Impact of competitive foods in public schools on child nutrition: effects on adolescent obesity in the United States an integrative systematic literature review

Figures & data

Table 1. MEDLINE and CINAHL Prescreened Combined Search.

Search Terms	Keywords	Number of articles
S1	School OR schools	3,155,522
S2	USA OR US OR US OR USA of America	1,885,884
S3	‘food’ OR ‘food preferences’ OR ‘food habits’ OR ‘health behavior’ OR ‘feeding behavior’ OR ‘child nutrition’	1,114,267
S4	‘weight gain’ OR ‘overweight’ OR ‘BMI’ OR ‘Body Mass Index’ OR ‘Body Composition’ OR ‘Obesity’ OR ‘Adolescent Obesity’ OR ‘Pediatric Obesity’ OR ‘body weight’	927,091
S5	‘nutrition policy’ OR ‘health promotion’ OR ‘health policy’ OR ‘school health policy’	442,801
S6	‘food services’ OR ‘competitive food’ OR ‘competitive foods’ OR ‘commercial food’ OR ‘beverages’ OR ‘carbonated beverages’ OR ‘sugar-sweetened beverages’ OR ‘vending machines’ OR ‘food dispensers’ OR ‘food dispensers, automatic’ OR ‘foods of minimal nutritional value’ OR ‘FMNV’	91,114
S7	S1 AND S2 AND S3 AND S4 AND S5 AND S6	366
	S1 AND S2 AND S3 AND S4 AND S5 AND S6 sorted with Limiters: (Publication date, source type of academic journal, subject age (adolescent 13–18), English language, geography (USA, USA, Texas, Baltimore, California, Illinois, Los Angeles, Midwestern USA, Alaska, Appalachian region, Colorado, Michigan, mid-Atlantic region, New York City, Washington)	125
	*Relevant number of articles after screening abstracts from the combined search of MEDLINE and CINAHL: 73

Table 2. EMBASE Prescreened Database Search.

Search Terms	Keywords	Results
#1EMBASE aspect #1 obesity terms 2017–03–022017–03-021193364	‘weight gain’/exp OR ‘weight gain’ OR ‘overweight’/exp OR ‘overweight’ OR ‘bmi’/exp OR ‘bmi’ OR ‘body mass index’/exp OR ‘body mass index’ OR ‘body composition’/exp OR ‘body composition’ OR ‘obesity’/exp OR ‘obesity’ OR ‘adolescent obesity’/exp OR ‘adolescent obesity’ OR ‘pediatric obesity’/exp OR ‘pediatric obesity’ OR ‘body weight’/exp OR ‘body weight’	1,193,364
#2 EMBASE aspect #1 obesity terms 2017–03–022017–03-0215	AND [adolescent]/lim AND ‘childhood obesity’/de AND ‘adolescent disease’/de AND (‘abdominal obesity’/de OR ‘adolescent obesity’/de OR ‘obesity’/de) AND (2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py) AND ‘Article’/it	15
#3 EMBASE aspect #2#039 food' terms 2017–03–022017–03-021748981	‘food’/exp OR ‘food’ OR ‘food preferences’/exp OR ‘food preferences’ OR ‘food habits’/exp OR ‘food habits’ OR ‘health behavior’/exp OR ‘health behavior’ OR ‘feeding behavior’/exp OR ‘feeding behavior’ OR ‘child nutrition’/exp OR ‘child nutrition’	1,748,981
#4 and #3 EMBASE aspect #2 ' food' terms2017-03–022017-03–023896	AND ‘obesity’/de AND [adolescent]/lim AND ‘Article’/it AND (2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py)	3,896
#5 EMBASE aspect #3 ' US" terms2017-03–022017-03–021541184	united AND states OR ‘u.s.’/exp OR u.s. OR us OR united AND states AND of AND (‘America’/exp OR America)	1,541,184
# 5 and #6 EMBASE aspect #3 ' US" terms2017-03–022017-03–021761	AND ‘obesity’/de AND ‘Article’/it AND [adolescent]/lim AND (2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py)	1,761
#7 EMBASE aspect #4 ' school' terms2017-03–022017-03–024727654	‘school’/exp OR school OR ‘schools’/exp OR schools	4,72 7,654
#8 and #7 EMBASE aspect #4 ' school' terms2017-03–022017-03–028174	AND ‘obesity’/de AND [adolescent]/lim AND ‘Article’/it AND (2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py)	8,174
#9 EMBASE aspect #5 ' nutrition policy' terms2017-03–022017-03–02316924	‘nutrition policy’/exp OR ‘nutrition policy’ OR ‘health promotion’/exp OR ‘health promotion’ OR ‘health policy’/exp OR ‘health policy’ OR ‘school health policy’	316,924
#10 and #9 EMBASE aspect #5 ' nutrition policy' terms2017-03-022017-03–021187	AND ‘obesity’/de AND [adolescent]/lim AND ‘Article’/it AND (2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py)	1,187
#11 EMBASE aspect #6 ' competitive food' terms2017-03-022017-03–02279682	‘food services’/exp OR ‘food services’ OR ‘competitive food’ OR ‘competitive foods’ OR ‘commercial food’ OR ‘beverages’/exp OR ‘beverages’ OR ‘carbonated beverages’/exp OR ‘carbonated beverages’ OR ‘sugar-sweetened beverages’ OR ‘vending machines’ OR ‘food dispensers’ OR ‘food dispensers, automatic’/exp OR ‘food dispensers, automatic’ OR ‘foods of minimal nutritional value’ OR ‘fmnv’	279,682
#12 and #11 EMBASE aspect #6 ' competitive food' terms2017-03-022017-03–02652	AND ‘obesity’/de AND [adolescent]/lim AND ‘Article’/it AND (2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py)	652
Aspects #1-#6 combined with limitations	Search with limitations: Disease (obesity), Age (adolescent), Publication types (article), Publication years (2006–2017)	39
	*Results After Screening for Duplicates with Other Databases that are Relevant to Aim:17

Figure 1. PRISMA Diagram.

