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. Author manuscript; available in PMC: 2024 Mar 28.
Published in final edited form as: J Youth Adolesc. 2021 Jan 30;50(9):1896–1910. doi: 10.1007/s10964-020-01373-0

Mechanisms Underlying Associations between Media Alcohol Exposure, Parenting, and Early Adolescent Drinking: A Moderated Sequential Mediation Model

Kristina M Jackson 1, Tim Janssen 1, Melissa J Cox 2, Suzanne M Colby 1, Nancy P Barnett 1, James Sargent 3
PMCID: PMC10975647  NIHMSID: NIHMS1964598  PMID: 33515374

Abstract

Exposure to alcohol content in the media, especially in movies, is a demonstrated risk factor for adolescent alcohol use. This paper examines processes underlying this association and whether parenting mitigates such harms. A mediational model of parental restriction of mature media (W1), alcohol content exposure (W2), alcohol expectancies, peer norms (W3), and alcohol outcomes (W4) was tested using annual assessments from a study of adolescent drinking (N = 879; 52% female; 21% Non-White; 12% Hispanic). When restrictions are not in place, adolescents report greater exposure to alcohol content, leading to higher perceived peer drinking. Parental monitoring did not buffer the link between exposure and peer norms. Parental media restriction and perceptions about peers comprise mechanisms by which alcohol-saturated media influences youth drinking.

Keywords: Alcohol, Media, Parent, Restriction, Norms

Introduction

Adolescents tend to drink in an opportunistic manner, often consuming large quantities per drinking episode (Miech et al. 2018) which may result in high rates of alcohol-related negative consequences (e.g., car accidents; unintentional injuries) (Hingson et al. 2000; McGue et al. 2001). Additionally, they may experience both acute and prolonged neurobiological effects due to their drinking (Clark et al. 2008). Researchers increasingly recognize the power of the environment in shaping adolescent drinking, with a particularly important contextual influence for adolescents being the media. Entertainment media often features favorable portrayals of alcohol, associating drinking with social, sexual, and financial success, and with little depiction of the hazards of drinking or discouragement of drinking (Stern and Morr 2013). Prior literature, including multiple systematic reviews, offers considerable evidence of a robust association between exposure to alcohol content in the media and alcohol use by adolescents, even after adjusting for potential interpersonal-level (parent, peer influence) and individual-level (socio-demographics, sensation seeking) confounders (Anderson et al. 2009; Koordeman et al. 2012; Ross et al. 2014; Smith and Foxcroft 2009). The present study is motivated by theoretical articulation of mechanisms underlying the association between exposure to alcohol content in films and drinking initiation in adolescents (Jackson and Bartholow 2020).

Mechanisms Underlying Exposure to Alcohol in the Media and Youth Alcohol Use

Positive alcohol outcome expectancies

Outcome expectancies are the anticipated social, physical, cognitive, or emotional outcomes of a behavior (Goldman et al. 1999). They represent a culmination of alcohol-related learning that begins with acquiring awareness of alcohol’s effects, initially through observations of others and ultimately through direct drinking experience. That is, individuals consume alcohol because it is expected to yield certain effects and these expected effects are transmitted in part through media images and portrayals. Viewers are influenced by why (for what purpose) a character is drinking alcohol, not just whether the character drinks or not. The literature supporting positive alcohol expectancies as a mechanism underlying media alcohol exposure’s influence on drinking, however, has produced mixed findings. Longitudinal (Dal Cin et al. 2009; Osberg et al. 2012) and ecological momentary assessment (Collins et al. 2017) studies show associations between media alcohol portrayals and positive outcome expectancies. However, several longitudinal studies have failed to detect associations between exposure and positive outcome expectancies. This includes work focusing on exposure to television alcohol advertising (Martino et al. 2006) and films (Janssen et al. 2018; Wills et al. 2009), although the Wills et al. (2009) expectancy measure consisted of a single item assessing whether the respondent would enjoy drinking alcohol. Findings of laboratory studies examining proximal change in positive expectancies using laboratory paradigms also are mixed (Brown et al. 2016). Few studies have conducted formal tests of mediation; a study of a sample of college freshmen showed that exposure to alcohol content in films influenced alcohol consumption and consequences through positive expectancies (Osberg et al. 2012) but another study with an adolescent sample did not detect significant mediation when looking at timing of first use (Janssen et al. 2018).

Social norms

Youth are preoccupied with personal image and identity (Giles and Maltby 2004) and are susceptible to the socializing influences of close friends, peers, and prevailing generational norms (Krosnick and Alwin 1989). Modeling by parents and media figures constitute a primary source of learning about alcohol (Bandura 1971). Popular media figures are powerful role models due to their visibility and larger-than-life status. They serve as influential “super peers” to young viewers that inform the norms and contexts for substance use and thereby socialize youth regarding its prevalence and acceptability (Elmore et al. 2017). Thus, media portrayals of alcohol use may increase its perceived normalcy, leading to overestimation of peer alcohol use and approval of use. Emerging research shows associations between portrayals of alcohol in the media, including in entertainment media such as films, and peer norms. Exposure to alcohol content in films is prospectively associated with descriptive norms, or estimations of others’ consumption levels, for both close friend drinking behavior (Gibbons et al. 2010; Wills et al. 2009) and alcohol use among general peers (Dal Cin et al. 2009). Formal tests of mediation support perceived social norms as an important mediator underlying the association between exposure to alcohol content in films and various indices of youth drinking (Dal Cin et al. 2009; Osberg et al. 2012).

