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

Effectiveness of school food environment policies on children’s dietary behaviors: A systematic review and meta-analysis

  • Renata Micha ,

    Contributed equally to this work with: Renata Micha, Dimitra Karageorgou

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    renata.micha@tufts.edu

    Affiliation Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States of America

  • Dimitra Karageorgou ,

    Contributed equally to this work with: Renata Micha, Dimitra Karageorgou

    Roles Data curation, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece

  • Ioanna Bakogianni,

    Roles Data curation, Formal analysis, Resources, Visualization, Writing – review & editing

    Affiliation Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece

  • Eirini Trichia,

    Roles Data curation, Formal analysis, Resources, Writing – review & editing

    Affiliation Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece

  • Laurie P. Whitsel,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation Policy Research, American Heart Association, Dallas, TX, United States of America

  • Mary Story,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation Global Health Institute and Community and Family Medicine, Duke University, Durham, NC, United States of America

  • Jose L. Peñalvo,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States of America

  • Dariush Mozaffarian

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

    Affiliation Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States of America

Abstract

Background

School food environment policies may be a critical tool to promote healthy diets in children, yet their effectiveness remains unclear.

Objective

To systematically review and quantify the impact of school food environment policies on dietary habits, adiposity, and metabolic risk in children.

Methods

We systematically searched online databases for randomized or quasi-experimental interventions assessing effects of school food environment policies on children’s dietary habits, adiposity, or metabolic risk factors. Data were extracted independently and in duplicate, and pooled using inverse-variance random-effects meta-analysis. Habitual (within+outside school) dietary intakes were the primary outcome. Heterogeneity was explored using meta-regression and subgroup analysis. Funnel plots, Begg’s and Egger’s test evaluated potential publication bias.

Results

From 6,636 abstracts, 91 interventions (55 in US/Canada, 36 in Europe/New Zealand) were included, on direct provision of healthful foods/beverages (N = 39 studies), competitive food/beverage standards (N = 29), and school meal standards (N = 39) (some interventions assessed multiple policies). Direct provision policies, which largely targeted fruits and vegetables, increased consumption of fruits by 0.27 servings/d (n = 15 estimates (95%CI: 0.17, 0.36)) and combined fruits and vegetables by 0.28 servings/d (n = 16 (0.17, 0.40)); with a slight impact on vegetables (n = 11; 0.04 (0.01, 0.08)), and no effects on total calories (n = 6; -56 kcal/d (-174, 62)). In interventions targeting water, habitual intake was unchanged (n = 3; 0.33 glasses/d (-0.27, 0.93)). Competitive food/beverage standards reduced sugar-sweetened beverage intake by 0.18 servings/d (n = 3 (-0.31, -0.05)); and unhealthy snacks by 0.17 servings/d (n = 2 (-0.22, -0.13)), without effects on total calories (n = 5; -79 kcal/d (-179, 21)). School meal standards (mainly lunch) increased fruit intake (n = 2; 0.76 servings/d (0.37, 1.16)) and reduced total fat (-1.49%energy; n = 6 (-2.42, -0.57)), saturated fat (n = 4; -0.93%energy (-1.15, -0.70)) and sodium (n = 4; -170 mg/d (-242, -98)); but not total calories (n = 8; -38 kcal/d (-137, 62)). In 17 studies evaluating adiposity, significant decreases were generally not identified; few studies assessed metabolic factors (blood lipids/glucose/pressure), with mixed findings. Significant sources of heterogeneity or publication bias were not identified.

Conclusions

Specific school food environment policies can improve targeted dietary behaviors; effects on adiposity and metabolic risk require further investigation. These findings inform ongoing policy discussions and debates on best practices to improve childhood dietary habits and health.

Introduction

Diets of most children and adolescents (hereafter referred to as children) remain poor, with tremendous consequences for metabolic diseases, overweight and obesity, and other nutrition-related illness [14]. Childhood is also a critical period to establish lifelong eating habits which influence future risk of obesity and cardiometabolic diseases [57]. Youth consume between one-third to one-half of meals at school, making this a crucial setting for interventions that alter the food environment [8]. Considering that almost all children obtain some years of schooling, and of diverse ethnic and socio-economic groups, health promotion efforts in schools could have a broader impact on eating behaviors and future disease risk.

Promising school food environment policies include direct provision of healthful foods/beverages such as fruits and vegetables (F&V), quality standards for competitive foods and beverages (foods and beverages sold outside of school meal programs), and quality standards (targets for foods, nutrients/energy) for school meals (lunch, breakfast) [8]. For example, in 2008, a US Fresh Fruit and Vegetable Program (FFVP) was expanded nationally for elementary schools with highest low-income enrolments to provide free F&V to students outside usual school meals [9]; and in 2007, a similar free school fruit programme was implemented in Norway to provide daily a free piece of fruit or vegetable to all secondary school students [10]. The Healthy, Hunger-Free Kids Act in 2010 [11] introduced Smart Snack Standards for competitive foods and beverages in schools receiving federal meal funding, including restriction of sugar-sweetened beverages (SSBs) to be fully implemented by 2014–15 [12]. In 2012, US National School Lunch and School Breakfast Programs nutrition standards were significantly updated to be more consistent with US Dietary Guidelines [13], and in 2015 the UK Department of Education mandated revised standards for all food served in schools [14].

Yet, effectiveness of these food environment policies for improving children’s habitual dietary habits, adiposity, or metabolic risk is not well-established. Understanding these effects is critical to estimate benefits of existing programs as well as need for their expansion; and to elucidate potential harms from their elimination as suggested by potential new federal priorities in the US [15,16]. Prior studies have reviewed whether a range of school dietary interventions increase F&V consumption but often without focusing on environmental policies [1722]; while other systematic reviews have been qualitative [23], assessed efficacy of competitive food/beverage standards informed mainly by cross-sectional studies [24], or focused on educational (rather than environmental) interventions [25]. Other reviews have grouped together highly varied programs, e.g., teacher training, child education, family components, labeling, pricing changes, behavioral techniques, and school gardens [2632]. Thus, effectiveness of school food environment policies remain unclear, including potential differences for in-school vs. habitual (within and outside school) intakes. To address these gaps in knowledge, we systematically investigated and quantified the effects of school food environment interventions -carefully exploring sources of heterogeneity-, including provision of healthful foods/beverages, competitive food/beverage standards, and school meal standards, on habitual and in-school dietary consumption, adiposity, and metabolic risk factors in children. This investigation was performed as part of the Food-PRICE (Policy Review and Intervention Cost-Effectiveness) Project (www.food-price.org).

Methods

PRISMA recommendations were followed throughout all stages of this meta-analysis (Appendix A in S1 File) [33]. The objective, search strategy, and selection criteria were specified in advance (Appendix B in S1 File).

Primary exposures and outcomes

The primary intervention was school food environment policies targeting food/beverage availability across the school setting (e.g., classroom, cafeterias, vending machines, tuck shops) including direct provision (free, reduced-price, or full-price) of healthful foods or beverages outside of usual school meals (e.g., fresh F&V programs, water fountains, increased availability of healthy foods at vending machines), nutritional quality standards for competitive foods/ beverages, and nutritional quality standards for school meals (lunch, breakfast). The primary outcome was the change in habitual consumption of the targeted food, beverage, or nutrient, evaluated by reported intakes or objective sales/purchases data as a proxy for consumption. Secondary outcomes included changes in in-school meal nutrient content and intake (to compare and contrast to findings for habitual intake), total caloric intake, adiposity (body mass index (BMI), prevalence of overweight (≥85th-95th percentile), obesity (≥95th percentile) or overweight/obesity combined); and metabolic measures (e.g., blood lipids, blood glucose, blood pressure).

Search strategy

Multiple online databases were systematically searched including PubMed, EconLit, CINAHL, CABI, Web of Science, PAIS, Cochrane Library, AGRIS, Open Grey, Faculty of 1000 and EMBASE earliest available through March 9, 2014 without restrictions on language or country. Online searches were updated in PubMed from March 10, 2014 to December 14, 2017 as this is the primary database for research in this field, and the majority (>95%) of relevant papers in the initial review were identified in PubMed. The intervention periods of identified publications largely preceded widespread implementation of the new US school lunch standards, Smart Snacks Standards, FFVP, or revised UK school meal standards. Search terms utilized 4 categories, including on the intervention, dietary target, outcome, and setting (Appendix C in S1 File); supplemented by hand-searching of citations and the first 20 “related articles” in PubMed for each final included article. Titles/abstracts were screened by one investigator; and for all potentially relevant articles, full-texts were retrieved.

Study selection

Full-text manuscripts were evaluated independently and in duplicate, with differences resolved by consensus or, if necessary, group discussion. Inclusion criteria were (a) all randomized or quasi-experimental interventions that (b) assessed the impact of school food environment policies in preschool, primary, or secondary schools on the outcomes of interest among generally healthy children age 2–18y; and (c) reported a quantitative change in the outcome (Appendix B in S1 File). We excluded cross-sectional, retrospective, case-control, modeling, methodology, and laboratory studies; reviews, commentaries, books, and studies for which full-text articles could not be retrieved. Studies were excluded if the policy focused on changes outside of food/beverage availability (e.g., student education, food labeling, price changes), if the food/beverage environmental policy was a minor component (qualitatively, <30%, as judged by two independent reviewers) of a multi-component intervention, if intervention duration was <4 weeks, or if only knowledge or attitudes were evaluated as outcomes.

