Annals of Epidemiology 95 (2024) 6–11 Available online 6 May 2024 1047-2797/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). Screen use in transgender and gender-questioning adolescents: Findings from the Adolescent Brain Cognitive Development (ABCD) Study Jason M. Nagata a,*, Priyadharshini Balasubramanian a, Puja Iyra a, Kyle T. Ganson b, Alexander Testa c, Jinbo He d, David V. Glidden e, Fiona C. Baker f,g a Division of Adolescent and Young Adult Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA b Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, Ontario, Canada c Department of Management, Policy and Community Health, University of Texas Health Science Center at Houston, TX, USA d School of Humanities and Social Science, The Chinese University of Hong Kong, 2001 Longxiang Boulevard, Longgang District, Shenzhen 518172, China e Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA f Center for Health Sciences, SRI International, Menlo Park, 333 Ravenswood Ave, Menlo Park, CA 94025, USA g School of Physiology, University of the Witwatersrand, 1 Jan Smuts Ave, Braamfontein, Johannesburg 2017, South Africa A R T I C L E I N F O Keywords: Screen time LGBTQ+ Adolescent Social media Video games Gender identity Gender minority Transgender A B S T R A C T Objective: To assess the association between transgender or gender-questioning identity and screen use (recre- ational screen time and problematic screen use) in a demographically diverse national sample of early adoles- cents in the U.S. Methods: We analyzed cross-sectional data from Year 3 of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®, N = 9859, 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 9859 adolescents (48.8% female, 47.6% racial/ethnic minority, 1.0% transgender, 1.1% gender-questioning), transgender adolescents reported 4.51 (95% CI 1.17–7.85) more hours of total daily rec- reational screen time including more time on television/movies, video games, texting, social media, and the internet, compared to cisgender adolescents. Gender-questioning adolescents reported 3.41 (95% CI 1.16–5.67) more hours of total daily recreational screen time compared to cisgender adolescents. 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 usage and detrimental consequences from excessive use including preoccupation, tolerance, relapse, withdrawal, and con- flict) [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 preva- lence of bullying (adjusted prevalence ratio [aPR] 1.88 and 1.62), sui- cide attempts (aPR 2.65 and 2.26), and binge drinking (aPR 1.80 and 1.50), respectively, compared to their cisgender peers [6–10]. Trans- gender and gender-questioning adolescents may engage in screen-based activities that are problematic and associated with negative health outcomes but also in a way that is different from their cisgender peers in Abbreviations: ABCD, Adolescent Brain Cognitive Development study; IRB, Institutional review board; MPIQ, Mobile Phone Involvement Questionnaire; SGM, Sexual and gender minority; SMAQ, Social Media Addiction Questionnaire; US;, United States; UCSD, University of California, San Diego; VGAQ, Video Game Addiction Questionnaire. * Correspondence to: 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143, USA. E-mail address: jason.nagata@ucsf.edu (J.M. Nagata). Contents lists available at ScienceDirect Annals of Epidemiology journal homepage: www.sciencedirect.com/journal/annals-of-epidemiology https://doi.org/10.1016/j.annepidem.2024.04.013 Received 28 November 2023; Received in revised form 25 April 2024; Accepted 30 April 2024 mailto:jason.nagata@ucsf.edu www.sciencedirect.com/science/journal/10472797 https://www.sciencedirect.com/journal/annals-of-epidemiology https://doi.org/10.1016/j.annepidem.2024.04.013 https://doi.org/10.1016/j.annepidem.2024.04.013 https://doi.org/10.1016/j.annepidem.2024.04.013 http://crossmark.crossref.org/dialog/?doi=10.1016/j.annepidem.2024.04.013&domain=pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ Annals of Epidemiology 95 (2024) 6–11 7 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), aged 13–18 years old, spent an average of 5 h per day online, approximately 45 min 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 aged 13–18 years old in the U.S., transgender adolescents had higher proba- bilities 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 differ- ences 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 screen time across several modalities including recrea- tional 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 rec- reational 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 2016–2018). 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 ob- tained from adolescent participants, and written informed consent was obtained from their caregivers. Measures Independent variable Transgender and gender-questioning: Adolescents were asked a ques- tion about transgender identity: “Are you transgender?” Response op- tions 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 adoles- cents, 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 h) 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 smart- phone 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 indi- cating 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 J.M. Nagata et al. Annals of Epidemiology 95 (2024) 6–11 8 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 9859 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). Compared to cisgender adolescents, transgender adolescents re- ported 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 mo- bile phone use compared to cisgender identification in adjusted models. 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. 