determinants of dietary behavior among youth: an umbrella

Sleddens et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:7 DOI 10.1186/s12966-015-0164-x

REVIEW Open Access

Determinants of dietary behavior among youth:an umbrella reviewEster FC Sleddens1*, Willemieke Kroeze2, Leonie FM Kohl1, Laura M Bolten2, Elizabeth Velema2, Pam J Kaspers3,Johannes Brug4 and Stef PJ Kremers1

Abstract

Background: The literature on determinants of dietary behavior among youth is extensive and unwieldy. Weconducted an umbrella review or review-of-reviews to present a comprehensive overview of the current knowledge.

Methods: Therefore, we included systematic reviews identified in four databases (i.e. PubMed, PsycINFO, The CochraneLibrary and Web of Science) that summarized determinants of observable child and adolescent dietary behaviors. Dataextraction included a judgment of the importance of determinants, strength of evidence and evaluation of themethodological quality of the eligible reviews.

Results: In total, 17 reviews were considered eligible. Whereas social-cognitive determinants were addressed mostintensively towards the end of the 20th century, environmental determinants (particularly social and physicalenvironmental) have been studied most extensively during the past decade, thereby representing a paradigm shift.With regard to environmental determinants, mixed findings were reported. Sedentary behavior and intention werefound to be significant determinants of a wide range of dietary behaviors in most reviews with limited suggestiveevidence due to the cross-sectional study designs. Other potential determinants such as automaticity, self-regulationand subjective norm have been studied in relatively few studies, but results are promising.

Conclusion: The multitude of studies conducted on potential determinants of dietary behavior provides quiteconvincing evidence of the importance of several determinants (i.e. quite some variables were significantly related todietary behavior). However, because of the often used weak research designs in the studies covered in the availablereviews, the evidence for true determinants is suggestive at best.

Keywords: Youth, Determinants, Dietary behavior, Umbrella review

BackgroundDietary behaviors have been found to track from child-hood into adulthood [1]. Unhealthy food habits in child-hood, therefore, can have a tremendous health impactlater in life. Given the high prevalence of nutrition-related disease and mortality in Western countries [2], itis necessary to develop effective behavioral interventionsto improve diet quality. But which factors determine aperson’s dietary behavior? Interventions to improvehealth-related behaviors should be tailored to the mostimportant and changeable determinants of these behav-iors, preferably applying behavior change theories [3].

* Correspondence: [email protected] of Health Promotion, NUTRIM School for Nutrition andTranslational Research in Metabolism, Maastricht University Medical Center+,PO Box 616, Maastricht 6200, MD, the NetherlandsFull list of author information is available at the end of the article

© 2015 Sleddens et al.; licensee BioMed CentrCommons Attribution License (http://creativecreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.

To facilitate improvement of relevant, effective programsand policies promoting healthy eating targeting dietarybehavior it is important to identify the various factorsthat may influence children’s and adolescents’ foodconsumption.Socio-cognitive models of (health) behavior and behav-

ior change, such as the Theory of Planned Behavior [4],Social-Cognitive Theory [5], and the Health Belief Model[6] have been applied frequently in development of nu-trition education interventions. In very general terms-and not paying attention to the richness of and also dif-ferences between these models- these theories regardnutrition behavior to be determined by beliefs and con-scious decisions, rational considerations of pros andcons of the behavior, perceived social influences, and as-sessment of personal efficacy and control. In additional

al. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,

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Sleddens et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:7 of 22

fields of research, the physiological and affective influ-ences on dietary behaviors have been studied, providingevidence for such basic factors as hunger and satiety,sensory perceptions, and perceived palatability of foods[7] as important drivers of food choice and dietary be-haviors. And somewhat more recently, the so-called foodenvironment that defines the availability and accessibility(i.e. physical environment), affordability (i.e. economicenvironment), social appropriateness or support (social-cultural environment), as well as rules, regulations andpolicies (i.e. political environment) regarding food choiceand dietary behaviors has been studied in relation tofood intake and dietary behaviors, as informed by (so-cial) ecological behavior models [8-12]. Kremers and col-leagues [13] proposed to integrate these insights in theirEnvironmental Research framework for weight Gain pre-vention (EnRG; Figure 1). EnRG is a dual-process modeland regards dietary behavior and physical (in) activity tobe the result of direct ‘automatic’ responses to environ-mental cues (e.g. meal patterns and routines) as well asof more rational decision making based on cognitionssuch as intentions and beliefs. Furthermore, EnRG in-cludes mediating pathways between environment andcognitions as well as potential moderators of the impactof these determinants such as habit strength and self-regulation skills.The purpose of this study was to get a comprehensive

and systematic overview of the scientific literature oncorrelates (referred to as potential determinants) anddeterminants of dietary behavior among children andadolescent (referred to as youth) to facilitate the im-provement of effective healthy eating promoting interven-tions and identify gaps for future research initiatives.Because the scientific literature on this topic is unwieldy

COGNITIVE ANDMEDIAT

Attitude, subjectiveself-efficacy/percecontrol, intention

Sensory perceptionsatiety, perceived pfoods

ENVIRONMENTPhysical Social-culturalEconomic/financialPolitical

MODERAHabit strength, autself-regulation

Figure 1 Environmental research framework for weight gain preventi

and has been documented in a number of systematic re-views in recent years, we aimed to conduct a review-of-reviews to provide a more comprehensive overview. Wewere interested in the association of all determinants thatare potentially modifiable (social-cognitive, environmental,sensory and automatic processes) with observable dietarybehavior (actual consumption behaviors like fruit con-sumption, beverage intake, snacking) among youth. Byconducting a review-of-reviews, the so-called umbrellareview, we aimed to (a) explore which determinant-behavior relationships have been studied so far, and (b)assess the importance and strength of evidence of po-tential determinants. The EnRG framework served tocategorize the findings. Parallel to this umbrella review,a separate review-of-reviews of studies among adultswas conducted by the same team with the same method-ology [14]. Some parts of these two reviews -especially thedescription of the methodology- are therefore very similar.

MethodsSearch strategy and eligibility criteriaTo identify all relevant systematic reviews, we con-ducted systematic searches in the bibliographic data-bases PubMed, PsycINFO (via CSA Illumina), TheCochrane Library (via Wiley) and Web of Science forarticles published between January 1, 1990 and May 1,2014. The search terms included controlled terms, e.g.MeSH in PubMed, Thesaurus in PsycINFO, as well asfree text terms (only in The Cochrane Library). Searchterms expressing ‘food and dietary behavior’ were usedin combination with search terms comprising ‘determi-nants’, ‘study design: (systematic) review’, ‘study popula-tion: humans’ and ‘time span (January 1, 1990 to May1, 2014)’. The PubMed search strategy can be found in

AFFECTIVE ORS

norm, ived behavioral

s, hunger and alatability of

TORSomaticity,

DIETARY BEHAVIORE.g., fruit, vegetable, snack, breakfast, beverage

on (EnRG), adapted from Kremers et al. [13].


Table 1. The search strategies used in the other data-bases were based on the PubMed strategy.Studies were included if they met the following cri-

teria: (i) systematic reviews on observable food and diet-ary behavior (i.e. consumption behaviors like fruit intakeand snacking consumption, not purchasing behavior);(ii) studies describing potential behavioral determinants;(iii) study design: (systematic) review; (iv) study popula-tion: humans and (v) time span: January 1, 1990 to May1, 2014. We excluded: (i) studies that were not writtenin English; (ii) studies in which dietary behavior was notan outcome of the study; (iii); studies about dietary behav-iors in disease management and treatment; (iv) studiesthat focused on specific population groups (e.g. chronic-ally ill, pregnant women, cancer survivors); (v) studies notpublished as peer reviewed systematic reviews in scientificjournals, e.g. theses, dissertations, book chapters, non-peer reviewed papers, conference proceedings, reviews ofcase studies and qualitative studies, design and positionpapers, umbrella reviews; (vi) reviews of studies on notdirectly observable dietary behavior (e.g. nutrient or en-ergy intake, appetite); (vii) reviews of studies on non-modifiable determinants (e.g. physiological, neurologicalor genetic factors); (viii) reviews of studies on the effect ofinterventions (but reviews of experimental manipulationof single determinants were included); (ix) reviews notconducted systematically (search strategy including key-words and databases used not identified, and/or with toolittle information of the included studies presented). Thecurrent umbrella review focuses on youth (<18 years). Asecond umbrella review using the same methodology

