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Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2021 Aug 4;82(4):460–469. doi: 10.15288/jsad.2021.82.460

Profiles of Parenting in the Digital Age: Associations With Adolescent Alcohol and Marijuana Use

Melissa J Cox a,*, Tim Janssen b, Joy Gabrielli c, Kristina M Jackson b
PMCID: PMC8356790  PMID: 34343077

Abstract

Objective:

This study assessed parental communication and behavior related to children's Internet and social media usage to delineate profiles of parenting regarding these newer forms of media and associated those profiles with youth alcohol and marijuana use.

Method:

Using data from 748 adolescents (mean age = 15.8, 52% female, 25% non-White) and their parents, latent class analysis was performed to identify classes based on items concerning device ownership, monitoring, and communication of online activities. The associations between class membership and ever use of alcohol and marijuana were then tested, controlling for screen time, general parenting, substance availability, and deviance.

Results:

We identified five classes: high media parenting (23%), low media parenting (20%), moderate media parenting with limited device access (11%), moderate media parenting with high device access (25%), and low monitoring but high communication about online activities (21%). Probability of class membership was differentially associated with contemporaneous and 1-year prospective alcohol and marijuana use. The low-device-access class had the highest percentage of abstainers at both time points. The lowest rate of abstaining was associated with membership in the high-device-access class but moderate levels of monitoring. Membership in the low media parenting class was associated with use of both substances.

Conclusions:

This study provides a novel exploration of media parenting, an important construct in the context of increased access to personalized media devices that allow for streaming of mature media content related to substance use.

Adolescent substance use is a serious and costly public health concern that is implicated in a range of negative health outcomes including increased risk for substance use disorder (Bonomo et al., 2004; Erskine et al., 2015; Gore et al., 2011; Johnson et al., 2005). Substance use that is initiated in adolescence is particularly alarming, since such behaviors are more difficult to change if they are sustained into adulthood (Gray & Squeglia, 2018). Thus, prevention of substance use in adolescence is a high priority in the promotion of future health outcomes (Fergusson & Boden, 2008; Hill et al., 2000; Moss et al., 2014). The present study seeks to enhance preventative efforts through improved identification of parenting practices that mitigate media-related risks for youth use of alcohol and marijuana, which continue to be the most commonly used substances among U.S. adolescents (Miech et al., 2019).

Media messaging related to substance use represents a modifiable environmental influence on youth alcohol and marijuana use (Jackson et al., 2018). A substantial literature supports the theoretical link between exposure to depictions of substance use in media content or digital marketing and subsequent initiation and progression of alcohol (Finan et al., 2020; Jernigan et al., 2017; Noel et al., 2020) and marijuana use (Cabrera-Nguyen et al., 2016; D’Amico et al., 2015). These findings have been replicated in a number of prospective studies with national and international samples of youth (Mejia et al., 2016; Morgenstern et al., 2011; Sargent et al., 2006). Media has been shown to affect key cognitive and social processes associated with adolescent alcohol and marijuana use (see reviews by Jackson et al., 2018, and Jackson & Bartholow, 2020). For example, exposure to alcohol marketing and media depictions of substance use behavior is associated with increased peer social norms including descriptive (e.g., how much peers drink) and injunctive (e.g., peer approval of drinking) norms (Dal Cin et al., 2009; Janssen et al., 2018). This is consistent with Social Learning Theory (Bandura, 1986), which describes how youth replicate the behaviors they see modeled by influential others, including “super peers” in the media (Elmore et al., 2017). With the emergence of personalized digital devices, streaming media content, and the rapid uptake of social media and Internet use by youth (Anderson & Jiang, 2018; Rideout, 2019), more recent studies have demonstrated a link between social and Internet-based media exposures to substance use and youth initiation and progression of alcohol (Jernigan et al., 2017; Nesi et al., 2017) and marijuana use (Whitehill et al., 2020).

