyond socioeconomic determination. To this extent, we adopted an anonymized large-scale record of food purchases in a major grocery store chain in the Greater London area to quantitatively model food consumption across the three complementary dimensions of macronutrients, food categories, and diet variety. Purchases were geographically aggregated at the level of fine-grained administrative areas (MSOA) and, unlikely most of previous work, we explored this relation in space with the adoption of spatial autoregressive models that aim at capturing the direct and indirect impacts that the spatial dependence induces. We observed that highly-educated areas tend to follow a healthier and more diverse diet, characterized by a higher consumption of fibers, fruits, vegetables, and fish products, along with a more balanced and diversified nutritional intake. On the contrary, a low educational attainment is generally connected to diets high in carbohydrates, sweets and red meats, as well as to a higher caloric intake and average portion size. These relations are consistent with the findings emerging from literature and they allow to map with an unprecedented spatial granularity the behavior of localized communities enabling the design of health policies and interventions that better adhere to the social, economic, and cultural contexts of a place.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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Narges Azizi Fard has been partially supported by the project “Countering Online hate speech through Effective on-line Monitoring” funded by Compagnia di San Paolo. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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current events conversation

What Students Are Saying About Making School Lunch Healthier

New nutrition guidelines will mean less salt and sugar in school meals. Teenagers share whether they think students will embrace the changes.

A student forks up some food from a red tray divided into compartments. There is also a small open carton of milk.

By The Learning Network

School meals will soon contain less salt and sugar under new nutrition guidelines released by the Biden administration. School cafeterias will have to cut sodium levels 15 percent by the 2027-28 academic year. And for the first time, schools will need to limit the amount of added sugars in cereals and yogurts, starting in the 2025-26 academic year.

While many parents and nutritionists applauded the stricter federal regulations, some school lunch administrators fretted that the results will be less tasty to students, reducing consumption and increasing waste.

We asked teenagers for their opinions: Should schools serve healthier meals if it changes students’ favorite foods?

They weighed in on the federal guidelines and whether “healthy” really means “less tasty.” They also shared about their experiences of eating in the school cafeteria, including what works well and what could be improved.

Thank you to everyone who participated in the conversation on our writing prompts this week, including students from schools in Dallas , St. Louis and Seoul .

Please note: Student comments have been lightly edited for length, but otherwise appear as they were originally submitted.

Many students supported the push for more healthful school lunches.

I feel as though we are being served foods that aren’t good for us because we don’t have all the food groups within the meal. Some students have health problems and need to be served healthier meals but the regular school lunches are all fats and carbs just blended in and quite frankly aren’t appetizing. Yes, some schools can’t afford a better lunch system but we still shouldn’t be served that unhealthy stuff. It’s not good for athletes or people with health problems. Schools can magically afford all this technology and all these fancier things in the school but we can’t afford a more healthy food option or better yet, something that actually tastes good. Me, personally, if we had a healthier school lunch I would eat it every day.

I think the lunches at our school are pretty satisfying. There is healthy and delicious Korean food. There is always a dessert for the students. However, I think the school should change the school lunch to a healthier meal because students need to eat a lot of vegetables, which are essential nutrients. Also, the school should provide more delicious dishes and different kinds of side dishes. The best solution is to have multiple options and dishes for vegan and vegetarians. I think junk food should not be part of a school lunch menu. School lunch is important since it hugely influences the students’ day.

— K K, south korea

Compared to other countries’ meals, America’s school lunches are not the most nourishing. Take a standard Japanese school lunch as an example. A balanced meal should have a source of carbohydrates, protein, dairy, and a source of vitamins and minerals that can be found in an average vegetable. A usual school lunch in Japan contains white rice, meat or fish, soup, a salad, and a bottle of milk. A quality, balanced meal such as this should be the standard for school lunches. Of course, this doesn’t mean that less healthy options should be out of the picture, as, who doesn’t want a treat now and then? But judging from my school’s lunches, which can be found as cheese pizza and spaghetti with meatballs, the concern for nutrition is understandable.

— Malaya, Philadelphia, PA

I think there should be a reduction of the amount of salt and sugar schools put into foods. Personally, I don’t even think many kids are considering it when they eat the food, as some people just eat school lunch every day. That being said, reducing the salts and sugars might make the foods taste better, as I find many foods to be over-sweetened and over-salted. Not only would making a change be healthier, but it might even be an improvement to the current menu.

— Livia, Greenbelt MS, Maryland

Others argued that making school food healthier will mean fewer students will eat it.

I believe that the more objectively correct option for student wellbeing is to make the foods healthier, but personally, I wouldn’t want that. Firstly, I don’t even eat school lunch, so my opinion on it is probably different from other people’s opinions … I think that more people might pack lunches if healthier meals that may not taste as good replace the current school lunches. Also, from what I can see, a lot of food gets thrown out, left behind, or just scattered all over the place. Replacing good (sometimes) tasting meals with foods that tend to not taste as good might increase the amount of food not eaten. In conclusion, healthier meals are objectively better for students, however, students may not prefer the healthier options.

— Max, J.R. Masterman School

Students will not embrace the change. Sadly if you take away the foods that taste good and swap them for foods that are healthy but don’t taste as good, there will be some dissatisfaction. I do think it’s important for students to have healthier diets but they might not think the same.

— Tanae, Greenbelt Middle School

I think that enforcing healthy eating habits at school is incredibly important, but flavorless green beans or corn might not be the best solution. For me, I don’t think that the fact that we are served healthy foods is an issue — I dislike many of the foods because they are simply not appetizing. I often enjoy salads at restaurants or at home, but the school cafeteria just seems to make everything taste worse. Judging by the amount of food left in trash cans around the steaming hot cafeteria, it is clear that my peers may feel similarly. Many people I know simply wait until they arrive home to eat, rather than indulge in the school’s delicacies. Snacks from home or vending machines are common ways to avoid cafeteria food. Healthy food is a good idea, but more needs to be done to make it both appetizing and energizing for the student body.

— Calla, Julia R. Masterman, Philadelphia, PA

Some suggested a middle ground, one in which nutritious options exist beside student favorites.

I have seen some school lunches some days in which I wonder how the school is able to serve considering how unhealthy it is. I put a big emphasis on healthy nutrition, so these types of lunches are unfortunate for me. However, we are kids and I do think we could be treated to things that are unhealthy at times. Making school lunches healthier could also build healthier habits for students when they are by themselves at home. This is because they could possibly get used to the health foods they are consuming everyday at lunch and make them want to crave healthier foods at home. Overall, I think it’s a good idea to give students healthier foods, but I don’t think it should be 100% healthy.

— Brendan, Baker High School

It really depends on the student body. Different people have different preferences. The best solution would be to have multiple options, including vegan, halal, healthy, and junk food for students. However, this can often lead to food waste. Junk food is unhealthy, but most students like it and food isn’t wasted a lot. On the other hand, not a lot of teenagers choose to eat vegetables and fruits. Food waste would be a huge problem if schools decide to serve healthier menus, and even worse, fresh fruit and vegetables are way more expensive than junk food and fast food, which not many educational districts can provide for.

— Jimin, Seoul

Students either look forward to school lunch or despise it, both breakfast and lunch: the school offers various options for one to choose from. However, within the options, they are not the best in a healthy manner. Therefore schools should consider serving healthier food to an extent. The reason is that students may complain about the lack of flavor, low salt, etc but in the long run it would be more beneficial to one’s health. A well-balanced mixture of a lunch tray that serves both nutrients and salt would be amazing and satisfy students.

— Valeria, John H. Francis Polytechnic High School

Schools should serve healthier food choices but not remove any of students’ favorite food options. Healthier food choices should increase since approximately 19.7% of children are obese. With the food provided, schools should set the demonstration of a healthy diet however not remove students’ favorite food choices. With healthier food choices for their bodies, the students will have the nutrients and the energy they need to learn since with unhealthy food, the children can have stomach aches and a lack of energy, which would affect them in their education, so I believe they should serve healthy options for students.

— Jose, Sun Valley

Several said teaching students about nutrition and letting them have a say in the menu would help them make healthier choices.

I have a mixed opinion about this topic. While I do believe promoting better health and nutrition among students is important, respecting students’ preferences is also important. Schools can introduce healthier foods slowly and involve students in the process. By finding ways to make healthy foods appealing and enjoyable, schools can help students develop a taste for nutritious foods they will carry into adulthood.

— Anngelin, Dallas, Texas

If I were responsible for keeping food waste to a minimum, step one would be to listen to students and serve what they like. There’s no reason to throw the food away if it is good. It’s impossible to cater to every student, so why not make sides available? If people, for example, like the breadstick that comes with the macaroni and cheese, why not give students the option to order a side and nothing else? This also works if someone hates the breadstick but loves the macaroni. Giving students options is a great way to prevent unnecessary food waste.

— Tate, Julia R. Masterman, Philadelphia, PA

I feel that throughout my school many food ends up getting wasted because of the lack of attention brought to people with regards to healthy eating. Because so many fruits and vegetables get thrown out on the daily at my school, many people are getting fueled on the unhealthy salt and sugar-filled items that are getting processed in places that aren’t good for you. If there were to be teachings about why it is so important to keep fruits and veggies in your day to day diet, it can really benefit you a lot. Also, lots of people around the world can afford healthy food items, so if schools were to start to make meals more healthy, it could really help not only American obesity but also help people get new healthy eating habits.

— Maddie, Connecticut

Educating students about the benefits of a low-salt, low-sugar diet and introducing flavorful alternatives could help promote acceptance of the new guidelines. Ultimately, it will be important for schools to engage with students, gather feedback, and involve them in the process of creating nutritious and appealing menu options to encourage healthier eating habits.

— Nebeyu, Greenbelt Middle

Students also told us what’s working in their own school cafeterias.

As a student attending public school, I was made aware of how the federal government regulated schools to follow specific nutritional guidelines, such as the healthy eating plate, which depicts a perfectly balanced meal consisting of ½ vegetables and fruits, ¼ carbohydrates, and ¼ protein. Most of the schools I attended followed this guideline. However, after switching to private schools, I noticed that their meal plans were more lenient, as they had more freedom to do what they wanted … At my school, our salad bar is very successful. Students can customize their salad with fresh fruits and vegetables that create a great food source that brings the body energy. Therefore, schools should have a balanced meal that includes healthy options, without eliminating all of students’ favorites. I personally think the healthy eating plate is a good guide to see if your main lunch source is pulling from all food types and energy sources.

— Sophia, St. Louis

I believe eating healthy, even if it is forced, is important. My previous school food, for example, had many options, including Asian, western, a salad bar, and different bread options. This helped students choose what kind of food they wanted. For breakfast and dinner, they balanced the sodium levels by giving under-seasoned food for breakfast if the dinner was going to have salty or sugary food. To help parents and students know how much sodium they are taking that day, they posted pictures with the sodium levels for every meal.

— Melinda, Korea

And what needs improvement.

I believe schools can improve on healthier food options. There are students who buy lunch everyday, some who don’t have a choice in this, so having higher quality food for them would be beneficial. I’ve seen plenty of questionable food in my cafeteria and they’re usually the healthier options. No student actively wants to eat a rotting salad or a fruit cup that has been sitting out for a few hours; it’s gross. So these usually get thrown away. Burgers, pizza, fries, etc. are always going to be served in school cafeterias. Those foods could also be improved with their high sodium levels. But, if schools offer good quality, healthier options, students may actually choose them. Overall, school lunches are tolerable in their current state, but I’m sure there can be steps taken to improve them.

— Ren, New York

To be honest, I am not completely satisfied with the food at my school. In our dining room, so-called “healthy food” is presented but, in my opinion, it cannot be called healthy. For example, let’s take the same chicken Caesar salad, which has the same fried patty that is served in burgers. The fruits that are presented are often of very questionable freshness. Also, compared to unhealthy food, the amount of healthy food is simply scant, so most students choose standard items: such as pizza, burgers, French fries and nuggets. I have nothing against it, but I don’t think it’s a good diet for every day, especially if you have lunch in the third period like me. I haven’t had lunch at school for a long time and I just take snacks from home, however, considering that we spend eight hours at school, I am always very hungry in the last periods. I wish my school had more healthy food options, even if it tasted worse than regular food.

— Sabrina, Hinsdale, IL

I believe that schools should continue to have healthy food options but improve on what they have. Some of the healthier options my school provides are fruits or small salads that are close to rotting. I think that the food that they give most times just ends up in the trash because the food is about to go bad and people don’t want to eat fruits that have gone brown or lettuce that is soggy. I also believe that if our school has to provide healthy options to students, they should also serve vegan/vegetarian options. Our school does have salads but they almost always have chicken or cheese in them and they aren’t nearly big enough to fill someone. I believe schools should provide free healthy options for students but improve the quality of food that they serve.

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Open access
Published: 17 May 2024

The impact of healthy nutrition education based on traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome: a randomized clinical trial

Fereshteh Sadeghi 1 ,
Shahzad Pashaeypoor 1 ,
Akbar Nikpajouh 2 &
Reza Negarandeh 3

BMC Public Health volume 24 , Article number: 1332 ( 2024 ) Cite this article

95 Accesses

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Metrics details

Acute Coronary Syndrome is the most common heart disease and the most significant cause of death and disability-adjusted life years worldwide. Teaching a healthy eating style is one preventive measure to prevent the disease’s recurrence. This study aimed to determine the effect of healthy nutrition education with the help of traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome.

This randomized, single-blinded clinical trial was conducted with 139 participants (66 in the intervention group and 73 in the control group) from January 2021 to August 2021 in Shaheed Rajaie Hospital, Tehran, Iran. The control group received standard training. The intervention group, besides this, received additional bedside training with an educational poster on traffic light labels from the research team during their final hospitalization days. Data were collected using a researcher-made questionnaire on food selection, preference, and consumption.

The Brunner-Munzel test showed no significant difference between the two groups in terms of selection ( P = 0.127), preference ( P = 0.852), and food consumption ( P = 0.846) in the baseline, while after the intervention, there were significant differences in selection ( P > 0.001), preference ( P > 0.001), and consumption ( p < 0.004). Comparing the difference between the two groups in the difference between the before and after scores for selection ( p < 0.001), preference ( p < 0.001), and food consumption ( p = 0.011) with the Brunner-Munzel test indicated a significant difference in all outcome variables.

Conclusions

Teaching healthy eating styles with the help of traffic light labels affected food selection, preference, and consumption and led to healthier diets in these patients.

Clinical trial registration number

Clinical trial registration: It was prospectively registered in the Iran Clinical Trials Registration Center on this date 30/10/2020 (IRCT20200927048857N1).

Peer Review reports

Acute coronary syndrome (ACS) is the most common heart disease and the most significant cause of death and disability-adjusted life years (DALYs) worldwide [ 1 ]. ACS is a group of clinical symptoms corresponding to acute myocardial ischemia and has a significant clinical and financial impact. Clinical variants of ACS include unstable angina and acute myocardial infarction (AMI) with or without ST-segment elevation [ 2 ]. Globally coronary artery diseases cause approximately 7 million yearly deaths [ 3 ]. Coronary artery diseases are also the first cause of death in people over 35 years of age in Iran and cause 39.3% of all deaths in the country [ 4 ].

These patients are discharged from the hospital after treatment, but due to the chronic nature of the disease, people who survive the first ischemic attack are at greater risk for cardiovascular events in the future [ 5 ]; ACS patients are 20% more at risk than people without coronary artery disease during the five years after an ischemic attack [ 6 , 7 ]. The most important behavioral risk factors for cardiovascular diseases are inactivity, an unhealthy diet, and smoking. By changing these risk factors, many cases of cardiovascular diseases can be prevented [ 8 ]. Secondary prevention guidelines encourage these patients to consume heart-protecting foods (fruits, vegetables, olive oil, and whole grains) and avoid consuming heart-damaging foods such as sweet drinks, processed meats, and foods containing trans fatty acids. It is also encouraged to limit the amount of sodium consumption.

Despite the availability of these guiding principles, previous studies have reported poor patient adherence to this advice, as patients, especially in low-income countries, tend to maintain their pre-heart attack diet [ 9 ].

The extraordinary increase in food-related diseases is due to poor eating habits [ 10 ]. Unfortunately, even consumers who are motivated to choose healthy foods sometimes fail to accurately assess the healthiness of food due to barriers to understanding nutrition information and utilizing it [ 11 ].