Table 1 Search strategy in PubMed: January 1st, 1990 to May

Set Search terms

#6 #1 AND #2 AND #3 AND #4 NOT #5

#5 (“addresses” [Publication Type] OR “biography” [Publication Type] OR “ca“directory” [Publication Type] OR “editorial” [Publication Type] OR “festsc[Publication Type] OR “legal cases” [Publication Type] OR “legislation” [PType] OR “newspaper article” [Publication Type] OR “patient education h“congresses” [Publication Type] OR “consensus development conference[Publication Type] OR “practice guideline” [Publication Type])

#4 “review” [tiab]

#3 “humans” [Mesh]

#2 (“food and beverages” [Mesh] OR “food” [tiab] OR “beverage” [tiab] OR ““eating” [tiab] OR “feeding behavior” [Mesh] OR “feeding behavior” [tiabdietary” [Mesh] OR “dietary sodium chloride” [tiab] OR “carbohydrates” [[tiab] OR “meals” [tiab] OR “meal pattern” [tiab]) NOT “dietary supplemeNOT “cannibalism” [Mesh]) NOT “carnivory” [Mesh]) NOT “herbivory” [Me“mastication” [Mesh])

#1 (“association” [Mesh] OR “association” [tiab] OR “associations” [tiab] OR ““correlations” [tiab] OR “correlated” [tiab] OR “correlates” [tiab] OR “relati[tiab] OR “relate” [tiab] OR “related” [tiab] OR “relates” [tiab] OR “factor” [“prediction” [tiab] OR “predictive” [tiab] OR “predicts” [tiab] OR “predictoOR “influence” [tiab] OR “influences” [tiab] OR “influencing” [tiab] OR “in

Note: Filters review; Publication data from 1990/01/01 to 2014/05/01; English.

about determinants of dietary behavior in adults is pub-lished elsewhere [14].

Selection processFigure 2 summarizes the manuscript selection process. Intotal, 17714 citations were obtained using PubMed (n =13156), PsycINFO (n = 961), The Cochrane Library (n =920), and Web of Science (n = 2677). The subsequentscreening of the citations was performed by multiple re-viewers (all citations were screened by ES and WK; somewere screened by LB, SK, and EV). All titles of the cita-tions were independently screened for relevance by tworeviewers (ES and WK). Any disagreement was resolvedby including the citation into the abstract screeningprocess. Subsequently, abstracts of the remaining 1031 ci-tations were retrieved for further screening. Another 729citations were removed, resulting in 292 articles for full-text assessment for eligibility. In case of doubt, potentialinclusion was discussed with a third reviewer (SK). Studiesthat did not meet the inclusion criteria (n = 257) were re-moved. Figure 2 displays the reasons for exclusion. Add-itionally, duplicates (n = 10) were removed. Thereafter, thereference lists of all review papers selected for inclusion(n = 25) were scanned for further relevant references. Thisreference tracking technique resulted in one additional re-view article appropriate for inclusion. In total, 26 reviewswere considered eligible. However, of these reviews, 9were only focused on determinants of adult dietary behav-ior (references reported in the umbrella review about de-terminants of dietary behavior in adults of Sleddens et al.[14]). Five reviews assessed dietary behavior of both youth

1st, 2014 (bottom-up): N = 13,156

se reports” [Publication Type] OR “comment” [Publication Type] ORhrift” [Publication Type] OR “interview” [Publication Type] OR “lectures”ublication Type] OR “letter” [Publication Type] OR “news” [Publicationandout” [Publication Type] OR “popular works” [Publication Type] OR” [Publication Type] OR “consensus development conference, nih”

beverages” [tiab] OR “diet” [Mesh] OR “diet” [tiab] OR “eating” [Mesh] OR] OR “feeding behaviour” [tiab] OR “drink” [tiab] OR “sodium chloride,Mesh:noexp] OR “food habit” [tiab] OR “food habits” [tiab] OR “meal”nts” [Mesh]) NOT “food additives” [Mesh]) NOT “micronutrients” [Mesh])sh]) NOT “bottle feeding” [Mesh]) NOT “breast feeding” [Mesh]) NOT

determinant” [tiab] OR “determinants” [tiab] OR “correlation” [tiab] ORon” [tiab] OR “relations” [tiab] OR “relationship” [tiab] OR “relationships”tiab] OR “factors” [tiab] OR “predict” [tiab] OR “predicted” [tiab] ORr” [tiab] OR “associate” [tiab] OR “associates” [tiab] OR “associated” [tiab]fluenced” [tiab] OR “effect” [tiab] OR “effects” [tiab])

17714 records screened by title (PubMed n = 13156,PsycINFO n = 961, Cochrane n = 920, Web of Science n = 2677)

1021 records screened by abstract (PubMed n = 674,PsycINFO n = 129, Cochrane n = 0, Web of Science n = 218)

16693 records excluded

729 records excluded

292 full-text articles assessed for eligibility (PubMed n = 134, PsycINFO n = 72, Cochrane n = 0, Web of Science n = 86)

25 studies eligible full-text screening

257 records excluded:- Manuscripts not written in English (n = 2)- Dietary behavior is not a primary outcome of the study (n = 42)- Studies about disease management and treatment (n = 1)- Studies solely assessing special populations (n = 1)- Theses, dissertations, book chapters, non-peer reviewed papers, conference proceedings, reviews of case studies and qualitative studies, design and position papers, umbrella reviews (n = 26)- Studies with no observable dietary behavior (n = 14)- Studies that do not address determinants that can be used in policy and practice (i.e. physiology, neurology, genes) (n = 4)- Studies about interventions effects or behavior change strategies (not reviews on manipulations of a single determinant) (n = 24)- Reviews not conducted systematically (i.e. search strategy (databases used and keywords) not specified) (n = 131)- Full-text could not be retrieved (n = 1)- Too little information of included studies presented (n = 11)

17714 records identified through database searching(PubMed, PsycINFO, Cochrane, Web of Science)

1 study eligible reference tracking

26 studies eligible

35 studies eligible

10 duplicates removed

9 studies solely focused on adults/ elderly removed17 studies eligible

(youth)

Figure 2 Flow diagram of literature search by database.


and adults [15-19]. Therefore, 17 reviews were consideredeligible for our umbrella review on determinants of youthdietary behavior [15-31].

Data extraction including rating of methodological qualityFour authors (ES, WK, LK, and LB) extracted data fromthe selected reviews. The extracted data included searchrange applied, total number of studies included in thereviews and number of studies included in the reviewsthat are eligible for the current umbrella review, totalnumber of participants of included studies in the reviewsand number of participants of the included studies thatare eligible for the current umbrella review, and age andcontinent of included eligible studies. For a descriptionof the results, correlate and outcome measures wereextracted, as well as overall results of the reviews andoverall limitations and recommendations of reviews.Additionally, the methodological quality of the reviewswas evaluated using quality criteria adapted from DeVet, De Ridder, & De Wit [32] and based on the QualityAssessment Tool for Reviews [33]. In total, a review wasscored on eight criteria (with a total quality scoring ran-ging from 0–8) (see Table 2); 0 when the criteria was notapplicable for the included review; 1 when the criteriawas applicable for the included review. Disagreement be-tween the reviewers on individual items were identified

and solved during a consensus meeting. The quality ofthe reviews could be labeled as weak (quality scores ran-ging from 0 to 3), moderate (quality scores ranging from4 to 6) or strong (quality scores ranging from 7 to 8).Furthermore, we judged the importance of included de-terminants in the reviews and judged its strength of evi-dence. The importance of a determinant refers to thestatistical significance of a potential determinant and/oreffect size estimate in relation to a particular type ofdietary behavior. It refers to the amount of reviews (oreligible studies within the reviews) that did or did notfind statistically significant results. For a particular deter-minant to receive the highest ranking (highest level ofimportance), all eligible studies in each review shouldhave found a significant relationship and/or reporteda (non)-significant effect size larger than 0.30. Thestrength of evidence represents the consistency betweenstudy findings and designs of the studies. Longitudinalobservational studies and -where relevant- experimentalstudies of sufficient size, duration and quality showingconsistent effects were given prominence as the highestranking study designs. For this judgment we applied twocoding schemes, see Tables 3 and 4 respectively. The cri-teria for grading evidence were adapted from those ofthe World Cancer Research Fund [34]. The data extrac-tion method is similar to the study of Sleddens et al. [14].

Table 2 Quality assessment of reviews of determinants of dietary behavior among youth

Qualityassessmentcriteria

Was there aclearly definedsearch strategy?1

Was the searchstrategycomprehensive?2

Are inclusion/exclusion criteriaclearly stated?

Are the designsand number ofincluded studiesclearly stated?

Has the qualityof primarystudies beenassessed?

Did the qualityassessment includestudy design, studysample, outcomemeasures or follow-up(at least 2 of 4)

Does the reviewintegrate findingsbeyond describingor listing findingsof primary studies?

Has more than oneauthor been involvedin the data screeningand/or abstractionprocess?

