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. 2024 Dec 24;27(6):1127–1136. doi: 10.1093/ntr/ntae309

Tobacco Susceptibility and Use Among Rural Adolescents: The Role of Tobacco Marketing Exposure and Screen Media Use

Sunny Jung Kim 1,2,✉, Kendall Fugate-Laus 3, Jeremy Barsell 4, Elizabeth K Do 5, Rashelle B Hayes 6, Bernard F Fuemmeler 7,8
PMCID: PMC13032087  PMID: 39716391

Abstract

Introduction

Tobacco marketing has been found to increase pro-tobacco attitudes and susceptibilities; yet its impact on rural adolescents lacks research. We aim to examine the association between tobacco marketing exposure, screen use, and susceptibility and use of tobacco among a rural youth sample.

Aims and Methods

Youth (N = 697) enrolled in grades 9–11 that resided in rural counties in Virginia were recruited to participate in a survey in September 2022. We assessed demographics, tobacco use, susceptibility to tobacco use, screen media use, and exposure to tobacco marketing and warning messages.

Results

One in five (n = 144, 20.66%) participants reported using any tobacco products and 394 (56.53%) indicated susceptibility. High engagement in social media and texting were more likely to be tobacco ever users than those who had low engagement with those screen media, X2(1) = 12.00 and X2(1) = 19.40, respectively (ps < .001). Greater exposure to pro-tobacco marketing on social media (odds ratio [OR]: 2.03, 95% CI [1.37 to 3.03]) and higher-grade level (OR: 1.77, 95% CI [1.29 to 2.43]) were significantly associated with a greater likelihood of reporting “ever-use” of tobacco products, while controlling for mother’s education, gender, and ethnicity. Among adolescents who never used tobacco products, a higher grade level was associated with greater susceptibility to initiating tobacco product use (OR: 1.40, 95% CI [1.05 to 1.86]).

Conclusions

In this rural sample, greater social media/text use and exposure to pro-tobacco marketing on social media were significantly associated with tobacco ever use. Identification of these risk factors can help inform potential targets and timing for future tobacco prevention campaigns for rural youth.

Implications

Screen media use, tobacco marketing/warning exposure, and their associations with tobacco use and susceptibility were examined in a study with 697 rural Virginia youth. Heavy social media/text users were more likely to have used tobacco products. Exposure to tobacco marketing on social media and higher grade levels were associated with ever using tobacco. Higher grade levels were linked to increased susceptibility to tobacco use among nonusers. These results highlight the importance of resilience to pro-tobacco marketing on social media, and self-regulation of social media/text use in interventions for tobacco ever users. Early interventions may benefit rural youth who never used tobacco.

Introduction

Despite steady declines in cigarette smoking among United States youth over the last 50 years, use of multiple nicotine and tobacco-containing products, such as electronic nicotine delivery systems (ENDS), has increased.1 In 2023, 22.2% of US middle and high school students reported ever using any tobacco product, and 10.0% reported current (past 30-day) use of these products. The most commonly used products by US youth are e-cigarettes, followed by cigars, cigarettes, nicotine pouches, and smokeless tobacco.2 This is concerning given recent findings that multiple tobacco product use and/or e-cigarette use during adolescence has been associated with dual use of combustible cigarettes, progression to cigarette use and uses of other substance in later adulthood.3,4 Furthermore, nicotine exposure during adolescence has been found to detrimental to neurodevelopment and can impair typical development of attention, impulsivity, and reward processing systems.5 Rural youth are found to have even higher rates of using ENDS and other tobacco products compared to urban youth.6 According to data from the US Monitoring the Future study covering 1998 to 2018, rural high school students reported 3% higher rates of regular smoking compared to their urban peers in 2018 (rural: 4.7%; urban: 1.7%).7 Overtime, this translates into significantly higher rates of tobacco-related cancers observed in rural communities.8 Risk factors that contribute to tobacco susceptibility and use among rural youth include pro-tobacco norms that are unique to rural communities with a history of growing tobacco,9 demographics such as low socioeconomic status, and exposure to tobacco retail marketing.6,10 Existing literature provides evidence for the association between these contextual and social factors and pro-tobacco attitudes and susceptibilities.11–13

One of the most pressing and prevalent factors influencing ENDS and other tobacco product use is exposure to tobacco marketing. Exposure to tobacco industry marketing has been associated with increased susceptibility to tobacco product initiation and progression to regular use among adolescents, as it alters their perceptions about tobacco use popularity and social norms,14 and increases curiosity among never users. Recent estimates suggest that more than 75% of US youth report exposure to marketing and advertisements for any tobacco product through a variety of channels, including tobacco retail outlets such as convenience stores and gas stations; television, streaming, and print media; and digital and social media.15 Although there are some restrictions on the display of certain tobacco products (eg, cigarettes, roll-your-own tobacco, covered tobacco products) on television, print media, and point-of-sale, there are fewer regulations, if any, that apply to digital and social media.16