Nutritional information is provided through nutritional education [ 12 ]. Nutritional education programs can increase nutritional knowledge and improve nutritional behaviors and therefore help prevent many chronic diseases such as cancer, diabetes and cardiovascular diseases [ 13 ]. The findings of Pem et al.‘s study showed that a nutrition education program is a promising strategy for nutritional behaviors. Participants who received nutrition education had fundamental changes in their behavior, knowledge, and attitude towards healthier eating, the amount of fruit consumption increased while the consumption of snacks rich in sugar and fat decreased. However, the consumption of vegetables, energy intake, body mass index and physical activity level did not change after training [ 14 ]. In Mohammadi et al.‘s study, the educational method was effective in improving the level of nutritional awareness, weight loss motivation, and people’s performance, but people’s attitudes towards the correct consumption of food did not change [ 15 ].

The traffic light label (TLL) is a type of front-of-package food labeling (FOPL) proposed by the British Food Standards Agency in 2006. The TLL is an effective tool for conveying complex nutritional information that, by being placed on the front of food packaging, can potentially contribute to reducing the consumption of products with high levels of fat, salt, and sugar. A feature of this label is that it categorizes according to color, with “little” recognized by green, “medium” by yellow, and “high” by red. The amount of energy is also shown in white [ 10 ]. In Iran, since 2014, the Food and Drug Organization has required TLL with five factors (energy, fat, salt, sugar, and trans fat) on all packaged food products, except for some exceptions. The energy is shown in kilocalories; the other factors are in grams per 100 g or 100 ml of the food. By providing nutritional information on the front of food packaging, TLL helps people choose food according to their physical conditions and personal needs and thus reduces the burden of chronic diseases [ 16 ]. This label can be understood without having nutritional literacy, so it has the potential to be used by the majority of people in society [ 17 ]. In addition, the need to disclose nutritional information can encourage food manufacturers to improve the nutritional profile of their products [ 18 ].

Previous evidence has shown that food labeling in a cafeteria in Boston, as well as in a sports recreation venue in Canada and a restaurant in Taiwan, increased the purchase of healthier products and decreased the purchase of unhealthy products [ 19 , 20 , 21 ]. In contrast, a study in England and Australia reported no change in the sale of healthier products after introducing the TLL label [ 22 , 23 ]. Despite the limited studies investigating the effect of TLL on the selection and consumption of food items in healthy populations, no study was found that used TLL to educate patients with a history of hospitalization. Therefore, we hypothesized that introducing TLL to ACS patients would improve their food choices, preferences, and consumption, enabling them to prevent chronic diseases. This study was conducted to determine the effectiveness of using TLL in patient education on food selection, preference, and consumption in ACS patients.

Study design

In this randomized, single-blind clinical trial, from January 2021 to August 2021, we enrolled 170 patients with ACS and who met the eligibility criteria. They had been referred to Shaheed Rajaie Cardiovascular Medical & Research Center, Tehran, Iran.

Study population

The inclusion criteria were: ACS diagnosis confirmed by a cardiologist, age 20 to 60 years, first admission due to ACS attack in Shaheed Rajaie Hospital, absence of communication and cognitive problems (with self-report and patient file contents), lack of vision problems or color blindness, having the ability to understand the Persian language, being able to read and write, and not having a history of participating in educational classes on the same subject. Exclusion criteria were: having special dietary restrictions, being hospitalized again during the study, death, or unwillingness to be in the study.

Intervention

During the course of their hospitalization, all participants were provided with standard training. In addition to this, the intervention group received supplementary training on Traffic Light Labelling (TLL) from the research team, facilitated through an educational poster. This additional training was administered bedside in the concluding days of their hospital stay. Each training session lasted 15 to 20 min. The training encompassed an overview of the TLL system and a detailed explanation of types of fat. An exemplar packaged food product, labeled with green (indicating salt), red (indicating sugar and fat), and yellow (indicating trans fatty acid), was utilized to illustrate the TLL system.

Randomization

After taking the informed consent and conducting the pre-test, the participants were randomly assigned to two groups: routine care (control) and TLL food label training (intervention).

Measuring study outcomes

Demographic information.

Demographic information includes age, gender, marital status, place of residence, body mass index, level of education, occupation, type of insurance, household income, smoking, family history of heart attack, family history of angina pectoris, history of diabetes, a history of high blood pressure, and a history of high cholesterol.

Food preference

The questionnaire pertaining to food preferences comprised four dichotomous questions, wherein participants were presented with a choice between healthy and unhealthy options. The options included a preference for low-salt (healthy) versus salty (unhealthy) cheese, high-fat (unhealthy) versus low-fat (healthy) milk, sugar-containing (unhealthy) versus sugar-free (healthy) soft drinks, and solid (unhealthy) versus liquid (healthy) vegetable oil. A scoring system was implemented where the selection of an unhealthy option was awarded one point, while the choice of a healthy option garnered two points.

Food selection

The food selection questionnaire comprised ten items. The respondents indicated their choice on a Likert scale ranging from 1 (never) to 5 (almost always) [ 5 ]. Choosing healthier food in this questionnaire means that the participants buy packaged food based on the nutrition label. That is, they read the nutrition label when buying food, specifically in search of information on the amount of sugar, salt, fat and trans fatty acid and other nutritional information. A higher score means healthier food choices.

Food consumption

The food consumption questionnaire asked about the frequency of 32 food items harmful to heart health and how much food they ate during the previous month. They received 1 point for eating something six or more times a month and 5 points for never eating it. If the participants used fewer of these items, they would get a higher score, indicating a healthier consumption.

Validity and reliability of the questionnaires

The food preference, selection, and consumption questionnaires was developed for this study by generating questions through a comprehensive review of existing literature. These questions were deliberately designed to encompass three distinct domains: preference, choice, and consumption of food. (please see supplementary file ). To ensure face and content validity, the questions were meticulously reviewed by an expert panel. This panel was composed of faculty members from various disciplines, providing a multidisciplinary perspective on the content. The reliability of the questionnaire was done by the test-retest method for 20 samples. The intra-cluster correlation coefficient was estimated for food selection, food preference, and food consumption questionnaires, and the results were ICC: 0.90 (95% CI: 0.75, 0.96), ICC: 0.99 (95% CI: 0.97, 0. 99), and ICC: 1, respectively. Internal consistency was also estimated using Cronbach’s alpha method, and it was 0.79 for food selection, 0.63 for food preference, and 0.72 for food consumption.

Sample size and statistical analysis

The sample size for comparing two independent means with an effect size of 0.5 was calculated using G*power. The type I error was 0.05, and the type II error was 0.20. The sample size was 67 per group, but it was increased by 20% to 85 people to allow for dropouts.

Data entry and cleaning were performed using SPSS software. Descriptive statistics and inferential tests were applied to compare the individual and disease characteristics of the two groups. chi-square or Fisher exact tests were used for comparing categorical variables between the intervention and control groups. Next, the assumptions for mixed model ANOVA were checked. The data violated the normality and homogeneity of variance assumptions, so a nonparametric alternative was employed. The Brunner-Munzel test was used to compare the scores of the three outcome variables at baseline and post-intervention, as well as the difference scores between pre-and post-intervention. The Brunner-Munzel test is a nonparametric test of the null hypothesis that when values are taken one by one from each group, the probabilities of getting large values in both groups are equal. The analysis was conducted using JAMOVI software version 2.3.18.0. A significance level of p < 0.05 was adopted for all tests. The study was blinded as the statistician did not know the allocation of the patients.

This study was approved by the Organizational Ethics Committee of the Tehran University of Medical Sciences Faculty of Medicine and was registered in the Iranian Clinical Trials Registration Center on this date 30/10/2020 (IRCT20200927048857N1). Before participating in the research, an informed consent form was signed by all participants. It was explained to the patients that refusing to be part of the study would not affect their treatment. Patients were also assured that their identity would remain anonymous and their data would be confidential.

Two hundred and sixty people were checked for eligibility to participate in the study, from January 2021 to August 2021 and 87 were excluded due to not meeting the inclusion criteria. Three people refused to sign informed consent to participate, and 31 people (12 in the control and 19 in the invention groups) did not attend to post-test. Finally, the data of 66 people in the intervention group and 73 in the control group were analyzed (Fig. 1 ).

The CONSORT diagram shows the participants’ flow through each stage of a randomized trial

Most participants in both groups were aged 50 to 60 (42 in the intervention and 47 in the control). The intervention and control groups were homogenous in demographic and medical history variables ( p > 0.05) except for diabetes history ( P = 0.012) (Table 1 ).

The baseline scores of the three outcome variables did not differ significantly between the two groups, indicating their comparability at the start of the intervention. However, after the intervention, the scores of the three outcome variables showed significant differences between the two groups, favoring the intervention group. To control for the effects of the baseline scores, the difference scores between pre- and post-intervention were calculated and compared using the Brunner-Munzel test. The results revealed a significant difference in scores of the three outcome variables, confirming the effectiveness of the intervention.

According to the Brunner-Munzel test, before the intervention, the selection of packaged foods between the control and intervention groups was not significantly different ( P = 0.127), but it became significantly different after the intervention ( P < 0.001). The pre-test and post-test differences were calculated, and the comparison of their median showed a significant difference between the two groups ( p < 0.001), showing that the intervention group had a higher median (Table 2 ). This result means the intervention group chose healthier food.

The results of the Brunner-Munzel test shows that the food preferences before the intervention were not significantly different between the control and intervention groups ( P = 0.852). But after the intervention, the intervention group obtained a higher score in terms of preference ( P < 0.001). This was also seen in the comparison of the median of the pre-test and post-test differences ( p < 0.001) (Table 3 ). This result means the intervention group preferred healthier food.

The results of the Brunner-Munzel test shows that food consumption was not significantly different before the intervention between the two groups ( P = 0.846), but the difference was significant after the intervention ( P = 0.004). This was also seen in the comparison of the median of the pre-test and post-test differences ( p = 0.011) (Table 4 ). This result means the intervention group consumed healthier food.

This study found that the intervention group, who received routine patient education by hospital staff and TLL label training by the research team, scored higher than the control group, who received only routine patient education by hospital staff. This result means the intervention group chose, preferred, and consumed healthier food than the control group. These findings suggest that teaching the use of TLL can enhance the effectiveness of patient education and help reduce the burden of ACS in the long run.

The findings from this study are consistent with previous studies that reported positive effects of TLL on food purchasing and consumption behaviors [ 19 , 20 , 24 ]. However, they contrast with some studies that found no relationship between TLL and sales of healthier products [ 22 , 23 ]. These discrepancies may be due to differences in the settings, populations, and methods of the studies.

To understand the complex nutritional information on food labels by the participants of the intervention group, even the participants who had a low level of education, we taught them to use the TLL label to prefer, choose and consume healthier food with only a few colors. Therefore, teaching the use of TLL can enhance the effectiveness of patient education and help reduce the burden of ACS in the long run.

For instance, Sonnenberg et al. conducted a study in an American hospital in 2010. They labeled foods sold in a cafeteria. A customer survey was carried out during a 2-week baseline period and a 7-week intervention period, with purchases monitored throughout. The study found that traffic light food labeling increased consumer awareness of food and beverage healthiness at the point of purchase. More consumers reported looking at nutrition information during the labeling intervention than the baseline period, and those who noticed the labels bought a higher proportion of healthy items.

Similarly, Olstad et al. used traffic light labels to improve food choices in a recreational and sports venue in Canada. They monitored product sales one week before and one week after food product labeling. The traffic light icons (green, yellow, and red) were placed on the menu screen or directly on the product shelf, and explanatory sheets were placed in the study environment. The study concluded that nutrition guidance labels effectively increased the sale of healthy foods and reduced the sale of unhealthy foods.

In Iran, Esfandiari et al. conducted a study to investigate the effectiveness of education on awareness, attitude, and performance based on the color indicator of appropriate food products. Students’ knowledge, attitude, and performance about the nutritional color indicator were measured using a questionnaire. Training was conducted face-to-face using an educational pamphlet. The questionnaire was filled out again by the participants 3–6 months after the training. The results show improvements in knowledge, attitude, and performance scores compared to before the training.

In contrast, Sacks et al.‘s study in the United Kingdom examined consumer food purchasing changes after TLL labeling to evaluate the label’s impact on the “healthiness” of purchased foods. The sales of two groups of products (prepared foods and sandwiches) were divided according to their healthiness. Comparisons were made before and four weeks after TLL labeling. The results show that the sales of prepared foods increased immediately after introducing the traffic light label. However, the sale of sandwiches did not change significantly after the introduction of labels. There was no association between changes in product sales and product healthiness [ 22 ].

A study conducted by Sacks and colleagues in Australia analyzed the changes in online food purchases over a period of 10 weeks, following the implementation of the Traffic Light Nutrition Information (TLNI) system. Four colored traffic light symbols indicated the amount of fat, saturated fat, sugar, and sodium. These were displayed for 53 products in the intervention store alongside the product list. No nutritional information was provided in the comparison shop. No association was seen between the sales changes in the two stores and the health value of the products [ 23 ].

This study had some limitations. First, it only assessed the short-term effect of patient education using TLL, so the long-term impact remains unknown. Second, it did not measure the effect of patient education using TLL on nutritional variables such as anthropometric, biochemical, clinical, and dietary indicators. Therefore, future studies should examine these endpoints and follow up with ACS patients for extended periods to evaluate the sustainability and impact of TLL label training on their health outcomes. The study also faced a limitation due to not completing the post-test, leading to the loss to follow-up of many participants. To mitigate this, we compared the demographic characteristics of participants who completed the post-test and those who didn’t. The analysis shows no significant differences in age, gender, or education level between these groups, suggesting that the attrition is unlikely to have introduced a significant bias in our results.

The present study was a randomized controlled trial (RCT) aimed at assessing the efficacy of traffic light labeling (TLL) training on food selection, preference, and consumption among individuals who recently experienced acute coronary syndrome (ACS). The findings indicate that combining TLL training with standard hospital patient education yields superior results compared to the latter alone. Although the study was restricted to ACS patients, it is recommended that future research extend this to healthy individuals with cardiovascular risk factors, as well as those with a history of ACS who have been discharged for a significant period. The results underscore the importance of incorporating TLL education into nutritional guidance for ACS patients and suggest integrating TLL training into hospital education programs. Moreover, placing TLL educational posters in hospital cafeterias and near vending machines may foster healthier dietary habits among patients and staff. The study further proposes extending TLL training to other environments such as schools, workplaces, and supermarkets to promote healthier eating habits in the wider public. It is important to note, however, that this recommendation is only applicable to countries that use TLL on their products, and other countries may require different types of education. Additionally, this educational strategy should be considered as a single component of a patient’s education plan for healthy nutrition.

Data availability

The data that support the findings of this study are available from the corresponding author, [Reza Negarandeh], upon reasonable request.

Abbreviations

Traffic light labeling

Acute coronary syndrome

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Acknowledgements

We would like to express our special thanks to all patients who participated in this study. Moreover, we are grateful to the Shahid Rajaee Hospital staff and authorities. We also sincerely appreciate the financial support of the Tehran University of Medical Sciences.

This study was founded by the Tehran University of Medical Sciences (Grant number: 49518).

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Reza Negarandeh

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RN and FS and ShP Conceived and designed the analysis, FS and AN and ShP Collected the data, RN and FS Contributed data or analysis tools, RN Performed the analysis, FS Wrote the paper, final all authors read, commented, and approved the manuscript.

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Sadeghi, F., Pashaeypoor, S., Nikpajouh, A. et al. The impact of healthy nutrition education based on traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome: a randomized clinical trial. BMC Public Health 24 , 1332 (2024). https://doi.org/10.1186/s12889-024-18805-2

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Want to become a nutrition coach? By following these 4 steps, you can help others eat healthier

Woman places vegetables in a bowl while a phone records on a tripod.

Do you eat enough fruit and vegetables? The odds are pretty slim that you do.

According to the CDC , fewer than 1 in 10 children and adults eat sufficient vegetables every day, and fewer than 1 in 7 adults eat enough fruit.

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Dietitians and nutritionists hope these numbers will soon change for the better—and so do their clients. The demand for the field is rising, with an expected growth of 7% between 2022 and 2032, says the U.S. Bureau of Labor Statistics . However, with avoidable diseases and conditions like diabetes and hypertension also on the rise, the Academy of Nutrition and Dietetics believes even more experts are needed to help Americans eat healthier.

If assisting others in setting and reaching nutrition goals, then becoming a nutrition coach may be a career hat for you. While it is important to keep in mind that nutrition coaching is an unregulated field and it is inadvisable for you to provide any sort of medically-related advice to clients, pairing nutrition coach knowledge with a health coach certification or personal trainer certification is a common choice.

If you follow these 4 tips, you may be on your way to becoming a nutrition coach:

Do your research and figure out your purpose

Complete a certification program, consider further education, keep up with the trends.

