Running head: JUNK FOOD INTAKE IN FRIENDSHIP … · Running head: JUNK FOOD INTAKE IN FRIENDSHIP NETWORKS 2 Abstract Adolescents’ consumption of low-nutrient, energy-dense (LNED)

Running head: JUNK FOOD INTAKE IN FRIENDSHIP NETWORKS 1

Adolescents’ Intake of Junk Food:

Processes and Mechanisms Driving Consumption Similarities Among Friends

Kayla de la Haye

RAND Corporation, Santa Monica, USA

Garry Robins

University of Melbourne, Melbourne, Australia

Philip Mohr

University of Adelaide, Adelaide, Australia

Carlene Wilson

Flinders Centre for Innovation in Cancer, Flinders University; and Cancer Council South

Australia, Adelaide, Australia

Please cite as: De la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2013). Adolescents' intake of

junk food: Processes and mechanisms driving consumption similarities among friends. Journal

of Research on Adolescence.

Running head: JUNK FOOD INTAKE IN FRIENDSHIP NETWORKS 2

Abstract

Adolescents’ consumption of low-nutrient, energy-dense (LNED) food often occurs out of home,

and friends may be an important source of influence. This study tested whether observed

similarities in LNED food intake among friends result from social influence, and also explored

underlying psychological mechanisms. Three waves of data were collected over one year from

grade 8 students in Australia (N = 378, 54% male), including measures of food intake and related

cognitions, and friendships to grade-mates. The results of longitudinal social network models

show that adolescent intake was predicted by their friends’ intake, accounting for pre-existing

similarities and other potentially confounding factors. Changes to adolescents’ beliefs about

LNED food do not appear to be the mechanisms underpinning influence from their friends.

JUNK FOOD INTAKE IN FRIENDSHIP NETWORKS 3

Adolescents’ Intake of Junk Food:

Processes and Mechanisms Driving Consumption Similarities Among Friends

Although adolescents’ diets are strongly governed by their family food environment

(Patrick & Nicklas, 2005), about half of their consumption of low-nutrient, energy-dense

(LNED) “junk” foods occurs out of home (Briefel, Wilson, & Gleason, 2009). In developed

countries such as the US and Australia, young peoples’ LNED food intake has increased

substantially in recent decades (Cook, Rutishauser, & Seelig, 2001; Jahns, Siega-Riz, &

Popkin, 2001), resulting in reduced diet quality and additional caloric intake (Jahns et al.,

2001). This has implications for both immediate and long-term health outcomes and is

thought to be one of many interrelated factors 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 are typically consumed in school and peer

contexts, and adolescent reports confirm that lunches and snacks are often eaten with friends

(Feunekes, de Graaf, Meyboom, & van Staveren, 1998). In cross-sectional studies,

consumption of snack foods and overall energy intake has been found to correlate with the

intake of an adolescents’ best friends (Feunekes et al., 1998), and male friends have been

found to be alike in their consumption of high-calorie foods (de la Haye, Robins, Mohr, &

Wilson, 2010). Even among adults, dietary similarities between socially connected peers

appear to be strongest for snack foods and alcohol, with effects over time suggestive of a

social influence process (Pachucki, Jacques, & Christakis, 2011).

Situational food norms, which entail factors such as social influence and portion size,

are theorized to have a powerful effect on food intake (Herman & Polivy, 2005). Although

social influence on food consumption is known to be pervasive and complex, one consistently

observed phenomenon is social modeling. Laboratory-based studies on social modeling of


eating, which pair naïve participants with experimental confederates, have shown that

individuals eat more when their eating companions eat more, and less when their companions

eat less (e.g. Conger, Conger, Costanzo, Wright, & Matter, 1980; Herman, Roth, & Polivy,

2003; Hermans, Larsen, Herman, & Engels, 2008; Rosenthal & McSweeney, 1979). This

“matching norm” effect has also been replicated in children (Salvy, de la Haye, Bowker, &

Hermans, 2012): for example, girls (aged 8-12) increased their intake of cookies when

exposed to a peer who ate a larger amount of cookies, relative to a peer eating a small number

of cookies (Romero, Epstein, & Salvy, 2009). Moreover, young people have been found to

match the food intake of friends more so than unfamiliar peers (Salvy, Howard, Read, &

Mele, 2009).

Similarities in diet and eating behaviors among adolescent friends have been observed

in a small number of cross-sectional survey-based studies (de la Haye et al., 2010; Feunekes

et al., 1998; Fletcher, Bonell, & Sorhaindo, 2011). Researchers have proposed that in these

naturalistic settings, the “matching” of eating behaviors arises through social influence

processes such as modeling (Monge-Rojas, Nunez, Garita, & Chen-Mok, 2002) and is

motivated by goals for peer approval (Unger et al., 2004). However, it is not clear from these

studies whether similarities in friends’ consumption patterns arise from socialization, or if

they can be explained by other confounding processes. One such process is friendship

choices that lead to dietary similarities, as friendships are often based on preferences for

similar others (Aboud & Mendelson, 1998). Although there is no evidence to suggest that

similarities in diet are a salient factor in adolescents’ friendship choices, friendships are based

on similarities in individual attributes and behaviors that are potentially correlated with diet

including gender, race or ethnic background, and obesity (de la Haye, Robins, Mohr, &

Wilson, 2011a; Simpkins, Schaefer, Price, & Vest, 2013). As such, accounting for friendship

selection processes when testing for socialization is critical (Veenstra, Dijkstra, Steglich, &


Van Zalk, 2013). To further this work, the current longitudinal study tested whether

associations in LNED food intake amongst adolescent friends, in the context of larger

friendship networks, could be explained by social influence, when potential confounding

factors were controlled.

The second aim of this study was to explore the mechanisms that might underpin

friends’ influence on adolescent LNED food intake. Despite the prominence of behavioral

modeling theory in social psychological research and evidence of social modeling effects on

food intake in adults and youth, a major limitation of the eating literature is the inability to

account for the reasons why people emulate each other (Herman et al., 2003).