Parental Influence on Exposure to Mature Media Content and Youth Drinking

Parental Restriction of R-rated Movie Watching and Movie Alcohol Exposure

A promising approach for limiting youth exposure to substance use in movies is through parental restriction of mature media content in films. Youth who report greater restrictions have lower exposure to substance use depictions in movies as compared to those without such restrictions. These associations have been identified in cross-sectional (Dalton et al. 2006; Mejia et al. 2016) and longitudinal (Tanski et al. 2010) analyses and are independent of important individual (e.g., sensation-seeking) and social (e.g., peer use) influences. As compared to those with unrestricted access to R-rated films, youth who did not watch them due to parental restrictions and those who watched the films with adult supervision were at lower risk for subsequent alcohol use (Cox et al. 2018). It is possible that the shared experience of watching a movie with a youth can provide active mediation whereby the adult communicates the harms of alcohol use and indicates disapproval of the behavior, thereby mitigating the negative effects of media on alcohol use (Clark 2011). Thus, parents play an important role in helping their children navigate the complex media messages they are presented with on a daily basis, and providing adequate media restrictions to limit alcohol exposure is one tactic in providing that support.

Parental monitoring as a buffering agent

Although limiting exposure may be a worthwhile endeavor to reduce negative media effects, adolescents are increasingly accessing media through personal mobile devices which permit them to circumvent restrictions. A substantial body of research supports the importance of parenting practices for adolescent behavior (Steinberg et al. 1994), including substance use behaviors (Ryan et al. 2010). Parental knowledge of their child’s whereabouts and activities have demonstrated protective effects on youth alcohol use (Dishion and McMahon 1998). In addition, parents may engage in monitoring behaviors specific to alcohol use. Adopting clear “house rules” related to youth alcohol use can have a protective effect against drinking (Janssen et al. 2014). To date, no studies have examined whether parental monitoring or rules about alcohol use are protective against the influence of exposure to alcohol content in entertainment media on adolescent alcohol use, although parental knowledge of their child’s behaviors has been shown to reduce the effect of other social influences (peer deviance) on substance use (Marschall-Lévesque et al. 2014). Parents who exhibit higher levels of monitoring are actively engaged in the activities of their child’s life and thus may guard against detrimental effects of alcohol media exposure. Higher parental monitoring is hypothesized to buffer the negative effects of exposure to alcohol content in films on alcohol expectancies and social norms, which in turn influence alcohol use behavior itself.

Current Study

The present study tests a novel comprehensive model that includes exposure to alcohol content in the media, cognitive and social processes, and parental influences. The framework was motivated by a theoretical model posited by Jackson and Bartholow (2020) but adds an important dimension of parental influences on media viewing. The following hypotheses were based on the conceptual model (Fig. 1): (1) parent restriction of mature media content, specifically exposure to R-rated movies, will be associated with less subsequent viewing of movies with alcohol content, (2) exposure to alcohol content in movies will be associated with early drinking outcomes and willingness to drink alcohol, (3) perceived norms and positive alcohol expectancies will serve as mediators of the association between exposure to alcohol content and alcohol outcomes, and (4) general and alcohol-specific parental monitoring will buffer the associations exposure to alcohol content and the proposed mediators. A strength of the model is the ability to test indirect effects from parental restriction of mature media content such as alcohol imagery to alcohol use through exposure to media alcohol content (distal mediators) via alcohol expectancies and perceived norms (proximal mediators) in a single sequential pathway. Three alcohol outcomes of increasing severity are considered to capture early drinking in a nuanced manner. This includes two successive early drinking measures: any consumption of a full drink of alcohol and frequency of alcohol use. Additionally, given evidence for prospective associations between exposure to media alcohol content and self-reported willingness (Gibbons et al. 2010) as well as controlled experimental studies documenting greater willingness to drink following exposure to film clips positively portraying alcohol (Gibbons et al. 2016), willingness to drink alcohol was included as an outcome.

Fig. 1.

Fig. 1

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Conceptual model of mechanisms affecting alcohol outcomes

Methods

Participants

The sample was comprised of 1023 adolescents enrolled in a prospective study on alcohol initiation and progression (citations blinded for review). At enrollment, participants were equally divided across grade (33%, 32%, and 35% in 6th, 7th, and 8th grades, respectively), with a mean age of 12.22 years (SD = 0.98). Seventy-six percent were Caucasian, 4.7% were Black, 7.9% were mixed race, and 11.8% were Other race/ethnicity; 12.2% self-identified as Hispanic, and 52.2% were female. The schools were a mix of rural (n = 231), suburban (n = 508), and urban (n = 284). Written informed consent was obtained from all participants and the Institutional Review Board approved project procedures.

Procedure

Participants were recruited through local middle schools and enrolled on a semi-annual basis between Fall 2009 and Fall 2011. Study information and consent forms were mailed to each student’s home; a second packet was distributed in schools. Across the schools, an average of 38% of students returned a consent form, 65% of forms returned were consents allowing for participation, and 88% of these individuals were enrolled; the remaining 12% provided consent but failed to complete the baseline survey. Thus, overall enrollment rate was 22%. As compared to Department of Education school data, the resulting sample was largely representative of the schools from which they were drawn, with samples obtained at five of six schools accurately representing the school gender and grade distribution. There was also accurate representation of the proportion of White youth for four of six schools and Hispanic youth in three schools, with a greater proportion of Hispanic students in the sample for the other three schools. Students receiving subsidized lunch were underrepresented in three schools, suggesting that the sample is more racially diverse than the school populations but also less disadvantaged.

Participants completed assessments at five semi-annual time points and an additional follow up one year later. They were re-enrolled (82% retention) into the study at the point of re-funding in 2014, after which they completed quarterly assessments through the end of high school. Students were given a $20 gift card for each follow-up survey completed. Present study data were selected from the full multi-wave assessment to correspond to four roughly annual waves. Wave 1 for the present study corresponded to the first survey that contained assessments of R-rated movie watching, administered on average one year after enrollment (Wave 1, W1; mean age 13.7 years), corresponding to Fall 2011 and Fall 2012. Subsequent study timepoints were selected in a manner to maintain temporal ordering, with follow-up waves separated by roughly one year (Wave 2, W2; mean age 14.9 years; Wave 3, W3; mean age 16.10 years; Wave 4, W4; mean age 17.10 years). Wave 2 data were collected in 2012–2013, Wave 3 data were gathered in 2013–2014, and Wave 4 data were gathered in 2014–2015.