Before you possibly spend several hundred or thousands of dollars learning the ins-and-outs of nutrition, take some time for discovery. Watch informational videos on YouTube or TikTok about the world of nutrition, what a nutrition coach does on a daily basis, and what sort of advice they can give clients. During this process, write down what strikes you as fascinating and really see if you can picture yourself in the shoes of a nutrition coach.

Shaina Painter , MS, CNS, and nutritionist at Being Health says one of the most important pieces of advice for any aspiring nutritionist is to simply figure out your goals—and early. This includes who you would like to serve, what sort of job would you like to have, and why do you want to go into the field?

By figuring out the answers to these questions, it will be easier to discern exactly which pathway you should take. The good news is that enrolling in a foundational certification program is one way to learn the basics and provide a direct way to figure out if you truly enjoy the profession.

If you know nutrition is something that fascinates you, and you are eager to learn more about the subject, then completing a certification is a great pathway toward gaining nutrition skills without a degree . Fortune has done some of the tedious work for you and ranked the best nutritionist certifications —denoting programs based on their strong suit, whether that be business development, clinical skill training, or affordability.

If you are interested in a namesake nutrition coach program—which is not much different than a simple nutritionist certification offered by many organizations—those exist, too. The National Academy of Sports Medicine (NASM), for example, has a self-paced program that can be completed in as little as one month and costs about $629.

With a certification, someone may have the experience to help individuals with their overall wellness and accountability, like weight loss , explains Michelle Routhenstein , MS, RD, CDCES, CDN, owner of Entirely Nourished.

“There’s a lot of holistic nutrition certifications out there, and they really look at body, mind and spirit and try to bring that all together,” she says—adding that those without the proper licensure should stray away from giving medical advice or helping those with medical conditions.

Don’t be fooled. While anyone can call themselves a nutritionist or nutritionist coach, in most states individuals are required to have at least a bachelor’s degree (or even a master’s or PhD) plus clinical training hours in order to become a state licensed nutritionist (the title may also depend on the state). As a result, going down a more traditional educational pathway can be especially useful.

Check your state’s licensure rules before considering a career as a nutritionist. Every state has different regulations when it comes to who can become a nutritionist, including age, education, and training hour requirements. For states with strict guidelines, you could get into legal troubles for using an improper title.

Finding a university to obtain more advanced nutrition skills is not necessarily a difficult task. Many schools have programs in subjects like dietetics, nutrition science, and food science. Plus, the coursework is oftentimes offered in an online form, allowing people with busy family schedules or existing work requirements to be able to learn on their own time. A quick search on the Internet will yield dozens of examples. The University of Arizona , the Unviersity of Alabama , and Purdue Global are just a few.

Companies are constantly innovating new products for the grocery store shelf. Sometimes, items may be advertised as a healthy choice, such as low sugar or sodium but may have alternate ingredients—like an artificial sweetener, for example—that still may not be great for an individual’s health. For these reasons, it is important that you are aware of new products and ingredients that are hitting the shelfs and to be able to share knowledge with clients.

With nutrition being such a popular subject on social media platforms, being aware of common trends and misconceptions is also a key part of your role as a nutrition coach. Overall, being an educated and informed nutrition expert will go a long way in building trustful relationships with clients—and propel you to a long-term career in the health and wellness world.

Frequently asked questions

Do nutrition coaches make money.

Yes, nutrition coaches can make money by providing services to clients. The more educational background you have, via certifications or degrees, the more likely potential clients will trust your expertise.

Can I be a nutrition coach without a degree?

Yes, everyone can technically call themselves a nutrition coach, though it may be best to at least have a certification. In order to become a licensed nutritionist in most states, you will need a degree.

What is the difference between a nutritionist and a nutrition coach?

Unlicensed nutritionists and nutrition coaches are largely the same. While a coach may emphasize relationships directly with clients more, they both work with wellness and accountability. If you compare the curriculum of a nutritionist versus nutrition coach certification, not many differences can be discerned.

Check out all of Fortune’ s rankings of degree programs , and learn more about specific career paths .

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Promoting Healthy Eating among Young People—A Review of the Evidence of the Impact of School-Based Interventions

Abina chaudhary.

1 Independent Researcher, Kastrupvej 79, 2300 Copenhagen, Denmark; moc.oohay@yrahduahcaniba

František Sudzina

2 Department of Materials and Production, Faculty of Engineering and Science, Aalborg University, A. C. Meyers Vænge 15, 2450 Copenhagen, Denmark

3 Department of Systems Analysis, Faculty of Informatics and Statistics, University of Economics, nám. W. Churchilla 1938/4, 130 67 Prague, Czech Republic

Bent Egberg Mikkelsen

4 Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark; kd.uk.ngi@imeb

Intro: Globally, the prevalence of overweight and obesity is increasing among children and younger adults and is associated with unhealthy dietary habits and lack of physical activity. School food is increasingly brought forward as a policy to address the unhealthy eating patterns among young people. Aim: This study investigated the evidence for the effectiveness of school-based food and nutrition interventions on health outcomes by reviewing scientific evidence-based intervention studies amongst children at the international level. Methods: This study was based on a systematic review using the PRISMA guidelines. Three electronic databases were systematically searched, reference lists were screened for studies evaluating school-based food and nutrition interventions that promoted children’s dietary behaviour and health aiming changes in the body composition among children. Articles dating from 2014 to 2019 were selected and reported effects on anthropometry, dietary behaviour, nutritional knowledge, and attitude. Results: The review showed that school-based interventions in general were able to affect attitudes, knowledge, behaviour and anthropometry, but that the design of the intervention affects the size of the effect. In general, food focused interventions taking an environmental approach seemed to be most effective. Conclusions: School-based interventions (including multicomponent interventions) can be an effective and promising means for promoting healthy eating, improving dietary behaviour, attitude and anthropometry among young children. Thus, schools as a system have the potential to make lasting improvements, ensuring healthy school environment around the globe for the betterment of children’s short- and long-term health.

1. Introduction

Childhood is one of the critical periods for good health and development in human life [ 1 , 2 ]. During this age, the physiological need for nutrients increases and the consumption of a diet high in nutritional quality is particularly important. Evidence suggests that lifestyle, behaviour patterns and eating habits adopted during this age persist throughout adulthood and can have a significant influence on health and wellbeing in later life [ 3 , 4 ]. Furthermore, the transition from childhood into adolescence is often associated with unhealthy dietary changes. Thus, it is important to establish healthful eating behaviours early in life and specially focus on the childhood transition period. A healthy diet during the primary age of children reduces the risk of immediate nutrition-related health problems of primary concern to school children, namely, obesity, dental caries and lack of physical activity [ 5 , 6 , 7 ]. Furthermore, young people adopting these healthy habits during childhood are more likely to maintain their health and thus be at reduced risk of chronic ailments in later life [ 7 , 8 , 9 ]. Thus, healthy behaviours learnt at a young age might be instrumental in reaching the goals of good health and wellbeing of the 2030 Sustainability Agenda which has implications at the global level.

Globally, the prevalence of overweight and obesity rose by 47.1% for children and 27.5% for adults between 1980 and 2013 [ 10 ]. A recent WHO (World Health Organization) Commission report [ 10 ] stated that if these same trends were to continue, then by 2025, 70 million children are predicted to be affected [ 11 ]. Hence, the increased prevalence might negatively affect child and adult morbidity and mortality around the world [ 12 , 13 ]. Worldwide the dietary recommendations for healthy diets recommend the consumption of at least five portions of fruits and vegetables a day, reduced intake of saturated fat and salt and increased consumption of complex carbohydrates and fibres [ 14 ]. However, studies show that most children and adolescent do not meet these guidelines [ 15 , 16 ] and, thus, as a result, childhood and adolescent obesity are alarming nearly everywhere [ 17 ]. Recent figures show that the prevalence has tripled in many countries, making it the major public health issue in the 21st century [ 18 , 19 , 20 , 21 ]. According to WHO [ 4 ], 1 in 3 children aged 6–9 were overweight and obese in 2010, up from 1 in 4 children of the same age in 2008.

The increased prevalence of overweight and obesity has fuelled efforts to counteract the development, as seen for instance in the action plan on childhood obesity [ 17 ]. Increasingly policy makers have been turning their interest to the school setting as a well-suited arena for the promotion of healthier environments [ 18 ]. As a result, schools have been the target of increased attention from the research community to develop interventions and to examine the school environment to promote healthful behaviours including healthy eating habits.

Globally, interventions in the school environment to promote healthier nutrition among young people have received considerable attention from researchers over the past years. But there is far from a consensus on what are the most effective ways to make the most out of schools’ potential to contribute to better health through food-based actions. Is it the environment that makes a difference? Is it the education or is it the overall attention given to food and eating that plays the biggest role? School food and nutrition intervention strategies have witnessed a gradual change from knowledge orientation to behavioural orientation [ 22 ] and from a focus on the individual to the food environment. Research evidence has shown that adequate nutrition knowledge and positive attitudes towards nutrition do not necessarily translate to good dietary practices. Similarly, research has shown that the food environment plays a far bigger role in behaviour than originally believed [ 23 , 24 ].

School-based interventions can a priori be considered as an effective method for promoting better eating at the population level. Schools reach a large number of participants across diverse ethnic groups. It not only reaches children, but school staffs, family members as well as community members [ 8 , 25 ]. Schools can be considered a protected place where certain rules apply and where policies of public priority can be deployed relatively easily. In addition, schools are professional spaces in which learning and formation is at the heart of activities and guided by a skilled and professional staff. Schools, as such, represent a powerful social environment that hold the potential to promote and provide healthy nutrition and education. Besides the potential to create health and healthy behaviours, good nutrition at school has, according to more studies, the potential to add to educational outcomes and academic performance [ 26 , 27 , 28 ].

However, taking the growth in research studies and papers in the field into account, it is difficult for both the research community and for policy makers to stay up to date on how successful school-based interventions have been in improving dietary behaviours, nutritional knowledge and anthropometry among children. Also, the knowledge and insights into how it is possible to intervene in the different corners of the school food environment has developed which obviously has influenced over recent decades how programs and interventions can be designed. It has also become clear that food at school is more than just the food taken but includes curricular and school policy components. The findings from school-based studies on the relationship between school, family as well as community-based interventions and health impact suggest that health impacts are dependent on the context in which they have been carried out as well as the methodology. Thus, an updated overview as well as a more detailed analysis of initiatives is needed in order to develop our understanding of the nature of the mechanisms through which the school can contribute to the shaping of healthier dietary behaviour among children and adolescents before more precise policy instruments can be developed. Our study attempted to fill the need for better insight into which of the many intervention components works best. It attempted to look at school food and nutrition interventions reported in the literature that have been looking at healthy eating programmes, projects, interventions or initiatives.

School-based interventions in the Western world are traditionally targeted at addressing obesity and over-nutrition, but school food interventions are also addressing under nutrition and, as such, their role in a double burden of disease perspective should not be underestimated. Many studies have reported on micronutrient malnutrition among school-aged children in developing countries (for instance [ 29 , 30 , 31 ]) but it has also been reported in the context of developed countries [ 32 ]. Against this backdrop, the aim of this study was to provide an analysis of the evidence of the effectiveness of school-based food interventions by reviewing recent scientific, evidence-based intervention studies on healthy eating promotion at school. The specific objectives of the study were to identify which interventions had an effect on primary outcomes, such as BMI, or on secondary outcomes such as dietary behaviour, nutritional knowledge and attitude.

2. Materials and Methods

The functional unit of the review were healthy eating programmes, projects or initiatives that have been performed using the school as a setting. We included only programmes, projects or initiatives that were studied in a research context, in the sense that they were planned by researchers, carried out under controlled settings using a research protocol, and reported in the literature. School-based programmes, projects, interventions or initiatives are, per definition, cluster samples where a number of schools first were chosen for intervention followed by performing an outcome measurement before and after the intervention and, in most cases, also in one or more control schools. The outcome measurement in the studies reviewed was performed on a sample of students that was drawn from each school (cluster).For this, the systematic review and meta-analysis (PRISMA) guidelines and the standardised quality assessment tool “effective public health practice project (EPHPP) quality assessment tool for quantitative studies” were used for analysing the quality assessment of the included studies [ 33 ]. This EPHPP instrument can be used to assess the quality of quantitative studies with a variety of study designs.

2.1. Literature Search

The literature review involved searches in PubMed, Web of Science and Cochrane Library database. The search strategy was designed to be inclusive and focused on three key elements: population (e.g., children); intervention (e.g., school-based); outcome (e.g., diet and nutrition, knowledge, attitude and anthropometrics). The search terms used in PubMed database were: “effectiveness of school food AND nutrition AND primary school children”, “effectiveness of school food AND nutrition AND interventions OR programs AND among primary school children AND increase healthy consumption”, “primary school children and education and food interventions”, “Effectiveness of school-based food interventions among primary school”, “effectiveness of school-based nutrition and food interventions”, “primary school interventions and its effectiveness”, and “obesity prevention intervention among Primary schools”. Search terms such as: “effectiveness of school-based food interventions among primary school”, “effectiveness of school based food and nutrition interventions”, “primary school interventions and its effectiveness” and “obesity prevention interventions”, were used in the Web of Science database. Lastly, search terms such as: “nutrition interventions in primary schools” and “Nutrition education interventions in school” were used in the Cochrane Library database to find the articles. In addition, reference lists of all retrieved articles and review articles [ 34 ] were screened for potentially eligible articles. The search strategy was initially developed in PubMed and adapted for use in other databases. In addition, snowballing of the reference list of the selected articles was conducted.

2.2. Inclusion Criteria

Studies selected for the inclusion were studies which investigated the effectiveness of a school-based interventions targeting food and nutrition behaviour, healthy eating and nutrition education as a primary focus during the intervention. Also, to be included in this review, only articles from 2014 to 2019 were selected and of those inclusion criteria included articles targeting primary school children aged between 5 and 14 years. Participants included both boys and girls without considering their socio-economic background. Study design included randomized controlled trial “RCT”, cluster randomized controlled trial “RCCT”, controlled trial “CT”, pre-test/post-test with and without control “PP”, experimental design “Quasi”. Studies which did not meet the intervention components/exposures, such as information and teaching (mostly for the target group and parents were additional), family focus on social support and food focus (which mainly focuses on the availability of free foods including food availability from school gardening), were excluded. Systematic review papers and studies written in different language except for English were excluded as well. Studies which met the intervention criteria but had after school programs were excluded.

2.3. Age Range

Since the review covers a broad range of different countries and since school systems are quite different, the sampling principle had to include some simplification and standardisation. The goal of the review was to cover elementary (primary) and secondary education and, as a result, the age range of 5–14 was chosen to be the best fit, although it should be noted that secondary education in some countries also covers those 15–18 years of age. In most countries, elementary education/primary education is the first—and normally obligatory—phase of formal education. It begins at approximately age 5 to 7 and ends at about age 11 to 13 and in some countries 14. In the United Kingdom and some other countries, the term primary is used instead of elementary. In the United States the term primary refers to only the first three years of elementary education, i.e., grades 1 to 3. Elementary education is, in most countries, preceded by some kind of kindergarten/preschool for children aged 3 to 5 or 6 and normally followed by secondary education.

2.4. Assessment of Study Eligibility

For the selection of the relevant studies, all the titles and abstracts generated from the searches were examined. The articles were rejected on initial screening if the title and abstract did not meet the inclusion criteria or met the exclusion criteria. If abstracts did not provide enough exclusion information or were not available, then the full text was obtained for evaluation. The evaluation of full text was done to refine the results using the aforementioned inclusion and exclusion criteria. Thus, those studies that met predefined inclusion criteria were selected for this study.

2.5. Analytical Approach

The first step of data collection was aimed at organizing all studies with their key information. In the second step, we created coded columns. A coded column served as a basis for being able to do further statistical analysis. In other words, in a coded column we added a new construct not originally found in the papers as a kind of dummy variable that standardized otherwise non-standardized information, allowing us to treat otherwise un-calculable data statistically. For the impact columns, we used the following approach to construct codes where impacts where put on a 1–4-point Likert scale with 1 being “ineffective”, 2 “partially effective”, 3 “effective” and 4 “very effective”.

