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Article |

Exposure to Smoking Depictions in Movies:  Its Association With Established Adolescent Smoking FREE

James D. Sargent, MD; Mike Stoolmiller, PhD; Keilah A. Worth, PhD; Sonya Dal Cin, PhD; Thomas A. Wills, PhD; Frederick X. Gibbons, PhD; Meg Gerrard, PhD; Susanne Tanski, MD
[+] Author Affiliations

Author Affiliations: Department of Pediatrics (Drs Sargent and Tanski) and Norris Cotton Cancer Center (Drs Sargent, Worth, Dal Cin, and Tanski), Dartmouth Medical School, Lebanon, New Hampshire; University of Oregon, Eugene (Dr Stoolmiller); Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York (Dr Wills); and Iowa State University, Ames (Drs Gibbons and Gerrard).


Arch Pediatr Adolesc Med. 2007;161(9):849-856. doi:10.1001/archpedi.161.9.849.
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Objective  To assess the association between exposure to movie smoking and established adolescent smoking.

Design  Longitudinal survey of a representative US adolescent sample.

Setting  Adolescents were surveyed by telephone in their homes.

Participants  Sixty-five hundred twenty-two US adolescents aged 10 to 14 years at baseline, resurveyed at 8 months (8M) (n = 5503), 16 months (16M) (n = 5019), and 24 months (24M) (n = 4575).

Main Exposure  Exposure to smoking in 532 box-office hits released in the 5 years prior to the baseline survey.

Outcome Measure  Established smoking (having smoked more than 100 cigarettes during lifetime).

Results  Of 108 incident established smokers with data at the 24M survey, 85% were current (30-day smokers) and 83% endorsed at least 1 addiction symptom. Established smoking incidence was 7.4, 15.8, and 19.7 per 1000 person-years of observation for the baseline-to-8M, 8M-to-16M, and 16M-to-24M observation periods, respectively. In a multivariate survival model, risk of established smoking was predicted by baseline exposure to smoking in movies with an adjusted overall hazard ratio of 2.04 (95% confidence interval, 1.01-4.12) for teens in the 95th percentile of movie-smoking exposure compared with the 5th percentile. This effect was independent of age; parent, sibling, or friend smoking; and sensation seeking. Teens low on sensation seeking were more responsive to the movie-smoking effect (hazard ratio, 12.7; 95% confidence interval, 2.0-80.6) compared with teens who were high on sensation seeking (hazard ratio, 1.01; 95% confidence interval, 0.4-2.6).

Conclusion  In this national US adolescent sample, exposure to smoking in movies predicted risk of becoming an established smoker, an outcome linked with adult dependent smoking and its associated morbidity and mortality.

Figures in this Article

Recent studies have implicated exposure to smoking in movies as a risk factor for initiation of cigarette smoking.16 Epidemiologic research indicates that the association of movie exposure with adolescent smoking shows a dose-response relation, that the movie exposure effect is independent of other risk factors for smoking, and that movie exposure precedes the initiation of smoking behavior.2,3,7 Smoking initiation during early adolescence is a sentinel event, indicative of risk for continued smoking and difficulty in subsequent cessation.810 However, not all adolescents who try smoking go on to become dependent smokers; half of high school seniors have tried smoking at some time, but only 7% are current daily smokers of half a pack or more.11 Longitudinal studies show that many adolescents remain at low levels of experimentation while others show steady escalation in frequency and intensity of use12,13; in one study of 164 smoking initiators (mean age, 12.6 years), 34% had progressed to high-frequency smoking while 38% remained sporadic smokers and 29% were abstinent at follow-up.14 Little is known about the factors that discriminate adolescents who progress to dependent smoking from those who do not.15,16

Movies are a mass medium, designed to reach a large audience, and they deliver billions of impressions of smoking to US adolescents each year.17 We hypothesized that movie-smoking exposure is a risk factor for entering later stages of smoking as well as for initiation. There are several grounds for this hypothesis. Among adolescents who are beginning to smoke, exposure to movie-smoking cues increases their positive expectancies about smoking.18,19 Exposure to smoking in movies is also associated with involvement in peer groups of adolescent smokers, who provide modeling and reinforcement for the behavior as well as greater access to cigarettes.20 Early experimental smoking tends to be discontinuous, and an increased frequency of smoking can put adolescents at risk for loss of autonomy over intake.14 The present research was designed to investigate the effect of exposure to movie smoking on transition to established smoking, an outcome closely aligned with nicotine dependence and one that predicts dependent adult smoking.