Moher et al. [72].

Table 3. Excluded Articles with Reasons.

References:	Reasons for Exclusion from Review:
Cisse-Egbuonye et al. [32]	Study identified the types of foods sold in vending machines and school stores in schools, discussed the availability and consumption associated with student purchase, but does not address weight/BMI/or caloric intake.
Gordon et al. [66]	Discusses availability of competitive foods in schools, school meal programs, in the prevalence of overweight and obesity. Does not address calories, BMI, or overall weight differences due to competitive foods in schools directly. Consumption, and availability, not weight.
Hawkes et al. [67]	Evaluates district policies addressing student wellness, developed an approach to determine best changes in policy to access to health food/drink in school cafeteria lunches. Does not address weight/BMI/caloric intake.
Kristensen et al. [68]	Discusses SSB taxes and impact on BMI in adolescents, but does not directly address this in school settings specifically.
Kubik et al. [20]	Discusses junk food in school vending machines and school stores and school nutrition policies, however does not address weight/BMI/caloric intake specifically. Consumption, not weight.
Schwartz et al. [69]	Discusses perceptions of weight and diet with regards to consumption of snack foods sold in schools, but actual weight, BMI, or calories were not measured or evaluated. Consumption, not weight.
Thompson et al. [70]	Primary purpose was to examine purchasing behaviors with school policy. Calculated purchasing behavior and dietary consumption, but did not address the research question on weight/BMI/or caloric intake.
Wordell et al. [71]	Study discusses school food environments, healthier school policies, and consumption and choice making of competitive foods. Does not address weight/BMI/caloric intake specifically. Consumption, not weight.

Table 4. Summary of Aspects Examined in Literature for Review.

Articles	Study Design	BMI/Obesity/Weight	Kcalories Consumed or measured	Availability of competitive foods in schools	Analysis of Policy	Other effects: SES/Race/Ethnicity/Sex
Anderson & Butcher [14]	Non-experimental design:Observational; cross sectional	Yes	No	Yes	Yes	No
Bauhoff, S. [51]	Non-experimental design: Observational: cohort and cross-section	Yes	Yes	No	Yes	Yes
Briefel et al. [63]	Non-experimental design: Observational; Cross-sectional	No	Yes	Yes	No	Yes
Briefel et al. [60]	Non-experimental design: Cross-sectional	No	Yes	Yes	No	Yes
Briefel et al. [61]	Non-experimental design: cross-sectional	No	Yes	Yes	Yes	Yes
Cradock et al. [43]	Quasi-experi-mental design:	Yes	Yes	Yes	Yes	Yes
Fletcher et al. [59]	Non-experimental design: Observational; Cross Sectional	Yes	Yes	Yes	Yes	No
Fox et al. [49]	Non-experimental design: Observational: cross sectional	Yes	Yes	Yes	No	No
Fox et al. [58]	Non-experimental design: Observational: cross-sectional	Yes	Yes	Yes	No	No
Hartstein et al. [44]	Non-Experimental Design: Observational; Cross-sectional	No	Yes	No	No	Yes
Hennessey et al. [26]	Non-experimental design: Observational; cross-sectional	Yes	No	Yes	Yes	Yes
Kakarala et al. [56]	Non-experimental design: Observational; Cross-sectional	Yes	Yes	Yes	No	Yes
Levy & Friend [62]	Experimental design: virtual experiment	No	Yes	Yes	Yes	No
Mendoza et al. [57]	Non-experimental design: Observational; Cross Sectional	Yes	Yes	No	Yes	Yes
Nanney et al. [47]	Non-experimental design: cross sectional; This study used quali-tative and quanti- tative methods	Yes	No	Yes	Yes	No
Park et al. [23]	Non-experimental design: Observational, cross sectional	Yes	No	Yes	No	Yes
Riis et al. [52]	Non-experimental design: Observational; Cross Sectional	Yes	No	Yes	Yes	No
Sanchez-Vaznaugh et al. [53]	Non-experimental design: Observational; cross sectional	Yes	No	No	Yes	Yes
Smith & Holloman [45]	Quasi-Experi- mental design: piloted inter-vention	Yes	No	Yes	No	Yes
Snelling et al. [46]	Non-experimental design: Observational; quasi-experi-mental	No	Yes	Yes	No	No
Taber et al. [3]	Non-experimental design: Observational: cross sectional	Yes	No	Yes	Yes	Yes
Taber et al. [54]	Quasi-experimental design: longi-tudinal study	Yes	No	Yes	Yes	Yes
Taber et al. [64]	Quasi-experimental design: observational study	No	Yes	No	Yes	Yes
Terry-McElrath et al. [50]	Non-experimental design;Observational; cross-sectional	Yes	No	Yes	No	Yes
Van Hook & Altman [16]	Quasi-experimental design: a longi-tudinal research design	Yes	No	No	No	Yes
Wiecha et al. [55]	Non-experimental design: Observational; cross-sectional analysis	Yes	Yes	Yes	No	Yes
Total		19	15	20	14	18

Table 5. Summary of Method of Analysis.

