DISPARITIES IN SLEEP DURATION, TIMING, VARIABILITY AMONG AMERICAN ADOLESCENTS: INTERSECTIONS OF IDENTITIES, DISCRIMINATION, AND STRUCTURAL STIGMA By Youchuan Zhang A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Human Development and Family Studies – Doctor of Philosophy 2023 ABSTRACT Drawing data from the Adolescent Brain Cognitive Development (ABCD) study, this dissertation investigated adolescents’ sleep health disparities based on double marginalized identities of ethnic-racial minority and sexual orientation minority (study 1). This dissertation further comprehensively examined the role of stigma, targeting both marginalized identities of ethnicity-race and sexual orientation, at both the structural level and the interpersonal level, in contributing to sleep disturbances among the specific group–adolescents with double marginalized identities of ethnic-racial minority and sexual orientation minority (i.e., LGB youth of color; study 2). The first study showed that LGB youth of color were at excessive risk of sleep disturbances, when either compared to the non-marginalized White heterosexual group, or to the groups with single marginalized identity (i.e., White LGB, Latinx heterosexual, Black heterosexual). Furthermore, the sleep disparities among LGB youth of color spanned across all the examined dimensions (i.e., duration, timing, and variability) and was more salient during weekdays than weekends. The second study provided initial evidence that state-level structural stigma directly affected sleep health, while the two forms of interpersonal discrimination impacted sleep in a multiplicative way. Taken together, these findings provided crucial insights in terms of on whom to target and what aspects to address in order to mitigate sleep health disparities based on ethnicity-race and sexual orientation. Copyright by YOUCHUAN ZHANG 2023 This dissertation is dedicated, with love, to my mom. iv ACKNOWLEDGEMENTS I would like to start by thanking my amazing advisor, Dr. Yijie Wang. Without your guidance and support, I won’t be able to make it at the finish line of grad school. You are the first person I always think of to turn to when I hit those tiny milestones or bumps on my academic road. You are my research role model. You have shaped me into the scholar I am today. I am very grateful to be your student. I would also like to express my gratitude to the other members of my committee: Drs. Kendal Holtrop, Linda Halgunseth, and Zhenqiang Zhao. Your invaluable feedback has greatly enhanced the quality of my dissertation at different stages. I really appreciate your efforts in pushing me to think more about linking theoretical frameworks and the implications. My gratitude goes to my family as well. My mom has always hoped I could be around her, but as I have pursued my education, our time together has become limited. However, she never stops providing me with unconditional love and support in every possible way. I know she will always be there for me no matter what. I also want to express my thanks to my husband for all that you have done for me, and to my in-laws for their love, support, and understanding. Heartfelt thanks to my HDFS friends. Mingzhang, Tiantian, and Meng, thank you for going grad school with me. We’ve been through so many ups and downs together, creating unforgettable memories. Grad school would have been a real pain without you guys. Lastly, I want to express my gratitude to my furry buddies, my puppy and kitten. Your woofs and mews never fail to cheer me up on my tough days. v TABLE OF CONTENTS CHAPTER 1. GENERAL INTRODUCTION ............................................................................... 1 CHAPTER 2. STUDY ONE: DISPARITIES IN SLEEP DURATION, TIMING, VARIABILITY AMONG AMERICAN ADOLESCENTS: INTERSECTIONS OF ETHNICITY -RACE AND SEXUAL ORIENTATION............................................................................................................. 6 CHAPTER 3. STUDY TWO: SLEEP DURATION, TIMING, VARIABILITY AMONG LGB YOUTH OF COLOR: UNDERSTANDING THE ROLES OF STRUCTURAL STIGMA AND INTERPERSONAL DISCRIMINATION FROM AN INTERSECTIONAL PERSPECTIVE... 30 CHAPTER 4. GENERAL CONCLUSION .................................................................................. 56 BIBLIOGRAPHY ......................................................................................................................... 58 APPENDIX A: TABLES.............................................................................................................. 72 APPENDIX B: FIGURES ............................................................................................................ 80 APPENDIX C: SUPPLEMENTARY TABLES........................................................................... 