Traditionally, health behavior theories emphasize the role of social-cognitive

mechanisms in mediating the influence of the social environment on behavior. For example,

the theory of planned behavior proposes that the behaviors we observe in others influence our

perceptions of social norms, which, along with attitudes and beliefs about behavior control,

shape our intentions and subsequent behaviors (Ajzen, 1991). More general theories of social

modeling also maintain that the observation of behavior in others shapes a range of

individuals’ beliefs and attitudes about these behaviors, guiding future actions (Bandura,

1977). For example, there is evidence that social factors influence not only perceptions of

social norms, but also attitudes towards the behavior and perceptions of behavior control

(Povey, Conner, Sparks, James, & Shepherd, 2000). Thus, we test a more general model of

social cognition, whereby friends’ food intake may predict adolescent intake by affecting a

range of cognitive constructs including norms, attitudes, perceived behavioral control, and

intentions (see the left panel of Figure 1).

However, other lines of thinking downplay the cognitive, considered aspects of food

choice, and instead highlight the imitative nature of social learning with regards to eating

behavior. This work proposes that environmental and social cues elicit somewhat automated,


imitative responses: akin to what is referred to as “mindless eating” (Wansink & Sobal,

2007). Support for this imitative process has been found in laboratory-based studies looking

at alcohol intake, where young people not only copied the quantity of alcohol consumed by

confederates, but also the rate at which they drank, suggesting a somewhat unconscious,

mimicked response (Larsen, Engels, Granic, & Overbeek, 2009; Larsen, Engels, Souren,

Granic, & Overbeek, 2010).

Aligned to this less cognitively-driven view of socio-environmental effects on eating

is Bem’s self-perception theory (1972), which proposes that beliefs associated with a

particular behavior tend to be fostered by reflecting on one’s past engagement in the

behavior. Thus, a substantially different model to social cognition theories might be

proposed, whereby youth “mindlessly” imitate the behavior of their peers, and subsequently

shape their beliefs and attitudes so that they are in line with the behaviors they have endorsed

(see the right panel of Figure 1).

As outlined in the literature reviewed above, despite consistent evidence of food

modeling in the laboratory, the extent to which this translates to natural settings, and how this

plays out over time, is unclear. Additionally, the mechanisms underpinning social influence

on food intake in youth and adults are not well understood, and additional work is needed to

test competing theories.

Current Study

The current study employed new statistical methods to 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 social

networks and behavior (Snijders, Steglich, & Schweinberger, 2007) allow us to test

simultaneously factors that predict changes in complete social networks (i.e., relationships

among a bounded set of social actors), and changes in network members’ behaviors. Thus,


we are able to test for effects of friend influence on LNED food intake, controlling for the

role of food intake and other potentially confounding factors, which initially predict

friendships and network structure. This flexible modeling framework allows us to test these

peer influence effects alongside other known individual and family predictors of eating

behaviors, and to explore processes that mediate friend influence on LNED food intake.

Whether or not friends influence adolescent LNED intake, in addition to any

similarities when friendships are formed, will be the primary focus of this study. If we find

evidence of friend influence on LNED food intake, potential psychosocial mechanisms

underpinning this process will also be explored. Based on general social cognition models of

behavior (Ajzen, 1991; Bandura, 1977), we anticipate that adolescents will emulate the

behaviors of their friends, and that this process will be partially mediated via perceptions of

peer norms, attitudes, perceived behavior control, and intentions. Failure to support this

mediation hypothesis would suggest 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 schools located in a major Australian city were

recruited in 2008 as part of a larger study looking at peer effects on obesity. At both schools

grade 8 is the first year of high school, with students feeding in from numerous primary

schools. The two schools were located in neighborhoods with similar middle-class socio-

demographic characteristics.

Information letters were mailed to students and their guardians at the start of the

school year, providing them with details about the study and the opportunity to opt-out.

Adolescents who joined the school throughout the year were also invited to participate. At

each wave of data collection, participating students were entered into a draw to win one of


several $20 gift vouchers. The study protocol was approved by the Human Research Ethics

Committees at the University of Adelaide and Australia’s Commonwealth Scientific and

Industrial Research Organisation (CSIRO).

A total of 378 students took part in the study, nested in two schools, with each school

cohort defined as a separate “friendship network”. Participation 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 among eligible

network members, such as these, are important for modeling complete longitudinal network

data (Huisman & Steglich, 2008). At the beginning of the study, the mean age of participants

was 13.6 years in school 1 (SD = 0.4; range 12.3 to 14.4), and 13.7 years in school 2 (SD =

0.4; range 12.3 to 15.6).

Procedure and Measures

Paper-based questionnaires were administered by teachers three times during the

school year. Questionnaire items assessed friendships with grade-mates, intake of various

LNED foods over the previous month, and beliefs about the regular consumption of “high-

energy foods”. The measures were part of a larger questionnaire that took 25 minutes to

complete.

Friendship networks. Friendships among grade-mates were assessed by having

participants list the first and last names of an unlimited number of peers in their grade who

were “friends you hang around with the most”. The instructions did not specify the number

or gender of friends to nominate, and although ten spaces were provided for responses, many

respondents listed fewer or more than ten friends. Participants were then instructed to circle

the names of their “best friends” from among the friends they had listed. The subsequent

analyses consider only best friend nominations. Knowledge of what peers are eating, likely

to occur via regular face-to-face contact that characterizes close friendships, is necessary for


food matching effects, and indeed the literature suggests that close peer relationships are

relevant to adolescent food intake (Feunekes et al., 1998; Salvy et al., 2009).

A friendship network for the set of participants in each grade cohort, at each wave,

was represented as a directed, asymmetrical adjacency matrix, where cells coded as 1 denoted

a unilateral friendship between participants i and j, and 0 the absence of a friendship.

Intake of LNED foods. Food frequency items required respondents to record how

often in the previous month they had consumed one serving of 14 specific types of LNED

foods, including chocolate, 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 were not asked to specify the context in which they ate the

foods, however many of the items listed were available for sale at the school canteens or

nearby shops and thus may have been eaten in both school and other environments.