Sumqualityscore

Williams [31] 1 1 1 1 1 1 1 1 8

Gardner [17]* 1 1 1 1 1 1 1 0 7

Pearson &Biddle [19]*

0 1 1 1 1 1 1 0 6

Adriaanse [15]* 0 1 1 1 0 0 1 1 5

McClain [24] 0 1 1 1 1 1 1 1 7

Van der Horst [29] 0 1 1 1 0 0 1 1 5

Pearson [25] 1 1 1 1 0 0 1 0 5

De Craemer [21] 1 1 1 1 0 0 1 1 6

Pearson [26] 1 1 1 1 1 1 1 0 7

Verloigne [30] 1 1 1 1 1 1 1 0 7

Ford [22] 1 1 1 1 1 1 1 1 8

Caspi [16]* 0 0 1 0 0 0 1 0 2

Moore &Cunningham [18]*

1 1 1 1 0 0 1 1 6

Lawman &Wilson [23]

0 1 1 1 1 1 1 0 6

Sleddens [28] 0 1 1 1 0 0 1 1 5

Berge [20] 0 0 1 1 1 1 1 0 5

Rasmussen [27] 0 1 1 1 1 1 1 1 7

8/17 15/17 17/17 16/17 10/17 10/17 17/17 9/17

Note: 1A search is rated clearly defined if at least search words and a flow chart is presented; 2A search is rated as comprehensive if at least two databases and the reference lists of examined papers were searched.*Reviews also including adults; weak (score ranging from 0–3) n = 1 (5.88%), moderate (score ranging from 4–6) n = 9 (52.94%), strong (score ranging from 7–8) n = 7 (41.18%).

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Table 3 Importance of a determinant

Five categories of importance are defined: ++ / + / 0 / - / --

The categories are defined as follows:

++ The variable has been found to be a statistically significantdeterminant in all identified reviews, without exception. This couldmean that only one review has included a particular variable, andshowed that this was a significant correlate and/or reported a(non)-significant effect size larger than 0.30, but it could also meanthat a number of reviews were conducted that included thisvariable and all of them concluded that the variable wassignificantly related to the particular behavioral outcome.

+ The variable has been found to be a statistically significantdeterminant and/or reported a (non)-significant effect size largerthan 0.30 in most reviews or studies within the review, with someexceptions. This implies that > 75% of the available reviewsconcluded the variable to be related, or the separate reviews reportthat 75% or more of the original studies concluded the factor to berelated. This could therefore mean that only one review hasincluded a particular variable, and showed that this was asignificant correlate in > 75% of studies. But it could also mean thata number of reviews were executed towards this variable andmost, but not all, concluded that the variable was significantlyrelated to the particular behavioral outcome.

0 The variable has been found to be a determinant and/or reporteda (non)-significant effect size larger than 0.30 in some reviews (25%to 75% of available reviews or of the studies reviewed in thesereviews), but not in others. This could mean that only one reviewhas included a particular variable, and showed ‘mixed findings’, butit could also mean that results are mixed across reviews.

- The variable has been found not to be a determinant, with someexceptions. This implies that <25% of the available reviews or ofthe original studies in the included reviews concluded that thevariable was related. This could thus mean that only one reviewhas included a particular variable, and generally showed ‘nullfindings’, with some exceptions. But it could also mean that anumber of reviews were executed towards this variable and most,but not all, concluded that the variable was not significantly relatedto the particular behavioral outcome.

-- The variable has been found not to be related to this particularoutcome. The absence of a relation was identified in all identifiedreviews, without exception. This could mean that only one reviewhas included a particular variable, and showed that this correlatewas not related to the behavior in question, but it could also meanthat a number of reviews were executed towards this variable andall of them concluded that the variable was unrelated to theparticular behavioral outcome.

Table 4 Criteria for grading evidence, see World CancerResearch Fund [34] for the full list

Strength of evidence:

Ideally the definition of the strength of evidence should be based on arelationship that has been established by multiple randomizedcontrolled trials of manipulations of single isolated variables, but thistype of evidence is often not available.

The following criteria were used to describe the strength of evidence inthis report. They are based on the criteria used by the World CancerResearch Fund (World Cancer Research Fund, 2007 [34]), but have beenmodified for the research question at hand. Four categories weredefined: convincing/probable/limited, suggestive/limited, no conclusion.

Convincing evidence: Evidence based on studies of determinantsshowing consistent associations between thevariable and the behavioral outcome. Theavailable evidence is based on a substantialnumber of studies including longitudinalobservational studies and where relevant,experimental studies of sufficient size, durationand quality showing consistent effects.Specifically, the grading criteria include evidencefrom more than one study type and evidencefrom at least two independent cohort studiesshould be available, and strong and plausibleexperimental evidence.

Probable evidence: Evidence based on studies of determinantsshowing fairly consistent associations betweenthe variable and the behavioral outcome, butthere are shortcomings in the available evidenceor some evidence to the contrary, whichprecludes a more definite judgment.Shortcomings in the evidence may be any ofthe following: insufficient duration of studies,insufficient studies available (but evidence fromat least two independent cohort studies or fivecase-control studies should be available),inadequate sample sizes, incomplete follow-up.

Limited, suggestiveevidence:

Evidence based mainly on findings from cross-sectional studies. Insufficient longitudinalobservational studies or experimental studies areavailable or results are inconsistent. More well-designed studies of determinants are requiredto support the tentative associations.

Limited, noconclusive evidence:

Evidence based on findings of a few studieswhich are suggestive, but are insufficient toestablish an association between the variableand the behavioral outcome. No evidence isavailable from longitudinal observational orexperimental studies. More well-designedstudies of determinants are required to supportthe tentative associations.


ResultsDescription of reviewsQuality assessment ratings are presented in Table 2.One review received a quality rating of 2 (weak). Theother reviews were rated as moderate (n = 9) or strong(n = 7). In all reviews, the inclusion and exclusion cri-teria were clearly stated and the review did integratefindings beyond describing or listing findings of pri-mary studies. Clearly defined search strategies were ab-sent in more than half of the reviews (9 out of 17reviews), as usually a flow chart of the data screeningprocess was missing.Table 5 provides an overview of the characteristics

of the included reviews. In three reviews [22-24] all

included studies were eligible for our current study.Most of the studies included in the reviews used across-sectional study design. Six studies did not provideany information about sample sizes [15,16,19,21,24,29].The remaining reviews included a total sample size of695 to 570,403. The target groups of the eligible studiesranged in ages between the different reviews, althoughthe focus was on primary school-aged children and ad-olescents. Most of the studies included in the reviewswere conducted in North-America, followed by Europe.

Table 5 Characteristics of analyzed systematic reviews among youth

Author anddate

Search rangeapplied

Number of eligible studiesincluded in the review/totalnumber of studies includedin the review

Designs of studies Total sample size of eligible studiesincluded in the review/Total samplesize of all studies included in thereview

Ages Continent

Williams et al.,2014 [31]

Up to 2013 13/30 Cross-sectional n = 12,longitudinal n = 1

Total n = 570,403, range 610 to 529,367/Total n = 1,550,415 (26 studies, NR: 4studies), range 319 to 926,018

11 to 17y North-America n = 6, Europe n = 5,Australasia n = 1, Asia n = 1

Gardner et al.,2011 [17]

No date limitswere set.

4 studies (2 samples)/22 studies(21 samples)

Cross-sectional n = 4 Total n = 695, range 312 to 383/Totaln = 6,121, range 93 to 876

High schoolstudents

Only Europe

Pearson &Biddle, 2011[19]

NR 31 studies (240 samples (total),33 samples (children), 207 samples(adolescents))/53 studies (111 samples)

Majority cross-sectional NR/Children: mean n = 1,184, range 66to 6,235, Adolescents: mean n = 8,356,range 60 to 14,407

<12, 12 to 18y Children: majority North-American = 13, Adolescents: halfNorth-America n = 13

Adriaanseet al., 2011[15]

Up to 2009 1 study/21 research articles describing23 empirical studies

Healthy eating: cross-sectionaln = 1, prospective n = 3,interventions n = 11;Unhealthy eating: longitudinaln = 1, interventions n = 8

NR/NR 11 to 16y NR

McClain et al.,2009 [24]

From 1990to 2009

50 studies/77 studies Overall results: cross-sectionaln = 64, prospective n = 11,interventions n = 2

NR/<50 n = 4, 51–99 n = 1, 100–499n = 39, 500–999 n = 15, 1000–2999n = 14, 3000–4999 n = 3, ≥5000 n = 3

<13, 13 to 18y Overall results: North-American = 48, Europe n = 32, Australasian = 3, Africa n = 3, Asia n = 2, othern = 3