Thus, another risk factor to consider is the use of screen media, including interactive and social media, as it has been found to be linked to risk behaviors and health problems.17–20 Screen media encompass any media content viewed on screens. This includes various formats and devices such as televisions, computer monitors, tablets, smartphones, smartwatches, video game consoles, and digital signage. Some forms of screen media, like social media, can facilitate interactions with a broad and/or targeted network of people. Social media can include a range of content types, such as news, entertainment, educational material, interactive experiences, advertisements, and promotions. As technology has advanced, the definition of screen media has expanded to incorporate new media devices and delivery methods, reflecting its growing impact on health, cognition, behavior, and development.

Media use among youth aged 13–17 years has increased to nine hours per day, not including screen time used for homework.21,22 In 2023, approximately one-third of teens aged 13–17 years report using social media sites (eg, TikTok, Instagram) “almost constantly.”23 Youth report seeing advertising for tobacco products while using the Internet and watching television or streaming services.15 A recent meta-analysis found that individuals exposed to pro-tobacco content on social media, specifically, were more likely to report lifetime tobacco use, tobacco use in the past 30 days, and increased susceptibility to tobacco use, compared to those who were not exposed.24 The study further reported the growing prevalence of digital tobacco marketing on social media and its influence on tobacco use and susceptibility by, in part, “normalizing tobacco use behaviors among regular social media users, such as adolescents and young adults” (p. 883).24 Given that these media platforms contain pro-tobacco marketing and advertisements and allow interaction with peers who may share similar social norms about tobacco and ENDS use,24,25 it is important to examine the role that screen time has on tobacco susceptibility and use among rural youth.

There is already growing literature that shows a relationship between the use of social media and e-cigarette use and susceptibility among youth,17,26–28 but this relationship has not been addressed among rural youth, an underserved population with a greater likelihood of ENDS use. Further, the extent to which screen time and exposure to pro-tobacco marketing and antitobacco warnings on various media platforms are associated with susceptibility and tobacco use among rural adolescents has not been empirically tested.

Thus, the aim of the current study is to examine the association between screen time, including social media use, exposure to pro-tobacco marketing and antitobacco warning available online and “on-site” (eg, tobacco retail outlets), and susceptibility to and use of tobacco products, including ENDS, among a population of rural youth. We hypothesize that: (H1) screen time for social media will be positively associated with greater tobacco use susceptibility and use among rural youth; (H2a) higher levels of exposure to on-site and social media messages promoting tobacco products (ie, pro-tobacco marketing) will be positively associated with greater tobacco use susceptibility and tobacco use among rural youth; and conversely, (H2b) exposure to on-site and social media antitobacco messages (ie, antitobacco warnings) will be negatively associated with tobacco use susceptibility and tobacco use among rural adolescents.

Materials and Methods

Study Sample and Procedures

In September 2022, the Southern Virginia—Adolescents, Place, and Behavior Study (SoVA-APB) surveyed youth enrolled in high school and who resided within rural counties in Southwestern Virginia (N = 697). According to the 2013 Rural-Urban Continuum Codes (RUCC), these areas that received RUCC between a score of 4 and 9 are considered rural, nonmetropolitan areas.29 RUCC distinguish counties by the population size of their area, the degree of urbanization, and adjacency to a metro area.29 Youth were eligible to participate if they were enrolled in grades 9 through 11 and able to speak and write in English. The study team worked closely with the superintendent and principals of school systems located in southwestern Virginia to administer the survey during the school day. Between 7 and 10 days prior to survey administration, parents received informational packets with opt-out consent forms. On the day the survey was administered, youth watched a video discussing the assent process, reviewed an informational sheet about the study, and were invited to complete a survey on the web-based platform, REDCap (Research Electronic Data Capture),30,31 which took approximately 20 to 30 min. Youth who provided assent to participate and completed the survey received a $10 e-gift card. Participants were also asked to provide their contact information for future study activities. All study procedures and protocols were approved by the [blinded for review] university Institutional Review Board (IRB).

Measures

Demographics

Youth reported their age, current grade level (9th grade, 10th grade, or 11th grade), gender (male, female, and other), race (American Indian or Alaska Native, Asian American, Black or African American, Native Hawaiian or Pacific Islander, White, Multiple race, Other race), ethnicity (Not of Hispanic, Latino, or Spanish origin; Cuban; Dominican; other Hispanic, Latino, or Spanish), phone type (ie, iOS and/or Android), and mother’s educational attainment.

