Adolescents’ Intake of Junk Food: Processes and Mechanisms Driving Consumption Similarities Among Friends

Adolescents’ Intake of Junk Food: Processes and Mechanisms Driving

Consumption Similarities Among Friends

Kayla de la HayeRAND Corporation

Garry RobinsUniversity of Melbourne

Philip MohrUniversity of Adelaide

Carlene WilsonFlinders University and Cancer Council South Australia

Adolescents’ consumption of low-nutrient, energy-dense (LNED) food often occurs out of home, and friends may bean important source of influence. This study tested whether observed similarities in LNED food intake among friendsresult from social influence and also explored underlying psychological mechanisms. Three waves of data werecollected over 1 year from Grade 8 students in Australia (N = 378, 54% male), including measures of food intake andrelated cognitions, and friendships to grademates. The results of longitudinal social network models show thatadolescent intake was predicted by their friends’ intake, accounting for pre-existing similarities and other potentiallyconfounding factors. Changes to adolescents’ beliefs about LNED food do not appear to be the mechanisms underpin-ning influence from their friends.

Although adolescents’ diets are strongly governedby their family food environment (Patrick & Nick-las, 2005), about half of their consumption of low-nutrient, energy-dense (LNED) “junk” foods occursout of home (Briefel, Wilson, & Gleason, 2009). Indeveloped countries such as the US and Australia,young peoples’ LNED food intake has increasedsubstantially in recent decades (Cook, Rutishauser,& Seelig, 2001; Jahns, Siega-Riz, & Popkin, 2001),resulting in reduced diet quality and additionalcaloric intake (Jahns et al., 2001). This has implica-tions for both immediate and long-term health out-comes and is thought to be one of many interrelatedfactors linked to increased body mass index (BMI)and rates of obesity in young people (Nicklas,Baranowski, Cullen, & Berenson, 2001; Spruijt-Metz,2011).

Foods eaten out of home by adolescents aretypically consumed in school and peer contexts, andadolescent reports confirm that lunches and snacksare often eaten with friends (Feunekes, de Graaf,Meyboom, & van Staveren, 1998). In cross-sectional

studies, consumption of snack foods and overallenergy intake has been found to correlate with theintake of an adolescent’s best friends (Feunekeset al., 1998), and male friends have been found to bealike in their consumption of high-calorie foods (dela Haye, Robins, Mohr, & Wilson, 2010). Evenamong adults, dietary similarities between sociallyconnected peers appear to be strongest for snackfoods and alcohol, with effects over time suggestiveof a social influence process (Pachucki, Jacques, &Christakis, 2011).

Situational food norms, which entail factorssuch as social influence and portion size, aretheorized to have a powerful effect on food intake(Herman & Polivy, 2005). Although socialinfluence on food consumption is known to bepervasive and complex, one consistently observedphenomenon is social modeling. Laboratory-basedstudies on social modeling of eating, which pairna€ıve participants with experimental confederates,have shown that individuals eat more when theireating companions eat more and less when theircompanions eat less (e.g. Conger, Conger, Cost-anzo, Wright, & Matter, 1980; Herman, Roth, &Polivy, 2003; Hermans, Larsen, Herman, & Engels,2008; Rosenthal & McSweeney, 1979). This “match-ing norm” effect has also been replicated in chil-dren (Salvy, de la Haye, Bowker, & Hermans,2012): for example, girls (aged 8–12) increased

Data were collected while K. de la Haye was being supportedby an Australian Postgraduate Award through the University ofAdelaide, and a Preventative Health Flagship Scholarship fromAustralia’s Commonwealth Scientific and Industrial ResearchOrganization (CSIRO). We would like to thank several anony-mous reviewers for their helpful comments on earlier versionsof this manuscript.Requests for reprints should be sent to Kayla de la Haye,

RAND Corporation, 1776 Main Street, PO Box 2138, SantaMonica, CA 90407-2138. E-mail: [email protected]

© 2013 The Authors

Journal of Research on Adolescence © 2013 Society for Research on Adolescence

DOI: 10.1111/jora.12045

JOURNAL OF RESEARCH ON ADOLESCENCE, 23(3), 524–536

their intake of cookies when exposed to a peerwho ate a larger amount of cookies, relative to apeer eating a small number of cookies (Romero,Epstein, & Salvy, 2009). Moreover, young peoplehave been found to match the food intake offriends more so than unfamiliar peers (Salvy,Howard, Read, & Mele, 2009).

Similarities in diet and eating behaviors amongadolescent friends have been observed in a smallnumber of cross-sectional survey-based studies(Feunekes et al., 1998; Fletcher, Bonell, & Sorhaindo,2011; de la Haye et al., 2010). Researchers haveproposed that in these naturalistic settings, the“matching” of eating behaviors arises through socialinfluence processes such as modeling (Monge-Rojas,Nunez, Garita, & Chen-Mok, 2002) and is motivatedby goals for peer approval (Unger et al., 2004).However, it is not clear from these studies whethersimilarities in friends’ consumption patterns arisefrom socialization, or if they can be explained byother confounding processes. One such process isfriendship choices that lead to dietary similarities, asfriendships are often based on preferences for simi-lar others (Aboud & Mendelson, 1998). Althoughthere is no evidence to suggest that similarities indiet are a salient factor in adolescents’ friendshipchoices, friendships are based on similarities in indi-vidual attributes and behaviors that are potentiallycorrelated with diet including gender, race or ethnicbackground, and obesity (de la Haye, Robins, Mohr,& Wilson, 2011a; Simpkins, Schaefer, Price, & Vest,2013). As such, accounting for friendship selectionprocesses when testing for socialization is critical(Veenstra, Dijkstra, Steglich, & Van Zalk, 2013). Tofurther this work, the current longitudinal studytested whether associations in LNED food intakeamong adolescent friends, in the context of largerfriendship networks, could be explained by socialinfluence, when potential confounding factors werecontrolled.

The second aim of this study was to explore themechanisms that might underpin friends’ influenceon adolescent LNED food intake. Despite theprominence of behavioral modeling theory in socialpsychological research and evidence of social mod-eling effects on food intake in adults and youth,a major limitation of the eating literature is theinability to account for the reasons why peopleemulate each other (Herman et al., 2003).

Traditionally, health behavior theories empha-size the role of social–cognitive mechanisms inmediating the influence of the social environmenton behavior. For example, the theory of plannedbehavior proposes that the behaviors we observe

in others influence our perceptions of socialnorms, which, along with attitudes and beliefsabout behavior control, shape our intentions andsubsequent behaviors (Ajzen, 1991). More generaltheories of social modeling also maintain that theobservation of behavior in others shapes a rangeof individuals’ beliefs and attitudes about thesebehaviors, guiding future actions (Bandura, 1977).For example, there is evidence that social factorsinfluence not only perceptions of social norms, butalso attitudes towards the behavior and percep-tions of behavior control (Povey, Conner, Sparks,James, & Shepherd, 2000). Thus, we test a moregeneral model of social cognition, wherebyfriends’ food intake may predict adolescent intakeby affecting a range of cognitive constructsincluding norms, attitudes, perceived behavioralcontrol, and intentions (see the left panel ofFigure 1).

