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One study found that sexual and gender minority (SGM) adolescents (e.g., lesbian, gay, bisexual, and transgender adolescents), 13-18 years old, spent an average of 5 hours per day online, approximately 45 minutes more than non-SGM adolescents in 2010-2011 [12]. However, this study grouped SGM together as a single group, conflating the experiences of gender minorities (e.g., transgender, gender-questioning) with those of sexual minorites (e.g., lesbian, gay, bisexual), and the data are now over a decade old. In a nationally representative sample of adolescents ages 13-18 years old in the U.S., transgender adolescents had higher probabilities of problematic internet use than cisgender adolescents. However, this analysis did not measure modality-specific problematic screen use such as problematic social media, video game, or mobile phone use, which may further inform the function that media use plays in the lives of gender minority adolescents [13]. While this prior research provides important groundwork to understand screen time and problematic use in gender minority adolescents, gaps remain in understanding differences in screen time and specific modalities of problematic screen use in gender minority early adolescents.
Our study aims to address the gaps in the current literature by studying associations between transgender and gender-questioning identity and recreational screen time across several modalities and problematic social media, video game, and mobile phone use in a large, national sample of early adolescents. We hypothesized that among early adolescents, transgender identification and questioning one’s gender identity would be positively associated with greater recreational screen time and problematic screen use compared to cisgender identification.
. The ABCD Study sample, recruitment, protocol, and measures have previously been described in detail [14]. Participants were mainly 12-13 years old during the 3-year follow-up, which was conducted between 2019 and 2021. Institutional review board (IRB) approval was received from the University of California, San Diego and the IRB of each respective study site for primary data collection, as well as the University of California, San Francisco for this secondary data analysis. Written assent was obtained from adolescent participants, and written informed consent was obtained from their caregivers.
Video Game Addiction Questionnaire (VGAQ): The six-question VGAQ was used to assess problematic video game use as reported by the adolescent participants who reported video game use during the week or on weekends. The questions were also modeled after the Bergen Facebook Addiction Scale [23]. Example questions include “I feel the need to play video games more and more” and “I play video games so much that it has had a bad effect on my schoolwork or job.” Likert-type scale responses ranged from 1 (never) to 6 (very often). To quantify the extent of problematic video game use, a mean score was calculated for the items in the questionnaire, with higher scores indicating greater problematic use.
Mobile Phone Involvement Questionnaire (MPIQ): The eight-question MPIQ was designed to assess problematic mobile phone use as reported by adolescents who reported having mobile phones use [24]. This questionnaire was previously used in a study to evaluate smartphone dependence in relation to digital multitasking while doing schoolwork among U.S. high school students [25]. Examples include “I interrupt whatever else I am doing when I am contacted on my phone” and “I lose track of how much I am using my phone.” Likert-type scale responses ranged from 1 (strongly disagree) to 7 (strongly agree). To quantify the extent of problematic mobile phone use, a mean score was calculated for the items in the questionnaire, with higher scores indicating greater problematic use.
Table 1. Sociodemographic and screen time characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N=9,859).
ABCD propensity weights were applied based on the American Community Survey from the US Census. SD = standard deviation
*
Asked among a subset who reported video game use (n=7,600)
†
Asked among a subset who reported social media use (n=5,656)
‡
Asked among a subset who reported mobile use (n=7,367)
Compared to cisgender adolescents, transgender adolescents reported 4.51 (95% CI 1.17-7.85) more hours of total screen time and reported higher time across all screen modalities except for video chat in adjusted models (Table 2). Furthermore, transgender identification was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification in adjusted models.
Table 2. Screen use associations with transgender vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study.
Bold indicates p<0.05. The estimated B coefficient in the cells represent abbreviated outputs from a series of linear regression models with transgender identification (yes vs no) as the independent variable and screen use (row header) as the outcome variable. Thus, the table represents the output from 22 different regression models in total (11 unadjusted and 11 adjusted). ABCD propensity weights were applied based on the American Community Survey from the US Census.