Parents serve an important role as gatekeepers to youth media access, and a growing literature on media parenting practices suggests that there are many ways in which parents can intervene upon youth media engagement (Collier et al., 2016; Padilla-Walker et al., 2018). Parents have long been recognized as influencers for youth substance use initiation and progression to risky substance use behaviors (Botzet et al., 2019; Ladis et al., 2019). From the general substance use literature, reports indicate that parents can influence youth outcomes through restriction of access to substance use products, discussion about risks related to youth substance use, monitoring of youth behavior, and modeling of healthy behaviors (Handley & Chassin, 2013; Jackson et al., 2016; Koning et al., 2012; van der Zwaluw et al. 2008). In the literature on media parenting, similar parental behaviors (e.g., mediation: discussion about media usage and risks; restriction: clear house rules about media access; co-use: modeling of healthy media practices) appear relevant for effective media parenting (Collier et al., 2016; Gabrielli et al., 2018; Padilla-Walker et al., 2018).

Prior work on media parenting practices to reduce youth risk for substance use has focused primarily on the construct of parental media restriction (i.e., R-movie restriction). Although such restriction has been associated with substance use outcomes (Collier et al., 2016; Cox et al., 2018; Tanski et al., 2010), additional literature on parenting practices around youth media access suggests that behaviors such as parental media mediation and co-viewing may be influential as well (Collier et al., 2016; Padilla-Walker et al., 2018). Moreover, the mobile nature of teens’ online media usage requires increased monitoring and intervention by parents—basic parental restriction is a less feasible approach for management of youth Internet use. Because of lack of industry regulation and ease of online posting, exposure to alcohol (Hendriks et al., 2018) and marijuana (Whitehill et al., 2020) content on digital media is high, particularly on social networking sites (e.g., Twitter, Instagram, Snapchat, TikTok). Youth now stream media content more frequently than they consume media during live air times on traditional media (Rideout et al., 2019). Youth also receive greater exposure to substance use through new media as compared with traditional media such as television and movies (Jernigan et al., 2017). Despite these risks, little is known about what parents do to manage their teens’ online access and presence, with only preliminary work examining the role of media parenting related to youth Internet use (Shin & Ismail, 2014). Further, there is little known about whether these parental media monitoring behaviors are effective in reducing or preventing youth substance use.

The present study seeks to address two gaps in the literature: (a) to provide a more comprehensive assessment of media parenting behaviors in the context of youth substance use, and (b) to address media parenting specifically for youth Internet use, which is arguably the fastest growing media influence for adolescents. Given the lack of prior quantitative evaluation of digital media parenting measures, we chose to use a person-centered approach to define profiles of digital media parenting and associate those profiles with youth alcohol and marijuana use. We hypothesized that unique media parenting profiles would emerge and that profiles reflecting media leniency would be associated with youth alcohol and marijuana use initiation.

Method

Study procedures were approved by the university's institutional review board (IRB#0808992539). Adolescents provided assent and parents provided consent for their child and for their own participation at study enrollment. A Certificate of Confidentiality was obtained to protect participant confidentiality. Data for this study were drawn from a longitudinal survey on adolescent substance use initiation and progression. Adolescents (N = 1,023; 52% female; 12% Hispanic; 76% White, 5% Black, 3% Asian, 2% American Indian, 8% mixed race, 6% other race/ethnicity; Mage at enrollment of 12.5 years) and their parents were recruited from six middle schools (Grades 6–8) in Rhode Island. Adolescents completed a baseline assessment, semi-annual surveys for 2 years, and a 3-year follow-up survey. At the point of new study funding, participants who continued in the study were assessed at quarterly intervals thereafter. All surveys were web-based, and participants were compensated $20 for completion of each follow-up survey (for further details on study design, see Jackson et al., 2015). Parents completed mailed pencil-and-paper surveys at study enrollment (87% of responding parents were mothers/female guardians) and three more times following enrollment, the latter two of which contained information about media parenting. For the present study, the first completed parental assessment that included media parenting was designated as Time 1 (T1). For most participants (95%; n = 711 of the 748 who had completed at least one assessment of media parenting), this corresponded to the first adolescent quarterly assessment; for the remaining 5% (n = 37) of participants it corresponded to the fifth quarterly adolescent assessment. Self-reports of youth substance use were selected from the same timepoint as the parental assessment (T1) and from the four quarterly assessments that followed (a composite measure to generate Time 2 [T2]). Other adolescent measures used as correlates in the analyses were also selected from T1. The analytical sample comprised those adolescents and their parents who completed a media parenting assessment. It did not differ from the full sample on gender, age, or race, but was significantly less likely to be Hispanic, χ2(1) = 8.8, p < .01, and less likely to have used lunch subsidies, the proxy for family socioeconomic status, χ2(1) = 11.6, p < .001.