A detailed description of the recruitment methods for study participants was published previously.5 Briefly, between June and October 2003, we conducted a random-digit-dial telephone survey of 6522 US adolescents aged 10 to 14 years. We obtained parental consent and adolescent assent prior to interviewing each respondent and obtained a certificate of confidentiality protecting information on risk behaviors. All aspects of the survey were approved by the institutional review boards at Dartmouth Medical School and the survey research firm (Westat; Rockville, Maryland). The completion rate for the survey was 66%. To confirm that the sample was representative, we compared the distributions of age, sex, household income, and census region in our unweighted sample with those of the 2000 US Census and showed that they were almost identical.5

FOLLOW-UP SURVEYS

Three follow-up surveys were conducted at 8-month intervals. The flow of subjects for the hazard analysis is shown in Figure 1. Subjects with complete data at the baseline survey (6295 of 6522, or 97%) were included in the hazard analysis until they either reported lifetime smoking of more than 100 cigarettes or missed their first follow-up survey. At each survey point, the hazard analysis risk-set “n” was therefore always smaller than the participation “n,” which included established smokers, subjects who missed a prior assessment and were recaptured, and the 227 (3%) subjects dropped because of missing baseline data. Because missing baseline data was minimal, we decided not to use methods to compensate for baseline missingness (ie, multiple imputation). The adolescents lost to follow-up from baseline to the 24-month survey watched more movies per week, were more likely to view movies at a theater, were more likely to view movies with their parents, were higher on movie-smoking exposure, were more likely to have friends who smoked, were more likely to be of nonwhite ethnicity (black, Hispanic, or other), were less likely to have excellent school performance, and were more likely to have parents who smoked and parents with lower educational attainment. Baseline smoking status (never smoker vs any level of lifetime smoking) did not predict the overall hazard of attrition or attrition at any individual follow-up.

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Figure 1.

Sample selection and attrition. 8M indicates 8-month survey; 16M, 16-month survey; and 24M, 24-month survey.

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EXPOSURE MEASUREMENT

We estimated adolescents' exposure to movie smoking using previously validated methods.3,6,21 We selected the top 100 US box-office hits per year for each of the 5 years preceding the baseline survey (1998-2002, n = 500) and 34 movies that earned at least $15 million in gross US box-office revenues during the first 4 months of 2003. Older movies were included because adolescents often watch these movies on videos or DVDs. The computer-assisted telephone interview survey was programmed to randomly select 50 movie titles from the larger pool of 532 movies for each adolescent interview; 2 of the movies were asked of all the adolescents and are not included in the analysis. Movie selection was stratified by the Motion Picture Association of America rating so that the distribution of movies in each list reflected the distribution of the full sample of movies (19% rated G or PG; 41%, PG-13; 40%, R). Respondents were asked (no/yes) whether they had ever seen each movie title on their unique list. We have previously demonstrated that adolescents reliably remember movies they have seen 1 to 2 years prior to a survey.6

Trained coders counted the number of smoking occurrences in each of the 532 movies using previously validated methods.22 A smoking occurrence was counted whenever a major or minor character handled or used tobacco in a scene or when tobacco was being used by an “extra” in the background. Occurrences were counted irrespective of the scene's duration or how many times the tobacco product appeared during the scene. For example, in a 3-minute bar scene where 2 major characters used tobacco (one for 1.5 minutes, the other for 5 seconds) and another character was seen smoking across the room, we would have counted 3 occurrences. Most tobacco use involved cigarettes or cigars with fewer than 1% of occurrences involving spit tobacco.22 To create a measure of exposure to movie smoking, we summed the number of smoking occurrences in films each adolescent had seen from his unique list of 50 movies. We then divided this number by the number of smoking occurrences that the adolescent would have seen had he seen all 50 movies in his unique list. This proportion was multiplied by the number of smoking occurrences in the entire parent sample of 532 movies to obtain the exposure measure.