Data extraction

Data were extracted independently and in duplicate using standardized electronic templates (Microsoft Access, Office 2010). Extracted information included first author, publication year, study location, design, population (age, sex, race, sample size), intervention characteristics (components, targets, duration), outcome data including habitual (within and outside school) and in-school (e.g., lunch, breakfast, total in-school) intakes (definition, ascertainment methods, effect size, precision estimate), covariates, and for multi-component interventions, the relative contribution of the food environment policy component to the overall intervention (low: 30–59%, medium: 60–89%, high: ≥90%; qualitatively assessed independently and in duplicate). Missing data or definitions were resolved by direct author contact, where possible.

For outcomes evaluated at multiple time-points, we extracted the latest follow-up measure at end-intervention. Sustainability findings based on follow-up after end-intervention were also extracted when available and ≥4 weeks duration. Study quality was assessed independently and in duplicate based on study design, assessment of exposure, assessment of outcome, control for confounding, and evidence of selection bias (Table A in S1 File). Differences in data extraction and quality assessment between investigators were infrequent (concordance >95%) and resolved by consensus.

Statistical analysis

Analyses were conducted using STATA14 (College Station, TX: StataCorp LP). For each policy, study-specific effect sizes were pooled using inverse-variance random-effects meta-analysis. For interventions with an external control group, we evaluated between-group continuous changes at follow-up, adjusted for baseline values and relevant covariates; for quasi-experimental studies with no control group, we evaluated within-group changes [34]. Statistical uncertainty (standard error, SE) was extracted or calculated based on other statistics (Appendix D in S1 File). For paired observations without reported covariance, we used a correlation of 0.5 for main analysis and 0.1 and 0.9 for sensitivity analyses [34]. In addition to continuous effect sizes, we extracted other relevant effect sizes (e.g., percentage meeting a cutpoint, odds ratio, ratio of the means, other relative changes) and their statistical uncertainty. Separate intervention arms or outcomes from the same study were included as separate estimates in the meta-analyses; subgroup findings from the same intervention arm or outcome (e.g., by sex, age) were first combined using study-specific meta-analysis.

We separately pooled findings for direct provision of healthful foods and beverages, competitive foods and beverage standards, and school meal standards. Effect sizes were standardized to consistent units: e.g., 80 g serving/d for F&V, 12-oz serving/d for SSBs, 8-oz serving (glass)/d for water, kcal/d for calories, % energy (E)/d, g/d or mg/d for nutrients, and kg/m2 or z-score for BMI. Endpoints that could not be standardized (e.g., consumption expressed as a score, proportion of children consuming a given level) or separately meta-analyzed were included in qualitative assessment of the evidence. When multiple overlapping outcomes were reported (e.g., fruit with vs. without 100% juice), we extracted the outcome mostly closely aligned to a standardized definition, e.g. total fruits (fresh, raw, canned, or dried), excluding fruit juice; total vegetables, excluding white potatoes; and total SSBs (soda, energy drinks, sweetened teas, etc.). For studies reporting subcomponents of these definitions (e.g., separate subtypes of vegetables, of sweet snacks, F&V separately), we first summed these subtypes.

Cochran's Q and I2 statistics assessed between-study heterogeneity [35]. Meta-regression and stratified/subgroup meta-analysis explored potential prespecified heterogeneity sources when at least 5 study estimates were present, including design (randomized, quasi-experimental), region (US/Canada, Europe/New Zealand), intervention level (national, statewide, citywide, local), executing agent (law, governmental policy, program), components (food environment policy only, multi-component), follow-up duration (≥ or <median), school level (preschool, primary, secondary, mixed), school type (public, private, mixed), outcome definition (primary, alternative), relative contribution of the food environment policy to the overall intervention (low: 30–59%, medium: 60–89%, high: ≥90%), type of provision (free vs. reduced/full cost; direct vs. indirect), targeted caloric intake (yes, no), outcome being a primary or secondary study endpoint, and study quality score (0–3, 4–5). Potential publication bias was assessed visually using funnel plots and statistically by Egger's and Begg's tests [36].

Results

Study characteristics

Of 6,636 identified articles, 91 interventions met inclusion criteria, including 39 randomized and 52 nonrandomized studies evaluating 1 or more food environment policy strategy (Fig 1, Table 1). These included direct provision of healthful foods/beverages (N = 40) [10,3775], competitive food/beverage standards (N = 29) [6672,7495], and school meal standards (N = 39) [7375,90126]. Most studies were conducted in the US (N = 55), followed by the UK (N = 11), Netherlands (N = 7), Norway (N = 6), Canada (N = 3), South Korea (N = 2) and others (N = 1 each). About half of interventions (N = 49, 54%) were multi-component, with the relative contribution of the food environment policy component ranging from 30–100%. Data on race, socioeconomics, response rate, and urban/rural setting were largely not reported. Longest follow-up was 47 months in randomized and 60 months in quasi-experimental interventions. Forty-seven intervention studies were in primary schools, 27 in secondary schools, 1 in preschool, and 13 in mixed schools; 1 did not specify. Two studies reported only sustainability effects. Given types of outcomes reported, 21 studies were only included in qualitative assessment.

thumbnail
Fig 1. Screening and selection process of interventions evaluating the impact of school food environment policies on dietary habits, adiposity, or metabolic risk factors in children.

https://doi.org/10.1371/journal.pone.0194555.g001

thumbnail
Table 1. Identified randomized and quasi-experimental interventions evaluating school food environment policy interventions and dietary habits, adiposity, or metabolic risk factors in children (N = 91 studies).

https://doi.org/10.1371/journal.pone.0194555.t001

Direct provision of healthful foods and beverages

Interventions providing healthful foods/beverages were mainly in classrooms (“direct” provision) or via increased availability in cafeterias, tuck shops or vending machines (“indirect” provision) (Table 1). F&V were most common.

Fruits.

Pooling 6 randomized and 9 quasi-experimental interventions with average duration 12 months, habitual fruit intake increased by 0.27 servings/d (95%CI: 0.17, 0.36) (Fig 2, Table B in S1 File). Effects were similar in randomized vs. quasi-experimental studies (Table 2, Figure A in S1 File). Effects appeared potentially higher with free provision [10,37,38,45,50,58,59,62,63] vs. reduced [48,71] or full [54,67,73,75] price, but this heterogeneity was not statistically significant (P = 0.07) (Figure A in S1 File). Findings were also similar in direct provision only vs. multi-component interventions; or in “direct” (n = 10; 0.29 (0.19, 0.39)) [10,37,38,45,48,50,58,59,62,63] vs. “indirect” (n = 5; 0.21 (-0.02, 0.44)) [54,67,71,73,75] interventions. Results were similar in 5 studies [10,37,38,45,71] assessing in-school fruit consumption (Table B in S1 File). Three studies [37,50,59] assessed sustainability at 6 weeks [37] or 12 months [50,59] after direct provision was removed; no significant effect was seen (-0.18 (-0.51, 0.15)).

thumbnail
Fig 2. Effect of direct provision of fruits and vegetables in schools on fruit and vegetable intake in children.

Intakes represent habitual (not just in-school) consumption. Solid squares represent study specific continuous changes in reported intakes; and lines, 95% confidence intervals (Cis). Vertical line represents pooled effect size (ES); and open diamond, corresponding 95% CI. Multi-component strategies were included only if the food environment policy was a major component, judged qualitatively to be at least 30% of the overall intervention. The relative contribution of the food environment policy component to the overall intervention was qualitatively assessed as low (30 to <60%), medium (60 to <90%), and high (≥90%). a A single estimate was obtained by summing separately reported outcomes (n = 2) that their total aligned to the single optimal definition (i.e., total vegetables, combined fruits and vegetables). b Same intervention reporting outcomes for different counties and ages. RCT, randomized controlled trial; QED, quasi-experimental intervention with external control group; QED, no C, quasi-experimental intervention without external control group; CA, Canada; DK, Denmark; F, Finland; N, Norway; NL, Netherlands; NZ, New Zealand; UK, United Kingdom; US, United States of America.

https://doi.org/10.1371/journal.pone.0194555.g002

thumbnail
Table 2. Prespecified sources of heterogeneity explored among interventions evaluating the effect of direct provision of fruits and vegetables in schools on habitual fruit and vegetable intake in children.

https://doi.org/10.1371/journal.pone.0194555.t002

Vegetables.

Pooling 3 randomized and 8 quasi-experimental interventions with average duration 13.4 months, habitual vegetable intake was slightly increased (0.04 servings/d (0.01, 0.08)) (Fig 2). In 7 interventions providing free vegetables, effects appeared higher, although this heterogeneity was not statistically significant (P = 0.22) (Table 2, Figure B in S1 File). Findings were similar stratified by other study characteristics and in 3 studies [10,38,45] assessing in-school intake (Table B in S1 File).

Combined fruits and vegetables.

Sixteen studies (6 randomized, 10 quasi-experimental) assessed combined F&V intake, with average duration 15.4 months (11 of these studies also separately evaluated fruits or vegetables, above). Combined intake increased by 0.28 servings/d (n = 16 (0.17, 0.40)) (Fig 2). Findings were not significantly different in randomized vs. quasi-experimental studies or by other population or intervention characteristics (Table 2, Figure C in S1 File). In 6 studies assessing in-school consumption [10,38,40,41,45,52], combined F&V intake increased by 0.38 servings/d (n = 6 (0.23, 0.53)) (Table B in S1 File).