For each of the 14 items, frequency of consumption was recorded on a 7 point scale

where 1 = 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. As only one factor emerged from these 14 items, with each item loading fairly

uniformly on this factor, an overall measure of LNED food intake was derived by taking the

mean score (school 1 α = .80 to .86, school 2 α = .79 to .85). Although there is some evidence

that self-report food frequency questionnaires overestimate energy intake in youth, they are

commonly used in survey research and have been found to have acceptable validity and

reliability (McPherson, Hoelscher, Alexander, Scanlon, & Serdula, 2000).

LNED food-related attitudes and cognitions. Standard items were used to measure

attitudinal and cognitive variables derived from social cognition theories (Ajzen, 1991).

These items have been used and validated in a number of studies (Armitage & Conner, 2001),

including studies of adolescent 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 these foods were defined as often having “lots of salt, sugar, or fat

(or all three)” with respondents instructed to consider the examples of these items listed in the

food frequency component of the questionnaire. These cognitive measures were rated on 7-

point scales anchored by two statements, unless otherwise noted.

Intention to eat high-energy foods in the coming month was measured by two items:

“In the next month, how often do you plan to eat high-energy food” (rated on a 7-point Likert

scale where 1 = once a 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 not intend

to do this - definitely intend to do this”. The correlation between these two items was

moderate to strong (r = .63 to .76 in school 1, and r =.55 to .68 in school 2, across the three

waves), and the mean was used as an overall measure of intentions.

Attitudes towards ‘high-energy foods’ were measured by two items: “Would you like

to eat high-energy food at least twice a day … definitely would not like to do this - definitely

would like to do this” and “I think that eating high-energy food at least twice a day would

be… unenjoyable - enjoyable”. The correlation between these two items was moderate to

strong (r = .65 to .80 in school 1, and r = .55 to .64 in school 2, across the three waves) and

the mean score was used as a measure of attitudes.

Perceived descriptive peer norms were measured by the item “Of your close friends at

school, how many eat high-energy food at least twice a day... none of my close friends - all of

my close friends”. Perceived injunctive peer norms were measured by the item “Do your

close friends at school think that you should eat high-energy food at least twice a day... they

definitely think I should not - they definitely think I should”. Descriptive and injunctive peer

norms were tested independently in our models, in line with evidence that they capture

different normative constructs (Rivis & Sheeran, 2003). The correlation between these two


items was moderate (r = .36 to .48 in school 1, and r = .45 to .54 in school 2, across the three

waves).

Self-efficacy and controllability over the intake of 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 was measured by

the question “Whether or not I eat high-energy food at least twice a day is entirely up to

me…strongly disagree – strongly agree.” Consistent with other research that identifies self-

efficacy and controllability as divergent constructs (e.g., Rhodes & Courneya, 2003), we

retained them as separate items in the analyses. The correlation between these two items was

weak to moderate (r = .30 to .54 in school 1, and r = .37 to.66 in school 2, across the three

waves).

Control attributes. Attributes known to be associated with adolescent friendships and

dietary intake were accounted for the in the models. Change in the friendship network

controlled for respondent gender (0 = female, 1 = male), ethnicity (1 = identify with an

ethnicity other than Anglo-Australian), and weekly allowance (i.e. pocket money) (4-point

scale, where 1 = less than $10 and 4 = more than $30). The role of weight status in friendship

choices was also controlled, given that overweight youth are marginalized in peer networks

(de la Haye et al., 2011a; Valente, Fujimoto, Chou, & Spruijt-Metz, 2009). At the dyad level,

the effect of sharing a home group class (meaning that they attended most core classes

together) on friendship choices was also included to account for greater opportunities for

certain pairs of students to become acquainted, and to account for shared environments.

Effects of gender, ethnicity, pocket money, and overweight on change in LNED food

intake were accounted for, as were perceptions of family LNED food intake. Perceived

family norms were assessed by the question “Do adults who are important to you (parents,


guardians, relatives) eat high-energy food at least twice a day” with responses anchored on a

7 point scale from they definitely do not this – they definitely do this.

Statistical analyses

SABMs for social networks and behavior. Stochastic actor-based models (SABMs)

for social networks and behavior (Snijders et al., 2007) were estimated to determine whether

friends’ intake of LNED foods influenced adolescent intake, controlling for a range of

predictors of friendships and diet. These models are implemented in the RSiena (Simulation

Investigation for Empirical Network Analysis) 4.0 software (Ripley, Snijders, & Preciado,

2012), and are described in Snijders, van de Bunt, and Steglich (2010), and Steglich,

Snijders, and Pearson (2010). Model parameters were estimated using a Method of Moments

procedure, whereby parameter vectors are adjusted to improve model fit through a series of

simulations. Effects were tested for significance based on a t-ratio (estimate divided by the

standard error).

Two parts of this model are estimated simultaneously: a network dynamics submodel

tests effects predicting changes to friendship ties, and a behavior dynamics submodel tests

effects predicting changes to the dependent behavior variable(s) (i.e., food intake).

Longitudinal measures of these variables (and covariates) represent the observed state of the

network at given points in time, and changes between the observed panels of data are

modeled using continuous time Markov chains to determine the most likely series of

unobserved micro-steps taken by actors when changing their ties or behavior. An evaluation

function determines the social “rules” that guide these changes, which are formalized as

specific parameters in the model and test for the hypothesized selection and influence effects.

A rate function estimates how many opportunities for change (in friendships and behavior)

occur between observations.


Model specification. For each school-based friendship network, two models were

estimated: the first tested the main effect of friend influence on food intake (basic model);

where evidence of influence was found, a second set of models were estimated testing for the

mediating role of cognitive variables (mediation models).

In the basic model, the hypothesis that friends influenced adolescent junk food intake

was tested with an effect of “friends’ total LNED food intake” (i.e., total similarity effect) on

changes to adolescent intake. A significant positive effect would indicate that actors’ overall

LNED food intake remained or became similar to the intake of their nominated friends.