87 vi CHAPTER 1. GENERAL INTRODUCTION Adolescent Sleep Health and Sleep Health Disparities Sleep is defined as “a reversible behavioral state of perceptual disengagement from and unresponsiveness to the environment (Carskadon & Dement, 2017, page 15)”. Healthy sleep (sufficient, with proper timing, and consistent; Buysse, 2014; Meltzer, Williamson, et al., 2021) is fundamental for adolescents’ healthy development across various domains (El-Sheikh et al., 2022; Matricciani et al., 2019). Compared to other developmental stages, early adolescence marks a period of increased vulnerability to sleep deprivation, delayed bedtime and early risetime, as well as large variability, due to the interplay of pubertal changes, heightened academic and social demands, and early school start times (Carskadon, 2011; Crowley et al., 2018). Sleep is a health indicator for which disparities have been observed among marginalized youth populations, especially those who are ethnic-racial (see Guglielmo et al., 2018 for a review) or sexual minorities (Dai et al., 2020; Kann et al., 2016; Kreski & Keyes, 2022). Health disparities refer to “a particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage” (U.S. Department of Health and Human Services, n.d.). These disadvantages can be broadly related to ethnicity and race, gender, sexual orientation, religion, socioeconomic status, gender, age, mental health, disability, geographic location (Centers for Disease Control and Prevention, n.d.). Identifying sleep disturbances among marginalized youth groups is the first step towards addressing sleep health disparities. Furthermore, as healthy sleep is critical for overall health, understanding the contributing factors to sleep health disparities can also help uncover opportunities for intervention programs to tackle health inequities in other domains. 1 Stigma and Health Disparities In addition to documenting health disparities based on marginalized identities of ethnicity-race and sexual orientation, it is imperative to further understand the underlying causes of these health disparities. It has been increasingly recognized that stigma plays a significant role in contributing to poorer health outcomes for marginalized groups (Hatzenbuehler et al., 2013). Various theories have emerged to elucidate how stigma associated with specific marginalized identities influences the experiences and well-being of stigmatized groups. The Integrative Model of Minority Child Development (García Coll et al., 1996) is one such theory that highlighted the central role of racism as a critical social mechanism through which social position factors (e.g., race and ethnicity, gender, social class) create the segregated environment for racial minority child development. Another theory that is influential on studies focusing on sexual minority group is the Minority Stress theoretical framework (Meyer, 2003), which is an explanatory model designed to elucidate why LGB populations are at excessive risk for mental disorders. In this framework, stigma tied to minority status (e.g., sexual orientation minority status, ethnic-racial minority status) was also proposed as a critical form of minority stress (different from general stress such as losing job), leading to disproportionate mental health problems among LGB populations. Although the Integrative Model of Minority Child Development and the Minority Stress theoretical framework concentrate on distinct marginalized identities, both theories recognize stigma tied to minority status as a fundamental cause of compromised health among marginalized groups (Hatzenbuehler et al., 2013). Stigma is a convergence of five interrelated components (i.e., labeling human differences, linking labeled groups to negative attributes, categorizing and separating labeled groups, labeled groups experiencing status loss and discrimination which lead to unequal outcomes, the 2 existence of social, economic, and political power differences that allow stigmatization; Link & Phelan, 2001), that can be associated with multiple labels, such as ethnicity-race, sexual orientation, gender, and physical or mental illness. Stigma has long been theorized to disadvantage the historically marginalized groups, such as ethnic-racial minorities or sexual minorities. Scholars have proposed two operating levels through which stigma jeopardizes the healthy development of stigmatized groups: structural processes at the macrolevel and interpersonal processes at the microlevel (Hatzenbuehler et al., 2013). The theoretical framework about stigma and health disparity suggests that when studying the health consequences of stigma among marginalized groups, it is crucial to consider systems of oppression at both the structural level (e.g., structural stigma) and at the interpersonal level (e.g., interpersonal discrimination; Hatzenbuehler et al., 2013). However, existing research has primarily focused on interpersonal level, investigating how encountering discrimination perpetrated by significant others (peers, educators) is associated with health outcomes (see the meta-analytic review Benner et al., 2018). Less attention has been paid to the structural facet (Link & Phelan, 2001). This is problematic, as structural stigma, as the “upstream” level inequalities, is regarded as the “root” cause that shapes “downstream” processes such as interpersonal discrimination (Cole, 2009), leading to health disparities. The limited research that did examine structural stigma has primarily focused on it in relation to ethnicity and race (i.e., structural racism). Structural racism, defined as “the totality of ways in which societies foster racial discrimination, through