Total calories.

Habitual caloric intake was reported in 6 studies [38,58,61,7375], yet wasn’t a target of direct provision in any of these. Pooling studies, no significant effect on habitual caloric intake was identified (-56 kcal/d; -174, 62) (Table B in S1 File). Only 1 study reported school caloric intake [56], which was unchanged.

Water.

Five studies increased access to free water mainly through installment of water coolers [47,53,55,64,65]. Of these, 3 reported nonsignificant trends toward increased habitual water consumption (0.33 glasses/d (-0.27, 0.93)) [47,55,64] (Table B in S1 File); and 3 reported changes in uptake, which decreased in 2 studies [55,65] and increased in one [53].

Adiposity and metabolic measures.

Four studies combining provision of fruits and vegetables with additional competitive food/beverage standards evaluated overweight or obesity, with average duration 26.8 months (range 2.3 to 60) [44,66,67,74]. Improvements were not identified in odds of overweight/obesity (n = 2; 1.04 (0.91, 1.19)) [44,66], overweight (n = 1; 1.03 (0.94, 1.12)) [74], or obesity (n = 2; 1.25 (1.07, 1.46)) [66,74]; BMI (n = 3; 0.19 kg/m2 (-0.12, 0.50)) [44,66,67]; or BMI z-score (n = 2; 0.01 (-0.04, 0.05)) [44,66]. Another 3 studies [43,55,60] focusing on water provision reported improvements in BMI z-score [60], prevalence of overweight/obesity [60] and odds of overweight [55], while obesity prevalence [60] and BMI percentile were unchanged [43]. Only 1 study [67] evaluated metabolic risk factors, finding significant decreases in total cholesterol, LDL cholesterol, and triglycerides, and blood pressure.

Other endpoints.

Three studies [46,68,72] evaluated odds of consuming F&V [72] or varying percentage changes in F&V intakes, reported dichotomously [46,68]; these outcomes were generally not significantly improved. One study reported only sustainability data for F&V intake after end-intervention, finding sustained benefits for both in-school intake at 3 years [42] and habitual F&V intake at 3 [42] and 7 years [39] although this weakened over time. A few interventions provided low-fat/low-calorie items [49,51,69], or milk [57]. No significant improvements were found in consumption of low-fat items. A milk provision study in Iran aimed to increase students’ weight, which was achieved.

Competitive food and beverage standards

Competitive food/beverage policies generally targeted SSBs and unhealthy snacks (Table 1). Strategies included product-specific restrictions; standards on nutrients, calories, or portion sizes; or both. All were performed prior to implementation of US national Smart Snacks guidelines in 2014.

Sugar-sweetened beverages.

Three interventions found decreased habitual SSB intake of 0.18 servings/d (n = 3 (-0.31, -0.05)) (Fig 3). In contrast, 4 separate studies assessing in-school intake did not identify a significant effect (n = 5; -0.02 servings/d (-0.04, 0.01)). No significant heterogeneity sources were identified (Table C and Figure D in S1 File).

thumbnail
Fig 3. Effect of competitive food and beverage standards in schools on sugar-sweetened beverage and unhealthy snack intake in children.

Intakes represent habitual or total in-school consumption, except for 1 study that assessed in-school lunch intake. Solid squares represent study specific continuous changes in reported intakes; and lines, 95% confidence intervals (Cis). Vertical line represents pooled effect size (ES); and open diamond, corresponding 95% CI. Multi-component strategies were included only if food environment policy was a major component, judged qualitatively to be at least 30% of the overall intervention. The relative contribution of the food environment policy component to the overall intervention was qualitatively assessed as low (30 to <60%), medium (60 to <90%), and high (≥90%). a A single estimate was obtained by summing separately reported outcomes (n = 2) that their total aligned to the single optimal definition (i.e., sweet snacks). SSBs, sugar-sweetened beverages; RCT, randomized controlled trial; QED, quasi-experimental intervention with external control group; QED, no C, quasi-experimental intervention without external control group; CA, Canada; UK, United Kingdom; US, United States of America.

https://doi.org/10.1371/journal.pone.0194555.g003

Unhealthy snacks.

Two interventions assessed habitual intake, which decreased by 0.17 servings/d (n = 3 (-0.22, -0.13)) (Fig 3). Four studies with 5 separate intervention arms assessed in-school intake, which decreased by 0.05 servings/d (n = 9; -0.08, -0.02) (Fig 3). No significant heterogeneity sources were identified (Table C and Figure D in S1 File).

Total calories.

Habitual caloric intake was reported in 5 studies [74,75,80,86,93], with no significant effect (-79 kcal/d; -179, 21) (Table B in S1 File). Findings were not significantly different in 2 studies [86,93] that specifically targeted calories (-40 kcal/d; -185, 104) or in 3 (2 additional) studies that assessed in-school lunch caloric intake [86,88,90].

Other targeted dietary factors.

Other targeted diet factors included total fat [88,90] and saturated fat [86,88] intake; habitual and in-school lunch total fat intake decreased (n = 3), but not in-school lunch saturated fat intake (n = 2) (Table B in S1 File).

Adiposity and metabolic measures.

Several studies assessed the prevalence or odds ratios of childhood overweight (n = 6 and n = 6 estimates, respectively), obesity (n = 10, n = 8), or overweight/obesity (n = 5, n = 2) (Figures G and H in S1 File), as well as BMI (n = 6; Figure I in S1 File) and BMI z-score (n = 5; Figure J in S1 File). Durations ranged from 2.3 to 69 months (mean 31.5). Competitive food/beverage standards did not significantly reduce any of these measures (Table B in S1 File), although the central effect estimate often tended to be slightly and nonsignificantly lower. Prevalence of overweight/obesity was nonsignificantly higher across 5 studies evaluating this outcome (n = 5; 0.24%; -0.54, 1.02), largely driven (70.51% of the weighted estimate) by 1 quasi-experimental study [84] that compared changes in rates among schoolchildren in California (n = ~600,000). Only 2 studies evaluated effects on metabolic risk factors and could not be pooled [67,91]; individually, these found significant improvements in various risk factors assessed [67] other than fasting glucose [91].

Other endpoints.

Eight studies [6870,72,76,85,89,92] reported odds of consuming SSBs and unhealthy snacks [72], changes in total caloric and total fat meal content [92], or changes in SSBs and unhealthy snack intakes reported dichotomously (e.g., percentage of sales, prevalence of students, score expressing frequency of intake) [6870,76,85,89] that could not be meta-analyzed due to outcome heterogeneity. Qualitatively, these studies reported conflicting findings regarding SSB and unhealthy snack intake, with some reporting decreases [76,85,89], others showing no change [6870,72], and one showing unhealthy snack increases [68]; total caloric and total fat school meal content decreased.

School meal standards

Policies on school meal (mainly lunch) standards (foods, nutrients/energy) generally targeted F&V, dietary fats, and sodium (Table 1). Five studies evaluated implementation of the 2012 US school lunch guidelines, while all studies were performed prior to the implementation of the revised 2015 UK school meal standards.

Fruits and vegetables.

Standards on F&V (e.g., serve at least one fruit or vegetable daily) generally targeted lunch, either alone or combined with direct provision. Habitual fruit intake increased by 0.76 servings/d (n = 2 (0.37, 1.16)) [73,102]; with nonsignificant trends toward increased habitual vegetable (n = 2; 0.30 servings/d (-0.001, 0.59)) [73,102] and F&V (n = 5; 0.12 servings/d (-0.08, 0.31)) consumption (Table B in S1 File) [73,74,95,102,108]. Findings were similar restricting to 3 studies [95,102,108] that did not include direct provision (n = 3; 0.23 servings/d of F&V; (-0.06, 0.51)). In one study assessing prevalence of students selecting F&V in lunch [117], fruit selection increased, while vegetable selection decreased.

Dietary fats.

Most studies specified target levels for dietary fats, which were generally consistent across studies; these ranged from 30–35%E/lunch for total fat and 10–11%E/lunch for saturated fat. Six studies assessed habitual total fat, which decreased by 1.49%E (-2.42, -0.57) (Fig 4). In g/d, the reduction in habitual fat intake was greater (~6 g/d total fat) in magnitude to achieved reductions in in-school meal content and intake (~3–4 g/d total fat) (Figure K in S1 File). Standards also reduced habitual saturated fat (n = 4; -0.93%E (-1.15, -0.70)), in-school lunch saturated fat (n = 9; -2.75%E (-4.39, -1.11)), and in-school meal (lunch or breakfast) saturated fat (n = 10; -2.46%E (-4.04, -0.89)) (Table B in S1 File). Again, absolute magnitudes of reduction were marginally higher for habitual intake (~3 g/d) compared with in-school meal intake and content (~1–2 g/d) (Figure L in S1 File). No significant heterogeneity sources were identified, with the exception of study region for in-school total fat intake (P = 0.042); larger reduction was observed for studies in Europe/New Zealand compared to US/Canada (Table D and Figures E and F in S1 File).

thumbnail
Fig 4. Effect of school meal standards on total fat and saturated fat intake in children.