Adjusted models include the adolescent’s age, sex assigned at birth, race/ethnicity, household income, parent education (all parent reported), and study site.
*
Asked among a subset who reported video game use
†
Asked among a subset who reported social media use
‡
Asked among a subset who reported mobile use
Screen use comparisons for gender-questioning adolescents (responding “maybe” compared to “no” for the transgender question) are shown in Table 3. Gender-questioning participants reported 3.41 (95% CI 1.16-5.67) more hours of total daily recreational screen time and higher problematic social media, video game, and mobile phone use scores compared to cisgender participants.
Table 3. Screen use associations with questioning one’s gender identity vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study.
Bold indicates p<0.05. The estimated B coefficient in the cells represent abbreviated outputs from a series of linear regression models with transgender identification (maybe vs no) as the independent variable and screen use (row header) as the outcome variable. Thus, the table represents the output from 22 different regression models in total (11 unadjusted and 11 adjusted). ABCD propensity weights were applied based on the American Community Survey from the US Census.
Adjusted models include the adolescent’s age, sex assigned at birth, race/ethnicity, household income, parent education (all parent reported), and study site.
*
Asked among a subset who reported video game use
†
Asked among a subset who reported social media use
‡
Asked among a subset who reported mobile use
Given no evidence of significant effect modification by sex assigned at birth (all p for interaction >0.05), we did not stratify by sex assigned at birth in the main analyses; however, analyses stratified by sex assigned at birth are shown in Appendix A.
The results of our study add to the literature by investigating how gender minority adolescents interact with digital technology. Previous work has found that SGM adolescents reported on average 45 more minutes of daily screen time than non-SGM adolescents [12]. Our study adds to this by centering around the historically understudied subgroup of transgender and gender-questioning early adolescents and finding much larger differences in transgender versus cisgender adolescents than previously reported.
Our study also adds descriptive nuance to the specific modalities of screen use among transgender and gender-questioning adolescents. Higher watching of TV shows/movies among transgender and gender-questioning adolescents has not been previously discussed in the literature, with most research focusing on social media use or media representation. Moreover, the elevated single-player video game utilization in transgender and gender-questioning adolescents compared to cisgender adolescents may be explained by the phenomenon that gender minority adolescents are more likely to use media as a mode of escapism [29], [30], and as an outlet for seeking out safety, engagement, and a sense of agency [31].
Our results also show that transgender and gender-questioning adolescents have higher rates of problematic video game use than cisgender adolescents. These findings are amplified by previous research that problematic video game use among gender minority adolescents is more significant at a younger age and associated with depression and interpersonal conflict [32].
Similarly, we also found that transgender and gender-questioning adolescents report higher problematic social media and mobile phone use compared to their cisgender peers. Previous work has shown that gender minority adolescents report higher problematic internet use, characterized by internet-related anxiety, withdrawal, or decreased motivation [30]. For gender minority adolescents, digital media may offer a nuanced duality, consisting of both problematic and resilience factors [30], [33]. One study found that among SGM young adults, higher problematic social media use was associated with depressive symptoms, internalized stigma, and less emotional support [34]. Conversely, another study focusing solely on gender minority adolescents aged 10-17 found that active social media use and cleaning/curating social media were associated with lower emotional problems and conduct issues [33]. Social media has been shown to provide social support, support networks, and online communities for SGM adolescents and young adults [35], [36], [37].
Despite the strengths of our study, several limitations should be noted. Given the cross-sectional nature of this study, temporality and causality of the associations cannot be determined. Additionally, problematic screen use was assessed via self-report survey, which is subject to reporting bias. The gender identity question focused on transgender identity and did not capture other diverse gender minority identities (e.g., nonbinary, genderqueer, etc.). Moreover, those who responded “maybe” to the question regarding transgender status were analyzed separately. It is difficult to assess if these adolescents did not understand the questions or are truly gender questioning; however, given the developmental stage of the population being studied, we would expect a greater proportion of adolescents aged 12-13 to explore nonnormative gender identity more fluidly as compared to older youth [38], [39]. Additionally, the time period for data collection included before and during the COVID-19 pandemic, when screen time increased substantially [40]. There could be differential impacts of the pandemic by geographic region due to differences in pandemic restrictions; therefore, we controlled for study site in the analyses which may help to account for some of these potential differences. This present study adds to the literature by studying a large, diverse, national dataset of younger (aged 12-13) transgender and gender-questioning adolescents that investigates overall screen time, subtype screen time, and problematic use behaviors.