The 879 (86%) participants who completed the W1 survey comprise the analytic sample for the present study. Compared to characteristics of the full sample (n = 1023) at the time of enrollment, present study participants did not differ from the original study sample in ethnicity (χ2 (1, N = 1023) = 1.92, p = 0.380), but were significantly younger (included: age 14.87; excluded: age 15.28) and more likely to be female (χ2 (1, N = 1023) = 7.45, p = 0.006) and White (χ2 (1, N = 1023) = 7.58, p = 0.006). At W2, 77% (n = 674) of the analytic sample provided alcohol exposure data. Across time points, age significantly predicted attrition (ts(878) = 11.5–14.0, ps < 0.001 across three tests), which is a consequnce of a subset of participants graduating high school prior to W4. There were no differences as a function of sex, race, ethnic group. All models control for the effect of age.

Measures

Parental restriction of R-rated movie viewing (Wave 1)

Youth indicated whether their parents permitted them to watch R-rated movies (“Do your parents/guardians let you watch R-rated movies?”), using the following response options: 0 = “No, and I never watch them”; 1 = “No, but I watch them anyway”; 2 = “Yes, if supervised by an adult”; 3 = “Yes, without adult supervision”. Three dummy codes were created for viewing of R-rated movies with the first response option (complete restriction) as the reference group: Permitted, if supervised, Permitted, and Not permitted, but do it anyway.

Exposure to alcohol content in films (Wave 2)

Exposure to alcohol content was assessed using a method that combines content analysis and random assignment of movie titles to youth surveys (Sargent et al. 2008). Prior to survey programming, alcohol exposure was coded from the top 100 box office hits (based on MPAA ratings) from each of the prior 5 years as well as the top-rated movies from earlier in that same calendar year; movies were updated annually to reflect the prior 5-year period and current year. Thus, depending on school cohort, movies were taken from 2007–2012 for three school cohorts and from 2008–2013 for two school cohorts. For each participant, 50 randomly sampled movies were presented in 5 blocks of 10 movies and participants were asked if they had seen each of the movies. Minutes of exposure to real or implied use of an alcoholic beverage by a character was coded by a team of two trained content coders. An estimate of movie alcohol exposure was derived based on the number of instances of movies that participants indicated having seen if all 530 movies had been assessed; see more details in (citation blinded for review).

Parenting moderators (Wave 2)

General parental monitoring was measured using the 9-item Parental Monitoring Questionnaire (PMQ; Kerr and Stattin 2000) which operationalizes parental monitoring as parental knowledge of child activities. Youth responded to items measured on a five-point scale ranging from 0 = No/never to 5 = Yes/always. A mean was taken across the first eight items (α = 0.93); one item, “In the last month, have there been nights when your parents had no idea where you were?” was dropped as it did not correlate well (r = 0.12) with the total scale.

Parental alcohol-specific rules was a 4-item measure of parents’ anticipated response to finding out the participant was drinking alcohol and was derived from a measure by Chassin et al. (1998). Items included: “Take away a privilege, like watching TV or using a cell phone”, “Take away something from you, like treats or allowance”, “Scold/yell at you”, and “Ground you” (α = 0.87). Response options ranged from 1 = Not at all likely to 5 = Very likely.

Social and cognitive mediators (Wave 3)

Positive alcohol outcome expectancies was a 10-item measure assessing beliefs about positive affective, cognitive, and behavioral consequences of alcohol use (Schell et al. 2005). Participants responded to the question: “How likely is it that the following things would happen to people your age if they had one or more drinks of alcohol?” Response options ranged from 1 = Very unlikely to 4 = Very likely. A mean was taken across the items (α = .93).

Perceived norms were gathered for two referent groups: close friends and peers. For close friend norms, a mean was taken across two items: “How do most of your close friends feel about kids your age drinking alcohol?” and “How do most of your close friends feel about kids your age drinking getting drunk?” (Wood et al. 2004). Response options ranged from 1 = Strongly Disapprove to 4 = Strongly Approve (α = 0.85). For the measure of general peer norms participants reported on perceptions about what is normative for their peers, with items customized to their own sex and grade referents (Wood et al. 2001). Perception of typical frequency included response options from 0 = They don’t drink to 9 = Twice a day or more. Typical peer quantity was number of drinks on any one occasion. Perception of frequency of peer heavy episodic drinking (3 + drinks/row) included response options ranging from 1 = None to 6 = Ten or more times, coded into ‘0’, ‘1’, ‘2’, and ‘3+ drinks’; this latter item was asked twice based on time of year (school year/summer). A mean across the four items (drinking frequency; school-year heavy drinking frequency; summer heavy drinking frequency; quantity) were averaged to construct a single item with high internal consistency (α = 0.88).

Drinking outcomes (Wave 4)

At each of the quarterly assessments, two items assessed willingness to drink if offered by a best friend and by a classmate (“If your best friend/classmate offered you an alcoholic beverage, would you drink it?”). Response options included 0 = Definitely not; 1 = Probably not; 2 = Probably yes; 3 = Definitely yes. Items were averaged across the four quarterly assessments to form a single outcome variable and then averaged with each other (α = 0.92). At each of the quarterly assessments, participants were also asked “In the past 3 months, how often have you had some kind of beverage containing alcohol?” with response options ranging from 1 = I didn’t drink to 9 = Every day. Any drinking was a binary outcome indicating alcohol use initiation that occurred between Waves 1 and 4, that is, endorsement of any drinking at any wave beteen these timepoints. Frequency of alcohol use was measured using the item: “In the past 3 months, how often have you had some kind of beverage containing alcohol”. The mean across responses at the four quarterly assessments between Wave 3 and Wave 4 was calculated and converted to a sum score which corresponded to a measure of alcohol use one year after Wave 3. The alcohol outcome measured at the immediately preceding timepoint (Wave 3) served as a control, as this is more conservative than controlling for the Wave 1 measure.