ASSESSING ESTABLISHED SMOKING

Assessment of established smoking was based on the question, “How many cigarettes have you smoked in your life?” (none, a few puffs, 1-19 cigarettes, 20-100 cigarettes, or more than 100 cigarettes). An adolescent who identified herself as a lifetime smoker of more than 100 cigarettes was defined as an established smoker. We validated the established smoking measure by its correspondence (in the 24-month survey) with other measures of smoking and addiction, including 30-day smoking, daily smoking, whether or not the adolescent considered herself a smoker, and 4 items (eg, “Have you ever felt you really needed a cigarette?” “Have you ever had strong cravings to smoke?”) from the Hooked on Nicotine Checklist.23 Each item had a binary (no/yes) response and the composite measure was scored on a 0-to-4 scale. The Hooked on Nicotine Checklist strongly predicts continued smoking among adolescents23 and has been shown to have high internal consistency and test-retest reliability.24

COVARIATES

Based on results from previous studies of adolescent smoking,3,6 we obtained data on a broad range of covariates. These included items for age, sex, race, parental education, self-reported school performance, extracurricular activities (level of participation in team sports, other sports without a coach, church or other religious activities, music or dance, school clubs, and other clubs), parent and sibling smoking, and peer smoking. We also included controls for other aspects of movie viewing besides seeing movie smoking. To account for overall movie attendance, we controlled for the number of movies watched each week as ascertained by the question, “About how many movies do you usually watch each week? Please include movies you see in movie theaters, on videotape or DVD, and on television” (none, 1 or 2, 3 or 4, 5 or more). Second, because adolescents who go to the theater often could be more exposed to other social influences to smoke there, we controlled for theater-going as ascertained by the question, “How do you see most of the movies you watch? Do you watch most (a) in movie theaters, (b) on videotape or DVD, or (c) on TV channels or pay-per-view?” Third, because adolescents could be more or less responsive to movie content based on who they watch movies with, we accounted for parental presence in the movie-viewing environment with the following question, “When you watch movies, how often do you watch them with your parents?” (never, once in a while, sometimes, most of the time).

Composite scores were obtained for measures of sensation seeking (eg, “I like to do scary things,” “I like to listen to loud music,” Cronbach α = .59)25,26; rebelliousness (eg, “I get in trouble at school,” “I do things my parent wouldn't want me to,” “I like to break the rules,” α = .73)27; and parenting style,28 which is assessed with respect to the adolescent's mother and includes 2 domains, responsiveness (eg, “she listens to what I have to say,” “she makes me feel better when I’m upset,” α = .71) and demandingness (eg, “she checks to see if I do my homework,” “she knows where I am after school,” α = .59). For these variables, increases in the scale indicate that the adolescent has (or perceives his mother to have) more of the characteristic.

STATISTICAL ANALYSIS

The available data consist of a baseline survey and then 3 consecutive follow-up assessments at 8-month intervals. Thus, we were able to ascertain the onset of established smoking (>100 lifetime cigarettes) only at the 8-month (8M), 16-month (16M), and 24-month (24M) surveys. An incident case was defined as an adolescent who became an established smoker from the pool of those who were not established smokers at the previous survey. The multivariate association between exposure to smoking in movies and the hazard of established smoking onset at each of these time points was assessed using discrete time hazard regression to estimate hazard ratios for event occurrence.2931 To aid in comparison of the adjusted hazard ratios, continuous covariates were all scaled so 0 corresponded to the 5th percentile and 1 to the 95th percentile for their distributions, and extreme values in either direction were recoded back to 0 or 1. This allowed for better comparison of the effect size between continuous, dichotomous, and ordered categorical variables and also limited the influence of outliers for skewed variables. Participants were censored from the analysis after they became established smokers. Participants who never became established smokers were censored at the final time interval, and participants who dropped out were censored at the time interval prior to attrition. Censoring was assumed to be independent of the hazard of initiating established smoking, conditional on covariates included in the model (ignorable missingness assumption).32

As a sensitivity check, we used a generalized additive model33 with a logistic link function to check continuous predictors for nonlinear trends in predicting initiation. As another sensitivity check to ensure that the statistically significant movie-smoking effects were not a result of undercontrolling for 3 key psychological constructs (parenting style, rebelliousness, and sensation seeking) due to measurement error, the regression model was re-estimated using the individual items for each scale score as indicators of these 3 underlying latent variables.34,35 The model was parameterized as a standard latent variable model assuming normally distributed latent variables and residual influences. The latent variables were included as predictors along with the other observed covariates to predict initiation of established smoking. All these models were estimated using the Mplus program.36

EXPOSURE TO SMOKING IN MOVIES

Movie smoking was present in 74% of movies in the total sample of 532 movies. In accordance with previous findings,21 the presence of movie smoking was directly associated with Motion Picture Association of America rating with smoking occurrences present in 25%, 44%, 77%, and 87% of G-, PG-, PG-13–, and R-rated movies, respectively. The 532 movies contained a total of 3795 smoking occurrences; estimated exposure to smoking in these movies among the adolescents was 500 occurrences (interquartile range, 210-954). For regression analysis, the movie exposure variable was scaled so that 0 corresponded to the 5th percentile of the distribution (25 smoking occurrences) and 1 corresponded to the 95th percentile (1811 smoking occurrences).

