Skip to main content
Download PDF

Problematic social media use and alcohol expectancies in early adolescents

BMC Public Health volume 23, Article number: 430 (2023) Cite this article

Abstract

Background

Alcohol expectancies are beliefs regarding positive (e.g., tension reduction) or negative (e.g., loss of motor coordination) effects of alcohol. Based on Social Learning Theory, social media can influence alcohol expectancies in adolescents. In particular, problematic social media use – which can reflect elements of addiction, including mood modification, tolerance, withdrawal, conflict, and relapse – could be linked to alcohol expectancies. We aimed to determine the associations between problematic social media use and alcohol expectancies in a national (U.S.) cohort of 10-14-year-old early adolescents.

Methods

We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 9,008) at the Year 2 assessment (2018–2020). Unadjusted and adjusted linear regression analyses were conducted to examine the associations between problematic social media use and alcohol expectancies (positive and negative), adjusting for race/ethnicity, sex, household income, parent education, sexual orientation, parental marital status, and study site. Furthermore, we computed marginal predicted probabilities to aid in interpreting findings.

Results

The sample was 48.7% female and racially and ethnically diverse (43.0% non-White), with a mean age of 12.02 ± 0.66 years old. In models adjusted for confounders including both time spent on social media and problematic social media use, time spent on social media was not associated with positive or negative alcohol expectancies, but higher problematic social media use score was associated with higher positive (B = 0.045, 95% confidence interval [CI] 0.020–0.069) and negative (B = 0.072, 95% CI 0.043–0.101) alcohol expectancies scores.

Conclusion

Problematic social media use was associated with both positive and negative alcohol expectancies in a demographically diverse national sample of early adolescents in the U.S. Given the small effect sizes of the current study, future studies should further examine these relationships prospectively, as well as the mechanisms linking problematic social media use to alcohol expectancies and alcohol consumption. Because alcohol expectancies are modifiable and linked with alcohol initiation, they could be a target for future prevention efforts.

Peer Review reports

Introduction

Excessive alcohol use accounts for over a quarter of deaths among U.S. young adults [1], and over one-third of 12th graders report past-month alcohol use [2]. Adolescents who drink alcohol before age 15 are six times more likely to develop alcohol use disorder later in life compared to those who wait until the legal drinking age of 21 [3, 4]. Alcohol expectancies are personal beliefs about behavioral, emotional, and/or cognitive effects that will occur when drinking alcohol [5]. Alcohol expectancies are learned through others and can affect behavior such as initiating or maintaining drinking [6]. Positive alcohol expectancies include beliefs about the positive effects of alcohol, while negative alcohol expectancies include beliefs about the adverse effects of alcohol [7]. Alcohol expectancies are modifiable and therefore may be important targets for efforts to prevent underage alcohol initiation among adolescents [8].

Although adolescents have access to several screen modalities, social media is uniquely positioned to influence alcohol expectancies given the public nature of alcohol content posts and the ability to “like,” react and comment on posts [9]. Social Learning Theory [10] offers a potential explanation for the effects of social media on alcohol use [11,12,13]. Social Learning Theory posits that learning occurs when people observe the consequences of others’ behaviors [10]. These behavioral observations can occur directly through social interactions with others or indirectly through the media. Observing peers is a major influence on adolescent health attitudes, intentions, and behaviors, particularly related to alcohol [14]. Adolescents report that 60% of their age-matched peers and 31% of their friends post alcohol content on social media [9].

Although prior studies have linked television advertisements to alcohol expectancies [5, 15], adolescents do not directly observe their own friends or classmates on television, and television is not interactive. Video games can be interactive, but the content is restricted to the game settings and generally does not focus on alcohol. Mobile phones and computers are devices that can be used for a range of functions and apps, including work, communication, videos, gaming, and social media, and therefore are a less specific exposure [15].

Social media users may post about the positive effects of alcohol, such as socialization, relaxation, or tension reduction. Alcoholic beverage companies also produce advertisements on social media exclusively portraying the positive effects of alcohol. These advertisements have been shown to increase alcohol consumption in current users and make previously non-drinking adolescents more likely to start using alcohol [16, 17]. Previous studies indicated that adolescents not only have access to official alcohol pages and related content but may be specifically targeted for alcohol advertising on social media sites such as Instagram and Twitter [18, 19]. Thus, individuals with problematic social media use may form beliefs that alcohol will produce the same positive effects as seen on social media.