Van der Horstet al., 2007[29]

From 1980to 2004

36 studies (44 samples)/58 studies(77 samples)

Cross-sectional 95%,longitudinal 3%, casecontrol 2%

NR/<100 n = 8, 100–199 n = 6, 200–299n = 1, 300–499 n = 5, 500–999 n = 2,1000–2999 n = 9; 3000–4999 n = 4,≥5000 n = 2

<13, 13 to 18y North-America 45 samples, Europe26 samples, Asia 4 samples,Oceania 2 samples

Pearson et al.,2009 [25]

Up to 2008 24 studies (33 samples)/ 24 studies(33 samples)

Majority cross-sectional n = 23 Children: mean n = 1,534, range 136to 4,314. Adolescents: mean n = 2,533,range 357 to 18,177

6 to 18y Majority Europe n = 12 (papers)

De Craemeret al., 2012[21]

From 1990to 2010

6 studies/43 studies Cross-sectional n = 35,longitudinal n = 6, cross-sectional and longitudinaln = 1, intervention n = 1

NR/<100 n = 3, 100–999 n = 28, >1000n = 12. Study sample sizes ranged from46 to 5,652

4 to 6y North-America n = 21, Europe n = 9,Australasia n = 12, Asia n = 1


Up to 2007 Total papers: n = 60. Children: 25studies (33 samples). Adolescents:38 studies (55 samples)/Total papers:n = 60. Children: 25 studies(33 samples). Adolescents: 38 studies(55 samples)

Majority cross-sectionaln = 24. Children: cross-sectional n = 31, longitudinaln = 2; Adolescent:cross-sectional n = 55 ,longitudinal n = 0

Mean n = 1,131, range 536 to 8,263.Children (6-11y): < 100 n = 8, 100–199n = 2, 200–299 n = 1, 300–499 n = 1,500–999 n = 6, 1000–2999 n = 10,3000–4999 n = 3, unknown n = 2;Adolescent (12-18y): < 100 n = 2,100–199 n = 3, 200–299 n = 3, 300–499n = 11, 500–999 n = 4, 1000–2999 n = 12,3000–4999 n = 8, >5000 n = 10

6 to 18yChildren: 6-11yAdolescents:12-18y

Children: North-America n = 15,Europe n = 15, Australasia n = 1,South-America n = 1, Asia n = 2;Adolescent: North- America n = 23,Europe n = 16, Australasia n = 9,South-America n = 2, Asia n = 5

Verloigneet al., 2012[30]

From 1990to 2010

17 studies/76 studies Cross-sectional n = 16,longitudinal n = 1

100-199 n = 1, 300–499 n = 1, 500–999n = 5, 1000–2999 n = 5, 3000–4999 n = 2,≥5000 n = 3/< 100 n = 3, 100–199 n = 9,200–299 n = 6, 300–499 n = 10, 500–999

10 to 12y North-America n = 9, Europe n = 4,Australasia n = 4

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Table 5 Characteristics of analyzed systematic reviews among youth (Continued)

n = 17, 1000–2999 n = 20, 3000–4999n = 5, ≥5000 n = 6

Ford et al.,2012 [22]

NR 9 studies /12 studies Cross-sectional n = 9 Total n = 13,280, range 240 to 4,983/Total n = 13,386, range 106 to 4,983

2 to 6y NR

Caspi et al.,2012 [16]

Up to 2011 5 studies /38 studies Majority cross-sectional,intervention n = 3

NR/NR Children5 to6y, 10 to 12y,youth, boyscouts

North-America n = 4,Australasia n = 1

Moore &Cunningham,2012 [18]

NR 2 studies /14 studies Cross-sectional n = 2 Total n = 5,144, range 824 to 4,320/Total n = 94,230, range 51 to 64,277

Preteens and14 to 17y

NR

Lawman &Wilson, 2012[23]

From 1995to 2010

11 studies /38 studies Cross-sectional n = 8,longitudinal n = 3.

Total n = 21,865, range 228 to 4,746/Total n = 51,396, range 52 to 7,907

9 to 21y North-America n = 32

Sleddenset al., 2011[28]

Up to 2010 10 studies /36 studies Cross-sectional: n = 9,longitudinal: n = 1


NR North-America n = 5,Europe n = 5

Berge, 2009[20]

From 2000onwards

48 studies /81 studies Cross-sectional n = 39,longitudinal n = 8,intervention n = 11


0 to 18y NR

Rasmussenet al., 2006[27]

Up to 2005 98 studies /98 studies Cross-sectional n = 90,longitudinal n = 8

<500 n = 24, 500–1000 n = 20, >1000n = 53, NR: n = 1/<500 n = 24, 500–1000n = 20, >1000 n = 53, NR: n = 1

NR North-America n = 50, Europen = 31, Australasia n = 16,South-America n = 1

Note: Designs of studies: cross-sectional, longitudinal observational, case control, and intervention studies (experimental, behavioral laboratory, filed studies in which interventions were studied); NR: not reported; wewere mainly interested to provide a more thorough description on the eligible studies of the included reviews (designs of studies, ages, continent).

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Findings of the reviewsTable 6 provides an overview of the correlates and out-comes (i.e. observable dietary behaviors) included in thereviews, and the overall findings, limitations and recom-mendations reported by the authors of these reviews. Inthe following two paragraphs we give an overview of thedeterminant-behavior relationships that have been stud-ied so far, and give an overview of the importance andstrength of evidence of potential determinants.

Determinant-behavior relationship: correlate andoutcome measuresPotential determinants of a range of dietary behavior out-comes among youth were explored, and many studies in-cluded multiple dietary behavior outcomes.Thirteen reviews explored associations between envir-

onmental factors and dietary behavior [16,18,20,21,23-31].Within the environmental determinants, the social-cultural environment was most often studied (n = 12)[16,18,20,21,23-30]. Thereafter, the physical environmentaldeterminants (n = 9) [16,21,23-26,29-31], the economic/fi-nancial environmental determinants (n = 4) [16,21,29,30],and the political environment (n = 1) [27]. Three re-views explored the associations between social-cognitivedeterminants and dietary behavior [15,24,27]. Thesesocial-cognitive determinants included attitude, self-efficacy/perceived behavioral control, and intention inthe study of McClain et al. [24] and Rasmussen et al. [27],subjective norm in the study of Rasmussen et al. [27], andself-regulation in the study of Adriaanse et al. [15]. Two ofthese reviews also examined the influence between sen-sory determinants and dietary behavior [24,27]. One re-view addressed the relation between habit strength anddietary behavior [17]. And finally, three reviews lookedat sedentary behavior in relation to dietary behavior[19,22,27].In total, four reviews solely explored associations be-

tween determinants and fruit and/or vegetable consump-tion: self-regulation [15]; physical, social-cultural andeconomic environmental determinants [16]; physical andsocial-cultural determinants [26]; and social-cultural,political, and social-cognitive determinants, sensory pro-cesses, and sedentary behavior [27]. Additionally, onereview solely explored associations between habit strengthand sugar-sweetened beverage intake [17], and one re-view solely explored association between physical andsocial-cultural environmental determinants and break-fast consumption [25]. The other 11 reviews exploreddeterminants of a variety of healthful and unhealthfuldietary behaviors (e.g. snacks, fruit and vegetables, softdrinks, milk, breakfast) [18-24,28-31]. Dietary behaviorsmost often included as outcomes in the included reviewswere fruit and/or vegetable consumption (n = 14)[15,16,18-24,27-29,31], followed by sugar-sweetened

beverage consumption (n = 10) [17,18,20-24,28-30], snackconsumption (n = 9) [18,19,21-24,28,29,31] and breakfastconsumption (n = 7) [18,20,22,23,25,28,30] (see Table 5).

The importance and strength of evidence of potentialdeterminantsWith regard to the importance of a determinant and itsstrength of evidence (Table 6), most determinant-behaviorrelationships were coded with a zero, indicating that thefindings are mixed. The following categories of determi-nants were found to be significantly related to dietary be-havior and/or reported a (non)-significant effect sizelarger than 0.30 in all identified eligible studies of the in-cluded reviews assessing these categories of determinants(++ in Table 3): some aspects of social-cognitive determi-nants (such as attitude, self-regulation, intention andself-efficacy) and dietary behavior [15,24,27]; habitstrength and sugar-sweetened beverage intake [17]; sen-sory processes and snacking [24]; and sedentary behaviorand sugar-sweetened beverage and breakfast consumption[22]. The following categories of determinants were foundto be significantly related to dietary behavior and/orreported a (non)-significant effect size larger than 0.30in more than 75% of the identified reviews assessingthese categories of determinants (+ in Table 3): thephysical environment and fruit intake [26]; the social-cultural environment and fruit and vegetable intake[16,18,21,24,27-29] and sugar-sweetened beverage con-sumption [18,20,21,23,24,28-30]; intention, sensory pro-cesses, and knowledge for fruit and vegetable intake[24,27]; and sedentary behavior and fruit intake [22],fruit and vegetable intake and snack intake [19,22]. Theevidence is mostly limited (limited, suggestive: Ls), pre-dominantly due to the abundance of studies with cross-sectional designs so that causal or predictive relationscould not be established. Systematic review on the in-fluence of political environments, self-regulation, subject-ive norm and automaticity were mostly lacking in theincluded reviews.