Intakes represent habitual or in-school lunch consumption. Solid squares represent study specific continuous changes in reported intakes; and lines, 95% confidence intervals (Cis). Vertical line represents pooled effect size (ES); and open diamond, corresponding 95% CI. Multi-component strategies were included only if the food environment policy was a major component, judged qualitatively to be at least 30% of the overall intervention. The relative contribution of the food environment policy component to the overall intervention was qualitatively assessed as low (30 to <60%), medium (60 to <90%), and high (≥90%). In secondary analysis, in-school meal (lunch or breakfast) consumption decreased for total fat by 7.12% energy (%E)/d (N = 10; -9.48, -4.75) and for saturated fat by 2.46%E/d (N = 10; -4.04, -0.89). RCT, randomized controlled trial; QED, quasi-experimental intervention with external control group; QED, no C, quasi-experimental intervention without external control group; CA, Canada; UK, United Kingdom; US, United States of America.

https://doi.org/10.1371/journal.pone.0194555.g004

Total calories.

School meal standards did not significantly decrease habitual caloric intake (n = 8; -38 kcal/d (-137, 62), in-school (lunch) calories (n = 11; -28 kcal/d (-76, 20)), or in-school (lunch+breakfast) calories (n = 12; -29 kcal/d (-76, 18)) (Figure M and Table B in S1 File). Results were similar in interventions specifically targeting total calories by aiming to provide adequate amounts of energy (habitual: n = 4; -19 kcal/d (-134, 95); in-school: n = 5; -60 kcal/d (-170, 50)). The magnitude of reduction was larger and significant for in-school meal content than for in-school meal intake or habitual intake (Figure M in S1 File). Differences were seen by study quality score for in-school lunch caloric intake (P = 0.01) but not for habitual caloric intake; nor for other heterogeneity sources (Table D in S1 File).

Sodium intake.

Target levels for sodium content in school meals varied across studies, ranging from 200–1200 mg/meal. School meal standards for sodium decreased habitual intake (n = 4; -170 mg/d (-242, -98)), in-school lunch intake (n = 6; -227 mg/d (-384, -69)), and in-school meal (lunch+breakfast) intake (n = 7; -221 mg/d (-371, -71)) (Figure N and Table B in S1 File). The magnitude of reduction in sodium was similar for in-school meal content (Figure N in S1 File). No significant sources of heterogeneity were identified (Table D in S1 File).

Other targeted dietary factors.

A few interventions set meal standards for other targets such as milk (one serving/d of milk/milk products; n = 2) [74,95], dietary fiber (grain-based foods with ≥2 g/serving of fiber; n = 7) [74,92,93,104,120122], whole grains (increase whole grains, e.g., by 1 daily serving; n = 5) [74,92,101,102,104], or total carbohydrates (> = 50% of food energy; n = 3) [74,109,120]. These studies found increased habitual consumption of milk/milk products (0.22 cups/d; 0.17, 0.28) and in-school lunch consumption of carbohydrate (8.17%E/d; 0.70, 15.65), but not habitual or lunch intakes of dietary fiber (0.08 g/d (-0.84, 1.00); 0.55 (-1.90, 3.00); respectively) or habitual or lunch intakes of whole grains (0.14 servings/d (-0.11, 0.39); 0.49 (-0.37, 1.35); respectively). Three studies (n = 5 estimates) targeted the proportion of schoolchildren selecting “less healthy” options (e.g., desserts, high-fat entrees, starchy foods in oil) [99,109,113]; no significant effects were seen (Table B in S1 File).

Adiposity and metabolic measures.

Six studies evaluated effects of school meal standards on adiposity, with average duration 34.3 months (range 4 to 60) [74,91,94,97,98,110]. Three of these [74,91,94] also combined competitive food/beverage standards. Two studies [97,110] assessed changes in BMI percentile, which decreased (-1.01, -1.62, -0.39), while other adiposity measures evaluated were unchanged (Table B in S1 File).

Other endpoints.

Two studies reported dichotomous changes in healthy food (e.g., fruit, vegetable, bread, milk, cereal [98,112], reported as % meeting a threshold) that could not be meta-analyzed due to varying cutpoints. Results were conflicting, with a reduced overall healthy food score and a higher intake of healthy items at breakfast [112]. One study [115] reported only sustainability data, evaluating total calories, total fat, and saturated fat in lunches 5 years after school meal standards were removed, finding further decreases in %E from total and saturated fat, but increased caloric content.

Publication bias

Visual inspection of funnel plots provided little evidence for publication bias (Figures O-Q in S1 File). Begg’s or Eggers test did not identify statistical evidence for publication bias.

Discussion

This systematic review and meta-analysis is the first, to our knowledge, to determine quantitative effects of school food environment policies on children’s habitual dietary intakes in interventional studies. Direct provision policies increased fruit intake by 0.27 servings/d and vegetable intake by 0.04 servings/d, but not water intake. Competitive food/beverage standards reduced SSBs by 0.18 servings/d and unhealthy snacks by 0.17 servings/d. School meal standards increased fruit intake by 0.76 servings/d, reduced total fat intake by ~1.5% energy and saturated fat intake by ~1% energy, and reduced sodium by 170 mg/d. All of these policies influenced dietary composition, without altering total calories. Measures of adiposity were generally unchanged; and few studies assessed metabolic factors, with mixed findings.

We separately evaluated in-school vs. habitual intakes to determine effects on children’s overall nutritional habits, given potential for compensatory changes outside of school. For example, restricting SSBs or unhealthy snacks at school could lead to increased consumption after school or at home. Such compensation is suggested in some cases; for instance, school meal standards significantly reduced meal calorie content, but not in-school meal calorie intake or habitual calorie intake. Conversely, reductions were similar for in-school vs. habitual sodium intake, suggesting that sodium reduction at school does not lead to meaningful compensation elsewhere. For some policy outcomes, e.g. for competitive food standards and SSBs and snacks, the pooled findings from interventions evaluating in-school effects were smaller than those evaluating habitual intakes. These were generally different studies, suggesting possibly other differences in the types of studies evaluating in-school intakes. Overall, our results support the importance of schools as a setting to improve overall dietary habits of children within and outside school.

Our findings suggest efficacy of a range of food environment policies, including direct provision, competitive food/beverage standards, and school meal standards. The results for both direct provision and school meal standards suggest greater efficacy for fruit intake, compared with vegetables; consistent with greater palatability of many fruits and generally less need for preparation or cooking. Water intake was unchanged in the limited studies that assessed this outcome, likely further due to difficulties in assessing fluid intake and measurement error. Our findings further highlight key gaps for many other dietary targets, such as other healthier foods (e.g., legumes, whole grains, fish, yogurt) or less healthy foods (e.g., processed meats) or other nutrients of concern (e.g., calcium, vitamin D, potassium, unsaturated fats, fiber). Given updated Dietary Guidelines for Americans that focus on healthier foods, overall diet patterns, and specific nutrients of concern [127], future studies are needed to assess how school food environment policies impact these priorities.

Evidence on the health impact of policies targeting the school food environment is especially relevant and timely given the potentially evolving priorities of the new federal US administration. Congress did not reauthorize the Healthy, Hunger-Free Kids Act (HHFKA) as scheduled in Sept 2015, so the future of Smart Snack Standards, now covering 99% of public and 83% of private schools [12], remains uncertain. Further, current policy debates include a focus on weakening or eliminating national school lunch standards [15,16]. A recent analysis indicated that in-school selections have improved with the new lunch standards [128]. Our findings build upon and expand this prior work by demonstrating changes in actual habitual intake, further supporting efficacy of meal and snack standards and informing ongoing debates. Similarly, the current national FFVP only applies to elementary schools with high proportions of low-income students [9], about 4 million students across the US [129]. Our investigation supports efficacy of such direct provision programs, which should be considered for a broader range of elementary, middle, and high schools. Finally, while identified dietary improvements were meaningful at a population level, these will not fully address the suboptimal diets of most children. Thus, our results confirm a need for multiple programmatic and policy interventions, including within and outside schools, to improve children’s diets.

While several dietary benefits were confirmed, changes in adiposity metrics were generally not significant. This may be because such policies improve dietary quality or composition (more relevant for general and metabolic health) but not dietary quantity (more relevant for obesity, at least in the short- to intermediate-term). Because dietary composition influences numerous pathways for health and well-being, the absence of a documented effect on obesity does not preclude efficacy of these interventions. Few studies evaluated metabolic risk factors, for which improvements may be more readily detected compared with adiposity. Also, establishing lifelong healthier dietary habits may have benefits decades later, during adulthood. Our findings provide quantitative summaries of how school food environment policies affect specific dietary targets, allowing modeling of potential effects on childhood obesity and future diets and disease risk in adulthood.

Prior reviews of a more varied range of school interventions identified effects of similar magnitude for total F&V consumption [18,19,21]. A previous systematic review on competitive foods/beverages was qualitative, and included mostly cross-sectional studies in the US alone [24]. Similarly, another systematic review on school food environment was also qualitative, excluded direct provision studies, and grouped together various heterogenous interventions [23]. Importantly, most prior reviews did not specifically evaluate potential effects of school food environment interventions on dietary intakes, and have grouped together highly varied programs potentially leading to biased inferences [2632]. Our findings extend these results by specifically evaluating school food environment policies and quantifying their effects on dietary intakes, as well as separately evaluating direct provision, competitive food/beverage standards, and school meal standards with careful consideration of potential heterogeneity. We also looked for sustainability: while few studies were identified, the results suggested that dietary improvements are difficult to sustain if school food environment policies are cancelled.