Though social media content often portrays alcohol in a positive light, social media use may also contribute to negative alcohol expectancies. While users may be more inclined to post about their positive experiences with alcohol [20], others may post about the negative impacts of alcohol (e.g., vomiting, experiencing hangover symptoms, and making poor decisions). Furthermore, public health agencies and alcohol abuse prevention advertisements produce content such as videos and user polls demonstrating the negative effects of alcohol [21]. While alcohol-promoting content has been shown to produce more positive alcohol expectancies, alcohol-warning content has been shown to produce negative emotions that reduce alcohol consumption [22]. Alcohol-warning content can lead social media users to believe that alcohol has negative effects, resulting in the development of negative alcohol expectancies. Negative alcohol expectancies were originally associated with less drinking [23]; however, the predictive role of negative expectancies has become less certain as other studies demonstrated negative alcohol expectancies to be associated with problematic drinking [24, 25].

One of the few studies to examine social media and alcohol expectancies did not find a significant association between time on social media or posting frequency and positive or negative alcohol expectancies [9]. However, recent scholarship on social media and screen time has noted that measures of time (e.g., hours per day) cannot capture important qualities of social media use, such as problematic use [26]. Problematic social media use can include elements of addiction, such as mood modification, tolerance, withdrawal, conflict, and relapse [27,28,29]. Problematic social media use also parallels to alcohol use disorder in that both can lead to functional impairment, such as failure to meet responsibilities at school or work [28,29,30]. Problematic social media use may have stronger associations with alcohol expectancies than time spent on social media as it captures thoughts, feelings, and behaviors related to social media.

Various factors could account for the associations between problematic social media use and alcohol expectancies, including normalization and peer influence. Problematic social media use may normalize activities such as excessive drinking, which can increase the likelihood of forming positive alcohol expectancies. Additionally, exposure to peers’ drinking on social media may create a group identity where excessive drinking is promoted. If there is a desire to affiliate with these peers and share their group identity, adolescents may develop more positive alcohol expectancies. In a previous survey, 75% of adolescents who saw pictures of teens using alcohol noted that it made them want to engage in similar behavior [31]. Lastly, adolescents with problematic social media use may be susceptible to peers’ “likes,” comments, and other reactions (e.g., emojis) [9]. Adolescents who post alcohol-related content may receive likes and positive comments on their posts that lead to the development of more positive alcohol expectancies, potentially leading to greater levels of alcohol use and alcohol-related problems [32].

Alcohol expectancies may play a critical role in the development of alcohol use disorder, mainly by shaping individuals’ alcohol-related attitudes. Positive alcohol expectancies can increase motivation to use alcohol as adolescents may believe it will result in desirable effects such as increased social belonging. If adolescents’ drinking behavior results in positive peer feedback, this may reinforce positive alcohol expectancies and make them more likely to engage in future alcohol use. In turn, adolescents may increase their alcohol consumption to achieve previously experienced positive effects, putting them at risk for tolerance formation and alcohol dependence. More positive alcohol expectancies have been associated with alcohol misuse and alcohol use disorder [33, 34].

It is also possible that problematic social media use could make adolescents less likely to engage in social gatherings where alcohol use is present, particularly in more introverted adolescents. Previous studies have shown problematic social media use to be associated with social isolation [35]. If problematic social media use leads to the adolescent withdrawing from social settings, they may be less likely to engage in social alcohol use. This is consistent with past studies that have found extroverted individuals to be more likely to engage in binge drinking relative to introverts [36].

The current study examines the association between problematic social media use and positive and negative alcohol expectancies in a diverse, national sample of 10-14-year-old adolescents participating in the Adolescent Brain Cognitive Development (ABCD) Study. We hypothesized that problematic social media use would be associated with both positive and negative alcohol expectancies.

Methods

We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD). The ABCD Study® started following a cohort of 11,875 9-10-year-olds (mean age = 12.02 years old) from 21 research sites within the U.S. at baseline (2016–2018). Further details about the ABCD Study participants, recruitment, protocol, and measures have been previously described [37]. Data analyzed in this study (Year 2 assessment; 2018–2020; 4.0 release) were collected by adolescents’ self-report. Participants with missing data for screen time use and alcohol expectancies data were excluded (Additional File; Table S1), leaving a total sample of 9,008 adolescents. Centralized institutional review board (IRB) approval was received from the University of California, San Diego (UCSD). Study sites obtained approval from their local IRBs. Caregivers provided written informed consent, and each child provided written assent.