However, other lines of thinking downplay thecognitive, considered aspects of food choice, andinstead highlight the imitative nature of sociallearning with regards to eating behavior. This workproposes that environmental and social cues elicitsomewhat automated, imitative responses: akin towhat is referred to as “mindless eating” (Wansink& Sobal, 2007). Support for this imitative processhas been found in laboratory-based studies lookingat alcohol intake, where young people not onlycopied the quantity of alcohol consumed byconfederates, but also the rate at which they drank,suggesting a somewhat unconscious, mimickedresponse (Larsen, Engels, Granic, & Overbeek,2009; Larsen, Engels, Souren, Granic, & Overbeek,2010).

Aligned to this less cognitively driven view ofsocioenvironmental effects on eating is Bem’sself-perception theory (1972), which proposes thatbeliefs associated with a particular behavior tend tobe fostered by reflecting on one’s past engagementin the behavior. Thus, a substantially differentmodel to social cognition theories might beproposed, whereby youth “mindlessly” imitate thebehavior of their peers and subsequently shape theirbeliefs and attitudes so that they are in line with thebehaviors they have endorsed (see the right panel ofFigure 1).

As outlined in the literature reviewed previ-ously, despite consistent evidence of food modelingin the laboratory, the extent to which this translatesto natural settings, and how this plays out overtime, is unclear. Additionally, the mechanismsunderpinning social influence on food intake inyouth and adults are not well understood,

JUNK FOOD INTAKE IN FRIENDSHIP NETWORKS 525

and additional work is needed to test competingtheories.

CURRENT STUDY

The current study employed new statistical methodsto model longitudinally individual (self-reported)behavior in the context of larger friendship networks(Veenstra & Dijkstra, 2011). These stochastic actor-based models (SABMs) for the co-evolution of socialnetworks and behavior (Snijders, Steglich, & Schwein-berger, 2007) allow us to test simultaneouslyfactors that predict changes in complete social net-works (i.e., relationships among a bounded set ofsocial actors) and changes in network members’behaviors. Thus, we are able to test for effects offriend influence on LNED food intake, controllingfor the role of food intake and other potentially con-founding factors, which initially predict friendshipsand network structure. This flexible modelingframework allows us to test these peer influenceeffects alongside other known individual and familypredictors of eating behaviors and to exploreprocesses that mediate friend influence on LNEDfood intake.

Whether or not friends influence adolescentLNED intake, in addition to any similarities whenfriendships are formed, will be the primary focusof this study. If we find evidence of friend influ-ence on LNED food intake, potential psychosocialmechanisms underpinning this process will also beexplored. Based on general social cognition modelsof behavior (Ajzen, 1991; Bandura, 1977), we antici-pate that adolescents will emulate the behaviors oftheir friends and that this process will be partiallymediated via perceptions of peer norms, attitudes,perceived behavior control, and intentions. Failureto support this mediation hypothesis wouldsuggest that an “imitation, self-perception” model,

in line with Bem (1972) and Wansink and Sobal’s(2007) theories (see Figure 1) is more plausible.

METHOD

Sample

Grade 8 students from two public high schoolslocated in a major Australian city were recruited in2008 as part of a larger study looking at peereffects on obesity. At both schools, Grade 8 is thefirst year of high school, with students comingfrom numerous primary schools. The two schoolswere located in neighborhoods with similar mid-dle-class sociodemographic characteristics.

Information letters were mailed to students andtheir guardians at the start of the school year, pro-viding them with details about the study and theopportunity to opt out. Adolescents who joined theschool throughout the year were also invited toparticipate. At each wave of data collection, partici-pating students were entered into a draw to winone of several $20 gift vouchers. The study proto-col was approved by the Human Research EthicsCommittees at the University of Adelaide and Aus-tralia’s Commonwealth Scientific and IndustrialResearch Organization.

A total of 378 students took part in the study,nested in two schools, with each school cohortdefined as a separate “friendship network.” Partic-ipation rates in each cohort were excellent: 92.9%of enrolled students in school 1 (N = 222, 52.7%male) and 90.2% of students in school 2 (N = 156,55.1% male). High rates of participation amongeligible network members, such as these, areimportant for modeling complete longitudinal net-work data (Huisman & Steglich, 2008). At thebeginning of the study, the mean age of partici-pants was 13.6 years in school 1 (SD = 0.4; range

FIGURE 1 Two models of food matching identifying different explanatory mechanisms. The social cognitions model proposes thatfood matching is partially mediated via cognitive mechanisms: individuals consume similar foods as their friends (A) in part becausetheir friends’ behavior influences their beliefs about food intake (B), and their beliefs in turn influence their own intake (C). The imita-tion, self-perception model proposes that food matching is not mediated by cognitive mechanisms: individuals unconsciously imitatethe food intake of their friends (D), and subsequently shape their beliefs about food intake by reflecting on their own intake (E).

526 DE LA HAYE, ROBINS, MOHR, AND WILSON

12.3–14.4), and 13.7 years in school 2 (SD = 0.4;range 12.3–15.6).

Procedure and Measures

Paper-based questionnaires were administered byteachers three times during the school year.Questionnaire items assessed friendships withgrade-mates, intake of various LNED foods over theprevious month, and beliefs about the regularconsumption of “high-energy foods.” The measureswere part of a larger questionnaire that took 25 minto complete.

Friendship networks. Friendships among grade-mates were assessed by having participants list thefirst and last names of an unlimited number ofpeers in their grade who were “friends you hangaround with the most.” The instructions did notspecify the number or gender of friends to nomi-nate, and although 10 spaces were provided forresponses, many respondents listed fewer or morethan 10 friends. Participants were then instructedto circle the names of their “best friends” fromamong the friends they had listed. The subsequentanalyses consider only best friend nominations.Knowledge of what peers are eating, likely tooccur via regular face-to-face contact that charac-terizes close friendships, is necessary for foodmatching effects, and indeed, the literature sug-gests that close peer relationships are relevant toadolescent food intake (Feunekes et al., 1998; Salvyet al., 2009).

A friendship network for the set of participantsin each grade cohort, at each wave, was representedas a directed, asymmetrical adjacency matrix, wherecells coded as 1 denoted a unilateral friendshipbetween participants i and j, and 0 the absence of afriendship.

Intake of LNED foods. Food frequency itemsrequired respondents to record how often in theprevious month they had consumed one serving of14 specific types of LNED foods, includingchocolate, candy (lollies), cookies (biscuits), cake,sweet pastry, savory pastry (pies or pasties), pizza,hamburgers, hot dogs, fried chicken, french fries(chips), and soda (soft drink). Respondents werenot asked to specify the context in which they atethe foods; however, many of the items listed wereavailable for sale at the school canteens or nearbyshops and thus may have been eaten in both schooland other environments.

For each of the 14 items, frequency of consump-tion was recorded on a 7-point scale where 1 = nonein the last month, 2 = less than once a week, 3 = one totwo times a week, 4 = three to six times a week, 5 = onea day, 6 = two times a day, and 7 = three times a dayor more. As only one factor emerged from these 14items, with each item loading fairly uniformly onthis factor, an overall measure of LNED food intakewas derived by taking the mean score (school 1a = .80 to .86, school 2 a = .79 to .85). Althoughthere is some evidence that self-report food fre-quency questionnaires overestimate energy intakein youth, they are commonly used in surveyresearch and have been found to have acceptablevalidity and reliability (McPherson, Hoelscher,Alexander, Scanlon, & Serdula, 2000).