Highlights
- •
We analyzed data from 9,859 early adolescents 12-13 years old - •
Transgender adolescents reported more screen time than cisgender peers - •
Transgender adolescents reported higher problematic screen use than cisgender peers
Abstract
Objective
To assess the association between transgender or gender-questioning identity and screen use (recreational screen time and problematic screen use) in a demographically diverse national sample of early adolescents in the U.S.Methods
We analyzed cross-sectional data from Year 3 of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®, N=9,859, 2019-2021, mostly 12-13-years-old). Multiple linear regression analyses estimated the associations between transgender or questioning gender identity and screen time, as well as problematic use of video games, social media, and mobile phones, adjusting for confounders.Results
In a sample of 9,859 adolescents (48.8% female, 47.6% racial/ethnic minority, 1.0% transgender, 1.1% gender-questioning), transgender participants reported 4.51 (95% CI 1.17-7.85) more hours of total daily recreational screen time including more time on television/movies, video games, texting, social media, and the internet, compared to cisgender participants. Gender-questioning participants reported 3.41 (95% CI 1.16-5.67) more hours of total daily recreational screen time compared to cisgender participants. Transgender identification and questioning one’s gender identity was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification.Conclusions
Transgender and gender-questioning adolescents spend a disproportionate amount of time engaging in screen-based activities and have more problematic use across social media, video game, and mobile phone platforms.Introduction
Screen-based digital media is integral to the daily lives of adolescents in multifaceted ways [1] but problematic screen use (characterized by inability to control and detrimental consequences from excessive use including preoccupation, tolerance, relapse, withdrawal, and conflict) [2], [3], has been linked with harmful mental and physical health outcomes, such as depression, poor sleep, and cardiometabolic disease [4], [5]. Transgender and gender-questioning adolescents (i.e., adolescents who are questioning their gender identity) experience a higher prevalence of bullying (adjusted prevalence ratio [aPR] 1.88 and 1.62), suicide attempts (aPR 2.65 and 2.26), and binge drinking (aPR 1.80 and 1.50), respectively, compared to their cisgender peers [6], [7], [8], [9], [10]. Transgender and gender-questioning adolescents may engage in screen-based activities that are problematic and associated with negative health outcomes but also in a way often different from their cisgender peers in order to form communities, explore health education about their gender identity, and seek refuge from isolating or unsafe environments [11].One study found that sexual and gender minority (SGM) adolescents (e.g., lesbian, gay, bisexual, and transgender adolescents), 13-18 years old, spent an average of 5 hours per day online, approximately 45 minutes more than non-SGM adolescents in 2010-2011 [12]. However, this study grouped SGM together as a single group, conflating the experiences of gender minorities (e.g., transgender, gender-questioning) with those of sexual minorites (e.g., lesbian, gay, bisexual), and the data are now over a decade old. In a nationally representative sample of adolescents ages 13-18 years old in the U.S., transgender adolescents had higher probabilities of problematic internet use than cisgender adolescents. However, this analysis did not measure modality-specific problematic screen use such as problematic social media, video game, or mobile phone use, which may further inform the function that media use plays in the lives of gender minority adolescents [13]. While this prior research provides important groundwork to understand screen time and problematic use in gender minority adolescents, gaps remain in understanding differences in screen time and specific modalities of problematic screen use in gender minority early adolescents.
Our study aims to address the gaps in the current literature by studying associations between transgender and gender-questioning identity and recreational screen time across several modalities and problematic social media, video game, and mobile phone use in a large, national sample of early adolescents. We hypothesized that among early adolescents, transgender identification and questioning one’s gender identity would be positively associated with greater recreational screen time and problematic screen use compared to cisgender identification.
