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Effects of Media and Social Standing on Smoking Behaviors

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Effects of Media and Social Standing onSmoking Behaviors among Adolescentsin ChinaGrace C. Huang, Janet Okamoto, Thomas W. Valente, Ping Sun,Yonglan Wei, C. Anderson Johnson & Jennifer B. Unger

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To cite this article: Grace C. Huang, Janet Okamoto, Thomas W. Valente, Ping Sun, Yonglan Wei,C. Anderson Johnson & Jennifer B. Unger (2012): Effects of Media and Social Standing on SmokingBehaviors among Adolescents in China, Journal of Children and Media, 6:1, 100-118

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EFFECTS OF MEDIA AND SOCIAL

STANDING ON SMOKING BEHAVIORS

AMONG ADOLESCENTS IN CHINA

Grace C. Huang, Janet Okamoto, Thomas W. Valente, Ping Sun,Yonglan Wei, C. Anderson Johnson and Jennifer B. Unger

Many studies have documented associations between peer influences and smoking among US and

Chinese adolescents. Few studies, however, have shown how these influences occur in relation

to adolescents’ positions in their social networks. Social media channels such as online social

networking and text messaging extend adolescents’ networks and sphere of influence. This study

examines the interplay between social media channels, peer influences, and smoking outcomes.

Data were collected from 6,073 students from 24 high schools in Chengdu, China. Multilevel models

suggest that mobile phone use (AOR ¼ 1.45, p , .0001) and social Internet activity are risk factors

for smoking (AOR ¼ 1.18, p ¼ .002), whereas informational Internet activity is protective

(AOR ¼ 0.87, p ¼ .038). High social status was also positively associated with smoking

(AOR ¼ 1.16, p ¼ .001), whereas the relationship with smoking intentions was moderated by

mobile phone use (AOR ¼ 1.11, p ¼ .013). Findings suggest that media usage and social standing

may have differential effects on smoking and other risky adolescent behaviors.

KEYWORDS adolescents; Internet; mobile phones; smoking intentions; social media; social

network analysis; social status

Introduction and Background

Tobacco use is a major public health concern worldwide, particularly among

adolescents (World Health Organization, 2009). In China, adolescent smoking is a growing

health problem, with initiation rates on the rise (Unger et al., 2001).

Social Influences and Smoking

The importance of peer influences on adolescent smoking behavior is well established

(Alexander, Piazza, Mekos, & Valente, 2001; Ennett et al., 2008; Hoffman, Sussman, Unger, &

Valente, 2006; Kobus, 2003). During adolescence, friendships are often formed and

behaviors adopted based on peer influence and selection processes, which are among the

most significant predictors of adolescent smoking (Hoffman, Monge, Chou, & Valente, 2007).

The existence of homophily, or shared behavior between existing social ties, is supported

by numerous US studies (Hoffman et al., 2006). Similar associations have been found among

Chinese adolescents (Li, Fang, & Stanton, 1996; Unger et al., 2002; Weiss, Spruijt-Metz,

Palmer, Chou, & Johnson, 2006; Zhang, Wang, Zhao, & Vartiainen, 2000).

Contagion effects of smoking have been affirmed through social network analyses,

providing evidence that smoking behaviors can spread through close and distant social

Journal of Children and Media, Vol. 6, No. 1, 2012ISSN 1748-2798 print/1748-2801 online/12/010100-118

q 2012 Taylor & Francis http://dx.doi.org/10.1080/17482798.2011.633411

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ties, and that smoking initiation and cessation patterns typically occur concurrently among

interconnected groups of people (Christakis & Flower, 2008). Longitudinal studies have

found that adolescents select friends based on similar smoking behavior (Mercken, Snijders,

Steglich, Vartiainen, & de Vries, 2010). Furthermore, research has shown associations

between popularity and early smoking initiation (Alexander et al., 2001; Valente, Unger, &

Johnson, 2005), suggesting that popular students contribute disproportionately to social

norms that favor tobacco use, or, alternatively, experience social pressures to stand out by

experimenting with smoking.

Social Influences and Media Consumption

Similar to smoking, media choices are impacted by social networks. New media

technologies greatly impact the way people access information and maintain social ties. In

recent years, Internet and mobile phone use have increased dramatically. In the US, 93% of

teens between ages 12 and 17 go online (Lenhart, Purcell, Smith, & Zickuhr, 2010), and 75%

of teens own a cell phone (Lenhart, Ling, Campbell, & Purcell, 2010). Popular online social

networking channels such as Facebook provide an outlet for self expression and peer

support. Mobile phones have been used by adolescents to create an “anytime-anywhere-

for whatever reason” relationship with other members in their mobile network (Ling, 2004),

satisfying the desire for instantaneous connection.

An increasing number of studies consider social motivations and media use. Quan-

Haase and Wellman (2004) review several interpretations of the Internet’s effect on social

capital, the social resources available through relationships in one’s network (Coleman,

1988). These studies suggest that online communities can supplement in-person and

telephone communication by providing a virtual forum for shared interests and social

support. However, when used for information-seeking purposes, the Internet can reduce

direct contact and weaken ties with family and friends. A study of 800 college students in

the US found associations between Facebook use and increased bridging social capital, the

formation and maintenance of weak ties or loose connections, due to its ability to facilitate

communication cheaply and easily (Ellison, Steinfield, & Lampe, 2007).