Measures

Predictors

Social media time

Time spent on social media was collected using adolescents’ self-reported hours and minutes on any typical weekday or weekend day they spent visiting “social media apps (e.g., Snapchat, Facebook, Twitter, Instagram, TikTok, etc.)” from the ABCD Youth Screen Time Survey. Total recreational daily social media use was calculated as the weighted sum ([weekday average x 5] + [weekend average x 2])/7. Self-reported screen use demonstrated a significant moderate positive correlation with an objective passively sensed smartphone app in the ABCD Study (r = 0.49, p < 0.001) [38].

Problematic social media use

The ABCD Study measured problematic social media use using the Social Media Addiction Questionnaire (SMAQ), comprised of six items modeled after the Bergen Facebook Addiction Scale [27]. Sample items included questions capturing mood modification (“I use social media apps so I can forget about my problems”), salience (“I spend a lot of time thinking about social media apps or planning my use of social media apps”), relapse (“I’ve tried to use my social media apps less but I can’t”), conflict (“I use social media apps so much that it has had a bad effect on my schoolwork or job”), and tolerance (“I feel the need to use social media apps more and more”). These scales were used among participants who reported having at least one social media account. The averages of each scale were calculated. Internal consistency (Cronbach’s alpha = 0.82) and test-retest reliability (r = 0.83) were sound [27]. The single-factor model of the SMAQ demonstrated adequate fit in a confirmatory factor analysis (χ2(9) = 597.23, p < 0.001, comparative fit index [CFI] = 0.943, root mean square error of approximation [RMSEA] = 0.108, 90% RMSEA CI [0.101,0.115]) [29].

Outcomes

Alcohol expectancies were assessed via adolescents’ self-report through the validated Alcohol Expectancy Questionnaire-Adolescent, Brief (AEQ-AB) [7]. The AEQ-AB questionnaire is a 7-item instrument meant to be used by clinicians and analyzed by components (positive alcohol expectancies and negative alcohol expectancies) [7]. On a 5-point Likert scale ranging from 1 to 5, youth reported how much they agreed/disagreed with statements about the effects of alcohol. Examples include, “Alcohol makes a person feel stronger and more powerful” (positive) and “Alcohol can hurt how well a person gets along with others” (negative). See Additional File Table S2 for a full list of the items and responses. We averaged scores on particular components as recommended in the literature [7]. For the positive component, we averaged scores from items 1, 2, 4 and 6 and for the negative component we averaged scores from items 3, 5 and 7. The minimum value is 1 and the maximum is 5. The higher the score on a component or scale, the higher the expectancy.

Covariates

Potential sociodemographic confounders in the screen time-alcohol expectancies relationship were selected based on previous literature [39, 40]. We adjusted for age (years), sex (female, male), sexual minority status (no, yes, questioning, don’t understand the question, declined to answer), race/ethnicity (White, Latino/Hispanic, Black, Asian, Native American, other), household income (greater than or less than $75,000 U.S. dollars), highest parent education (high school or less vs. college or more), parental marital status (partnered/married, unpartnered/single) and study site.

Statistical analysis

All statistical analyses were conducted in 2022 using Stata 17.0 (StataCorp). Average scores on a particular component (positive alcohol expectancies; negative alcohol expectancies) were calculated as described above [7]. Adjusted linear regression analyses were conducted to estimate cross-sectional associations between social media use variables and alcohol expectancies (positive and negative), adjusted for age, race/ethnicity, sexual orientation, household income, parent education, parent marital status, and study site [5, 24]. For each outcome, we ran three models with different exposure combinations: Model 1: social media time only; Model 2: problematic social media use only; and Model 3: social media time and problematic social media use. In Model 3, we checked for multicollinearity; however, variance inflation factors for the two exposures were < 1.20, so we retained both exposures in the model [41]. Marginal predicted probabilities were computed following the Model 3 linear regressions to get standardized estimates of alcohol expectancies among adolescents with problematic social media use to aid with interpretation of the findings (Fig. 1). Propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the U.S. [42]. Given the large sample size, two-sided alpha was set at p < 0.01.