Measures

Demographics

Participants reported their sex, race, and Hispanic ethnicity at enrollment. Age at social media assessment was calculated based on date of birth. Parent-reported child eligibility for free or reduced-price lunch was used as a proxy for socioeconomic status (SES).

Media parenting

A set of 29 parent-reported items was developed for this study based on several sources available at the time regarding media and parenting (Ellison et al., 2007; Madden et al., 2013; Pew Research Center, 2012; Ross et al., 2009). Three of the 29 indicators assessed parent's own use of the Internet and social media and 26 assessed parenting practices specific to the child's access to media devices and use of the Internet and social media (Table 1). One parent in the household completed the media parenting assessment.

Table 1.

Endorsement rates and separation scores for media parenting items (n = 748)

graphic file with name jsad.2021.82.460tbl1.jpg

Indicator n % yes % separation
A: Parent uses Internet 721 97 20
B: Parent sends/receives email 710 96 40
C: Parent uses online social network website 702 80 40
D: Child has cell phone 746 92 50
E: Child cell phone has Internet capability 520 81 40
F: Child has own computer 741 71 60
G: Child can keep computer in bedroom 742 69 50
H: Child has TV in bedroom 744 53 70
I: Child uses Internet 743 98 30
J: Child uses online social network website 735 90 70
K: Parent friends with child on social network 671 77 40
L: Parent has seen images of alcohol on child social network account 656 4 0
M: Parent believes child has posted alcohol images on social network account 649 2 10
N: Parent has seen references to alcohol on social network account 643 3 0
O: Parent believes child has made references to alcohol on social network account 638 2 20
P: Parent friends with child's friends on social network website 652 56 80
Q: Parent read privacy policy for website or social network child uses 686 36 60
R: Parent searched for child's name online 705 41 50
S: Parent helped child set up privacy settings for social network website 689 33 60
T: Parent talked with child because concerned with something they posted 671 31 50
U: Parent commented or responded to child's social network account 672 46 70
V: Parent uses parental controls for online activity 715 30 50
W: Parent checks which websites child visits 713 49 50
X: Parent uses controls to restrict child's use of their cell phone 691 20 40
Y: Parent has suggested ways to use Internet safely 734 71 40
Z: Parent suggested ways to behave toward others online 734 75 40
AA: Parent discussed with child what they do online 732 77 10
BB: Parent discussed what should and should not be shared online 728 86 10
CC: Child has indicated they saw something online that concerned them 709 15 40
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Notes: Separation score refers to the proportion of significant differences in 10 possible pairwise comparisons of item endorsements in a five-class solution.

Substance use involvement

At both the time of the media parenting assessment (contemporaneous), and at four quarterly time points following (1-year prospective), adolescents reported on their alcohol and marijuana use. For alcohol, a single item asked: “Have you ever had a full drink of alcohol?” with response options “yes” and “no.” For marijuana, a single item asked, “Have you ever used marijuana (pot, hash, hash oil, etc.)?” with response options “yes” and “no.” From these responses we computed a four-level categorical variable at each time point reflecting ever use of the two substances: neither alcohol nor marijuana (0), alcohol only (1), marijuana only (2), both alcohol and marijuana (3).

Correlates

We selected individual, social, and environ mental correlates of youth media exposure and substance use to include in analyses as control variables that are consistent with prior studies on media influences on youth substance use (e.g., Koordeman et al., 2012; Morgenstern et al., 2011; Sargent et al., 2006). To measure general screen time, youth reported how often they watched TV/videos/DVDs on a scale from never (0) to more than once a day (5). We used a binary measure of perceived availability of substances, “If you wanted to get some beer, wine, or hard liquor, could you get some?” (Arthur et al., 2000). A parallel item was asked separately for marijuana. Response options were no (0) or yes (1). We used two items (Arthur et al, 2000; Zucker et al., 1994) to assess peer alcohol and marijuana use with the prompt, “Think of your three best friends (the friends you feel closest to). In the past six months, have any of your friends tried beer, wine, or hard liquor when their parents didn't know about it?”, with a parallel item for “Used marijuana?”. Response options were no (0) or yes (1).