Several scholars who have examined the social meanings attached to adolescent

mobile phone use suggest that phones enhance the maintenance of social groups and

the feeling of belongingness. Stald (2008) asserts that the social networks supported by

mobile communication allow adolescents to test values and norms that define their

personal identities. Another study of 909 college students in Taiwan found that social

utility, or using mobile phones to chat, pass time, or relax, was a strong predictor of

mobile phone use compared to other uses and gratifications such as information seeking,

fashion status, mobility, and accessibility (Wei & Lo, 2006). Early adopters of mobile

phones used them primarily to socialize and foster intimacy among existing ties, whereas

late adopters were less socially connected and used them primarily as a marker of

fashion and status to help foster a sense of belongingness to their perceived community

(Wei & Lo, 2006). Others have found that use of a technologically advanced phone can

improve social status among peers (Ozcan & Kocak, 2003). Although these studies

delineate some of the functions of Internet and mobile phone use based on self-report

data, further study is needed to understand the influence of media use on health

behaviors.

ADOLESCENTS, MEDIA, AND SMOKING STATUS 101

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Mobile Phone and Internet Use in China

In China, mobile phone penetration has grown 34-fold over the past 10 years, with up

to 650 million users compared to 340 million landline users (China Ministry of Industry &

Information Technology, 2009). Adolescents are among the primary consumers of mobile

phones and use them for the affordability and convenience of text messaging. Some

mobile phone attributes are symbols of prestige and social status, such as specific phone

number prefixes or certain mobile carriers. About 90% of users purchase prepaid phone

cards, which allow the versatility to frequently change their handsets, another means for

youth to maintain “face” or gain social prestige among peers (Wang, 2005).

In December of 2008, China reached 298 million Internet users (a 22.6% penetration

rate), with youth as the fastest growing online population (Wallis, 2009). Chinese

technology owners and users are younger, more educated, and reside in more urban areas

(Pew Research Center, 2008). Among 18- to 29-year-olds, 48% report owning a computer.

Although ownership rates are similar, young men are more likely to access the Internet

(82%) than young women (73%) (Pew Research Center, 2008). Online social networking

sites in China, such as QZone and Renren, are gaining popularity rapidly, connecting as

many as 388 million Chinese users, predominantly young teens, across urban and rural

cities.

Media Use and Smoking

Media effects are compounded by new communication technologies, which offer

increased interactivity, access to information, and communication. Few studies have

examined relationships between the use of these new media channels and risky behaviors

such as tobacco use. Studies show contradictory evidence on the effects of media use on

social identity formation among adolescents. Media usage may serve as a complementary

behavior to health compromising behaviors (Leena, Tomi, & Arja, 2005), where heavier use

is associated with increased likelihood of smoking due to the conformity to mass media

culture or their elevated social network position. Alternatively, media may also serve as a

displacement for smoking and another way to obtain peer approval and popularity

(Cassidy, 2006). This study addresses these questions, guided by two theoretical

frameworks.

Theoretical Overview

Diffusion of Innovations theory (Rogers, 2003) asserts that individuals vary in the rate

they adopt new ideas, beliefs, or behaviors. Diffusion occurs through communication

channels among people with similar attributes (homophily), or from dissimilar people who

bring new ideas to a homogenous group (heterophily). Among adolescents, Internet and

mobile phone adoption may occur because of external factors such as cost, compatibility,

and practicality, but also because of social factors such as social status, pressures of peer

norms, and the desire to fit in.

With recent technological advances, media channels now serve as an extension of

one’s social network and sphere of influence. Social Contagion Theory asserts that the

transmission of information, attitudes, norms, beliefs, and behaviors occur through

exposure to one’s social network (Valente, 1995). Modern technology is therefore likely to

102 GRACE C. HUANG ET AL.

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increase the frequency and reach of exposure to members and likely to speed the

transmission rates of, in our case, smoking. Peer influences and the need to conform to

normative behaviors are salient during the adolescent years, so being major consumers of

new technologies not only keeps them apprised of the rapidly changing social mores, but

also intensifies contagion effects as a result of increased exposure.

Study Aims and Hypotheses

This study examines the use of social media by Chinese adolescents. The associations

between social position in an adolescent’s network and smoking outcomes is also

investigated in relation to different media use patterns. In this paper, the network

characteristics considered include one’s social status and popularity.

The aims of this paper are twofold (Figure 1). First, we investigate the relationship

between different types of media usage and social network characteristics among

adolescents in China (H1). Specifically, we hypothesize that higher levels of social media use

(mobile phone and social Internet use) will be positively associated with one’s social status

and popularity, whereas nonsocial types of media use (Informational Internet use) will not.

Second, we expect that social status will be positively associated with smoking

outcomes (H2a), and that the strength of association between social status and smoking

outcomes will be moderated by media use behaviors (H2b). Specifically, that increased use

of media types that are social in nature will increase the strength of association between

social status and smoking.

Methods

Study Design

The Claremont Graduate University/University of Southern California Pacific Rim

Transdisciplinary Tobacco and Alcohol Use Research Center (TTAURC) was established to

investigate the influence of personal characteristics, social circumstances, cultural setting,

Social status(indegree)

Smokingintentions

Tobaccouse

Media use(mobile phone, Internet, television)

H1H2b

H2a

FIGURE 1

Conceptual model of research hypotheses

ADOLESCENTS, MEDIA, AND SMOKING STATUS 103

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dispositional phenotypes, and genes on the course of tobacco and alcohol use trajectories

in youth.