COVARIATES AND THEIR RELATION WITH EXPOSURE TO MOVIE SMOKING

At the baseline survey, 5637 (90%) of the adolescents were never smokers, 461 (7%) had smoked less than 1 cigarette, 135 (2%) had smoked 1 to 19 cigarettes, 29 (0.5%) had smoked 20 to 100 cigarettes, and 33 (0.5%) had smoked more than 100 cigarettes. Other baseline covariates of the study population appear in Table 1 along with their relationship to below-median exposure to movie smoking. An association with movie-smoking exposure was significant for all the covariates at the P < .001 level. Exposure to movie smoking increased with older age; male sex; black race; smoking by peers, siblings, and parents; more weekly movie viewing; more movie theater viewing; less movie viewing with parents; and higher levels of sensation seeking and rebelliousness. Lower parental education, lower parent demandingness and responsiveness, poorer school performance, and fewer extracurricular activities were also associated with higher exposure to movie smoking. To account for these associations, we adjusted for these variables in the multivariate analysis.

Table Graphic Jump LocationTable 1. Covariates and Their Relation to Movie-Smoking Exposure
VALIDATING THE ESTABLISHED SMOKING MEASURE

By the 24M survey, 125 adolescents had become established smokers at 1 or more of the follow-up surveys. To further characterize their smoking status, we examined their responses for other smoking measures at the 24M survey for the 108 adolescents who were not lost earlier to follow-up. Of these, 85% were current (30-day) smokers and 51% were daily smokers. With regard to symptoms of smoking dependence, 83% endorsed at least 1 Hooked on Nicotine Checklist loss-of-autonomy symptom and 53% endorsed all 4 symptoms included in the interview; so by the criteria of DiFranza et al,37 almost all of these adolescents experienced symptoms of smoking dependence.

MOVIE-SMOKING EXPOSURE AND INCIDENCE OF ESTABLISHED SMOKING

The incidence of established smoking for each observation period, grouped by whether exposure to movie smoking was less than or greater than or equal to the median, is shown in Figure 2. The incidence of established smoking was less than 6 per 1000 person-years for adolescents with below-median exposure to movie smoking compared with between 14 and 36 per 1000 person-years for those with exposure that was greater than or equal to the median. The crude incidence rate ratio (95% confidence interval [CI]) for movie exposure on incident established smoking was 12.6 (3.0-12.6); 6.4 (2.9-14.4); and 6.5 (3.0-13.8) for the baseline-to-8M, 8M-to-16M, and 16M-to-24M periods, respectively, with none being significantly different from each other. Tabulation of the transitions indicated that at the previous wave, 37% of the incident established smokers had smoked 20 to 100 cigarettes, 30% had smoked 1 to 19 cigarettes, 16% had puffed on a cigarette, and 17% had never smoked. Thus transitions to established smoking were predominantly but not exclusively among adolescents with prior smoking experience.

Place holder to copy figure label and caption
Figure 2.

Incidence of established smoking by observation period. 8M indicates 8-month survey; 16M, 16-month survey; 24M, 24-month survey; CI, confidence interval; and IRR, incidence rate ratio.

Graphic Jump Location
MULTIVARIATE HAZARD MODEL

After controlling for other baseline covariates, movie-smoking exposure significantly increased the hazard ratio for established smoking (Table 2). The hazard ratio of initiating established smoking for teens at the 95th percentile for movie-smoking exposure (compared with teens at the 5th percentile) was 2.04 (95% CI, 1.01-4.12). Other significant predictors of incident established smoking included age, baseline smoking, parent or friend smoking, and sensation seeking. Protective factors included black or Hispanic compared with white ethnicity, extracurricular activities, and parenting style. Parental education, sibling smoking, poor school performance, rebelliousness, movies per week, viewing movies with parent, movie viewing venue, other nonwhite ethnicity, and sex were not significant predictors of established smoking in the multivariate model.

Table Graphic Jump LocationTable 2. Multivariate Hazard Models for Baseline Predictors of Established Smoking
EFFECT MODIFICATION

The effect of exposure to movie smoking on established smoking was significantly stronger for adolescents at the 5th percentile for sensation seeking (12.7 [95% CI, 2.0-80.6]) compared with the effect for teens at the 95th percentile (1.01 [95% CI, 0.4-2.6]). Thus, the movie-smoking exposure effect was about 12 times larger for teens who were low on sensation seeking than for teens who were high on sensation seeking. This moderation effect is presented in Figure 3, which illustrates the continuous decline in the adjusted hazard ratio for established smoking as sensation seeking increases.