For the design column, the following approach was adopted as illustrated in the Table 1 . Quasi experimental/pre–post studies were labelled QED and were considered to always include a baseline and follow-up outcome measurement. As the simplest design with no comparison but just a pre/post study of the same group, we constructed a power column and assigned 1 to this for a QED design. For the controlled trial (CT), we assigned the power 2. A controlled trial is the same as QED but with a comparison/control in which no interventions are made and with no randomization. We considered a study to be of that kind if some kind of controls were made which could be, for instance, matching. All CTs in our study included 2 types of comparisons: pre and post (baseline and follow-up) as well as a comparison between intervention/no intervention. For the RCT/RCCT—a trial that is controlled through the randomization—we assigned the power 3. This “top of hierarchy” design includes the case (intervention) and a control (no intervention) and normally two types of comparisons (pre and post) as well as an intervention/no intervention. For the context of this study, we did not differentiate between RCTs and RCCTs. The latter is sometimes used to stress the fact that the school (or the class) is the sampling unit from which the subjects are recruited. But since in the context of schools RCCT is simply a variation of RCT, we coded them in the same class of power. We simply assumed that when authors spoke about an RCT, they in fact meant an RCCT since they could not have been sampling subjects without using the school as the unit.

Coding table for study designs. The table shows the types of studies examined in the review and the power assigned to them.

Codes and categorization were used to standardize the information found in the papers for our statistical analysis. Categorisation of the age/class level, such as EA—Early age, EML—Early middle late, EL—Early late, was used.

For the intervention components (“what was done”) we translated all studies into three columns: information and teaching, family and social support and environmental components, food provision and availability. The latter was further expanded into three columns labelled as: focus on and provisioning of F & V; free food availability through school gardening and availability of food and healthier food environment. Our inclusion criteria were that studies should contain at least one of these components. For the environmental component—food provision and availability intervention components—we identified 2 distinct types: either a broad healthier eating focus or a narrow and more targeted fruit and vegetable focus. After the coding, we started to ask questions about the data. Most importantly, we were interested in knowing whether there existed a relationship between “what was done” and “what was the impact”. In other words, we were interested in knowing more whether there was a pattern in the way the studies intervened and the outcomes.

2.6. Queries Made

We performed queries for each intervention component (the independent variable in columns K, L and M) for each single outcome measure.

Is there a relationship between age and outcome? We used the coded column (EA, EML, etc.) to study that relationship.

In addition, we made queries regarding the relationship among study designs. For instance, would the duration of studies influence whether an effect could be found or not? Would more powerful designs result in more impact?

Furthermore, we made queries on the relationship between one intervention and a multi-interventional component and their effect on the outcome measure. Also, the queries on target groups were made. Codes such as S and NS (refer Table 4) in the column were used to study the relationship. In our analysis a distinction was made between “standard” and “extreme” (special cases). From the reviewed papers, it was clear that some studies put little emphasis on the school selected. We classified those as standard (S). However, a few papers used a stratification approach and case/cluster selection that can be classified as an “extreme” or non-standard case. We coded these as non-standard (NS). For instance, studies could be targeted to include only refugees or subjects of low socio-economic status. It can be speculated that being a “special case” or extreme case could have an influence. As a result, we reserved a code for these cases, although it became clear that they represented only a minority.

In our study, availability plays a central role, since it is used in many food-at-school intervention studies. Availability signals that food is “pushed” as opposed to being used in the “pull” mode, where individuals are expected to request food in the sense that is the behaviour of the individual that becomes the driving force rather than the “out thereness”. Availability is in most studies used in combination with the idea of a food environment. The literature shows that availability can be of two types. One is when food is made available for the individual to take where visibility, salience, product placement, etc., are used as factors. The other type of availability is when it is made free and the individual as a result does not have to pay. Free availability has been studied extensively in intervention studies but for obvious reason it is difficult to implement “post-study” since there needs to be a permanent financing present. The only exceptions to this are the collective meal models found in countries such as Sweden, Finland, Estonia and Brazil as well as in the EU scheme where the EU subsidizes the fruit.

Study design and other characteristics are provided in Table 2 , and their findings are provided in Table 3 .

The review sample: study design/characteristics. The table shows the 43 studies of the review Illustrating study design and study characteristics of the included studies.

The review sample-findings. The table shows the findings from the 43 studies of the review.

The information from abstracts were organized in a table with the following information:

Column A: Authors. The column lists the researchers/authors conducting the study.

Column B: Year. The column shows the year of the publication of the article.

Column C: Title/Reference. The column lists the title of the article.

Column D: Main aim. The column lists the main aim presented by authors in the abstract of each article.

Column E: Main aim in brief. This column is a constructed variable that refers to the main aim of each study. The idea was to give in brief the study idea and which outcome measures was focused on in the study.

Column F: Program name. The column gives the name of the project, program or intervention reported in in the article.

Column G: Location and Country. The column lists the specific place or location where the study was performed.

Column H: Study design. The column shows research design of the study according to authors.

Column I: Study design coded. This column is a constructed variable to capture the research design of the study and used to make an analysis of power possible, see Column J.

Column J: Power. The column was constructed to express the strength of the design. It is a dummy variable that was assigned a numerical value that allowed for a quantitative analytical approach.

Column K, L and M: Intervention components. The column shows which intervention components that was used in the study. We used a model that categorizes components into three different mechanisms of influence: cognitive (K), environmental (L, M, N) and social (O).

The environmental component includes actions where availability of meals—or fruit and vegetable (F & V)—were increased. Either through passive provision (F & V and meals) or through active participation such as gardening. The social category included actions where families and/or peers were actively influencing the participants. The cognitive category included teaching and learning.

Column L: Environmental/food focus on F & V. In this column, interventions which were targeted towards fruits and vegetables were flagged. This includes interventions whose focus was providing cooking lessons and maintaining healthy cafeterias during the intervention periods. Also, maintaining healthy cafeteria here refers to school canteens providing healthy options to its menu where children’s while buying food have healthier options to choose.

Column M: Environmental/food focus on increasing availability through school gardening. In this column, interventions which provided free foods among participants through gardening within the school were listed.

Column N: Environmental/food interventions focused on healthy meal availability. Interventions which provided healthy meals, breakfast, snacks during the school hours and distributed fresh fruits among the participants were listed in this column.

Column O: Family/social support. In this column interventions that included social components were flagged. These interventions included peer and family influence mechanisms.

Column P: Age. The column lists the age of the targeted groups of the intervention expressed in years according to the primary article data provided by authors.

Column Q: Age construct EA. This column shows a constructed variable for the age categorization based on the primary data given by authors. The constructed code was made to make statistical analyses possible. The construct Early Age (EA) was assigned if intervention were carried out in early school.

Column R: Age construct EML. This column shows a constructed variable for the age categorization based on the primary data given by authors. The code Early Middle Late (EML) was assigned if intervention was targeted all age groups.

Column S: Age construct EL. This column shows a constructed variable for the age categorization based on the primary data given by authors. The code EL refers to Early late and was assigned if the intervention was targeted early and early and late school.

Column T: Sample size. The number of young people enrolled in the intervention was listed in this column.

Column U: Time duration. This column shows the length of the intervention expressed in months. It is a constructed variable based on the primary data given by authors and was made to standardize duration and make it ready for cross study analysis.

Columns V, W, X, Y: Outcome measures. In Columns T, U, V, W, the outcome measures named as Anthropometry, HE/FV (healthy eating fruits and vegetables), Nutritional knowledge, and Attitude, respectively, were listed according to our outcome model shown in Figure 1 . Only a few include all outcome measures, but all studies included at least one of them.

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Outcome measures model. The figure illustrates the four types of outcome measures found in the interventions.

Columns X, AA, AB, AC: Effectiveness. The effectiveness as measured by the outcomes measured are listed in this column. Each outcome measure was rated using a Likert scale from 0–4. The effectiveness of outcome measures among participants as measured by the measures in our model ( Figure 1 ): attitude, anthropometry, HE/FV, nutritional knowledge and attitude were listed in the Columns X, Y, Z, AA, respectively.

Column AD: Target group. This column provides information on the target group of interventions such as information on grades of subjects and municipalities.

Columns AE, AF: Target group. This column is a constructed variable created to capture if the intervention had a special ethnic or socio-economic focus. Columns AC and AD consisted of coded target group named as Standard (S) and Non-Standard (NS). The “NS” here represents the target group either from refugees or immigrants or lower socio-economic classes.

Column AG: Keywords. This column lists the keywords found in the interventions.

Ordinary least squares regression was applied in this study; specifically, we used the linear regression function in IBM SPSS 22. We opted for a multi-variate approach; i.e., multiple linear regression was used. Anthropometry, behaviour (healthy eating and food focus), attitude and nutritional knowledge were used as dependent variables. In order to better account for control variables, such as sample size and study length, a dummy variable was introduced for study length of one year and more; and a logarithm of the sample size was used instead of the actual sample size to eliminate scaling effects. We grouped countries by continents (while splitting Europe into North and South as there were enough studies and no countries in between) and introduced related dummy variables. The remaining variables were used as independent variables without any additional manipulations.

Since the aim was to create models consisting only of independent variables that significantly influence the dependent variables, we used the backwards function. Because there were too many independent variables for the backwards function for the attitude model (with only eight observations), the stepwise function was used instead.

Information and teaching was present in all but one study. Free food was found only in two studies and focus on fruit and vegetables in three studies. Therefore, it is not surprising that neither of the three variables were found to be significant in any of the models.

2.7. Study Sample

The search strategy resulted in 1826 titles which were screened for duplicates and potential relevance. After this initial screening, 345 titles and abstracts were assessed against the inclusion and exclusion criteria. Articles that studied school interventions after school hours were excluded. In addition, articles which studied interventions among children in out of school context such as at community level were excluded. The justification is that both “after school” and “out of school” since can be regarded as non-typical school environments. We aimed to study the “school” as an artefact that can be considered as a “standard” across countries despite some national differences. For both “after school” and “out of school”, we argue that there are considerable differences among countries and that an inclusion of such studies would negatively influence our analytical approach. In total, 42 articles were identified as relevant and full papers were obtained as the final sample. Figure 2 below illustrates the search terms and selection process of articles.

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Review flow chart. The figure shows the progress of the literature review process following the PRISMA 2009 approach.

2.8. Intervention Study Characteristics

For all 43 items in our sample, Table 2 provides the information about the study, intervention methodologies, characteristics strategies, etc. In our extract of studies, the sample size ranged from 65-2997 subjects/participants, and the intervention duration ranged from 1 and half month to 36 months. The systematic review locations identified by the author were: 26 from Europe [ 21 , 36 , 38 , 39 , 40 , 44 , 46 , 49 , 52 , 54 , 57 , 58 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], six from Asia [ 35 , 42 , 48 , 59 , 60 , 62 ], 10 from America [ 37 , 41 , 43 , 45 , 47 , 50 , 51 , 53 , 55 , 61 ] and one from Africa [ 56 ]. We categorized all interventions according to their intervention components. To this end, we had constructed three classes: Information and Teaching, Food Focus and Family/Social support as illustrated. The interventions characteristics of each included study are shown in Table 2 .

Of the total study sample, the majority of studies ( n = 41) involved “Information and Teaching” components consisting mainly of classroom-based activities (e.g., an adapted curriculum and distribution of educational materials, health and nutrition education program). Another 12 studies along with “Information and Teaching” involved a food focus and availability component. These food and availability components which consisted mainly of supervised school gardening, environmental modifications to stimulate a more healthful diet, such as increased availability and accessibility of healthy foods, distributions free food programmes, school provided free breakfast, school lunch modifications and incentives. Only two studies combined all the three intervention components of this study. Family/social support intervention was clearly focused on in nine study. In other studies, even though their interventions were not primarily or secondarily focused on family/social support component, they indirectly acknowledged the importance of parents and included them in their studies.

All of the reviewed studies included intervention components that were delivered in school settings and within school hours. Our sample showed that consumption of fruit and vegetables was the most used intervention component and was include in more than half of the interventions. Most studies were designed and carried in a way where a research assistant was trained by senior researchers/co-authors to ensure that each members of the research team followed same procedures for data collection. Since all studies were “in situ” studies included a close researcher/school staff cooperation component. In most of the listed studies, teachers being the responsible person to implement the interventions were trained beforehand.

2.9. Types of Interventions

Table 2 shows an overview of the programmes and their intervention components. From the table, it can be seen that studies differed according to how broadly they intervened. Some studies have included a narrow intervention (i.e., only one intervention components which targeted behavioural components), whereas others included multicomponent approaches where all three intervention components were used in the study.

Finding the right approach to intervening for healthier eating at school is a major challenge. In other words, which interventions create which impacts and how should the public best invest in new policies, strategies, and practices at school if long term health is the intended end point?

The purpose of this review was to compile the evidence regarding the effectiveness of successful school-based interventions in improving dietary behaviours, nutritional knowledge, attitudes and anthropometry among children. The analysis of the data showed a number of relationships between outcome effect and a number of other characteristics of the intervention (i.e., age, location/region, intervention type, duration). Descriptive statistics are provided in Table 4 .

Descriptive statistics.

The linear regression models carried out for each intervention component is added in the text and the tables have been referred to each associated result. Out of 42 studies, 36 studies reported the outcome on HE/FV behaviour scale while anthropometry and attitude impacts were observed in 18 and six studies, respectively. The item one of the results in this article presents the most general finding from the literature review, item two describes the variable found significant in two cases, while the remaining variables were significant in once case each. Additionally, item four, five and six are related “design” phenomena effects in the sense that they are not related to intervention components but to the study was designed your study. The rest is related to (intervention components rather than designs. In Table 5 , the outcome measures for which an effect could be seen has been listed. The linear regression model describing what influences the attitude is provided in Table 6 .

Linear regression model for attitude.

Linear regression model for anthropometry.

With regards to the explanatory power of the model, R 2 = 0.789, R 2 adj. = 0.719, and significance = 0.009.

The linear regression model describing what influences the anthropometry is provided in Table 6 .

With regards to the explanatory power of the model, R 2 = 0.683, R 2 adj. = 0.586, and significance = 0.003.

The linear regression model describing what influences the behaviour is provided in Table 7 .

Linear regression model for behaviour.

With regards to the explanatory power of the model, R 2 = 0.121, R 2 adj. = 0.096, and significance = 0.037.

An alternative linear regression model describing what influences the behaviour is provided in Table 8 .

Alternative linear regression model for behaviour.

With regards to the explanatory power of the model, R 2 = 0.449, R 2 adj. = 0.432, and significance < 0.001.

3.1. School-Based Interventions in General Create Impact

Looking across the whole study sample, it can be seen that in general the interventions created an impact in one or more ways either on knowledge, intentions, eating habits and/or anthropometry. In other words, it was hard to find studies that created no impact. This finding adds to the body of evidence that suggests that food-based interventions are a well-suited and effective policy tool when it comes to promoting healthier eating among young people.

3.2. Family Support Affects Healthier Eating Behaviour and Attitude

Out of all the included studies, nine studies focused on family support as an intervention component. But out of those, our analysis showed that the family involvement was impactful among participants when it comes to promoting healthier food choices. Parents being influencers and role models in the family in these studies seemed to help to influence children’s dietary habits. Studies which involved participants’ parents in the intervention and provided them with nutritional knowledge and healthy cooking skills (i.e., knowledge about the importance of healthy food and nutrition during the early age of their children), seemed to be able to help young people prepare more healthy and nutritious food at home. As studies showed, this seemed to increase children’s intentions towards eating more fruits and vegetables and eventually resulted in consumption of more healthy foods. However, this did not seem to be the case for all ages. Intention to eat more fruits and vegetables was seen among early age participants (EA) either alone or with family support. It should be noted that the regression models did not include interactions, since the number of analysed studies was only ~40. It was not possible to include age as a continuous variable in the models because (as it can be seen in Table 5 ) age was a range, and sometimes even a wide range, e.g., 8–11 or 4–11. Family support increases the outcome measure by approximately 1 in both cases. Please refer to Table 5 and Table 7 for detailed linear regression model used for attitude and behaviour.

3.3. Interventions Done in Northern Europe (7 Studies) Had a Smaller Impact on Behaviour than the Studies Conducted in the Rest of the World (22 Studies)

The results from the models which was created to measure the efficiency of HE/FV highlighted the fact that HE/FV scale depends only on region where the intervention was done. The behaviour outcome for Northern Europe was on average 1.5 while the average for the rest was 3.2 (please refer to Table 8 ).

3.4. Effect of Anthropometry Measures Increases with Study Power

The results suggested that the design of the study plays a role when it comes to be able to show impact of interventions. From the findings, it was clear that the anthropometry measured among the participants were increasing with the power of the study. That is, the stronger the design the greater the likelihood of being able to measure impact on anthropometric outcomes—a unit increase in the design power is associated with an outcome increase of approximately 1.5 (please refer to Table 6 ). To examine the influence of study design we used the score that was constructed for the purpose (please refer to Table 1 ). This score assigns a higher power to randomized designs than non-randomized ones.