Tobacco Ever Use

We assessed tobacco ever use status by asking “Which of the following tobacco products have you ever used or tried? Please select all that apply.” Response options included: Cigarettes; Cigars (including little cigars, cigarillos, bidis, Black & Milds); Hookah (including shisa, waterpipe); e-cigarettes/vapes (electronic cigarettes, vape sticks, vape pens, e-hookah, and Juuls); other tobacco products (including smokeless tobacco, pipe tobacco, dip/snuff, chewing tobacco, snus, and dissolvables); and “I have never used or tried a tobacco product.” Responses were re-coded to reflect ever use status (ie, ever use vs. never use).

Tobacco Use Susceptibility

Tobacco use susceptibility was assessed using three items (Cronbach’s α = .93): “Do you think you will try any type of tobacco product in the next year?”; “Do you think you will try any type of tobacco product soon?”; and “If one of your best friends were to offer you a tobacco product, would you use it?” These items were adapted from previous work examining susceptibility to cigarette use.32 Response options included “definitely not, probably not, probably yes, definitely yes, don’t know, and refused to answer.” Survey participants were considered “not susceptible” only if they selected “definitely not” to each item. Participants that reported any level of uncertainty (ie, “probably not,” “probably yes,” “don’t know”), or selected “definitely yes” or “refused to answer” were coded as susceptible to tobacco use. This coding method was based on the work described in Pierce et al. studies.1,32

Screen Time

Youth responded to screen time questionnaires that were adopted from the Adolescent Brain Cognitive Development Youth Screen Time Survey.33 We measured a wide range of media use activities as exposure to pro-tobacco or antitobacco messages can occur across various media platforms. The items read, “On a typical weekday, how many hours do you spend…”: (1) watching TV shows or movies; (2) watching videos (eg, YouTube); (3) playing video games, (4) texting, and (5) visiting social networking sites. The same set of five items were measured with the following leading statement, “On a typical weekend day, how many hours do you spend…” to assess screen use on both a typical weekday and a typical weekend day. These items on screen media use were assessed with these response scales, “none,” “less than 30 minutes,” “30 min,” “1 h,” “2 h,” “3 h,” “4 or more hours.” Using the overall median point (2 h) of these items, we dichotomized screen media use intensity by two hours or more per a typical weekday or weekend day as heavy media use; and less than two hours per a typical weekday or weekend day as light media use.

On-Site Exposure to Pro-Tobacco Marketing and Antitobacco Warning Messages

Using items adapted from the Population Assessment of Tobacco and Health Study survey,34 we asked how often participants saw advertisements or promotions for cigarettes and other tobacco products on-site (ie, at convenience or corner stores; grocery stores; gas stations; and drug stores/pharmacies). Youth also reported how often they saw signs (not including warning stickers found on packages) warning of the dangers of tobacco use on-site. Responses to these on-site exposure items were measured using a 5-point Likert scale (“Never,” “Rarely,” “Sometimes,” “Most of the time,” and “Always”).

Exposure to Pro-Tobacco Marketing and Antitobacco Warning Messages on Social Media

Participants were asked to indicate how often they saw posts or advertisements promoting cigarettes and other tobacco products on each of the following social media platforms, including Facebook, Instagram, Twitter, Tumblr, Reddit, Pinterest, TikTok, YouTube, and Snapchat using the four-point Likert scale (“Never”; “Rarely”; “Sometimes”; “Most of the time”). Participants were also asked to indicate how often they saw messages warning of the dangers of tobacco on the same list of social media platforms.

Prior to data analysis, we generated four composite mean scores: (1) exposure to pro-tobacco marketing messages on-site (Cronbach’s α = .78); (2) exposure to antitobacco warning messages on-site (Cronbach’s α = .87); (3) exposure to pro-tobacco marketing messages on social media (Cronbach’s α = .87); and (4) exposure to antitobacco warning messages on social media (Cronbach’s α = .92). Participants were also provided the response options “don’t know” and “refused to answer” for all exposure-related items. To avoid coding errors or bias, “don’t know” or “refuse to answer” responses were marked as missing data and excluded from data analyses.

Statistical Analysis

First, we determined how demographics (race, ethnicity, grade), exposure to pro- and antitobacco messages, and screen media use (types and intensity) vary with tobacco use status (ever use vs. never use) and susceptibility (susceptible vs. not susceptible) by constructing (corrplot) two separate heatmaps using Spearman correlations in R.35 The first heatmap was used to visually present the association across the types and intensity of screen media use, and ENDS/tobacco susceptibility and use. The second heatmap was generated to visually present the associations between the message types (pro-tobacco vs. antitobacco messages), the exposure locations (four on-site and nine social media platforms), and ENDS/tobacco susceptibility and use. For correlational heatmaps, we used raw ordinal data of the variables that were not dichotomized.

