Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

School and community drivers of child diets in two Arab cities: The SCALE protocol and innovative tools to assess children’s food environments

  • Hala Ghattas ,

    Roles Conceptualization, Funding acquisition, Methodology, Supervision, Visualization

    hg15@aub.edu.lb

    Affiliation Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon

  • Zeina Jamaluddine,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliations Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon, London School of Hygiene and Tropical Medicine, London, United Kingdom

  • Aline Semaan,

    Roles Conceptualization, Methodology, Project administration, Writing – review & editing

    Affiliations Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium

  • Nehmat El-Helou,

    Roles Project administration, Writing – original draft, Writing – review & editing

    Affiliation Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon

  • Gloria Safadi,

    Roles Project administration, Writing – original draft, Writing – review & editing

    Affiliation Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon

  • Tatiana Elghossain,

    Roles Writing – review & editing

    Affiliation Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon

  • Christelle Akl,

    Roles Project administration, Writing – original draft, Writing – review & editing

    Affiliation Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon

  • Shady Elbassuoni,

    Roles Conceptualization, Methodology, Software, Writing – review & editing

    Affiliation Department of Computer Science, Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon

  • Ali Chalak,

    Roles Conceptualization, Methodology, Software, Writing – review & editing

    Affiliation Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon

  • Jalila El Ati,

    Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

    Affiliation INNTA (National Institute of Nutrition and Food Technology), SURVEN (Nutrition Surveillance and Epidemiology in Tunisia) Research Laboratory, Tunis, Tunisia

  • the SCALE Research Group

    Members of the SCALE Research Group are listed in the Acknowledgments section.

Abstract

Background

In the context of the rapid nutrition transition experienced by middle-income countries of the Arab region, children and adolescent’s food choices and dietary behaviors are early risk factors for the development of non-communicable diseases. Assessment of factors influencing food choices among this age group is challenging and is usually based on self-reported data, which are prone to information and recall bias. As the popularity of technologies and video gaming platforms increases, opportunities arise to use these tools to collect data on variables that affect food choice, dietary intake, and associated outcomes. This protocol paper describes the SCALE study (School and community drivers of child diets in Arab cities; identifying levers for intervention) which aims to explore the environments at the level of households, schools and communities in which children’s food choices are made and consequently identify barriers and enablers to healthy food choices within these environments.

Methods

Field studies are being conducted in primary schools, among children aged 9–12 years, in Greater Beirut, Lebanon and Greater Tunis, Tunisia. A stratified random sample of 50 primary schools (public and private) are selected and 50 children are randomly selected from grades 4-5-6 in each school. The study includes surveys with children, parents/caregivers, school directors, teachers, and nutrition/health educators to assess individual diets and the contextual factors that influence children’s food choices. Innovative locally adapted tools and methods such as game-based choice experiments, wearable cameras and neighborhood mapping are used to describe the environments in which children’s food choices are made.

Discussion

The SCALE study will generate contextual knowledge on factors in school and neighborhood environments that influence child dietary behaviors and will inform multi-level interventions and policies to address childhood malnutrition (under-and over-nutrition). By integrating methods from various disciplines, including economics, data science, nutrition, and public health and by considering factors at various levels (home, school, and neighborhood), the study will identify levers for intervention with the potential to improve children’s dietary behaviors. This will help fill existing gaps in research on food systems and consequently guide positive change in Lebanon and Tunisia, with the potential for replicability in other contexts.

Introduction

The last two decades have been marked by substantial dietary and lifestyle changes in the Arab region, where the prevalence of overweight and obesity among children is estimated to have doubled during this period [13]. In fact, diets have included ever-increasing shares of processed foods high in fats, sugar, refined grains and salt [4], together with low levels of physical activity [5, 6]. In middle-income countries of the Arab region, while stunting and micronutrient deficiencies persist, overweight prevalence estimates in children and adolescents are similar to those of high-income countries of the region, reaching about 30% in Lebanon and 20% in Tunisia [2]. Overweight in Arab adolescents has been linked to early metabolic syndrome, hypertension, cardiovascular diseases, diabetes, and musculoskeletal disorders; all conditions that are cause for higher proportions of disability-adjusted life years in the region [7]. Nearly a third of the region’s population is aged under 15 years [8], and if diet and overweight trends are not curbed, this youth bulge risks adding economic pressures on health systems, straining their capacity to deal with non-communicable disease (NCD) burdens.

Evidence suggests that children’s dietary habits are established early in life and continue into adolescence and beyond [9, 10]. Children’s weight status and food habits are influenced by many factors that span the socio-ecological model, including individual physiology and behaviors, family characteristics and interactions, as well as structural forces at the level of community, society, and policy environments [1115]. Given that children spend a large proportion of their time at school, their eating patterns are also influenced by the food choices available in schools [1620]. Beyond the confines of schools, aspects of the neighborhood environment, including accessibility to, amount and types of food outlets, as well as food marketing and advertising, are also associated with children’s food choices and body weight [2124].

The ways in which obesogenic environments influence food choices have been shown to be context-specific [25, 26]. While there is considerable research from high-income countries [27, 28] and from studies that investigate the specific influences of external environments only [29], a better understanding of children’s immediate environments in a middle-income context is essential to inform interventions that address childhood dietary patterns, behaviors, food choices and overweight in the region.

Assessment of factors influencing food choices among children and adolescents is challenging, and is usually based on self-reported data, which are prone to measurement error including information and recall bias [30]. Technology-based tools enable an objective and comprehensive measurement of children’s nutrition-related behaviors and experiences around food [3133]. As the popularity and use of mobile technologies and video gaming platforms increase, opportunities arise to use these tools to collect data on variables that affect food choices and diets. Such tools can also foster a ‘people-based’ approach to measuring these exposures [34]. These include factors in the child’s environment such as setting, social interactions and media exposure [32]. Digital technologies can give insight on children’s lived experiences and engage younger participants in documenting their own behavior, which may also lead to more accurate and representative data collection [30, 33, 35]. Such technologies have been positively received by younger participants and are feasible and acceptable [32, 33, 36, 37].

Conceptual framework

Fig 1 depicts the “School and community drivers of child diets in Arab cities; identifying levers for intervention” (SCALE) study conceptual framework showing the levels of influence on children’s food choices, the variables to be collected by the SCALE study at each of these levels and the tools needed to collect each set of variables. The macro level includes the economic and policy environment. The meso level includes school food environments, the neighborhood environment of the school, and exposure to the wider neighborhood food environment in children’s trajectories to and from schools. The micro level includes the child’s household and individual behaviors and characteristics.

thumbnail
Fig 1. Ecological framework of the study.