Methods
We conducted a cross-sectional analysis of the Year 3 follow-up of the Adolescent Brain Cognitive Development (ABCD) Study (5.0 release), the most recent year with full data available and the highest prevalence of adolescents who identify as transgender or gender-questioning. The ABCD Study is the largest long-term longitudinal study of health and cognitive development in 11,875 children from 21 recruitment sites across the U.S. (baseline 2016–201. The ABCD Study sample, recruitment, protocol, and measures have previously been described in detail [14]. Participants were mainly 12-13 years old during the 3-year follow-up, which was conducted between 2019 and 2021. Institutional review board (IRB) approval was received from the University of California, San Diego and the IRB of each respective study site for primary data collection, as well as the University of California, San Francisco for this secondary data analysis. Written assent was obtained from adolescent participants, and written informed consent was obtained from their caregivers.Measures
Independent Variable
Transgender and gender-questioning: Adolescents were asked a question about transgender identity: “Are you transgender?” Response options included: yes, maybe, no, don’t understand the question, and decline to answer [15], [16]. For the purposes of terminology in this study, participants who responded “yes” were considered transgender adolescents, those who responded “maybe” were considered gender-questioning adolescents, and those who responded “no” were considered cisgender adolescents. When referring to the “yes” and “maybe” transgender groups together, we used a more inclusive term “gender minority” given its use in past literature [6], [15], [17].Dependent Variables
Recreational Screen Use
Adolescents self-reported their recreational screen use for the following modalities by hours (0-24 hours) and minutes (0-60 min) of use on a typical weekday and weekend: multi-player gaming, single-player gaming, texting, social media, video chatting, browsing the internet, and watching/streaming television shows or movies [18]. The total typical daily screen use was calculated as the weighted sum of hours/minutes ([weekday average x 5] + [weekend average x 2])/7) across all modalities as has been done previously [2], [19].Problematic Screen Use
Social Media Addiction Questionnaire (SMAQ): The six-question SMAQ was used to assess problematic social media use as reported by adolescents who had at least one social media account. The questions were modeled after the Bergen Facebook Addiction Scale [2], [3], [20], which assesses Facebook addiction (e.g., overuse, tolerance, relapse, conflict) in a questionnaire with a unidimensional factor structure. Its application has been extended to broader social media and video game addiction among high school and college students [21], [22]. Examples include “I’ve tried to use my social media apps less but I can’t” and “I’ve become stressed or upset if I am not allowed to use my social media apps.” Likert-type scale responses ranged from 1 (never) to 6 (very often). To quantify the extent of problematic social media use, a mean score was calculated for the items in the questionnaire, with higher scores indicating greater problematic use.Video Game Addiction Questionnaire (VGAQ): The six-question VGAQ was used to assess problematic video game use as reported by the adolescent participants who reported video game use during the week or on weekends. The questions were also modeled after the Bergen Facebook Addiction Scale [23]. Example questions include “I feel the need to play video games more and more” and “I play video games so much that it has had a bad effect on my schoolwork or job.” Likert-type scale responses ranged from 1 (never) to 6 (very often). To quantify the extent of problematic video game use, a mean score was calculated for the items in the questionnaire, with higher scores indicating greater problematic use.
Mobile Phone Involvement Questionnaire (MPIQ): The eight-question MPIQ was designed to assess problematic mobile phone use as reported by adolescents who reported having mobile phones use [24]. This questionnaire was previously used in a study to evaluate smartphone dependence in relation to digital multitasking while doing schoolwork among U.S. high school students [25]. Examples include “I interrupt whatever else I am doing when I am contacted on my phone” and “I lose track of how much I am using my phone.” Likert-type scale responses ranged from 1 (strongly disagree) to 7 (strongly agree). To quantify the extent of problematic mobile phone use, a mean score was calculated for the items in the questionnaire, with higher scores indicating greater problematic use.