DiscussionMain resultsThe multitude of studies conducted on determinants ofdietary behavior among youth provides mixed and some-times quite convincing evidence regarding associationsbetween potential determinants and a range of dietarybehaviors. However, because of the general use of cross-sectional designs in the studies covered in the availablereviews, the evidence for true determinants is suggestiveat best.In particular, environmental determinants (mainly the

social-cultural environment) and social-cognitive determi-nants have been studied quite extensively for their associ-ation with different dietary behaviors, with somewhat

Table 6 Results of the reviews about determinants of dietary behavior among youth

Author, date Outcome measures Correlate measures Overall results of the reviews Overall limitations of the view Overall recommendations of thereview

Williams et al.,2014 [31]

Sugar-sweetenedbeverages, fast food,fruit and vegetables

Food and retail outlets Little evidence for an associationbetween retail food environmentsurrounding schools and foodconsumption.

1) Meta-analysis not possib due todifferent conceptualization ndmeasures of the food env mentsurrounding schools.

1) Longitudinal studies needed.

2) Integrate validated classificationsystems of the retail foodenvironment, explore the capacity ofalternative methods for validatingexposure data.

2) Loss of detail; a reviewdependent upon outcome ndanalyses that individual pa rsreported. 3) Specify individual-level measures of

exposure to the food environment.

4) Collecting complementarymeasures of both qualitative andquantitative measures of food access.

5) Collect outcome measures that areappropriate relative to the exposures.

6) Take age and ethnicity differencesinto account.

Gardner et al.,2011 [17]

Sugar-sweetenedsoft drinks

Habit strength The weighted habit–behaviorcorrelation effect estimate fornutritional habits was moderate tostrong in size (fixed: r + =0.43; random:r + =0.41), and effects were of equalmagnitude across healthful (fixed:r + =0.43; random: r + = 0.42) andunhealthful (fixed: r + =0.42; random:r + =0.41) dietary habits. The medium-to-large grand weighted mean habit–behavior correlation (r + ≈0.45)suggests that habit alone can explainaround 20% of variation in nutritionrelated behaviors (i.e. R2 ≈ 0.20).

1) While it was not possib o meta-analyze interaction effects bit oftenmoderated the relationshi etweenintention and behavior, su thatintentions had reduced im ct onbehavior where habit was ong. Thisfinding must be interprete autiouslyas it may reflect a bias tow dspublication of studies whi findsignificant interaction, and anoverestimation of the robu essof this effect.

1) Explorations of the role of counter-intentional habits on the intention–be-havior relationship, such as the cap-acity for habitual snacking to obstructintentions to eat a healthful diet, areneeded.

2) Healthful behaviors can habituate.The formation of healthful (‘good’)habits, so as to aid maintenance ofbehavior change, thus represents arealistic goal for health promotioncampaigns.

3) More methodologically rigorousresearch is required to provide moreconceptually coherent and less biasedobservations of the influence of habiton action.

2) Many studies were cros ctional,and so modeled habit as redictorof past behavior. This failsacknowledge the expecte mporalsequence between habit abehavior, and is also conc uallyproblematic given that, at st inearly stages of habit forma n,repeated action strengthe habit.

4) A more comprehensiveunderstanding of nutrition behaviors,and how they might be changed, willbe achieved by integrating habitualresponses to contextual cues intotheoretical accounts of behavior.

3) Reliance on self-reports behavior.

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Table 6 Results of the reviews about determinants of dietary behavior among youth (Continued)

Pearson &Biddle, 2011[19]

Fruit, vegetables, fruitand vegetable intakecombined, energy-densesnacks, fast foods, energy-dense drinks

Sedentary behavior: screentime (TV viewing,video/DVD, computer use,sitting while talking/reading/homework)

Sedentary behavior, usually assessed asscreen time and predominantly TVviewing, is associated with unhealthydietary behaviors in children andadolescents. There appears no clearpattern for age acting as a moderator.There appears to be more consistentassociations between sedentarybehavior and diets for women/girlsthan for men/boys.

1) Many studies were cross-sectional. 1) More studies using objectivemeasures of sedentary behaviors andmore valid and reliable measures ofdietary intake are required.

2) Use of self-report measures ofsedentary and dietary behaviors thatlack strong validity.

2) Examine the longitudinalassociation between sedentarybehavior and dietary intake, and thetracking of the clustering of specifıcsedentary behaviors and specifıcdietary behaviors. For example, itappears from the mainly cross-sectional evidence presented that TVviewing isassociated with unhealthy dietarypatterns. Much less is known aboutdiet and either computer use orsedentary motorizedtransport. It is likely that the mainassociations will be with TV, but thisneeds testing.

3) Sedentary behavior is largelyoperationally defined as screen time,and this is mainly TV viewing, makingit diffıcult to draw any conclusionsregarding non-screen time and dietaryintake.

4) Although “screen time” can includeTV and computer use, this does nothelp in identifying whether it is TV,computer use, or both, that isassociated with unhealthy diets.

3) A focus on sedentary behaviors anddietary behaviors that “share”determinants as well as determinantsof the clustering of sedentary anddietary behaviors will aid thedevelopment of targeted interventionsto reduce sedentary behaviors andpromote healthy eating.

Adriaanseet al., 2011[15]

Fruit and vegetableconsumption

Use of implementationintentions

Considerable support was found for thenotion that implementation intentionscan be effective in increasing healthyeating behaviors, with twelve studiesshowing an overall medium effect size ofimplementation intentions on increasingfruit and vegetable intake. However, whenaiming to diminish unhealthy eatingpatterns by means of implementationintentions, the evidence is less convincing,with fewer studies reporting positiveeffects, and an overall effect size thatis small.

NR 1) Although implementation intentioninstructions were not included as amoderator in the present meta-analysis due to the limited amount ofstudies, it seems prudent that futureresearch takes into account theimportance of using autonomysupportive instructions.

2) Stricter control conditions as well asbetter outcome measures arerequired.

3) Investigate efficacy ofimplementation intentions indiminishing unhealthy eatingbehaviors. In doing so, these studiesshould also compare the efficacy ofdifferent types of implementationintentions, as these may havedifferential effects on unhealthy foodconsumption.

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McClain et al.,2009 [24]

Fruit, juice and vegetableconsumption, sugar,snacking, sweetenedbeverage consumption

Psychosocial correlates like:attitude, availability,intention, knowledge,norms, self-efficacy,preferences, parental factors,and more.

Perceived modeling and dietaryintentions to make healthy or lesshealthy dietary changes (such asintentions to decrease consumptionof sugary beverages or intentions toincrease consumption of medium fatmilk) have the most consistent andpositive associations with dietarybehavior. Other psychosocial correlatessuch as liking, norms, and preferenceswere also consistently and positivelyassociated with dietary behavior inchildren and adolescents. Availability,knowledge, outcome expectations, self-efficacy and social support did not showconsistent relationships across dietaryoutcomes.

1) Many studies were cross-sectional. 1) Future intervention research maybenefit from the incorporation offindings from this review to createmore effective adolescent andchildhood dietary interventions bytargeting the variables shown in thisreview that are most consistentlyassociated with the various eatingbehaviors such as intentions,modeling, norms, liking, andpreferences.

2) Not possible to conductmeta-analysis.

3) Authors combined conceptuallysimilar psychosocial determinantsinto one category, which may haveintroduced bias.

4) Most studies relied on self-reportof dietary intake.

2) Investigate variables that have beeninsufficiently examined to date,particularly the variables rooted inaffective theories. It is quite plausiblethat affective factors, such asmotivation, executive control, ormeanings of behavior might drive thedietary behavior of children andadolescents.

5) Bias might have been introduceddue to possible lack of validity orreliability of both dietary andpsychosocial measures.

6) Certain studies reported onlysignificant findings and did notaddress non-significant findings.

3) Investigate psychosocial correlatesof several dietary behaviors that areknown to influence weight andmetabolic health such as fat and fiberthat have been understudied.

7) Only studies included that werepublished in English in peer-reviewedjournals (electronic databases).