Fig. 1
figure 1

Predicted scores of problematic screen use and alcohol expectancies. (A) Problematic social media score and positive alcohol expectancies. (B) Problematic social media scores and negative alcohol expectancies

Full size image

Results

Among the 9,008 adolescents of ABCD Study® that met inclusion criteria, 48.7% of the participants were female and 43.0% racial/ethnic minorities (Table 1). Average time spent on social media was 0.71 (SD = 1.62) hours per day. Mean positive and negative alcohol expectancies were 2.99 (SD = 0.61) and 2.80 (SD = 0.78), respectively.

Table 1 Sociodemographic, screen time, and alcohol expectancies characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N = 9,008) at Year 2 assessment 2016–2018
Full size table

Table 2 shows results from linear regression models examining associations between social media variables and alcohol expectancies. Time spent on social media was not associated with positive alcohol expectancies and was weakly associated with negative alcohol expectancies (Model 1). Problematic social media use score was associated with higher positive and negative alcohol expectancies scores (Model 2). In models including both time spent on social media and problematic social media use (Model 3), time spent on social media was not associated with positive or negative alcohol expectancies, but problematic higher social media use score was associated with higher positive (B = 0.045, 95% confidence interval [CI] 0.020–0.069) and negative (B = 0.072, 95% CI 0.043–0.101) alcohol expectancies scores.

Table 2 Associations among social media and alcohol expectancies in the Adolescent Brain Cognitive Development (ABCD) Study, 2018–2020
Full size table

Predicted probabilities computed after linear regressions (Model 3) of problematic social media use and alcohol expectancies (Fig. 1) reveal that across a visual examination of the figures, alcohol expectancies scores rise along higher values of problematic social media use.

Discussion

In a demographically diverse nationwide sample of 9,008 10-14-year-old adolescents in the United States, the present study found problematic social media use to be associated with both positive and negative alcohol expectancies, even after adjusting for potential confounders. Time spent on social media was not associated with positive or negative alcohol expectancies after considering problematic social media use.

Social Learning Theory may help explain the association between problematic screen use and positive alcohol expectancies. As posited by Social Learning Theory, adolescents learn by observing and interacting with others. Social media posts including alcohol overwhelmingly (97%) depict it in a positive social context, display people holding drinks (67%), and are mostly (54%) placed on participants’ timelines by peers rather than themselves [20]. These powerful peer comparisons and connections on social media can lead to positive alcohol expectancies.

Problematic social media use was also associated with negative alcohol expectancies. Although a majority of social media posts related to alcohol depict it in a positive social context, those depicting it in a negative context could reveal struggles with alcoholism, alcohol use disorder, or other consequences. Adolescents with problematic social media use may be sensitive to issues of addiction, tolerance, conflict, withdrawal, and relapse with their social media use. These risk behaviors could cluster together with both social media and alcohol use. Of note, negative alcohol expectancies have been shown to be associated with problematic alcohol drinking [24, 25], although findings have been mixed [23].

The differences in the associations between problematic social media use versus time spent on social media and alcohol expectancies may be explained by conceptual differences in the measures [26]. Time spent on social media does not capture specific thoughts, feelings, or behaviors related to social media, whereas the problematic social media use measure included elements relevant to addiction with parallels to alcohol use disorder such as mood modification, tolerance, withdrawal, conflict, and relapse [27, 28]. A prior study of 435 older adolescents 16–20 years in the Northeast U.S. found no association between social media time and positive alcohol expectancies [9]; our study builds upon these prior findings of no association between social media time and alcohol expectancies in a younger, larger, national sample.

The present study has several limitations. Given its cross-sectional design, causal inferences are limited. The present study adjusted for several potential confounders including race, gender, household income, parent education, sexual orientation, and parental marital status; however, residual confounders like the social media content that was consumed, as well as parenting and cultural differences, were not examined. Furthermore, underlying psychopathology or related factors (e.g., lack of self-control, attention deficit hyperactivity disorder comorbidity, social problems) could confound the relationship between problematic social media use and alcohol expectancies. There is a high chance of clustering of certain behaviors among adolescents making them more vulnerable to multiple risk behaviors [43]. The social media measures were also self-reported, which increases the risk for social-desirability bias. Given that the associations between problematic social media and alcohol expectancies were relatively small, which may be due to the young age of the cohort, further research is necessary to analyze the relationships between problematic social media and alcohol expectancies. We did not include alcohol use disorder given the young age of the current cohort, but associations between problematic social media use and alcohol use disorder could be an area of future research when the cohort is older, as prior research has shown that alcohol use disorders are most prevalent between ages 18 to 29 years [44]. The current study also had several strengths, including the large, diverse, nationwide sample that focused on early adolescents and examined a measure of problematic social media use.