Statistical Analyses
Data analyses were performed in 2023 using Stata 18 (StataCorp, College Station, TX) using a complete case analysis. Multiple linear regression analyses were conducted to estimate associations between transgender identification (“yes” compared to “no”) or gender-questioning (“maybe” compared to “no”) and recreational screen time (seven modalities in total) as well as three forms of problematic screen use (video game, social media, mobile phone), adjusting for potential confounders including the adolescent’s age, sex assigned at birth, race/ethnicity, parent education, household income (all parent reported), and study site. We checked for effect modification of the associations by sex assigned at birth given prior research showing differences in mental health and substance use by sex assigned at birth among transgender adolescents [26], [27]. Propensity weights provided by the ABCD Study were applied to yield representative estimates based on key demographic and socioeconomic distributions of early adolescents in the American Community Survey from the U.S. Census [28].Results
In a sample of 9,859 adolescents (48.8% female, 47.6% racial/ethnic minority), 1.0% were transgender (responding “yes” to the transgender question) and 1.1% were gender-questioning (responding “maybe” to the transgender question, Table 1).Table 1. Sociodemographic and screen time characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N=9,859).
| Sociodemographic characteristics | Mean (SD) / % |
|---|---|
| Age (years) | 12.91 (0.65) |
| Sex assigned at birth (%) | |
| Female | 48.8% |
| Male | 51.2% |
| Race/ethnicity (%) | |
| White | 52.4% |
| Latino / Hispanic | 20.1% |
| Black | 17.3% |
| Asian | 5.5% |
| Native American | 3.2% |
| Other | 1.5% |
| Household income (%) | |
| Less than $75,000 | 56.8% |
| $75,000 and greater | 43.2% |
| Parents' highest education (%) | |
| High school education or less | 16.2% |
| College education or more | 83.8% |
| Transgender identification (%) | |
| No | 94.7% |
| Yes | 1.0% |
| Maybe | 1.1% |
| I don't understand the question | 2.6% |
| Decline to answer | 0.6% |
| Screen time | |
| Total recreational screen time | 9.13 (8.87) |
| Television and movies | 2.58 (2.31) |
| Single-player video games | 1.27 (1.90) |
| Multi-player video games | 1.54 (2.12) |
| Texting | 1.15 (2.05) |
| Social media | 1.28 (2.13) |
| Video chat | 0.81 (1.76) |
| Browsing the internet | 0.52 (1.10) |
| Problematic screen use measures | |
| Video Game Addiction Questionnaire Score* | 2.21 (1.09) |
| Social Media Addiction Questionnaire Score† | 2.08 (0.97) |
| Mobile Phone Involvement Questionnaire Score‡ | 3.34 (1.12) |
ABCD propensity weights were applied based on the American Community Survey from the US Census. SD = standard deviation
*
Asked among a subset who reported video game use (n=7,600)
†
Asked among a subset who reported social media use (n=5,656)
‡
Asked among a subset who reported mobile use (n=7,367)
Compared to cisgender adolescents, transgender adolescents reported 4.51 (95% CI 1.17-7.85) more hours of total screen time and reported higher time across all screen modalities except for video chat in adjusted models (Table 2). Furthermore, transgender identification was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification in adjusted models.
Table 2. Screen use associations with transgender vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study.