Articles	Factor Analysis (Clusters)	Association Analysis (multi-variate; regression; bi-variate; odds ratio)	Comparative Analysis (t-tests; comparisons; chi-Squared; correlations; ANOVA)	Descriptive statistical Analysis
Anderson & Butcher [14]		X	X
Bauhoff, S. [51]				X
Briefel et al. [63]		X		X
Briefel et al. [61]			X	X
Briefel et al. [60]			X	X
Cradock et al. [43]		X		X
Fletcher et al. [59]			X	X
Fox et al. [49]		X		X
Fox et al. [58]			X	X
Hartstein et al. [44]	X
Hennessy et al. [26]		X		X
Kakarala et al. [56]			X	X
Levy & Friend [62]				X
Mendoza et al. [57]			X
Nanney et al. [47]	X
Park et al. [23]		X	X	X
Riis et al. [52]		X
Sanchez-Vaznaugh et al. [53]		X		X
Smith & Holloman [45]			X	X
Snelling et al. [46]				X
Taber et al. [64]		X
Taber et al. [3]	X	X
Taber et al. [54]	X		X
Terry-McElrath et al. [50]	X
Van Hook & Altman [16]	X			X
Wiecha et al. [55]	X	X	X
Total:	7	11	11	16

Appendix 1. Sample of Variables for Data Collection Table (inspired by Ursi [65]; Souza et al [38]).

1. Identification
Title of the Article
Title of the Journal
Authors
Country	United States
Language	English
Year of publication
2. Type of publication
Nursing publication
Medical publication
Publication in another area of health? Which area?
1. Methodological characteristics of the study
2. Type of publication	3. Research () Quantitative approach () Experimental design () Quasi-experimental design () Non-experimental design:
1. Objective or investigation question/AIM:
2. Sample	3. Selection 4. () Random 5. () Convenience 6. () Other: 7. Size (n) 8. () Initial: 9. () Final: 10. Characteristics 11. Age: 12. Sex: M () F () 13. Race ______________________________________________ 14. Inclusion/Exclusion criteria of subjects:
15. Data Analysis
16. Interventions performed	17. Independent variable: 18. Dependent variable: 19. Control group: yes () no (): 20. Measurement instrument yes () no (): 21. Duration of the study: 22. Methods employed to measure the intervention:
23. Evidence level and Quality based on: Ackley et al. [41] Detsky et al. [42]

Appendix 2. Summary of the 26 articles in this Review.