mutually reinforcing systems”, operates across the processes of residential segregation, disadvantaged economic and social status, lack of access to health care systems, and incarceration (Bailey et al., 2017). Prior studies have consistently linked structural racism to health disparities such as obesity (Browne et al., 3 2022), asthma and allergy related disease (Grant et al., 2022; Martinez et al., 2021), cardiovascular disease (Mannoh et al., 2021; Skolarus et al., 2020), depression and anxiety (Ahmed et al., 2023), as well as suicide (Alvarez et al., 2022). Research has also investigated structural stigma in relation to sexual orientation (i.e., structural heterosexism), conceptualizing it as state-level policies towards sexual minority populations (e.g., same-sex marriage bans, policies regarding hate crimes and employment discrimination towards sexual minorities; Hatzenbuehler & Keyes, 2013). State-level heterosexism has been found to be associated with mood disorder and alcohol use disorder among the stigmatized lesbian, gay, and bisexual (LGB) groups (Hatzenbuehler et al., 2009, 2010). A quasi-experimental study also showed that LGB groups’ mental distress increased by 46% after the pass of laws allowing refusal of providing services to same-sex couple (Raifman et al., 2018). An Intersectional Approach Existing research on adolescent health disparities has a critical limitation in that it often focused on a single type of marginalized identity 1 (e.g., ethnic-racial minority, sexual minority), failing to consider various marginalized identities simultaneously and neglecting the intersecting nature of adolescents’ multiple social identities. As a result, currently, we know little about sleep disturbances among adolescents with more than one type of marginalized identity. This lack of knowledge prevents us from understanding whether this group is most vulnerable to sleep disturbances, and therefore should be the target on which future intervention programs should pay extra efforts. Informed by intersectionality theory stressing the importance to consider intersecting identities (Crenshaw, 1989; Kern et al., 2020), my dissertation investigated disparities in sleep health among U.S. adolescents with intersecting identities of ethnicity-race 1 In the manuscript, “identity” was used to specifically refer to an individual’s ethnic-racial affiliations and self- defined sexual orientation. 4 and sexual orientation. Intersectionality theory can also shed light on how stigma tied to both marginalized identities at structural level and interpersonal level (i.e., racism and heterosexism at structural level; ethnic-racial discrimination and sexual orientation discrimination at the interpersonal level) function to compromise adolescents’ health, including sleep health. Specifically, intersectionality theory highlights the importance of examining the intersecting systems of oppressions to understand the unique and complex experiences of individuals who need to navigate multiple marginalized identities (Crenshaw, 1989). Informed by this, the current study investigated, for LGB youth of color, how intersecting structural stigma (interaction between structural racism and heterosexism) is associated with sleep disturbances, and tested whether ethnic-racial discrimination and sexual orientation discrimination at the interpersonal level are additively or multiplicatively (Raver & Nishii, 2010) associated with sleep disturbances. 5 CHAPTER 2. STUDY ONE: DISPARITIES IN SLEEP DURATION, TIMING, VARIABILITY AMONG AMERICAN ADOLESCENTS: INTERSECTIONS OF ETHNICITY-RACE AND SEXUAL ORIENTATION Introduction Sufficiency, proper timing (e.g., bedtime and risetime), and consistency are key aspects of healthy sleep for adolescents (Buysse, 2014; Meltzer, Williamson, et al., 2021). It has been well established in the literature that insufficient sleep duration is associated with various developmental problems, such as depression, anxiety, obesity, and aggression (Galván, 2020; Shochat et al., 2014; Hart et al., 2011). Although less studied, problematic sleep timing (e.g., delayed bedtime and early risetime) and large sleep variability (i.e., there is a lack of day-to-day consistency in sleep duration and timing; Bei et al., 2016), independent from sleep duration, has been linked to adolescent health problems in physical (e.g., obesity, Kjeldsen et al., 2014, Hart et al., 2011; metabolic disease risk, Abbott et al., 2019), and emotional (e.g., negative mood, Asarnow et al., 2014, Fuligni & Hardway, 2006) domains. Early adolescence is a period of development during which young people are at heightened risk of sleep disturbances including deprivation and late bedtime (Carskadon, 2011; Crowley et al., 2018), as well as increased variability (Moore et al., 2011). Unfortunately, youth are not equally susceptible to these sleep disturbances: Historically marginalized youth, including ethnic-racial minorities or sexual minorities, are at even higher risks for sleep disturbances. Identifying sleep disparities during early adolescence has crucial implications for evidence-based programs to address health inequities and optimize developmental outcomes. Adolescent Sleep Disparities by Ethnicity-race