The current study examines data from the baseline wave of a TTAURC study of

adolescents in Chengdu, China. Chengdu is a densely populated and prosperous city in

southwest China, with a population of over ten million (Chengdu Science & Technology

Bureau, 2010). It is the capital of Sichuan Province, a progressive city that has experienced

rapid changes in recent decades; moving from a primarily agricultural provincial capital

to an urban industrial city. The city was selected as the site of the intervention trial due to

a strong collaborative relationship with the Chengdu Center for Disease Control and

Prevention (CDC). The study was approved by Institutional Review Boards in the US and

China.

Sampling

China has two primary high school types, academic (regular) and vocational.

Students’ test scores in middle school determine which school type they will attend.

Generally, those attending academic schools have a rigorous schedule for three years of

high school (tenth through twelfth grades) to prepare them for college. Those attending

vocational schools are enrolled in a career track, such as computers/IT or public relations,

and complete an internship during their final year(s) in high school with the goal of

entering the workforce upon graduation. Both school types were included in the study,

with an equal number of schools and students from each. The study included a

convenience sample of twelve academic schools and twelve vocational schools. Academic

schools that were ranked with the highest levels of achievement were not included due to

students’ rigorous class and test schedules and because of their extremely low prevalence

of tobacco use. All vocational schools in the Chengdu urban districts and almost all schools

in the surrounding Shuang Liu County were included. A total of 124 tenth grade classes

(36% of all tenth grade classes—approximately four to six classes in each school) were

invited to take the survey. The overall participation rate among students was 91.2% in

vocational school classes and 96.5% among students in academic school classes.

Data Collection

Assessments were administered via paper-and-pencil surveys during regular school

class sessions between November and December 2007. Active parental consent and

student assent were obtained. Questions were translated and back-translated to ensure

quality and consistency.

Measures

Outcome variables. The outcome variable, past month smoking “During the past 30

days, on how many days did you smoke cigarettes?”, was dichotomized to 0 ¼ “zero days”

and 1 ¼ “one or more days” due to the small percentage of students who indicated

smoking one or more days out of the past month, which created an extremely skewed

distribution (Unger et al., 2002). Smoking intentions were measured using the item “Do you

think you will be smoking cigarettes 5 years from now?” with response options

dichotomized into 0 ¼ “definitely not” and 1 ¼ “maybe no, maybe yes, and definitely yes.”

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Independent variables. Four main independent variables are considered. These

include two social network measures, “popularity” and “social status” and two media use

measures for the degree of “Internet” and “mobile phone” consumption.

The first social network question that was included, “who are your best friends in this

class,” was used to calculate popularity within the classroom. Popularity was measured

using students’ indegree centrality scores, calculated by summing the total number of best

friend nominations received and dividing the total by the number of participating students

in each class to adjust for differences in class size across schools. The second question “who

is well-liked in this class” measured students’ social status in the classroom. Social status

was calculated and normalized in the same way as popularity. The social status variable was

further categorized into five groups (Ostberg, 2003) to generate a more normal distribution

and to facilitate the moderation analyses described below.

For Internet use, students were asked to check any of twelve different types of

Internet use activities following the question “During the last 7 days, did you conduct the

following computer- and Internet-based activities?” These included: read news online,

emailed, read/participated in online forum discussion, did homework online, played online

games, played offline games, watched movies or video clips online, chatted with friends in

real daily life, chatted with friends met online, chatted with unknowns online, shopped

online, and visited porn site.

Mobile phone use habits were assessed using one item with five response categories:

1 ¼ “I do not have a mobile phone and do not want to have one,” 2 ¼ “I do not have a

mobile phone but wish to have one,” 3 ¼ “I have a mobile phone but rarely use it,” 4 ¼ “I

have a mobile phone and use it often,” and 5 ¼ “I have a mobile phone and cannot be

without it.”

Imputation of network exposure variables. Additional independent variables for

students’ exposure to their friends’ smoking and media use were calculated based on “past

month smoking” and “media use” items of the friend they nominated first (or best friend).

“Best friend smoking,” was coded as 0 ¼ “smoked zero days” and 1 ¼ “smoked one or more

days.” “Best friend media use” was coded the same way as described earlier for mobile

phone and Internet activity.

Covariates. Covariates that were entered into the regression models included age,

gender, urbanicity (family residence in the city, suburb, town, or village), allowance,

academic performance, highest parent educational level, and Internet addiction (Davis,

Flett, & Besser, 2002).

Data Analysis

Students’ network indegree centrality scores for popularity and social status were

calculated using UCINet 6 for Windows (Borgatti, Everett, & Freeman, 2002). Exposure to

friends’ media use habits, smoking behavior, and other statistical analyses were conducted

in STATA 11.0.

Descriptive and bivariate analyses were first conducted to explore trends and

correlations among variables. Exploratory factor analysis using principal component factors

with Varimax rotation of the 12 Internet use items was conducted. Four items (offline

games, movies, email, chatting with unknowns online) were excluded from the factor

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analysis because they were either activities that generally do not require Internet

connectivity, or because they could not be distinctly categorized as either social,

informational or risky. For instance, email can be used for both social and informational

purposes and online chatting with unknowns may be viewed as both social and risky.

Factor analysis with the remaining items revealed three distinct types of Internet activity,

with factor loadings of .50 or higher for all items in each factor. “Social activity” included

online gaming (0.78), chatting with real friends (0.62), and chatting with online friends

(0.76), which are activities that constitute real time social interaction with others.