The figure shows the variables to be collected at each level and the methods to be used (blue boxes).

https://doi.org/10.1371/journal.pone.0264963.g001

Macro-level factors such as food system policies have been recognized to improve diet quality, particularly among children and adolescents [38]. For younger children, the effects of macro-level and policy environments are mediated through their caregivers, whereas older children engage more directly with these environments [39]. The presence of, and proximity to, food markets that children and adolescents often encounter play a crucial role in defining children’s food choices, especially as these markets often sell non-nutritious foods with concerns about quality and safety [40]. Also, there are massive promotions of unhealthy snacks that are cheap, nutrient-poor and energy dense, and very limited investments to promote healthy foods [29, 41]. Additionally, nutritious foods recommended to children and adolescents can be expensive, particularly in low-income settings [42].

Intermediate (meso) structures such as school, neighborhood, and community level environments mediate the effects of societal forces on children’s eating behaviors by influencing exposures, perceptions, understandings, and behaviors.

Usually, children consume over a quarter of their daily caloric intake at school; as such, eating patterns in schools contribute significantly to diet quality, as well as subsequent health outcomes, including increased risk of obesity [16, 4345]. With food availability being one of the most salient factors associated with food choice [43], the ready availability of nutrient-poor and energy-dense competitive foods [16, 43] at schools lead to increased energy intake and body mass index (BMI), and decreased fruit and vegetable consumption [16, 17, 46], with negative health implications. School food policies can modify the school environment and alter social norms relating to appropriate foods for children [47]. Food-related policies in schools, however, have not received enough attention in low and middle-income countries, and there is increasing financial dependence by schools on revenues generated by food sales [45], which are used to support food service and other school activities [16]. A better understanding of enablers and barriers to healthy eating behaviors in schools in such contexts is warranted to inform interventions to address childhood overweight [48].

Neighborhood and community-level environments represent another set of meso structures that influence children’s dietary intake beyond the confines of schools [49]. Reviews have reported a number of associations between, on the one hand, neighborhood environment characteristics–including the accessibility, amount, and types of food outlets–and, on the other hand, food choices and body weight among boys and girls [21, 22]. For example, living closer to convenience stores has been associated with an increased intake of unhealthy snacks, such as chocolate and crisps, among children [50, 51]. The price of food has also been associated with consumption [22], as documented in a study that found that a dollar increase in the price of fruits and vegetables decreased consumption by 6.3% [22, 52]. These environmental influences on food choice have been shown to be context-specific and to manifest differences in dietary intake [25, 53]. The complexity of food choice warrants further study of the contexts in which these choices are made [54]. Classifying both proximity and density of food outlets is a common method used to map spatial accessibility to food in different neighborhoods, and to assess associations between the food environment and individual behaviors [55]. Studies that have categorized food outlets and retailers and mapped them according to proximity to children’s homes and neighborhood density, have reported associations between these factors and food intake among children [51].

Being at the center of the framework, the micro level elaborates the child’s characteristics and autonomy in making their own food choices and decisions. Eating patterns in children and adolescents are influenced by several factors such as physical activity, taste preferences, and habits [40]. In addition, many other household-level factors shape children’s food choices such as food availability, accessibility and affordability, as socio-economic status can dictate food environments that children and their caregivers are exposed to [56]. Caregivers, play a key role in determining young children’s eating behaviors since they procure, prepare, cook and provide food [40].

This conceptual framework guides the design of the study in two urban centers of the Arab world: Greater Beirut, in Lebanon and Greater Tunis, in Tunisia. These two urban agglomerations provide examples of the nutritional shift occurring in many Arab countries. Lebanon and Tunisia are both middle-income countries that have experienced rapid rates of urbanization and economic development [1, 57] paralleled by a rapidly proceeding nutrition transition [3, 58] with overweight prevalence in children having long surpassed that of stunting [59]. This transition has typically been most rapid in urban contexts, where the consumption of starchy carbohydrates, food away from home, and processed foods including sugary snacks among children are on the rise [3, 60].

Objectives

The SCALE study uses a cross-disciplinary approach and employs innovative locally relevant tools to assess school and community-level drivers of children’s dietary behaviors. The specific study objectives are to:

  1. Develop innovative, context-specific methods that measure and account for the complex set of factors that play a role in children’s food choices and dietary behaviors
  2. Describe the environments at the level of households, schools and communities in which children’s food choices are made
  3. Identify points in the daily routine of children that represent threats to/opportunities for healthy eating
  4. Quantify the impact of the obesogenic environment on schoolchildren’s food choices and their nutritional outcomes
  5. Define context-specific multi-level interventions for influencing children’s food behaviors and diets related to the home, school and neighborhood environments

Materials and methods

Study area

In each country, the study focuses on the urban area around the capital city (Greater Beirut, in Lebanon and Greater Tunis, in Tunisia).

Study design

We design this study to investigate and identify school and community drivers of children’s diets. We use mixed-methods to develop the innovative research tools. The tools are applied in a cross-sectional design to gather quantitative data on children’s individual, familial, school-level and neighborhood-level characteristics.

Study participants

In both countries, the study population includes students enrolled in grades 4-5-6, in both public and private schools. The study also includes surveys with parents/caregivers, school directors, teachers, and nutrition/health educators.

Sample size calculations were based on simulations derived by Moineddin et al. [61] to reduce relative bias of estimates in multi-level logistic regression models. The recommended minimum group size was found to be 50 with at least 50 observations per group for the derivation of valid estimates. The assumptions underlying this recommendation were verified for several key study indicators using data from published literature (e.g. expected prevalence and intra-class correlation coefficients for diet diversity categories and overweight/obesity within schools) [62, 63].

We therefore use a two-stage sampling approach to recruit a representative sample of 8 to 12-year-old schoolchildren. A random sample of 50 schools, stratified by school type (public and private), is selected from the list of schools provided by the respective Ministries of Education in both countries. Accounting for the expected non-response rates, the team contacts the schools to schedule a meeting with the directors to explain the study objectives, protocol, and timeline. From the schools that agree to participate in the study, we randomly select students from grades 4-5-6 and send parental consent forms home with the selected students, with the aim of enrolling 50 students per school: for a total of 2,500 students per city.

In the schools that agree to participate, we invite the school/section director, teachers and nutrition/health educators that teach grades 4, 5 and 6 to participate in the study. We recruit one director, 5–10 teachers, and 1–3 nutrition/health educators present at school on the day of data collection to participate in the study.

Study procedures

This study protocol employs various research methods which address different levels of the socio-ecological model. Based on the aforementioned conceptual framework (Fig 1) we present hereafter the details of the different tools employed at each level in the study.