Covariates

(Wave 1) Sex, race (collapsed into to White/non-White given low baserates of nonWhite participants), ethnicity (Hispanic/non-Hispanic), and age were assessed at enrollment. Perceived availability of alcohol was measured with a binary item from Arthur et al. (2000): “If you wanted to get some beer, wine, or hard liquor (for example: vodka, whiskey, or gin) could you get some?”

Analytic Strategy

To investigate direct and indirect effects of parent restriction of R-rated movie watching and exposure to movie alcohol content in movies on expectancies, perceived norms, and drinking outcomes, two models were tested. The first of these models tested mediational effects only, whereas the second of these models tested moderated mediation effects, to test whether general and alcohol-specific parental monitoring buffered these hypothesized mediated effects.

In Model 1, alcohol content exposure was regressed on parent restriction of R-rated movie watching and age. AOEs, close friend norms, and peer norms were regressed on parent restriction of R-rated movie watching, parental monitoring, parental alcohol-specific rules, alcohol content exposure, and age. Alcohol use outcomes (Any Drinking assumed binary, Willingness and Frequency assumed continuous) were regressed on all of the above variables as well as sex, race, ethnicity, W1 alcohol availability, and the corresponding Wave 1 alcohol outcome. To avoid exclusion of cases with missing data on predictors, the models estimated means for parental alcohol-specific rules, parental monitoring, W1 willingness, and W3 alcohol frequency, so that these variables were treated as endogenous to the model.

In addition to all model parameters from Model 1, Model 2 included interaction terms for the effects of alcohol content exposure with parental monitoring, and alcohol content exposure with parental alcohol-specific rules. These interaction terms were calculated as product terms. Product terms were converted to be endogenous variables in the model and means were estimated to prevent exclusion of cases with missing data. Significant overall improvement in model fit based on BIC and loglikelihood ratio tests are probed for significant individual moderation effects using a regions of significance testing approach (Bauer et al. 2006; Preacher et al. 2007).

We obtained bias-corrected bootstrapped confidence intervals for the indirect effects, using 5000 bootstrapped samples. Percent mediated for each indirect effect served as an index of effect size. Alpha was set at .05 (corresponding to 95% confidence intervals) with the exception of the tests of indirect effects, where alpha was set at .01 (corresponding to 99% confidence intervals) given the large number of tests being conducted (27 per model). To facilitate interpretation of intercepts, continuous predictors (exposure to alcohol content, parental alcohol-specific rules, and parental monitoring) were standardized. Models were estimated in Mplus 8.4 (Muthén and Muthén 2017). Missing data were handled under the assumption of missing at random (predicted by covariates included in the model) using the full information maximum likelihood (FIML) estimator.

Results

Descriptive Statistics

Any alcohol use was reported by 13.5% of the sample at Wave 1 and 55.1% of the sample at Wave 4. Extrapolating from a sample of 50 films, participants viewed on average 5.06 hours (SD = 3.99) of alcohol content. Median hours of exposure was 4.10 (interquintile range = 1.65–8.05). At W4, 57.1% of participants had endorsed ever drinking a full drink of alcohol. Table 1 contains descriptive information and inter-item correlations. Notably, drinking outcomes were negatively associated with parental monitoring and parental alcohol-specific rules as well as supervised viewing of mature media content, but positively associated with unsupervised viewing of mature media content. Exposure to movie alcohol content was greater when parental monitoring was low and when mature media content was not restricted but was not correlated with parental alcohol-specific rules or other categories of parental restriction. Both alcohol-specific and general monitoring were negatively associated with unrestricted viewing and positively associated with supervised viewing; the relatively small magnitude of these associations suggest that restriction and monitoring are unique constructs.

Table 1.

Descriptive statistics and correlation matrix among study variables

Movie Exposurea Positive AOEs Close Friend Norms General Peer Norms Freq of Alcohol Use Willingness Parent Monitor Parental Alcohol Rules Availability
Positive Expectancies (W3) 0.09
Close Friend Norms (W3) 0.22 0.22
General Peer Norms (W3) 0.22 0.22 0.29
Frequency of Alcohol Use (W4) 0.06 0.04 0.24 0.18
Willingness (W4) 0.21 0.20 0.59 0.31 0.46
Parental Monitoring (W2) −0.07 −0.09 −0.27 −0.10 −0.13 −0.28
Parental Alcohol Rules (W2) 0.02 0.12 −0.12 −0.02 −0.15 −0.11 0.39
Alcohol Availability 0.18 0.10 0.27 0.15 0.18 0.34 −0.24 −0.09
Restriction: Not permitted, do it anyway (W1; 7%) 0.02 0.04 0.14 −0.03 0.06 0.14 −0.10 −0.01 0.12
Restriction: Permitted, if supervised (W1; 30%) 0.02 −0.04 −0.08 −0.11 −0.06 −0.09 0.14 0.15 −0.14
Restriction: Permitted (W1; 43%)) 0.19 0.07 0.19 0.28 0.09 0.17 −0.22 −0.24 0.20
Mean (SD) 5.06 (3.99) 2.75 (0.76) 2.19 (2.04) 2.05 (1.30) 3.80 (13.03) 1.45 (1.62) 3.86 (0.97) 4.02 (1.33) 0.29 (0.45)
Interquintile Range (20%-80%) 3.09, 8.05 2.70, 3.30 1.00, 4.00 1.50, 3.25 0.00, 3.00 0.33, 3.00 3.75, 4.88 4.25, 5.00 0.00, 1.00
Min—Max 0/25 1/4 0/8 1/6 0/168 0/6 1/5 1/5 0/1
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Note. The restriction variable is a four-level categorical variable; three of the four response options are presented here. Complete restriction (“No, and I never watch them”) serves as the reference group. Exposure to movie alcohol content was assessed at W2. AOE = Alcohol Outcome Expectancies. Although some of the predictor and outcome variables were measured on multiple occasions, the table includes the first assessment measured for that variable.

a.