LNED food-related attitudes and cogni-tions. Standard items were used to measureattitudinal and cognitive variables derived fromsocial cognition theories (Ajzen, 1991). These itemshave been used and validated in a number of stud-ies (Armitage & Conner, 2001), including studies ofadolescent health behavior (e.g., Kassem, Lee,Modeste, & Johnston, 2003; Marcoux & Shope,1997). All items referred to beliefs about eating“high-energy foods at least twice a day,” and thesefoods were defined as often having “lots of salt,sugar, or fat (or all three)” with respondentsinstructed to consider the examples of these itemslisted in the food frequency component of thequestionnaire. These cognitive measures were ratedon 7-point scales anchored by two statements,unless otherwise noted.

Intention to eat high-energy foods in the comingmonth was measured by two items: “In the nextmonth, how often do you plan to eat high-energyfood” (rated on a 7-point Likert scale where 1 = oncea week or less and 7 = four or more times every day)and “In the next month, do you intend to eat high-energy food at least twice a day … definitely do notintend to do this—definitely intend to do this.” Thecorrelation between these two items was moderateto strong (r = .63 to .76 in school 1, and r = .55 to .68in school 2, across the three waves), and the meanwas used as an overall measure of intentions.

Attitudes toward “high-energy foods” weremeasured by two items: “Would you like to eathigh-energy food at least twice a day … definitelywould not like to do this—definitely would like to do this”and “I think that eating high-energy food at leasttwice a day would be… unenjoyable—enjoyable”. Thecorrelation between these two items was moderateto strong (r = .65 to .80 in school 1, and r = .55 to .64


in school 2, across the three waves) and the meanscore was used as a measure of attitudes.

Perceived descriptive peer norms were measuredby the item “Of your close friends at school, howmany eat high-energy food at least twice a day…none of my close friends—all of my close friends.” Per-ceived injunctive peer norms were measured by theitem “Do your close friends at school think that youshould eat high-energy food at least twice a day…they definitely think I should not—they definitely think Ishould.” Descriptive and injunctive peer norms weretested independently in our models, in line withevidence that they capture different normative con-structs (Rivis & Sheeran, 2003). The correlationbetween these two items was moderate (r = .36 to.48 in school 1, and r = .45 to .54 in school 2, acrossthe three waves).

Self-efficacy and controllability over the intakeof LNED foods were measured by two items. Self-efficacy was assessed by the question “If I wanted,I could eat high-energy food at least twice a day…definitely false—definitely true.” Controllability wasmeasured by the question “Whether or not I eathigh-energy food at least twice a day is entirely upto me…strongly disagree—strongly agree.” Consistentwith other research that identifies self-efficacy andcontrollability as divergent constructs (e.g., Rhodes& Courneya, 2003), we retained them as separateitems in the analyses. The correlation betweenthese two items was weak to moderate (r = .30 to.54 in school 1, and r = .37 to .66 in school 2, acrossthe three waves).

Control attributes. Attributes known to be asso-ciated with adolescent friendships and dietaryintake were accounted for the in the models. Changein the friendship network controlled for respondentgender (0 = women, 1 = men), ethnicity (1 = iden-tify with an ethnicity other than Anglo-Australian),and weekly allowance (i.e., pocket money; 4-pointscale, where 1 = less than $10 and 4 = more than $30).The role of weight status in friendship choices wasalso controlled, given that overweight youth aremarginalized in peer networks (de la Haye et al.,2011a; Valente, Fujimoto, Chou, & Spruijt-Metz,2009). At the dyad level, the effect of sharing a homegroup class (meaning that they attended most coreclasses together) on friendship choices was alsoincluded to account for greater opportunities for cer-tain pairs of students to become acquainted and toaccount for shared environments.

Effects of gender, ethnicity, pocket money, andoverweight on change in LNED food intake wereaccounted for, as were perceptions of family LNED

food intake. Perceived family norms were assessedby the question “Do adults who are important toyou (parents, guardians, relatives) eat high-energyfood at least twice a day” with responses anchoredon a 7-point scale from they definitely do not do this—they definitely do this.

Statistical Analyses

SABMs for social networks and behavior. Sto-chastic actor-based models for social networks andbehavior (Snijders et al., 2007) were estimated todetermine whether friends’ intake of LNED foodsinfluenced adolescent intake, controlling for a rangeof predictors of friendships and diet. These modelsare implemented in the RSiena (Simulation Investi-gation for Empirical Network Analysis) 4.0 software(Ripley, Snijders, & Preciado, 2012) and aredescribed in Snijders, van de Bunt, and Steglich(2010), and Steglich, Snijders, and Pearson (2010).Model parameters were estimated using a methodof moments procedure, whereby parameter vectorsare adjusted to improve model fit through a series ofsimulations. Effects were tested for significancebased on a t-ratio (estimate divided by the standarderror).

Two parts of this model are estimated simulta-neously: a network dynamics submodel tests effectspredicting changes to friendship ties, and a behaviordynamics submodel tests effects predicting changes tothe dependent behavior variable(s) (i.e., food intake).Longitudinal measures of these variables (and cova-riates) represent the observed state of the network atgiven points in time, and changes between theobserved panels of data are modeled using continu-ous time Markov chains to determine the most likelyseries of unobserved microsteps taken by actorswhen changing their ties or behavior. An evaluationfunction determines the social “rules” that guidethese changes, which are formalized as specificparameters in the model and test for the hypothesizedselection and influence effects. A rate function esti-mates how many opportunities for change (in friend-ships and behavior) occur between observations.

Model specification. For each school-based friend-ship network, two models were estimated: the firsttested the main effect of friend influence on foodintake (basic model); where evidence of influence wasfound, a second set of models were estimated testingfor the mediating role of cognitive variables (media-tion models).

In the basic model, the hypothesis that friendsinfluenced adolescent junk food intake was tested


with an effect of “friends’ total LNED food intake”(i.e., total similarity effect) on changes to adolescentintake. A significant positive effect would indicatethat actors’ overall LNED food intake remained orbecame similar to the intake of their nominatedfriends. Effects of covariates (gender, ethnicity,pocket money, and overweight) on changes inintake were controlled, and linear and quadraticshape effects were included to model the overalldistribution of scores (Snijders et al., 2010; Veenstraet al., 2013).

This basic model simultaneously accounted forfactors predicting friendship selection and mainte-nance. Associations between LNED food intakeand friendship nominations were controlled usingfour effects, the most relevant being intake similaritythat captures the extent to which friendships wereestablished or maintained between peers withexisting dietary similarities. The model alsoincluded an effect of actors’ food intake on theiroutgoing friendship nominations (intake ego), aneffect of peers’ food intake on them receiving anactor’s friendship nomination (intake alter), as wellas a squared effect of peers’ intake (intake squaredalter) to control for nonlinearity in this effect. Theroles of gender, ethnicity and pocket money onfriendship choices were controlled using the samefriendship selection effects (covariate ego, covariatealter, same or similar covariate), and a dyad-leveleffect of sharing the same home group class wasalso included (same classroom). Finally, modelsalso controlled for endogenous network effects,including tendencies for actors to reciprocatefriendships (reciprocity) and to befriend friends’ offriends (transitivity) and highly nominated peers(indegree popularity). The inclusion of thesestructural effects is standard and necessary whenmodeling network data, to account for dependen-cies between respondents who are connected via asocial network (Snijders et al., 2007).