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Figure 3.

Effect of sensation seeking on the effect of exposure to movie smoking. The black line reflects the established smoking hazard ratio comparing the 95th percentile with the 5th percentile of movie-smoking exposure. Sensation seeking is also scaled so that 0 equals the 5th percentile and 1 equals the 95th percentile for the distribution. The hazard ratio is adjusted for other media variables (movie-viewing venue, movies viewed per week, viewed movies with parent), social and other environmental influences (friend smoking, sibling smoking, parent smoking, poor school performance, parenting style, extracurricular activities), and characteristics of the adolescent (age, sex, parent education, race, tried smoking at baseline, and rebelliousness).

Graphic Jump Location
SENSITIVITY ANALYSIS

Probes of nonlinear effects indicated that, of the continuous predictors, only movie-smoking exposure and sensation seeking had significant nonlinear effects on established smoking. These higher-order effects were rendered nonsignificant by the other predictors and the sensation seeking × movie exposure interaction. Results from models with polynomial contrasts indicated that none of the ordered categorical covariates had nonlinear effects on initiation. Additionally, the movie-smoking exposure effect on established smoking remained statistically significant when the 3 psychological covariate constructs were entered into the model as latent variables, lowering concern that measurement error for these variables was an issue.

This study showed that exposure to smoking in movies predicts established smoking, an advanced stage of smoking present in only about 3% of younger adolescents. The effect of movie-smoking exposure persisted after controlling for tried smoking at baseline and was independent of a number of demographic, social, individual, and other-media covariates. In addition, the effect was consistent across 3 observation points as the adolescents aged over a 2-year period. To our knowledge, this is the first study to link viewing smoking in movies with established adolescent smoking. Combined with previous findings showing that young persons who view more smoking in movies are at increased risk for initiating cigarette smoking,3,5 the present findings heighten concern about the public health implications of movie-smoking exposure by linking it with an outcome that predicts smoking-related morbidity and mortality in the future.

Although this study demonstrated an effect of exposure to movie smoking on established smoking, it did not address possible mechanisms for the effect. The context of current theory and research suggests the most plausible explanation is that frequent exposure to smoking cues in movies leads to more positive expectancies about effects of smoking, more favorable perceptions of smokers, and a greater tendency to affiliate with teens who smoke,3840 all factors that increase risk for smoking. Along with repeated exposure to movie smoking, these processes may work in concert to reinforce smoking behavior until adolescents reach a point where they crave nicotine and become dependent.41 This explanation is based on the assumption that it is the smoking in movies that prompts adolescents to smoke, an explanation that is simplest and fits best with social cognitive theory and the present analyses, which controlled for other characteristics of the adolescent and his media environment (eg, where he mainly sees movies and who he sees them with). Still some caution about causal inference is indicated in any observational study, and further research is warranted using epidemiologic designs and controlled laboratory studies to explicate the mechanisms through which movie-smoking exposure influences earlier and later stages of adolescent smoking.

Another notable finding is effect modification by sensation seeking, with adolescents who are low on sensation seeking being most responsive to movie-smoking exposure whereas adolescents who are high on sensation seeking showed less response. It has been known for some time that a high level of sensation seeking predicts substance use4245 and high media use.21,46 However, moderation effects have been less studied, and we are not aware of previous research documenting lower responsiveness to pro–substance use media messages among high sensation seekers. It is possible that low sensation seekers have relatively little exposure to risky behavior, hence are more influenced by viewing smoking models in movie situations.47 There may also be a convergence of personality and social risk factors among the high sensation seekers such that exposure to movie cues has less additional impact for this group of high-risk teens. The fact that movie exposure influences established smoking among low-risk teens, who otherwise might not become dependent smokers, needs to be considered in terms of its implications for the prevention of adolescent smoking.