| Empty Cell | Unadjusted | Adjusted | ||
|---|---|---|---|---|
| Empty Cell | B (95% CI) | p | B (95% CI) | p |
| Screen time | ||||
| Total recreational screen time | 4.20 (1.10, 7.31) | 0.008 | 4.51 (1.17, 7.85) | 0.008 |
| Television and movies | 0.98 (0.35, 1.60) | 0.002 | 0.82 (0.16, 1.49) | 0.016 |
| Single-player video games | 0.51 (0.01, 1.01) | 0.045 | 0.89 (0.36, 1.42) | 0.001 |
| Multi-player video games | 0.33 (-0.52, 1.17) | 0.449 | 0.96 (0.04, 1.87) | 0.040 |
| Texting | 0.29 (0.20, 1.63) | 0.408 | 0.81 (0.04, 1.59) | 0.040 |
| Social media | 1.06 (0.32, 1.81) | 0.005 | 0.83 (0.06, 1.61) | 0.035 |
| Video chat | -0.04 (-0.31, 0.24) | 0.802 | -0.30 (-0.55, -0.05) | 0.020 |
| Browsing the internet | 0.44 (0.09, 0.79) | 0.014 | 0.49 (0.12, 0.87) | 0.010 |
| Problematic screen use measures | ||||
| Video Game Addiction Questionnaire Score* | 0.08 (-0.18, 0.35) | 0.537 | 0.46 (0.19, 0.74) | 0.001 |
| Social Media Addiction Questionnaire Score† | 0.45 (0.20, 0.70) | <0.001 | 0.43 (0.17, 0.69) | 0.001 |
| Mobile Phone Involvement Questionnaire Score‡ | 0.43 (0.16, 0.70) | 0.002 | 0.34 (0.07, 0.61) | 0.012 |
Bold indicates p<0.05. The estimated B coefficient in the cells represent abbreviated outputs from a series of linear regression models with transgender identification (yes vs no) as the independent variable and screen use (row header) as the outcome variable. Thus, the table represents the output from 22 different regression models in total (11 unadjusted and 11 adjusted). ABCD propensity weights were applied based on the American Community Survey from the US Census.
Adjusted models include the adolescent’s age, sex assigned at birth, race/ethnicity, household income, parent education (all parent reported), and study site.
*
Asked among a subset who reported video game use
†
Asked among a subset who reported social media use
‡
Asked among a subset who reported mobile use
Screen use comparisons for gender-questioning adolescents (responding “maybe” compared to “no” for the transgender question) are shown in Table 3. Gender-questioning participants reported 3.41 (95% CI 1.16-5.67) more hours of total daily recreational screen time and higher problematic social media, video game, and mobile phone use scores compared to cisgender participants.
Table 3. Screen use associations with questioning one’s gender identity vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study.
| Empty Cell | Unadjusted | Adjusted | ||
|---|---|---|---|---|
| Empty Cell | B (95% CI) | p | B (95% CI) | p |
| Screen time | ||||
| Total recreational screen time | 2.37 (0.23, 4.52) | 0.030 | 3.41 (1.16, 5.67) | 0.003 |
| Television and movies | 0.83 (0.22, 1.44) | 0.008 | 1.12 (0.48, 1.77) | 0.001 |
| Single-player video games | 0.17 (-0.23, 0.56) | 0.414 | 0.43 (0.07, 0.7 | 0.018 |
| Multi-player video games | -0.14 (-0.61, 0.34) | 0.576 | 0.47 (-0.05, 1.00) | 0.079 |
| Texting | 0.38 (-0.30, 1.00) | 0.565 | 0.33 (-0.38, 1.04) | 0.362 |
| Social media | 0.51 (0.02, 0.99) | 0.041 | 0.42 (-0.09, 0.93) | 0.108 |
| Video chat | 0.15 (-0.39, 0.69) | 0.580 | 0.14 (-0.45, 0.73) | 0.637 |
| Browsing the internet | 0.50 (0.02, 0.9 | 0.042 | 0.50 (-0.01, 1.01) | 0.055 |
| Problematic screen use measures | ||||
| Video Game Addiction Questionnaire Score* | 0.28 (-0.03, 0.5 | 0.077 | 0.57 (0.27, 0.8 | < 0.001 |
| Social Media Addiction Questionnaire Score† | 0.44 (0.20, 0.69) | <0.001 | 0.46 (0.21, 0.71) | 0.001 |
| Mobile Phone Involvement Questionnaire Score‡ | 0.60 (0.29, 0.92) | <0.001 | 0.56 (0.21, 0.90) | 0.001 |
Bold indicates p<0.05. The estimated B coefficient in the cells represent abbreviated outputs from a series of linear regression models with transgender identification (maybe vs no) as the independent variable and screen use (row header) as the outcome variable. Thus, the table represents the output from 22 different regression models in total (11 unadjusted and 11 adjusted). ABCD propensity weights were applied based on the American Community Survey from the US Census.