Reference	Aim/ Objective	Design	Methods/ Data1. Definition of the subjects 2. Size of the sample 3. Methodology 4. Measuring variables 5. Method of analysis	Eval. Meas. Wt. BMI Kcal	Level of Evidence Ackley, et al. [41] Quality Detsky et al. [42]
Anderson & Butcher [14]	“We examine whether schools under financial pressure tend to adopt potentially unhealthful food policies and whether students’ Body Mass Index (BMI) is higher where they are more likely to be exposed to these food policies.”	Non-experimental design: Observational; cross sectional	1. Grades 1–12. 2. 451 public middle and high schools. 3. Two-sample methodology. ‘We use school food policy information from the representative sample of public schools surveyed in the SHPPS to create our proxy variable.’ 4. Three different policies: junk food availability in schools, whether schools have “pouring rights” contracts, and whether soda and snack food advertisements are allowed at schools or school events. 5. Statistical analysis; ‘our approach uses an auxiliary regression to predict exposure to a food policy, which is unobservable in the data. Standard errors were adjusted for arbitrary correlation within the state.’	BMI	Level IV; Low Quality
Bauhoff, S. [51]	“This paper evaluates the impact of an early nutrition policy, Los Angeles Unified School District’s food-and-beverage standards of 2004, using two large datasets on food intake and physical measures.”	Non-experimental design: Observational: cohort and cross-section	1. 12–13 and 14–15. 2. Middle school (206,718), High School (198,262). 3. ‘A data-driven approach was used to construct a “synthetic”’ control group that closely resembles the treatment unit in the pre-intervention period. Then generated the synthetic control group as a preprocessing step and then estimate a two-period difference-in-difference with additional individual and school level controls. The synthetic districts are constructed in three steps that were applied separately for the two datasets because of their different geographic coverage.’ 4. ‘Estimate the treatment effect using these four cohort and cross-section synthetic control groups, one for each the PFT and CHKS data, and for the cohort and cross-section samples. Since the Los Angeles beverage policy starts in January 2004, data were compared from Spring 2003 and Spring 2005.’ 5. Descriptive Statistical Analysis used.	Kcal	Level IV: Low Quality
Briefel et al. [60]	“To describe patterns of consumption of “empty calories”—low-nutrient, energy-dense foods, including sugar-sweetened beverages—by eating location among National School Lunch Program (NSLP) participants and nonparticipants.”	Non-experimental design: Observational; Cross-sectional	1. Grades 1–12. 2. 2,314 children, including 1,386 NSLP participants. 3. Twenty-four–hour dietary recalls, child surveys, and parent surveys were collected for each selected child. 4. One 24-hour recall was collected for each child and a second day’s intake on a subsample using the USDA Automated Multiple Pass Method software. 5. Descriptive Statistical Analysis used. ‘Comparisons, using t tests, of the proportion of children consuming low-nutrient, energy dense foods and beverages, mean daily energy and energy from low-nutrient, energy-dense foods, and energy density by NSLP participation status.’	Kcal	Level IV: Low Quality
Briefel et al. [63]	“To estimate the effects of school food environments and practices, characterized by access to competitive foods and beverages, school lunches, and nutrition promotion, on children’s consumption of sugar-sweetened beverages, low-nutrient energy-dense foods, and fruits/ vegetables at school.”	Non-experimental design: Cross-sectional	1. 287 schools (in 94 districts) with child-level dietary recall data; 2,314 children, grades 1–12. 2. Children aged 6 to 18 years. 3. ‘Based on the questionnaire, checklist, and menu data, we created 20 binary variables that indicated the presence of a healthful school food policy, practice, or environmental characteristic and grouped them into three domains: wellness policies and nutrition promotion practices of the district or school, competitive foods and beverages and related school practices, and characteristics of USDA lunches offered and practices related to school meals.’ 4. Food and nutrient consumption for this analysis was based on a single 24-hour dietary recall collected using the USDA Automated Multiple Pass Method software. 5. ‘Ordinary least squares regression was used to identify the association between school food environments and practices and dietary outcomes, controlling for other school and child/ family characteristics. To examine whether school food environments and practices were associated with children’s dietary outcomes at school, binary and multivariate analyzes were conducted. Descriptive analysis was conducted to estimate the prevalence of individual school food policies and practices by school type and boys’ and girls’ consumption of beverages, low-nutrient, energy-dense foods, and fruits and vegetables. Multivariate analysis was conducted to identify the relationship between school food environments and practices and dietary outcomes.’	Kcal	Level IV: Low Quality
Briefel et al. [61]	“The objective of this study was to estimate the mean calories from added sugars potentially saved by healthier beverage selections at both home and school. The study tested the hypothesis that calories saved would be greater for SSB’s than flavored milks, and that away-from-school savings would be greater than at-school savings.”	Non-experimental design: cross-sectional	1. Grades 1–12. 2. 2,314 students in grades 1 through 12 (ages 6 to 18 years), distributed among 287 schools at the elementary (n732), middle (n787), and high school (n795). 3. 24-hour dietary recalls, child/parent surveys on demographics, school meal program participation, and family eating habits were collected for each selected child in spring of 2005. 4. Descriptive analyzes of calories and added sugars from SSB’s and flavored milks by location and replacement simulation were completed using SAS, incorporating appropriate sampling weights for schoolchildren and design effects caused by the SNDA-III complex sample design. 5. Descriptive analyzes used. ‘A t test of mean differences between population subgroups was conducted to determine which groups would benefit most from beverage changes.’	Kcal	Level IV: Low Quality