The study has some additional limitations that could be noted. The fact that data were based on self-reports of smoking (not biochemical measures) could pose a limitation, although empirical research has indicated that adolescent self-reports collected under confidential conditions have good validity.48,49 Other limitations involve sample selection and attrition. We cannot know whether those who cooperated in the baseline sample and those who refused differed in terms of their movie exposure history and propensity to smoke, and there was differential attrition of higher-risk participants.50 While such attrition tends to work against finding significant effects for substance use, the fact that the longitudinal sample is different from the adolescent population could affect the generalizability of the results. However, it is important to point out that there is now a sizable published literature pointing to the generalizability of this finding in 5 published adolescent samples—4 in this country2,3,5,7 and 1 in Germany.51

In summary, this study demonstrated a relation between exposure to movie smoking and established smoking during adolescence, suggesting that this media exposure is an independent risk factor not just for trying smoking, but also for onset of more advanced stages of smoking. From a public health perspective, this study adds strong scientific support for the public health program Smoke Free Movies, which has been endorsed by the American Academy of Pediatrics, and which aims at reducing adolescent exposure to movie smoking through voluntary changes in how movies are rated. Pediatricians should support these efforts and encourage parents to limit movie viewing to no more than 2 movies per week as part of a set of general recommendations on media limits.52 Our prior research has shown particular grounds for limits on viewing of R-rated movies by adolescents.27

Correspondence: James D. Sargent, MD, Department of Pediatrics, Children's Hospital at Dartmouth, One Medical Center Drive, Lebanon, NH 03756 (james.d.sargent@dartmouth.edu).

Accepted for Publication: May 11, 2007.

Author Contributions:Study concept and design: Sargent, Stoolmiller, Gibbons, and Gerrard. Analysis and interpretation of data: Stoolmiller, Worth, Dal Cin, Wills, Gibbons, Gerrard, and Tanski. Drafting of the manuscript: Sargent, Stoolmiller, Wills, Gibbons, and Gerrard. Critical revision of the manuscript for important intellectual content: Sargent, Stoolmiller, Worth, Dal Cin, Wills, Gibbons, Gerrard, and Tanski. Statistical analysis: Sargent, Stoolmiller, Worth, Dal Cin, Wills, Gibbons, and Gerrard. Administrative, technical, and material support: Worth and Tanski.

Financial Disclosure: None reported.

Funding/Support: This work was supported by grant CA-77026 from the National Cancer Institute, the American Legacy Foundation, and grant DA12623 from the National Institute on Drug Abuse.

Additional Contributions: We would like to acknowledge the thoughtful input of an anonymous Archives reviewer. Cindy Patch provided editorial assistance. Jennifer J. Tickle, PhD, assisted in the development of the content analysis; Dan Nassau, BA, and Balvinder Rakhra, BA, coded the movies; and Diana B. Nelsen, BA, supervised the content analysis.