1. Macro and meso levels

This study aims to map structures at the macro and meso levels; that is the economic and policy environment along with school food environments, the school neighborhood environments, and exposures to the wider neighborhood food environment in children’s trajectories, respectively. These structures are mapped using four main methods.

1.1 Desk review

A desk review of the broader food-related economic and policy environment at the country-level for Tunisia and Lebanon is performed. This provides comparable information on the macro-level context within which intermediate structures (schools and neighborhoods) exist and operate in each country. For this, we systematically collect and analyze published articles, available reports and policy documents describing the policies and economic, social and political factors that influence child diets and nutritional status in Tunisia and Lebanon; we include sources specific to each country, as well as more general sources on the region if relevant. Documentation pertaining to existing policies and initiatives established to reduce childhood overweight in Lebanon and Tunisia are also compiled. Other complementary sources are collected to document economic and agricultural policies of the country, including those that influence exposure to food promotion (exposure to food advertisements, purchase prompts at food outlets, food labeling and packaging), food outlet density (supermarket density, fast food outlet density), market prices (imported vs locally produced goods, food subsidies), societal initiatives related to child nutrition, social norms related to household decision making around food choices, and other market regulations and policies affecting food choices.

1.2 School survey

We conduct a school survey, administered to school principals and teachers in order to effectively profile school food environments and assess children’s exposures to different components of this environment. This survey builds on standardized tools to examine school food-related policies and compliance with these [64], the extent of nutrition education available in the curriculum, knowledge and attitudes of teachers towards nutrition and food, food availability, as well as price and quality in school food outlets [45, 65, 66]. This survey consequently generates two sets of data: first, school profiles that can be used to inform advocacy and intervention development; second, a list of school-level variables to include in multi-level analyses of the determinants of children’s food choice and diets.

1.3 Wider neighborhood food environment and trajectory mapping

To capture exposure to different factors in children’s food environments, we use an innovative set of tools that build on simple digital technologies to collect comparable data that can be quantified and analyzed across the two countries. We adopted a user-centered design approach to develop an innovative tool using wearable cameras with the aim of capturing neighborhood food environments and food trajectories of children. Focus groups and design activities were conducted with parents, children and school staff. These data informed the design of this component in an ethical and culturally acceptable manner with the detailed methods described elsewhere [67]. Based on ethical frameworks [31, 68, 69] and findings from the formative research phase, we developed a comprehensive camera model and data collection protocol detailed in Zoughby [70]. This consists of a wearable camera, attached to a strap worn over the child’s clothes, that automatically captures continuous footage of what children are exposed to in their daily trajectories to and from school. Only children in grade 6 are invited to participate in this component. Those whose parents agree to participate, wear the camera for one full day, outside the confines of school, mainly on the road/trajectory to and from school, and inside their homes (if parental consent and child assent is obtained). Timestamped images are extracted from the recorded footage and consequently filtered using machine learning models to only retain the images relevant to the study; those related to food advertisement, food outlets, food purchasing and food/beverage consumption and to blur all faces in the images, therefore generating a dataset of pseudonymized images related to food exposures. These are then further classified according to the NOVA classification–a classification that categorizes foods into four groups according to the extent of food processing [71]. These data will allow us to uncover and quantify the magnitude and the quality of exposures and cues to food choices and eating behaviors in children’s daily trajectories.

1.4 School neighborhood food environment mapping

We also map the community food environments that children are exposed to, including food outlets’ proximity to schools, typology, and density, and child exposure to food advertisement in the neighborhoods of the selected schools [51, 72, 73] A data collection module is designed to identify, enumerate, classify, and map food stores/advertisements within an 800-meter road network buffer around selected schools [73]. This is programmed on a mobile phone using two applications: Collector Classic and Survey123 (ESRI Inc., Redlands, CA). Data collectors are given the programmed mobile phones and instructed to (a) walk around the school according to a planned route using Collector Classic, and (b) collect data on food outlets and food advertisements using Survey123. The geographic coordinates of each food outlet/food advertisement, and a geotagged picture of the outlet/advertisement are collected. These are categorized into typologies of outlets (e.g. supermarket, ambulant vendor, pastry shop) as well as food groups for advertisements (e.g. fresh fruits and vegetables, processed meats, sugar-sweetened beverages). Then, they are all further classified according to the NOVA classification [71]. The geocoded locations of schools, and detailed food outlet and food advertisement classifications are then projected to GIS software in order to analyze the proximity, density, and clustering of food stores and advertisements relative to schools (ArcGIS 10, ESRI Inc. CA). This method is described in detail elsewhere [74].

2. Micro level

The macro and meso-level data collected are complemented by structured interviews with schoolchildren and parents, and anthropometric measurements and micronutrient assessment of schoolchildren. For this, two methods are used allowing us to explore correlations between behavioral and nutritional variables as well as factors in the school and neighborhood environments that may influence them. By doing so, we generate individual-level variables, including socio-demographic status, dietary behaviors, food choices, nutritional outcomes, and sedentary behaviors [75, 76].

2.1 Parents

Family/household-level indicators are collected from children’s parents/caregivers through a 15-20-minute telephone interview using a structured questionnaire programmed on tablets. This questionnaire includes general socio-demographic family and household characteristics (age, sex, education, employment, household size, assets, expenditures, wealth). The questionnaire also includes a section on family food-related habits and food-related parenting style and modeling [7779]. We collect parents’ perceptions of the home food environment [80, 81] and administer the Arab Family Food Security Scale (AFFSS), previously validated in Lebanon [82, 83]. The household diet diversity scale is additionally used as a proxy of household access to a variety of foods [84]. We lastly assess parents’ level of nutrition knowledge using eight questions adapted from the General Nutrition Knowledge Questionnaire (GNKQ) [85] and collect a self-report of parental anthropometric measurements.

2.2 Children

To collect child-level outcomes and co-variates, we employ four tools. First, students in grades 4-5-6 participate in a structured face-to-face interview and answer the children’s questionnaire. The tablet-programmed-questionnaire lasts 30 minutes and includes questions on demographic variables (age and gender), food consumption and food purchase habits in and around school [8692]. It also includes a module on child diet diversity, which is used as a proxy for children’s diet quality and nutrient adequacy [84]. We further collect data on children’s physical activity levels and sedentary behaviors [93, 94] as well as assessing children’s level of nutrition knowledge. Second, trained data collectors record students’ anthropometric measurements, including height, weight (to be able to calculate BMI for age) and waist circumference (WC). These measurements are taken individually, in a private space using a standard stadiometer for height, a calibrated weighing scale for weight, and a measurement tape for WC. Third, a registered nurse conducts a finger stick to obtain around 400 μL of blood, part of which is used to measure hemoglobin on the spot using a portable hematofluorometer (hemocue) to gage anemia, and the other for further micronutrient analyses [95]. Serum aliquots (between 50–100 μl each) are shipped to a laboratory in Germany, where a single ELISA has been developed to measure plasma ferritin, soluble transferrin receptor, retinol binding protein (RBP), C-reactive protein (CRP), and Alpha-1-acid glycoprotein (AGP) [95] allowing us to establish children’s iron and vitamin A status.