Our evaluation has several strengths. Evidence was based on interventions, most of which were randomized, increasing reliance in validity of results. We evaluated changes in diet, adiposity, and metabolic risk factors, providing a more coherent and comprehensive picture of the evidence. We focused on habitual (within and outside school) dietary intakes, rather than in-school intake alone. A systematic search of multiple databases made it less likely that major relevant reports were missed. Standardized methods and analytic techniques and duplicate full text reviews and data extractions reduced errors and bias. Standardization of interventions and outcomes facilitated quantitative pooling. We explored multiple factors for potential modifying effects.

Potential limitations should be considered. Educational systems and schools vary within and across nations, which could contribute to unmeasured heterogeneity. Intensity or success of policy implementation could modify results, but these are difficult to quantify; e.g., due to varying professional education or technical assistance for food service directors; differences in how schools prepare, offer, sell, serve, or purchase food; and policy nutritional guidelines. Most studies did not report details by socioeconomic indicators, which could modify efficacy of some programs. Costs and cost-effectiveness were generally not reported. Several studies included other intervention components that might contribute to impact. Some studies were judged to have lower quality scores, that could weaken or bias results. Evaluation of heterogeneity and publication bias is dependent on total numbers of studies, limiting statistical power for some endpoints. Most studies were from high-income Western countries, highlighting the need for research in lower-income nations.

In conclusion, this systematic review and meta-analysis demonstrates that specific school food environment policy interventions can improve targeted dietary behaviors. These findings inform ongoing policy discussions and debates on best practices to improve childhood dietary habits and health.

Supporting information

S1 File. Supplementary material.

Appendix A. PRISMA Checklist. Appendix B. Study protocol. Appendix C. Search query for PubMed/ Medline. Appendix D. Statistical Analysis. Table A. Quality Assessment Criteria. Table B. Meta-analyses of randomized and quasi-experimental interventions evaluating school food environment policies and dietary habits or adiposity in children. Table C. Prespecified sources of heterogeneity explored among interventions evaluating the effect of competitive food and beverage standards in schools on dietary intakes or adiposity in children. Table D. Prespecified sources of heterogeneity explored among interventions evaluating the effect of school meal standards on dietary intakes or meal contents in children. Figure A. Effect of direct provision of fruits and vegetables in schools on fruit intake in children by prespecified sources of heterogeneity. Figure B. Effect of direct provision of fruits and vegetables in schools on vegetable intake in children by prespecified sources of heterogeneity. Figure C. Effect of direct provision of fruits and vegetables in schools on fruit and vegetable intake in children by prespecified sources of heterogeneity. Figure D. Effect of competitive food and beverage standards in schools on sugar-sweetened beverages and unhealthy snack intake in children by prespecified sources of heterogeneity. Figure E. Effect of school meal standards in schools on total fat intake in children by prespecified sources of heterogeneity. Figure F. Effect of school meal standards in schools on saturated fat intake in children by prespecified sources of heterogeneity. Figure G. Effect of competitive food and beverage standards in schools on overweight and obesity prevalence in children. Figure H. Effect of competitive food and beverage standards in schools on odds of overweight and obesity in children. Figure I. Effect of competitive food and beverage standards in schools on BMI in children. Figure J. Effect of competitive food and beverage standards in schools on BMI z-score in children. Figure K. Effect of school meal standards on total fat intake or meal content in children. Figure L. Effect of school meal standards on saturated fat intake or meal content in children. Figure M. Effect of school meal standards on total caloric intake or meal content in children. Figure N. Effect of school meal standards on sodium intake or meal content in children. Figure O. Begg’s funnel plots for graphical evaluation of potential publication bias for the effect of direct provision of fruits and vegetables in schools on fruit, vegetable and caloric intake in children. Figure P. Begg’s funnel plots for graphical evaluation of potential publication bias for the effect of competitive food and beverage standards in schools on dietary intakes or adiposity in children. Figure Q. Begg’s funnel plots for graphical evaluation of potential publication bias for the effect of school meal standards on dietary intakes or meal contents in children.

https://doi.org/10.1371/journal.pone.0194555.s001

(DOCX)

Acknowledgments

The authors thank all of the collaborators and advisory groups in the Food Policy Review and Intervention Cost-Effectiveness (Food-PRICE) project (www.food-price.org).

Funding: This research was supported by the NIH, NHLBI (R01 HL115189, PI Mozaffarian; R01 HL130735, PI Micha).

Role of the funding source: The funding agency did not contribute to design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Author contributions: RM and DM designed the research and provided overall guidance. RM and DK performed statistical analyses and drafted the manuscript. RM, DK, IB, and ET acquired, evaluated, and extracted data. All authors checked and interpreted results, revised the manuscript for important intellectual content, read and approved the final manuscript. DM and RM had primary responsibility for final content.

Non author contributions: We thank Dr. Saman Fahimi, for assistance with creating the Access database; and Masha L. Shulkin, Siyi Shangguan, and Michael Kashaf, for assistance with initial data extractions.