Sensation seeking was measured using six items from the UPPS+P Impulsive Behavior Scale (e.g., “I quite enjoy taking risks”; Lynam, 2006). Responses ranged from disagree strongly (1) to agree strongly (4) and were averaged across the six items (α = .87). Delinquency was measured using six items from the Problem Behavior Frequency Scale (e.g., “How often in the past 30 days did you skip school/damage property/been suspended?”; Farrell et al., 1992). Responses ranged from never (1) to 20 or more times (6) and were summed across the six items. Youth completed the Sources of Parental Knowledge Scale (Kerr & Stattin, 2000), which included five items representing parental solicitation (α = .85), five items representing parental control (α = .91), and five items representing child disclosure (α = .77). Items were measured on a five-point scale, and averages were created for each parental knowledge subscale.

Analysis strategy

We performed latent class analysis (LCA; Bartholomew, 1987; McCutcheon, 1987) to examine whether there were identifiable subclasses of Internet and social media parenting styles in the sample. LCA identifies subpopulations based on similar threshold values (i.e., similar probabilities of endorsing certain items for dichotomous indicators). To determine the optimal number of classes, we evaluated four comparative fit indices including the Akaike's Information Criterion (AIC), Akaike's Information Criterion with Correction (AICC), Bayesian Information Criteria (BIC), and sample size–adjusted BIC (Schwarz, 1978). We computed a likelihood ratio test (LRT), Satorra-Bentler corrected LRT (SB-LRT; Satorra, 2000), Vuong–Lo–Mendell–Rubin LRT (VLMR LRT; Lo et al., 2001), and bootstrapped LRT for relative improvement in model fit. We also considered whether a suitable number of participants were assigned to each class (minimum of 5% of the sample) when identifying the optimal class solution and we consulted the entropy value, the average posterior class probability, and the odds of correct classification for each potential class solution.

The latent class solution produces item endorsement probabilities within each class as well as individual class membership probabilities, which reflect the proportion of individuals who are categorized in a given class. Auxiliary models can be used to test whether probability of class membership is related to individual, social, and environmental correlates as well as substance use, while preserving the meaning of class membership (Asparouhov & Muthén, 2014a). To investigate the bivariate relation between class membership and these correlates, we used a set of arbitrary second models in which certain predictors are included (an auxiliary approach referred to as the Dcat method for categorical variables or the Bolck et al., 2004, method for continuous predictors; also see Asparouhov & Muthén, 2014b; Vermunt, 2010). These methods avoid “contamination” of class membership (differences in classification without change to the indicators) because of the addition of covariates to the structural model. Next, as a sensitivity analysis we repeated those regressions and included age as a covariate using the three-step approach method (Asparouhov & Muthén, 2014b), since the methods from the first step do now allow for multivariate comparisons. Using this approach, substance use variables were regressed on age, and probabilities of endorsement and their standard errors were then estimated independently within each latent class. Full information maximum likelihood estimation was used to account for missing data. All analyses were conducted in Mplus Version 8.4 (Muthén & Muthén, 2017).

Results

Media parenting classes description

Table 1 shows the endorsement rates for each media parenting item. To delineate the role of each item in distinguishing classes, we calculated a percentage separation value that reflects the percentage of the 10 possible pairwise comparisons of item endorsement between classes in a five-class solution (the optimal solution described below) that are significantly different. Thus, a higher percentage separation value indicates that the item was more discriminatory in determining class membership. Indicators with the highest separation score reflected child's use and ownership of devices, monitoring behaviors such as whether the parent was friends with their child's friends on a social networking website, whether they read and set up privacy restrictions on those websites, and whether they had engaged with their child's account. Parental awareness of alcohol-related content on their child's social network account and parental discussion of what is shared online were not good discriminators of class membership.

Table 2 shows the fit indices for the estimated class enumeration solutions. As shown, a five-class solution was considered optimal based on the significance of the VLMR criterion (p < .01), and the AICC (16,378), BIC (16,992), and sample size–adjusted BIC values (16,518). The average posterior probabilities per class, defined as the mean of the Class k posterior class probabilities across all individuals whose maximum posterior class probability is for Class k, as well as the odds of correct classification, are also provided for the chosen five-class solution. As shown in Figure 1, the five classes were defined as follows: (1) moderate levels of media parenting and low device access (referring to whether a child has their own personal phone and/or computer) (n = 130; 11%; “moderate media parenting + low device access”), (2) low monitoring behaviors but high communication regarding online behaviors (n = 122; 21%; “low monitoring + high communication”), (3) low media parenting overall in regard to monitoring and communication practices (n = 121; 20%; “low media parenting”), (4) high levels of both monitoring and communication practices (n = 164; 23%; “high media parenting”), and (5) moderate levels of monitoring and communication but high levels of device access (n = 211; 25%; “moderate media parenting + high device access”).