and Sexual Orientation Most studies on adolescent sleep disparities have focused on deficits in sleep among 6 ethnic-racial minority groups. Within the U.S. context, racial minority groups refer to American Indian or Alaska Native, Asian, Black or African American, and Native Hawaiian or other Pacific Islander, multi-race, while ethnic minority groups refer to Hispanic or Latino (National Institute on Minority health and Health Disparities, n.d.). Robust evidence showed that youth from ethnic-racial minority groups disproportionately exhibit shorter sleep duration than their White peers. A recent review showed that ethnic-racial minority youth generally sleep shorter (e.g., 20 to 22 minutes fewer) and are more likely (e.g., 1.07 to 1.25 times) to have insufficient sleep (i.e., sleeping less than recommended hours, for instance, less than 9 hours for youth under 12 years and less than 8 hours for 13 to 18 years, Centers for Disease Control and Prevention, n.d.) than their White peers (Guglielmo et al., 2018). Importantly, while socioeconomic factors attenuate the ethnic-racial disparities in adolescent sleep duration, they cannot fully explain the disparities (Guglielmo et al., 2018), indicating that other factors specifically related to ethnicity- race may play significant roles in these disparities. The more recent U.S. data on a national sample of youth from 9 to 13 years old provided by ABCD study also indicated ethnic-racial sleep duration disparity, with Black youth sleeping 34 minutes less than their White peers (Giddens et al., 2022). Compared to duration, much less attention has been paid to ethnic-racial disparities on other aspects of sleep health, such as timing and variability. Given the important implications of sleep timing and variability on adolescents’ various domains of health suggested by emerging evidence (Kjeldsen et al., 2014; Abbott et al., 2019; Asarnow et al., 2014; Fuligni & Hardway, 2006), including those aspects when examining ethnic-racial sleep health disparities can provide a more comprehensive understanding about the aspects of sleep that ethnic-racial minority adolescents are disadvantaged at, thus elucidating on the potential targets for interventions. Some 7 initial evidence has indicated that adolescents from ethnic-racial minority backgrounds tend to have later bedtimes (Spilsbury et al., 2004; Giddens et al., 2022), earlier wakeup or risetime (Adam et al., 2007) and greater duration or timing variability (Buckhalt et al., 2007; Moore et al., 2011; Suratt et al., 2007) than their non-Hispanic White peers. A small but emerging body of research has highlighted the sleep disparities experienced by sexual minority youth (i.e., youth self-identify as an inclination of being sexually attracted to people of the same-sex or both sexes, such as gay, lesbian, and bisexual or LGB youth; Saewyc, 2011). During adolescence, sexual orientation starts to develop as youth mature in sexual and cognitive domains and tends to be stable throughout adolescence and beyond (Saewyc, 2011). Although sexual orientation has a salient influence on adolescent development (see Goldbach et al., 2014 and di Giacomo et al., 2018 for reviews), a very limited number of studies have examined sleep as an outcome in this population. The limited studies that do so have provided initial evidence of sleep duration disparities that disadvantage sexual minority youth. For example, according to the 2015 National Youth Risk Behavior Survey, U.S. LGB adolescents (9- 12 graders) were less likely than their heterosexual peers to sleep the recommended 8 or more hours (Kann et al., 2016), and tended to have very short sleep (≤ 5 hours; Dai et al., 2020). National data on Chinese adolescents have revealed similar pattern: compared to heterosexual peers, sexual minority adolescents sleep 20 minutes less (Li et al., 2017). Another study on South Korean adolescent population suggested subgroup variations: sexual minority male adolescents but not females are more likely to have insufficient sleep (Seo et al., 2014). All these studies, however, have relied on adolescents’ one-time self-reports on a single item to assess sleep duration, which may produce less reliable or accurate assessment (Matthews et al., 2018). In addition, none of the studies have examined other aspects of sleep other than duration. This 8 precludes our in-depth understanding of the sleep disparities that disadvantage this marginalized group of adolescents. Investigating Youth Sleep Disparities Using an Intersectional Approach Another limitation of the existing research examining youth sleep disparities was the sole focus on one type of marginalized identity, thereby limiting our understanding of sleep disparities among youth with multiple marginalized identities, such as those who identify as both ethnic-racial and sexual minorities (i.e., LGB youth of color). According to intersectionality theory (Crenshaw, 1989), individuals who sit at the intersection of multiple marginalized identities are more likely than others to experience multiplicative risks of health problems. Intersectionality theory has been applied to understand mental and physical health disparities among adolescents from various