To avoid issues of collinearity, a forward selectionapproach was used to specify the basic model(described in Burk, Steglich, & Snijders, 2007;Snijders et al., 2010). Effects for each covariate werescore tested against a null model (Schweinberger,2012), and if any were significant the group of effectswas retained in the final model. Additionally, thefinal model tested for time heterogeneity in the food-intake effects (Lospinoso, Schweinberger, Snijders,& Ripley, 2010), and dummies were added, whenneeded, to account for significant differences inthese effects across time periods.

To examine whether friend influence on LNEDintake was partially mediated by adolescents’ beliefs

about eating junk foods, we specified a second setof models for the co-evolution of the friendship net-works, LNED food intake and related attitudes andcognitions (mediation models). Thus, each modelincluded rate and evaluation functions for threedependent variables: friendships, food intake, andone cognitive measure. The same parametersspecified in the basic model were included in themediation model, including the effect of friends’food intake on actor intake (i.e., behavior influence).To test for the hypothesized mediation effects, thisbehavior influence effect was tested alongside aneffect of friends’ food intake on actors’ food-relatedcognition. In other words, did friends’ consumptionof LNED foods influence adolescents’ intake ofthese foods as well as their beliefs about eatingthese foods? Evidence of mediation would requirethat the effect of friend intake on actors’ cognitionbe statistically significant and that the addition ofthis effect partially (or fully) accounts for the effectof friends’ intake on actors’ intake.

In addition to this mediation effect, the follow-ing parameters were also included as controls inthe mediation models: (1) the effect of actors’ cog-nition on change in actor’s food intake, and (2) theeffect of actors’ food intake on actors’ cognition.

RESULTS

Descriptive Statistics of the Network and LNEDFood Intake

A summary of descriptive statistics is presented inTable 1; these data show that the two cohorts werecomparable on the covariates measured. Table 1also presents a summary of the LNED food intakeand cognition variables, with mean values suggest-ing that these variables were fairly stable over timeand that there are consistencies between the twoschools. On average, participants consumed oneserve of each LNED food item less than once aweek. Attitudes and beliefs about regular intake ofthese foods tended to be negative or neutral,although they showed upward trends over time,suggesting that cognitions became somewhat morepositive, especially between the first two waves.

Structural characteristics of the two friendshipnetworks are summarized in Table 2. Across eachwave, students nominated an average of 3–4 bestfriends, and about one-third of these friendshipnominations were reciprocated (reciprocity index).Between each of the three waves (Period 1 andPeriod 2), students, on average, maintained twofriendships, but also dissolved one friendship tie


and nominated one new friend. Changes to thecomposition of the network as a result of studentsjoining or leaving the school (Table 2) were mod-eled as exogenous events at specified time points(Huisman & Snijders, 2003).

Observed similarities on LNED food intakeamong friends, called network autocorrelation, is alsosummarized in Table 2. Moran’s I is a measure ofspatial correlation, and coefficient values close to 0indicate that individuals who are connected in thematrix are not more similar on the behavior thanwould be expected if they were randomly paired.Values close to 1 indicate that connected individualsare very similar. Friend similarities on LNED foodintake were found to be modest, with increases insimilarity over the school year observed only inschool 1.

Statistical Models for the Evolution of Networksand LNED Food Intake

The basic model (Table 3) tested for friend influenceon LNED food intake (food intake dynamics) byincluding an effect of friend intake on adolescentintake. In both schools, this effect was positive andsignificant, indicating that over time respondentsemulated the LNED food consumption of theirfriends. Food intake dynamics were predicted byfew actor-attributes: only pocket money significantlyand positively predicted intake in school 2, indicat-ing the more pocket money the more likely youthadopted higher intake levels. Effects of gender,ethnicity, and perceived parent intake norms were

not found to predict food intake. The shape effects(negative linear and negative quadratic shapeeffects), interpreted in consideration of the meanLNED intake values, indicated that there was anoverall tendency for actors to have low values on theLNED food intake scale (1 or 2) and that this effectwas curvilinear so that the behavior function wasgreatest for low LNED intake scores.

The tendency for adolescents to adopt similar eat-ing patterns as their friends was significant control-ling for effects predicting friendship choices(friendship network dynamics). In school 1, therewas some evidence that adolescents with higherfood intake were less likely to make or maintainfriend nominations (negative intake ego), althoughthis trend became weaker over time (negative intakeego*period 2 dummy). In this same school, foodintake was also associated with popularity: thecombination of negative intake alter and negativeintake squared alter effects indicates that actors’preference was to befriend peers with LNED valuesslightly above the mean (3), more so than peers withlow (1, 2) or very high values. There was no evi-dence that food intake was associated with friend-ship choices in school 2, and also no evidence thatactors in either school selected friends whose intakelevels were similar to their own (intake similarity).

Covariates also predicted friendship choices,although these effects differed slightly betweenschools. In school 1, actors preferred friends whowere of the same gender, the same ethnic back-ground, who had similar amounts of pocket money,and who were in their home group class. In this

TABLE 1Individual Descriptive Statistics

Characteristics

School 1 (N = 222) School 2 (N = 156)

Wave 1 Wave 2 Wave 3 Wave 1 Wave 2 Wave 3

N classes (avg. N per class) 9 (25) 12 (13)% other ethnicity 31.1 29.5M (SD) pocket moneya 1.8 (0.9) 1.7 (0.8) 1.9 (0.9) 2.0 (0.9) 2.0 (1.0) 2.0 (0.9)% overweight or obese 25.8 24.0 23.6 17.2 22.7 21.0M (SD) LNED food variablesIntakeb 2.3 (0.6) 2.2 (0.5) 2.1 (0.6) 2.3 (0.6) 2.3 (0.6) 2.3 (0.6)Intentionsc 2.5 (1.5) 2.9 (1.3) 3.1 (1.4) 2.1 (1.2) 3.3 (1.5) 3.2 (1.5)Attitudesc 3.6 (1.7) 4.2 (1.4) 4.3 (1.6) 3.2 (1.5) 4.3 (1.6) 4.2 (1.6)Descriptive peer normc 3.7 (1.6) 4.2 (1.3) 4.2 (1.3) 3.4 (1.5) 4.1 (1.3) 4.1 (1.3)Injunctive peer normc 3.0 (1.6) 3.5 (1.5) 3.7 (1.4) 2.8 (1.7) 3.6 (1.7) 3.8 (1.5)Descriptive adult normc 2.4 (1.6) 3.2 (1.6) 3.3 (1.7) 2.4 (1.7) 3.6 (1.7) 3.6 (1.8)

Note. LNED, low-nutrient, energy-dense.a1 = less than $10, 2 = $10 to $20, 3 = $20 to $30, 4 = more than $30.b1 = none in the last month, 2 = less than once a week, 3 = one to two times a week, 4 = three to six times a week, 5 = one a day,6 = two times a day, and 7 = three times a day or more (average score over 14 food items).c7-point scale anchored by strongly negative (1) and strongly positive (7) statements.


school, youth with more pocket money were morelikely to nominate friends (money ego) but were lesslikely to receive friend nominations (money alter),although this latter preference was bimodal, indicat-ing that actors preferred friends with both low andhigh values of pocket money (money squared alter).In school 2, actors preferred friends of the samegender and who were in their home group class.Males in this school also tended to make more friendnominations (male ego), but were less attractive asfriends (male alter); and students who identified withrace or ethnic backgrounds other than Anglo-Austra-lian were less attractive as friends (race or ethnicityalter). There was also a tendency for friendships toform among youth with the same weight status.