Cradock et al. [43]	“The Boston Public Schools passed a policy restricting sale of sugar-sweetened beverages in Boston schools in June 2004. The objective of this study was to determine whether high school students’ consumption of sugar-sweetened beverages declined after this new policy was implemented.”	Quasi-experi-mental design:	1. Students in grades 9 through 12 (15–19). 2. 2004, 1,079 students from 17 H.S. participated; in 2006, 1,233 students from 18 H.S.’s. 3. Used data on consumption of SSB’s by H.S. students who participated in the Boston Youth Survey during February - April 2004 and February - April 2006 (N = 2,033). ‘We compared the observed change with national trends by using data from the 2003–2004 and 2005–2006 Health and Nutrition Examination adjusted for student demographics. We obtained dietary recall data from adolescents 15–19 years surveyed during the NHANES 2003–2004 and 2005–2006 periods. For analysis of national trends in consumption of SSB’s, we used the 24-hour recall interview component of the NHANES survey that documented the type, quantity, and location of each beverage consumed, researchers coded beverage items according to the (USDA) Food and Nutrient Database.’ 4. The outcome measure of per capita total k/cal consumed per day SSB’s included consumption of soda, sport drinks, fruit drinks and punches, low-calorie drinks, sweetened tea, and other sweetened beverages. 5. Descriptive Statistical Analysis. ‘We used linear regression analysis to examine changes in mean servings per day of SSB’s between 2004 and 2006, adjusting for potential differences in student composition.’	Kcal	Level III: Mod. Quality
Fletcher et al. [59]	“In this paper, we outline important concepts related to these policies. We present new empirical evidence of the likely effectiveness of school vending machine restrictions and increasing taxes on soft drinks on reducing obesity rates in children. We conclude by discussing how the effectiveness of these policies might be increased.”	Non-experimental design: Observational; Cross Sectional	1. 5th (2004) −8th grades (2007). 2. None provided. 3. ‘Using data on soft drink consumption and vending machine restrictions from the nationally representative ECLS-K. We focused on the 5th grade (2004) and 8th grade (2007) survey waves, to measure soft drink consumption and accessing data from (NHANES) III (1988–1994) and IV (1999– 2006). Individual foods and beverages from the dietary recall were coded and classified using the USDA’s Survey Nutrient Databases. We examined the effects of soft drink taxes on soft drink consumption and weight among children and adolescents using data from (NHANES) III (1988–1994) and IV (1999– 2006).’ 4. Soft drink consumption and accessing data; Indiv. foods and beverages were coded and classified; soft drink consumption and weight were examined. 5. Descriptive Statistical Analysis used. Comparisons used for BMI levels across ages and sexes.	BMI	Level IV: Low Quality
Fox et al. [49]	“To examine the association between school food environments and practices and children’s body mass index (BMI; calculated as kg/m2). In this article, we examine associations between school food environments and practices and children’s weight status, as measured using BMI.”	Non-experimental design: Observational: cross sectional	1. Grades 1–12. 2. 287 schools and 2,228 children. 3. 287 schools (in 94 school food authorities) in which site visits were completed to inventory competitive food sources and collect child-level data, and 2,228 children who had valid height and weight data; Foodservice managers completed a menu survey that provided information about the foods offered in school meals each day during a specified “target week.” 4. Students and parents completed brief interviews that collected data on sociodemographic characteristics of the child’s household, as well as information about the child’s level of physical activity, the amount of time the child spent watching television or videos, using the computer, or playing video games, recent dieting behavior, and parental perceptions about how the child’s usual food intake and activity level compared to other children of the same age and sex. Sampled children were measured and weighed. 5. Descriptive Statistical Analysis used. ‘Ordinary least squares regression was used to estimate the associations between school food environments and practices and BMI z scores and logistic regression was used to estimate associations between school food environments and practices and the likelihood of obesity.’	BMI	Level IV: Low Quality
Fox et al. [58]	“To describe the availability of competitive foods in US public schools, consumption of competitive foods by children, and contributions of competitive foods to energy intakes.”	Non-experimental design: Observational: cross-sectional	1. Grades 1–12. 2. 287 schools and 2,314 children. 3. SNDA-III data were collected between January and June 2005. Food service managers in each sampled school completed a menu survey that provided information about the foods offered in school meals each day during a specified “target week. Trained field interviewers completed observation checklists that documented the competitive food sources available in each school and the specific types of food that were offered. Three separate checklists were completed to document competitive foods available in each school from three major sources: foods available for à la carte purchase in the cafeteria; vending machines; and other on-campus food sources. Observations were conducted on one randomly selected day during the time interviewers were at the school to collect 24-hour dietary recalls from children. Interviewers also identified all of the specific locations within the school where children could obtain foods and beverages and assigned a separate code to each location. Interviewers used information provided by the food service manager to rank each location on the relative availability of competitive foods. Rankings indicated that all, most, about half, a small amount, or none of the foods sold/served at the location were part of subsidized school meals. Sampled children were interviewed in person. 4. Foods available for à la carte purchase in the cafeteria; vending machines; and other on-campus food sources; location of food sources; 24-hour recall interviews. 5. Descriptive Statistical Analysis used. ‘Most analyzes were limited to estimation of means and proportions. Two-tailed t tests were used to test the significance of differences between children who did and did not eat a school lunch.’	Kcal	Level IV: Low Quality
Hartstein et al. [44]	“Document whether a 6-week snack bar/a la carte line intervention improved kilocalories, macronutrients, and food offerings purchased per student and per item sold (nutrient density) compared to baseline.”	Non-Experimental Design: Observational; Cross-sectional	1. Students grades 6–8. 2. 6,248 students. 3. Reducing all reg. chips serv. size bags to <1.5 oz., increase lower-fat chip offerings by 25%; offer bottled water in a 20-oz. size, and limit all sweetened beverages to < 12 oz. 4. To assess if nutrient density of items sold was improved by the intervention, kilocalories, fat, and % fat from items sold per day were divided by the total number of items sold that day. 5. Statistical analyzes used. ‘Mixed models, accounting for the clustering of observations within schools, were used to analyze changes between baseline and week 6 for two models.’	Kcal	Level IV Low Quality