The last tool used is a digital game that employs a series of choice experiments designed to vary key factors that shape children’s food choices [9699]. The choice experiments are tailored to the context of schools and homes in Lebanon and Tunisia and present the most frequently consumed foods by schoolchildren based on previous literature. We worked with a software developer and a graphic designer to ensure a modular, customizable and appealing game (Fig 2). The choice experiment is composed of six different contexts or meal types within which children are asked to select hypothetical food choices: (1) breakfast, (2) road to school, (3) recess, (4) lunch, (5) snack and (6) dinner. In each section, children are presented with repeated choice tasks, each composed of nine items: three beverages, three side meals and three meals. Children get to choose their preferred items based on items’ attributes and contextual effects that apply. Attributes that are being explored are item placement (accessible or hidden in a cupboard), preparation (ready to eat or requires an additional step to prepare), and price, when applicable. Contextual effects are applied to the choice tasks in general and hence equally to all items within this task (e.g. supervision by parents, food choices of friends and peers). Also, the level of healthiness of each item is studied implicitly. Each of the three items representing a beverage, side meal or meal, was selected to the choice task from an exhaustive menu of ‘healthy’, ‘medium healthy’ and ‘unhealthy’ articles, respectively, that are typically present in the Lebanese or Tunisian diet. The econometric analysis [100] of these choices enables the estimation of preference intensities for each food item as inflected by the type of food/beverage articles, its attributes and the contextual effects under which it is presented.

thumbnail
Fig 2. Examples of different scenarios and choice tasks from the choice experiment.

https://doi.org/10.1371/journal.pone.0264963.g002

Analytical approach

Data collected from the multiple levels will be merged and analyzed using standard statistical methods. Fig 3 illustrates the set of expected outcomes per phase and analysis level.

thumbnail
Fig 3. Meso and micro level factors that influence children’s diet diversity, nutritional status, and food choice.

https://doi.org/10.1371/journal.pone.0264963.g003

Descriptive statistics will be used to describe child diets and socio-demographic variables at the child and household level. School-level variables will be generated in regard to policy, nutrition education and food availability patterns. At the school neighborhood-level, data will be used to generate variables related to proximity, density, and typology of food outlets. For the wider neighborhood-level, we will use deep learning models to conduct photo analysis and quantify the intensity, frequency, types and healthiness of food exposures captured by wearable cameras, stratified by gender, socio-economic status, geographic areas, and nutritional status.

The determinants of children’s food choices, diet quality, and nutritional status will be examined using logistic and linear regression models. Multilevel models [101, 102] will be used to analyze the various levels of influence on child diets and nutritional status, allowing for the analysis of hierarchical data [103106]. Neighborhood and school environmental factors, individual socio-economic characteristics and contextual effects in choice tasks, will be accommodated by the econometric analysis as preference intensity shifters. We will take a comparative approach to analyze the similarities and differences across the two study sites, as well as across neighborhood typologies, school typologies, socio-economic strata, and gender. All statistical analyses will be conducted using STATA version 15 (STATA Corporation, College Station, Texas, USA).

The multiple layers of analysis included in this project will ensure the triangulation of data from different sources and will allow us to fill knowledge gaps that exist at each of the levels of analysis. The documentation of children’s trajectories informs data collection at every level and allows for the validation of data collected from child and parent reports. The photo-documentation of trajectories can also be used to illustrate salient findings in the dissemination of study results.

Ethics approval and consent to participate

All applicable institutional and governmental regulations on the ethical use of human volunteers were respected. In Lebanon, the study has been approved by the American University of Beirut (AUB) Institutional Review Board (IRB) on December 23, 2019 (IRB ID: SBS-2019-0306). In Tunisia, the survey protocol was reviewed and approved by the Ethics Committee on Human Research of the National Institute of Nutrition and Food Technology (INNTA) (Visa n° 03/2019) on July 12, 2019 and by the National Council of Statistics in Tunisia (Visa n° 06/2019) on April 28, 2019. All parents/caregivers gave their written informed consent.

The information we collect herein is treated with the utmost confidentiality; every reasonable effort is made to keep the records confidential and preserve participants’ anonymity across the various study components. Consent for participation is to be sought from all participants either in oral or written form, both documented in the records. All participation is voluntary; participants are free to opt-out of any component of the study including the fingerstick and wearable camera components.

Current study status and timeline

Data collection began in Tunisia in January 2020 and was completed in November 2020. As for Beirut, data collection is set to commence in September 2021.

Tunisia.

Data collection began on January 30th, 2020 with the implementation of child surveys, anthropometry, biomarker collection, school environment survey, and the wearable cameras protocol. However, the emergent COVID-19 pandemic, which resulted in school closures, forced the team to halt data collection on March 11th, 2020. The team completed 29 out of the 50 schools at the individual, family, and school level. Following this, extensive field work was conducted to collect the neighborhood mapping data. School-level data collection resumed on October 12, 2020 under stringent COVID-19 precaution measures. The second wave was slower because of the adopted hybrid system in schools and infection mitigation protocol put in place in the country. Data collection was conducted on two consecutive days for each school in turn allowing us to reach the desired sample size on November 16, 2020, despite the difficult circumstances. In parallel, phone interviews with parents were conducted.

Lebanon.

As for Lebanon, data collection was planned to commence in October 2019. However, as a result of worsening economic conditions, social movements led to road and school closures in October-November 2019 and postponement of fieldwork to 2020. The emergent COVID-19 pandemic then shuttered schools from March 2020 with schools in Lebanon only opening for a total of four non-consecutive weeks of hybrid schooling thus far in the academic year 2020–2021. Beirut was also exposed to one of the largest non-nuclear explosions in history in August 2020 causing death, injury, destruction and further economic hardship. These overlaps between the economic, COVID-19 and Beirut blast crises have contributed to an increase in poverty and food insecurity, changes in markets including the food environment, and an overall increase in vulnerability of residents of Greater Beirut. It will remain essential to conduct this study when schools reopen in the aftermath of these crises; data collected will capture the new reality of this population and allow us to identify key points of intervention to improve children’s diets—essential to “building back better” post crises.