Effects of covariates (gender, ethnicity, pocket money, overweight) on changes in intake

were controlled, and linear and quadratic shape effects were included to model the overall

distribution of scores (Snijders et al., 2010; Veenstra et al., 2013).

This basic model simultaneously accounted for factors predicting friendship selection

and maintenance. Associations between LNED food intake and friendship nominations were

controlled using four effects, the most relevant being intake similarity that captures the extent

to which friendships were established or maintained between peers with existing dietary

similarities. The model also included an effect of actors’ food intake on their outgoing

friendship nominations (intake ego), an effect of peers’ food intake on them receiving an

actor’s friendship nomination (intake alter), as well as a squared effect of peers’ intake

(intake squared alter) to control for non-linearity in this effect. The roles of gender, ethnicity

and pocket money on friendship choices were controlled using the same friendship selection

effects (covariate ego, covariate alter, same or similar covariate), and a dyad-level effect of

sharing the same home group class was also included (same classroom). Finally, models also

controlled for endogenous network effects, including tendencies for actors to reciprocate

friendships (reciprocity), and to befriend friends’ of friends (transitivity) and highly-

nominated peers (indegree popularity). The inclusion of these structural effects is standard


and necessary when modeling network data, in order to account for dependencies between

respondents who are connected via a social network (Snijders et al., 2007).

To avoid issues of collinearity, a forward selection approach was used to specify the

basic model (described in Burk, Steglich, & Snijders, 2007; Snijders et al., 2010). Effects for

each covariate were score tested against a null model (Schweinberger, 2012), and if any were

significant the group of effects was retained in the final model. Additionally, the final model

tested for time heterogeneity in the food-intake effects (Lospinoso, Schweinberger, Snijders,

& Ripley, 2010), and dummies were added, when needed, to account for significant

differences in these effects across time periods.

To examine whether friend influence on LNED intake was partially mediated by

adolescents’ beliefs about eating junk foods, we specified a second set of models for the co-

evolution of the friendship networks, LNED food intake, and related attitudes and cognitions

(mediation models). Thus, each model included rate and evaluation functions for three

dependent variables: friendships, food intake, and one cognitive measure. The same

parameters specified in the basic model were included in the mediation model, including the

effect of friends’ food intake on actor intake (i.e., behavior influence). To test for the

hypothesized mediation effects, this behavior influence effect was tested alongside an effect

of friends’ food intake on actors’ food-related cognition. In other words, did friends’

consumption of LNED foods influence adolescents’ intake of these foods as well as their

beliefs about eating these foods? Evidence of mediation would require that the effect of

friend intake on actors’ cognition be statistically significant, and that the addition of this

effect partially (or fully) accounts for the effect of friends’ intake on actors’ intake.

In addition to this mediation effect, the following parameters were also included as

controls in the mediation models: a) the effect of actors’ cognition on change in actor’s food

intake, and b) the effect of actors’ food intake on actors’ cognition.


Results

Descriptive statistics of the network and LNED food intake

A summary of descriptive statistics is presented in Table 1; these data show that the

two cohorts were comparable on the covariates measured. Table 1 also presents a summary of

the LNED food intake and cognition variables, with mean values suggesting that these

variables were fairly stable over time, and that there are consistencies between the two

schools. On average, participants consumed one serve of each LNED food item less than

once a week. Attitudes and beliefs about regular intake of these foods tended to be negative

or neutral, although showed upward trends over time suggesting that cognitions became

somewhat more positive, especially between the first two waves.

Structural characteristics of the two friendship networks are summarized in Table 2.

Across each wave, students nominated an average of 3 to 4 best friends, and about one third

of these friendship nominations were reciprocated (reciprocity index). Between each of the

three waves (Period 1 and Period 2), students, on average, maintained two friendships, but

also dissolved one friendship tie and nominated one new friend. Changes to the composition

of the network, as a result of students joining or leaving the school (Table 2) were modeled as

exogenous events at specified time points (Huisman & Snijders, 2003).

Observed similarities on LNED food intake among friends, called network

autocorrelation, is also summarized in Table 2. Moran’s I is a measure of spatial correlation,

and coefficient values close to 0 indicate that individuals who are connected in the matrix are

not more similar on the behavior than would be expected if they were randomly paired.

Values close to 1 indicate that connected individuals are very similar. Friend similarities on

LNED food intake were found to be modest, with increases in similarity over the school year

observed only in school 1.

Statistical Models for the Evolution of Networks and LNED food intake


The basic model (Table 3) tested for friend influence on LNED food intake (food

intake dynamics) by including an effect of friend intake on adolescent intake. In both schools,

this effect was positive and significant indicating that over time, respondents emulated the

LNED food consumption of their friends. Food intake dynamics were predicted by few

actor-attributes: only pocket money significantly and positively predicted intake in school 2,

indicating the more pocket money the more likely youth adopted 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 shape effects), interpreted in

consideration of the mean LNED intake values, indicate that there was an overall tendency

for actors to have low values on the LNED food intake scale (1 or 2), and that this effect was

curvilinear so that the behavior function was greatest for low LNED intake scores.

The tendency for adolescents to adopt similar eating patterns to their friends was

significant controlling for effects predicting friendship choices (friendship network

dynamics). In school 1, there was some evidence that adolescents with higher food intake

were less likely to make or maintain friend nominations (negative intake ego), although this

trend became weaker over time (negative intake ego * period 2 dummy). In this same school,

food intake was also associated with popularity: the combination of negative intake alter and

negative intake squared alter effects indicates that actors’ preference was to befriend peers

with LNED values slightly above the mean (3), more so than peers with low (1, 2) or very

high values. There was no evidence that food intake was associated with friendship choices in

school 2, and also no evidence that actors in either school selected friends whose intake levels

were similar to their own (intake similarity).