Finally, friendship network dynamics were alsoexplained by similar structural effects in bothschools, including a tendency for actors to recipro-cate friend nominations (reciprocity), to befriendfriends of their current friends (transitive ties), andto not make friend nominations arbitrarily (outgoingties). In school 2, there was also an aversion tobefriending peers who were already popular (i.e.,high indegree). Although contrary to the customary“Matthew effect” where popular individuals attractthe most friends, a rejection of popular or successfulindividuals is embedded in Australian culture andreferred to as “tall poppy syndrome”.

Statistical Models for the Evolution of Networks,LNED Food Intake, and Cognitions

Given that friends’ intake of LNED foods wasfound to predict adolescent intake in both schools,

we also examined if this process was mediated bycognitive mechanisms. Specifically, we tested ifactors’ tendency to adopt similar levels of foodintake to their friends was explained by friends’intake influencing changes in adolescents’ beliefsand attitudes about regular consumption of LNEDfoods.

A summary of the results obtained from themediation models, which tested mediation effectsfor six different cognitive constructs, are presentedin Table 4 (only estimates that directly tested effectsof the cognitive variables are reported). There wasno evidence in either school that friends’ food intakepredicted changes in adolescent beliefs or attitudestowards regular LNED food intake (Table 4, column1). Thus, the hypothesis that friends’ influence onadolescent food intake was partially mediated bychanges in adolescents’ intentions, attitudes,perceived peer norms, or perceived control over thisbehavior was not supported. Moreover, there wasnegligible evidence that adolescents’ beliefs aboutLNED food intake predicted changes in theirreported levels of food intake (Table 4, column 2).Only adolescents’ perceptions of injunctive peernorms were found to marginally and positivelypredict changes in their food intake in school 1,suggesting that adolescents who reported that theirschool friends thought they should regularlyconsume LNED foods were somewhat more likelyto increase their food intake. Interestingly, intake ofLNED foods was found to predict changes in cogni-tions (Table 4, column 3). There was some evidencethat intake positively predicted changes in inten-tions, attitudes, and perceived descriptive peer

TABLE 2Friendship Network Descriptive Statistics

Characteristics

School 1 (N = 222) School 2 (N = 156)

Wave 1 Wave 2 Wave 3 Wave 1 Wave 2 Wave 3

% Nonrespondents 14.4 14.9 11.7 13.5 12.8 10.3M (SD) friends nominated 3.8 (2.5) 4.0 (2.6) 4.0 (2.8) 3.4 (2.5) 3.6 (2.3) 3.5 (2.4)Reciprocity index 0.34 0.37 0.34 0.33 0.33 0.37Transitivity index 0.44 0.43 0.43 0.41 0.41 0.39Moran’s I for LNED food intake 0.09 0.12 0.17 0.09 0.08 0.09

Period 1 Period 2 Period 1 Period 2

M new friendship ties 1.4 1.3 1.6 1.5M stable friendship ties 2.2 2.4 1.9 2.1M friendship ties dissolved 1.1 1.2 1.5 1.4Composition change (joined, left) 3, 3 3, 1 6, 1 5, 0Jaccard index 0.48 0.49 0.38 0.42

Note. LNED, low-nutrient, energy-dense.


norms: these effects were significant in school 1, andmarginally significant in school 2, so that greaterintake predicted stronger intentions to consume

LNED foods, more positive attitudes toward thesefoods, and stronger beliefs that friends regularlyconsumed these foods. In school 1, greater intake ofLNED foods also predicted perceived injunctivepeer norms, meaning that respondents with higherintake tended to adopt the view that “my friendsthink I should eat high-energy foods.”

DISCUSSION

Adolescents’ consumption of LNED foods was foundto be predicted by the LNED food intake of theirschool friends, in two cohorts of Australian highschool students. Specifically, adolescents’ intakebecame or remained similar to the intake of theirsame-grade best friends over the course of the schoolyear, over and above more general tendencies of thegrade cohort to adopt low levels of LNED foodintake, and the positive effect of pocket money onintake (school 1). Additionally, these peer effectswere significant controlling for potentiallyconfounding associations between LNED food intakeand friendship choices (as found in school 1),together with other factors predicting friendshipchoices (covariates and network structure) and LNEDfood intake. Thus, students with “low consuming”friends were especially likely to adopt or maintainlow levels of consumption, whereas those with “highconsuming” friends were likely to emulate ormaintain similar high levels of consumption.

These findings are in line with laboratory-basedstudies that have consistently demonstrated thatadults and youth emulate the eating behaviors oftheir peers (Conger et al., 1980; Hermans et al.,2008; Romero et al., 2009; Rosenthal & McSweeney,1979), and cross-sectional observational researchshowing that adolescent friends tend to have simi-lar dietary patterns, particularly with regard tosnack foods (Feunekes et al., 1998; de la Hayeet al., 2010). Our work extends the findings on“matching norms” by supporting hypotheses thatthese effects hold in a naturalistic setting, control-ling for dietary similarities when these peer rela-tionships were formed, and further suggesting thatfriends may influence dietary behaviors over time.

Social cognition models, such as the theory ofplanned behavior, propose that behaviors are influ-enced by the social environment via cognitive mech-anisms, in particular perceptions of social norms(Ajzen, 1991). However, in this study, we did notfind evidence that the effect of actual food intakenorms (i.e., friends’ self-reported LNED consump-tion) on adolescents’ consumption was mediatedvia changes in adolescents’ attitudes, perceived peer

TABLE 3Stochastic Actor-Based Models Parameter Estimates (P.E.) and

Standard Errors (SE) for the Basic Models

Parameter

School 1 School 2

P.E. (SE) P.E. (SE)

Food intake dynamicsFriend intake (influence)a 0.88 (.41)* 1.07 (.46)*Individual covariatesPocket money 0.16 (.14) 0.22 (.10)*Overweight �0.50 (.29)+ N.S.Parent intake norm N.S. 0.05 (.05)

Shape effectsLinear shape �0.29 (.08)** �0.03 (.07)Quadratic shape �0.16 (.07)* �0.11 (.05)*

Friendship network dynamicsLNED food intake effectsIntake ego �0.07 (.04)+ 0.03 (.05)Intake ego*period 2 dummy �0.17 (.08)*Intake alter 0.11 (.06)* 0.02 (.05)Intake sq. alter �0.14 (.04)** 0.05 (.03)Intake similarity �0.06 (.47) 0.61 (.59)

Covariate effectsMale ego 0.03 (.09) 0.27 (.10)**Male alter �0.06 (.09) �0.28 (.10)**Same male 0.72 (.08)** 0.77 (.09)**Ethnicity ego 0.00 (.08) 0.04 (.10)Ethnicity alter 0.11 (.08) �0.23 (.10)*Same ethnicity 0.23 (.07)** 0.12 (.09)Money ego 0.09 (.05)* �0.05 (.04)Money alter �0.17 (.05)** �0.02 (.05)Money sq. alter 0.12 (.04)** �0.07 (.04)Money similarity 0.56 (.15)** 0.11 (.14)Same classroom 0.85 (.07)** 0.28 (.09)**Overweight ego �0.06 (.09) 0.15 (.10)Overweight alter �0.01 (.09) �0.14 (.12)Same overweight �0.09 (.08) 0.15 (.09)+