marginalized groups (e.g., ethnicity-race, sex, immigration status, and family income in Evans & Erickson, 2019; ethnicity-race, sex, and socioeconomic status in Hargrove, 2018). The findings emerged from those studies generally support inequalities across those various groups, with those with multiple marginalized identities more susceptible to health problems. Focusing on sleep outcomes, however, studies that examine adult populations have yielded inconsistent results. For example, one study found that adults with double marginalized identities (i.e., LGB people of color) tended to have shorter sleep duration than those with only one type of marginalized identity (i.e., heterosexual White or LGB non-White; Caceres et al., 2019). In contrast, another study suggested the opposite pattern, showing that bisexual White individuals were more likely than LGB people of color to report daytime sleepiness (Hsieh & Ruther, 2016), likely due to insufficient sleep at night (Hershner & Chervin, 2014). However, no studies have directly examined youth sleep disparities using the intersectionality approach, particularly considering youth with intersecting identities of ethnicity- 9 race and sexual orientation. This study purposefully focused on early adolescence when sleep deprivation and inconsistency start to emerge (Carskadon, 2011; Crowley et al., 2018). Early adolescence is also a critical period for both ethnic-racial and sexual orientation identity development (Calzo et al., 2011; Huang & Stormshak, 2011). At this age, youth are expanding their social world and developing an increased capacity for reflecting on their social experiences, rendering them more sensitive and susceptible to those experiences (Mills et al., 2014). Evidence suggested that social experiences increasingly affect youth sleep during early adolescence (Sladek & Doane, 2015). Therefore, it is possible that sleep health disparities based on youth intersecting identities may be particularly salient during this period. Variations in Sleep Disparities between Weekdays and Weekends Another notable feature of sleep for youth in their early adolescence is the increasing discrepancy between weekday vs weekend nights. In general, compared to weekdays, adolescents tend to go to bed and get up later and consequently sleep longer during weekends, potentially to catch up sleep loss accumulated throughout the weekdays (Lo et al., 2017). This weekday-weekend discrepancy might be explained by either different constraints on adolescents’ weekday vs. weekend sleep or the same constraints operating at different magnitudes. For instance, during weekdays, adolescents’ sleep patterns are heavily influenced by external factors, such as social obligations and school schedules. They also need to conform to early school start time, which in turn impacts their risetime (Meltzer, Wahlstrom, et al., 2021). Another example is that interactions with peers and school personnel throughout the day shapes their sleep patterns to some extent (Zeringue et al., 2021). Conversely, on weekends when many of the social and school demands are relaxed, adolescents’ sleep patterns become more reflective of their 10 developing internal bioregulatory rhythms and regulatory processes, such as greater tolerance of daily sleep pressure accumulation and circadian phase delay (Carskadon et al., 1998; Carskadon et al., 2011). Relating these weekday vs. weekend sleep differences to adolescents’ sleep disparities, it’s plausible that because the factors regulating weekday vs. weekend sleep operate differently for adolescents with multiple vs one vs no marginalized identity, potentially leading to variations in sleep disparities between weekdays and weekends. Nevertheless, limited studies have delved into this topic. Some emerging studies have primarily focused on a single dimension of adolescents’ marginalized identity (mostly ethnic-racial), providing initial insights that shorter sleep duration among ethnic-racial minority groups when compared to their non-Hispanic White peers only emerged during weekends (Adam et al., 2007) or were more salient during weekends than weekdays (James et al., 2020). Leveraging the valuable daily actigraphy assessments of sleep data across 21 days in the ABCD study, this study created reliable and accurate weekday and weekend sleep indicators for each individual based on more days of data (15 days for weekday and 6 days for weekend). With this enhanced weekday and weekend sleep data, this study aims to provide a more nuanced picture in terms of weekday and weekend differences for sleep disparities based on the intersecting identities of ethnicity-race and sexual orientation. The Present Study The current study used data from the Adolescent Brain Cognitive Development (ABCD) study (https://abcdstudy.org/), a prospective longitudinal study designed to understand child health and brain development in the United States. Using parental reports on their children’s ethnicity-race at baseline, adolescents’ self-reports on sexual orientation identity from baseline to two-year follow-up, actigraphy assessed sleep data in early adolescence at two-year follow-up, 11 the current study examined how intersecting identities of