The current study offers several implications within the realm of public health and healthcare. Findings are especially relevant amidst the vast increase in adolescent screen time, adolescent alcohol use, and parental permissibility of adolescent alcohol use throughout the COVID-19 pandemic [45, 46]. Our findings indicate that problematic social media use may lead to the development of positive and negative alcohol expectancies in early adolescents. Because alcohol expectancies predict alcohol initiation and are easily modifiable, they could be a prime target for prevention efforts [8]. Social media platforms could implement stricter rules regarding alcohol advertising targeted at minors, which persists despite recommendations by the Federal Trade Commission and resulting digital media alcohol advertising guidelines developed by the alcohol industry regarding age verification requirements and moderation of user-generated content [18]. Social media platforms could flag posts from minors depicting underage drinking. Parents can consider implementing parental controls on devices, enhancing privacy settings on social media, and monitoring their children’s social media activity to dissuade the propagation of underage drinking [47]. The American Academy of Pediatrics advocates for parents to develop a family media use plan, which could involve regular conversations and open communication with adolescents about online activities and promoting digital literacy [47, 48]. Digital literacy courses could provide adolescents with guidance regarding problematic social media use and the potential consequences of posting underage drinking (e.g., images discoverable by future employers, and cancel culture). Healthcare providers can ask patients and families about media use concerns, including problematic social media use as part of routine health maintenance, and encourage families to develop a family media use plan [47, 48]. Future research should examine the prospective relationships and mechanisms among problematic social media use, alcohol expectancies, alcohol use, and alcohol use disorder in later adolescence and young adulthood.

Data availability

Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). Investigators can apply for data access through the NDA (https://nda.nih.gov/).

Abbreviations

ABCD:

Adolescent Brain Cognitive Development Study

AEQ-AB:

Alcohol Expectancy Questionnaire- Adolescent, Brief

IRB:

Institutional review board

MBIQ:

Mobile Phone Involvement Questionnaire

SMAQ:

Social Media Addiction Questionnaire

UCSD:

University of California, San Diego

VGAQ:

Video Game Addiction Questionnaire

References

  1. Esser MB, Leung G, Sherk A, Bohm MK, Liu Y, Lu H, et al. Estimated deaths attributable to excessive Alcohol Use among US adults aged 20 to 64 years, 2015 to 2019. JAMA Netw Open. 2022;5:e2239485.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tapert SF, Eberson-Shumate S. Alcohol and the adolescent brain: what we’ve learned and where the data are taking us. Alcohol Res Curr Rev. 2022;42:07.

    Article  Google Scholar 

  3. Boden JM, Newton-Howes G, Foulds J, Spittlehouse J, Cook S. Trajectories of alcohol use problems based on early adolescent alcohol use: findings from a 35 year population cohort. Int J Drug Policy. 2019;74:18–25.

    Article  PubMed  Google Scholar 

  4. Yuen WS, Chan G, Bruno R, Clare P, Mattick R, Aiken A, et al. Adolescent Alcohol Use Trajectories: risk factors and adult outcomes. Pediatrics. 2020;146:e20200440.

    Article  PubMed  Google Scholar 

  5. Chen YY, Chiu YC, Ting TT, Liao HY, Chen WJ, Chen CY. Television viewing and alcohol advertising with alcohol expectancies among school-aged children in Taiwan. Drug Alcohol Depend. 2016;162:219–26.

    Article  PubMed  Google Scholar 

  6. Goldman M, Boca FD, Darkes J. Alcohol Expectancy Theory: the application of cognitive neuroscience. Psychol Theor Drink Alcohol; 1999.