3.5. Study Duration Impacts Anthropometric Outcomes

It was also clear that the intervention duration does have impact on the outcome, i.e., the longer the duration better the anthropometric results among the children. Interventions that lasted a year or more, had the outcome measure on average almost one unit higher than shorter studies (please refer to Table 6 ).

3.6. Larger Samples Impacts Anthropometry Measures

Results showed that anthropometric outcome decreased within the sample size. Increasing the sample size by a factor of 10, from approximately 100 to 1000, decreased the outcome measure by almost 2.5 (please refer to Table 6 ). Thus, bigger the sample size a reverse effect on outcome was obtained. The studies whose intervention was done for long period of time (i.e., couple of months or year and among small participants) were found to be effective in the outcome. It might be the case that it was hard to administer the same thing to large sample size post intervention and thus could have decreased the anthropometry outcome among the participants.

3.7. Food Availability Interventions Influence Anthropometric Outcomes

Our analyses showed that a food focus, specifically healthy meal availability had an impact on the children’s anthropometric outcomes—increasing it by almost 3.5 on average (please refer to Table 6 ).

3.8. Interventions among Younger Students Influence Attitude Among Participants

Results showed that the younger the study subjects were, the more influence interventions had on attitudes (the outcome was on average 0.75 higher than for other age groups). Thus, the result suggests that the participants’ attitude increases when they are in their early age (EA) i.e., 4–7 years old. Furthermore, results suggest that increased family support associated with participants’ attitude towards healthy eating helps in changing the behaviour among them. Early age (EA) and family support seemed to impact positively both alone and together. Meaning that the intervention had positive impacts on participants (i.e., EA participants) attitudes towards healthy eating either with the involvement of their family support or without the involvement of family support. Please refer to Table 5 for detail linear regression model for attitude.

3.9. No Effect of School Based Interventions on Nutritional Knowledge

Findings showed that nutritional knowledge among participants (i.e., of all age group) does not depend on school-based interventions. Thus, none of the collected variables have influences on nutritional knowledge.

4. Discussion

4.1. discussion of results of this review in relation to others.

In the discussion we aim to relate our findings with what has been found in previous studies, discuss our methodological approach and reflect on what are the policy implications. Since the discussion on how to counteract the unhealthy eating pattern and the worrying increase in nutrition related disorders among young people is attracting much attention and since the discussion on how the school could contribute we aim to give policy makers and practitioners an up to date insight into the potentials of the school to act as a hub for promotion of healthier eating and provide inspiration for the development of new types of school-based interventions and strategies.

The huge interest in using the infrastructure of the school to initiate and promote healthier eating among young people has resulted in a large number of interventions studies over the past decades. This research interest per definition as the same time creates a need for syntheses of the findings in order to make them feed into the public health and school policy cycle and to “send the results to work”. Taken the huge investment that better food at school strategies at school will cost for states it is worth appreciating that the Evidence-Informs-Policy pathway seems to be working. At the same time the conceptual approaches and the understanding of what intervention components might work better than others, which age groups might benefit the most etc. as developed considerably which again adds to the rationale for synthesis of intervention study findings. Most recent reviews by Julie et al. [ 76 ], Noguera el al. [ 77 ], Evans et al. [ 78 ], Cauwenberghe et al. [ 34 ] and Brown et al. [ 79 ] has created a time gap of almost five years. Covering the last five years of research our review makes a needed contribution and in addition we argue it makes a needed contribution to a standardization and conceptualization of both sampling and intervention design methodologies.

Overall, the findings from this review suggest that school-based interventions that include intervention components such as information and teaching, food focus and family support are effective in improving the HE/FV, anthropometric measurements and attitude towards healthy dietary behaviour among the participants. On the other hand, nutritional knowledge among participants did not seem to be influenced much by any of the intervention components used.

Impacts on HE/FV behaviours were observed, but mostly among early age children revealing a distinct age pattern in the findings. Thus, age was seen as a significant factor in determining effectiveness in several study [ 35 , 37 , 39 , 42 ]. Impact was greater on young children in the 4–7 year old age range, suggesting that dietary influences may vary with age.

Multicomponent approaches that includes good quality instruction and programs, a supportive social environment both at school and home, family support has been effective in addressing childhood related diseases through focusing on diet and physical activity. Most of the studies in this review implemented with combination of school staff and intervention specialists provide evidence for the effectiveness of the program. Thus, evidence supports that family involvement and nutrition education curriculum delivered by the teacher under supervision of intervention specialists can alter the intake of fruit and vegetables while impacting positively on anthropometric measurements. Teacher led interventions have been effective and can be the most sustainable approach for long term impact of the program. The same conclusion was found in a review done in investigating the effectiveness of school-based interventions in Europe which provided the effectiveness of multicomponent intervention promoting a healthy diet in school aged children in Europe [ 34 ].Studies with a food focus in their intervention approaches showed significant improvements in BMI [ 35 , 54 , 58 ]. Significant improvements in BMI here refers to the studies whose probability value was less or equal to 0.05. This means that the interventions in that case showed reduction in body mass of participants. We looked at studies whose aim was to focus on interventions of obesity prevention or reduction among primary school children’s. Thus, search term such as: “obesity prevention intervention among primary schools”, was used as explained in the methods section. When performing the search for school-based interventions we did not encounter any studies that were focusing on underweight. Making the options for healthy choices of food in the school cafeterias and having the option of free food from the school gardens decreases the sugar sweetened beverages and junk options among the children’s and thus resulting in improvements in BMI. This review evidence further highlights that duration of the intervention, i.e., a year or more has an impact on anthropometric measurements. This is in contrast to reviews of Julie et al. [ 76 ] and Cauwenberghe et al. [ 34 ] review that found that making the better options of food choices and duration of the studies were effective in reducing the sedentary behaviour and noting improvements in BMI. This study also found that larger sample sizes reverse the outcome of anthropometric measurements (i.e., sample size negatively influences the outcome). This might be the case because it might be harder to administer the same thing to more individual. Thus, more studies are needed to examine the effects of bigger sample sizes.

Our study is far from being the first to create overview of the large number of studies that are studying interventions that can promote healthier eating habits and that can counteract the worrying increase in obesity and overweight among young people the general. The huge interest is reflected in the number of studies trying to assess the impact and effectiveness of school-based interventions as well as in the number of reviews aiming to synthesize the findings from the growing body of evidence of the effect of school-based food interventions into actionable school food policies. Our study adds to this body of knowledge and fills a gap since our study looks at the most recent studies.

Comparing our review with others we find that the majority of the studies on school food-based interventions have been conducted in high income countries. This is also the case in our study and this fact is important to keep in mind since it introduces a bias in the insight created from school food effectiveness reviews. It is also important to keep in mind that studies—and as a result also reviews-covers different types of school food cultures. These cultures can roughly be divided in collective, semi collective and non-collective types. In the collective type found in countries such as Sweden, Finland, Estonia and Brazil school food provision is an integrated—and mainly free—part of the school day. In semi-collective approaches food is in most cases traditionally a part of what is offered at school, but due to payment. In the non-collective approach found in countries such as Denmark, Norway and the Netherlands there is little infrastructure and tradition for school organized foodservice. In this approach parents organized lunch boxes as well as competitive foods traditionally play a bigger role.

A further important note to make is the distinction between narrow F & V approaches and broader healthier eating intervention approaches. This classification can also be seen in previous studies and in more recent reviews. The first type of interventions that follow the six-a-day tradition that to some extent has been fuelled by the European School Fruit program introduced by the EU in 2009 was reviewed by Noguera et al. [ 77 ] and by Evans et al. [ 78 ]. In a study by Noguera el al. [ 77 ] a meta-analysis on F&V interventions was done but limited to educational interventions in the sense that it only looked at computer-based interventions and covering mostly European research. The study showed that this targeted but narrowed approach was effective in increasing FV consumption but that broader multicomponent types of interventions including free/subsidized FV interventions were not effective. In the review paper from 2012 by Evans et al. [ 78 ] examined studies done in United Kingdom, United States, Canada, Denmark, New Zealand, Norway and the Netherlands. Evans and co-workers [ 78 ] found that school-based interventions were able to moderately improve fruit intake but that they had only minimal impact on vegetable intake. These reviews and previous ones generally conclude that F&V targeted interventions are able to improve young people’s eating patterns towards higher intake of fruit.

In the category of reviews taking a broader approach to healthier lifestyle promotion we find studies and reviews that looks at promotion of healthier eating in general—and that in some cases include physical activity. A review by Julie et al. [ 76 ] covered studies from United States, United Kingdom, Australia, Spain and the Netherlands. This review also included physical activity as part of broader school-based obesity prevention interventions. In particular, interventions should focus on extending physical education classes, incorporating activity breaks, and reducing sedentary behaviours to improve anthropometric measures. Julie et al. concluded that interventions taking a broader approach should include employing a combination of school staff and intervention specialists to implement programs; that they should include psychosocial/psychoeducational components; involve peer leaders; use incentives to increase fruit and vegetable consumption and should involve family. In a study by Cauwenberghe et al. [ 34 ] intervention studies done in a European union studies were reviewed. This review—as our study do—made an age distinction in the sense that a categorization was done between children and adolescents. Among children the authors found a strong evidence of effect for multicomponent interventions on fruit and vegetable intake. For educational type of interventions Cauwenberghe et al. [ 34 ] found limited evidence of effect as found when looking at behaviour and fruit and vegetable intakes. The study found limited evidence on effectiveness of interventions that specifically targeted children from lower socio-economic status groups. For adolescents Cauwenberghe et al. [ 34 ] found moderate evidence of effect was found for educational interventions on behaviour and limited evidence of effect for multicomponent programmes on behaviour. In the same way as our review authors distinguished between behaviour and anthropometrics and found that effects on anthropometrics were often not measured in their sample. Therefore, evidence was lacking and resulted in inconclusive evidence. Cauwenberghe et al. [ 34 ] concluded that there was evidence was found for the effectiveness of especially multicomponent interventions promoting a healthy diet but that evidence for effectiveness on anthropometrical obesity-related measures was lacking. In a review by Brown et al. [ 79 ] studies mostly from Europe but also covering United States, New Zealand, Canada and Chile it was found that intervention components most likely to influence BMI positively included increased physical activity, decreased sugar sweetened beverages intake, and increased fruit intake.

Our review adds to the increasing support for the idea that school should play a role in promoting healthier eating habits among young people. As such the school can be seen as an important actor when it comes to the promotion of human rights. In particular; the right to adequate food, the right to the highest attainable standard of health and right to the education, school plays an integral part which has also been highlighted in the “United Nations System Standing Committee on Nutrition” new statement for school-based and nutrition interventions [ 25 ]. Furthermore, Mikkelsen and colleagues [ 80 ] in their study have also suggested the fact that the international framework of human rights should invoke its strategies, policies, and regulations in the context of school and that national, regional, and local level actors has important roles to play. Additionally, they have highlighted that ensuring healthy eating in school environment can be a good investment in children short- and long-term health and education achievements. Thus, schools, as a system have the potential to make lasting improvements in students nutrition both in terms of quality and quantity and simultaneously contribute to realization of human rights around the globe [ 25 ].

4.2. Discussion of Methods

Strengths and limitations.

All attempts to reduce complexity of research studies in a research field suffers from in built weaknesses. Standardising the work of others in attempts to make generalizations is always difficult. As per definition a review includes attempts to standardize its study material in order to create an overview of “what works” and what “this that works” depends on. For obvious reasons research protocols depends very much on the context of the study: What is doable in one study setting on one country might not work on other settings. Additionally, reporting procedures vary among authors. The aim of a review is to standardize this heterogeneity to something that is homogenous and computable. So, in our case our constructs represent an attempt to make different studies with similar but slightly different approaches and methodologies comparable by making them computable. This has obviously some disadvantages.

Another limitation is that our review restricted itself to cover only published English language articles. Therefore, publication bias cannot be excluded, as it is possible that the inclusion of unpublished articles written in other languages than English will have affected the results of this review. Second, most of the studies included in the present were carried out in countries from Southern and Northern parts of Europe. This raises questions about the generalisability of these results to other countries in Europe, especially because contextual variables were often lacking in the included studies. And the same questions about the generalisability could be raise in other parts of the world i.e., in Latin America, North America, Asia and Africa, as very few studies were reported from this part of the world.

On the other hand, large dropouts were reported in many listed studies and the study follow up were reported in few studies and was for short time period. Among these studies which did follow up, was right after the end of the intervention period and thus this could have affected the effectiveness among this study outcomes. Long-term follows-up post-interventions would help to study the retention of behaviour change and effect on the body composition among the participants. Thus, long terms studies post interventions are needed to draw the conclusion about the sustainability of an intervention. Additionally, in future studies to improve the quality of the evidence of effectiveness in this kind of interventions, studies with high quality, rigorous design, appropriate sample size, post interventions long term follow up, assessment of implementation issues and cost effectiveness of the intervention should be executed.

On the strength side the standardisation approach helps to find patterns and to create overview of a large material within a given field of research. The strength of this study is that it provides a broad up to date overview of what is known about the relationship between school-based intervention and policies and healthy eating outcomes among children and that it contributes to the deeper understanding of the fact that current research findings are quite limited. This is among the very few recent reviews which evaluated the effect of school-based food at nutrition interventions among children only. A systematic review approach of this study attempted efficiently to integrate existing information and provide data for researchers’ rationale in the decision making of future research. Furthermore, the applied explicit methods used in this limited bias and, contributed to improved reliability and accuracy of drawn conclusions. Other advantages are that this study looks specifically at the evidence available in Northern and Southern Europe. Statistical analyses of pooled data have facilitated a more through synthesis of the result is one of the biggest strengths of this study.

4.3. Policy Implications

The evidence of the impact of school intervention derived from our review suggests several topics to be dealt with in future research not only in Europe but also the other part of the world. First, this review highlights the need for researchers to recognize the importance of further investigations on the measures of anthropometrics, nutritional knowledge, and attitude. Among these 42 studies carried out in different regions very few looked upon the effects on participants’ attitudes and anthropometrics measures. And of those showed positive impact if family support was provided, if started at early age and lastly if food focus was part of the intervention. Additionally, most of the included studies were not aiming to contribute to obesity prevention. Thus, it is highly recommendable that there is urgent need for more studies to be done that includes more measures of efficiency of participants’ attitude towards the healthy behaviour and healthy lifestyle and measures for anthropometrics. Second, to increase the comparability between studies and to facilitate the assessment of effectiveness, more agreement is needed for best measures of the diet and questionnaires. Third, more research is needed to be done among specific groups like low socio-economic group, immigrants or minorities. As mention earlier, only few listed studies included this specific group in their studies. Furthermore, evidence suggest that health inequalities such as prevalence of overweight are as a result of dietary habits and ethnicity and socio-economic status are identified as determinants of health eating. Thus, future research should not exclude these specific groups as European countries have become ethnically diverse.

To improve or decrease childhood diseases such as overweight and obesity and other aspects of health, many policy documents have been calling for the development of the effective strategies among children’s and adolescents. Even though the limited to moderate impact and evidence was found among these school-based interventions, it should be noted that interventions were not primarily targeting obesity prevention but, in many cases, had a broader scope. Thus, in order to deliver these evidence-based recommendations to policy makers factors such as sustainability of intervention, context and cost effectiveness should be considered. Additionally, the policy makers should ensure school policies and the environment that encourage physical activity and a healthy diet.

5. Conclusions

Findings from this systematised review suggest that applying multicomponent interventions (environmental, educational, and physical strategies) along with parental involvement and of long-term initiatives may be promising for improving dietary habits and other childhood related diseases among primary school children. Despite being challenging to find experimental studies done in related fields, those studies found showed positive trend. Thus, to conclude, evidence of the effect was found among school-based food and nutrition initiatives among primary school children. However, to strengthen the perspectives of this study, further systematic review targeting the more long-term studies assessing the long-term sustainability of the interventions should be considered. Also, studies with goal to increase efficiency of anthropometric measurements in their future school-based interventions could include increasing PA, increasing fruit and vegetable intake and decreasing sedentary behaviour. This study has provided fundamentals background on which further research could be done in this area of school-based food and nutrition interventions. Thus, the findings from this systematic review can be used as guidelines for future interventions in school settings related to food and nutrition. Also, the categorization of intervention components we see as useful for the planning of future interventions.