Structural effectsOutdegree �3.21 (.22)** �3.01 (.21)**Reciprocity 1.42 (.11)** 1.79 (.12)**Transitive ties 0.49 (.03)** 0.47 (.03)**Indegree popularity (sqrt.) �0.11 (.08) �0.19 (.08)*

Note. LNED, low-nutrient, energy-dense; N.S., nonsignificant.These effects were not included in the final model because theywere found to be nonsignificant during the forward selectionmodel specification.aSeveral variations for modeling this influence effect are avail-able in RSiena, and because there was no strong theoretical rea-son to select on over another, three different specifications werescore-tested. An effect of friends’ total food intake on actor intake(total similarity), defined as the sum of centered similarity scoresbetween adolescents and their nominated friends, was found tobe the best fit and was estimated in the final models. For thiseffect, larger differences in intake scores between friends arelikely to be highly salient and result in intake change.+p < .10, two-tailed; *p < .05, two-tailed; **p < .01, two-tailed.


norms, perceived behavioral control, or intentions.Rather, intake of LNED foods tended to predictchanges to beliefs about regularly consuming thesefoods, including related intentions, attitudes, andperceived peer norms.

These findings are more line with health behav-ior or food intake theories that view socializationeffects as automatic rather than deliberative (Bem,1972; Wansink & Sobal, 2007; see Figure 1), as wellas findings from similar research looking at peernetwork effects on physical activity (de la Haye,Robins, Mohr, & Wilson, 2011b). From this perspec-tive, the modeling of eating behaviors in our socialenvironment elicits a somewhat “mindless” imita-tion (Wansink & Sobal, 2007). Conscious cognitiveprocesses are therefore not expended on makingdecisions about these recurrent, everyday behav-iors, and so do not strongly guide future behavior.Rather, beliefs and cognitions about eating may beformed during opportunities to reflect on pastbehavior (Bem, 1972).

Although our findings lend support to “mindlessimitation” social modeling process on food intake,additional work is needed to evaluate a widerrange of potential mediating factors, includingexplicit and implicit cognitions related to foodintake (e.g., Coronges, Stacy, & Valente, 2011;Nosek, Greenwald, & Banaji, 2007). For example, totest whether eating behaviors observed in one’ssocial network influence implicit cognitions aboutconsumption of LNED foods could be exploredusing the Implicit Association Test (see Corongeset al., 2011). Future research should also explore therole of social goals in social modeling effects; ado-lescents’ adoption of their friends’ behaviors may

be more strongly motivated by the desire to estab-lish and maintain affiliations with peers in a newschool setting (Brown, Bakken, Ameringer, & Ma-hon, 2008), rather than their own beliefs about junkfood.

Results of the current study highlight the impor-tant role of situational food norms: referring to exter-nal influences and opportunities, such as friends’behaviors and the availability of pocket money, thatfacilitate the adoption of unhealthy dietary practicesby youth. Opportunities to consume unhealthyfoods presented by peers, or the ability to purchasethem, appear to have important influences on theseeating behaviors that are not mediated by beliefs orattitudes. Indeed, an alternate explanation to the“influence” effects described above could be sharedopportunities to consume LNED foods that arejointly experienced by friends. For example, iffriends engage in similar activities together theywill be exposed to similar environments and food-related stimuli, which may result in similar con-sumption patterns. To test competing explanatorymechanisms of social influence versus shared envi-ronments, future work needs to assess explicitly theextent to which friends are exposed to similar envi-ronments and eating opportunities.

Together, these results suggest that to mitigatesuccessfully negative social–environmental influ-ences on adolescent junk food intake associatedwith behavior by peers, it will be crucial to alteryouths’ actual environments and peer behaviors, asopposed to their perceptions of, or beliefs about,these social contexts. Creating peer contexts thathave limited availability to LNED foods, andtargeting friendship clusters that are high consumers

TABLE 4Summary of Select Stochastic Actor-Based Models Parameter Estimates (P.E.) and Standard Errors (SE) for the Mediation Models

Dependent LNEDfood variable

Effects of friend foodintake on actor

cognitive variableEffect of actor cognitive

variable on actor food intakeEffect of actor food intake on

actor cognitive variable

School 1 School 2 School 1 School 2 School 1 School 2

P.E. (SE) P.E. (SE) P.E. (SE) P.E. (SE) P.E. (SE) P.E. (SE)

Intention 0.06 (.17) 0.05 (.18) 0.11 (.11) �0.04 (.13) 0.24 (.07)** 0.11 (.07)+

Attitude 0.08 (.16) �0.02 (.15) 0.06 (.08) 0.07 (.10) 0.11 (.05)* 0.11 (.06)+

Descriptive peer norm 0.04 (.18) 0.02 (.19) �0.20 (.13) 0.05 (.11) 0.09 (.05)+ 0.10 (.06)+

Injunctive peer norm 0.07 (.19) 0.15 (.18) 0.16 (.09)+ 0.06 (.09) 0.11 (.05)* 0.05 (.05)Self-efficacy 0.11 (.12) �0.05 (.15) 0.02 (.08) �0.01 (.07) 0.04 (.04) 0.05 (.06)Perceived behavior control 0.12 (.13) 0.22 (.19) 0.01 (.07) �0.09 (.08) �0.03 (.04) �0.03 (.05)

Note. LNED, low-nutrient, energy-dense. All behavior and cognition models controlled for the same effects that were included inthe behavior-only models. Only parameter estimates that directly tested effects of the cognitive variables are reported here.+p < .10, two-tailed; *p < .05, two-tailed; **p < .01, two-tailed.


of LNED foods, could therefore be effective inreducing intake and diffusing more healthful eat-ing practices through adolescent peer networks.Because LNED food intake was found to predictpositively changes in beliefs about the regular con-sumption of these foods, providing youth withsocial referents that model healthy behaviors mayhave a positive effect in reinforcing this behaviorpattern in future. Although cognitions about LNEDfood intake did not predict behavior over the 1school year tracked in the current study, the widerliterature shows that attitudes and beliefs predictbehavior over time, and as such the relationshipsbetween cognitions and behaviors is likely to bebidirectional.

Limitations of the current study are largely dueto measurement issues. Although self-report foodfrequency questionnaires are a commonly appliedand valid measure of dietary patterns in youth,they have been found to overestimate energyintake and may not be as accurate as food diaries(McPherson et al., 2000). Innovative methods toassess dietary behavior more reliably and validly,without compromising participation rates or creat-ing excessive respondent burden, will be importantin future research. The use of hand-held mobiledevices or ecological momentary assessments maybe useful approaches to pursue in future studiesseeking to investigate the role of peers on childrenand adolescents’ food intake in naturalistic settings.This will be especially useful to tease apart furtherthe extent to which friends’ joint experiences ofdifferent food environments might explain the foodmodeling effects we observed. In the current study,the only shared environment controlled for wasenrolment in the same home group class.