Discussion

In the context of the rapid nutrition transition experienced by middle-income countries of the Arab region, children’s food choices and dietary behaviors affect growth and development and are early risk factors for the development of NCDs. The current research base on overweight among children in the Arab region has considerable gaps in how it has been problematized, often limited to assessment of individual-level risk factors [107, 108]. In order to inform policy and community-based interventions, there is a need to measure factors beyond the individual that shape children’s exposure to the food environment, their food choices and subsequent health outcomes. To our knowledge, this is the first study that takes an ecological approach to build a robust evidence based on school and neighborhood-level food systems and assess their influence on food choices and behaviors among children in the Arab region.

For this, we have developed innovative and contextually validated tools and methods to inform effective solutions to unhealthy diets in children. By mapping children’s food-related trajectories to and from schools and assessing the influence of these trajectories and exposures on children’s dietary behaviors, the study takes a systems approach to generate an in-depth understanding of the drivers of children’s diets in two middle-income countries experiencing a nutrition transition.

This investigation of how factors at the individual, family, and neighborhood levels influence food behaviors and practices among boys and girls will provide an evidence base for efforts targeting the rising prevalence of unhealthy diets and overweight, which has implications for growth and development in the short to medium term and NCD prevention in the long-term. The knowledge generated from this research will ultimately present practical solutions at different levels (policy, school, advertisement, and neighborhood), that enable and consequently promote mechanisms that have the potential to change dietary behaviors among children. The findings of this study will further inform culturally appropriate and context-specific interventions which could be relevant to the broader Arab world; including urban low-income countries that are experiencing similar rapid epidemiological and nutrition transitions.

Acknowledgments

We acknowledge the Lebanese Ministry of Education and Higher Education, the Tunisian National Institute of Nutrition and Food Technology for their support and assistance; the data collectors for supporting in the data collection across all 50 schools in Tunisia; including Samir Bechaouch, Samia Ben Chérifa, Zohra Berriche, Wafa Zarrouki and Monia Zayati.

The SCALE Research Group comprised of Chaza Akik1, Houda Ben Gharbia2, Radhouene Doggui2,3, Marwa Khammassi2, Jessika Nicolas4, Sonia Sassi2, Zoulfikar Shmayssani5, Hajer Skhiri6, Tarek Trabelsi2, Pierre Traissac7, Zeina Kamareddine8, Yorgo Zougby5.

1 Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut Lebanon

2 INNTA (National Institute of Nutrition and Food Technology), SURVEN (Nutrition Surveillance and Epidemiology in Tunisia) Research Laboratory, Tunis 1007, Tunisia