To explore how different types of screen media use and intensity are associated with tobacco ever use status, we conducted a series of chi-square tests on each media type and intensity (heavy screen media use vs. light screen media use) by smoking ever use status (ever use vs. never use). We anticipate that greater social media use will be associated with a higher likelihood of ever using tobacco (H1). To determine the extent to which pro-tobacco marketing or antitobacco warning exposure relates to tobacco use susceptibility and tobacco use (H2a and H2b, respectively), we used a series of logistic regression models controlling for key covariates that were found in the literature to influence tobacco susceptibility and youth smoking (ie, grade, gender, mother’s education, and ethnicity). In the first model, we used multiple logistic regression examine the effect the exposure to pro-tobacco marketing on-site, pro-tobacco marketing on social media, antitobacco warning on-site, and antitobacco warning on social media have on tobacco ever use. The second model examined the effect of exposure to on-site pro-tobacco marketing, social media pro-tobacco marketing, on-site antitobacco warning, and social media antitobacco warning on future tobacco susceptibility among never users (n = 506), the subgroup of participants who marked “not susceptible” to the three susceptibility items.

Results

Sample Characteristics (Table 1)

Table 1.

Demographic Sample Characteristics by Tobacco Products Use

Total (n = 697) Tobacco use status Tobacco susceptibilitya
Ever users (n = 144, 20.66%) Never users (n = 506, 72.60%) Yes (n = 394, 56.53%) No (n = 303, 43.47%)
Age, M(SD) 15.06 (.96) 15.29 (.99)** 14.98 (.93)** 15.13(.98)* 14.98 (.92)*
Gender (n = 697)
 Male, n (%) 301 (43.2) 57 (40.4) 22 (44.4) 155 (40.6) 146 (48.8)
 Female, n (%) 362 (51.9) 82 (58.2) 260 (52.4) 216 (56.5) 146 (48.8)
 Other, n (%) 18 (2.6) 2 (1.4) 16 (3.2) 11 (2.9) 7 (2.3)
Grade (n = 682)
 9th grade, n (%) 249 (35.7) 36 (25.5)** 198 (39.7)** 135 (35.3)* 114 (38)*
 10th grade, n (%) 237(34.0) 48 (34) 175 (35.1) 121 (31.7) 116 (38.7)
 11th grade, n (%) 196 (28.1) 57 (40.4) 126 (25.3) 126 (33) 70 (23.3)
Race (n = 649)
 White, n (%) 405 (62.4) 84 (60.9) 302 (64) 227 (61.5) 178 (63.6)
 Non-White, n (%) 244 (37.6) 54 (39.1) 170 (36) 142 (38.5) 102 (36.4)
Hispanic (n = 598)
 Yes, n (%) 80 (13.4) 17 (13.8) 57 (12.7) 45 (13.5) 35 (13.3)
 No, n (%) 518 (86.6) 106 (86.2) 392 (87.3) 289 (86.5) 229 (86.7)
Mother’s education
 < College, n (%) 238 (42.3) 49 (41.5) 178 (42.7) 138 (43.4)* 100 (41)*
 College, n (%) 265 (47.2) 58 (49.2) 198 (47.5) 139 (43.7) 126 (51.6)
 Don’t Know, n (%) 59 (10.5) 11 (9.3) 41 (9.8) 41 (12.9) 18 (7.4)
Phone device
 iOS, n (%) 469 (81.4) 118 (88.7)* 351 (79.2)* 287 (83.2) 221 (79.6)
 Android, n (%) 85 (14.8) 10 (7.5)* 75 (16.9)* 47 (13.6) 42 (15.8)
 Other, n (%) 22 (3.8) 5 (3.8) 17 (3.8) 11 (3.2) 12 (4.5)
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*p < .05, **p < .01. Tobacco use status by age = t(644) = 3.48, p < .001, Cohen’s d = .33; Tobacco use status by grade = X2(2, 640) = 14.91, p < .001; Tobacco use status by phone device type = X2(2, 576) = 7.27, p = .026.

aTobacco susceptibility reported in Table 1 was a composite score of three items based on the original ordinal scales. The variable reported here includes both tobacco ever users and never users, totaling n = 697.

Tobacco susceptibility by age = t(644) = 3.48, p < .001, Cohen’s d = .33; Tobacco susceptibility by grade = X2(2, 682) = 8.14, p = .02; Tobacco susceptibility by mother’s education = X2(2, 562) = 6.03, p = .05.

For the “ever use” variable, 21 participants self-reported “do not know” and 26 participants answered “refuse to answer.”.