“Informational activity” included reading the news (0.65), reading and/or participating in

online discussion forums (0.50), and doing homework (0.75), which are mostly activities that

involve information seeking and passive engagement. Lastly, “Risky activity” included

shopping (0.77) and pornography (0.81), activities that have been associated with other

offline risk-seeking behaviors (Chang & Chen, 2008; Chang, Cheung, & Lai, 2005; Dowell,

Burgess, & Cavanaugh, 2009). These activities are also deemed as inappropriate for Chinese

youth and are likely conducted in seclusion for personal gratification. Scales for the three

types of Internet activity were generated by taking the average of the items that loaded on

each factor. The scales for the three types of activity were used for all subsequent analyses.

Intraclass correlation coefficients (ICC) were calculated to determine the extent to which

school-level and class-level differences would significantly bias our findings. The ICC was

.0917 at the school level and .00191 at the class level, indicating that both between-school,

and especially between-class differences were very small and not likely to bias our findings.

One-way ANOVA tests were conducted to explore potential differences in smoking

behavior across the different types of mobile phone use, followed by the Bonferroni

multiple-comparison correction. Multilevel logistic regression analyses were then used to

confirm these findings and further determine the associations between social network

measures and other independent variables on smoking outcomes. For the regression

analyses, we included only students who owned mobile phones by recoding the mobile

phone use variable into a 3-point scale (1 ¼ “have but rarely use,” 2 ¼ “have and use

often,” 3 ¼ “have and cannot be without it”). Due to the small ICC at the class level, only

adjusting for school-level random effects was deemed adequate to correct for potential

biases from the clustering of characteristics within schools. Interaction effects between

social status and the different media use behaviors (mobile phone and Internet use) were

tested. All independent variables as well as covariates were centered to school means to

account for differences (demographic composition, concentration areas, or social norms)

across schools.

Results

Demographic Characteristics

Table 1 lists the demographic and smoking characteristics of the participants.

Respondents were on average 15 to 16 years old, with approximately the same number of

male and female students. While age was evenly represented across the school types

and across gender, there were some noticeable differences in other indicators. Students

attending the academic schools lived predominantly in the city, compared to students

attending vocational schools (82% vs. 36%). Students attending academic schools generally

had higher allowances (18% vs. 9% 51 Yuan/week), and parents with higher educational

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levels (43% vs. 7% who have parents with college degree or above). Given the educational

criteria that determine the school type students are enrolled, it is not surprising that

academic school students on average show higher academic performance than their

vocational school counterparts (18% vs. 7% who reported mostly scoring As).

TABLE 1

Demographic and smoking characteristics of study sample, by school type and gender

Academic schools Vocational schoolsAll Females Males Total Females Males Total

N 6,073 1,482 1,449 2,931 1,542 1,600 3,142Mean age (range) 15.8

(13–19)15.6

(14–18)15.8

(13–19)15.7

(13–19)15.9

(13–19)16.0

(13–19)16

(13–19)t ¼ 214.8, p , .0001

GenderFemale 49.8 – – 50.6 – – 49.0

*x2 ¼ 1.34, p ¼ .247

Urbanicity (family residence)City 58.8 82.0 82.5 82.3 33.1 38.9 36.0Suburb 8.6 10.6 9.4 10.0 6.8 7.6 7.2Country town 13.2 4.6 5.0 4.8 21.9 20.9 21.4Village 19.0 2.4 2.9 2.6 37.6 32.2 34.9Other 0.4 0.4 0.2 0.3 0.6 0.4 0.5

x2 ¼ 16,000, p , .0001Allowance0–10 Yuan 22.6 15.0 21.3 18.1 25.3 28.6 27.011–30 Yuan 28.2 25.0 25.8 25.4 32.9 28.9 30.931–50 Yuan 21.3 24.9 21.6 23.2 20.1 18.9 19.551–90 Yuan 14.2 16.1 13.8 15.0 13.4 13.5 13.5. 90 Yuan 13.7 19.0 17.5 18.2 8.4 10.1 9.2

x2 ¼ 187, p , .0001Parent highest educationElementary school or less 8.4 4.3 2.7 3.5 13.6 12.8 13.2Junior high graduate 36.9 23.5 20.7 22.1 52.5 49.7 51.1Senior high or vocationalschool graduate

30.5 31.4 32.5 31.9 28.5 29.7 29.1

College graduate 10.2 16.7 16.2 16.5 3.3 5.0 4.2University graduateor higher

14.0 24.0 27.9 26.0 2.1 2.8 2.4

x2 ¼ 13,000, p , .0001Academic performanceSuperior (mostly As) 12.4 19.8 16.9 18.3 7.0 6.3 6.6Very good (mostly Bs) 28.2 30.3 31.7 31.1 27.3 23.8 25.5Average (mostly Cs) 32.2 29.3 28.4 28.9 36.7 34.3 35.5Below average (mostly Ds) 19.8 15.2 16.1 15.7 23.6 24.0 23.8Failing (mostly Fs) 7.3 5.3 6.9 6.1 5.4 11.6 8.6

x2 ¼ 249, p , .0001Past 30-day Smoking 23.0 4.7 18.6 11.6 16.1 51.7 34.2

x2 ¼ 405, p , .0001Intention to smokein next 5 years

43.2 22.8 46.0 34.3 32.9 70.4 51.8

x2 ¼ 188, p , .0001Best friend smoking 22.0 4.9 18.4 11.2 16.6 50.0 32.9

x2 ¼ 322, p , .0001

*T-test and x2 statistics indicate differences between academic and vocational school students.