References

  1. 1. Writing Group M, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, et al. (2016) Executive Summary: Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association. Circulation 133: 447–454. pmid:26811276
  2. 2. Craigie AM, Lake AA, Kelly SA, Adamson AJ, Mathers JC (2011) Tracking of obesity-related behaviours from childhood to adulthood: A systematic review. Maturitas 70: 266–284. pmid:21920682
  3. 3. Simmonds M, Burch J, Llewellyn A, Griffiths C, Yang H, et al. (2015) The use of measures of obesity in childhood for predicting obesity and the development of obesity-related diseases in adulthood: a systematic review and meta-analysis. Health Technol Assess 19: 1–336.
  4. 4. Simmonds M, Llewellyn A, Owen CG, Woolacott N (2016) Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev 17: 95–107. pmid:26696565
  5. 5. Birch LL (1998) Development of food acceptance patterns in the first years of life. Proc Nutr Soc 57: 617–624. pmid:10096125
  6. 6. Mikkila V, Rasanen L, Raitakari OT, Pietinen P, Viikari J (2005) Consistent dietary patterns identified from childhood to adulthood: the cardiovascular risk in Young Finns Study. Br J Nutr 93: 923–931. pmid:16022763
  7. 7. Welker E, Lott M, Story M (2016) The School Food Environment and Obesity Prevention: Progress Over the Last Decade. Curr Obes Rep 5: 145–155. pmid:27066793
  8. 8. Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, et al. (2012) Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation 126: 1514–1563. pmid:22907934
  9. 9. U.S. Department of Agriculture Food and Nutrition Service. Fresh Fruit and Vegetable Program [cited 2016 Jan 31]. Available from: https://www.fns.usda.gov/ffvp/fresh-fruit-and-vegetable-program.
  10. 10. Bere E, Hilsen M, Klepp KI (2010) Effect of the nationwide free school fruit scheme in Norway. The British journal of nutrition. pp. 589–594. pmid:20350345
  11. 11. U.S. Department of Agriculture Food and Nutrition Service. School Meals: Healthy Hunger-Free Kids Act [cited 2016 Jan 31]. Available from: https://www.fns.usda.gov/school-meals/healthy-hunger-free-kids-act.
  12. 12. U.S. Department of Agriculture Food and Nutrition Service. Healthier School Day. Tools for Schools: Focusing on Smart Snacks [cited 2016 Jan 31]. Available from: https://www.fns.usda.gov/healthierschoolday/tools-schools-focusing-smart-snacks.
  13. 13. U.S. Department of Agriculture Food and Nutrition Service. Nutrition Standards for School Meals [cited 2016 Jan 31]. Available from: https://www.fns.usda.gov/school-meals/nutrition-standards-school-meals.
  14. 14. UK Department of Education School food standards: actions in the School Food Plan London2015. Available from: http://www.schoolfoodplan.com/actions/school-food-standards/.
  15. 15. Huehnergarth NF (Forbes; 2016 [cited 2016 Dec 16]. Available from: https://www.forbes.com/sites/nancyhuehnergarth/2016/12/16/house-conservatives-to-trump-axe-michelle-obamas-school-food-standards/#673d1895ea30.) House Conservatives To Trump: Axe Michelle Obama's School Food Standards:.
  16. 16. School Nutrition Association (2016 [cited 2016 Jun 15]. Available from: https://schoolnutrition.org/NewsPublications/PressReleases/BlockGrantsWouldSlashMillionsFromSchoolMealBudgets/.) Block Grants Would Slash Millions from School Meal Budgets and Compromise Meal Service for Students.
  17. 17. de Sa J, Lock K (2008) Will European agricultural policy for school fruit and vegetables improve public health? A review of school fruit and vegetable programmes. Eur J Public Health 18: 558–568. pmid:18719006
  18. 18. Delgado-Noguera M, Tort S, Martinez-Zapata MJ, Bonfill X (2011) Primary school interventions to promote fruit and vegetable consumption: a systematic review and meta-analysis. Prev Med 53: 3–9. pmid:21601591
  19. 19. Evans CE, Christian MS, Cleghorn CL, Greenwood DC, Cade JE (2012) Systematic review and meta-analysis of school-based interventions to improve daily fruit and vegetable intake in children aged 5 to 12 y. Am J Clin Nutr 96: 889–901. pmid:22952187
  20. 20. Ganann R, Fitzpatrick-Lewis D, Ciliska D, Peirson LJ, Warren RL, et al. (2014) Enhancing nutritional environments through access to fruit and vegetables in schools and homes among children and youth: a systematic review. BMC Res Notes 7: 422. pmid:24996963
  21. 21. Howerton MW, Bell BS, Dodd KW, Berrigan D, Stolzenberg-Solomon R, et al. (2007) School-based nutrition programs produced a moderate increase in fruit and vegetable consumption: meta and pooling analyses from 7 studies. J Nutr Educ Behav 39: 186–196. pmid:17606244
  22. 22. Van Cauwenberghe E, Maes L, Spittaels H, van Lenthe FJ, Brug J, et al. (2010) Effectiveness of school-based interventions in Europe to promote healthy nutrition in children and adolescents: systematic review of published and 'grey' literature. Br J Nutr 103: 781–797. pmid:20070915
  23. 23. Driessen CE, Cameron AJ, Thornton LE, Lai SK, Barnett LM (2014) Effect of changes to the school food environment on eating behaviours and/or body weight in children: a systematic review. Obes Rev 15: 968–982. pmid:25266705
  24. 24. Chriqui JF, Pickel M, Story M (2014) Influence of school competitive food and beverage policies on obesity, consumption, and availability: a systematic review. JAMA Pediatr 168: 279–286. pmid:24473632
  25. 25. Levy DT, Friend KB, Wang YC (2011) A review of the literature on policies directed at the youth consumption of sugar sweetened beverages. Adv Nutr 2: 182S–200S. pmid:22332051
  26. 26. Katz DL, O'Connell M, Njike VY, Yeh MC, Nawaz H (2008) Strategies for the prevention and control of obesity in the school setting: systematic review and meta-analysis. Int J Obes (Lond) 32: 1780–1789.
  27. 27. Khambalia AZ, Dickinson S, Hardy LL, Gill T, Baur LA (2012) A synthesis of existing systematic reviews and meta-analyses of school-based behavioural interventions for controlling and preventing obesity. Obes Rev 13: 214–233. pmid:22070186
  28. 28. Nixon CA, Moore HJ, Douthwaite W, Gibson EL, Vogele C, et al. (2012) Identifying effective behavioural models and behaviour change strategies underpinning preschool- and school-based obesity prevention interventions aimed at 4-6-year-olds: a systematic review. Obes Rev 13 Suppl 1: 106–117.
  29. 29. Wang Y, Wu Y, Wilson RF, Bleich S, Cheskin L, et al. (2013). Childhood Obesity Prevention Programs: Comparative Effectiveness Review and Meta-Analysis. Rockville (MD).
  30. 30. Waters E, de Silva-Sanigorski A, Hall BJ, Brown T, Campbell KJ, et al. (2011) Interventions for preventing obesity in children. Cochrane Database Syst Rev: CD001871. pmid:22161367
  31. 31. Sobol-Goldberg S, Rabinowitz J, Gross R (2013) School-based obesity prevention programs: a meta-analysis of randomized controlled trials. Obesity (Silver Spring) 21: 2422–2428.
  32. 32. Vargas-Garcia EJ, Evans CEL, Prestwich A, Sykes-Muskett BJ, Hooson J, et al. (2017) Interventions to reduce consumption of sugar-sweetened beverages or increase water intake: evidence from a systematic review and meta-analysis. Obes Rev 18: 1350–1363. pmid:28721697
  33. 33. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6: e1000097. pmid:19621072
  34. 34. Fu R, Vandermeer BW, Shamliyan TA, O'Neil ME, Yazdi F, et al. (2008) Handling Continuous Outcomes in Quantitative Synthesis. Methods Guide for Effectiveness and Comparative Effectiveness Reviews. Rockville (MD).
  35. 35. Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21: 1539–1558. pmid:12111919
  36. 36. Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50: 1088–1101. pmid:7786990
  37. 37. Ashfield-Watt PA, Stewart EA, Scheffer JA (2009) A pilot study of the effect of providing daily free fruit to primary-school children in Auckland, New Zealand. Public health nutrition.
  38. 38. Bartlett S, Olsho L, Klerman J LPK, Blocklin M, Connor P, et al. Evaluation of the Fresh Fruit and Vegetable Program (FFVP): Final Evaluation Report. Abt Associates Inc. Alexandria, VA; USDA Food and Nutrition Service, 2013.
  39. 39. Bere E, te Velde SJ, Smastuen MC, Twisk J, Klepp KI (2015) One year of free school fruit in Norway—7 years of follow-up. Int J Behav Nutr Phys Act 12: 139. pmid:26556692
  40. 40. Bere E, Veierød MB, Bjelland M, Klepp KI (2006) Free school fruit—sustained effect 1 year later. Health education research. pp. 268–275. pmid:16219630
  41. 41. Bere E, Veierod MB, Klepp KI (2005) The Norwegian School Fruit Programme: evaluating paid vs. no-cost subscriptions. Prev Med 41: 463–470. pmid:15917042
  42. 42. Bere E, Veierod MB, Skare O, Klepp KI (2007) Free School Fruit—sustained effect three years later. Int J Behav Nutr Phys Act 4: 5. pmid:17309800
  43. 43. Bogart LM, Elliott MN, Cowgill BO, Klein DJ, Hawes-Dawson J, et al. (2016) Two-Year BMI Outcomes From a School-Based Intervention for Nutrition and Exercise: A Randomized Trial. Pediatrics 137.
  44. 44. Bonsergent E, Agrinier N, Thilly N, Tessier S, Legrand K, et al. (2013) Overweight and obesity prevention for adolescents: a cluster randomized controlled trial in a school setting. Am J Prev Med 44: 30–39. pmid:23253647
  45. 45. Coyle KK, Potter S, Schneider D, May G, Robin LE, et al. (2009) Distributing free fresh fruit and vegetables at school: results of a pilot outcome evaluation. Public Health Rep 124: 660–669. pmid:19753944
  46. 46. Davis EM, Cullen KW, Watson KB, Konarik M, Radcliffe J (2009) A Fresh Fruit and Vegetable Program improves high school students' consumption of fresh produce. J Am Diet Assoc 109: 1227–1231. pmid:19559140
  47. 47. Elbel B, Mijanovich T, Abrams C, Cantor J, Dunn L, et al. (2015) A water availability intervention in New York City public schools: influence on youths' water and milk behaviors. Am J Public Health 105: 365–372. pmid:25521867