Author Contributions

Conceptualization, B.E.M. and A.C.; methodology, B.E.M., A.C. and F.S.; validation B.E.M.; formal analysis, F.S.; investigation, A.C.; resources B.E.M. and A.C.; data curation, A.C. and F.S.; writing—original draft preparation, B.E.M., A.C. and F.S.; writing—review and editing, B.E.M., A.C. and F.S.; project administration, B.E.M. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Open access
Published: 13 May 2024

The impact of the world’s first regulatory, multi-setting intervention on sedentary behaviour among children and adolescents (ENERGISE): a natural experiment evaluation

Bai Li ORCID: orcid.org/0000-0003-2706-9799 1 ,
Selene Valerino-Perea 2 ,
Weiwen Zhou 3 ,
Yihong Xie 4 ,
Keith Syrett 5 ,
Remco Peters 1 ,
Zouyan He 4 ,
Yunfeng Zou 4 ,
Frank de Vocht 6 , 7 &
Charlie Foster 1

International Journal of Behavioral Nutrition and Physical Activity volume 21 , Article number: 53 ( 2024 ) Cite this article

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Regulatory actions are increasingly used to tackle issues such as excessive alcohol or sugar intake, but such actions to reduce sedentary behaviour remain scarce. World Health Organization (WHO) guidelines on sedentary behaviour call for system-wide policies. The Chinese government introduced the world’s first nation-wide multi-setting regulation on multiple types of sedentary behaviour in children and adolescents in July 2021. This regulation restricts when (and for how long) online gaming businesses can provide access to pupils; the amount of homework teachers can assign to pupils according to their year groups; and when tutoring businesses can provide lessons to pupils. We evaluated the effect of this regulation on sedentary behaviour safeguarding pupils.

With a natural experiment evaluation design, we used representative surveillance data from 9- to 18-year-old pupils before and after the introduction of the regulation, for longitudinal ( n = 7,054, matched individuals, primary analysis) and repeated cross-sectional ( n = 99,947, exploratory analysis) analyses. We analysed pre-post differences for self-reported sedentary behaviour outcomes (total sedentary behaviour time, screen viewing time, electronic device use time, homework time, and out-of-campus learning time) using multilevel models, and explored differences by sex, education stage, residency, and baseline weight status.

Longitudinal analyses indicated that pupils had reduced their mean total daily sedentary behaviour time by 13.8% (95% confidence interval [CI]: -15.9 to -11.7%, approximately 46 min) and were 1.20 times as likely to meet international daily screen time recommendations (95% CI: 1.01 to 1.32) one month after the introduction of the regulation compared to the reference group (before its introduction). They were on average 2.79 times as likely to meet the regulatory requirement on homework time (95% CI: 2.47 to 3.14) than the reference group and reduced their daily total screen-viewing time by 6.4% (95% CI: -9.6 to -3.3%, approximately 10 min). The positive effects were more pronounced among high-risk groups (secondary school and urban pupils who generally spend more time in sedentary behaviour) than in low-risk groups (primary school and rural pupils who generally spend less time in sedentary behaviour). The exploratory analyses showed comparable findings.

Conclusions

This regulatory intervention has been effective in reducing total and specific types of sedentary behaviour among Chinese children and adolescents, with the potential to reduce health inequalities. International researchers and policy makers may explore the feasibility and acceptability of implementing regulatory interventions on sedentary behaviour elsewhere.

The growing prevalence of sedentary behaviour in school-aged children and adolescents bears significant social, economic and health burdens in China and globally [ 1 ]–[ 3 ]. Sedentary behaviour refers to any waking behaviour characterised by an energy expenditure equal or lower than 1.5 metabolic equivalents (METs) while sitting, reclining, or lying [ 3 ]. Evidence from systematic reviews, meta-analyses and longitudinal studies have shown that excessive sedentary behaviour, in particular recreational screen-based sedentary behaviour, affect multiple dimensions of children and adolescents’ wellbeing, spanning across mental health [ 4 ], cognitive functions/developmental health/academic performance [ 5 ], [ 6 ], quality of life [ 7 ], and physical health [ 8 ]. In China, over 60% of school pupils use part of their sleep time to play mobile phones/digital games and watch TV programmes, and 27% use their sleep time to do homework or other learning activities [ 9 ]. Screen-based, sedentary entertainment has become the leading cause for going to bed late, which is linked to detrimental consequences for children’s physical and mental health [ 10 ]. Notably, academic-related activities such as post-school homework and off campus tutoring also contribute to the increasing amounts of sedentary behaviour. According to the Organisation for Economic Co-operation and Development (OECD) report, China is the leading country in time spent on homework by adolescents (14 h/week on average) [ 11 ].

The COVID-19 pandemic exacerbated this global challenge, with children and adolescents reported to have been the most affected group [ 12 ]. Schools are a frequently targeted setting for interventions to reduce sedentary behaviour [ 13 ]. However, school-based interventions have had limited success when delivered under real-world conditions or at scale [ 14 ]. School-based interventions alone have also been unsuccessful in mitigating the trend of increasing sedentary behaviour that is driven by a complex system of interdependent factors across multiple sectors [ 13 ]. Even for parents and carers who intend to restrict screen-based sedentary behaviour and for children who wish to reduce screen-based sedentary behaviour, social factors including peer pressure often form barriers to changing behaviour [ 15 ]. In multiple public health fields such as tobacco control and healthy eating promotion, there has been a notable shift away from downstream (e.g., health education) towards an upstream intervention approach (e.g., sugar taxation). However, regulatory actions for sedentary behaviour are scarce [ 16 ]. World Health Organization (WHO) 2020 guidelines on sedentary behaviour encourage sustainable and scalable approaches for limiting sedentary behaviour and call for more system-wide policies to improve this global challenge [ 8 ]. Up-stream interventions can act on sedentary behaviour more holistically and have the potential to maximise reach and health impact [ 13 ]. In response to this pressing issue, and to widespread demands from many parents/carers, the Chinese government introduced nationwide regulations in 2021 to restrict (i) the amount of homework that teachers can assign, (ii) when (and for how long) online gaming businesses can provide access to young people, and (iii) when tutoring businesses can provide lessons [ 17 ], [ 18 ]. Consultations with WHO officials and reviewers of international health policy interventions confirmed that this is currently the only government-led, multi-setting regulatory intervention on multiple types of sedentary behaviour among school-aged children and adolescents. A detailed description of this programme is available in the Additional File 1 .

We evaluated the impact of this regulatory intervention on sedentary behaviour in Chinese school-aged children and adolescents. We also investigated whether and how intervention effects differed by sex, education stage, geographical area, and baseline weight status.

Study design

The introduction of the nationwide regulation provided a unique opportunity for a natural experiment evaluation where the pre-regulation comparator group data (Wave 1) was compared to the post-regulation group data (Wave 2). Multiple components of the intervention (see Additional File 1 ) were introduced in phases from July 2021 with all components being fully in place by September 2021 [ 17 ], [ 18 ]. This paper follows the STROBE reporting guidance [ 19 ], [ 20 ].

Data source, study population and sampling

We obtained regionally representative data on 99,947 pupils who are resident in the Chinese province of Guangxi as part of Guangxi Centre for Disease Control and Prevention’s (CDC) routine surveillance. The data, available from participants in grade 4 (aged between 9 and 10 years) and higher, were collected using a multi-stage random sampling design (Fig. 1 ) through school visits by trained health professionals following standardised protocols (see Supplementary Fig. 1 , Additional File 1 ). In Wave 1 (data collected from September to November 2020), pupils were randomly selected from schools in 31 urban/rural counties from 14 cities in Guangxi. At least eight schools, including primary, secondary, high schools, and ‘vocational high schools’, were selected from urban counties. Five schools were selected from rural counties. Approximately 80 students were randomly selected from each grade at the schools selected. The same schools were invited to participate in Wave 2 (data collected from September to November 2021), and new schools were invited to replace Wave 1 schools that no longer participated. Children with available data at both Wave 1 and Wave 2 represented approximately 10% of the sample ( n = 7,587). Paper-based questionnaires were administrated to students by trained personnel or teachers. The questionnaires were designed and validated by China National Health Commission, and have been utilised in routine surveillance throughout the country.

Flow diagram of participants included in the ENERGISE study

We used data from the age groups 7–18 years for most analyses. For specific analyses of homework and out-of-campus tutoring, we excluded high school pupils (16–18 years) because the homework and out-of-campus tutoring regulations apply to primary (7–12 years) and middle (13–15 years) school pupils only. Furthermore, participants without socio-demographic data or those who reported medical history of disease, or a physical disability were excluded. This gave us a total sample of 7,054 eligible school-aged children and adolescents with matching data (longitudinal sample).

Outcomes and subgroups

Guangxi CDC used purposively designed questions for surveillance purposes to assess sedentary behaviour outcomes (Table 1 ).

The primary outcomes of interest included: (1) total sedentary behaviour time, (2) homework time, (3) out-of-campus learning (private tutoring) time, and (4) electronic device use time (Table 1 ). We considered electronic device use time, including mobile phones, handheld game consoles, and tablets, the most suitable estimator of online game time (estimand) in the surveillance programme since these are the main devices used for online gaming in China [ 23 ]. Secondary outcomes were: (1) total screen-viewing time, (2) internet-use time, (3) likelihood of meeting international screen-viewing time recommendations, and (4) likelihood of meeting the regulation on homework time (Table 1 ).

We calculated total sedentary behaviour time as the sum of total screen-viewing time (secondary outcome), homework time, and out-of-campus learning time (Table 1 ). Total screen-viewing time represents the sum of electronic device use time per day, TV/video game use time per day, and computer use time per day (Table 1 ). Total screen-viewing time was considered as an alternative estimator of online game time (estimand) since TV/videogame console use time and computer time could also capture the small proportion of children who use these devices for online gaming (Table 1 ). The international screen-viewing time recommendations were based on the American Academy of Paediatrics guidelines [ 21 ]. We did not include internet use time (secondary outcome) in total screen-viewing time, and total sedentary behaviour time, because this measure likely overlaps with other variables.

We defined subgroups by demographic characteristics, including the child’s sex (at birth: girls or boys), date of birth, education stage [primary school or secondary school [including middle school, high school, and ‘occupational schools’]), children’s residency (urban versus rural) and children’s baseline weight status (non-overweight versus overweight/obesity). Each sampling site selected for the survey was classified by the surveillance personnel as urban/rural and as lower-, medium-, or higher-economic level based on the area’s gross domestic product (GDP) per capita. The area’s GDP per capita was measured by the Chinese Centre for Disease Control and Prevention (CDC). Trained personnel also measured height, and weight using calibrated stadiometers and scales. Children’s weight/height were measured with light clothing and no shoes. Measurements during both waves were undertaken when students lived a normal life (no lockdowns, school were opened normally). We classified weight status (normal weight vs. overweight/obesity) according to the Chinese national reference charts [ 24 ].

Statistical analyses

We treated sedentary behaviour values that exceeded 24-hours per day as missing. We did not exclude extreme values for body mass index from the analyses 25 . Additional information, justifications, and results of implausible and missing values can be found in the Supplementary Table 1 , Additional File 1 .

The assumptions for normality and heteroscedasticity were assessed visually by inspecting residuals. We assessed multicollinearity via variance inflation factors. The outcome variables for linear regression outcomes were transformed using square roots to meet assumptions. We reported descriptive demographic characteristics (age, sex, area of residence, socioeconomic status), weight status, and outcome variables using means (or medians for non-normally distributed data) and proportions [ 26 ]

We ran multilevel models with random effects nested at the school and child levels to compare the outcomes in Wave 1 against Wave 2. We developed separate models for each sedentary behaviour outcome variable. We treated the introduction of the nationwide regulation as the independent binary variable (0 for Wave 1 and 1 for Wave 2). We ran linear models for continuous outcomes, logistic models for binary outcomes, and ordered logistic models for ordinal outcomes in a complete case analysis estimating population average treatment effects [ 27 ]. For the main analysis, in which participants had measurements in both Waves (longitudinal sample), only those with non-missing data at both time points were included.

We estimated marginal effects for each sedentary behaviour outcome. With a self-developed directed acyclic graph (DAG) we identified age (continuous), sex (male/female), area of residence (urban/rural), and socioeconomic status (high/medium/low) as confounders (see Supplementary Figs. 2–4, Additional File 1 ).

We evaluated subgroup effects defined by child’s sex at birth (boys versus girls), child’s stage of education (primary school versus secondary school [including middle school, high school, and ‘occupational schools’]), children’s residency (rural versus urban), and children’s baseline weight status (non-overweight versus overweight/obesity). We also repeated the covariate-adjusted model with interaction terms (between Wave and sex; Wave and child stage of education; Wave and residency; and Wave and weight status). We adjusted for multiple testing using Bonferroni correction ( p 0.05 divided by the number of performed tests for an outcome). The resulting cut-off point of p < 0.005 was used to determine the presence of any interaction effects.

We also conducted exploratory analyses (including subgroup analyses) by evaluating the same models with a representative, cross-sectional sample of 99,947 pupils. This cross-sectional sample included different schools and children at Wave 1 and Wave 2. We therefore used propensity score (PS) weighting to account for sample imbalances in the socio-demographic characteristics. Propensity scores were calculated by conducting a logistic regression, which calculated the likelihood of each individual to be in Wave 2 (dependent variable). Individual’s age, sex, area of residence and the GDP per area were treated as independent variables. Subsequently, inverse probability of treatment weighting was applied to balance the demographic characteristics in the sample in Wave 1 (unexposed to the regulatory intervention) and Wave 2 (exposed to the regulatory intervention). The sample weight for individuals in Wave 1 were calculated using the Eq. 1/ (1-propensity score). The sample weight for individuals in Wave 2 were calculated using the Eq. 1/propensity score [ 28 ].

We only ran linear models for continuous outcomes since it was not possible to run PS-weighted multilevel models with this sample size in Stata. We conducted all statistical analyses in Stata version 16.0.

Participant sample

In our primary, longitudinal analyses, we analysed data from 7,054 children and adolescents. The mean age was 12.3 years (SD, 2.4) and 3,477 (49.3%) were girls (Table 2 ). More detailed information on characteristics of subgroups in the longitudinal sample are presented in the Supplementary Tables 2–5, Additional File 2 .

Primary outcomes

Children and adolescents reported a reduction in their daily mean total sedentary behaviour time by 13.8% (95% CI: -15.9 to -11.7), or 46 min, on average between Waves 1 and 2. Participants were also less likely to report having increased their time spent on homework (adjusted odd ratio/AOR: 0.39; 95% CI: 0.35–0.43) and in out-of-campus learning (AOR: 0.53; 95% CI: 0.47 to 0.59) in Wave 2 in comparison to Wave 1, respectively (Tables 3 and 4 ). We did not find any changes in electronic device use time.

Secondary outcomes

Participants reported reducing their mean daily screen-viewing time by 6.4% (95% CI: -9.6 to -3.3%), or 10 min, on average (Tables 3 and 4 ). Participants were also 20% as likely to meet international screen time recommendations (AOR: 1.20; 95% CI: 1.09 to 1.32) and were 2.79 times as likely to meet the regulatory requirement on homework time (95% CI: 2.47 to 3.14) compared to the reference group (before the introduction of the regulation).

Subgroup analyses

Most screen- and study-related sedentary behaviour outcomes differed by education stage ( p < 0.005) (see Supplementary Tables 6–13, Additional File 2 ), with the reductions being larger in secondary school pupils than in primary school pupils (Tables 3 and 4 , and Table 5 ). Only secondary school pupils reduced their total screen-viewing time (-8.4%; 95% CI: -12.4 to -4.3) and were also 1.41 times as likely to meet screen-viewing recommendations (AOR: 1.41; 95% CI: 1.23 to 1.61) at Wave 2 compared to Wave 1.

Conversely, at Wave 2, primary school pupils reported a lower likelihood of spending more time doing homework (AOR: 0.30; 95%: 0.26 to 0.34) than secondary school pupils (AOR: 0.58; 95% CI: 0.50 to 0.67) compared to their counterparts at Wave 1. At Wave 2, primary school pupils also had a higher likelihood of reporting meeting homework time recommendations (AOR: 3.61; 95% CI: 3.09 to 4.22) than secondary school pupils (middle- and high school) (AOR: 2.11; 95% CI: 1.74 to 2.56) compared to their counterparts at Wave 1 (Table 5 ). There was also a residence interaction effect ( p < 0.001) in total sedentary behaviour time, with participants in urban areas reporting larger reductions (-15.3%; 95% CI: -17.8 to -12.7) than those in rural areas (-11.2%; 95% CI: -15.0 to -7.4). There was no evidence of modifying effects by children’s sex or baseline weight status (Tables 4 and 5 ).