  7. Stein LAR, Katz B, Colby SM, Barnett NP, Golembeske C, Lebeau-Craven R, et al. Validity and reliability of the Alcohol Expectancy Questionnaire-Adolescent, brief. J Child Adolesc Subst Abuse. 2007;16:115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Smit K, Voogt C, Hiemstra M, Kleinjan M, Otten R, Kuntsche E. Development of alcohol expectancies and early alcohol use in children and adolescents: a systematic review. Clin Psychol Rev. 2018;60:136–46.

    Article  PubMed  Google Scholar 

  9. Meisel SN, Nesi J, Janssen T, Jackson KM. Adolescent (mis)perceptions of peer alcohol posts on social media: prospective associations with alcohol attitudes and use. Alcohol Clin Exp Res. 2022;46:2054–67.

    Article  PubMed  Google Scholar 

  10. David L. Social Learning Theory Bandura Social Learning Theory. Learn Theor. 2015; October.

  11. Wesche R, Kreager DA, Lefkowitz ES. Sources of Social Influence on Adolescents’ Alcohol Use. J Res Adolesc Off J Soc Res Adolesc. 2019;29:984–1000.

    Google Scholar 

  12. Lonczak HS, Huang B, Catalano RF, Hawkins JD, Hill KG, Abbott RD, et al. The social predictors of adolescent alcohol misuse: a test of the social development model. J Stud Alcohol. 2001;62:179–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Strickland DE, Pittman DJ. Social learning and teenage Alcohol Use: interpersonal and observational influences within the Sociocultural Environment. J Drug Issues. 1984;14:137–50.

    Article  Google Scholar 

  14. Moreno MA, Whitehill JM. Influence of Social Media on Alcohol Use in Adolescents and Young adults. Alcohol Res Curr Rev. 2014;36:91.

    Google Scholar 

  15. Tebar WR, Christofaro DGD, Diniz TA, Lofrano-Prado MC, Botero JP, de Correia M. Increased screen time is Associated with Alcohol Desire and Sweetened Foods Consumption during the COVID-19 pandemic. Front Nutr. 2021;8:78.

    Article  Google Scholar 

  16. Engels RCME, Hermans R, van Baaren RB, Hollenstein T, Bot SM. Alcohol portrayal on television affects actual drinking behaviour. Alcohol Alcohol Oxf Oxfs. 2009;44:244–9.

    Article  Google Scholar 

  17. Smith LA, Foxcroft DR. The effect of alcohol advertising, marketing and portrayal on drinking behaviour in young people: systematic review of prospective cohort studies. BMC Public Health. 2009;9:51.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Barry AE, Johnson E, Rabre A, Darville G, Donovan KM, Efunbumi O. Underage access to online alcohol marketing content: a YouTube case study. Alcohol Alcohol Oxf Oxfs. 2015;50:89–94.

    Article  Google Scholar 

  19. Barry AE, Bates AM, Olusanya O, Vinal CE, Martin E, Peoples JE, et al. Alcohol Marketing on Twitter and Instagram: evidence of directly advertising to Youth/Adolescents. Alcohol Alcohol. 2016;51:487–92.

    Article  PubMed  Google Scholar 

  20. Hendriks H, den Putte BV, Gebhardt WA, Moreno MA. Social drinking on Social Media: Content Analysis of the Social Aspects of Alcohol-Related posts on Facebook and Instagram. J Med Internet Res. 2018;20:e9355.

    Article  Google Scholar 

  21. Döring N, Holz C. [Alcohol in social media: where is the space for prevention?]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2021;64:697–706.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Stautz K, Frings D, Albery IP, Moss AC, Marteau TM. Impact of alcohol-promoting and alcohol‐warning advertisements on alcohol consumption, affect, and implicit cognition in heavy‐drinking young adults: a laboratory‐based randomized controlled trial. Br J Health Psychol. 2017;22:128–50.

    Article  PubMed  Google Scholar 

  23. Goldman M, Brown S, Christiansen B. Expectancy theory - thinking about drinking. Psychol Theor Drink Alcohol; 1987.

  24. Chisolm DJ, Manganello JA, Kelleher KJ, Marshal MP. Health literacy, alcohol expectancies, and alcohol use behaviors in teens. Patient Educ Couns. 2014;97:291–6.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zamboanga BL, Schwartz SJ, Ham LS, Borsari B, Van Tyne K. Alcohol expectancies, pregaming, drinking games, and hazardous alcohol use in a multiethnic sample of college students. Cogn Ther Res. 2010;34:124–33.