A majority of participants were female (n = 362, 51.9%), White (n = 405, 62.4%; whereas Black or African American, n = 134, 20.6%), non-Hispanic (n = 518, 86.6%; whereas Hispanic, n = 80, 13.4%), and used an iOS smartphone device (n = 469, 81.4%). The average age was 15.1 years old (SD = .96). Of all participants (n = 697), 20.7% of the participants (n = 144) reported ever using any tobacco products, 394 (56.53%) out of 697 were considered susceptible to tobacco products, and 506 participants (72.6%) reported never using any types of tobacco products, including e-cigarettes/vapes, cigarettes, cigars, and other tobacco products (eg, chewing tobacco, snus, or dissolvable tobacco). Less than one-tenth (n = 63, 9.5%) reported not using social media at all on a typical weekday or weekend. More than three out of ten reported using social media for four hours or more on a typical weekday (n = 208, 31.2%) or weekend (n = 233, 35.1%).

Heatmap and Associations

Figure 1 presents a heatmap of a typical Spearman correlation matrix visualization, with the outcome variables (tobacco ever use and tobacco use susceptibility) at the right and the bottom rows. Social media use and texting during the weekdays and weekends as well as grade showed positive correlations with tobacco ever use status (yes = 1, no = 0) and susceptibility (susceptible = 1, not susceptible = 0).

Figure 1.

Correlation matrix heatmaps for ever use and tobacco susceptibility.

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(A) Spearman correlation coefficient between screen media use and the ever use status (0 = never use; 1 = ever use) and susceptibility (0 = not susceptible; 1 = susceptible) variables. Primary outcome variables (ie, ever use and susceptibility) are highlighted in boxes. Race (0 = non-White; 1 = White). 30-day use (0 = No; 1 = Yes; 2 = I have never used any tobacco product). Phone type (1 = iOS; 2 = Android; 3 = Other). Ethnicity (0 = non-Hispanic; 1 = Hispanic). Raw ordinal data for screen media use variables were used. (B) Spearman correlation coefficient between screen media use and the ever use status (0 = never use; 1 = ever use) and susceptibility (0 = not susceptible; 1 = susceptible) variables. Primary outcome variables (ie, ever use and susceptibility) are highlighted in yellow squares. Race (0 = non-White; 1 = White). 30-day use (0 = No; 1 = Yes; 2 = I have never used any tobacco product). Phone type (1 = iOS; 2 = Android; 3 = Other). Ethnicity (0 = non-Hispanic; 1 = Hispanic). Raw ordinal data for screen media use variables were used.

Associations Between Screen Media Use and Tobacco Use Status (Table 2)

Table 2.

Use of Social Media and Screen Devices by Tobacco Use Status

Total (n = 650a) Ever users (n = 144) Never users (n = 506) Chi-square
On a typical weekday, how many hours do you . . . .
 Watch TV shows or movies? Less than 2 h 332 (52.4%) 68 (47.9%) 264 (53.8%) X 2(1) = 1.53, p = .22
2 or more hours 301 (47.6%) 74 (52.1%) 227 (46.2%)
 Watch videos (such as YouTube)? Less than 2 h 318 (50.2%) 77 (54.2%) 241 (49%) X 2(1) = 1.21, p = .27
2 or more hours 316 (49.8%) 65 (45.8%) 251 (51%)
 Play video games on a computer, console, phone or other devices (Xbox, PlayStation, iPad)? Less than 2 h 325 (51.2%) 71 (50%) 254 (51.5%) X 2(1) = .10, p = .75
2 or more hours 310 (48.8%) 71 (50%) 239 (48.5%)
 Text on a cell phone, tablet, or computer (eg, GChat, Whatsapp, etc.)? Less than 2 h 224 (35.5%) 28 (19.9%) 196 (40%) X 2(1) = 19.40, p < .001
2 or more hours 407 (64.5%) 113 (80.1%) 294 (60%)
 Visit social networking sites/apps like Facebook, Twitter, Instagram, etc.? Less than 2 h 271 (42.9%) 43 (30.3%) 228 (46.6%) X 2(1) = 12.00, p < .001
2 or more hours 360 (57.1%) 99 (69.7%) 261 (53.4%)
On a typical weekend, how many hours do you . . . .
 Watch TV shows or movies? Less than 2 h 278 (44.2%) 52 (36.9%) 226 (46.3%) X 2(1) = 3.95, p = .047
2 or more hours 351 (55.8%) 89 (63.1%) 262 (53.7%)
 Watch videos (such as YouTube)? Less than 2 h 300 (47.2%) 69 (48.6%) 231 (46.9%) X 2(1) = 1.33, p = .72
2 or more hours 335 (52.8%) 73 (51.4%) 262 (53.1%)
 Play video games on a computer, console, phone or other device (Xbox, Play Station, iPad)? Less than 2 h 293 (46.4%) 70 (49.3%) 223 (45.6%) X 2(1) = 0.60, p = .44
2 or more hours 338 (53.6%) 72 (50.7%) 266 (54.4%)
 Text on a cell phone, tablet, or computer (eg, GChat, Whatsapp, etc.)? Less than 2 h 230 (36.3%) 32 (22.7%) 198 (40.2%) X 2(1) = 14.47, p < .001
2 or more hours 404 (63.7%) 109 (77.3%) 295 (59.8%)
 Visit social networking sites/apps like Facebook, Twitter, Instagram, etc.? Less than 2 h 246 (39%) 40 (28.4%) 206 (42.1%) X 2(1) = 8.70, p = .003
2 or more hours 384 (61%) 101 (71.6%) 283 (57.9%)
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Significant differences between “ever users” and “never users” are noted in bold.

aThe total sample (n = 650) includes those who self-reported the ever use variable.