8) This review did not separatechildren and adolescents into distinctcategories, although research hassuggested that children andadolescents exhibit different healthbehaviors.

Van der Horstet al., 2007[29]

Fruit intake, vegetableintake, juice intake,composite measure offruit and vegetable intake,composite measure of fruitjuice and vegetable intake,fast food consumption,snack food intake, pizzaand snack, soft drinkconsumption.

Correlates are categorizedunder home/household,educational institutions,neighborhood,city/municipality.

Consistent evidence, for the relationshipbetween parental intake and children’sfruit and vegetable intake, and for parenteducational level with adolescent’s fruitand vegetable intake. A positiveassociation was found for the relationshipbetween availability and accessibility withchildren’s fruit and vegetable intake.Further positive associations were foundfor modeling (fruit/vegetable), parentalintake (soft drink), parenting style (fruit/vegetable), family connectedness (fruit/vegetable) and encouragement toincrease food intake (fruit/vegetable).

1) Many potential environmentaldeterminants have been examined fora variety of dietary behaviors, but onlyfew studies have been conducted onthe same specific environmentalfactor—dietary behavior combination.

1) Replication of studies on the samespecific environmental factors isnecessary, to generate morecompelling evidence for associationsbetween environmental factors anddietary intake.

2) The finding that parental behavioris associated with child andadolescent intakes implies thatinterventions should take the behaviorof parents into account, or desensitizeadolescents for the (unfavorable)behavior of their parents. Parentsshould be more strongly encouragedto give the right example, especially

2) Many studies were cross-sectional.

3) Reliance on self-report measures.

4) Similar environmental determinantswere collapsed conceptually into onecategory, although potentialdeterminants in the same categorywere often dissimilar or measured indifferent ways.

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where fat and energy intakes areconcerned.

3) Fruit and vegetable promotionshould focus especially on adolescentsfrom parents with lower levels ofeducation.

5) Only studies included that werepublished in English in peer-reviewedjournals (electronic databases).

4) Studies are needed that target theenvironmental levels and factors thathave found to be (nearly) empty inthe ANGELO framework, such asphysical, socio-cultural, economic andpolitical factors in the school (e.g.school food policy and food prices),neighborhood (e.g. availability andaccessibility of foods in shops) andcity/ municipality environment (e.g.food policy, food prices, marketing).Factors such as availability andaccessibility at home, school andneighborhood should be studied inrelation to energy, fat, soft drink,snacks and fast food intake.

6) Studies were heterogeneous in theconceptualization, measurement ofthe environmental determinant and/or dietary intakes, samples andanalyses used: not possible to assessthe overall strength of associations.

7) Multiple environmental factorsexamined in one study were includedin the review, so these associationsare not independent.

5) Need for longitudinal studies withvalid or objective measures.


Breakfast consumption,breakfast skipping

Physical (availability andaccessibility), food poverty,socio-cultural (e.g. twoparent family, modeling,family communication,monitoring, food rules,parental presence),demographic (SES, parentaleducation level andemployment)

This review reported support for threefamily variables: Parental breakfasteating and living in two parentfamilies were positively associated withadolescent breakfast consumption; andsocio-economic deprivation wasinversely associated with breakfastconsumption.

1) Several studies may not have beenpowered to detect significantassociations between family correlatesand breakfast behaviors.

1) Future studies should clearly definebreakfast foods (e.g. breakfast cereal,breads, milk, snacks on the run) beingmeasured as this will allow for anunderstanding of the healthfulness ofthis behavior and will provide scopefor interventions to promote healthybreakfast consumption.

2) Diversity in the definition ofbreakfast across the literature.

2) Importance of family structureshould be considered when designingprograms to promote breakfastconsumption.

3) Future qualitative studies areneeded to further explicate themechanisms of the complexrelationship between SES andadolescent breakfast behaviors.

De Craemeret al., 2012[21]

Sweet beverages, fruitand vegetable intakecombined, snacks,milk intake

Demographic andbiological variables,behavioral variables,physical environmentalvariables

TV viewing was positively associatedwith the intake of sweet beverages,snacks and inversely associated withfruit and vegetable intake. Parentalmodeling was associated with fruitand vegetable intake. No associationwith fruit and vegetable intake was

NR 1) Future research should investigatesimilar correlates of physical activity,sedentary behavior and eatingbehavior to develop more efficientinterventions.

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found for restriction of eating, andan indeterminate result was foundfor pressuring the child to eat. Foodavailability was not associated withfruit and vegetable intake andsnacking but had an indeterminateresult for sweet beverages.

2) Future research should be oninterventions to predict whetherinterventions targeting thesecorrelates will have an impact.

3) Future research should focus onidentifying the common correlates ofphysical activity, sedentary behaviorand eating behavior in preschool-aged children so that better tailoredinterventions could be developed.

4) More longitudinal studies areneeded.


Fruit and vegetableconsumption separately.fruit, fruit juice andvegetable consumptioncombined.

Physical (e.g. availability,accessibility), socio-cultural(parental modeling,parental intake, familyrules), and demographiccorrelates (e.g. SES).

Children: home availability, familyrules (demand/allow) and parentalencouragement were positivelyassociated with children’s fruit andvegetable intake. Parental modelingand parental intake were positivelyassociated with children’sconsumption of fruit and fruit juiceand vegetable intake. Adolescents:parental intake and parentaloccupational status were found tobe positively associated withadolescents’ consumption of fruit.Parental intake was also positivelyassociated with adolescents’vegetable consumption. There isalso evidence for a positiveassociation between parentaleducation and adolescents’ fruitjuice and vegetable intake.

1) Diversity in character (e.g. measuresused and correlates studied)


2) More studies are needed to testunderstudied correlates to generatemore convincing evidence forassociations between correlates anddietary behaviors. 3) Studies shouldreport the validity and reliability ofmeasures used to assess predictorvariables.

2) Difficult to assess overallconsistency of associations.

3) Several studies may not have beenpowered to detect significantassociations between family correlatesand dietary behaviors.

4) Few studies have examined thesame specific combination of familycorrelate and dietary behavior, thuslimiting the possibilities of drawingstrong or consistent conclusions.


6) Reliance on self-report measures.

7) Little data on reliability and validityof measures of dietary outcomes andphysical and socio-cultural familycorrelates.

8) Only studies included that werepublished in English

Verloigneet al., 2012[30]

Breakfast consumption,soft drink consumption.

Family and schoolenvironment: physical,socio-cultural, economic,political correlates.

Parental descriptive/ injunctivenorms and control/supervisionwere positively related to breakfast.Parental catering on demands,avoidance of negative modelingbehavior, permissiveness, and areadeprivation were inversely related.School SES was negatively relatedand teacher injunctive norms waspositively related to breakfast.Availability at home, parental softdrink, and permissive parenting style

1) Only studies included that werepublished in English


2) Interventions could help parents tocreate a supportive environment fortheir children to promote healthybehavior.

2) Did not take possible moderatorsand covariates into account.

3) More research is needed to focuson important school-environmentalfactors when developing anintervention program.

3) Not all existing studies on this topicwere covered.

4) Focused on the consistency of theassociation and not on the strengthof the association.

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were positively related to soft drink.Having family dinners, householdincome, parental employment status,and limits were inversely related.Availability of soft drinks at schooland intake at school were positivelyrelated with soft drink. Participationin healthy school lunches wasinversely related.

5) Conceptually similar variables werecombined into a single category, evenif variables were measured in adifferent way.


Ford et al.,2012 [22]

Vegetables, fruit andvegetable intake, (non)-fruit juice, high-energy/sugar-sweetened drinks,whole or 2% milk, fastfoods, breakfast

TV, video, and computertime in minutes.

Eleven of the 12 included studiesreported significant associationsbetween TV and adverse dietarybehaviors in young children. Sixstudies reported significant inverserelationships between TV viewingand fruit and vegetable intake.

1) Reliance on parent-reportedmethods to assess child TV viewing.

1) Guidelines for TV viewing use inyoung children should be furtherdelimited.2) Many studies were cross-sectional.2) More longitudinal studies areneeded.

3) Direct measurement of TV use.

Caspi et al.,2012 [16]

Fruit and/or vegetablesintake, 100% fruit juiceconsumption.

Food environment: 5dimensions of food access(availability, accessibility,affordability, accommodation,acceptability).

Moderate evidence in support ofthe causal hypothesis thatneighborhood food environmentsinfluence dietary health. Perceivedmeasures of availability wereconsistently related to multiplehealthy dietary outcomes.

NR 1) More standardized/validatedmeasures for food environmentassessment needed.

2) Develop/refine understudiedmeasures.

3) Abandon purely distance-basedmeasures of accessibility, and combinemultiple environmental assessmenttechniques.