3 Department of family medicine, Université de Sherbrooke, Sherbrooke, Canada

4 Department of Geology, Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon

5 Department of Computer Science, Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon

6 University of Tunis El Manar, Tunis, Tunisia

7 MoISA—Univ Montpellier, CIRAD, CIHEAM-IAMM, INRAE, Institut Agro, IRD, Montpellier, France

8 Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon

References

  1. 1. Nasreddine L, Naja F., Chamieh M. C., Adra N., Sibai A. M., & Hwalla N. Trends in overweight and obesity in Lebanon: evidence from two national cross-sectional surveys (1997 and 2009). BMC Public Health. 2012;12(1):798. pmid:22984791
  2. 2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9945):766–81. pmid:24880830
  3. 3. Aounallah-Skhiri H, Traissac P, El Ati J, Eymard-Duvernay S, Landais E, Achour N, et al. Nutrition transition among adolescents of a south-Mediterranean country: dietary patterns, association with socio-economic factors, overweight and blood pressure. A cross-sectional study in Tunisia. Nutrition journal. 2011;10(1):38. pmid:21513570
  4. 4. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3–21. pmid:22221213; PubMed Central PMCID: PMC3257829.
  5. 5. Caballero B. The nutrition transition: diet and disease in the developing world: Elsevier; 2002.
  6. 6. Batal M A. Al-Hakimi F. Pelat. Dietary Diversity in Lebanon and Yemen: A tale of two countries. Ecohealth Research in Practice: Springer; 2012. p. 69–80.
  7. 7. Obermeyer CM, Bott S, Sassine AJ. Arab adolescents: health, gender, and social context. Journal of Adolescent Health. 2015;57(3):252–62.
  8. 8. UNDP. Arab Human Development Report 2016: Youth and the prospects for human development in a changing reality. New York: UNDP, 2016.
  9. 9. Lytle LA, Seifert S, Greenstein J, McGovern P. How Do Children’s Eating Patterns and Food Choices Change over Time? Results from a Cohort Study. American Journal of Health Promotion. 2000;14(4):222–8. pmid:10915532.
  10. 10. Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study. Pediatrics. 2001;108(3):712–8. pmid:11533341
  11. 11. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. The Lancet. 2011;378(9793):804–14. pmid:21872749
  12. 12. Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, Finegood DT, et al. Changing the future of obesity: science, policy, and action. The Lancet. 2011;378(9793):838–47.
  13. 13. Davison KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obesity reviews. 2001;2(3):159–71. pmid:12120101
  14. 14. Stok FM, Hoffmann S, Volkert D, Boeing H, Ensenauer R, Stelmach-Mardas M, et al. The DONE framework: Creation, evaluation, and updating of an interdisciplinary, dynamic framework 2.0 of determinants of nutrition and eating. PloS one. 2017;12(2):e0171077. pmid:28152005
  15. 15. Hawkes C, Fox E, Downs SM, Fanzo J, Neve K. Child-centered food systems: Reorienting food systems towards healthy diets for children. Global Food Security. 2020;27:100414.
  16. 16. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietary behaviors of US public school children. Journal of the American Dietetic Association. 2009;109(2):S91–S107. pmid:19166677.
  17. 17. Fox MK, Dodd AH, Wilson A, Gleason PM. Association between school food environment and practices and body mass index of US public school children. Journal of the American Dietetic Association. 2009;109(2):S108–S17. pmid:19166665.
  18. 18. Harrison F, Jones AP. A framework for understanding school based physical environmental influences on childhood obesity. Health & place. 2012;18(3):639–48. pmid:22281440
  19. 19. Neumark-Sztainer D, French SA, Hannan PJ, Story M, Fulkerson JA. School lunch and snacking patterns among high school students: associations with school food environment and policies. International Journal of Behavioral Nutrition and Physical Activity. 2005;2(1):14. pmid:16209716
  20. 20. Larson N, Story M. Are ‘competitive foods’ sold at school making our children fat? Health Affairs. 2010;29(3):430–5. pmid:20194984
  21. 21. Burgoine T, Jones AP, Brouwer RJN, Neelon SEB. Associations between BMI and home, school and route environmental exposures estimated using GPS and GIS: do we see evidence of selective daily mobility bias in children? International journal of health geographics. 2015;14(1):8. pmid:25656299
  22. 22. Engler-Stringer R, Le H, Gerrard A, Muhajarine N. The community and consumer food environment and children’s diet: a systematic review. BMC Public Health. 2014;14(1):522. pmid:24884443
  23. 23. Pearce A, Kirk C, Cummins S, Collins M, Elliman D, Connolly A, et al. Gaining children’s perspectives: A multiple method approach to explore environmental influences on healthy eating and physical activity. Health & place. 2009;15(2):614–21. pmid:19083258
  24. 24. Barr M, Signal L, Jenkin G, Smith M. Capturing exposures: using automated cameras to document environmental determinants of obesity. Health promotion international. 2014;30(1):56–63. pmid:25301856
  25. 25. Sobal J, Bisogni CA. Constructing food choice decisions. Annals of Behavioral Medicine. 2009;38(suppl_1):s37–s46. pmid:19787306.
  26. 26. Kearney J, Hulshof K, Gibney M. Eating patterns–temporal distribution, converging and diverging foods, meals eaten inside and outside of the home–implications for developing FBDG. Public Health Nutrition. 2001;4(2b):693–8. pmid:11683564
  27. 27. Turner C, Kalamatianou S, Drewnowski A, Kulkarni B, Kinra S, Kadiyala S. Food environment research in low-and middle-income countries: a systematic scoping review. Advances in Nutrition. 2020;11(2):387–97. pmid:31079142
  28. 28. Carducci B, Oh C, Keats EC, Roth DE, Bhutta ZA. Effect of Food Environment Interventions on Anthropometric Outcomes in School-aged Children and Adolescents in low-and Middle-Income Countries: A Systematic Review and Meta-Analysis. Current Developments in Nutrition. 2020. pmid:32666031
  29. 29. Downs S, Demmler KM. Food environment interventions targeting children and adolescents: A scoping review. Global Food Security. 2020;27:100403.
  30. 30. Boushey CJ, Kerr DA, Wright J, Lutes KD, Ebert DS, Delp EJ. Use of technology in children’s dietary assessment. European journal of clinical nutrition. 2009;63 Suppl 1(Suppl 1):S50–S7. pmid:19190645.
  31. 31. Doherty AR, Hodges SE, King AC, Smeaton AF, Berry E, Moulin CJ, et al. Wearable cameras in health: the state of the art and future possibilities. American journal of preventive medicine. 2013;44(3):320–3. pmid:23415132
  32. 32. Gemming L, Doherty A, Utter J, Shields E, Mhurchu CN. The use of a wearable camera to capture and categorise the environmental and social context of self-identified eating episodes. Appetite. 2015;92:118–25. pmid:26002278
  33. 33. Briggs L, Lake AA. Exploring school and home food environments: perceptions of 8–10-year-olds and their parents in Newcastle upon Tyne, UK. Public health nutrition. 2011;14(12):2227–35. pmid:21859512
  34. 34. Williams J, Scarborough P, Matthews A, Cowburn G, Foster C, Roberts N, et al. A systematic review of the influence of the retail food environment around schools on obesity‐related outcomes. Obesity reviews. 2014;15(5):359–74. pmid:24417984
  35. 35. Prosser J, Loxley A. Introducing visual methods. 2008.
  36. 36. Schrempft S, van Jaarsveld CH, Fisher A. Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview. Journal of medical Internet research. 2017;19(10):e332–e. pmid:29025695.
  37. 37. Signal LN, Smith MB, Barr M, Stanley J, Chambers TJ, Zhou J, et al. Kids’ Cam: an objective methodology to study the world in which children live. American journal of preventive medicine. 2017;53(3):e89–e95. pmid:28455122