Hennessy et al. [26]	“This study attempted to determine whether state laws regulating low nutrient, high energy-dense foods and beverages sold outside of the reimbursable school meals program (referred to as ‘‘competitive foods’’) are associated with children’s weight status.”	Non-experimental design: Observational; cross-sectional	1. 11–14. 2. 16,271 interviews. 3. ‘The National Cancer Institute’s Classification of Laws Associated with School Students (CLASS) database was used to evaluate the stringency of codified school nutrition laws for each of the 50 states and the District of Columbia. Competitive food laws were scored in relation to the Institute of Medicine standards.’ 4. Independent variable: a composite score of competitive food and beverage laws for middle schools. Dependent variable: overweight and obesity weight status defined as (BMI). 5. Descriptive Statistical Analysis used. Bivariate and multivariate logistic analyzes performed.	BMI	Level IV: Low Quality
Kakarala et al. [56]	“Excluding a` la carte items from competitive foods, the objectives were to: (1) assess competitive food use by gender, ethnicity, eligibility for free or reduced-price meals, and participation in school meals programs, (2) determine differences between grade levels in energy intakes obtained from food sources, (3) determine the nutrient intake derived from competitive foods for students who consumed them, and (4) determine energy-adjusted differences in 24-hour nutrient intakes of protein, calcium, iron, & other selected nutrients betwn compet. food consumer and non-consumers.”	Non-experimental design: Observational; Cross-sectional	1. Grades 1–12. 2. 2309 students. 3. ‘Competitive foods/beverages use, excluding a la carte items, was examined using the third School Nutrition Dietary Assessment Study (SNDA III), a nationally representative sample of 2309 schoolchildren in grades 1 to 12. Trained interviewers administered questionnaires to obtain 24-hour food recall data on a school day. All students of middle schools and high schools self-reported their dietary intakes.’ 4. (SNDA III). Data on food source, Competitive foods/beverages use, dietary interview data were collected, reported food items to food composition data; gender (male, female), ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), income eligibility for NSLP/SBP (eligible for free meals, eligible for reduce-price meals, not eligible), and participation in NSLP/SBP. 5. Descriptive statistical analysis used. Analysis of variance techniques were employed to test significance of differences between groups while controlling for covariates.	Kcal	Level IV: Low Quality
Levy & Friend [62]	“In this paper, we develop a simulation model of the pathways of policy effects of school-based access to nutrition, school-based education and SSB tax policies on the consumption of SSB’s by youth, and the resulting effects on caloric intake. The model will embody a systematic approach that considers the effect of various school nutrition policies on the number of SSB calories consumed inside and outside of school, and how changes in SSB consumption affect the consumption of other foods (more specifically, their associated caloric intake).”	Experimental design: virtual experiment	1. Review of literature and policies. 2. None provided. 3. ‘We reviewed the literature on the direct effects of school nutrition policies on SSB consumption, the effect of price on SSB consumption (with a focus on youth), the effects of reduced consumption of SSB’s in school on the consumption of other foods in school (LNEDs or otherwise), the effects of SSB consumption in school on the consumption of SSB’s and other foods outside of school, and the effects of SSB consumption on caloric intake (both through direct reductions in the amount consumed and the effect on the consumption of other foods).’ 4. ‘We apply those estimates where feasible in our current modeling approach and incorporate new studies conducted since that review. We limit the analysis to the effect of SSB policies on overall caloric intake.’ 5. Statistical Descriptive Analysis used.	Kcal	Level II-III; Mod. Quality
Mendoza et al. [57]	“The study’s objective was to assess the impact of the Texas Public School Nutrition Policy on children’s energy density by using a pre- and post-policy evaluation. Specifically, the research objectives were to examine the changes in energy density after the policy, determine whether socioeconomic status moderated the impact of the policy on energy density, identify changes in the contribution of individual food groups to energy intake after the policy was implemented, and determine whether socioeconomic status mod. changes by individual food groups to energy intake.”	Non-experimental design: Observational; Cross Sectional	1. 6–8th grade students. 2. 2,616 students 2001–2002; 10,172 students 2005–2006. 3. 6–8th grade students completed anonymous lunch food records immediately after lunch in the cafeteria. ‘On each school day during the week, research assistants selected either one or two tables of students at each lunch period and asked students to complete a food record for lunch consumption only. Lunch tables were selected starting at one side of the cafeteria and then on subsequent days, tables closer to the opposite side of the cafeteria were selected until all tables had been sampled. This process was then repeated throughout the entire school year, September through May. Research staff did not collect data on refusals.’ 4. ‘Graphic data were collected in year 1. From the food records, data were entered into the Nutrition Data System to obtain average daily lunch intake of calories and the average gram weight of food at lunch.’ 5. Analysis of variance (ANOVA) was used to address the study objectives. Two-way ANOVAs, with year and (SES) as factors, were used to identify differences in energy density between academic years and school SES. To control for inflated type I errors caused by multiple testing, the Bonferroni correction was used and the significance level was set to 0.0167.	Kcal	Level IV: Low Quality
Nanney et al. [47]	“The primary aim of this paper was to develop a comprehensive evaluation approach to describe the policy environment related to school-based obesity prevention efforts in each of the 50 US states. A secondary aim was to examine the cross-sectional associations between current state policy environments and youth obesity prevalence.”	Non-experimental design: cross sectional; This study used quali-tative and quanti- tative methods	1. Ages 10 –17. 2. US student populations 10–17 across 50 states in the US 3. ‘Using 2006 (SHPPS) state data, qualitative and quantitative methods were used (2008 –2009) to construct domains of state-level school obesity prevention policies and practices, establish the validity and reliability of the domain scales, and examine their associations with state-level obesity prevalence among youth aged 10 –17 years from the 2003 National Survey of Children’s Health’ 4. Used state-level data from SHPPS 2006. scaled scores were developed for all state-level obesity-related policies and practices, for areas of FSN, PAD, and WA. 5. Statistical Analysis. To manage the extensive SHPPS policy data set, investigators used principal component analysis (PCA) to create an initial set of policy clusters.	Wt.	Level IV: Low Quality