Table 2.

Latent class analysis class enumeration fit indices and counts (n = 748)

graphic file with name jsad.2021.82.460tbl2.jpg

Comparative fit indices Classification diagnostics VLMR
Variable AIC AICC BIC SA-BIC LRT SB-LRT LRT BLRT Entropy
1 class 18,496 18,498 18,630 18,538 — — 1
2 class 17,137 17,148 17,410 17,222 <.001 <.001 <.001 <.001 .799
3 class 16,707 16,732 17,118 16,836 <.001 <.001 .458 <.001 .801
4 class 16,450 16,495 16,999 16,621 <.001 <.001 .086 <.001 .813
5 class 16,304 16,378 16,992 16,518 <.001 <.001 .013 <.001 .826
C1 C2 C3 C4 C5
1 class 748
2 class 414 334
3 class 304 239 205
4 class 219 190 208 131
5 class 130 122 121 164 211
avePPk .884 .885 .933 .887 .875
OCC 53.07 56.38 102.70 45.86 34.57
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Notes: AIC = Akaike's Information Criterion; AICC = Akaike's Information Criterion with correction; BIC = Bayesian Information Criterion; SA-BIC = sample size–adjusted BIC; LRT = log likelihood ratio test; SB-LRT = Satorra-Bentler corrected LRT; VLMR LRT = Vuong–Lo–Mendell–Rubin log likelihood ratio test; BLRT = bootstrapped LRT; avePPk = average posterior probabilities per class for the chosen five-class solution; OCC = odds of correct classification for the chosen five-class solution. Bold indicates statistical significance at α less than .05.

Figure 1.

Figure 1.

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Item endorsement probability within class (n = 748). Probability of endorsing each of the 29 media parenting indicators within each class in the optimal five-class solution. Indicators reflect the following items: A–C: parental use of Internet and social media; D–H: child access to media devices; I–J: child use of Internet and social media; K: parent friends with child on social network; L–O: parental knowledge of references to alcohol on social network sites; P–X: parental monitoring of child's online behavior; Y–CC: parental communication with child about online behaviors.

Table 3 shows differences in individual, social, and environmental correlates across classes. There were no differences across classes with respect to child's gender or ethnicity. There was a significant difference in race (p = .034), age (p < .001), and SES (p < .001). There were no differences across classes with respect to general screen time, peer alcohol and marijuana use, sensation seeking, deviancy, and child disclosure or parental solicitation. There were significant differences across classes for alcohol availability (p = .019), marijuana availability (p = .045), and parental control (p = .019). Endorsement of perceived substance availability was highest among the low monitoring and high communication group (alcohol: 60%, marijuana: 49%) and the highest levels of parental control were among those in the high media parenting class (M = 4.21, SD = 0.10).

Table 3.

Class differences in sociodemographic and individual correlates and substance use (n = 748)