    Article  Google Scholar 

  26. Kaye K, Orben L, Ellis AA, Hunter DC, Houghton S. The conceptual and methodological mayhem of “screen time”. Int J Environ Res Public Health. 2020;17:3661.

    Article  Google Scholar 

  27. Andreassen CS, TorbjØrn T, Brunborg GS, Pallesen S. Development of a Facebook Addiction Scale. Psychol Rep. 2012;110:501–17.

    Article  PubMed  Google Scholar 

  28. Nagata JM, Singh G, Sajjad OM, Ganson KT, Testa A, Jackson DB, et al. Social epidemiology of early adolescent problematic screen use in the United States. Pediatr Res. 2022;92:1443–9.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bagot KS, Tomko RL, Marshall AT, Hermann J, Cummins K, Ksinan A, et al. Youth screen use in the ABCD® study. Dev Cogn Neurosci. 2022;57:101150.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Arlington, VA: American Psychiatric Publishing; 2013.

    Book  Google Scholar 

  31. Survey of American Attitudes on Substance Abuse. 2012. Partnership to End Addiction. https://drugfree.org/reports/national-survey-on-american-attitudes-on-substance-abuse-xvii-teens/. Accessed 5 Feb 2023.

  32. Curtis BL, Lookatch SJ, Ramo DE, McKay JR, Feinn RS, Kranzler HR. Meta-analysis of the association of alcohol-related social media use with alcohol consumption and alcohol-related problems in adolescents and young adults. Alcohol Clin Exp Res. 2018;42:978–86.

    Article  PubMed  PubMed Central  Google Scholar 

  33. King SE, Waddell JT, Corbin WR. Examining the moderating role of behavioral willingness on Indirect Relations between Alcohol Expectancies and negative consequences. Alcohol Alcohol. 2022;57:755–61.

    PubMed  Google Scholar 

  34. Coates JM, Gullo MJ, Feeney GFX, Young RM, Dingle GA, Connor JP. Alcohol expectancies pre-and post-alcohol use disorder treatment: clinical implications. Addict Behav. 2018;80:142–9.

    Article  PubMed  Google Scholar 

  35. Meshi D, Ellithorpe ME. Problematic social media use and social support received in real-life versus on social media: Associations with depression, anxiety and social isolation. Addict Behav. 2021;119:106949.

    Article  PubMed  Google Scholar 

  36. Adan A, Forero DA, Navarro JF. Personality Traits Related to Binge Drinking: A Systematic Review.Front Psychiatry. 2017;8.

  37. Barch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, et al. Demographic, physical and mental health assessments in the adolescent brain and Cognitive Development study: rationale and description. Dev Cogn Neurosci. 2018;32:55–66.

    Article  PubMed  Google Scholar 

  38. Wade NE, Ortigara JM, Sullivan RM, Tomko RL, Breslin FJ, Baker FC, et al. Passive sensing of preteens’ smartphone use: an adolescent brain Cognitive Development (ABCD) cohort substudy. JMIR Ment Health. 2021;8:e29426.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hatzenbuehler ML, Nolen-Hoeksema S, Erickson SJ. Minority stress predictors of HIV risk behavior, substance use, and depressive symptoms: results from a prospective study of bereaved gay men. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2008;27:455–62.

    Google Scholar 

  40. Jackson KM, Rogers ML, Sartor CE. Parental divorce and initiation of Alcohol Use in Early Adolescence. Psychol Addict Behav J Soc Psychol Addict Behav. 2016;30:450.

    Article  Google Scholar 

  41. Allison P. When can you safely ignore multicollinearity? Statistical Horizons. 2012;5:1–2.

  42. Heeringa S, Berglund P. A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data. bioRxiv. 2020. https://doi.org/10.1101/2020.02.10.942011

  43. Mayne SL, Virudachalam S, Fiks AG. Clustering of unhealthy behaviors in a nationally representative sample of U.S. children and adolescents. Prev Med. 2020;130:105892.

    Article  PubMed  Google Scholar 

  44. Connor JP, Weier M, Hall WD. The age of Onset of Alcohol Use Disorders. In: de Girolamo G, McGorry PD, Sartorius N, editors. Age of Onset of Mental Disorders: Etiopathogenetic and Treatment Implications. Cham: Springer International Publishing; 2019. pp. 169–82.