Additional limitations are that this studyexplored socialization processes over the course ofone school year among early adolescents in theirfirst year of high school, limiting its generalizabil-ity. Whether or not friends influence LNED foodintake among a more diverse sample of youth, andwhether these effects persist outside of the schoolsetting or have a lasting impact on dietary behav-iors, needs to be explored in future work. Forexample, differences in adolescents’ socioeconomicstatus may be important to consider given healthinequalities and differing rates of family break-down, with the potential for peers to be evenstronger referents among some youth. A furtherlimitation of studying food-modeling effects amonggrademates only is that we do not know the extentto which school-based friends in other grades, non-school-based friends, and respondents’ broader

social networks are important to these behaviors.Although many of the LNED foods measured inthis study were available at or near the schoolsand so were likely to be consumed with schoolpeers, these foods would also be eaten in otherenvironments and it is unclear if school friends orother peers would influence intake in these othersettings. Finally, as power analyses are not yetavailable for RSiena models, we were not able toassess if we had sufficient power to detect thehypothesized effects. Nonetheless, we identifiedsignificant predictors of behaviors and cognitions,and the standard errors for our model parameterestimates were typically reasonable, giving usgreater confidence in our interpretation of the nulleffects. Of note, the standard errors for the parame-ters testing the mediating role of cognitions wereslightly larger, and we cannot rule out the possibil-ity that the models were underpowered to detectthese effects.

In conclusion, this study applied novel longitu-dinal social network models (Snijders et al., 2007)to investigate the dynamic relationship betweenadolescent friendship networks and intake of“junk” foods. The results suggest that adolescents’friends influence their intake of LNED foods overtime, though not through commonly assumedcognitive mechanisms. This highlights the impor-tance of the peer context for addressing LNEDfood intake in young people and providesinsights into strategies that may be useful inestablishing contexts that support more healthfuleating.

REFERENCES

Aboud, F. E., & Mendelson, M. J. (1998). Determinants offriendship selection and quality: Developmental per-spectives. In W. M. Bukowski, A. F. Newcomb, &W. W. Hartup (Eds.), The company they keep: Friendshipin childhood and adolescence (pp. 87–112). New York,NY: Cambridge University Press.

Ajzen, I. (1991). The theory of planned behavior. Organi-zational Behavior and Human Decision Processes, 50, 179–211. doi:10.1016/0749-5978(91)90020-T

Armitage, C. J., & Conner, M. (2001). Efficacy of the the-ory of planned behaviour: A meta-analytic review.British Journal of Social Psychology, 40, 471–499.

Bandura, A. (1977). Social learning theory. EnglewoodCliffs, NJ: Prentice-Hall.

Bem, D. J. (1972). Self-perception theory. Advances inExperimental Social Psychology, 6, 1–62. doi:10.1016/S0065-2601(08)60024-6

Briefel, R. R., Wilson, A., & Gleason, P. M. (2009). Con-sumption of low-nutrient, energy-dense foods and bev-erages at school, home, and other locations among


school lunch participants and nonparticipants. Journal ofthe American Dietetic Association, 109(2 Suppl. 1), S79–S90.doi:10.1016/j.jada.2008.10.064

Brown, B. B., Bakken, J. P., Ameringer, S. W., & Mahon,S. D. (2008). A comprehensive conceptualization of thepeer influence process in adolescence. In M. J. Prin-stein & K. A. Dodge (Eds.), Understanding peer influencein children and adolescents (pp. 45–71). New York, NY:Guilford Press.

Burk, W. J., Steglich, C. E. G., & Snijders, T. A. B. (2007).Beyond dyadic interdependence: Actor-oriented mod-els for co-evolving social networks and individualbehaviors. International Journal of Behavioral Develop-ment, 31, 397–404. doi:10.1177/0165025407077762

Conger, J. C., Conger, A. J., Costanzo, P. R., Wright,K. L., & Matter, J. A. (1980). The effect of social cueson the eating behavior of obese and normal subjects.Journal of Personality, 48, 258–271. doi:10.1111/j.1467-6494.1980.tb00832.x

Cook, T., Rutishauser, I., & Seelig, M. (2001). Comparabledata on food and nutrient intake and physical measurementsfrom TEH 1983, 1985 and 1995 National Nutrition Sur-veys. Canberra, Australia: Australian Food and Nutri-tion Monitoring Unit.

Coronges, K., Stacy, A. W., & Valente, T. W. (2011).Social network influences of alcohol and marijuanacognitive associations. Addictive Behaviors, 36, 1305–1308. doi:10.1016/j.addbeh.2011.07.008

Feunekes, G. I. J., de Graaf, C., Meyboom, S., & van Stav-eren, W. A. (1998). Food choice and fat intake of adoles-cents and adults: Associations of intakes within socialnetworks. Preventive Medicine, 27, 645–656. doi:10.1006/pmed.1998.0341

Fletcher, A., Bonell, C., & Sorhaindo, A. (2011). You arewhat your friends eat: Systematic review of social net-work analyses of young people’s eating behavioursand bodyweight. Journal of Epidemiology and CommunityHealth, 65, 548–555. doi:10.1136/jech.2010.113936

de la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2010).Obesity-related behaviors in adolescent friendship net-works. Social Networks, 32, 161–167. doi:10.1016/j.soc-net.2009.09.001

de la Haye, K., Robins, G., Mohr, P., & Wilson, C.(2011a). Homophily and contagion as explanations forweight similarities among adolescent friends. Journal ofAdolescent Health, 49, 421–427. doi:10.1016/j.jadohealth.2011.02.008

de la Haye, K., Robins, G., Mohr, P., & Wilson, C.(2011b). How physical activity shapes, and is shapedby, adolescent friendships. Social Science & Medicine,73, 719–728. doi:10.1016/j.socscimed.2011.06.023

Herman, C. P., & Polivy, J. (2005). Normative influenceson food intake. Physiology and Behavior, 86, 762–772.doi:10.1016/j.physbeh.2005.08.064

Herman, C. P., Roth, D. A., & Polivy, J. (2003). Effects ofthe presence of others on food intake: A normative inter-pretation. Psychological Bulletin, 129, 873–886. doi:10.1037/0033-2909.129.6.873

Hermans, R. C. J., Larsen, J. K., Herman, C. P., &Engels, R. C. M. E. (2008). Modeling of palatable foodintake in female young adults: Effects of perceivedbody size. Appetite, 51, 512–518. doi:10.1016/j.appet.2008.03.016

Huisman, M., & Snijders, T. A. B. (2003). Statistical analy-sis of longitudinal network data with changing compo-sition. Sociological Methods and Research, 32, 253–287.doi:10.1177/0049124103256096

Huisman, M., & Steglich, C. (2008). Treatment of non-response in longitudinal network studies. Social Net-works, 30, 297–308. doi:10.1016/j.socnet.2008.04.004

Jahns, L., Siega-Riz, A. M., & Popkin, B. M. (2001). Theincreasing prevalence of snacking among US childrenfrom 1977 to 1996. The Journal of Pediatrics, 138, 493–498. doi:10.1067/mpd.2001.112162

Kassem, N. O., Lee, J. W., Modeste, N. N., & Johnston,P. K. (2003). Understanding soft drink consumptionamong female adolescents using the theory of plannedbehavior. Health Education Research, 18, 278–291.doi:10.1093/her/cyf017