Adjusted models include the adolescent’s age, sex assigned at birth, race/ethnicity, household income, parent education (all parent reported), and study site.
*
Asked among a subset who reported video game use
†
Asked among a subset who reported social media use
‡
Asked among a subset who reported mobile use
Given no evidence of significant effect modification by sex assigned at birth (all p for interaction >0.05), we did not stratify by sex assigned at birth in the main analyses; however, analyses stratified by sex assigned at birth are shown in Appendix A.
Discussion
In a demographically diverse national sample, the present study found that transgender adolescents reported over four more hours of daily screen time than their cisgender peers. Transgender adolescents reported higher time spent on all screen modalities except for video chat compared to cisgender adolescents. Notably, transgender and gender-questioning adolescents had higher problematic phone, social media, and video game use compared to cisgender adolescents.The results of our study add to the literature by investigating how gender minority adolescents interact with digital technology. Previous work has found that SGM adolescents reported on average 45 more minutes of daily screen time than non-SGM adolescents [12]. Our study adds to this by centering around the historically understudied subgroup of transgender and gender-questioning early adolescents and finding much larger differences in transgender versus cisgender adolescents than previously reported.
Our study also adds descriptive nuance to the specific modalities of screen use among transgender and gender-questioning adolescents. Higher watching of TV shows/movies among transgender and gender-questioning adolescents has not been previously discussed in the literature, with most research focusing on social media use or media representation. Moreover, the elevated single-player video game utilization in transgender and gender-questioning adolescents compared to cisgender adolescents may be explained by the phenomenon that gender minority adolescents are more likely to use media as a mode of escapism [29], [30], and as an outlet for seeking out safety, engagement, and a sense of agency [31].
Our results also show that transgender and gender-questioning adolescents have higher rates of problematic video game use than cisgender adolescents. These findings are amplified by previous research that problematic video game use among gender minority adolescents is more significant at a younger age and associated with depression and interpersonal conflict [32].
Similarly, we also found that transgender and gender-questioning adolescents report higher problematic social media and mobile phone use compared to their cisgender peers. Previous work has shown that gender minority adolescents report higher problematic internet use, characterized by internet-related anxiety, withdrawal, or decreased motivation [30]. For gender minority adolescents, digital media may offer a nuanced duality, consisting of both problematic and resilience factors [30], [33]. One study found that among SGM young adults, higher problematic social media use was associated with depressive symptoms, internalized stigma, and less emotional support [34]. Conversely, another study focusing solely on gender minority adolescents aged 10-17 found that active social media use and cleaning/curating social media were associated with lower emotional problems and conduct issues [33]. Social media has been shown to provide social support, support networks, and online communities for SGM adolescents and young adults [35], [36], [37].
Despite the strengths of our study, several limitations should be noted. Given the cross-sectional nature of this study, temporality and causality of the associations cannot be determined. Additionally, problematic screen use was assessed via self-report survey, which is subject to reporting bias. The gender identity question focused on transgender identity and did not capture other diverse gender minority identities (e.g., nonbinary, genderqueer, etc.). Moreover, those who responded “maybe” to the question regarding transgender status were analyzed separately. It is difficult to assess if these adolescents did not understand the questions or are truly gender questioning; however, given the developmental stage of the population being studied, we would expect a greater proportion of adolescents aged 12-13 to explore nonnormative gender identity more fluidly as compared to older youth [38], [39]. Additionally, the time period for data collection included before and during the COVID-19 pandemic, when screen time increased substantially [40]. There could be differential impacts of the pandemic by geographic region due to differences in pandemic restrictions; therefore, we controlled for study site in the analyses which may help to account for some of these potential differences. This present study adds to the literature by studying a large, diverse, national dataset of younger (aged 12-13) transgender and gender-questioning adolescents that investigates overall screen time, subtype screen time, and problematic use behaviors.