Unit of exposure is hours

Analytic Models

Tests of mediation

Figure 2 depicts results from Model 1, the basic mediation model without tests of moderation. Table 2 contains standardized summarized results from bootstrapped indirect effects estimation, including the residual direct effect, the total effect, and the total indirect effect. Overall, alcohol expectancies and perceived social norms robustly mediated the relation between movie exposure and willingness, with 73% of the effect mediated. They also mediated the relation between movie exposure and any drinking status, with 40% of the total effect mediated. There was inconsistent mediation of the relationship between movie exposure and frequency of alcohol use, as the residual direct effect was non-significantly negative (B = −0.03, 95% bCI = −0.09 to 0.03), although the total indirect effect was positive and significant (B = 0.03, 95% bCI = 0.01–0.05).

Fig. 2.

Fig. 2

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Overarching model depicting the mechanisms underlying prospective associations between parental restriction of R-rated movie content and exposure to alcohol content in movies and alcohol outcomes through cognitive processes. Note. Dashed lines in the top panel reflect the tests of parenting as a buffer of these associations (analytic Model 2). Thin elements in grey in the bottom panel denote non-significant pathways. Effects of parental movie restriction are relative to the complete restriction (“No, and I never watch them”) category. Not shown: residual direct effect of Parental Movie Restriction (Yes, while supervised) predicting full drink initiation (B = 0.16, p < 0.01); Parental Movie Restriction (Yes) predicting close friend and general peer norms (B = 0.14, p = 0.005; B = 0.23, p < 0.001), and Parental Movie Restriction (No, do it anyway) predicting close friend norms (B = 0.15, p < 0.001). Further, parental monitoring and rules predicting alcohol expectancies (B = −0.14, p = 0.007; B = 0.19, p < 0.001), and monitoring predicting close friend norms (B = −0.20, p < 0.001). All other residual direct effects were non-significant. All estimates are standardized; effects on full drink initiation are standardized effects on the logit scale. Thick elements in black were significant at p < 0.05

Table 2.

Standardized indirect effects from Model 1

95% LB mean 95% UB % Mediated
Target 1: movie exposure and alcohol outcomes
Willingness 72.8%
 Residual direct effect −0.046 0.025 0.093
 Total effecta 0.092
 Total indirect effect 0.026 0.067 0.110
Any Drink 40.3%
 Residual direct effect −0.045 0.080 0.182
 Total effect 0.134
 Total indirect effect 0.022 0.054 0.093
Frequency --b
 Residual direct effect −0.090 −0.034 0.032
 Total effect −0.008
 Total indirect effect 0.010 0.026 0.052
Target 2: direct and indirect effect of movie parenting restriction
Willingness by “No, but 42.1%
 I do it anyway”
 Residual direct effect −0.024 0.044 0.110
 Total effect 0.076
 Total indirect effect (2 indirect paths)c 0.023
 Total indirect effect (sequential) 0.003 0.009 0.020
Willingness by “Yes, if supervised” --b
 Residual direct effect −0.071 0.001 0.068
 Total effect 0.012
 Total indirect effect (2 indirect paths) −0.007
 Total indirect effect (sequential) 0.007 0.018 0.032
Willingness by “Yes” --b
 Residual direct effect −0.113 −0.027 0.057
 Total effect 0.001
 Total indirect effect (2 indirect paths) 0.006
 Total indirect effect (sequential) 0.008 0.022 0.039
Any Drink by “No, but I do it anyway” 13.3%
 Residual direct effect 0.003 0.098 0.189
 Total effect 0.113
 Total indirect effect (2 indirect paths) 0.008
 Total indirect effect (sequential) 0.002 0.007 0.017
Any Drink by “Yes, if supervised” 20.1%
 Residual direct effect 0.049 0.159 0.278
 Total effect 0.167
 Total indirect effect (2 indirect paths) −0.006
 Total indirect effect (sequential) 0.006 0.014 0.027
Any Drink by “Yes” --b
 Residual direct effect −0.052 0.081 0.221
 Total effect 0.101
 Total indirect effect (2 indirect paths) 0.002
 Total indirect effect (sequential) 0.007 0.018 0.033
Frequency by “No, but I do it anyway” --b
 Residual direct effect −0.064 0.010 0.117
 Total effect 0.012
 Total indirect effect (2 indirect paths) −0.001
 Total indirect effect (sequential) 0.001 0.003 0.009
Frequency by “Yes, if supervised” --b
 Residual direct effect −0.101 −0.025 0.048
 Total effect −0.030
 Total indirect effect (2 indirect paths) −0.012
 Total indirect effect (sequential) 0.003 0.007 0.014
Frequency by “Yes” --b
 Residual direct effect −0.165 −0.067 0.010
 Total effect −0.074
 Total indirect effect (2 indirect paths) −0.015
 Total indirect effect (sequential) 0.003 0.008 0.018
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Note. Complete restriction (“No, and I never watch them”) serves as the reference group for the Restriction variable. Indirect effects are adjusted for covariates so percent mediated is an adjusted estimate.

Total effects were derived from mean additions, and did not have normally distributed errors, so no confidence intervals were calculated.

These indirect effects could not be calculated due to negative residual direct effects.

The first indirect effect reflects paths through both mediators, but not sequentially. The sequential path reflects the complex chain.