4) Researchers should continue toexpound upon the conceptualdefinitions of food access as theydevelop and refine new combinationsof measure for the food environment.

Moore &Cunningham,2012 [18]

Daily fruit and vegetableconsumption, snacking,breakfast consumption,soda consumption, meatintake.

Social status, stress. Higher stress is related to less healthydietary behaviors. The majority ofstudies reported that higher socialposition is related to healthier diet.

1) Only studies included that werepublished in English.

1) More quantitative dietaryassessment tools such as FFQ,repeated 24-hr recalls, and food diariesare needed.2) Many studies were cross-sectional.


3) Because obesity results from aprolonged period of positive energyimbalance, assessment of dietarybehaviors at a single point in timemakes inferences related to diet andobesity difficult.

3) Important to acknowledgeadditional factors that influenceenergy intake, such as SES and stresslevels.

4) Implementing appropriatemonitoring and evaluation is essentialto identifying successful, holisticstrategies that can be used toimprove quality of care.

4) Heterogeneity of measures.

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Lawman &Wilson, 2012[23]

Fruit, vegetables, fast food,soft drink, dairy, milk,breakfast.

Parenting (parental supportfor health behaviors,parenting style and parentalmonitoring surroundinghealth behaviors) and/orenvironmental factors (homeavailability/access,neighborhood availability/access and the builtenvironment, neighborhoodsafety, neighborhoodsocial factors).

The current review found supportfor some parenting and physicalenvironmental factors for healthbehaviors, particularly parentalmonitoring and neighborhoodsocial factors.

1) Many studies were cross-sectional. 1) More longitudinal studies areneeded on at-risk youth.2) Reliance on self-report measures.

2) More objective measures of healthbehaviors or multiple reporters, whomay hold different perspectives areneeded, when objective measures arenot feasible.

3) Future research should beconscious of reporting results in a waythat facilitates systematic review of theliterature

4) Examine additional levels of thebio-ecological model such asinterpersonal and other macro- orsociety and policy level factors.

5) Future research should explore therelation between home/environmentand health behaviors, particularlyneighborhood social contextualfactors such as social cohesion, andhow factors at multiple bio-ecologicallevels may be influencing them (e.g.moderators).

6) More research monitoring isneeded.

7) Development of more validmeasures of parenting, family, andhome environment variables iswarranted.

8) Examine how parenting style isrelated to health behavior outcomes.

Sleddenset al., 2011[28]

Vegetables, fruit, sugar-sweetened beverages,soft drinks, breakfast,snacks/sweets

General parenting In many studies significantassociations with general parentingwere found. Generally, childrenraised in authoritative homes werefound to eat healthier.

1) Reliance on questionnaires andparental self-report measures.

1) Additional research is needed tofurther study the influence ofmediating and moderating factorsinfluencing the general parenting -child weight relationship, preferablyemploying a longitudinal design withmore extended follow-up periods.

2) Differences in conceptualization ofparenting constructs across studies.

3) Different categorizations to classifyparents into styles across studies.

2) More longitudinal studies areneeded using diverse ethnic samplesand age groups.

4) Heterogeneity of measurementsacross studies and lacking informationabout distribution of independentand outcome variables. 3) Larger samples of fathers should be

included to allow for comparisonsbetween mothers and fathers.

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5) Few studies examined the role ofgeneral parenting as a contextualfactor that can influence theeffectiveness of food-related parentingpractices in predicting children’s dietary intake behaviors (moderationanalyses).

4) Intervention developers shouldincrease their attention to the familycontext as it is an important factorinfluencing outcomes of overweightinterventions for children.

Berge, 2009[20]

Fruit and vegetableconsumption, sugarsweetened beverages,dairy products, breakfastconsumption, etc.

Parental domain (e.g.parenting style, parentingpractices), familyfunctioning domain(e.g. family meals, familyemotional closeness/connection, familyweight teasing).

Parental domain: authoritative parentingstyle is positively associated with dietaryintake. Family functioning domain: fromcross-sectional and longitudinal researchthere is convincing evidence that familymeals have an enduring protective factorfor children and adolescents, girls andboys, and across diverse ethnic groupsrelated to healthy dietary intake.

1) Many studies were cross-sectional. 1) More longitudinal, experimentaland direct observational research in allfamily domains is needed.2) Many studies used single-group

designs.

3) Reliance on self-report measures. 2) Beneficial to incorporate mixedqualitative and quantitative designs.

4) Many studies used single informantmeasures to measure family-level data 3) There is a need for more within-in

family measurements that utilizemulti-level and multi-measurementapproaches.

5) Many studies used single itemmeasures.

6) Many studies adjusted for gender,SES and ethnicity as covariates, butleft out other influential covariatessuch as maternal BMI and parentalperception of child/adolescentweight.

4) There is a need to use systemicoutcome variables. More family systemvariables should be studies.

5) Examine possible mediator ormoderator effects of the familydomains.

6) Important to include covariateswhen studying familial correlates ofchild/adolescent obesity.

Rasmussenet al., 2006[27]

Fruit and/or vegetableintake

Socio-demographic factors,personal factors, family-related, friends-relatedfactors, school-related factors,meal patterns, TV watching,eating fast food.

The determinants supported by thegreatest amount of evidence are social-economic position, preferences, parentalintake, and home availability/accessibility.For nutritional knowledge, self-efficacyand shared family meals the evidence forpositive associations is rather convincing.

1) Publications may have been misseddue to the search strategy.

1) More studies on the influence ofthe family setting for influencing fruitand vegetable intake among childrenand adolescents are needed to enablehealth promoters to make evidencebased decisions.

2) Within this review only significantassociations are considered.

2) Observational studies analyzing fruitand vegetable intake in a schoolsetting are still lacking.

3) Many papers include analysesbased on small study samples andsamples that are non-representativeor only representative of a restrictedgeographical area. 3) Future international comparative

surveys should enable investigationsof national level factors of importancee.g. price levels, policy, guidelines,supply, and exposure to mass mediaand commercials.

4) Often the validity of the appliedinstruments are only considered verysuperficially or not mentioned at all.

4) Future research should study theinfluence of e.g. local access to fruitand vegetables through grocerystores, local food policies, exposure tomass media and commercials, and

5) There is insufficient confoundercontrol.

6) Large variety of approaches forconceptualizing, operationalizing,

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fruit and vegetable availability inleisure time facilities for children andadolescents, like for instance localsport clubs.

measuring and coding the outcomevariable(s) exist.

5) Future research would benefit fromimprovements in design andmethodology.

6) More longitudinal studies ofchildren and adolescents’ fruit andvegetable intake are needed.

7) Lack of knowledge about predictorsof FVI among children andadolescents from non-western parts ofthe world.

8) Future studies should keep a verybroad and comprehensive theoreticalscope, in order not to excludeimportant etiological components ofimportance for child and adolescentFVI.

Note: The overall results/limitations/recommendations of the reviews that are reported are those reported by the review authors themselves.

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mixed results. The included reviews suggest that in thepast decade, environmental determinants have been stud-ied most extensively. This is an important finding in itself,suggesting a paradigm shift in the field, i.e. from a focuson social-cognitive determinants to environmental factors.This shift towards more consideration of the social-ecological approach was also seen in our umbrella reviewon determinants of dietary behavior in adults [14]. Otherpotential determinants of dietary behavior, such as auto-maticity, self-regulation, and subjective norm, have beenstudied in relatively few studies, but study results arepromising. With regard to the outcomes investigated,most reviews explored relations of potential determinantswith fruit and/or vegetable intake.In the reviewed papers we found evidence that the

social-cultural environment, such as the familial influ-ence (e.g. [21,24,27,28]) is a significant correlate of fruitand vegetable intake and snack consumption in youth inmore than 75% of the available studies (see Table 7). Par-ents, as gatekeepers of the home food supply, can influ-ence children’s eating behavior either through the use ofspecific food parenting practices (i.e. context-specificacts of parenting on child eating including encouragingof food variety and controlling a child’s intake of un-healthy products) or through the indirect influence ofgeneral parenting [28]. Social-cognitive determinantshave been studied often, but the evidence regarding theirimportance is limited (i.e. suggestive at best). Intention,a proximal indicator of actual behavior, was found to bea significant determinant of fruit and vegetable intake,snack intake, and sugar-sweetened beverage intake[24,27]. Socio-cognitive theories such as the Theory ofPlanned Behavior [6] are indicated to have limited valuein predicting the translation of intention into action.This limitation is addressed in reviews on the con-structs of habit [17] and implementation intentions[15]. The review on implementation intentions showedconsiderable support for the effect of implementationintentions on increasing fruit and vegetable intake(medium effect size) among youth. However, the effectof implementation intentions on the reduction of un-healthy eating patterns was less convincing. Habitstrength was one of the factors to be significantly re-lated to sugar-sweetened beverage intake with moderateto strong effect sizes in all identified eligible studies ofthe review of Gardner et al. [17]. Automatic processes,including habit strength appears to reduce the utility ofcognitive factors for the prediction or association withdietary behavior [17]. Additionally, screen time wasfound to be consistently associated with dietary behav-ior [19,22,27]. The included reviews provide evidencethat the amount of screen time was significantly relatedto dietary behavior; screen time was positively associ-ated with snack and sugar-sweetened beverage intake