Covariates also predicted friendship choices, although these effects differed slightly

between schools. In school 1, actors preferred friends who were of the same gender, the same

ethnic background, who had similar amounts of pocket money, and who were in their home


group class. In this school, youth with more pocket money were more likely to nominate

friends (money ego), but were less likely to receive friend nominations (money alter),

although this latter preference was bimodal, indicating that actors preferred friends with both

low and high values of pocket money (money squared alter). In school 2, actors preferred

friends of the same gender, and who were in their home group class. Males in this school also

tended to make more friend nominations (male ego), but were less attractive as friends (male

alter); and students who identified with race or ethnic backgrounds other than Anglo-

Australian were less attractive as friends (race or ethnicity alter). There was also a tendency

for friendships to form among youth with the same weight status.

Finally, friendship network dynamics were also explained by similar structural effects

in both schools, including a tendency for actors to reciprocate friend nominations

(reciprocity), to befriend friends of their current friends (transitive ties), and to not make

friend nominations arbitrarily (outgoing ties). In school 2, there was also an aversion to

befriending peers who were already popular (i.e., high indegree). Although contrary to the

customary “Matthew effect” where popular individuals attract the most friends, a rejection of

popular or successful individuals is embedded in Australian culture and referred to as “tall

poppy syndrome”.

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

Given that friends’ intake of LNED foods was found to predict adolescent intake in

both schools, we also examined if this process was mediated by cognitive mechanisms.

Specifically, we tested if actors’ tendency to adopt similar levels of food intake to their

friends was explained by friends’ intake influencing changes in adolescents’ beliefs and

attitudes about regular consumption of LNED foods.

A summary of the results obtained from the mediation models, which tested

mediation effects for six different cognitive constructs, are presented in Table 4 (only


estimates that directly tested effects of the cognitive variables are reported). There was no

evidence in either school that friends’ food intake predicted changes in adolescent beliefs or

attitudes towards regular LNED food intake (Table 4, column 1). Thus, the hypothesis that

friends’ influence on adolescent food intake was partially mediated by changes in

adolescents’ intentions, attitudes, perceived peer norms, or perceived control over this

behavior, was not supported. Moreover, there was negligible evidence that adolescents’

beliefs about LNED food intake predicted changes in their reported levels of food intake

(Table 4, column 2). Only adolescents’ perceptions of injunctive peer norms were found to

marginally and positively predict changes in their food intake in school 1, suggesting that

adolescents who reported that their school friends thought they should regularly consume

LNED foods were somewhat more likely to increase their food intake. Interestingly, intake of

LNED foods was found to predict changes in cognitions (Table 4, column 3). There was

some evidence that intake positively predicted changes in intentions, attitudes, and perceived

descriptive peer norms: these effects were significant in school 1, and marginally significant

in school 2, so that greater intake predicted stronger intentions to consume LNED foods,

more positive attitudes towards these foods, and stronger beliefs that friends regularly

consumed these foods. In school 1, greater intake of LNED foods also predicted perceived

injunctive peer norms, meaning that respondents with higher intake tended to adopt the view

that “my friends think I should eat high-energy foods”.

Discussion

Adolescents’ consumption of LNED foods was found to be predicted by the LNED

food intake of their school friends, in two cohorts of Australian high school students.

Specifically, adolescents’ intake became or remained similar to the intake of their same-grade

best friends over the course of the school year, over and above more general tendencies of the

grade cohort to adopt low levels of LNED food intake, and the positive effect of pocket


money on intake (school 1). Additionally, these peer effects were significant controlling for

potentially confounding associations between LNED food intake and friendship choices (as

found in school 1), together with other factors predicting friendship choices (covariates and

network structure) and LNED food intake. Thus, students with “low consuming” friends were

especially likely to adopt or maintain low levels of consumption, whereas those with “high

consuming” friends were likely to emulate or maintain similar high levels of consumption.

These findings are in line with laboratory-based studies that have consistently

demonstrated that adults and youth emulate the eating behaviors of their peers (Conger et al.,

1980; Hermans et al., 2008; Romero et al., 2009; Rosenthal & McSweeney, 1979), and cross-

sectional observational research showing that adolescent friends tend to have similar dietary

patterns, particularly with regards to snack foods (de la Haye et al., 2010; Feunekes et al.,

1998). Our work extends the findings on “matching norms” by supporting hypotheses that

these effects hold in a naturalistic setting, controlling for dietary similarities when these peer

relationships were formed, and further suggesting that friends may influence dietary

behaviors over time.

Social cognition models, such as the theory of planned behavior, propose that

behaviors are influenced by the social environment via cognitive mechanisms: in particular

perceptions of social norms (Ajzen, 1991). However, in this study we did not find evidence

that the effect of actual food intake norms (i.e., friends’ self-reported LNED consumption) on

adolescents’ consumption was mediated via changes in adolescents’ attitudes, perceived peer

norms, perceived behavioral control, or intentions. Rather, intake of LNED foods tended to

predict changes to beliefs about regularly consuming these foods, including related

intentions, attitudes, and perceived peer norms.

These findings are more line with health behavior or food intake theories that view

socialization effects as automatic rather than deliberative (Bem, 1972; Wansink & Sobal,


2007) (see Figure 1), as well as findings from similar research looking at peer network effects

on physical activity (de la Haye, Robins, Mohr, & Wilson, 2011b). From this perspective, the

modeling of eating behaviors in our social environment elicits a somewhat “mindless”

imitation (Wansink & Sobal, 2007). Conscious cognitive processes are therefore not

expended on making decisions about these recurrent, everyday behaviors, and so do not

strongly guide future behavior. Rather, beliefs and cognitions about eating may be formed

during opportunities to reflect on past behavior (Bem, 1972).

Although our findings lend support to “mindless imitation” social modeling process

on food intake, additional work is needed to evaluate a wider range of potential mediating

factors, including explicit and implicit cognitions related to food intake (e.g., Coronges,

Stacy, & Valente, 2011; Nosek, Greenwald, & Banaji, 2007). For example, to test whether

eating behaviors observed in one’s social network influences implicit cognitions about

consumption of LNED foods could be explored using Implicit Association Test (IAT) (see

(Coronges et al., 2011) . Future research should also explore the role of social goals in social

modeling effects; adolescents’ adoption of their friends’ behaviors may be more strongly

motivated by the desire to establish and maintain affiliations with peers in a new school

setting (Brown, Bakken, Ameringer, & Mahon, 2008), rather than their own beliefs about

junk food.