ADOLESCENTS, MEDIA, AND SMOKING STATUS 107

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Smoking Characteristics

Gender differences are especially pronounced in terms of smoking behaviors.

Academic females reported the lowest rates in actual 30-day smoking (5%) followed by

vocational females (16%), academic males (18.69%), and vocational males who reported the

highest rates of smoking (52%). Similar trends were found for smoking intentions.

Media Use Characteristics

Table 2 lists the different media use types analyzed in this study. Consistent with

previous studies (Li & Kirkup, 2007), our data show that males conducted social Internet

activities at higher rates compared to females. Informational Internet activities were more

popular among academic compared to vocational school students (63% vs. 39% using at

least one type of informational Internet activity). Risky Internet usage consisted of only a

very small percentage of all respondents. Specific uses of risky Internet behavior varied by

gender, with a higher likelihood of males engaging in pornography and females in online

shopping.

Approximately two-thirds of respondents reported owning a mobile phone,

with more ownership among academic compared to vocational students. Based on the

TABLE 2

Media usage behaviors of study sample, by school type and gender

Academic schools Vocational schoolsAll Females Males Total Females Males Total

N 5,693 1,424 1,387 2,811 1,432 1,450 2,882Social Internet activityNone 26.5 28.7 24.7 26.7 30.7 22.1 26.31 activity 26.4 33.5 23.9 28.8 25.8 22.3 24.12 activities 25.6 25.2 25.0 25.1 25.5 26.8 26.13 activities 21.5 12.6 26.5 19.4 18.0 28.8 23.5

x2 ¼ 23.4, p , .0001Informational Internet activityNone 49.3 34.6 39.7 37.1 59.6 62.7 61.21 activity 31.8 38.3 34.3 36.3 28.1 26.6 27.32 activities 13.8 20.0 17.9 19.0 9.6 7.7 8.73 activities 5.2 7.2 8.2 7.7 2.7 3.0 2.8

x2 ¼ 378, p , .0001Risky Internet activityNone 91.4 92.3 88.8 90.5 94.5 90.1 92.3Shopping 5.9 7.4 5.4 6.4 4.8 5.9 5.3Pornography 4.8 2.4 8.9 5.6 1.8 6.1 4.0Shopping or pornography 6.5 5.7 8.2 6.9 4.5 7.7 6.1Shopping and pornography 2.1 2.0 3.0 2.5 1.1 2.1 1.6

x2 ¼ 7.97, p ¼ .019Mobile phone usageNot have and do not want one 11.0 7.5 14.3 10.9 9.5 13.0 11.2Not have but want one 21.2 15.4 18.5 16.9 25.8 25.1 25.4Have but rarely use 23.3 22.6 26.5 24.5 20.6 23.6 22.1Have and use often 29.6 36.8 28.3 32.6 27.6 25.8 26.7Have and cannot be without it 14.9 17.8 12.4 15.1 16.6 12.6 14.6

x2 ¼ 69.1, p , .0001

*x2 statistics indicate differences between academic and vocational school students.

108 GRACE C. HUANG ET AL.

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descriptive findings, academic (18%) and vocational (17%) females tend to report more

attachment to their phones (“cannot be without”) compared to academic (12%) and

vocational (13%) males. A larger percentage of vocational students report a greater desire

to have a mobile phone (25%) compared to their academic counterparts (17%).

H1: Social media use and social network positions are positively associated.

As seen in Table 3, Pearson correlations of media use and indegree centrality

measures (popularity and social status), revealed that both mobile phone use and social

Internet activity were positively correlated with one’s popularity and social status. However,

informational and risky Internet activity were not significantly correlated with either of the

centrality measures.

Comparing Social Status and Smoking by Varying Levels of MobilePhone Use

The sociogram in Figure 2 is a graphical example of the social status network

structure in a vocational high school classroom. Although this may not be representative of

the entire study sample, it provides a visualization of the relationships formed between

students in a classroom. Arrows point to students who were nominated as “well-liked” in

the classroom. The figure shows that students who occupy central positions in the class as

most well-liked (high social status) tend to report greater use of their mobile phones (Nodes

46, 40, and 41) and having smoked in the past 30 days.

One-way ANOVA analyses (Table 4) confirmed the hypothesized trends of mobile

phone usage, showing that the likelihood for smoking and social status differed

significantly across groups. Based on Bonferroni post hoc analyses, students who have

a mobile phone and could not be without it were significantly more likely to smoke

compared to all other groups. Students who used their phone often were significantly more

likely to smoke than those who do not want one, and those who rarely use their phone.

Similarly, social status was equally associated with students who used their mobile phones

often or could not be without (p ¼ .873), and significantly higher for these two groups

compared to the rest of the participants.

Differential Effects of Media Use and Social Status on SmokingOutcomes

Multilevel logistic regression analyses confirmed the above trends and showed

the differential effects of media use and social status on smoking and smoking intentions

TABLE 3

Bivariate correlation between media use and centrality measures

Mobilephoneusage

SocialInternetactivity

InformationalInternetactivity

RiskyInternetactivity

Popularity (best friend nominations) .070*** .039* .033 2 .025Social status (well-liked nominations) .106*** .061*** .026 .001

*p , .01; ***p , .0001.

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(a ¼ .05) while controlling for school-level random effects and subject-level covariates such

as age, gender, socioeconomic factors, academic performance, and other related variables.