  48. 48. Eriksen K, Haraldsdottir J, Pederson R, Flyger HV (2003) Effect of a fruit and vegetable subscription in Danish schools. Public Health Nutr 6: 57–63. pmid:12581466
  49. 49. Fiske A, Cullen KW (2004) Effects of promotional materials on vending sales of low-fat items in teachers' lounges. Journal of the American Dietetic Association. pp. 90–93. pmid:14702590
  50. 50. Fogarty AW, Antoniak M, Venn AJ, Davies L, Goodwin A, et al. (2007) Does participation in a population-based dietary intervention scheme have a lasting impact on fruit intake in young children? Int J Epidemiol 36: 1080–1085. pmid:17602183
  51. 51. French SA, Story M, Fulkerson JA, Hannan P (2004) An environmental intervention to promote lower-fat food choices in secondary schools: outcomes of the TACOS Study. American journal of public health. pp. 1507–1512. pmid:15333303
  52. 52. He M, Beynon C, Sangster Bouck M, Onge R, Stewart S, et al. (2009) Impact evaluation of the Northern Fruit and Vegetable Pilot Programme—a cluster-randomised controlled trial. Public health nutrition. pp. 2199–2208. pmid:19476675
  53. 53. Loughridge JL, Barratt J (2005) Does the provision of cooled filtered water in secondary school cafeterias increase water drinking and decrease the purchase of soft drinks? Journal of human nutrition and dietetics: the official journal of the British Dietetic Association. pp. 281–286.
  54. 54. Lytle LA, Murray DM, Perry CL, Story M, Birnbaum AS, et al. (2004) School-based approaches to affect adolescents' diets: results from the TEENS study. Health Educ Behav 31: 270–287. pmid:15090126
  55. 55. Muckelbauer R, Libuda L, Clausen K, Toschke AM, Reinehr T, et al. (2009) Promotion and provision of drinking water in schools for overweight prevention: randomized, controlled cluster trial. Pediatrics. pp. e661–667. pmid:19336356
  56. 56. Olsho LE, Klerman JA, Ritchie L, Wakimoto P, Webb KL, et al. (2015) Increasing Child Fruit and Vegetable Intake: Findings from the US Department of Agriculture Fresh Fruit and Vegetable Program. J Acad Nutr Diet 115: 1283–1290. pmid:25746429
  57. 57. Rahmani K, Djazayery A, Habibi MI, Heidari H, Dorosti-Motlagh AR, et al. (2011) Effects of daily milk supplementation on improving the physical and mental function as well as school performance among children: results from a school feeding program. J Res Med Sci 16: 469–476. pmid:22091261
  58. 58. Ransley JK, Greenwood DC, Cade JE, Blenkinsop S, Schagen I, et al. (2007) Does the school fruit and vegetable scheme improve children's diet? A non-randomised controlled trial. J Epidemiol Community Health 61: 699–703. pmid:17630369
  59. 59. Reinaerts E, Crutzen R, Candel M, Vries NK, Nooijer J (2008) Increasing fruit and vegetable intake among children: comparing long-term effects of a free distribution and a multicomponent program. Health education research. pp. 987–996. pmid:18550582
  60. 60. Schwartz AE, Leardo M, Aneja S, Elbel B (2016) Effect of a School-Based Water Intervention on Child Body Mass Index and Obesity. JAMA Pediatr 170: 220–226. pmid:26784336
  61. 61. Slusser WM, Cumberland WG, Browdy BL, Lange L, Neumann C (2007) A school salad bar increases frequency of fruit and vegetable consumption among children living in low-income households. Public Health Nutr 10: 1490–1496. pmid:17610759
  62. 62. Tak NI, Te Velde SJ, Brug J (2009) Long-term effects of the Dutch Schoolgruiten Project—promoting fruit and vegetable consumption among primary-school children. Public Health Nutr 12: 1213–1223. pmid:18940029
  63. 63. te Velde SJ, Brug J, Wind M, Hildonen C, Bjelland M, et al. (2008) Effects of a comprehensive fruit- and vegetable-promoting school-based intervention in three European countries: the Pro Children Study. British Journal of Nutrition 99: 893–903. pmid:17953787
  64. 64. van de Gaar VM, Jansen W, van Grieken A, Borsboom G, Kremers S, et al. (2014) Effects of an intervention aimed at reducing the intake of sugar-sweetened beverages in primary school children: a controlled trial. Int J Behav Nutr Phys Act 11: 98. pmid:25060113
  65. 65. Visscher TL, Hal WC, Blokdijk L, Seidell JC, Renders CM, et al. (2010) Feasibility and impact of placing water coolers on sales of sugar-sweetened beverages in Dutch secondary school canteens. Obesity facts. pp. 109–115. pmid:20484944
  66. 66. Coleman KJ, Shordon M, Caparosa SL, Pomichowski ME, Dzewaltowski DA (2012) The healthy options for nutrition environments in schools (Healthy ONES) group randomized trial: using implementation models to change nutrition policy and environments in low income schools. The international journal of behavioral nutrition and physical activity. pp. 80. pmid:22734945
  67. 67. Eagle TF, Gurm R, Smith CA, Corriveau N, DuRussell-Weston J, et al. (2013) A middle school intervention to improve health behaviors and reduce cardiac risk factors. Am J Med 126: 903–908. pmid:23932159
  68. 68. Kim KR, Hanyang University, Seoul, Republic of Korea, Hong SA, Hanyang University, Seoul, Republic of Korea, Yun SH, Hanyang University, Seoul, Republic of Korea, Ryou HJ, Health Promotion Division, Seoul Metropolitan Government, Seoul, Republic of Korea, Lee SS, Hanyang University, Seoul, Republic of Korea, et al. ((Apr 2012)) The effect of a healthy school tuck shop program on the access of students to healthy foods. v. 6(2) p. 138–145. pmid:22586503
  69. 69. Kocken PL, Eeuwijk J, C M., Dusseldorp E, Buijs G, et al. (2012) Promoting the Purchase of Low-Calorie Foods From School Vending Machines: A Cluster-Randomized Controlled Study. Journal of School Health 82: 115–122. pmid:22320335
  70. 70. Kocken PL, van Kesteren NM, Buijs G, Snel J, Dusseldorp E (2015) Students' beliefs and behaviour regarding low-calorie beverages, sweets or snacks: are they affected by lessons on healthy food and by changes to school vending machines? Public Health Nutr 18: 1545–1553. pmid:25591446
  71. 71. Moore L, Tapper K (2008) The impact of school fruit tuck shops and school food policies on children's fruit consumption: a cluster randomised trial of schools in deprived areas. Journal of epidemiology and community health. pp. 926–931. pmid:18483061
  72. 72. Wordell D, Daratha K, Mandal B, Bindler R, Butkus SN (2012) Changes in a middle school food environment affect food behavior and food choices. J Acad Nutr Diet 112: 137–141. pmid:22709644
  73. 73. Anderson AS, Porteous LE, Foster E, Higgins C, Stead M, et al. (2005) The impact of a school-based nutrition education intervention on dietary intake and cognitive and attitudinal variables relating to fruits and vegetables. Public Health Nutr 8: 650–656. pmid:16236195
  74. 74. Fung C, McIsaac JLD, Kuhle S, Kirk SFL, Veugelers PJ (2013) The impact of a population-level school food and nutrition policy on dietary intake and body weights of Canadian children. Preventive Medicine.
  75. 75. Hoppu U, Lehtisalo J, Kujala J, Keso T, Garam S, et al. (2010) The diet of adolescents can be improved by school intervention. Public Health Nutr 13: 973–979. pmid:20513268
  76. 76. Bae SG, Kim JY, Kim KY, Park SW, Bae J, et al. (2012) Changes in dietary behavior among adolescents and their association with government nutrition policies in Korea, 2005–2009. J Prev Med Public Health 45: 47–59. pmid:22389758
  77. 77. Bauhoff S (2013) The effect of school district nutrition policies on dietary intake and overweight: A synthetic control approach. Economics and Human Biology.
  78. 78. Blum JE, Davee AM, Beaudoin CM, Jenkins PL, Kaley LA, et al. (2008) Reduced availability of sugar-sweetened beverages and diet soda has a limited impact on beverage consumption patterns in Maine high school youth. J Nutr Educ Behav 40: 341–347. pmid:18984489
  79. 79. Cradock AL, McHugh A, Mont-Ferguson H, Grant L, Barrett JL, et al. (2011) Effect of school district policy change on consumption of sugar-sweetened beverages among high school students, Boston, Massachusetts, 2004–2006. Prev Chronic Dis 8: A74. pmid:21672398
  80. 80. Foster GD, Sherman S, Borradaile KE, Grundy KM, Vander Veur SS, et al. (2008) A policy-based school intervention to prevent overweight and obesity. Pediatrics. pp. e794–802. pmid:18381508
  81. 81. Jensen CD, Sato AF, McMurtry CM, Hart CN, Jelalian E (2012) School nutrition policy: an evaluation of the Rhode Island healthier beverages policy in schools. ICAN: Infant, Child & Adolescent Nutrition 4: 276–282.
  82. 82. Palakshappa D, Fiks AG, Faerber JA, Feudtner C (2016) Association between state school nutrition laws and subsequent child obesity. Prev Med 90: 107–113. pmid:27370166
  83. 83. Sanchez-Vaznaugh EV, Sanchez BN, Baek J, Crawford PB (2010) 'Competitive' food and beverage policies: are they influencing childhood overweight trends? Health Aff (Millwood) 29: 436–446.
  84. 84. Sanchez-Vaznaugh EV, Sanchez BN, Crawford PB, Egerter S (2015) Association between competitive food and beverage policies in elementary schools and childhood overweight/obesity trends: differences by neighborhood socioeconomic resources. JAMA Pediatr 169: e150781. pmid:25938657
  85. 85. Schwartz MB, Novak SA, Fiore SS (2009) The impact of removing snacks of low nutritional value from middle schools. Health Educ Behav 36: 999–1011. pmid:19196862
  86. 86. Taber DR, Chriqui JF, Chaloupka FJ (2012) Differences in nutrient intake associated with state laws regarding fat, sugar, and caloric content of competitive foods. Arch Pediatr Adolesc Med 166: 452–458. pmid:22566546
  87. 87. Taber DR, Chriqui JF, Perna FM, Powell LM, Chaloupka FJ (2012) Weight status among adolescents in States that govern competitive food nutrition content. Pediatrics 130: 437–444. pmid:22891223
  88. 88. Williamson DA, Champagne CM, Harsha DW, Han H, Martin CK, et al. (2012) Effect of an environmental school-based obesity prevention program on changes in body fat and body weight: a randomized trial. Obesity (Silver Spring) 20: 1653–1661.