Results of the current study highlight the important role of “situational food norms”:

referring to external influences and opportunities, such as friends’ behaviors and the

availability of pocket money, that facilitate the adoption of unhealthy dietary practices by

youth. Opportunities to consume unhealthy foods presented by peers, or the ability to

purchase them, appear to have important influences on these eating behaviors that are not

mediated by beliefs or attitudes. Indeed, an alternate explanation to the “influence” effects

described above could be shared opportunities to consume LNED foods that are jointly


experienced by friends. For example, if friends’ engage in similar activities together they will

be exposed to similar environments and food-related stimuli, which may result in similar

consumption patterns. To test competing explanatory mechanisms of social influence vs.

shared environments, future work needs to assess explicitly the extent to which friends’ are

exposed to similar environments and eating opportunities.

Together, these results suggest that to mitigate successfully negative social-

environmental influences on adolescent junk food intake associated with behavior by peers, it

will be crucial to alter youths’ actual environments and peer behaviors, as opposed to their

perceptions of, or beliefs about, these social contexts. Creating peer contexts that have limited

availability to LNED foods, and targeting friendship clusters that are high consumers of

LNED foods, could therefore be effective in reducing intake and diffusing more healthful

eating practices through adolescent peer networks. Because LNED food intake was found to

predict positively changes in beliefs about the regular consumption of these foods, providing

youth with social referents that model healthy behaviors may have a positive effect in

reinforcing this behavior pattern in future. Although cognitions about LNED food intake did

not predict behavior over the 1 school year tracked in the current study, the wider literature

shows that attitudes and beliefs predict behavior over time, and as such the relationships

between cognitions and behaviors is likely to be bidirectional.

Limitations of the current study are largely due to measurement issues. Although self-

report food frequency questionnaires are a commonly applied and valid measure of dietary

patterns in youth, they have been found to overestimate energy intake and may not be as

accurate as food diaries (McPherson et al., 2000). Innovative methods to assess dietary

behavior more reliably and validly, without compromising participation rates or creating

excessive respondent burden, will be important in future research. The use of hand-held

mobile devices or ecological momentary assessments may be useful approaches to pursue in


future studies seeking to investigate the role of peers on children and adolescents’ food intake

in naturalistic settings. This will be especially useful to tease apart further the extent to which

friends’ joint experiences of different food environments might explain the food modeling

effects we observed. In the current study, the only shared environment controlled for was

enrolment in the same home group class.

Additional limitations are that this study explored socialization processes over the

course of one school year among early adolescents in their first year of high school, limiting

its generalizability. Whether or not friends influence LNED food intake among a more

diverse sample of youth, and whether these effects persist outside of the school setting or

have a lasting impact on dietary behaviors, needs to be explored in future work. For example,

differences in adolescents’ socio-economic status may be important to consider given health

inequalities and differing rates of family breakdown, with the potential for peers to be even

stronger referents among some youth. A further limitation of studying food-modeling effects

among grade-mates only is that we do not know the extent to 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 in this study were

available at or near the schools and so were likely to be consumed with school peers, these

foods would also be eaten in other environments and it is unclear if school friends or other

peers would influence intake in these other settings. Finally, as power analyses are not yet

available for RSiena models, we were not able to assess if we had sufficient power to detect

the hypothesized effects. Nonetheless, we identified significant predictors of behaviors and

cognitions, and the standard errors for our model parameter estimates were typically

reasonable, giving us greater confidence in our interpretation of the null effects. Of note, the

standard errors for the parameters testing the mediating role of cognitions were slightly


larger, and we cannot rule out the possibility that the models were underpowered to detect

these effects.

In conclusion, this study applied novel longitudinal social network models (Snijders et

al., 2007) to investigate the dynamic relationship between adolescent friendship networks and

intake of “junk” foods. The results suggest that adolescents’ friends influence their intake of

LNED foods over time, though not through commonly assumed cognitive mechanisms. This

highlights the importance of the peer context for addressing LNED food intake in young

people, and provides insights into strategies that may be useful in establishing contexts that

support more healthful eating.


Acknowledgements

Data were collected while K. de la Haye was being supported by an Australian Postgraduate

Award through the University of Adelaide, and a Preventative Health Flagship Scholarship

from Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO).

We would like to thank several anonymous reviewers for their helpful comments on earlier

versions of this manuscript.

Authors addresses and affiliations

Kayla de la Haye, RAND Corporation, Santa Monica, USA

Garry Robins, University of Melbourne, Melbourne, Australia

Philip Mohr, University of Adelaide, Adelaide, Australia

Carlene Wilson, Flinders Centre for Innovation in Cancer, Flinders University; and Cancer

Council South Australia, Adelaide, Australia

Author note

Correspondence regarding this paper should be addressed to Kayla de la Haye, RAND

Corporation, 1776 Main Street, PO Box 2138, Santa Monica, CA, 90407-2138, U.S.A.

Electronic mail may be addressed to [email protected]

Keywords: adolescents, friendship, diet, influence, selection, stochastic actor-based models,

social network


References

Aboud, F. E., & Mendelson, M. J. (1998). Determinants of friendship selection and quality:

Developmental perspectives The company they keep: Friendship in childhood and

adolescence. (pp. 87-112): New York, NY, US: Cambridge University Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human

Decision Processes, 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-

analytic review. British Journal of Social Psychology, 40(4), 471-499.

Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, N.J.: Prentice Hall.

Bem, D. J. (1972). Self-perception theory. Advances in Experimental Social Psychology, 6, 1-

62. doi: 10.1016/S0065-2601(08)60024-6

Briefel, R. R., Wilson, A., & Gleason, P. M. (2009). Consumption of low-nutrient, energy-

dense foods and beverages at school, home, and other locations among school lunch

participants and nonparticipants. Journal of the American Dietetic Association, 109(2,

Supplement 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 the peer influence process in adolescence. In M. J. Prinstein & K.