ethnicity-race and sexual orientation were associated with various adolescent sleep indicators (i.e., duration average, bedtime average, risetime average, duration variability, bedtime variability, and risetime variability). Informed by intersectionality theory (Crenshaw, 1989) and previous studies, I expected to find adolescents with marginalized identities on more dimensions were more likely to have worse sleep. More specifically, I hypothesized that LGB youth of color would have shorter sleep duration, later bedtime, earlier risetime, and greater variability on duration, bedtime, and risetime, either when compared to non-marginalized group (i.e., heterosexual White group) or when compared to groups with single marginalized identity (i.e., White LGB group, heterosexual ethnic-racial minority group). Informed by evidence that adolescents’ sleep patterns were different during weekdays vs. weekends, and that ethnic-racial sleep disparities differed for weekday vs. weekend sleep, this study also explored how sleep disparities based the intersecting identities of ethnicity- race and sexual orientation may differ during weekdays vs. weekends by examining the above- mentioned comparisons separately for weekday vs. weekend sleep. Methods Participants Data were drawn from the ABCD study 4.0 release (https://abcdstudy.org/). ABCD is a prospective longitudinal study designed to understand child health and brain development in the U.S. Using a multi-stage, stratified sampling approach, the study sampled 11,876 children aged about 9 to 11 years old across 21 cites in the U.S. at baseline. Each cite was selected from one of the four major regions (Northeast, Midwest, South, West) in the U.S., where over 20% of the nation’s eligible 9- to 10-year-olds live. At each site, a stratified probability sample of schools was identified based on student socio-demographic data from the National Center for Education 12 Statistics. Each school was considered based on its geographical location, racial, ethnic, and gender composition, and proportion of students receiving free or subsidized lunches (SES proxy). The baseline sample participated between September 2016 and November 2018, and is being followed up annually. This study used data of baseline, one-year follow up and two-year follow-up, and focused on White, Black, and Latinx participants, excluding participants from other ethnic-racial minority groups (due to the limited group size of LGB youth of those ethnic-racial minority groups). To account for the feature of some participants nested by family, I randomly selected one participant from each family where there were multiple youth participating. Given the focus of the study on youth in early adolescence, individuals below the age of 10 or above 13 years old were excluded (Labergel et al., 2001). To make sure assessment of sleep indicators were reliable, participants who had less than seven days of actigraphy sleep data were also excluded (Giddens et al., 2022). Details for each step excluding participants are presented in Figure 1. Detailed participant characteristics for the analytic sample are presented in Table 1. Measures Actigraphy sleep Adolescent sleep was assessed using Fitbit Charge HR at two-year follow-up, the only wave in the ABCD Study when sleep was assessed objectively. Fitbit uses multiple sensors, such as a three-axis accelerometer and a skin temperature sensor, to track various sleep-related data (e.g., motion, skin temperature, heart rate). A closed algorithm was used to identify wake and sleep status in a one-minute epoch, logging the first minute identified as being asleep and the first minute identified as being awake. Participants were instructed to wear the Fitbit for consecutive 21 days. Two facets (i.e., average level and variability) of three sleep indicators (i.e., 13 duration, bedtime, and risetime) were examined in the current study. For sleep duration, I used the interval that participants were identified as in the actual state of asleep, which was provided by the ABCD study as the sum of three sleep phases (i.e., light, deep, and rapid eye movement) each night. ABCD study also provided bedtime – the first minute that the participant was identified as staying in bed, and risetime – the first minute that the participant was identified as being out of bed. Both average and variability of the three sleep indicators were created based on the daily assessments. The mean values across the available studied days of the three sleep indicators were created to indicate an individual’s habitual average sleep. The variances across the available studied days of the three sleep indicators were created to indicate an individual’s habitual sleep variability. To address the high positive skewness in the original scores of variances, I conducted natural log transformation for all the three sleep variability indicators. Intersecting identities Adolescents’ intersecting identities were created based on information of ethnicity and race, as well as sexual orientation. Adolescents’ ethnicity and race was assessed at baseline. Parents reported if their participating child belonged to one of the 17 racial groups (i.e., White, Black/African American, American