The sequential mediation of the effects of parental restriction categories on drinking outcomes through movie exposure and in turn, alcohol cognitions, are also included in Table 2, and the full set of individual indirect effects are available in supplemental materials (Table S1). Proportions for specific mediated effects are included in Table 2 and for those that could be calculated, ranged from 13% to 42%; it was not possible to calculate cumulative mediated effects of all the parental restriction categories on drinking outcomes, since several of the simpler indirect effects and residual direct effects were negative (albeit non-significant). The nine separate sequential indirect effects from parental restriction categories (No, but I do it anyway, Yes when Supervised, Yes, compared to No) to each three drinking outcomes were each significant based on 99% bCI. The pattern of mediation differed across outcomes and mediators, such that the effects of close friend norms impacted all outcomes; in fact, all nine indirect effects through close friend norms being significant at the 99% bCI. General peer norms only impacted the probability of having consumed a full drink; three indirect effects through general peer norms were significant at the 99% bCI. Mediation through positive alcohol expectancies was generally not supported, with one out of nine indirect effects being significant at the 99% bCI. Only one out of nine residual direct effects (that of “Yes, Supervised” predicting any full drink) was significant, once mediation pathways were accounted for. The full mediation model accounted for greater variance (willingness R2 = 0.39, any drinking R2 = 0.18, frequency R2 = 0.13) than a model featuring only movie exposure, parental restriction, and the covariates as predictors (willingness R2 = 0.25, any drinking R2 = 0.11, frequency R2 = 0.12) or a model featuring only parental restriction and the covariates as predictors (willingness R2 = 0.24, any drinking R2 = 0.10, frequency R2 = 0.12).

Sensitivity analysis

To ensure that associations among the parental restriction and monitoring varibles and adolescent drinking were not be confounded by parental drinking, a sensitivity analysis compared Model 1 against a supplemental model that included the effect of parental drinking measured using AUDIT scores for both parents as a covariate (Babor et al. 2001). The supplemental model did not significantly improve model fit (χ2(7) = 2.94, p = 0.89; dBIC= 41.58). This suggests that controlling for parental drinking did not alter the relations among movie restriction, exposure, alcohol cognitions, and drinking behavior in this sample. None of the focal effects changed in significance in this supplemental model.

Tests of moderation

Models comparing the overall effects of the additions of moderating effects of parental behaviors suggest that adding parental monitoring, adding parental rules, or adding both, as moderators, resulted in less parsimonious models (Model 1: 122 parameters, Bayesian Information Criterion (BIC) = 33217; moderation by monitoring only: 125 parameters, BIC: 33234.51, LRT χ2 (3) = 1.27, p = 0.74; moderation by parental rules only: 125 parameters, BIC: 33235, LRT χ2 (3) = 0.68, p = 0.88; Moderation by both: 128 parameters, BIC: 33254, LRT χ2 (6) = 1.68, p = 0.94). Therefore, interpreting moderation results from Model 2 was not appropriate. In conclusion, parental behaviors did not buffer the influence of exposure to alcohol content in films on alcohol cognitions.

Discussion

Exposure to alcohol content in the media is consistently associated with increased risk for adolescent alcohol use, pointing to the need for effective strategies to reduce and mitigate exposure to such content. However, research on the mechanisms underlying the association between exposure to media alcohol content and youth drinking use is under-developed, and the modifiable factors that mitigate against likelihood of exposure to alcohol content are poorly understood. The present study tested a conceptual model that combines empirically-tested relationships between exposure to alcohol content in films and parental and peer sources of influence to elucidate pathways through which mature media content affects youth alcohol use. Findings indicated that restrictive media parenting influences both alcohol use and willingness to use alcohol over and above general parental monitoring and parental alcohol rules. Further, results revealed that when parental restrictions are not in place, adolescents report greater exposure to alcohol content in films, which in turn increases perceptions of close friend and general peer alcohol use, but not positive expectancies about alcohol. Thus, consistent with other literature (Jackson and Bartholow 2020), perceptions about peers play a role in the mechanisms by which alcohol-saturated media influences youth drinking behaviors.

Social-cognitive Processes Underlying the Effects of Alcohol Exposure on Youth Drinking

Perceived peer drinking norms was found to be an important mediating process linking exposure to alcohol media content and drinking outcomes; significant indirect effects were found through close peer norms for all three drinking outcomes and through general peer norms for two drinking outcomes (probability of having any drink of alcohol and willingness to drink). Notably, the effect of peer norms is robust to the potentially confounding influences of general and alcohol-specific parenting behaviors as well as demographic factors, which were controlled in the models. The effects of close peer norms were greater in magnitude, which reflects prior work indicating stronger effects of norms associated with important or proximal individuals than norms associated with more general referents (Cox et al. 2019; Kenney et al. 2017). This is supported by Social Comparison Theory (Festinger 1954) which states that one’s own behavior is more aligned with the behavior and beliefs of individuals closest to them and reflective of the fact that personal behaviors are more deeply influenced by individuals deemed to be important (Miller and Prentice 2016). Close peer norms are indicative of injunctive peer norms which capture the extent to which the particpant perceives their close friends to approve of alcohol use. Youth who are exposed to models of alcohol use in movies exhibit higher acceptability of alcohol use (Sargent et al. 2006). Moreover, exposure to alcohol content in movies has more pronounced effects on early drinking outcomes when movies are viewed with a friend (Jackson et al. 2018); therefore, the shared experience of watching media portrayals of alcohol use with friends may imply implicit approval by peers. The role of media in shaping normative perceptions of peer alcohol use may be particularly important in samples with low base rates of alcohol use in that such exposure may lead youth to sense greater approval of alcohol use by their peers than they would otherwise suspect by direct observation of the behavior alone. Research that examines the shared reactions of one’s close peers to mature media content would be a next step in developing interventions that address perceived peer norms of alcohol use among early adolescents.

In contrast to the mediating effects of perceived peer norms, the proposed indirect effects of exposure to alcohol use through positive alcohol expectancies were not significant. While several previous studies have demonstrated mediation of media alcohol exposure on alcohol use through positive alcohol expectancies (Dal Cin et al. 2009; Osberg et al. 2012), study results align with other studies that did not find such an association (Martino et al. 2006; Wills et al. 2009). Findings are consistent with observations that alcohol content communicates more about who you are, or who you could be, if you consume alcohol than about what might result if you drink alcohol (Martino et al. 2016). It may be that after being exposed to a given amount of alcohol imagery, expectancies are already formed, making an individual less susceptible to subsequent exposure.