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Allison  PD Event History Analysis: Regression for Longitudinal Event Data.  Thousand Oaks, CA Sage Publications, Inc1984;
Singer  JDWillett  JB Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence.  New York, NY Oxford University Press2003;
Muthen  BMasyn  K Discrete-time survival mixture analysis. J Educ Behav Statistics 2005;30 (1) 27- 58
Link to Article
Schafer  JLGraham  JW Missing data: our view of the state of the art. Psychol Methods 2002;7 (2) 147- 177
PubMed Link to Article
Hastie  TJChambers  JM, Hastie TJ, eds.ed Generalized additive models. Statistical Models in S. Pacific Grove, CA Wadsworth & Brooks/Cole1992;608
Palta  MLin  C Latent variables, measurement error and methods for analysing longitudinal binary and ordinal data. Stat Med 1999;18 (4) 385- 396
PubMed Link to Article
Reeves  GKCox  DRDarby  CWhitley  E Some aspects of measurement error in explanatory variables for continuous and binary regression models. Stat Med 1998;17 (19) 2157- 2177
PubMed Link to Article
Muthen  LMuthen  B Mplus Users' Guide.  Los Angeles, CA Muthen & Muthen2004;
DiFranza  JRSavageau  JARigotti  NA  et al.  Development of symptoms of tobacco dependence in youths: 30 month follow up data from the DANDY study. Tob Control 2002;11 (3) 228- 235
PubMed Link to Article
Gerrard  MGibbons  FXReis-Bergan  MTrudeau  LVande Lune  LSBuunk  B Inhibitory effects of drinker and nondrinker prototypes on adolescent alcohol consumption. Health Psychol 2002;21 (6) 601- 609
PubMed Link to Article
Gibbons  FXGerrard  MOuellette  JBurzette  B Cognitive antecedents to adolescent health risk: discriminating between behavioral intention and behavioral willingness. Psychol Health 1998;13319- 340
Link to Article
Sargent  JDWills  TAStoolmiller  MGibson  JJGibbons  FX Alcohol use in motion pictures and its relation with early-onset teen drinking. J Stud Alcohol 2006;67 (1) 54- 65
PubMed
DiFranza  JWellman  R A sensitization-homeostasis model of nicotine craving, withdrawal, and tolerance: integrating the clinical and basic science literature. Nicotine Tob Res 2005;7 (1) 9- 26
PubMed Link to Article
Stacy  ANewcomb  MDBentler  PM Cognitive motivations and sensation seeking as long-term predictors of drinking problems. J Soc Clin Psychol 1993;12 (1) 1- 24
Link to Article
Wills  TAVaccaro  DMcNamara  G Novelty seeking, risk taking, and related constructs as predictors of adolescent substance use: an application of Cloninger's theory. J Subst Abuse 1994;6 (1) 1- 20
PubMed Link to Article
Lipkus  IMBarefoot  JCWilliams  RBSiegler  IC Personality measures as predictors of smoking initiation and cessation in the UNC Alumni Heart Study. Health Psychol 1994;13 (2) 149- 155
PubMed Link to Article
Donohew  RLHoyle  RHClayton  RRSkinner  WFColon  SERice  RE Sensation seeking and drug use by adolescents and their friends: models for marijuana and alcohol. J Stud Alcohol 1999;60 (5) 622- 631
PubMed
Hall  A Sensation seeking and the use and selection of media materials. Psychol Rep 2005;97 (1) 236- 244
PubMed Link to Article
Yanovitzky  I Sensation seeking and adolescent drug use: the mediating role of association with deviant peers and pro-drug discussions. Health Commun 2005;17 (1) 67- 89
PubMed Link to Article
Patrick  DLCheadle  AThompson  DCDiehr  PKoepsell  TKinne  S The validity of self-reported smoking: a review and meta-analysis. Am J Public Health 1994;84 (7) 1086- 1093
PubMed Link to Article
Wills  TACleary  SD The validity of self-reports of smoking: analyses by race/ethnicity in a school sample of urban adolescents. Am J Public Health 1997;87 (1) 56- 61
PubMed Link to Article
Wills  TAWalker  CResko  JASloboda  Z, ed.ed Longitudinal studies of drug use and abuse. Epidemiology of Drug Abuse. New York, NY Springer2005;
Hanewinkel  RSargent  JD Exposure to smoking in popular contemporary movies and youth smoking in Germany. Am J Prev Med 2007;32 (6) 466- 473
PubMed Link to Article
Roberts  DFFoehr  UGRideout  V Generation M: media in the lives of 8-18 year olds.  Menlo Park, CA Kaiser Family Foundation2005;7251

Figures

Place holder to copy figure label and caption
Figure 1.

Sample selection and attrition. 8M indicates 8-month survey; 16M, 16-month survey; and 24M, 24-month survey.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Incidence of established smoking by observation period. 8M indicates 8-month survey; 16M, 16-month survey; 24M, 24-month survey; CI, confidence interval; and IRR, incidence rate ratio.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Effect of sensation seeking on the effect of exposure to movie smoking. The black line reflects the established smoking hazard ratio comparing the 95th percentile with the 5th percentile of movie-smoking exposure. Sensation seeking is also scaled so that 0 equals the 5th percentile and 1 equals the 95th percentile for the distribution. The hazard ratio is adjusted for other media variables (movie-viewing venue, movies viewed per week, viewed movies with parent), social and other environmental influences (friend smoking, sibling smoking, parent smoking, poor school performance, parenting style, extracurricular activities), and characteristics of the adolescent (age, sex, parent education, race, tried smoking at baseline, and rebelliousness).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Covariates and Their Relation to Movie-Smoking Exposure
Table Graphic Jump LocationTable 2. Multivariate Hazard Models for Baseline Predictors of Established Smoking