Indian, etc.), on a binary scale of 0 (No) and 1 (Yes). Parents also reported on their child’s ethnicity on a separate question, indicating if the child was Hispanic/Latino/Latina on a binary scale of 0 (No) and 1 (Yes). Given that the number of participants who belong to both racial minority group and ethnic minority group was small (e.g., n = 16 for Black Latinx), I did not consider race and ethnicity as two dimensions but treated them as different categories on one ethnic-racial dimension of race-ethnicity. Therefore, participants were grouped into eight mutually exclusive categories: White, Black, Latinx, 14 Multiracial, Pacific Islanders, Native Americans, Asian Americans, and Other. Adolescents reported their sexual orientation (“Are you gay or bisexual?”) at each wave. To capture youth’s sexual orientation development and its accumulating influence, information from baseline to two-year follow-up was integrated. The original response scale was 1 (Yes), 2 (Maybe), 3 (No), 4 (I do not understand this question), 777 (Decline to answer). I coded responses 1 and 2 into 1 (Yes), 3 into 0 (No), and 4 and 777 into missing. The final sexual orientation variable was a binary variable (1 = LGB at any wave from BY to Y2; 0 = no). It is important to note that although a significant portion of youth reported not understanding the question at baseline (25%; Potter et al., 2022), this rate declined to 9% at Y2, suggesting that youth were developing their sexual identities over time. Youth intersecting identities was coded as one of the six categories based on ethnicity- race and sexual orientation: heterosexual White, White LGB, heterosexual Latinx, heterosexual Black, Latinx LGB, and Black LGB. Covariates. Covariates included demographics and factors that were found to influence adolescent sleep at individual, family, school levels, and within broader context. Demographic factors included sex assigned at birth (1 = Female, 0 = Male), grade (age was not included due to its high correlation with grade), and generation status (0 = 1st [both the child and the parents were foreign born] or 2ed [the child was US born while at least one of the parents were foreign born] generation, 1 = 3rd generation [both the child and the parents were US born]). Factors that are influential of adolescent sleep at the individual level included obese status (1 = above 95% BMI percentile calculated based on CDC Growth Chart, 2000; 0 = No), and youth religiosity (i.e., how important is the participant’s religious and spiritual beliefs, with higher scores indicating greater importance). Factors at the family level included SES (a latent factor score 15 indicated by parental education [λ = .71], employment status [λ = .48], and family economic hardship [λ = -.39]) and structure (1 = a child lived in a two-parent household, 0 = No). Factors at the school level included school start time (i.e., the exact time which their usual school schedule starts at) and positive school environment (a latent factor score indicated by positive school climate [λ = .64], school involvement [λ = .97], and school engagement [λ = .50]). Within the broader context, I included neighborhood poverty, which was indicated by the area deprivation index, created based on a neighborhood’s condition on various relevant dimensions (i.e., education, employment, income, housing conditions, poverty rate, and infrastructure). Data Analysis Plan All primary analyses were conducted in path analysis models in a structural equation framework using Mplus 8.8 (Muthén & Muthén, 2017). Maximum likelihood estimator with robust standard errors (MLR) was used to provide robust estimates with non-normal data. CLUSTER option with TYPE = COMPLEX function in Mplus was used to handle the non- independence by study sites. I conducted a series of path analyses to examine the associations between intersecting identities and sleep. Dummy coded intersecting identity variables (i.e., White LGB, heterosexual Black, heterosexual Latinx, Black LGB, Latinx LGB; White heterosexual as reference group) were included as exogenous predictors. Of particular interest, I examined the extent to which youth with both marginalized identities (Black LGB, Latinx LGB) exhibit worse sleep than non- marginalized youth (i.e., heterosexual White). I also examined the extent to which youth with both marginalized identities (i.e., Latinx LGB and Black LGB) exhibit worse sleep than those with only one marginalized identity (White LGB, Latinx heterosexual, and Black heterosexual, respectively). To obtain those comparisons of interest, I rotated the reference groups by treating 16 White LGB, Latinx heterosexual, and Black heterosexual as reference groups. Sleep indicators (i.e., duration average, bedtime average, risetime average, duration variability, bedtime variability, risetime variability) were treated as endogenous variables. Each sleep indicator was examined in separate models. To address the potential issue of inflated Type-I error rates due to multiple comparisons among intersecting groups, I applied the False Discovery Rate (FDR) controlling procedure (Benjamini & Hochberg, 1995). Specifically, I ordered the p values of the 15 tests for each sleep indicator in an