The Role of Parents in Understanding and Addressing Media Effects on Youth Alcohol Use

Previous work has demonstrated that parental restriction of movies, particularly R-rated films, is associated with reduced alcohol use among adolescents (Tanski et al. 2010). The results of the present study expand such findings by showing that parental movie restrictions impact the exposure youth have to alcohol content in movies. While all three categories of parental movie restrictions (not allowed to watch, but do anyway; allowed to watch with adult supervision; allowed to watch without adult supervision) allow for exposure to alcohol content, the effects of exposure on processes associated with alcohol use behaviors may differ. Alcohol use was higher for youth who reported unrestricted access to R-rated movies. This direct influence of parental behavior on levels of exposure is important given that youth are increasingly exposed to media content on multiple devices that are easily accessible at all times. The rise in Internet-enabled personal devices to stream media indicates that parents will have a continued role to play in actively reducing their teen’s access to mature media content (Shin and Ismail 2014). The need for parental engagement in their child’s interactions with media is highlighted in the American Academy of Pediatrics Family Media Plan to generate personalized goals for media use within the home. Targeted media interventions for parents have been proposed to facilitate parental media management; for example, a recently developed TECH parenting model addresses four key behavioral domains: (1) Talking to kids about media use and monitoring their media activity, (2) Educating youth about risks associated with media content, (3) Actively co-viewing and co-using media with children, and (4) Establishing house rules for media use (Gabrielli et al. 2018). Parental restrictions on media content, in addition to active knowledge and discussion of how media content is being perceived by their child and their peers, will be necessary to mitigate the harms associated with increased exposure to alcohol content youth receive as the proliferation of media devices and access to mature content rises.

Although parental movie restriction proved important, neither general nor alcohol-specific parental monitoring behaviors moderated the association between alcohol exposure and the three tested mediators. However, reduced parental monitoring was associated with greater drinking and willingness to drink, albeit with relatively small effects, and parenting behaviors had effects on the social-cognitive processes themselves. Specifically, greater general parental monitoring was associated with lower positive expectancies about the effects of alcohol and lower alcohol-related close friend norms. Thus, not only can parents have a direct effect on reducing the amount of alcohol exposure a child has via media restrictions, they can also influence key processes associated with the effect of exposure on alcohol use through increased knowledge of their child’s activities and behaviors. Although adolescence is a developmental period marked by increased importance of peer relationships (Steinberg and Morris 2001), there is also a clear role for parents through their influence on peer processes. Active involvement by parents with their child’s friends and parents of those friends are shown to be directly associated with reductions in deviant behaviors (Cleveland et al. 2012). Extending this influence, it is plausible that parental effects may occur in more indirect pathways related to peer influence. Although studies have shown that adolescents may form beliefs from observing parental drinking behaviors (Ouellette et al. 1999), this is the first study to show that expectancies are affected by parental monitoring behaviors.

Strengths and Limitations

The data in this study are derived from a prospective study of adolescent alcohol use behavior that uniquely collected a rich array of contextual data related to media exposure and behaviors along with psychosocial measures and extensive alcohol outcomes data. The measure of movie alcohol content was based on a rigorous survey method that combined content analysis of contemporary movies and random assignment of movie titles to each participant. This study was able to test theoretically derived mechanistic pathways underlying exposure to alcohol alcohol in movies and adolescent alcohol use in a single analytic model, maintaining strict temporal ordering at roughly equivalent (annual) intervals. Such analyses increases understanding of the complex processes associated with youth alcohol use.

Study results should be viewed within the context of several limitations. Consent form return rates were relatively low. Notably, the sample was comprised of youth whose parents provided consent for participation in an intensive study on alcohol use. This in turn may have yielded a relatively low-risk sample who were naïve with regard to initial drinking experiences. The lower response rates are reflective of previous findings that use of active parental consent procedures can lead to lower than desired participation rates (Rojas et al. 2008). Parental nonresponse to active consent procedures may be a result of parents failure to act on the request (e.g., due to inconvenience) as opposed to explicit refusal (Frissell et al. 2004). Active consent requires more parental effort (Liu et al. 2017); thus, it is possible that these procedures impacted the characteristics of those who enrolled in the study. The sample was predominately White, and racial and ethnic group categorization was relatively crude (White vs non-White; Hispanic vs non-Hispanic) but was required based on sample characteristics.

It is also important to acknowledge that the present study found small effect sizes for indirect effects. Small effects may naturally arise from observational studies, where there is no experimental assignment, nor mediators that are able to be assessed with complete accuracy. There is a compounding effect of sequential mediators with less-than-perfect reliability since the subsequent parameter values that are limited by these reliabilities are multiplied with each other. For this reason, these small measured effects are likely to represent real-life effects that are much larger in magnitude. Parental media restriction was a single-item measure and thus did not capture the full breadth of parent control behaviors regarding their child’s media use. Alcohol exposure data were extracted from movies released in 2007–2013 and are thus somewhat dated, although the films were contemporary at the point of movie data collection in 2012–2013. However, adolescents may be viewing older movies through DVD or streaming devices, given that they often view movies on multiple occasions (Jackson et al. 2018). Finally, the possibility remains that some of the associations observed reflect exposure to other forms of media (e.g., social media, YouTube).

It is also possible that there are common influences underlying associations among measures of parenting and alcohol use such as impulsivity or shared genetic predisposition toward risk taking and alcohol use, or contexts supportive of alcohol use. Findings were robust to controls for perceived availability of alcohol in the home and parental drinking but it is possible that other omitted variables may account for links between parental restriction of mature media content and risk behaviors such as drinking.