  38. 38. Haddad L, Hawkes C, Waage J, Webb P, Godfray C, Toulmin C. Food systems and diets: Facing the challenges of the 21st century. 2016.
  39. 39. Fox EL, Timmer A. Children’s and adolescents’ characteristics and interactions with the food system. Global Food Security. 2020;27:100419.
  40. 40. Raza A, Fox EL, Morris SS, Kupka R, Timmer A, Dalmiya N, et al. Conceptual framework of food systems for children and adolescents. Global Food Security. 2020;27:100436.
  41. 41. UNICEF, GAIN. Food Systems for Children and Adolescents. Working Together to Secure Nutritious Diets. NEW YORK: UNICEF, 2019.
  42. 42. Headey DD, Alderman HH. The relative caloric prices of healthy and unhealthy foods differ systematically across income levels and continents. The Journal of nutrition. 2019;149(11):2020–33. pmid:31332436
  43. 43. Neumark-Sztainer D, French SA, Hannan PJ, Story M, Fulkerson JA. School lunch and snacking patterns among high school students: associations with school food environment and policies. Int J Behav Nutr Phys Act. 2005;2(1):14. pmid:16209716; PubMed Central PMCID: PMC1266392.
  44. 44. Larson N, Story M. Are ’competitive foods’ sold at school making our children fat? Health Aff (Millwood). 2010;29(3):430–5. pmid:20194984.
  45. 45. Kubik MYL, Leslie A.; Hannan, Peter J.; Perry, Cheryl L.; Story M. The association of the school food environment with dietary behaviors of young adolescents. American Journal of Public Health. 2003;93(7):1168–73. pmid:12835204
  46. 46. Keeley B, Little C, Zuehlke E. The State of the World’s Children 2019: Children, Food and Nutrition—Growing Well in a Changing World. UNICEF. 2019.
  47. 47. Pettigrew S, Pescud M, Donovan RJ. Stakeholder perceptions of a comprehensive school food policy in Western Australia. Health policy. 2012;108(1):100–4. pmid:22975118
  48. 48. Hansen S, Kanning M, Lauer R, Steinacker JM, Schlicht W. MAP-IT: A Practical Tool for Planning Complex Behavior Modification Interventions. Health Promotion Practice. 2017:1524839917710454. pmid:28557551
  49. 49. Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy nutrition environments: concepts and measures. American Journal of Health Promotion. 2005;19(5):330–3. pmid:15895534
  50. 50. Galvez MP, Hong L, Choi E, Liao L, Godbold J, Brenner B. Childhood obesity and neighborhood food-store availability in an inner-city community. Academic pediatrics. 2009;9(5):339–43. pmid:19560992
  51. 51. Skidmore P, Welch A, van Sluijs E, Jones A, Harvey I, Harrison F, et al. Impact of neighbourhood food environment on food consumption in children aged 9–10 years in the UK SPEEDY (Sport, Physical Activity and Eating behaviour: Environmental Determinants in Young people) study. Public health nutrition. 2010;13(7):1022–30. pmid:20082745
  52. 52. Powell LM, Auld C.M., Chaloupka F.J., O’Malley P.M., Johnson L.D. Access to fast food and food prices: Relationship with fruit and vegetable consumption and overweight among adolescents. The economics of obesity: Emerald Group Publishing Limited; 2006. p. 23–48.
  53. 53. Kearney JM, Hulshof K, Gibney MJ. Eating patterns–temporal distribution, converging and diverging foods, meals eaten inside and outside of the home–implications for developing FBDG. Public Health Nutrition. 2001;4(2b). pmid:11683564
  54. 54. Devine CM. A Life Course Perspective: Understanding Food Choices in Time, Social Location, and History. Journal of Nutrition Education and Behavior. 2005;37(3):121–8. pmid:15904575
  55. 55. Charreire H, Casey R, Salze P, Simon C, Chaix B, Banos A, et al. Measuring the food environment using geographical information systems: a methodological review. Public health nutrition. 2010;13(11):1773–85. pmid:20409354
  56. 56. Downs SM, Ahmed S, Fanzo J, Herforth A. Food Environment Typology: Advancing an Expanded Definition, Framework, and Methodological Approach for Improved Characterization of Wild, Cultivated, and Built Food Environments toward Sustainable Diets. Foods. 2020;9(4):532.
  57. 57. Mokhtar N, Elati J, Chabir R, Bour A, Elkari K, Schlossman NP, et al. Diet culture and obesity in northern Africa. The Journal of nutrition. 2001;131(3):887S–92S. pmid:11238780
  58. 58. Doggui R, El Ati J, Sassi S, Ben Gharbia H, Al‐Jawaldeh A, El Ati‐Hellal M. Unbalanced intakes of sodium and potassium among Tunisian adults: A cross‐sectional study. Food Science & Nutrition. 2021. pmid:33841839
  59. 59. UNICEF. Enquête par grappes à indicateurs multiples (MICS) 2018: rapport final des résultats. Enquête par grappes à indicateurs multiples (MICS) 2018: rapport final des résultats2019.
  60. 60. Hawkes C HJ, Gillespie S. Changing diets: Urbanization and the nutrition transition. Global Food Policy Report. Washington, DC: International Food Policy Research Institute; 2017. p. 34–41.
  61. 61. Moineddin R, Matheson FI, Glazier RH. A simulation study of sample size for multilevel logistic regression models. BMC medical research methodology. 2007;7(1):1–10. pmid:17634107
  62. 62. Townsend N. Shorter lunch breaks lead secondary-school students to make less healthy dietary choices: multilevel analysis of cross-sectional national survey data. Public health nutrition. 2015;18(9):1626–34. pmid:25191898
  63. 63. Saelens BE, Sallis JF, Frank LD, Couch SC, Zhou C, Colburn T, et al. Obesogenic neighborhood environments, child and parent obesity: the Neighborhood Impact on Kids study. American journal of preventive medicine. 2012;42(5):e57–e64. pmid:22516504
  64. 64. School Health Profiles 2014: Characteristics of Health Programs among Secondary Schools: U.S. Department of Health and Human Services; 2014. Available from: https://www.cdc.gov/healthyyouth/data/profiles/questionnaires.htm.
  65. 65. Lytle LA, Fulkerson JA. Assessing the dietary environment: examples from school-based nutrition interventions. Public health nutrition. 2002;5(6a):893–9. pmid:12633512
  66. 66. Story M, Neumark-Sztainer D, French S. Individual and environmental influences on adolescent eating behaviors. Journal of the American Dietetic Association. 2002;102(3):S40–S51. pmid:11902388
  67. 67. Semaan A, Zoughby Y, Akl C, Talhouk R, Gharbia HB, Doggui R, et al. Developing an innovative Tool to Quantify Schoolchildren’s Exposure to Obesogenic Environments. Current Developments in Nutrition. 2020;4(Supplement_2):1178–.
  68. 68. Nebeker C, Lagare T, Takemoto M, Lewars B, Crist K, Bloss CS, et al. Engaging research participants to inform the ethical conduct of mobile imaging, pervasive sensing, and location tracking research. Translational behavioral medicine. 2016;6(4):577–86. pmid:27688250
  69. 69. Mok TM, Cornish F, Tarr J. Too much information: visual research ethics in the age of wearable cameras. Integrative Psychological and Behavioral Science. 2015;49(2):309–22. pmid:25537955
  70. 70. Zoughby Y. Jr AI System In The Real World: Capture Children Food Exposure Using Wearable Cameras 2020.
  71. 71. Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, et al. NOVA. The star shines bright. World Nutrition. 2016;7(1–3):28–38.
  72. 72. Chow CK, Lock K, Madhavan M, Corsi DJ, Gilmore AB, Subramanian S, et al. Environmental Profile of a Community’s Health (EPOCH): an instrument to measure environmental determinants of cardiovascular health in five countries. PloS one. 2010;5(12):e14294. pmid:21170320
  73. 73. Mackenbach JD, Rutter H, Compernolle S, Glonti K, Oppert J-M, Charreire H, et al. Obesogenic environments: a systematic review of the association between the physical environment and adult weight status, the SPOTLIGHT project. BMC Public Health. 2014;14(1):233. pmid:24602291