graphic file with name jsad.2021.82.460tbl3.jpg

C1: Moderate media parenting + low device access (n = 130) C2: Low monitoring + high communication (n = 122) C3: Low media parenting overall (n = 121) C4: High media parenting overall (n = 164) C5: Moderate media parenting + high device access (n = 211)
Variable M/% SD M/% SD M/% SD M/% SD M/% SD Overall p
Male gender 51.80 54.80 47.40 41.80 45.60 .380
White race 76.80 89.10 79.30 79.00 93.00 .034
Hispanic ethnicity 12.00 8.80 13.00 13.00 7.50 .621
Age 15.52 0.10 15.91 0.11 16.17 0.10 15.58 0.09 16.05 0.08 <.001
SES 40.60 13.50 26.70 48.00 31.70 <.001
General screen time 3.18 0.19 2.72 0.21 3.10 0.19 3.48 0.17 3.25 0.15 .073
Alcohol availability 37 60 54 39 55 .019
Marijuana availability 36 49 46 36 56 .045
Peer alcohol use 15 22 24 25 29 .184
Peer marijuana use 18 18 24 25 23 .729
Sensation seeking 2.34 0.09 2.38 0.10 2.33 0.08 2.23 0.09 2.29 0.07 .633
Delinquency 0.59 0.23 0.50 0.15 0.79 0.20 0.67 0.17 0.73 0.15 .788
Par know: child disclosure 3.66 0.10 3.78 0.10 3.55 0.09 3.60 0.09 3.60 0.08 .538
Par know: parental control 4.13 0.12 4.08 0.12 3.73 0.11 4.21 0.10 3.97 0.10 .019
Par know: parental solicitation 3.04 0.12 3.25 0.11 3.08 0.11 3.12 0.11 3.11 0.09 .800
Contemporaneous substance
use involvement
No alcohol, no marijuana 65.70 63.20 55.20 54.70 45.00
Alcohol only 7.40 12.90 14.70 16.50 23.70
Marijuana only 6.30 10.40 5.60 7.80 9.30
Both alcohol and marijuana 20.60 13.50 24.50 21.00 21.90 .048
Prospective substance
use involvement
No alcohol, no marijuana 56.80 46.90 44.00 44.80 30.70
Alcohol only 14.60 16.20 12.00 24.60 24.50
Marijuana only 5.70 9.90 5.00 8.00 3.90
Both alcohol and marijuana 22.90 26.90 39.00 22.60 40.90 .03
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Notes: SES = Family socioeconomic status defined by eligibility for free or reduced-price lunch; general screen time = frequency of watching TV/DVDs/videos; Alcohol availability = endorsement youth could get beer, wine, or liquor if they wanted; marijuana availability = endorsement youth could get marijuana if they wanted; peer alcohol use = perceived friend use of alcohol in past 6 months; peer marijuana use = perceived friend use of marijuana in past 6 months; sensation-seeking = subscale of UPPS+P Impulsive Behavior Scale (Lynam, 2006); delinquency = subscale of Problem Behavior Frequency Scale (Farrell et al., 1992); par know = subscales of Sources of Parental Knowledge Scale (Kerr & Stattin, 2000). Bold indicates statistical significance at α less than .05.

Associations between media parenting class membership and adolescent substance use

Table 3 describes the endorsement rates for each category of substance use at both time points. There were significant overall differences between classes for contemporaneous substance use, χ2(12) = 21.1, p = .048, and prospective substance use, χ2(12) = 22.7, p = .03. The moderate media parenting + low device access class had the largest proportion of abstainers of both substances in contemporaneous (65.7%) and prospective (56.8%) analyses, whereas the moderate media parenting + high device access class had the lowest proportion of abstainers of alcohol and marijuana in contemporaneous (45.0%) and prospective (30.7%) analyses. The low media parenting group reported the highest level of use of both alcohol and marijuana in contemporaneous analyses (24.5%) and had high levels in the prospective (39.0%) analyses along with the moderate media parenting + high device access group (40.9%). In sensitivity analyses accounting for age in a multivariate model, although some trends listed above were still significant, the overall differences in contemporaneous, χ2(12) = 15.3, p = .225, and prospective, χ2(12) = 12.9, p = .374, substance use were no longer significant.

Discussion

Given the widespread exposure to substance use content on Internet-based media, we identified profiles of parenting based on behaviors parents use to monitor and communicate with their children about Internet and social media usage. We used a person-centered approach to identify five digital media parenting profiles that reflected access to devices and the Internet, monitoring of their child's online activities, and communication about online behavior. Our results indicated that variability exists in how parents approach their child's online behaviors and that these constellations of digital parenting behaviors are uniquely associated with youth alcohol and marijuana use as well as correlates such as peer substance use. One class reflected low levels of media parenting overall, one indicated high levels of media parenting, two consisted of moderate monitoring and communication behaviors but deviated based on child's access to devices, and a final class exhibited low monitoring but high communication practices.