H1 was supported: heavy social media users (ie, two or more hours of social media use on a typical weekday or weekend day) were more likely to be tobacco ever users (n = 99, 69.7% for a typical weekday; n = 101, 71.6% for a typical weekend day) than light social media users (n = 43, 30.3% for a typical weekday; n = 40, 28.4% for a typical weekend), X2(1) = 12.00, p < .001 and X2(1) = 8.70, p = .003 for a typical weekday and weekend, respectively (Table 2). Likewise, heavy text users (ie, text on a cell phone, tablet, or computer using GChat, Whatsapp, etc. for two hours or more on a typical weekday or weekend day) were more likely to be tobacco ever users (n = 109–113, 77.3%–80.1%) than light text users (n = 28–32, 19.9%–22.7%), X2(1) = 14.47–19.40, ps < .001.

The Association Between Pro-Tobacco Marketing and Antitobacco Warning Exposure, Tobacco Use, and Tobacco Use Susceptibility

H2a was partially supported: higher levels of exposure to pro-tobacco marketing on social media were associated with tobacco use but not with susceptibility to tobacco use. H2b was rejected: exposure to on-site and social media antitobacco warnings was not associated with tobacco use susceptibility or tobacco use among rural adolescents. As shown in Table 3, after controlling for covariates, participants exposed to social media pro-tobacco marketing (odds ratio [OR] = 2.03, 95% CI: 1.37 to 3.03, Wald Χ2 = 12.19, p < .01) and being in a higher grade (OR = 1.77, 95% CI: 1.29 to 2.43, Wald Χ2 = 12.52, p < .01) were associated with significantly greater odds of having been a tobacco ever user. Among tobacco never users (n = 506), grade was significantly related to the future susceptibility of using tobacco products (OR = 1.40, 95% CI: 1.05 to 1.86, Wald Χ2 = 5.37, p = .02), such that a higher grade was associated with greater tobacco product susceptibility regardless of exposure to on-site and social media pro- or antitobacco messages.

Table 3.

Multiple Regression Models for Tobacco Ever Use and Susceptibility

Multivariate logistic regression for tobacco ever user status (n = 697) Multiple logistic regression for tobacco susceptibility (n = 506)a
Covariates OR 95% CI Wald p OR 95% CI Wald p
Exposure to on-site pro-tobacco marketing 1.14 0.84 to 1.53 0.71 0.4 1.21 0.92 to 1.58 1.89 0.17
Exposure to social media pro-tobacco marketing 2.03 1.37 to 3.03 12.19 0 0.94 0.65 to 1.36 0.10 0.76
Exposure to on-site antitobacco warning 1.10 0.85 to 1.42 0.53 0.47 1.18 0.93 to 1.50 1.88 0.17
Exposure to social media antitobacco warning 0.82 0.58 to 1.16 1.29 0.26 1.34 1.00 to 1.80 3.77 0.052
Grade 1.77 1.29 to 2.43 12.52 0 1.40 1.05 to 1.86 5.37 0.02
Mother’s education
 Less than college graduate Ref Ref Ref Ref
 College graduate 1.010 0.60 to 1.69 0.00 0.98 0.66 0.41 to 1.05 3.04 0.08
 Don’t know 0.966 0.40 to 2.36 0.01 0.94 1.71 0.75 to 3.88 1.65 0.20
Gender (female = 0; male = 1) 1.140 0.69 to 1.89 1.89 0.61 0.69 0.44 to 1.10 2.44 0.12
Ethnicity (non-Hispanic = 0) 1.457 0.69 to 3.08 0.97 0.32 1.13 0.54 to 2.34 0.10 0.75
Constant 0.01 . 32.48 0 0.96 0.12 7.73
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Values in bold indicate statistically significant estimates.

aThe Susceptibility model only includes those who self-identified as tobacco never users (n = 506), and ever users and missing responses were excluded from the susceptibility model. Among never users (n = 506), 281 adolescents (55.5%) self-identified as not susceptible to any type of tobacco products, and 225 (44.5%) reported as susceptible to initiating any type of tobacco products.