Park et al. [23]	“This cross-sectional study examined the prevalence of students buying snacks or beverages from school vending machines instead of buying school lunch and predictors of this behavior. the purpose of this study was to examine the prevalence and behavioral predictors of students in middle school who purchase items from school vending machines instead of purchasing a traditional cafeteria lunch.”	Non-experimental design: Observational, cross sectional	1. Grades 6–8. 2. 4,322 students. 3. Students were asked, “During the previous five school days, how many days did you buy a snack or beverage from the vending machine instead of buying lunch?” 4. Analyzes were based on the 2003 YPANS using a representative sample of 4,322 students in grades 6-8 in 73 Florida public middle schools. Student and school characteristics were examined. Data were weighted by sex, grade, and region to represent all Florida public middle school students in grades 6–8. Outcome measure was buying a snack or beverage from vending machines 2 or more days during the previous 5 days instead of buying lunch. 5. Statistical Analyzes included Chi-Squared tests and logistic regression. Descriptive statistics were expressed as proportions. Chi-Squared tests were used to examine differences within categories. Odds ratios and 95% confidence intervals (CIs) for risk factors for buying lunch from vending machines were calculated using multivariable logistic regression. Interactions between exposure variables were also examined.	BMI	Level IV: Low Quality
Riis et al. [52]	“The objective of this study was to examine relations between state-level school policies and childhood obesity for youth ages 10–17 years.”	Non-experimental design: Observational; Cross Sectional	1. Students grade 5–12. 2. Children aged 10–17. 3. Wilcoxon signed rank sum tests were used to examine the extent of change in state nutrition and PE policies from 2003 to the most recent assessment (2006 for nutrition and 2007 for PE policies). Tests were also used to compare the comprehensiveness of the most recent nutrition and PE policy domain scores across school-type (ES, MS and HS). 4. 11 nutrition and 5 physical education (PE) domains were examined for elementary (ES), middle (MS), and high school (HS) children. 5. Bivariate logistic regression examined the relations of each independent variable, including individual, family and neighborhood factors, with the odds of being obese.	BMI	Level IV: Low Quality
Sanchez-Vaznaugh et al.[53]	“This is one of the first studies examining the postulated population-level influence of recently implemented policies aimed at sales of competitive foods and beverages in schools.”	Non-experimental design: Observational; cross sectional	1. Fifth and seventh graders. 2. 475,389 (2001); 602,567 (2008) 3. ‘The Fitnessgram database includes information on children’s (BMI) age, sex, race and ethnicity, grade, and physical fitness test results. To account for school- and district-level characteristics, we merged 2001–2008 Fitnessgram data with school and district data from the California Department of Education’s databases and 2000 census data.’ 4. BMI values were compared to the (CDC’s) 2000 growth charts. ‘Student-level explanatory variables included age, race and ethnicity, and physical fitness. characteristics of the students, schools, and districts, overall and by year, for the LA Unified School District and the rest of California were measured separately.’ 5. ‘We estimated the characteristics of the students, schools, and districts, overall and by year, for the LA Unified School District and the rest of California separately, using means and standard deviations or frequencies as appropriate. We estimated adjusted overweight prevalence for each study year and the rate of change, or trend, of overweight prevalence using multilevel multiple logistic regression models.’	BMI	Level IV; Low Quality
Smith & Holloman [45]	“The purpose of the project was to examine school-based purchasing patterns of SSB and explore the impact of a school-based and student-led intervention aimed at limiting short-term and longer-term SSB consumption. To understand SSB consumption patterns, both daily servings and the numbers of days per week that SSB were consumed were examined.”	Quasi-Experi- mental design: piloted inter-vention	1. Grades 9–12. 2. 186. 3. The piloted intervention consisted of the creation of a TAC at each of 2 high schools to design specific components of the intervention: a ‘‘Sodabriety’’ 30-day challenge. Each TAC consisted of 12 members overall: 2 teachers and at least 2 students from each grade in school (grades 9–12). To gather baseline data, pre-intervention an assessment of school vending machine beverage choices was completed. Data was collected at 3 time-points: pre-intervention, immediately post-intervention, and at 30 days’ post-intervention. Recruitment for the ‘‘Sodabriety’’ project occurred in 2 phases. (1) focused on recruitment of members to serve on each TAC; (2) focused on recruitment of students attending each school to participate. 4. Demographic survey. A Vending Machine Survey was designed and completed by study personnel prior to implementation of the intervention. A 10-item beverage survey was completed by each participant at 3 times: preintervention, immediately post-intervention, and 30 days’ post-intervention. Each participant was provided a beverage log to complete during the 30-day intervention. 5. Descriptive variables were collected by self-report during the pre-intervention data collection. Data were analyzed using both descriptive and inferential methods. Descriptive data were analyzed by calculating frequency, means, standard deviations, and ranges of values. Paired t tests were conducted.	Kcal	Level III: Mod. Quality
Snelling et al. [46]	“The aim of this study was to compare the NSLP and competitive food offerings with the food purchased at 3 public high schools, collectively.”	Non-experimental design: Observational; quasi-experi-mental	1. High School students (grades 9–12). 2. 5249. 3. ‘Over a 4-week cycle, daily food purchases were gathered and the proportions of green, yellow, and red foods offered and purchased was compared. Competitive food items were coded according to nutrient information obtained from the food label. Purchasing data for both competitive and NSLP food items was collected each day by the food service staff at 3 public high schools.’’ 4. The food items were coded by a system based on the Stoplight Diet for analysis, placing food in 3 categories: green, yellow, or red. 5. Statistical Descriptive Analysis.	Kcal	Level III: Mod. Quality
Taber et al. [64]	“To determine whether nutrient intake is healthier among high school students in California, which regulates the nutrition content of competitive foods sold in high schools, than among students in states with no such standards.”	Non-experimental design: Observational: cross sectional	1. Grades 9–12. 2. 680 high school students. 3. State laws governing fat, sugar, and nutritional content of competitive foods sold in vending machines, school stores, and à la carte venues were studied. Student data on nutrient intake were obtained from the (NYPANS), conducted by the (CDC). 4. Estimated differences in (1) total caloric intake, overall and stratified by location of consumption; (2) proportions of total calories consumed in the forms of protein, carbohydrates, and different types of fat, and (3) total intake of macronutrients and micronutrients. 5. General linear models were used to estimate differences in average dietary intake between students in California and states that did not have laws regarding the nutrition content of competitive foods.	Kcal	Level IV; Low Quality