Findings from the exploratory repeated cross-sectional analyses were similar to the findings of the main longitudinal analyses including total sedentary behaviour time, electronic device use time, total screen-viewing time and internet use time (see Supplementary Tables 14–23, Additional File 2 ).

Principal findings

Our study evaluated the impact of the world’s first regulatory, multi-setting intervention on multiple types of sedentary behaviour among school-aged children and adolescents in China. We found that children and adolescents reduced their total sedentary behaviour time, screen-viewing time, homework time and out-of-campus learning time following its implementation. The positive intervention effects on total screen-viewing time (-8.4 vs. -2.3%), and the likelihood of meeting recommendations on screen-viewing time (1.41 vs. 1.02 AOR) were more pronounced in secondary school pupils compared with primary school pupils. Intervention effects on total sedentary behaviour time (-15.3 vs. -11.2%) were more pronounced among pupils living in the urban area (compared to pupils living in the rural area). These subgroup differences imply that the regulatory intervention benefit more the groups known to have a higher rate of sedentary behaviour [ 29 ].

Interestingly, the observed reduction in electronic device use itself did not reach statistical significance following implementation of regulation. This could be viewed as a positive outcome if this is correctly inferred and not the result of reporting bias or measurement error. International data indicated that average sedentary and total screen time have increased among children due to the COVID-19 pandemic [ 12 ]. However, such interesting finding might be explained by the absence of lockdowns in Guangxi during both surveillance waves when most school-aged students outside China were affected by pandemic mitigation measures such as online learning.

Strengths and weaknesses

Our study has several notable strengths. This is the first study to evaluate the impact of multi-setting nationwide regulations on multiple types of sedentary behaviour in a large and regionally representative sample of children and adolescents. Still, to gain a more comprehensive view of the regulatory intervention on sedentary behaviour across China, similar evaluation research should be conducted in other regions of China. Furthermore, access to a rich longitudinal dataset allowed for more robust claims of causality. The available data also allowed us to measure the effect of the intervention on multiple sedentary behaviours including recreational screen-time and academic-related behaviours. Lastly, the large data set allowed us to explore whether the effect of the regulatory intervention varied across important subgroups, suggesting areas for further research and development.

Some limitations need to be taken into consideration when interpreting our findings. First, a common limitation in non-controlled/non-randomised intervention studies is residual confounding. We aimed to limit this by adjusting our analysis for confounders known to impact the variables of interest, but it is impossible to know whether important confounding may still have been present. With maturation bias, it is possible that secular trends are the cause for any observed effects. However, this seems unlikely in our study as older children may spend more time doing homework [ 23 ] and engage more in screen-viewing activities [ 30 ]. In this study, we observed reductions in these outcomes. The use of self-reported outcomes (social desirability bias) was a limitation and might have led to the intervention effects being over-estimated [ 13 ]. However, since our data were collected as part of a routine surveillance programme, pupils were unaware of the evaluation. This might mitigate reporting bias. In addition, the data were collected in Guangxi which might not representative of the whole population in China. Another limitation is using electronic device use time as a proxy measure of online gaming time. It is possible that electronic devices can be used for other purposes. However, mobile phones, handheld game consoles and tablets are the main devices used for online gaming. In this study, electronic device use time provided a practical means of assessing the broad effects of regulatory measures on screen time behaviours, including online gaming, in a large (province level) surveillance programme. In the future, instruments specifically designed to capture online gaming behaviour should be used in surveillance and research work.

Comparisons with other studies

Neither China nor other countries globally have previously implemented and evaluated multi-setting regulatory interventions on multiple types of sedentary behaviour, which makes comparative discussions challenging. In general, results of health behaviour research over the past decades have shown that interventions that address structural and environmental determinants of multiple behaviours to be more effective in comparison with individual-focussed interventions [ 31 ]. Furthermore, the continuous and universal elements of regulatory interventions may be particularly important explanations for the observed reductions in sedentary behaviour. Standalone school and other institution-led interventions may struggle with financial and logistic costs which threaten long-term implementation [ 13 ]. In contrast, the universality element of regulatory intervention can reduce or remove peer pressures and potential stigmatisation among children and teachers that are often associated with more selective/targeted interventions [ 24 ]. Our findings support WHO guidelines for physical activity and sedentary behaviour that encourage sustainable and scalable approaches for limiting sedentary behaviour and call for more system-wide policies to improve this global challenge[ 8 ].

Implications for future policy and research

Our study has important implications for future research and practice both nationally and internationally. Within China, future research should focus on optimising the implementation of the regulatory intervention through implementation research and assess long-term effects of the regulation on both behavioral and health outcomes. Internationally, our findings also provide a promising policy avenue for other countries and communities outside of China to explore the opportunities and barriers to implement such programmes on sedentary behaviour. This exploratory process could start with assessing how key stakeholders (including school-aged children, parents/carers, schoolteachers, health professionals, and policy makers) within different country contexts perceive regulatory actions as an intervention approach for improving health and wellbeing in young people, and how they can be tailored to fit their own contexts. Within public health domains, including healthy eating promotion, tobacco and alcohol control, regulatory intervention approaches (e.g., smoking bans and sugar taxation) have been adopted. However, regulatory actions for sedentary behaviour are scarce [ 19 ]. Within the education sector, some countries recently banned mobile phone use in schools for academic purpose [ 25 ]. While this implies potential feasibility and desirability of such interventions internationally, there is little research on the demand for, and acceptability of, multi-faceted sedentary behaviour regulatory interventions for the purpose of improving health and wellbeing. It will be particularly important to identify and understand any differences in perceptions and feasibility both within (e.g., public versus policy makers) and across countries of differing socio-cultural-political environments.

This natural experiment evaluation indicates that a multi-setting, regulatory intervention on sedentary behaviour has been effective in reducing total sedentary behaviour, and multiple types of sedentary behaviour among Chinese school-aged children and adolescents. Contextually appropriate, regulatory interventions on sedentary behaviour could be explored and considered by researchers and policy makers in other countries.

Data availability

Access to anonymised data used in this study can be requested through the corresponding author BL, subject to approval by the Guangxi CDC. WZ and SVP have full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Abbreviations

Centre for disease control and prevention

Directed acyclic graph

Gross domestic product

Metabolic equivalents

Organisation for Economic Co-operation and Development

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Acknowledgements

We would like to acknowledge Dr Peter Green and Dr Ruth Salway for providing feedback on the initial data analysis plan, and Dr Hugo Pedder and Lauren Scott who provided feedback on the statistical analyses.

This work was funded by the Wellcome Trust through the Global Public Health Research Strand, Elizabeth Blackwell Institute for Health Research. The funder of our study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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BL conceived the study idea and obtained the funding with support from WZ, CF, KS, YX, YZ, ZH and RP. BL, CF, FdV and KS designed the study. WZ led data collection and provided access to the data. YX, SVP and ZH cleaned the data. SVP analysed the data with guidance from BL, FdV and CF. BL, SVP and RP drafted the paper which was revised by other authors. All authors read and approved the final manuscript for submission.

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Correspondence to Bai Li .

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Li, B., Valerino-Perea, S., Zhou, W. et al. The impact of the world’s first regulatory, multi-setting intervention on sedentary behaviour among children and adolescents (ENERGISE): a natural experiment evaluation. Int J Behav Nutr Phys Act 21 , 53 (2024). https://doi.org/10.1186/s12966-024-01591-w

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Determinants of low birth weight and its effect on childhood health and nutritional outcomes in Bangladesh

Md. Zahidul Islam 1 na1 ,
Mohammad Rocky Khan Chowdhury ORCID: orcid.org/0000-0003-1934-1748 1 , 2 na1 ,
Manzur Kader 3 ,
Baki Billah 2 ,
Md. Shariful Islam 1 &
Mamunur Rashid ORCID: orcid.org/0000-0001-7558-4168 4

Journal of Health, Population and Nutrition volume 43 , Article number: 64 ( 2024 ) Cite this article

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The high incidence of low birth weight (LBW) is associated with an increased risk of infant mortality, adverse pregnancy outcomes for mothers, and a decline in overall health and well-being. The current study aimed to identify the various determinants of LBW and its effect on adverse health and nutritional outcomes of children aged 0–23 months in Bangladesh.

Bangladesh Demography and Health Survey (BDHS) 2017-18 data was used. A chi-square test and multivariable logistic regression analysis were used to find out the associations between independent variables and outcomes (e.g., LBW, child illness and undernutrition).

The overall prevalence of LBW among was 16.3%. Mother with no formal education (AOR = 2.64, 95% CI = 0.55–3.30, p = 0.01), female child (AOR = 1.31, 95% CI = 1.04–1.65, p = 0.023); and poorest economic status (AOR = 1.69, 95% CI = 1.13–2.51, p = 0.010), were identified significant determinants of LBW. Of home environment and hygiene factors, unimproved toilet facilities (AOR = 1.38, 95% CI = 1.03–1.84, p = 0.030) had a significant effect on LBW. In addition, children born with LBW were more likely to suffer fever (AOR = 1.26, 95% CI = 1.05–1.60, p = 0.050), stunting (AOR = 2.42, 95% CI = 1.86–3.15, p = < 0.001), wasting (AOR = 1.47, 95% CI = 1.02–2.25 p = 0.049), and underweight (AOR = 3.19, 95% CI = 2.40–4.23, p = < 0.001).

One out of five children was LBW in Bangladesh. Maternal education, sex of child, wealth index, and toilet facilities had significant effects on LBW. In addition, LWB contributed to children’s poor health and nutritional outcomes. Enhancing maternal pregnancy, and child health outcomes necessitates policies addressing poverty, gender inequality, and social disparities. Key strategies include promoting regular prenatal care, early medical intervention, reproductive health education, and safe hygiene practices. To combat the negative impacts of LBW, a comprehensive strategy is vital, encompassing exclusive breastfeeding, nutritional support, growth monitoring, accessible healthcare, and caregiver education.

Low birth weight (LBW) of children poses a serious public health problem in low- and middle-income countries [ 1 ]. Early childhood is a critical window for children’s physical and mental development and LBW contributes as a leading cause of illness and death among children during this period [ 2 ]. Two physiological conditions among others, Intrauterine Growth Restriction and/or preterm birth during pregnancy can basically lead to children’s born with LBW [ 3 ]. LBW is responsible for 60–80% of the total mortality in children under one month of age and one-third of total deaths among children aged less than one year [ 4 , 5 ]. Further, the likelihood of infant mortality is 40 times higher among LBW children compared to normal children [ 4 ]. Apart from mortality, it hinders normal growth and raises the risk of developing chronic illnesses, such as ischemic heart disease, diabetes, dementia, osteoarthritis, stroke, and hypertension, later in life [ 6 , 7 , 8 ]. LBW also increases the chances of developing behavioral and psychological disorders, as well as sensory and learning disabilities [ 9 , 10 ]. Furthermore, compared to normal infants, those who are born with LBW are at a greater risk of experiencing prolonged and intense infections, such as diarrhea and acute respiratory infection (ARI), which are the leading causes of child mortality [ 6 ].

Around 30 million infants worldwide, accounting for 23.4% of all newborns annually, are born underweight [ 7 ]. This condition can result in numerous immediate and extended health and nutritional complications. The prevalence of LBW is considerably higher in low- and middle-income countries, with the estimation of South Asia (28%) and Sub-Saharan Africa (13%) was being most affected regions. This highlights the existing health inequalities between different parts of the world [ 11 , 12 ]. The rate of LBW in Bangladesh dropped to 14.5% in 2022, showing a significant decline from the 20% recorded in 2012 [ 2 , 9 ].

Despite substantial efforts were made to uncover the etiology of LBW in several research, yet the etiology of LBW is not well understood [ 13 , 14 ]. LBW was determined by the complex interplay of several factors including biological (such as, premature birth, intrauterine growth restriction, genetic factors, etc.) maternal (age, body mass index, education, occupation, maternal mental stress, maternal weight gain during pregnancy, mother’s access to prenatal care, diet during pregnancy and others), environmental (natural disaster, type of toilet facilities, type of drinking water, used solid waste for cooking, etc.), child (sex of child); and contextual (place of residence, region of residence) factors [ 2 , 4 , 5 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Some previous studies in Bangladesh showed that maternal characteristics; child, and contextual factors were significantly associated with LBW [ 2 , 9 , 12 , 22 , 23 , 24 , 25 ]. Additionally, a previous study conducted in Bangladesh identified that LBW was a significant factor in the likelihood of stunting and being underweight among under-five children [ 26 , 27 ]. Most of those studies that previous carried out considered maternal perceptions of baby size at birth as proxy indicator for birth weight [ 19 , 28 , 29 ]. However, the current understanding of the determinants of LBW and its association with adverse health and nutritional outcomes has not been adequately studied using estimated weight of birth from more recent nationally representative sample of Bangladesh. Moreover, environmental factors other than household air pollution have not been broadly studied as potential risk factors for LBW in Bangladesh [ 30 ]. Hence, the current study aimed to identify various determinants, related to maternal factors, children characteristics, contextual factors and environmental factors, of LBW and further extended to determine the effect of low birthweight on adverse health and nutritional outcomes of children 0–23 months using a nationally representative cross-sectional survey.

Data and sampling

A cross sectional nationally representative data from Bangladesh Demography and Health Survey (BDHS) 2017–2018 was used in this study. Demographic Health Survey (DHS) covers information regarding demographic and social factors as well as health and nutritional indicators for adults (both male and female) and children to monitor a wide range of the population. The BDHS 2017-18 was a multistage sampling. In the first stage, 675 primary sampling units (PSUs) were selected of which 250 PSUs were from urban and 425 PSUs were from rural areas. The PSUs were based on enumeration areas (clusters) listed in the population census 2011 conducted by the Bangladesh Bureau of Statistics. The second stage involved selecting an average of 30 households from each PSU using an equal probability systematic sampling technique. The multistage sampling and corresponding sampling weight might help to reduce potential sampling bias. In addition, all ever-married women aged 15–49 years (with or without children aged less than 5 years) from the preselected households were interviewed without replacement and change in the implementing stage to prevent selection bias. A total of 20,127 women aged 15–49 years were interviewed from 19,457 households with a response rate of 98.8% [ 31 ]. In BDHS 2017-18, a total of 8,759 children under-five were listed and birth weight was able to collect from a written record for 2,408 children aged 0–23 months of age (Fig. 1 and Additional file: Table S1 ).

Sample size selection

Major outcome variable

LBW, the child’s adverse health (e.g., fever, cough, acute respiratory infection (ARI), diarrhea), and child’s nutritional status (e.g., stunting, wasting and underweight) were considered outcome variables in this study. All outcome variables were coded as binary (1 for yes and 0 for no).

LBW: Child’s birth weight below 2.5 kg regardless of gestational age was considered LBW. If the child’s birth weight less than 2.5 kg coded as 1, otherwise coded as 0 [ 31 ].

Other outcome variables

Child’s adverse health outcomes.

Fever: Children who had a fever prior two weeks before the survey was categorized as 1; otherwise categorized as 0 [ 31 ].

Cough: Children who had a cough prior two weeks of the survey was categorized as 1; otherwise categorized as 0 [ 31 ].

ARI: Children had symptoms of ARI (short, rapid breathing which was chest-related, and/or difficult breathing which was chest-related) in the 2 weeks preceding the survey was categorized as 1; otherwise categorized as 0 [ 31 ].

Diarrhea: Children who had diarrhea in the 2 weeks preceding the survey was categorized as 1; otherwise categorized as 0 [ 31 ].

Had at least one illness: Children who had at least one of the conditions among fever, cough, ARI, and diarrhea in the 2 weeks preceding the survey was considered having at least one illness and categorized as 1; otherwise categorized as 0.

Child’s nutritional status

Stunting: A child was considered to be stunted (short stature for age), if the height-for-age, index was 2 standard deviations or more below the respective median of the World Health Organization reference population and was categorized as 1; otherwise categorized as 0 [ 32 ].

Wasting: A child was considered wasted (perilously thin) if the weight-for-height index was 2 standard deviations or more below the respective median of the World Health Organization reference population and was categorized as 1; otherwise categorized as 0 [ 32 ].

Underweight: A child was considered to be underweight (low weight for age) if the weight-for-age index was 2 standard deviations or more below the respective median of the World Health Organization reference population and was categorized as 1; otherwise categorized as 0 [ 32 ].

At least one undernutrition condition: Children who had at least one of the conditions among stunting, wasting, and/or underweight were considered having at least one undernutrition condition and was categorized as 1; otherwise categorized as 0.