Discussion

This study examined the associations between different types of tobacco marketing and warning exposure, screen media use, tobacco use and future susceptibility to tobacco products among a rural youth sample. We measured various media use activities, including those related to social media for several reasons. Firstly, exposure to pro- or antitobacco messages can occur across various media platforms, and we wanted to ensure that no potential influences were overlooked. Television shows, video games, and texting with peers all present opportunities for exposure to tobacco-related content. Secondly, we aim to include data from adolescents who may have minimal or no social media use and examine all forms of media broadly. With this inclusive approach, we found that heavy use of screen media that enable peer interactivity and social networking, such as texting apps and social media, was a salient characteristic among those who ever used tobacco products. This may indicate that peer influence transmitted via texting and consumption of unregulated social media content are the notable risk factors for tobacco use in rural teens.

According to the Gallup Familial and Adolescent Health Survey reporting a probability-based sample of 1,567 US adolescents, 51% of US teens spent more than 4 h daily on social media, including YouTube, in 2023.36 Note that we assessed hours per day spent on YouTube on a separate item. When the number of hours spent on social media and YouTube were combined, our sample also showed that more than 50% spent 4 hours or more on these media platforms. This indicates that rural youth might be exposed to as much content, both promoting and opposing tobacco products, on social media as general youth population.37

Our findings are consistent with another study demonstrating that heavy use of screen media (apps) and exposure to tobacco-related content on social media predicted experimental smoking in youth.19 Social media use and other screen time were connected to reduced physical and mental health.38,39 These health conditions resulting from heavy use of screen media can co-occur with other risk behaviors, such as smokeless tobacco initiation.40 A recent study showed that lower mental health (eg, depression among youth) can result from increased social media use that in turn influenced future tobacco product use.17 Our study is not able to examine these relationships given the cross-sectional nature of our survey assessments, but future studies examining mechanisms for this relationship are warranted, particularly among rural youth. Future research may clarify elements contributing to the association between social media use and tobacco use, such as the content on social media or the interaction of adolescents via social media platforms that induce peer influence promoting tobacco use.

In our initial unadjusted regression model, we found that rural adolescents exposed to social media pro-tobacco marketing had a greater chance of being an ever tobacco user (OR: 1.95, 95% CI: 1.42 to 2.68, Χ2 = 17.00, p < .01) and this relationship continued even after controlling for important covariates that were found to be associated with youth tobacco use (ie, gender, mother’s education, and ethnicity). Conversely, there was no association between on-site and social media antitobacco warning messages and tobacco ever use, and no relationship between on-site pro-tobacco marketing and tobacco ever use, with or without controlling for covariates (ps = ns). Among tobacco never users, when those covariates were controlled, students in a higher-grade level showed a greater susceptibility to initiating ENDS or tobacco products. Findings indicate that exposure to pro-tobacco marketing and antitobacco warning messages, either on-site or on social media, was not associated with future susceptibility among never users, which may imply that these messages that encourage or discourage tobacco use have little impact among adolescents who never used tobacco products.

There are a few possible explanations for the null associations found between antitobacco warning exposure and tobacco use or susceptibility. For example, it could be that antitobacco messages were not tailored to rural youth, or that those who are not using tobacco products might be less receptive of antitobacco messages since they might find these messages irrelevant to them. Adolescents who are already smoking may likely ignore or not pay attention to antitobacco warnings, especially if it elicits negative emotional arousal that leads to message avoidance.41 Or, if the social norms around tobacco use are high among rural youth (eg, “everyone is doing it”), the message of not using it might not be as effective.24

Additionally, we were not able to not account for the content of the antismoking warnings. If those antitobacco messages used negative emotional arousal tactics, those messages might not resonate as much as personal narrative stories.42 There might be a greater degree of resistance to messages from specific populations (eg, lower health literacy levels, less perception that smoking increases changes of negative health effects). Future research should track the characteristics of the antismoking messages adolescents were exposed to, and identify specific message elements that were not effective or effective in reducing tobacco use or susceptibility.

For these never users, rather, being in a higher grade level was associated with tobacco susceptibility, which aligns with previous findings that teens aged 15 or 16 (11th grade) are at the greatest risk for smoking initiation.43 Tobacco use tends to be established in the teenage years, as shown in national data reporting that 87% of adult daily smokers initiated their first cigarette use before or at 18 years old.44 Sustainable educational and tobacco prevention programs should be considered in high school and even in later years. Often timing of a message can be influential on behavioral change and although youth may have initially heard about the risks of substance and nicotine use, it may be particularly salient when they are older and have a peer network that has greater likelihood of using and accessing tobacco products. Future prevention efforts may focus on this particular age group that might be most vulnerable to tobacco susceptibility and initiation.

Contrary to previous findings that exposure to pro-tobacco marketing at retail outlets directly impacts tobacco use initiation among youth,45 we found that exposure to antitobacco warning messages—both online and on-site (eg, at pharmacies and gas stations)—was not associated with preventing tobacco use or reducing susceptibility. It’s important to note that this is based on associations, not causation. Further exploration of the causal mechanisms between antitobacco message exposure, tobacco use, and future susceptibility, as well as potential interventions for rural youth, is warranted.