A. Dodge (Eds.), Understanding Peer Influence in Children and Adolescents (pp. 45-

71). New York: Guilford.

Burk, W. J., Steglich, C. E. G., & Snijders, T. A. B. (2007). Beyond dyadic interdependence:

Actor-oriented models for co-evolving social networks and individual behaviors.

International Journal of Behavioral Development, 31(4), 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 cues on the eating behavior of obese and normal subjects. Journal of

Personality, 48(2), 258-271. doi: 10.1111/j.1467-6494.1980.tb00832.x

Cook, T., Rutishauser, I., & Seelig, M. (2001). Comparable data on food and nutrient intake

and physical measurements from teh 1983, 1985 and 1995 national nutrition surveys.

Canberra: Australian Food and Nutrition Monitoring Unit.

Coronges, K., Stacy, A. W., & Valente, T. W. (2011). Social network influences of alcohol

and marijuana cognitive associations. Addictive Behaviors, 36(12), 1305-1308. doi:

10.1016/j.addbeh.2011.07.008

de la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2010). Obesity-related behaviors in

adolescent friendship networks. Social Networks, 32(3), 161-167. doi:

10.1016/j.socnet.2009.09.001

de la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2011a). Homophily and contagion as

explanations for weight similarities among adolescent friends. Journal of Adolescent

Health, 49(4), 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 shaped by, adolescent friendships. Social Science & Medicine, 73(5), 719-728.

doi: 10.1016/j.socscimed.2011.06.023

Feunekes, G. I. J., de Graaf, C., Meyboom, S., & van Staveren, W. A. (1998). Food choice

and fat intake of adolescents and adults: Associations of intakes within social

networks. Preventive Medicine, 27(5), 645-656. doi: 10.1006/pmed.1998.0341

Fletcher, A., Bonell, C., & Sorhaindo, A. (2011). You are what your friends eat: Systematic

review of social network analyses of young people's eating behaviours and

bodyweight. Journal of Epidemiology and Community Health, 65(6), 548-555. doi:

10.1136/jech.2010.113936


Herman, C. P., & Polivy, J. (2005). Normative influences on food intake. Physiology &

Behavior, 86(5), 762-772. doi: 10.1016/j.physbeh.2005.08.064

Herman, C. P., Roth, D. A., & Polivy, J. (2003). Effects of the presence of others on food

intake: A normative interpretation. Psychological Bulletin, 129(6), 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 food intake in female young adults. Effects of perceived body size. Appetite,

51(3), 512-518. doi: 10.1016/j.appet.2008.03.016

Huisman, M., & Snijders, T. A. B. (2003). Statistical analysis of longitudinal network data

with changing composition. Sociological Methods Research, 32(2), 253-287. doi:

10.1177/0049124103256096

Huisman, M., & Steglich, C. (2008). Treatment of non-response in longitudinal network

studies. Social Networks, 30(4), 297-308. doi:

http://dx.doi.org/10.1016/j.socnet.2008.04.004

Jahns, L., Siega-Riz, A. M., & Popkin, B. M. (2001). The increasing prevalence of snacking

among US children from 1977 to 1996. The Journal of Pediatrics, 138(4), 493-498.

doi: 10.1067/mpd.2001.112162

Kassem, N. O., Lee, J. W., Modeste, N. N., & Johnston, P. K. (2003). Understanding soft

drink consumption among female adolescents using the Theory of Planned Behavior.

Health Education Research, 18(3), 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 alcohol consumption in same-sex dyads. Alcohol and Alcoholism,

agp002. 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-perspective: Imitation of alcoholic and non-alcoholic beverages.

Addictive Behaviors, 35(1), 49-52. doi: 10.1016/j.addbeh.2009.08.002

Lospinoso, J., Schweinberger, M., Snijders, T., & Ripley, R. (2010). Assessing and

accounting for time heterogeneity in stochastic actor oriented models. Advances in

Data Analysis and Classification, 1-30. doi: 10.1007/s11634-010-0076-1

Marcoux, B. C., & Shope, J. T. (1997). Application of the Theory of Planned Behavior to

adolescent use and misuse of alcohol. Health Education Research, 12(3), 323-331.

doi: 10.1093/her/12.3.323

McPherson, R. S., Hoelscher, D. M., Alexander, M., Scanlon, K. S., & Serdula, M. K. (2000).

Dietary assessment methods among school-aged children: Validity and reliability.

Preventive Medicine, 31(2), 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 adolescents' eating and physical activity patterns. Journal of Adolescent

Health, 31(2), 212-219. doi: 10.1016/S1054-139X(02)00376-2

Nicklas, T. A., Baranowski, T., Cullen, K. W., & Berenson, G. (2001). Eating patterns,

dietary quality and obesity. Journal of the American College of Nutrition, 20(6), 599-

608.

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The implicit association test at age

7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social Psychology

and the Unconscious: The Automaticity of Higher Mental Processes. (pp. 265-292).

New York: Psychology Press.

Pachucki, M. A., Jacques, P. F., & Christakis, N. A. (2011). Social network concordance in

food choice among spouses, friends, and siblings. American Journal of Public Health,

101(11), 2170-2177. doi: 10.2105/ajph.2011.300282


Patrick, H., & Nicklas, T. A. (2005). A Review of family and social determinants of

children's eating patterns and diet quality. Journal of the American College of

Nutrition, 24(2), 83-92.

Povey, R., Conner, M., Sparks, P., James, R., & Shepherd, R. (2000). The theory of planned

behaviour and healthy eating: Examining additive and moderating effects of social

influence variables. Psychology & Health, 14(6), 991-1006. doi:

10.1080/08870440008407363

Rhodes, R. E., & Courneya, K. S. (2003). Investigating multiple components of attitude,

subjective norm, and perceived control: An examination of the theory of planned

behaviour in the exercise domain. British Journal of Social Psychology, 42(1), 129-

146. doi: 10.1348/014466603763276162

Ripley, R. M., Snijders, T. A. B., & Preciado, P. (2012). Manual for RSiena. Retrieved from

http://www.stats.ox.ac.uk/siena/RSiena_Manual.pdf

Rivis, A., & Sheeran, P. (2003). Social influences and the theory of planned behaviour:

Evidence for a direct relationship between prototypes and young people's exercise

behaviour. Psychology & Health, 18(5), 567-583. doi:

10.1080/0887044032000069883

Romero, N. D., Epstein, L. H., & Salvy, S. J. (2009). Peer modeling influences girls' snack

intake. Journal of the American Dietetic Association, 109(1), 133-136. doi:

10.1016/j.jada.2008.10.005

Rosenthal, B., & McSweeney, F. K. (1979). Modeling influences on eating behavior.