Independent variables

Various maternal and child characteristics and contextual and environmental factors found significant in previous literature and/or available in BDHS 2017-18 dataset were used as independent variables in this study [ 2 , 4 , 5 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Maternal factors included mother’s age in years (15–19, 20–24, 25–29, 30–34, 35 and above); parents’ educational status (both parents were uneducated, only father was uneducated, only mother was uneducated, both parents were educated); mother currently working (no, yes); underweight mother (no, yes); mothers’ decision-making autonomy (not practiced, practiced); mother’s attitudes towards violence (not justified, justified); mothers received antenatal care (ANC) (no, yes); the number of living children (≤ 2, ≥ 3); age at first sex in years (< 15, 15–24, 25–34); wanted last child (wanted then, wanted later, wanted no more); ever had terminated pregnancy (no, yes); last birth with a caesarean section (no, yes); and a sign of pregnancy complication (no, yes). Sex of child (male, female) was listed as child characteristics. Contextual factors included mass media exposure (no, yes); wealth index (poorest, poorer, middle, richer, richest), and place of residence (urban, rural). Home environmental factors were types of drinking water (improved, unimproved); type of toilet facility (improved, unimproved); solid waste used for cooking (nonsolid, solid)( Additional file: Table S1 ).

Statistical analysis

Descriptive statistics were used to evaluate the background characteristics of the respondents. A Chi-square test was used to find out the association between outcome and independent variables. The statistical significance was set at p < 0.25 (two-tailed), rather than the typical cut-off point of 0.05, which may aid to include the factors that are considered to be important [ 33 ]. Multivariable logistic regression analyses was used to find out the effects of independent variables on outcome measures. Factors found significant in the Chi-square test were simultaneously entered into the Multivariable logistic regression model. In this study, factors significantly associated with LBW were identified using multivariable logistic regression analysis. Further, multivariable logistic regression analysis was used to find out the effect of LBW on adverse nutritional and health outcomes. The magnitude of the association was assessed using adjusted odds ratio (AOR) and confidence interval (CI) in multivariable logistic regression. The significance level for multivariable logistic regression analyses was set at p < 0.05 (two-tailed). Multicollinearity was checked by examining the standard errors (SEs) of regression coefficients in the logistic regression analyses. An SE > 2.0 indicates multicollinearity among the independent variables [ 34 ]. The SEs for the independent variables in the adjusted models for each outcome were < 1, indicating an absence of multicollinearity. Akaike information criterion (AIC), and Bayesian information criterion (BIC) were assessed for model’s evaluation. Stata version 14.2 (StataCorp LP, College Station, Texas) was used for all analyses. To adjust the complex nature of the sampling, such as, sampling weight, cluster, and strata; the Stata command ‘svyset’ was prepared and used.

Background characteristics

More than one-third (36.6%) of all mothers belong to the age group 20–24 years. Only 2.9% of mothers of children were uneducated (only mother 1.6% and both parents 1.3%). Approximately one out of ten mothers (12.1%) was underweight, and 98% of mothers received antenatal care. About two-thirds (65.5%) of the total mothers were rural dwellers, and only 10.9% were from the poorest section the society. Around 38.1% of children living in households had unimproved toilet facilities and 59.2% of children in households used solid waste for cooking. The detailed background characteristics are presented elsewhere (Table 1 ).

According to Fig. 1 , around 15.7% and 6.7% of children aged 0–23 months suffered from ARI and diarrhea respectively. More than half of the children (51.5%) had at least one illness among fever, cough, ARI and Diarrhea. Around 24.1% of children had stunting, 7.9% had wasting and 15.2% were underweight. Around 29.8% of children had at least one under-nutritional condition ( Fig. 2 ).

Children’s adverse health and nutritional outcomes (0–23 months of age)

Prevalence and determinants of LBW

The prevalence of LBW was significantly higher among children of mothers with no formal education (fathers were educated) (41.5%), children from the poorest socioeconomic status (22.4%), mothers who had more than 3 living children (18.8%), wanted child later (19.8%), and children were born by normal delivery (18.6%) (Table 2 ). The prevalence of LBW was significantly higher in household with unimproved toilet facilities (18.3%) (Table 2 ).

From regression analysis results, mothers with no formal education (fathers were educated), female children, and children from the poorest socio-economic status had significant effect on the LBW. Children were 2.64 times (AOR = 2.64, 95% CI = 0.55–3.30, p = 0.010) more likely to born with LBW among mothers with no formal education than educated mothers. Female children had 1.3 times (AOR = 1.31, 95% CI = 1.04–1.65, p = 0.023) higher chances of being LBW than their counterparts. Children from the poorest socioeconomic background (AOR = 1.69, 95% CI = 1.13–2.51, p = 0.010) were more likely to born LBW than the children from the richest socio-economic status. Similarly, the likelihood of being LBW at birth was 1.26 times (AOR = 1.34, 95% CI = 1.03–1.84, p = 0.030) higher among children living in household with unimproved toilet facilities (Table 2 ). The unadjusted regression models were presented in Table S2 (Additional file: Table S2 ).

Effects of LBW on adverse health and nutritional status

The prevalence of LBW was significantly higher among children who had at least one under-nutritional condition (48.1%), had stunting (40%), being underweight (30.4%), and wasting (10.7%) (Table 3 ).

Furthermore, LBW had significant effect on children who had a fever, with stunting, wasting, being underweight and with at least one under-nutritional condition (see Table 2 ). Children who were LBW had 1.26 times (AOR = 1.26, 95% CI = 1.02–1.60, p = 0.047) higher chance of getting fever than a normal child. Children born with LBW were 2.4 times (AOR = 2.42, 95% CI = 1.86–3.15, p < 0.001); 3 times (AOR = 3.19, 95% CI = 2.40–4.23, p < 0.001) and 1.49 times (AOR = 1.49, 95% CI = 1.02–2.25, p = 0.049) respectively, more likely of being stunted, wasted and underweight than normal children. Similarly, LBW had a significant effect on children with at least one undernutrition condition (AOR = 2.39, 95% CI = 1.83–3.03, p < 0.001) (Table 3 ). The unadjusted regression models were presented in Table S3 (Additional file: Table S3 ).

The current study extensively assessed determinants of LBW and identified its effect on adverse health and nutritional outcome of children using a nationally represented sample in Bangladesh. This study found that prevalence of LBW in Bangladesh stood at 16.3%, similar to rates in neighboring countries like India with 16.4% and Pakistan with 16.9% [ 10 , 14 , 35 , 36 ]. The prevalence of LBW was slightly lower in Nepal, and Sri Lanka which accounted for 15.4%, and 14.6%, respectively [ 14 , 37 ]. Countries in South Asia exhibited comparable patterns of prevalence for LBW; it is perhaps due to similarities between countries in terms of geography, culture, economy, and quality of life [ 38 ].

The study showed that the prevalence of LBW was higher among children of mothers with no formal education and children from the poorest socio-economic status. Additionally, children of mothers with no formal education, being a female child and children from the poorest socio-economic status were more likely of being LBW. In previous literatures, mother’s education, child sex and wealth index were found significant factors of LBW in Bangladesh [ 2 , 23 , 24 , 25 ]. Findings of the present study were consistent with previous studies conducted in other neighboring countries [ 5 , 10 , 14 , 39 ]. Mothers who belonged to poor socio-economic background may also lack an educational profile, often experience difficulties in accessing nutrition and health care, which can result in inadequate maternal nutrition during pregnancy leading to maternal undernutrition and consequently LBW [ 13 , 40 ]. Lack of education can also limit access to prenatal care, which might hinder the mother’s ability to receive proper medical care [ 21 , 41 ]. Although Bangladesh has gained substantial improvement in female education over the past few decades, unfortunately approximately 36% of females still remain illiterate [ 42 ]. Female dropout rates were very high including 13.3% in primary and 40.29% in secondary school level [ 43 ]. The government of Bangladesh has taken initiatives such as stipends, allowances, and free education facilities to reduce the female dropout rate at school. Still, it needs to strengthen administrative coordination, establish a monitoring and evaluation framework, and increase multidimensional investment in education to improve female education and consequently health status. Furthermore, no preference for female children is often responsible for poor ANC visits and inadequate nutritional practice among mothers during pregnancy results in adverse birth outcome like LBW [ 44 ]. Despite substantial progress in primary health care over the last decades, only 47% of pregnant women in Bangladesh receive at least four ANC visits [ 45 ]. A lack of access to health providers and facilities has contributed to nearly one in two mothers in Bangladesh not receiving four or more ANC visits from skilled health professionals [ 46 ]. In addition, gender inequality, cultural and religious behavior and restrictions among women; illiteracy and poverty are often considered the preference of male child as well as poor ANC visit in Bangladesh [ 47 ]. Improving access to quality ANC and sustaining its implementation must be prioritized for the country to achieve better health sustainability.

The study also revealed that the odds of being LBW was higher in household with unimproved toilet facility as well as it was estimated higher prevalence of LBW in those households in Bangladesh. Recent studies conducted in Bangladesh did not find any correlation between the type of toilet facilities and LBW [ 23 ]. Open defecation and unsafe bowel disposal negatively affect the nutrition and health status of pregnant women and promote chronic infections [ 48 ]. Due to unimproved toilet facilities, especially in urban slums and rural areas, women also suffer from diarrhea and hookworm infestation which lead to maternal anemia, undernutrition, and infectious diseases that results in poor pregnancy outcomes like LBW [ 8 , 10 , 13 , 48 , 49 ]. Sufficient budget allocation and ensuring effective implementation of resources under national sanitation program can provide a framework for addressing sanitation issues and improving access to clean water and hygienic toilet facilities. In addition, promoting good sanitation practices and increasing awareness about the importance of sanitation and hygiene can help prevent the spread of disease and improve maternal and child health outcomes.

the children with LBW were more likely to suffer from fever and undernutrition than normal children. Previous studies based on data from Bangladesh showed that LBW was identified as an important risk factor for various forms of undernutrition [ 26 , 27 ]. Several neighboring countries like India, and Pakistan found comparable results [ 50 , 51 ]. Another study in Africa (Malawi) also found higher odds of stunting, wasting, and being underweight among LBW children [ 52 ]. LBW infants often had difficulties in feeding due to underdeveloped digestive systems or a weak sucking reflex, which can lead to inadequate intake of nutrients [ 50 , 53 ]. Moreover, LBW infants may have higher metabolic rates, which means they require more energy and nutrients per kilogram of body weight than a normal infant and this supply-demand imbalance leads to undernourishment [ 54 ]. The children of LBW had lower immune substances and improper formation of the respiratory tract which lead to various infectious diseases like pneumonia and often suffer from fever, and cough [ 55 , 56 ]. LBW children and their mothers need adequate parenteral care and nutritional education including regular checkup and nutritional counselling, initiation of early and exclusive breast feeding, and nutrient-dense complementary foods to reduce the incidence of child morbidity and undernutrition in children born with LBW.

The main strength of this study was the utilization of nationally representative cross-sectional sample which covers both rural and urban areas of all districts of the country as well as aids to generalize the findings. Additionally, BDHS 2017–2018 data was collected by using a standard questionnaire, designing a complex survey strategy, and global study model to provide credible results. Despite these advantages, we acknowledged several limitations of this study. As the data was collected based on the mother’s self-reported information, the information might be affected by recall bias. This differential misclassification could cause either an overestimation or underestimation of the study findings. The cross-sectional nature of the data interferes with drawing causal associations between dependent and independent variables. This study might limit to generalize the findings only for low- and middle- income countries.

One out of five children were born with LBW in Bangladesh. Poor maternal education, female child, poorest socio-economic status, and unimproved toilet facilities were significantly associated with LBW. Further, the likelihood of gaining illness and being undernutrition was higher in LBW children. To improve maternal pregnancy, and child health outcomes, it is crucial to implement policies that tackle poverty, gender inequality, and social disparities. Encouraging regular antenatal care visits and early medical intervention is essential, as is promoting education and awareness about reproductive health, hygiene and safe sanitation practices. Further, treating a low birth weight (LBW) child to reduce adverse health and nutritional outcomes needs child malnutrition multifaceted approach including exclusive breastfeeding promotion, nutritional intervention, growth monitoring, accessible medical care, and education of caregivers.

Data availability

The BDHS 2017-18 data is publicly available on the DHS Program’s page at https://dhsprogram.com/data/ .

Abbreviations

Antenatal care

adjusted odds ratio

Acute respiratory infection

Bangladesh Demography and Health Survey

Confidence interval

Low birth weight

Primary sampling unit

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Acknowledgements

We thank MEASURE DHS for granting us permission to use the data. We also acknowledge the support of Department of Public Health, First Capital University of Bangladesh, Chuadanga, Bangladesh, where this study was conducted.

This research did not receive any fund.

Open access funding provided by University of Gävle.

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Md. Zahidul Islam and Mohammad Rocky Khan Chowdhury have equal contribution.

Authors and Affiliations

Department of Public Health, First Capital University of Bangladesh, Chuadanga, Bangladesh

Md. Zahidul Islam, Mohammad Rocky Khan Chowdhury & Md. Shariful Islam

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

Mohammad Rocky Khan Chowdhury & Baki Billah

Department of Medical Science, School of Health and Welfare, Dalarna University, Falun, Sweden

Manzur Kader

Department of Public Health and Sports Sciences, University of Gävle, Gävle, Sweden

Mamunur Rashid

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Contributions

MZI and MRKC designed the concept of the study. MRKC prepared the dataset for analysis, and carried out the analyses. MZI prepare the first draft. MRKC and MSI edited the first draft. MK, BB and MR critically reviewed the manuscript. All authors reviewed the study findings and read and approved the final version before submission. MZI and MRKC are responsible for the overall content as guarantor.

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Correspondence to Mamunur Rashid .

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The BDHS 2017-18 dataset is a publicly available data and authors do not need ethical approval for using it. Informed consent was obtained verbally from each mother of children (every married woman aged 15–49 years) before being enrolled into the study.

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Islam, M.Z., Chowdhury, M.R.K., Kader, M. et al. Determinants of low birth weight and its effect on childhood health and nutritional outcomes in Bangladesh. J Health Popul Nutr 43 , 64 (2024). https://doi.org/10.1186/s41043-024-00565-9

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Lastly, search terms such as: "nutrition interventions in primary schools" and "Nutrition education interventions in school" were used in the Cochrane Library database to find the articles. In addition, reference lists of all retrieved articles and review articles [ 34 ] were screened for potentially eligible articles.
The impact of the world's first regulatory, multi-setting intervention
Regulatory actions are increasingly used to tackle issues such as excessive alcohol or sugar intake, but such actions to reduce sedentary behaviour remain scarce. World Health Organization (WHO) guidelines on sedentary behaviour call for system-wide policies. The Chinese government introduced the world's first nation-wide multi-setting regulation on multiple types of sedentary behaviour in ...
Determinants of low birth weight and its effect on childhood health and
Background The high incidence of low birth weight (LBW) is associated with an increased risk of infant mortality, adverse pregnancy outcomes for mothers, and a decline in overall health and well-being. The current study aimed to identify the various determinants of LBW and its effect on adverse health and nutritional outcomes of children aged 0-23 months in Bangladesh. Methods Bangladesh ...

Healthy Eating Learning Opportunities and Nutrition Education

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Nutrition education in schools: experiences and challenges

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The effect of physical activity intervention and nutritional habits on anthropometric measures in elementary school children: the health oriented pedagogical project (HOPP)

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Nutrition Education: Linking Research, Theory, and Practice

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School health and nutrition

What is the state of school health and nutrition around the world?

What does health and nutrition mean for learners and schools?

What are some of the key challenges?

What can we do about it?

How does UNESCO work to advance school health and nutrition?

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On the interplay between educational attainment and nutrition: a spatially-aware perspective

1 Introduction

2 Related work

3.2 Spatial modeling

4.1 Diet composition

Product categories

4.2 Diet variety

4.3 Sensitivity analysis

5 Discussion

Limitations

6 Conclusion

Availability of data and materials

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Explorative spatial data analsysis (PDF 11.7 MB)

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What Students Are Saying About Making School Lunch Healthier

Many students supported the push for more healthful school lunches.

Others argued that making school food healthier will mean fewer students will eat it.

Some suggested a middle ground, one in which nutritious options exist beside student favorites.

Several said teaching students about nutrition and letting them have a say in the menu would help them make healthier choices.

Students also told us what’s working in their own school cafeterias.

And what needs improvement.

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The impact of healthy nutrition education based on traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome: a randomized clinical trial

Conclusions

Clinical trial registration number

Study design

Study population

Intervention

Randomization

Measuring study outcomes

Food selection

Validity and reliability of the questionnaires

Sample size and statistical analysis

Data availability

Abbreviations

Acknowledgements