Limitations

There are limitations of this study imposed by study designs. First, the survey was based on a cross-sectional design that limits testing causality or time-lagged effects of screen media use or marketing/warning exposure on tobacco use and susceptibility.

Second, our participants were recruited through a particular school system in Virginia, and thus findings may not be readily generalizable to other states or regions. To build evidence for generalizability, future studies should administer the current survey questionnaires among rural teens nationwide to replicate the associations we examined. Alternatively, future research may replicate key findings in this study using existing national datasets. Note that we used a median score to categorize screen time use (eg, watching TV shows or movies) into two groups (heavy use vs. light use). This decision was made because the distributions of these items were not symmetrically distributed but right skewed, and the median is less affected by outliers and skewed data than the mean. In some cases, median split can yield more robust results, when the distribution is heavily skewed.46 However, this median-based dichotomization may have discarded valuable information available in a continuous variable and could result in oversimplified findings due to reduced statistical power.46 Future research should compare the effect sizes and consistency of the findings using these two analytic approaches.

Third, it is possible that exposure to marketing and warning messages as well as screen time may have been under-, over-, or misreported as the measures were based on self-reported recalls. To remove threat to validity due to recall bias or human errors, a better measurement technique could be implemented to ensure the accuracy of recall for screen media use and tobacco marketing/warning exposure.

Fourth, due to potential data missingness, we did not control for other potential risk factors for tobacco use in teens, such as social norms and peer influence. We explored a posteriori model by adding a variable indicating whether participants had close friends who used tobacco products (yes vs. no) to account for social influence. Even after adjusting for this effect in the model, exposure to pro-tobacco marketing on social media remained a significant factor associated with ever using tobacco. However, the sample size decreased due to increased listwise deletion from missing values. For future studies, it will be important to include variables that assess social and peer influence, such as peer attitudes toward smoking and the number of close friends who use ENDS and tobacco products.

Conclusion

Our study identified several risk factors for tobacco susceptibility and use among rural teens, in relation to exposure to pro-tobacco marketing and antitobacco warning, as well as screen media use. Greater social media and text use, as well as exposure to pro-tobacco marketing on social media were significantly associated with tobacco ever use. Among never users, both on-site and social media pro-tobacco marketing and warning message were not associated with tobacco susceptibility. Rather, higher grade levels were associated with greater susceptibility to tobacco products. For rural teens who have ever used tobacco products, future interventions may focus on how to address unregulated online marketing and advertisements that promote tobacco products. For rural youth, it may be beneficial to provide tobacco prevention campaigns that focus on regulating the intensity of social media and texting use while building their resilience to social media pro-tobacco marketing.

Acknowledgments

The authors thank Dr. David Wheeler.

Contributor Information

Sunny Jung Kim, Department of Social and Behavioral Sciences, School of Population Health, Virginia Commonwealth University, Richmond, VA, USA; Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA, USA.

Kendall Fugate-Laus, Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA.

Jeremy Barsell, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, USA.

Elizabeth K Do, Schroeder Institute, Truth Initiative, Washington, DC, USA.

Rashelle B Hayes, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.

Bernard F Fuemmeler, Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, USA.

Funding

This research was funded by the Virginia Foundation for Healthy Youth (RFP 852R010, PI: BFF).

Declaration of Interests

None declared.

Author Contributions

Sunny Jung Kim (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Funding acquisition [supporting], Investigation [lead], Methodology [lead], Validation [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]), Kendall Fugate-Laus (Data curation [supporting], Investigation [supporting], Project administration [supporting], Writing—review & editing [supporting]), Jeremy Barsell (Data curation [supporting], Methodology [supporting], Project administration [lead], Writing—review & editing [supporting]), Elizabeth Do (Funding acquisition [supporting], Investigation [supporting], Methodology [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Rashelle Hayes (Funding acquisition [supporting], Investigation [supporting], Methodology [supporting], Writing—review & editing [supporting]), and Bernard Fuemmeler (Conceptualization [supporting], Funding acquisition [lead], Investigation [supporting], Methodology [supporting], Writing—review & editing [supporting])

Data Availability

This study was not formally registered. The analysis plan was not formally pre-registered. De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author (SJK). Analytic code used to conduct the analyses presented in this study is not available in a public archive. They may be available by emailing the corresponding author. Materials used to conduct the study are not publicly available.

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Associated Data

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

Data Availability Statement

This study was not formally registered. The analysis plan was not formally pre-registered. De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author (SJK). Analytic code used to conduct the analyses presented in this study is not available in a public archive. They may be available by emailing the corresponding author. Materials used to conduct the study are not publicly available.

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