Taber et al. [3]	“To determine if state laws regulating nutrition content of foods and beverages sold outside of federal school meal programs (“competitive foods”) are associated with lower adolescent weight gain.”	Quasi-experimental design: longi-tudinal study	1. Middle school’s grades 5th −8th. 2. 6300 students. 3. The Westlaw legal database identified state competitive food laws that were scored by using the (CLASS) criteria. States were classified as having strong, weak, or no competitive food laws in 2003 and 2006 based on law strength and comprehensiveness. Objective height and weight data were obtained from 6300 students in 40 states in 5 and 8th grade (2004 and 2007). 4. ECLS-K used. General linear models estimated the association between baseline state laws (2003) and within-student changes in BMI, overweight status, and obesity status. Identity measures were examined: gender, age, race/ethnicity (non-Hispanic white, non Hispanic black, Hispanic, non-Hispanic other), socioeconomic status (SES) 5. ‘Fixed-effect models estimated the association between law changes during follow-up (2003–2006) and within-student changes in BMI and weight status. A robust SE was used to account for within-state clustering.’	BMI Kcal	Level III: Mod. Quality
Taber et al. [54]	“To determine if state laws with stricter school meal nutrition standards are inversely associated with adolescent weight status, while controlling for unmeasured state-level confounders.”	Quasi-experimental design: observational study	1. 8th grade students in 40 states. 2. 4,870 students. 3. ‘Codified state statutory and administrative laws governing school meals were collected as part of the Bridging the Gap research program.’ Student data from the (ECLS-K). Other ECLS-K data in this study was gotten from interviews with parents/guardians and questionnaires done by students. 4. Independent variable: Type of School Lunch they usually obtained. Dependent variable: primary outcomes of interest were (BMI) percentile and obesity status. Control group: Students who do not obtain lunch at school 5. Statistical Analysis. ‘All analyzes used generalized estimating equations with an exchangeable correlation matrix and identity link to control for state-level clustering’.	BMI	Level III: Mod. Quality
Terry-McElrath et al. [50]	“Identify trends in the availability of various food choices in USA’ middle and high schools from 2004 to 2007, and examines the potential associations between such food availability and students’ self-reported eating habits and body mass index (BMI)–related outcomes.”	Non-experimental design Observational; cross-sectional	1. Students 8th, 10th, and 12th grades. 2. 45,000 students interviewed each year/ 2004 to 2007. 3. Report uses data from two studies: Monitoring the Future (MTF), and Youth, Education, and Society (YES). 4. Independent variables were all measured at the school level, and included: types of foods offered, locations such foods could be obtained, if the school provided breakfast, and school start time. 5. Trend analyzes were conducted at the school level using the Mantel-Haenszel c2 test for significance. Multivariate analyzes were conducted to assess the association between the school food environment and student food consumption, with correction for design effects resulting from clustered sampling using the survey logistic procedure.	BMI	Level IV Low Quality
Van Hook & Altman [16]	“We use longitudinal data to estimate the association between the introduction of competitive food sales in children’s schools and weight gain from fifth grade to eighth grade, and we exploit variation in the timing of the transition from elementary to middle school to estimate exposure to competitive foods in middle school environments. Second, we assess whether the estimated associations of competitive foods with weight gain vary significantly by gender, race/ethnicity, and family socioeconomic status (SES).”	Quasi-experimental design: a longi-tudinal research design	1. Fifth and eighth grades. 2. 19,450 children who attended school in the same county in both fifth and eighth grades. 3. ‘In the 5th and 8th-grade waves of data collection, school administrators were asked a series of questions about the availability of competitive foods for students to purchase. Children were also asked the types of food available for purchase at school. For both 5th and 8th grades, we created a dichotomous measure of competitive food sales for schools that sold food through at least one competitive food venue (i.e. vending machines, snack bars, or a` la carte). We conducted supplementary analyzes of the types of food sold, using two competitive food scales.’ 4. ‘Height and weight measures were collected from the children during the spring and fall of kindergarten and 1st grade and during the spring of 3rd, 5th, and 8th grades, which were converted to percentile BMI in accordance with CDC guidelines. We used data from the of (ECLS-K) 1998–1999.’ 5. Used estimate fixed effects models, which model changes in children’s weight as a function of changes in competitive foods. Descriptive statistical analysis used.	BMI	Level III- Level IV; Mod. Quality
Wiecha et al. [55]	“To examine associations between use of school vending machines and fast-food restaurants and youth intake of sugar-sweetened beverages.”	Non-experimental design: Observational; cross-sectional analysis	1. Middle School grade 6–7. 2. 1,474 students. 3. ‘From a group randomized obesity intervention, we analyzed baseline data from 1,474 students in 10 Massachusetts middle schools with vending machines that sold soda and/or other sweetened drinks. Students completed a self-report survey of 83 questions during a single class period in Fall 2002 administered by trained study staff. Vending machine presence, availability to students, and contents were determined in Spring 2003 by study staff using a standardized data collection instrument.’ 4. ‘Daily SSB consumption (regular soda, fruit drinks, and iced tea), purchases from school vending machines, and visits to fast-food restaurants in the preceding 7 days were estimated by self-report. Heights and weights were obtained by school nurses standardized in measurement techniques by study staff. BMI was generated using an age- and sex-specific algorithm from the CDC.’ 5. Statistical analyzes performed and nonparametric tests were performed on unadjusted data; multivariable models adjusted for sex, grade, body mass index, and race/ ethnicity, and accounted for clustering within schools.	BMI	Level IV: Low Quality

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