Models for past 30-day smoking and smoking intentions are listed in Table 5.

As expected, higher academic achievement and being female were protective

against smoking and smoking intentions, whereas having more allowance was associated

with greater odds of smoking outcomes. The strongest predictor of smoking was if their

nominated best friend was also a smoker, AOR ¼ 2.99, p , .0001. Best friend smoking was

similarly the strongest predictor for intentions to smoke, AOR ¼ 2.17, p , .0001.

H2a: Social status and smoking outcomes are positively associated.

While popularity (number of friend nominations received) was not a significant

predictor of smoking intentions, higher number of nominations as “best friend” appeared

TABLE 4

ANOVA of mobile phone use and smoking

30-day smoking Social status

No mobile phone and do not want one 0.17 3.95No mobile phone but want one 0.21 3.96Have a mobile phone but rarely use 0.20 3.91Have a mobile phone and use often 0.24 5.28Have a mobile phone and cannot be without it 0.32 5.77

F(4, 5522) ¼ 15.85 F(4, 5661) ¼ 17.88p , .0001 p , .0001

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FIGURE 2

Social status network of one vocational school classroom. Square ¼ male; circle-in-

square ¼ female; dark grey ¼ smoker; light grey ¼ nonsmoker; size ¼ degree of mobile

phone use, larger size indicates greater use (see categories in Table 4)

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to be protective against smoking, AOR ¼ .95, p ¼ .016. The social status measure (number

of well-liked nominations), however, had a significant positive association with both

smoking and smoking intentions, AOR ¼ 1.17, p ¼ .001, and AOR ¼ 1.10, p ¼ .007,

respectively, suggesting that higher social status may be a risk factor for smoking

outcomes.

In terms of media use effects, social Internet activity was found to be a significant risk

factor for smoking, AOR ¼ 1.63, p ¼ .002, and smoking intentions, AOR ¼ 1.38, p ¼ .014.

Informational Internet activity was found to have an overall protective effect for smoking,

AOR ¼ .66, p ¼ .038. Frequency of mobile phone use was significantly associated with

smoking and smoking intentions, AOR ¼ 1.45, p , .0001, and AOR ¼ 1.30, p , .0001,

respectively. Interestingly, best friend mobile phone use was also associated with smoking

and smoking intentions.

H2b: Social status and smoking outcomes are moderated by social media use.

As demonstrated in Table 5, social status was a significant predictor for both smoking

intentions and past 30-day smoking. Significant interaction effects were found between

mobile phone use and social status for smoking intentions, AOR ¼ 1.1, p ¼ .013, but not for

past 30-day smoking, AOR ¼ 1.07, p ¼ .198. Results suggest that among students who own

mobile phones, the association between students’ social status and smoking intentions are

stronger for those who use their mobile phones more often, compared to students who use

their mobile phones less often.

TABLE 5

Effects (adjusted odds ratios) of social status and media usage on smoking outcomes

5-year smokingintentionsn 5 2,959

Past 30-daysmokingn 5 2,931

AOR AOR AOR AOR

Age 1.03 1.03 1.02 1.02Female 0.28*** 0.27*** 0.21*** 0.21***Academic achievement 0.86** 0.86** 0.82*** 0.82***Urbanicity 1.10 1.10 1.11 1.11Allowance 1.10*** 1.10*** 1.12*** 1.12***Parent education 1.04 1.04 1.03 1.03Popularity (best friend nominations) 0.99 0.99 0.95† 0.95†

Social Status (well-liked nominations) 1.10* 1.00 1.17** 1.09Mobile phone use 1.30*** 1.07 1.45*** 1.26Social Internet activity 1.38† 1.41* 1.63* 1.65*Informational Internet activity 0.78 0.77 0.66† 0.66†

Risky Internet activity 1.56 1.57 1.19 1.20Internet addiction 1.12* 1.12* 1.09 1.09Best friend smoking 2.17*** 2.15*** 2.99*** 2.98***Best friend media useMobile phone use 1.18*** 1.18*** 1.12* 1.13*Internet social activity 1.16 1.16 1.08 1.08Internet informational activity 0.78 0.77 0.72 0.72Internet risky activity 1.20 1.20 1.12 1.12Social status £ Mobile phone use 1.11* 1.07

Logistic models control for intraclass correlations at the school level. †p # .05, *p # .01, **p # .001,

***p # .0001.

ADOLESCENTS, MEDIA, AND SMOKING STATUS 111

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Discussion

Findings from this cross-sectional study of Chinese adolescents reveal interesting

patterns of social media usage in association with their peer-defined social status among

class members, and with their intentions to smoke and actual smoking behavior. This work

supplements findings from previous smoking prevalence studies by examining the role of

media use as a factor associated with social status and smoking outcomes.

Media Use and Social Status

The first aim of the study was to investigate the relationship between different types

of media usage and social network characteristics among adolescents in China. In

accordance with Hypothesis 1, mobile phone usage and social Internet activity were found

to be associated with higher social status and popularity. This suggests that media use may

serve as a function of one’s network position if the media activity involves a degree of

socialization with others, in this case, conversing or texting with a friend through their

mobile phone, or chatting and game playing with someone over the Internet. Informational

Internet activity and risky Internet activity tend to be less social and were thus less likely to

be associated with network status. The intention is to absorb information for personal use,

such as doing homework, reading the news, or reading the latest posts on a discussion

forum. Online gaming and social status were also positively correlated; however, extremely

high levels of online gaming among vocational school males were negatively associated

with their social status. Females, on the other hand, chat with their friends more frequently,

and higher levels of chatting were associated with higher social status as well as a higher

tendency to smoke.