Addictive Behaviors, 4(3), 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 children's and adolescents' eating and activity behaviors. Physiology &

Behavior, 106(3), 369-378. doi: 10.1016/j.physbeh.2012.03.022


Salvy, S.-J., Howard, M., Read, M., & Mele, E. (2009). The presence of friends increases

food intake in youth. American Journal of Clinical Nutrition, 90(2), 282-287. doi:

10.3945/ajcn.2009.27658

Salvy, S.-J., Roemmich, J. N., Bowker, J. C., Romero, N. D., Stadler, P. J., & Epstein, L. H.

(2009). Effect of peers and friends on youth physical activity and motivation to be

physically active. Journal of Pediatric Psychology, 34(2), 217-225. doi:

10.1093/jpepsy/jsn071

Schweinberger, M. (2012). Statistical modelling of network panel data: Goodness of fit.

British Journal of Mathmatical and Statistical Psychology, 65(2), 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 physical activity: Untangling selection and influence through longitudinal

social network analysis. Journal of Research on Adolescence. 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 and behavior. In K. van Montfort, H. Oud & A. Satorra (Eds.),

Longitudinal Models in the Behavioral and Related Sciences (pp. 41-71). Mahwah,

NJ: Erlbaum.

Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic

actor-based models for network dynamics. Social Networks, 32(1), 44-60. doi:

10.1016/j.socnet.2009.02.004

Spruijt-Metz, D. (2011). Etiology, treatment, and prevention of obesity in childhood and

adolescence: A decade in review. Journal of Research on Adolescence, 21(1), 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 selection from influence. Sociological Methodology, 40(1), 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, physical activity, and fast-food consumption among Asian-American

and Hispanic adolescents. Journal of Community Health, 29(6), 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(2), 202-204. doi: 10.1016/j.jadohealth.2009.01.007

Veenstra, R., & Dijkstra, J. K. (2011). Transformations in adolescent peer networks. In B.

Laursen & W. A. Collins (Eds.), Relationship Pathways: From Adolescence to Young

Adulthood (pp. 135-154). Los Angeles: Sage.

Veenstra, R., Dijkstra, J. K., Steglich, C., & Van Zalk, M. (2013). Network-behavior

dynamics. Journal of Research on Adolescence.

Wansink, B., & Sobal, J. (2007). Mindless eating. Environment and Behavior, 39(1), 106-

123. doi: 10.1177/0013916506295573


Table 1. Individual Descriptive Statistics

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

Characteristic 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 29.5

M (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 21.0

M (SD) LNED food variables

Intakeb 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)

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). c 7-point scale anchored by strongly negative (1) and strongly positive (7) statements


Table 2. Friendship Network Descriptive Statistics

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

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

% non-respondents 14.4 14.9 11.7 13.5 12.8 10.3

M (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 .34 .37 .34 .33 .33 .37

Transitivity index .44 .43 .43 .41 .41 .39

Moran’s I for LNED food

intake

.09 .12 .17 .09 .08 .09

Period 1 Period 2 Period 1 Period 2

M new friendship ties 1.4 1.3 1.6 1.5

M stable friendship ties 2.2 2.4 1.9 2.1

M friendship ties dissolved 1.1 1.2 1.5 1.4

Composition change (joined,

left)

3, 3 3, 1 6, 1 5, 0

Jaccard index 0.48 0.49 0.38 0.42


Table 3. SABM Parameter Estimates (P.E.) and Standard Errors (S.E.) for the Basic Models

Parameter School 1 School 2

P.E. (S.E.) P.E. (S.E.)

Food Intake Dynamics

Friend intake (influence)a 0.88 (.41) * 1.07 (.46) *

Individual covariates

Pocket money 0.16 (.14) 0.22 (.10) *

Overweight -0.50 (.29)+ N.S.

Parent intake norm N.S. 0.05 (.05)

Shape effects

Linear shape -0.29 (.08) ** -0.03 (.07)

Quadratic shape -0.16 (.07) * -0.11 (.05) *

Friendship network dynamics

LNED food intake effects

Intake 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 effects

Male 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 effects

Outdegree -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) *

+ p < .10, two-tailed. *p < .05, two-tailed. ** p < .01, two-tailed.

Note. N.S. = non-significant. These effects were not included in the final model because they were found to be non-significant during the forward selection model specification. aSeveral variations for modeling this influence effect are available in RSiena, and because there was no strong theoretical reason to select on over another, three different specifications were score-tested. An effect of friends’ total food intake on actor intake (total similarity), defined as the sum of centered similarity scores between adolescents and their nominated friends, was found to be the best fit and was estimated in the final models. For this effect, larger differences in intake scores between friends are likely to be highly salient and result in intake change.


Table 4. Summary of Select SABM Parameter Estimates (P.E.) and Standard Errors (S.E.) for the Mediation Models

Dependent LNED food variable

Effects of friend food intake on actor

cognitive variable

Effect of actor cognitive variable on

actor food intake

Effect of actor food intake on actor

cognitive variable

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

P.E. (S.E.) P.E. (S.E.) P.E. (S.E.) P.E. (S.E.) P.E. (S.E.) P.E. (S.E.)

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)

+ p < .10, two-tailed. *p < .05, two-tailed. ** p < .01, two-tailed.

Note. All behavior and cognition models controlled for the same effects that were included in the behavior-only models. Only parameter estimates that directly tested effects of the cognitive variables are reported here.


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