Larsen, H., Engels, R. C. M. E., Granic, I., & Overbeek, G.(2009). An experimental study on imitation of alcoholconsumption in same-sex dyads. Alcohol and Alcohol-ism, 44, 250–255. doi:10.1093/alcalc/agp002

Larsen, H., Engels, R. C. M. E., Souren, P. M., Granic, I.,& Overbeek, G. (2010). Peer influence in a micro-per-spective: Imitation of alcoholic and non-alcoholic bev-erages. Addictive Behaviors, 35, 49–52. doi:10.1016/j.addbeh.2009.08.002

Lospinoso, J., Schweinberger, M., Snijders, T., & Ripley,R. (2010). Assessing and accounting for time heteroge-neity in stochastic actor oriented models. Advances inData Analysis and Classification, 5, 147–176, doi:10.1007/s11634-010-0076-1

Marcoux, B. C., & Shope, J. T. (1997). Application of thetheory of planned behavior to adolescent use and mis-use of alcohol. Health Education Research, 12, 323–331.doi:10.1093/her/12.3.323

McPherson, R. S., Hoelscher, D. M., Alexander, M., Scan-lon, K. S., & Serdula, M. K. (2000). Dietary assessmentmethods among school-aged children: Validity andreliability. Preventive Medicine, 31, S11–S33. doi:10.1006/pmed.2000.0631

Monge-Rojas, R., Nunez, H. P., Garita, C., & Chen-Mok,M. (2002). Psychosocial aspects of Costa Rican adoles-cents’ eating and physical activity patterns. Journal ofAdolescent Health, 31, 212–219. doi:10.1016/S1054-139X(02)00376-2

Nicklas, T. A., Baranowski, T., Cullen, K. W., & Beren-son, G. (2001). Eating patterns, dietary quality andobesity. Journal of the American College of Nutrition, 20,599–608.

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). Theimplicit association test at age 7: A methodological andconceptual review. In J. A. Bargh (Ed.), Social psychologyand the unconscious: The automaticity of higher mental pro-cesses (pp. 265–292). New York, NY: Psychology Press.


Pachucki, M. A., Jacques, P. F., & Christakis, N. A.(2011). Social network concordance in food choiceamong spouses, friends, and siblings. American Journalof Public Health, 101, 2170–2177. doi:10.2105/ajph.2011.300282

Patrick, H., & Nicklas, T. A. (2005). A review of familyand social determinants of children’s eating patternsand diet quality. Journal of the American College of Nutri-tion, 24, 83–92.

Povey, R., Conner, M., Sparks, P., James, R., & Shepherd,R. (2000). The theory of planned behaviour andhealthy eating: Examining additive and moderatingeffects of social influence variables. Psychology andHealth, 14, 991–1006. doi:10.1080/08870440008407363

Rhodes, R. E., & Courneya, K. S. (2003). Investigatingmultiple components of attitude, subjective norm, andperceived control: An examination of the theory ofplanned behaviour in the exercise domain. British Jour-nal of Social Psychology, 42, 129–146. doi:10.1348/014466603763276162

Ripley, R. M., Snijders, T. A. B., & Preciado, P. (2012).Manual for RSiena. Retrieved March 2, 2012, fromhttp://www.stats.ox.ac.uk/siena/RSiena_Manual.pdf

Rivis, A., & Sheeran, P. (2003). Social influences and thetheory of planned behaviour: Evidence for a directrelationship between prototypes and young people’sexercise behaviour. Psychology and Health, 18, 567–583.doi:10.1080/0887044032000069883

Romero, N. D., Epstein, L. H., & Salvy, S. J. (2009). Peermodeling influences girls’ snack intake. Journal of theAmerican Dietetic Association, 109, 133–136. doi:10.1016/j.jada.2008.10.005

Rosenthal, B., & McSweeney, F. K. (1979). Modelinginfluences on eating behavior. Addictive Behaviors, 4,205–214. doi:10.1016/0306-4603(79)90029-7

Salvy, S.-J., de la Haye, K., Bowker, J. C., & Hermans, R.C. J. (2012). Influence of peers and friends on chil-dren’s and adolescents’ eating and activity behaviors.Physiology & Behavior, 106, 369–378. doi:10.1016/j.phys-beh.2012.03.022

Salvy, S.-J., Howard, M., Read, M., & Mele, E. (2009). Thepresence of friends increases food intake in youth.American Journal of Clinical Nutrition, 90, 282–287.doi:10.3945/ajcn.2009.27658

Schweinberger, M. (2012). Statistical modelling of net-work panel data: Goodness of fit. The British Journal of

Mathematical and Statistical Psychology, 65, 263–281.doi:10.1111/j.2044-8317.2011.02022.x

Simpkins, S. D., Schaefer, D. R., Price, C. D., & Vest, A.E. (2013). Adolescent friendships, BMI, and physicalactivity: Untangling selection and influence throughlongitudinal social network analysis. Journal of Researchon Adolescence, 23, 537–549. doi:10.1111/j.1532-7795.2012.00836.x

Snijders, T. A. B., Steglich, C. E. G., & Schweinberger, M.(2007). Modeling the co-evolution of networks andbehavior. In K. van Montfort, H. Oud, & A. Satorra(Eds.), Longitudinal models in the behavioral and relatedsciences (pp. 41–71). Mahwah, NJ: Lawrence Erlbaum.

Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G.(2010). Introduction to stochastic actor-based modelsfor network dynamics. Social Networks, 32, 44–60.doi:10.1016/j.socnet.2009.02.004

Spruijt-Metz, D. (2011). Etiology, treatment, and preven-tion of obesity in childhood and adolescence: A decadein review. Journal of Research on Adolescence, 21, 129–152. doi:10.1111/j.1532-7795.2010.00719.x

Steglich, C., Snijders, T. A. B., & Pearson, M. (2010).Dynamic networks and behavior: Separating selectionfrom influence. Sociological Methodology, 40, 329–393.doi:10.1111/j.1467-9531.2010.01225.x

Unger, J. B., Reynolds, K., Shakib, S., Spruijt-Metz, D.,Sun, P., & Johnson, C. A. (2004). Acculturation, physi-cal activity, and fast-food consumption among Asian-American and Hispanic adolescents. Journal of Commu-nity Health, 29, 467–481. doi:10.1007/s10900-004-3395-3

Valente, T. W., Fujimoto, K., Chou, C.-P., & Spruijt-Metz,D. (2009). Adolescent affiliations and adiposity:A social network analysis of friendships and obesity.Journal of Adolescent Health, 45, 202–204. doi:10.1016/j.jadohealth.2009.01.007

Veenstra, R., & Dijkstra, J. K. (2011). Transformations inadolescent peer networks. In B. Laursen & W. A. Col-lins (Eds.), Relationship pathways: From adolescence toyoung adulthood (pp. 135–154). Los Angeles, CA: Sage.

Veenstra, R., Dijkstra, J. K., Steglich, C., & Van Zalk, M.(2013). Network behavior dynamics. Journal of Researchon Adolescence, 23, 399–412. doi:10.1111/jora.12070

Wansink, B., & Sobal, J. (2007). Mindless eating. Environmentand Behavior, 39, 106–123. doi:10.1177/0013916506295573


Documents

Adolescents’ Intake of Junk Food: Processes and Mechanisms Driving Consumption Similarities Among Friends