Extensions and Future Directions

The present study is focused on films, a more traditional form of entertainment media. In the past decade, there has been an explosion of “new” (digital) media, including social media, YouTube, and the Internet. These platforms are highly interactive, allowing for exchange and manipulation of information (“electronic communication”) (Moreno et al. 2016). Teens today have been dubbed the “digital generation” and use of social media such as Instagram, Snapchat, Facebook, and Twitter is ubiquitous (Buckingham 2013). A recent (2018) Pew Research Center report (Anderson and Jiang 2018) indicates that among U.S. teens age 13–17, 85% report using YouTube, 72% Instagram, and 69% Snapchat. Data from the 2015 nationally representative Common Sense Census (Wartella et al. 2016) indicates that teenagers (13–18 years) use an average of 9 hours of entertainment media daily; this includes watching TV, movies, and online videos; playing video, computer, and mobile games; using social media; using the Internet; and listening to music. Further, 58% of teenagers watch movies, TV, and online videos “every day”, likely on YouTube or through streaming services such as Netflix, Amazon Prime, Hulu, and the like that allow for on-demand viewing. Youth may be viewing media on multiple occasions, in shorter segments of time, and concurrently with other apps (e.g., multi-window/split screen; using iPad and iPhone simultaneously). Thus, both traditional and new media are highly utilized by adolescents, and this is in part due to the ease of access to media on personal mobile devices such as Smartphones and tablets.

As with popular films, exposure to alcohol content in digital and social media is shown to predict alcohol use (Boyle et al. 2016; Nesi et al. 2017a, b). A recent meta-analysis found moderate effect sizes between alcohol‐related social media viewing and engagement (e.g., posting, liking, commenting) and alcohol use and problems (Curtis et al. 2018). Little is known with respect to the processes underlying these associations, although emerging work documents social norms as an important mechanism underlying the association between exposure to alcohol content in social media (Geusens and Beullens 2018) and youth drinking. New media allows the user to create and distribute, as well as to receive, content which is discussed and exchanged within social networks (Collins et al. 2011). Additionally, when a message source is peers and close friends, there is greater authenticity and the pro-alcohol message may be communicated below explicit levels of awareness (McCreanor et al. 2008).

Parental restriction of media use may be exceedingly difficult in the current media landscape. Youth typically view new media on portable devices that provide anytime, anywhere access to mature entertainment and digital media content, using streaming services or YouTube, and they may have multiple social media accounts. Traditional restrictions on content are easily circumvented (e.g., lying about a birthdate on age-gated websites or social media accounts) (Madden et al. 2013). Parents very likely enact different rules for media today but parental controls such as restrictions on screen time and type of apps and accounts are not consistently utilized with adolescents and when they are used, there is often non-compliance (Toh et al. 2019). Although parents continue to have an influence on their children through young adulthood (Abar and Turrisi 2008), the expanded autonomy and independence associated with maturing through adolescence would apply to media usage. Thus, the particular restrictive media parenting behaviors included in this study may be unrealistic and perhaps even counterproductive at later ages where higher levels of independence is normative. Indeed, restriction of online content appears to reduce the development of skills to effectively cope with online risks (Wisniewski et al. 2015).

Future research also is recommended to take a closer look at racial and ethnic influences. Preliminary investigations with these data do suggest that exposure to movie alcohol content is less predictive of alcohol use for non-White youth possibly because minority youth are less responsive to White actors, which predominate in popular movies (Tanski et al. 2012); research has yet to test whether parental restrictions of mature media content vary across racial/ethnic group heterogeneity given differential effects of parental socialization behaviors between White and non-White youth (Moreno et al. 2017).

Conclusion

Previous studies have specified unique and robust negative effects of exposure to alcohol content in the media on youth drinking as well as protective parenting behaviors to reduce these harms of media influence. Guided by a comprehensive model of media, parenting, and social-cognitive processes, the current study examined theoretically-derived mechanisms by which exposure to mature media content influences three levels of early drinking- willingness to drink, drinking initiation, and frequency of alcohol use. Youth reported greater exposure to alcohol content in films when they perceived their parents to not employ media restrictions, and that exposure in turn increased peer norms about drinking, particularly among close peers. Positive alcohol expectancies did not serve as mediator of the association between exposure and drinking outcomes. The findings suggests an important role that peer perceptions about alcohol use play in the undestanding of how acohol-saturated media is associated with youth drinking.

Supplementary Material

Suppl Material Jackson et al.

Funding

This study was funded by the National Institute on Alcohol Abuse and Alcoholism (R01 AA016838, PI Jackson; K01 AA026335, PI: Janssen; T32 AA007459, PI Monti).

Biographies

Kristina M. Jackson is a Professor (Research) at the Center for Alcohol and Addiction Studies at Brown University. Her major research interests center on the developmental course of substance use among adolescents and young adults and to examine individual- and contextual-level risk factors for substance use initiation and progression, including the influence of exposure to alcohol content in social and entertainment media.

Tim Janssen is an Assistant Professor (Research) at the Center for Alcohol and Addiction Studies at Brown University. His major research interests include context effects on the decision to drink and the application advanced analytic approaches to study mechanisms underlying substance use initiation and escalation.

Melissa J. Cox is an Assistant Professor at East Carolina University. Her research examines the social ecology of youth and youth adult alcohol misuse.

Suzanne M. Colby is a Professor (Research) at the Center for Alcohol and Addiction Studies at Brown University. Her major research interests include smoking and nicotine addiction in adolescents and young adults as well as intervention development.

Nancy P. Barnett is a Professor (Research) at the Center for Alcohol and Addiction Studies at Brown University. Her major research interests include peer influences within social networks, behavior change following alcohol-related experiences, and the utility of alcohol biosensors for research and clinical applications.

James Sargent is a Professor at the C. Everett Koop Institute at the Geisel School of Medicine at Dartmouth. His major research interests include media and marketing exposures on child and adolescent substance use (tobacco and alcohol), aggression, and violence.

Footnotes

Supplementary information The online version of this article (https://doi.org/10.1007/s10964-020-01373-0) contains supplementary material, which is available to authorized users.

Compliance with Ethical Standards

Conflict of Interest The authors declare that they have no conflict of interest.

Ethical Approval The Brown University Institutional Review Board approved project procedures.

Informed Consent Written informed consent was obtained from all participants.

Data Sharing and Declaration

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Suppl Material Jackson et al.
NIHMS1964598-supplement-Suppl_Material_Jackson_et_al_.docx (32.6KB, docx)

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request

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