  89. 89. Woodward-Lopez G, Gosliner W, Samuels SE, Craypo L, Kao J, et al. (2010) Lessons learned from evaluations of California's statewide school nutrition standards … [corrected] [published erratum appears in AM J PUBLIC HEAL 2011 OCT; 101(10):1815. American Journal of Public Health 100: 2137–2145. pmid:20864696
  90. 90. Cullen KW, Watson K, Zakeri I (2008) Improvements in middle school student dietary intake after implementation of the Texas Public School Nutrition Policy. Am J Public Health 98: 111–117. pmid:18048778
  91. 91. Foster GD, Linder B, Baranowski T, Cooper DM, Goldberg L, et al. (2010) A school-based intervention for diabetes risk reduction. N Engl J Med 363: 443–453. pmid:20581420
  92. 92. Group HS, Mobley CC, Stadler DD, Staten MA, El Ghormli L, et al. (2012) Effect of nutrition changes on foods selected by students in a middle school-based diabetes prevention intervention program: the HEALTHY experience. J Sch Health 82: 82–90. pmid:22239133
  93. 93. Kaufman F, Hirst K, Buse J, Foster GD, Goldberg L, et al. (2011) Effect of secular trends on a primary prevention trial: the HEALTHY study experience. Childhood Obesity 7: 291–297.
  94. 94. Marcus C, Nyberg G, Nordenfelt A, Karpmyr M, Kowalski J, et al. (2009) A 4-year, cluster-randomized, controlled childhood obesity prevention study: STOPP. Int J Obes (Lond) 33: 408–417.
  95. 95. Mullally ML, Taylor JP, Kuhle S, Bryanton J, Hernandez KJ, et al. (2010) A province-wide school nutrition policy and food consumption in elementary school children in Prince Edward Island. Can J Public Health 101: 40–43. pmid:20364537
  96. 96. Amin SA, Yon BA, Taylor JC, Johnson RK (2015) Impact of the National School Lunch Program on Fruit and Vegetable Selection in Northeastern Elementary Schoolchildren, 2012–2013. Public Health Rep 130: 453–457. pmid:26327723
  97. 97. Anderson LM, Aycock KE, Mihalic CA, Kozlowski DJ, Detschner AM (2013) Geographic Differences in Physical Education and Adolescent BMI: Have Legal Mandates Made a Difference? Journal of School Nursing 29: 52–60. pmid:22815346
  98. 98. Ask AS, Hernes S, Aarek I, Vik F, Brodahl C, et al. (2010) Serving of free school lunch to secondary-school pupils—a pilot study with health implications. Public health nutrition. pp. 238–244. pmid:19650962
  99. 99. Bartholomew JB, Jowers EM (2006) Increasing frequency of lower-fat entrees offered at school lunch: an environmental change strategy to increase healthful selections. Journal of the American Dietetic Association. pp. 248–252. pmid:16442873
  100. 100. Bergman EA, Englund T, Taylor KW, Watkins T, Schepman S, et al. (2014) School lunch before and after implementation of the Healthy Hunger-Free Kids Act. J Child Nutr Manag 38.
  101. 101. Burgess-Champoux TL, Chan HW, Rosen R, Marquart L, Reicks M (2008) Healthy whole-grain choices for children and parents: a multi-component school-based pilot intervention. Public Health Nutr 11: 849–859. pmid:18062842
  102. 102. Cohen JF, Kraak VI, Choumenkovitch SF, Hyatt RR, Economos CD (2014) The CHANGE study: a healthy-lifestyles intervention to improve rural children's diet quality. J Acad Nutr Diet 114: 48–53. pmid:24126295
  103. 103. Cohen JF, Richardson S, Parker E, Catalano PJ, Rimm EB (2014) Impact of the new U.S. Department of Agriculture school meal standards on food selection, consumption, and waste. Am J Prev Med 46: 388–394. pmid:24650841
  104. 104. Cohen JF, Smit LA, Parker E, Austin SB, Frazier AL, et al. (2012) Long-term impact of a chef on school lunch consumption: findings from a 2-year pilot study in Boston middle schools. J Acad Nutr Diet 112: 927–933. pmid:22504283
  105. 105. Cullen KW, Chen TA, Dave JM, Jensen H (2015) Differential Improvements in Student Fruit and Vegetable Selection and Consumption in Response to the New National School Lunch Program Regulations: A Pilot Study. J Acad Nutr Diet 115: 743–750. pmid:25556770
  106. 106. Cummings PL, Welch SB, Mason M, Burbage L, Kwon S, et al. (2014) Nutrient content of school meals before and after implementation of nutrition recommendations in five school districts across two U.S. counties. Prev Med 67 Suppl 1: S21–27.
  107. 107. Dwyer JT, Hewes LV, Mitchell PD, Nicklas TA, Montgomery DH, et al. (1996) Improving school breakfasts: effects of the CATCH Eat Smart Program on the nutrient content of school breakfasts. Prev Med 25: 413–422. pmid:8818065
  108. 108. Folta SC, Kuder JF, Goldberg JP, Hyatt RR, Must A, et al. (2013) Changes in diet and physical activity resulting from the Shape Up Somerville community intervention. BMC Pediatr 13: 157. pmid:24093936
  109. 109. Haroun D, Harper C, Wood L, Nelson M (2011) The impact of the food-based and nutrient-based standards on lunchtime food and drink provision and consumption in primary schools in England. Public Health Nutr 14: 209–218. pmid:20701821
  110. 110. Hollar D, Lombardo M, Lopez-Mitnik G, Hollar TL, Almon M, et al. (2010) Effective multi-level, multi-sector, school-based obesity prevention programming improves weight, blood pressure, and academic performance, especially among low-income, minority children. J Health Care Poor Underserved 21: 93–108. pmid:20453379
  111. 111. Luepker RV, Perry CL, McKinlay SM, Nader PR, Parcel GS, et al. (1996) Outcomes of a field trial to improve children's dietary patterns and physical activity. The Child and Adolescent Trial for Cardiovascular Health. CATCH collaborative group. JAMA 275: 768–776. pmid:8598593
  112. 112. Murphy S, Moore GF, Tapper K, Lynch R, Clarke R, et al. (2011) Free healthy breakfasts in primary schools: a cluster randomised controlled trial of a policy intervention in Wales, UK. Public health nutrition. pp. 219–226. pmid:20602868
  113. 113. Nelson M (2011) The School Food Trust: transforming school lunches in England. Nutrition Bulletin 36: 381–389.
  114. 114. Nicklas TATUSoPHaTM, New Orleans, LA.), Dwyer J, Yang M, Stone E, Lytle L, et al. ((1996)) The impact of modifying school meals on dietary intakes of school-aged children. v. 20(suppl.) p. 21–26.
  115. 115. Osganian SK, Hoelscher DM, Zive M, Mitchell PD, Snyder P, et al. (2003) Maintenance of effects of the eat smart school food service program: results from the CATCH-ON study. Health Educ Behav 30: 418–433. pmid:12929894
  116. 116. Perry CL, Bishop DB, Taylor GL, Davis M, Story M, et al. (2004) A randomized school trial of environmental strategies to encourage fruit and vegetable consumption among children. Health education & behavior: the official publication of the Society for Public Health Education. pp. 65–76.
  117. 117. Schwartz MB, Henderson KE, Read M, Danna N, Ickovics JR (2015) New school meal regulations increase fruit consumption and do not increase total plate waste. Child Obes 11: 242–247. pmid:25734372
  118. 118. Simons-Morton BG, Parcel GS, Baranowski T, Forthofer R, O'Hara NM (1991) Promoting physical activity and a healthful diet among children: results of a school-based intervention study. Am J Public Health 81: 986–991. pmid:1854016
  119. 119. Snyder MP, Story M, Trenkner LL (1992) Reducing fat and sodium in school lunch programs: the LUNCHPOWER! Intervention Study. J Am Diet Assoc 92: 1087–1091. pmid:1512366
  120. 120. Spence S, Delve J, Stamp E, Matthews JN, White M, et al. (2013) The impact of food and nutrient-based standards on primary school children's lunch and total dietary intake: a natural experimental evaluation of government policy in England. PLoS One 8: e78298. pmid:24205190
  121. 121. Spence S, Delve J, Stamp E, Matthews JN, White M, et al. (2014) Did school food and nutrient-based standards in England impact on 11-12Y olds nutrient intake at lunchtime and in total diet? Repeat cross-sectional study. PLoS One 9: e112648. pmid:25409298
  122. 122. Spence S, Matthews JN, White M, Adamson AJ (2014) A repeat cross-sectional study examining the equitable impact of nutritional standards for school lunches in England in 2008 on the diets of 4-7y olds across the socio-economic spectrum. Int J Behav Nutr Phys Act 11: 128. pmid:25342153
  123. 123. Story M, Snyder MP, Anliker J, Weber JL, Cunningham-Sabo L, et al. (2003) Changes in the nutrient content of school lunches: results from the Pathways study. Prev Med 37: S35–45. pmid:14636807
  124. 124. Whitaker RC, Wright JA, Finch AJ, Psaty BM (1993) An environmental intervention to reduce dietary fat in school lunches. Pediatrics 91: 1107–1111. pmid:8502510
  125. 125. Williams CL, Bollella MC, Strobino BA, Spark A, Nicklas TA, et al. (2002) "Healthy-start": outcome of an intervention to promote a heart healthy diet in preschool children. Journal of the American College of Nutrition. pp. 62–71. pmid:11838889
  126. 126. Williamson DA, Copeland AL, Anton SD, Champagne C, Han H, et al. (2007) Wise Mind project: a school-based environmental approach for preventing weight gain in children. Obesity (Silver Spring) 15: 906–917.
  127. 127. U.S. Department of Agriculture (2015. 8th:[Available from: http://health.gov/dietaryguidelines/2015/guidelines/.) U.S. Department of Health and Human Services. 2015–2020 Dietary Guidelines For Americans: U.S. Department of Health and Human Services and U.S. Department of Agriculture.
  128. 128. Johnson DB, Podrabsky M, Rocha A, Otten JJ (2016) Effect of the Healthy Hunger-Free Kids Act on the Nutritional Quality of Meals Selected by Students and School Lunch Participation Rates. JAMA Pediatr 170: e153918. pmid:26747076
  129. 129. U.S. Department of Agriculture ([cited 2016 Jan 31]. Available from: http://www.unitedfresh.org/content/uploads/2014/06/FFVP-SY-2015-2016-final-7-6-15.pdf.) Food and Nutrition Service. The Fresh Fruit and Vegetable Program: “A Win for Children, Schools, Public Health and Agriculture".