    Google Scholar 

  45. Maggs JL, Cassinat JR, Kelly BC, Mustillo SA, Whiteman SD. Parents who First Allowed Adolescents to drink Alcohol in a Family Context during Spring 2020 COVID-19 emergency shutdowns. J Adolesc Health. 2021;68:816–8.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Sen LT, Siste K, Hanafi E, Murtani BJ, Christian H, Limawan AP, et al. Insights into adolescents’ Substance Use in a low–middle-income country during the COVID-19 pandemic. Front Psychiatry. 2021;12:1743.

    Article  Google Scholar 

  47. Radesky J, Chassiakos YR, Ameenuddin N, Navsaria D, Ameenuddin N, Boyd R, et al. Digital advertising to children. Pediatrics. 2020;146:e20201681.

    Article  PubMed  Google Scholar 

  48. Chassiakos YR, Radesky J, Christakis D, Moreno MA, Cross C, Hill D, et al. Children and adolescents and digital media. Pediatrics. 2016;138.

Download references

Acknowledgements

The authors thank Anthony Kung and Sanya Dhama for editorial assistance. The ABCD Study was supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report.

Funding

J.M.N. was funded by the National Institutes of Health (K08HL159350), the American Heart Association Career Development Award (CDA34760281), and the Doris Duke Charitable Foundation (2022056).

Author information

Authors and Affiliations

  1. Division of Adolescent and Young Adult Medicine, Department of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA, 94143, USA

    Jason M. Nagata, Natalia Smith & Gabriel Zamora

  2. Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Rd, Hanover, NH, 03755, USA

    Omar M. Sajjad

  3. Factor-Inwentash Faculty of Social Work, University of Toronto, 246 Bloor St W, Toronto, ON, M5S 1V4, Canada

    Kyle T. Ganson

  4. Department of Management, Policy and Community Health, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA

    Alexander Testa

  5. Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, USA

    Dylan B. Jackson

Authors
  1. Jason M. Nagata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel Zamora
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omar M. Sajjad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kyle T. Ganson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexander Testa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dylan B. Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jason Nagata - conceptualization, analysis, writing-original draft and revisions, supervision. Natalia Smith – conceptualization, data analysis, writing –original draft and revisions. Gabriel Zamora – conceptualization, writing –original draft and revisions. Omar Sajjad – conceptualization, writing –critical revisions. Kyle Ganson, Alexander Testa, Dylan Jackson – writing -critical revisions. All authors approve of the final submitted version.

Corresponding author

Correspondence to Jason M. Nagata.

Ethics declarations

Ethics approval and consent to participate

Written informed consent and assent were obtained from the parent/guardian and adolescent, respectively, to participate in the ABCD Study. The University of California, San Diego provided centralized institutional review board (IRB) approval, and each participating site received local IRB approval: 1) Children’s Hospital Los Angeles, Los Angeles, California. 2) Florida International University, Miami, Florida. 3) Laureate Institute for Brain Research, Tulsa, Oklahoma. 4) Medical University of South Carolina, Charleston, South Carolina. 5) Oregon Health and Science University, Portland, Oregon. 6) SRI International, Menlo Park, California. 7) University of California San Diego, San Diego, California. 8) University of California Los Angeles, Los Angeles, California. 9) University of Colorado Boulder, Boulder, Colorado. 10) University of Florida, Gainesville, Florida. 11) University of Maryland at Baltimore, Baltimore, Maryland. 12) University of Michigan, Ann Arbor, Michigan. 13) University of Minnesota, Minneapolis, Minnesota. 14) University of Pittsburgh, Pittsburgh, Pennsylvania. 15) University of Rochester, Rochester, New York. 16) University of Utah, Salt Lake City, Utah. 17) University of Vermont, Burlington, Vermont. 18) University of Wisconsin—Milwaukee, Milwaukee, Wisconsin. 19) Virginia Commonwealth University, Richmond, Virginia. 20) Washington University in St. Louis, St. Louis, Missouri. 21) Yale University, New Haven, Connecticut. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors have no conflict to declare. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Role of the funder/sponsor

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nagata, J.M., Smith, N., Zamora, G. et al. Problematic social media use and alcohol expectancies in early adolescents. BMC Public Health 23, 430 (2023). https://doi.org/10.1186/s12889-023-15298-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-15298-3

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us