[19-22] and inversely associated with fruit and vege-table intake [19-27]. An important mechanism linkingscreen time to unhealthy dietary behavior is exposureto marketing of unhealthy foods and beverages throughscreens [35,36]. The food and beverages depicted in theseadvertisements are predominantly unhealthy foods high infat, salt and sugar [35,37]. Sedentary activities and un-healthy dietary behavior have repeatedly been found tocluster [38-40] and may also share similar environmentalcues causing these behaviors to co-occur. Sedentary be-havior offers a context for the consumption of energy-dense food products, disrupting the habituation to foodcues.Systematic reviews on the influence of political envi-

ronments, self-regulation, subjective norm and habitualbehavior were mostly lacking in the included reviews. Inaddition, some types or categories of potential determi-nants were not covered in the present umbrella reviewbecause we did not come across systematic reviews ofsuch determinants. For instance, although we found twosystematic reviews on sensory determinants of dietarybehavior [24,27], most of the reviews were excluded asthey did not comply to the quality standards of system-atic reviews that we used as an inclusion criteria, e.g.[41]. This does not necessarily imply that such factors astaste and preferences are not important, but just thatthese have not been covered well at present in system-atic reviews. Furthermore, it should be noted that lack ofevidence for the importance of a possible determinant isnot the same as evidence that the determinant is not im-portant; since lack of well-designed studies is often themain reason for lack of evidence. We need to try to dis-tinguish between well-researched determinants and stillno evidence for importance, and determinants that havejust not been studied (well enough) to make meaningfulconclusions.

Limitations and methodological issuesSeveral limitations should be taken into considerationin reviewing these findings. These include the cross-sectional nature of many studies relying on self-reportmeasures; heterogeneity of conceptualization, measure-ment, samples and analyses used, making it difficult tocompare results between studies; inability to conduct ameta-analysis; lack of validity and reliability of dietaryintake and correlate measures; and categorization of de-terminants into more global categories thereby losingimportant information. Additionally, the systematic re-views included a wide age range, i.e. respondents frombirth to 18 years. During childhood many developmentaltransitions take place that may imply differential import-ance of distinct behavioral determinants. For instance,parents are highly responsible as gatekeepers of the homefood supply for their children’s dietary intake behavior.

Table 7 Summary of the results from reviews about determinants of dietary behavior among youth: Importance of a determinant and strength of evidence

Dietary behavior

Determinants Fruit Vegetable Fruit & vegetable Snack/fast food Sugar-sweetened beverage Breakfast

Physical environment +, Ls [26] 0, Ls [26] 0, Ls [15,21,24,29,31] 0, Ls [21,23,24,29,31] 0, Ls [21,23,24,29,30] 0, Ls [23,25]

Social-cultural environment 0, Ls [20,23,26-28] 0, Ls [20,23,26-28] +, Ls [16,18,21,24,27-29] 0, Ls [18,21,23,24,28,29] +, Ls [18,20,21,23,24,28-30] 0, Ls [18,20,23,25,28,30]

Economic/financial environment 0, Ls [16,21,29] 0, Ls [21,29] -, Ls [21,29,30] -, Lnc [30]

Political environment 0, Ls [27]

Attitude 0, Lnc [24] 0, Lnc [24] 0, Ls [24,27] ++, Ls [24] ++, Lnc [24]

Subjective norm ++, Lnc [27]

Self-efficacy/perceived behavioralcontrol

0, Ls [24] 0, Ls [24] 0, Ls [24,27] 0, Ls [24] ++, Lnc [24]

Intention ++, Ls [24] ++, Lnc [24] +, Ls [24,27] ++, Ls [24] ++, Lnc [24]

Self-regulation ++, Ls [15](implementation intentions)

Habitual behavior, automaticity ++, Lnc [17]

Sensory perceptions, perceivedpalatability foods

+, Ls [24,27] ++, Lnc [24] –, Lnc [24]

Other, knowledge 0, Ls [24] 0, Ls [24] +, Ls [24,27] 0, Ls [24] ++, Lnc [24]

Other, sedentary behavior +, Ls [19,22,27] 0, Ls [19,22,27] +, Ls [19,22,27] +, Ls [19,22] ++, Ls [22] ++, Ls [22]

Note: Importance of a determinant: ++, +, 0, −, −- (see Table 3); strength of evidence (see Table 4): Co (Convincing evidence), Pr (Probable evidence), Ls (Limited, suggestive evidence), Lnc (Limited, no conclusion);studies including determinants such as stress and risks and dietary behavior such as milk and meat intake not included in this table.

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However, parental influence decreases with advancing ageof the child as the child is increasingly exposed to otherenvironments (e.g. school environment, peer influences).In addition to the quality of the research design, the

fact that some determinants have not been extensivelystudied yet, studies of some types of determinants havenot been reviewed systematically, and the lack of robustresults from this umbrella review may also be explainedby the fact that groups or types of determinants areoften studied in relative isolation. For instance, studiesin which different categories of determinants - e.g. sensorydeterminants, self-regulation, and political environmentalfactors - were studied with integrative approaches arelargely lacking. Such studies would allow for explorationand testing of mediating and moderating pathways be-tween these determinants in influencing dietary behav-ior. Already some studies combining environmentaland social-cognitive determinants have been reportedin recent years and do support such mediating andmoderating pathways, e.g. [42-45].This is the first umbrella review that provides an

overview of reviewed research regarding a broad rangeof potential determinants of dietary behavior in youth.Umbrella reviews in itself are, however, also prone tobias in various ways. Differences in reviewing method-ology and reporting were apparent, as well as differencesin for example categorizations of the determinants. By na-ture, umbrella reviews lead to loss of detail. In addition,some individual studies are included in multiple reviewswhich may have led to an overrepresentation of singlestudies in our results. Finally, we excluded reviews thatprimarily addressed biological determinants or paperswith summative outcomes such as caloric intake, and wealso did not include reviews that focused on qualitativedata.

Conclusions and recommendationsThe evidence gathered in our umbrella review suggeststhat intention and sedentary behavior have the strongestevidence base as determinants of healthy and unhealthydietary behavior in youth. The influence of distinct de-terminants may, however, be stronger in interaction withother influences. We would advocate for studies that ad-dress combined, mediating and interactive influences ondietary behavior [46]. Such studies are advocated to in-clude behaviors that have been found to cluster withdietary behavior, such as sedentary behavior. Other rec-ommendations include the need for better designedstudies, beyond mere cross-sectional research, −i.e. morelongitudinal and experimental or intervention research,and research using natural experiments-, larger samplesamong specific age groups, and more valid and reliablemeasures (dietary behavior and correlates). Our resultsunderline the importance of embracing theories and

factors additional to determinants derived from socio-cognitive theories that are often used to inform interven-tions to promote healthy dietary behaviors. Theories thatare promising of further research for determinants ofdietary behavior research include habit theory and (so-cial-) ecological models of health behavior.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsES, WK, SK and JB conceptualized the study. EV and PK performed theliterature search; ES, WK, EV, LB and SK were involved in the screeningprocess; ES, WK, LK and LB extracted data. ES drafted the manuscript. Allauthors reviewed draft versions of the manuscript and provided criticalfeedback. All authors have made a significant contribution to thismanuscript, and all authors read and approved the final manuscript.

AcknowledgmentsThis research was funded by The Netherlands Organization for HealthResearch and Development, project number 115100008. The authors wouldlike to thank Professor C. de Graaf for his cooperation on this project.

Author details1Department of Health Promotion, NUTRIM School for Nutrition andTranslational Research in Metabolism, Maastricht University Medical Center+,PO Box 616, Maastricht 6200, MD, the Netherlands. 2Department of HealthSciences and the EMGO+ Institute for Health and Care Research, VUUniversity Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, TheNetherlands. 3Medical Library, VU University Amsterdam, PO Box 70571007MB Amsterdam, The Netherlands. 4Department of Epidemiology &Biostatistics and the EMGO+ Institute for Health and Care Research, VUUniversity Medical Center, van der Boechorststraat 7, 1081 BT Amsterdam,the Netherlands.

Received: 24 June 2014 Accepted: 6 January 2015

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