Social Status and Smoking at Varying Levels of Mobile PhoneUse/Nonuse

The second aim was to test whether the strength of association between social status

and smoking outcomes were stronger for heavier use of certain media types. The multilevel

logistic regression models confirm previous analyses that higher social status, or being well-

liked by classmates, is associated with smoking outcomes (Alexander et al., 2001). However,

being someone’s best friend is protective against smoking. This suggests that friendship

bonds are not driven by delinquent behaviors such as smoking and that nonsmokers may

have more friends or closer friendships than smokers. The link between higher social status

and smoking outcomes, however, might be explained by the image one has with peers, and

less about true friendships. Smoking, much like the ownership of a mobile phone or

presence in a chat room, may serve to boost one’s social identity and social status among

peers.

Similar to the correlations found with social status, mobile phone use and social

Internet activity were also found to be a risk factor for smoking outcomes, and

informational use of the Internet a protective factor. This confirms the findings by Kraut,

Kiesler, Boneva, and Shklovski (2006) that there are discrete types of Internet use, which

influence users in very distinctive ways. Whereas social Internet activity may serve as a

complementary behavior to delinquent health behaviors, informational Internet use may

act as a displacement. Students who spend more time reading the news or researching

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information for school work may be less concerned with their social image and less likely to

have the leisure time to engage in behaviors that can lead to negative health outcomes.

Finally, only mobile phone use was found to moderate the relationship between

social status and smoking outcomes. Our findings suggest that students who are most well-

liked have a much higher likelihood of smoking, especially if they use their mobile phones

frequently. Perhaps this may apply to certain status-conscious students who smoke for

social status, compared to others who are less status conscious and therefore only smoke

for physiological reasons. For the status-conscious smokers, mobile phone use may serve

as a symbolic means for perpetuating their status as a trendsetter, and similarly perpetuate

behaviors that are deemed risky or extreme, such as smoking, by intensifying their need to

retain a position of high status and thus increasing pressure to smoke.

Limitations

The study sample was taken from one cross-sectional wave of a multiwave

intervention trial, and therefore we are unable to establish directional or causal influences.

Although associations between media use, social status and smoking can be established,

the etiology behind these associations is limited. For instance, based on our quantitative

analysis, it is difficult to distinguish whether students were nominated as “well-liked” due to

their smoking behavior or media use patterns, or whether students adopt smoking and

media use as an expression of their social status predetermined by reasons other than

ones investigated in this study. Furthermore, the generalizability of findings is limited to

academic and vocational schools in moderate to urban cities in China. Social network

analysis techniques are used to collect objective data on students’ social status, but they are

only reflective of the context within each student’s classroom. Ties with students outside of

the classroom or with members in the community were not accounted for. Lastly, the

measures for risky Internet activity were limited by the items that were available in the

survey instrument. Although these two types of Internet activity loaded highly onto one

factor, it is unclear the degree to which they represent online risk behaviors in general.

Future studies may wish to include additional items for a more comprehensive measure of

risky Internet behavior.

Implications

Social media may symbolize one’s modernity and high social status. Despite the

limitations just summarized, this study contributes to the understanding of adolescent

media use, and its relation with social status among peers. Mobile phone and social Internet

use have similar implications for the way they are perceived by peers. The study supports

and builds on the idea that use of social media channels and mobile technology may

symbolize modernity among peers, and being on the cutting edge. These marks of social

status are then likely to coincide with smoking behaviors, which may also be seen as a sign

of maturity and progressiveness. Future studies might consider investigating the rationale

for selecting peers as “well-liked” in a classroom, gender differences in the use of various

social media types, as well as the degree to which social media use contributes to an

adolescent’s social identity. Furthermore, given the significant demographic differences

between academic and vocational school students, further exploration of our hypothesized

associations across the two school types may be warranted.

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Cell phones and social Web-activity may accelerate smoking uptake. The direction of

causality cannot be established in this cross-sectional study design, but one might

conjecture that social Internet activity and mobile phone use increases socialization

through remote settings and trickles into greater socialization in offline, face-to-face

settings. Given the bonding experience formed during game play or in chat settings, these

people are likely to relate better with other peers, thus placing themselves in a central

position within their social network. In an environment where smoking is viewed favorably,

one who occupies a central position may seek to maintain that status by adopting novel

behaviors that perpetuate their leadership identity (Valente et al., 2005). The increased

levels of friendship exposure that these students have with each other are then likely to

accelerate the transmission of information, attitudes, and perpetuate the normative beliefs

about smoking through conversations over the phone and through Internet chat sites that

friends share in common. In future studies, one might compare the relative social status

levels by smoking status to determine the degree to which nonsmokers are selected as

opinion leaders compared to those who do smoke.

Media Use and Increased Access to Information. Another explanation for the

associations between smoking, Internet use, and mobile phone use may be the increase in

access to information that may in turn increase one’s physical proximity to cigarettes. Virtual

smoking networks may exist, whereby people with mobile texting or mobile Internet access

can arrange a time and a place to smoke or to acquire cigarettes.