References

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Zuckerman  MBone  RNNeary  RMangelsdorff  DBrustman  B What is the sensation seeker? personality trait and experience correlates of the Sensation-Seeking Scales. J Consult Clin Psychol 1972;39 (2) 308- 321
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Sargent  JDBeach  MLDalton  MA  et al.  Effect of parental R-rated movie restriction on adolescent smoking initiation: a prospective study. Pediatrics 2004;114 (1) 149- 156
PubMed Link to Article
Jackson  CHenriksen  LFoshee  VA The Authoritative Parenting Index: predicting health risk behaviors among children and adolescents. Health Educ Behav 1998;25 (3) 319- 337
PubMed Link to Article
Allison  PD Event History Analysis: Regression for Longitudinal Event Data.  Thousand Oaks, CA Sage Publications, Inc1984;
Singer  JDWillett  JB Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence.  New York, NY Oxford University Press2003;
Muthen  BMasyn  K Discrete-time survival mixture analysis. J Educ Behav Statistics 2005;30 (1) 27- 58
Link to Article
Schafer  JLGraham  JW Missing data: our view of the state of the art. Psychol Methods 2002;7 (2) 147- 177
PubMed Link to Article
Hastie  TJChambers  JM, Hastie TJ, eds.ed Generalized additive models. Statistical Models in S. Pacific Grove, CA Wadsworth & Brooks/Cole1992;608
Palta  MLin  C Latent variables, measurement error and methods for analysing longitudinal binary and ordinal data. Stat Med 1999;18 (4) 385- 396
PubMed Link to Article
Reeves  GKCox  DRDarby  CWhitley  E Some aspects of measurement error in explanatory variables for continuous and binary regression models. Stat Med 1998;17 (19) 2157- 2177
PubMed Link to Article
Muthen  LMuthen  B Mplus Users' Guide.  Los Angeles, CA Muthen & Muthen2004;
DiFranza  JRSavageau  JARigotti  NA  et al.  Development of symptoms of tobacco dependence in youths: 30 month follow up data from the DANDY study. Tob Control 2002;11 (3) 228- 235
PubMed Link to Article
Gerrard  MGibbons  FXReis-Bergan  MTrudeau  LVande Lune  LSBuunk  B Inhibitory effects of drinker and nondrinker prototypes on adolescent alcohol consumption. Health Psychol 2002;21 (6) 601- 609
PubMed Link to Article
Gibbons  FXGerrard  MOuellette  JBurzette  B Cognitive antecedents to adolescent health risk: discriminating between behavioral intention and behavioral willingness. Psychol Health 1998;13319- 340
Link to Article
Sargent  JDWills  TAStoolmiller  MGibson  JJGibbons  FX Alcohol use in motion pictures and its relation with early-onset teen drinking. J Stud Alcohol 2006;67 (1) 54- 65
PubMed
DiFranza  JWellman  R A sensitization-homeostasis model of nicotine craving, withdrawal, and tolerance: integrating the clinical and basic science literature. Nicotine Tob Res 2005;7 (1) 9- 26
PubMed Link to Article
Stacy  ANewcomb  MDBentler  PM Cognitive motivations and sensation seeking as long-term predictors of drinking problems. J Soc Clin Psychol 1993;12 (1) 1- 24
Link to Article
Wills  TAVaccaro  DMcNamara  G Novelty seeking, risk taking, and related constructs as predictors of adolescent substance use: an application of Cloninger's theory. J Subst Abuse 1994;6 (1) 1- 20
PubMed Link to Article
Lipkus  IMBarefoot  JCWilliams  RBSiegler  IC Personality measures as predictors of smoking initiation and cessation in the UNC Alumni Heart Study. Health Psychol 1994;13 (2) 149- 155
PubMed Link to Article
Donohew  RLHoyle  RHClayton  RRSkinner  WFColon  SERice  RE Sensation seeking and drug use by adolescents and their friends: models for marijuana and alcohol. J Stud Alcohol 1999;60 (5) 622- 631
PubMed
Hall  A Sensation seeking and the use and selection of media materials. Psychol Rep 2005;97 (1) 236- 244
PubMed Link to Article
Yanovitzky  I Sensation seeking and adolescent drug use: the mediating role of association with deviant peers and pro-drug discussions. Health Commun 2005;17 (1) 67- 89
PubMed Link to Article
Patrick  DLCheadle  AThompson  DCDiehr  PKoepsell  TKinne  S The validity of self-reported smoking: a review and meta-analysis. Am J Public Health 1994;84 (7) 1086- 1093
PubMed Link to Article
Wills  TACleary  SD The validity of self-reports of smoking: analyses by race/ethnicity in a school sample of urban adolescents. Am J Public Health 1997;87 (1) 56- 61
PubMed Link to Article
Wills  TAWalker  CResko  JASloboda  Z, ed.ed Longitudinal studies of drug use and abuse. Epidemiology of Drug Abuse. New York, NY Springer2005;
Hanewinkel  RSargent  JD Exposure to smoking in popular contemporary movies and youth smoking in Germany. Am J Prev Med 2007;32 (6) 466- 473
PubMed Link to Article
Roberts  DFFoehr  UGRideout  V Generation M: media in the lives of 8-18 year olds.  Menlo Park, CA Kaiser Family Foundation2005;7251

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