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 more 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 Table 1 Sociodemographic and screen time characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N = 9859). Sociodemographic and screen use 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 Study propensity weights were applied based on the American Commu- nity Survey from the US Census. SD = standard deviation * Asked among a subset who reported video game use (n = 7600) † Asked among a subset who reported social media use (n = 5656) ‡ Asked among a subset who reported mobile use (n = 7367) Table 2 Screen use associations with transgender vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study. Unadjusted Adjusted 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 Study 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 J.M. Nagata et al. Annals of Epidemiology 95 (2024) 6–11 9 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 liter- ature, with most research focusing on social media use or media rep- resentation. Moreover, the elevated single-player video game utilization in transgender and gender-questioning adolescents compared to cis- gender 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 ad- olescents have higher rates of problematic video game use than cis- gender 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 is- sues [33]. Social media has been shown to provide social support net- works and online communities for SGM adolescents and young adults [35–37]. 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, prob- lematic 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 adolescents [38,39]. Additionally, the time period for data collection included before and during the COVID-19 pandemic, when screen time increased substan- tially [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 in- vestigates overall screen time, subtype screen time, and problematic use behaviors. Conclusion Our findings support the need for digital media technology in- terventions to be scaffolded and tailored to the unique needs of gender minority adolescents. Given the differential uses and functions of screen time for gender minority adolescents, school media literacy programs can meaningfully support and empower adolescents through their identity formation journeys. The literature has shown that strong school media literacy programs were associated with less depression among gender minority adolescents, highlighting the importance of institu- tionalized media skill-building for youth [33]. Our findings should be interpreted with thought and deliberation given that there is a complex nuance to how gender minority adolescents interact with screen-based media and how it may be associated with both negative and positive outcomes. Further research should focus on the specific online activities in which gender minority adolescents engage and the function of these digital interactions. Moreover, future studies that analyze other mea- sures of gender (e.g., gender expression, contentedness [16]), other adolescent age groups, and intersections with sexual orientation [41], may shed light on differences in screen time by developmental stage and Table 3 Screen use associations with questioning one’s gender identity vs cisgender identification in the Adolescent Brain Cognitive Development (ABCD) Study. Unadjusted Adjusted 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, 0.78) 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, 0.98) 0.042 0.50 (− 0.01, 1.01) 0.055 Problematic screen use measures Video Game Addiction Questionnaire Score* 0.28 (− 0.03, 0.58) 0.077 0.57 (0.27, 0.88) < 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 Study 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 J.M. Nagata et al. Annals of Epidemiology 95 (2024) 6–11 10 offer insight into differential associations with mental and physical health outcomes across adolescence. Ethics approval The University of California, San Diego provided centralized insti- tutional review board (IRB) approval and each participating site received local IRB approval. The University of California, San Francisco provided additional approval for this secondary data analysis. Funding J.M.N. was funded by National Institutes of Health (K08HL159350 and R01MH135492) and the Doris Duke Charitable Foundation (2022056). 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. The ABCD Study was supported by the National Institutes of Health (Bethesda, Maryland) and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy. org/federal-partners/. 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. CRediT authorship contribution statement Jason M. Nagata: Conceptualization, Formal analysis, Supervision, Writing – original draft, Writing – review & editing. Priyadharshini Balasubramanian: Formal analysis, Writing – original draft, Writing – review & editing. Puja Iyra: Writing – original draft, Writing – review & editing. Kyle T. Ganson: Writing – review & editing. Alexander Testa: Writing – review & editing. Jinbo He: Writing – review & editing. David V. Glidden: Writing – review & editing. Fiona C. Baker: Conceptuali- zation, Investigation, Methodology, Writing – review & editing. Declaration of Competing Interest The authors have no conflict to declare. References [1] Twenge JM, Martin GN, Spitzberg BH. Trends in U.S. Adolescents’ media use, 1976–2016: the rise of digital media, the decline of TV, and the (near) demise of print. Psychol Pop Media Cult 2019;8:329–45. https://doi.org/10.1037/ ppm0000203. [2] 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. Pedia Res 2022;92:1443–9. https://doi.org/10.1038/s41390-022-02176-8. 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