A child's access to their own cell phone, computer, and TV and the ability to have those devices in their bedrooms varied across classes. Personal access to devices that are Internet-enabled without restrictions provides access to mature media content. Marketing of substances on social media platforms has increased over time, and that marketing is tailored to appeal to youth (Freeman, 2012; Winpenny et al., 2013). Youth often violate age restrictions for media that include thematic content that is appealing to them such as romance and individuality (Noel et al., 2017). Social media content often depicts alcohol intoxication with humor and attractiveness and infrequently depicts negative outcomes (Cavazos-Rehg et al., 2015; Primack et al., 2015), which in turn can influence important cognitions such as social norms and outcome expectancies that are known correlates of substance use (Collins et al., 2017; Janssen et al., 2018). In the present study, the class with the lowest device access had the highest rate of abstainers of both alcohol and marijuana use, whereas the class with higher device access but lower monitoring of behaviors on those devices had the highest rates of substance use. This suggests that mere access to digital devices has an impact on health outcomes. Current regulations to restrict underage access to substance-related content such as age confirmations on websites are weak at best. Without such restrictions to online media, adolescents have access to a full range of mature content that they can view on demand (Barry et al., 2015). We also found variability in parental use of controls for online activity, which may be a necessary parental mediation behavior given the lack of controls otherwise.

In line with our hypotheses, the class defined by low levels of media parenting was associated with elevated reports of contemporaneous and prospective alcohol and marijuana use. This aligns with previous research indicating that a lack of parental media restrictions with traditional media formats is risk-inducing for marijuana use (Cox et al., 2018; Dalton et al., 2002). Items concerning parental monitoring of social network websites such as reading privacy policies for the websites and actively engaging with the child's account were distinguishing factors among the classes of media parenting. When a parent is connected to their child's friends on social networking sites they may be more actively engaging in their child's online presence and connected to what their child is exposed to through peers. Further, results indicate that if a child has expanded access to devices and the Internet then providing some monitoring of online behaviors may not be enough. Communication about online behaviors is necessary to reduce risks for substance use. This affirms previous work indicating that talking about media messages and co-viewing media to facilitate those conversations is a needed complement to other media parenting practices (Collier et al., 2016; Padilla-Walker et al., 2018).

Contrary to expectations, membership in the high media parenting class was not associated with protective effects for adolescent alcohol and marijuana use. In cross-sectional analyses, we are unable to determine the temporal ordering of this effect. Parents may reactively exert greater control over their child's Internet use following a negative or inappropriate online event. The high media parenting class was also associated with high levels of peer alcohol and marijuana use; thus, parents may be reacting to deviant peer behavior. Further, it may be that effects are attenuated, or even risk-inducing, if those behaviors are perceived as harsh or overly intrusive (Stice et al., 1993). Over-engaged media parenting might also indicate highly media-involved parents who are modeling inappropriate media behavior or promoting mature content themselves.

Importantly, when age was included in models associating class membership to substance use, significant class differences for substance use were attenuated. This is consistent with the finding that mean age was higher in the high access group than the low access group. This reflects needed attention to developmentally appropriate levels of parental control as it pertains to media. As adolescents age and seek greater autonomy, parental control of their Internet and social media use that occurs on a personal mobile device may seem overly intrusive. It will be important to identify developmentally appropriate levels of parental media restrictions as the landscape of device ownership and media access evolves.

The results of this study should be viewed in the context of several strengths and limitations. Data on parental behaviors were only collected from a single parent and thus may not represent the full scope of these behaviors if more than one adult is present in the home. Parental behavior was identified through self-report and therefore may reflect social desirability bias. Future studies that triangulate media parenting behavior constructs are needed. The present study focused on a simple index of substance use; future work should examine more nuanced indices of alcohol and marijuana consumption and problem use, particularly given the increases in marijuana use and cultural norms surrounding its use. Our study did not assess cannabis-specific parenting items as the literature on media effects at the time of study administration focused on alcohol and tobacco. Even today the literature on cannabis-specific parenting remains scant (Sternberg et al., 2019; Vermeulen-Smit et al., 2015) and is an area for additional research. Further, research should explore the effects of media parenting on new substance products, most notably e-cigarettes given the rise in their use and vaping behaviors by U.S. adolescents (Wang et al., 2020). Despite these limitations, results indicate that there are meaningful differences in how parents behave with regard to their child's Internet and social media usage, and those parenting behaviors are associated with youth alcohol and marijuana use. This study provides novel insight into how parents monitor and communicate with their children in a digital world, which will continue to be important as youth exposure to substance use increases with the proliferation of technology and media.

Footnotes

This research was supported by National Institute on Alcohol Abuse and Alcoholism Grant Nos. R01 AA016838 (principal investigator: Kristina M. Jackson), K01 AA026335 (principal investigator: Tim Janssen), and T32 AA007459 (principal investigator: Peter M. Monti). The National Institutes of Health had no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or the decision to submit the article for publication.

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