Media may be an effective channel for health education. Given the pervasiveness of

certain media types among adolescents, public health research should continue to explore

ways in which these socialization patterns can be harnessed for modern, interactive, and

tailored interventions for health promotion. China is a ripe market for Internet and mobile-

phone based interventions given the popularity of mobile text messaging and virtual

communities. Furthermore, health messaging may be an effective way to reach high risk

adolescent groups who are the heavy users of these social media channels. Perhaps healthy

lifestyle norms can be rapidly diffused through mobile or online networks in the same

manner that unhealthy norms are spread. Such studies are now increasingly popular, and

have been conducted in the areas of smoking cessation, weight management, physical

activity promotion, and healthy sexual practices (Cole-Lewis & Kershaw, 2010; Fjeldsoe,

Marshall, & Miller, 2009). Mobile texting and the Internet provides ways to send messages,

collect data in real time, and even reach high risk individuals who are not as accessible

through traditional community- or school-based settings (Noar & Kennedy, 2009).

Further analyses should be conducted towards a more in-depth understanding of

societal factors that serve as proxies to established determinants of smoking intentions and

smoking behaviors. The link between problematic mobile phone use (Walsh & White, 2007)

and problematic health behaviors should be explored to determine how they may be

antecedents associated by common cognitive processes, psychological attributes such as

sensation seeking, and self-esteem (Leung, 2008). Perhaps certain media use types are more

conducive as complementary or substitute behaviors. Communication patterns within

online and mobile social networks might reveal the cognitive and emotional processes by

which information is diffused and social norms are formed. One could determine if similar

patterns of association found in this study exist with other deviant adolescent behaviors

such as binge drinking and use of other illegal substances. Longitudinal analyses are

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warranted for determining how social dynamics change over time and whether changes in

smoking status can be attributed to changes in one’s social status, and whether mediation

between these relationships exist. Future analyses may also examine potential Interaction

effects between social status or media use and other psychosocial (Tyas & Pederson, 1998;

Weiss, Palmer, Chou, Mouttapa, & Johnson, 2008), cognitive (Guo et al., 2010), or genetic

(Munafo & Johnstone, 2008; Unger et al., 2011) determinants of adolescent smoking

behaviors, which may offer more robust explanations of why these centralized people are

likely to smoke.

In sum, we shed some light on the interplay between various media channels, social

status, and smoking outcomes. Due to the increasing pervasiveness and accessibility of

these media channels, especially among youth, it is important for public health researchers

to understand the effects they have on one’s day-to-day functioning, socialization patterns,

peer influences, and ultimately how these changes can affect one’s decision making about

healthy choices.

ACKNOWLEDGEMENT

This study was supported by Transdisciplinary Tobacco Use Research Center (grant

number 7P50CA084735), funded by the National Cancer Institute, National Institute on

Drug Abuse, and National Institute on Alcohol Abuse and Alcoholism.

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Grace C. Huang is a PhD student in Health Behavior Research at University of Southern

California’s Keck School of Medicine. Her research interests include media effects and

social network influences on health behavior. She formerly held a research position at

Hollywood, Health & Society, a program of the USC Annenberg Norman Lear Center,

where she studied the health impact of entertainment television. Correspondence to:

Grace C. Huang, Institute for Health Promotion and Disease Prevention Research,

Keck School of Medicine, University of Southern California, 2001 N Soto Street,

3rd floor, MC 9239, Los Angeles, CA 90033-9045, USA. Tel.: 858-336-9411; E-mail:

grace.huang@usc.edu

Janet Okamoto received her PhD from USC and is a Cancer Research Training Award

(CRTA) postdoctoral fellow at the National Cancer Institute. Her research interests

include social and cultural determinants of health behaviors among adolescents. In

particular, the use of social network measures to better understand peer relationships

and their influence on health attitudes, norms and beliefs, decisions, and behaviors.

She also investigates how the use of these measures increases the effectiveness of

prevention interventions.

Thomas W. Valente is a Professor in Preventive Medicine at the University of Southern

California. He is author of Social Networks andHealth:Models, Methods, and Applications

(2010, Oxford University Press), two other books, and over 100 articles/chapters on

social networks, behavior change, and program evaluation. Valente uses social

network analysis, health communication, andmathematical models to implement and

evaluate health promotion programs designed to prevent tobacco and substance

abuse, unintended fertility, and STD/HIV infections. Correspondence to: Thomas W.

Valente, Institute for Health Promotion and Disease Prevention Research, Keck School

of Medicine, University of Southern California, 2001 N Soto Street, Room 302W, MC

9239, Los Angeles, CA 90033-9045, USA. Tel.: 323-442-8238; E-mail: tvalente@usc.edu

Ping Sun is an Assistant Professor of Research at the Institute for Health Promotion and

Disease Prevention at the University of Southern California. His recent research

interests are exploring the etiology of substance use and Internet addiction.

Yonglan Wei is an Assistant Professor of the Chengdu Center for Disease Control and

Prevention. Her research area is in non-communicable disease control and

prevention.

C. Anderson Johnson is a Professor and the Dean of the School of Community and Global

Health at Claremont Graduate University. His research interests include the

transnational prevention of tobacco, alcohol, and drug abuse, HIV-AIDS, and

obesity, social and environmental influences on health-related behavior and health

outcomes, and community and mass media approaches to prevention of chronic

diseases and promotion of healthy lifestyles.

Jennifer B. Unger is a Professor of Preventive Medicine at the University of Southern

California Keck School of Medicine. Her research interests include the psychosocial

and cultural risk and protective factors for adolescent health behaviors. She was a

coinvestigator on the China Seven Cities Study.

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