  74. 74. Akl C. School neighborhood food environment and schoolchildren’s diets and nutrition in a middle-income Arab city. [Unpublished doctoral’s thesis, American University Of Beirut]. In press 2022.
  75. 75. Van Der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, van Lenthe F, et al. A systematic review of environmental correlates of obesity-related dietary behaviors in youth. Health education research. 2006;22(2):203–26. pmid:16861362
  76. 76. Rasmussen M, Krølner R, Klepp K-I, Lytle L, Brug J, Bere E, et al. Determinants of fruit and vegetable consumption among children and adolescents: a review of the literature. Part I: quantitative studies. International Journal of Behavioral Nutrition and Physical Activity. 2006;3(1):22. pmid:16904006
  77. 77. Birch LL, Fisher JO, Grimm-Thomas K, Markey CN, Sawyer R, Johnson SL. Confirmatory factor analysis of the Child Feeding Questionnaire: a measure of parental attitudes, beliefs and practices about child feeding and obesity proneness. Appetite. 2001;36(3):201–10. pmid:11358344
  78. 78. Kaur H, Li C, Nazir N, Choi WS, Resnicow K, Birch LL, et al. Confirmatory factor analysis of the child-feeding questionnaire among parents of adolescents. Appetite. 2006;47(1):36–45. pmid:16624444
  79. 79. Vereecken CA, Keukelier E, Maes L. Influence of mother’s educational level on food parenting practices and food habits of young children. Appetite. 2004;43(1):93–103. pmid:15262022
  80. 80. Mujahid MS, Diez Roux AV, Morenoff JD, Raghunathan T. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. American journal of epidemiology. 2007;165(8):858–67. pmid:17329713
  81. 81. Ma X, Barnes TL, Freedman DA, Bell BA, Colabianchi N, Liese AD. Test–retest reliability of a questionnaire measuring perceptions of neighborhood food environment. Health & place. 2013;21:65–9. pmid:23434497
  82. 82. Sahyoun NR, Nord M, Sassine AJ, Seyfert K, Hwalla N, Ghattas H. Development and validation of an Arab family food security scale. The Journal of nutrition. 2014;144(5):751–7. pmid:24598883
  83. 83. Poulsen MN, Bailey-Davis L, Pollak J, Hirsch AG, Schwartz BS. Household food insecurity and home food availability in relation to youth diet, body mass index, and adiposity. Journal of the Academy of Nutrition and Dietetics. 2019;119(10):1666–75. pmid:30858071
  84. 84. Food and Agriculture Organization (FAO). Guidelines for measuring household and individual dietary diversity 2012.
  85. 85. Parmenter K, Wardle J. Development of a general nutrition knowledge questionnaire for adults. European journal of clinical nutrition. 1999;53(4):298–308. pmid:10334656
  86. 86. Berge JM, Wall M, Larson N, Loth KA, Neumark-Sztainer D. Family functioning: associations with weight status, eating behaviors, and physical activity in adolescents. Journal of adolescent health. 2013;52(3):351–7.
  87. 87. Centers for Disease Control and Prevention (CDC). National Health and and Nutrition Examination Survey—Diet behavior and nutrition questionnaire 2018. Available from: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017.
  88. 88. Finch M, Begley A, Sutherland R, Harrison M, Collins C. Development and reproducibility of a tool to assess school food‐purchasing practices and lifestyle habits of Australian primary school‐aged children. Nutrition & Dietetics. 2007;64(2):86–92.
  89. 89. Gwynn JD, Flood VM, D’Este CA, Attia JR, Turner N, Cochrane J, et al. The reliability and validity of a short FFQ among Australian Aboriginal and Torres Strait Islander and non-Indigenous rural children. Public health nutrition. 2011;14(3):388–401. pmid:20633315
  90. 90. Hare-Bruun H, Nielsen BM, Kristensen PL, Møller NC, Togo P, Heitmann BL. Television viewing, food preferences, and food habits among children: a prospective epidemiological study. BMC Public Health. 2011;11(1):311.
  91. 91. Shah S, Foley BC, Molinari I, Lim K-S, Shrewsbury VA. The Students As LifeStyle Activists (SALSA) program. British Journal of Sports Medicine. 2017;51(19):1445–6. pmid:28583995
  92. 92. Flood V, G J., G J., T N., H L. Short survey instruments for children’s diet and physical activity: the evidence. 2016.
  93. 93. Al Sabbah H, Vereecken C, Kolsteren P, Abdeen Z, Maes L. Food habits and physical activity patterns among Palestinian adolescents: findings from the national study of Palestinian schoolchildren (HBSC-WBG2004). Public health nutrition. 2007;10(7):739–46. pmid:17381946
  94. 94. Centers for Disease Control and Prevention (CDC). Global School-based Student Health Survey (GSHS) Questionnaire. 2016. Available from: https://www.cdc.gov/gshs/questionnaire/index.htm.
  95. 95. Erhardt JG, Estes JE, Pfeiffer CM, Biesalski HK, Craft NE. Combined measurement of ferritin, soluble transferrin receptor, retinol binding protein, and C-reactive protein by an inexpensive, sensitive, and simple sandwich enzyme-linked immunosorbent assay technique. The Journal of nutrition. 2004;134(11):3127–32. pmid:15514286
  96. 96. Heard AM, Harris JL, Liu S, Schwartz MB, Li X. Piloting an online grocery store simulation to assess children’s food choices. Appetite. 2016;96:260–7. pmid:26409642
  97. 97. Mikkelsen BE, Høeg E, Mangano L, Serafin S. The virtual foodscape simulator gaming, designing and measuring food behaviour in created food realities. Proceedings of Measuring Behavior 2016. 2016.
  98. 98. Mötteli S, Keller C, Siegrist M, Barbey J, Bucher T. Consumers’ practical understanding of healthy food choices: a fake food experiment. British Journal of Nutrition. 2016;116(3):559–66. pmid:27256562
  99. 99. Wiseman N, Harris N, Downes M. Validation of an iPad activity to measure preschool children’s food and physical activity knowledge and preferences. International Journal of Behavioral Nutrition and Physical Activity. 2017;14(1):11. pmid:28143528
  100. 100. Soekhai V, de Bekker-Grob E, Ellis A, Vass C. PMU107-DISCRETE CHOICE EXPERIMENTS IN HEALTH ECONOMICS: PAST, PRESENT AND FUTURE. Value in Health. 2018;21:S325.
  101. 101. Huang TT, Drewnowski A, Kumanyika SK, Glass TA. A systems-oriented multilevel framework for addressing obesity in the 21st century. Preventing chronic disease. 2009;6(3). pmid:19527584
  102. 102. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health education quarterly. 1988;15(4):351–77. pmid:3068205
  103. 103. Duncan C, Jones K, Moon G. Health-related behaviour in context: A multilevel modelling approach. Social Science & Medicine. 1996;42(6):817–30. pmid:8778995
  104. 104. Jones K, Duncan C. Individuals and their ecologies: analysing the geography of chronic illness within a multilevel modelling framework. Health & Place. 1995;1(1):27–40.
  105. 105. Krull JL, MacKinnon DP. Multilevel Modeling of Individual and Group Level Mediated Effects. Multivariate Behavioral Research. 2001;36(2):249–77. pmid:26822111
  106. 106. Snijders TAB. Multilevel Analysis. In: Lovric M, editor. International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 879–82.
  107. 107. Hillier-Brown FC, Bambra CL, Cairns J-M, Kasim A, Moore HJ, Summerbell CD. A systematic review of the effectiveness of individual, community and societal level interventions at reducing socioeconomic inequalities in obesity amongst children. BMC public health. 2014;14(1):1–18. pmid:25113624
  108. 108. Rutter H, Bes-Rastrollo M, De Henauw S, Lahti-Koski M, Lehtinen-Jacks S, Mullerova D, et al. Balancing upstream and downstream measures to tackle the obesity epidemic: a position statement from the European Association for the Study of Obesity. Obesity facts. 2017;10(1):61–3. pmid:28245444