ascending order. Based on the formular: pi = Q × 𝑖 𝑚 , where Q denotes the original false discovery rate (i.e., .05 in the current study), i denotes the rank of the p value in ascending order (i.e., from 1 to 15 in the current study), and m demotes the number of multiple testing (i.e., 15 in the current study), the adjusted critical values were p1 = .003, p2 = .006, p3 = .01, …, p15 = .05. Then I compared each p value to its corresponding critical value. When the actual p value was smaller than the corresponding critical value, the effect was determined to be significant. To examine if the primary findings were different for weekday vs. weekend nights of sleep, I conducted the primary analyses by selecting weekday and then weekend nights of sleep indicators respectively. To obtain reliable assessment of weekday and weekend sleep, weekday sleep with less than five valid days and weekend sleep with less than three valid days were coded as missing. Descriptive Statistics of Youth Sleep Results I first examined the descriptive statistics of all sleep indicators (see lower portion in 17 Table 2). In the overall sample, on average, adolescents slept 7.43 hours every day, went to bed (bedtime) at 11.05pm (11:08pm), and got out of bed (risetime) at 7.11am (7:18am). The standard deviation for duration, bedtime, and risetime is 1.11, 1.22, and 1.72, respectively, indicating that, a typical individual would sleep between 6.32 to 8.54 hours, go to bed at between 9.83pm (9:50pm) and 12.27am (00:16am), and get out of bed between 5.39am (5:23am) and 8.83am (8:50am). The bivariate correlations among sleep indicators were presented in the upper portion in Table 2. The correlations among the three indicators of sleep average level were between small to moderate. Variability in sleep duration, bedtime, and risetime were strongly, positively correlated with each other. The correlation between the mean level and variability of sleep was moderate for duration and bedtime, while strong for risetime. I also examined how demographics and factors that were influential on youth sleep were correlated with sleep indicators. Their correlations were generally between small to moderate. Female adolescents were more likely to have longer duration, later bedtime, and greater variability of all three indicators. Adolescents in higher grades were more likely to have shorter duration, later bedtime and risetime, as well as greater risetime variability. Compared to 1st and 2ed generation, 3rd generation had greater variability in duration and bedtime. Obesity was associated with shorter duration, earlier risetime, and greater variability in duration, bedtime, and risetime. Adolescents with more positive attitudes toward their religion were more likely to have shorter duration, later bedtime, and greater variability in duration and bedtime. Better family SES, living in a two-parent household, and perceiving a more positive school environment was associated with better sleep pattern (longer duration, earlier bedtime, later risetime, and less variability in all three indicators). Later school start time was associated with longer duration, 18 later bedtime and risetime. Adolescents living in poorer neighborhoods were more likely to have poorer sleep (shorter duration, later bedtime, later risetime, and greater variability in all three indicators). Sleep Disparities by Intersecting Identity Groups I then examined sleep disparities in various indicators (i.e., duration average, bedtime average, rise time average, duration variability, bedtime variability, risetime variability) by intersecting identity groups (i.e., White heterosexual, White LGB, Latinx heterosexual, Latinx LGB, Black heterosexual, Black LGB; see Table 3 for the estimates of primary interest; see Table S2 in supplements for the full model estimates; see Figure 2 for detailed mean level for each group), controlling for demographics factors and other factors that were found to impact adolescent sleep. I am particularly interested in sleep disparities among LGB youth of color, when compared to non-marginalized group and groups with single marginalized identity. Focusing on the comparisons between LGB youth of color and non-marginalized group, for indicators of sleep average level, compared to heterosexual White, Latinx LGB had later bedtime, while Black LGB had shorter duration, later bedtime. For sleep variability indicators, Latinx LGB had greater duration variability and bedtime variability, while Black LGB had greater bedtime variability and risetime variability than heterosexual White group. Next, focusing on the comparisons between LGB youth of color and their same ethnic- racial heterosexual peers, no significant associations emerged for sleep indicators of average level, either for Latinx LGB or Black LGB. Moving to sleep variability indicators, significant associations emerged for Latinx LGB: Latinx LGB had greater variability in duration and bedtime than their Latinx heterosexual peers. I did not observe any significant associations for Black LGB. 19 As for the comparisons between LGB youth of color and their White peers also identify as LGB, for indicators of sleep average levels, significant associations emerged for Black LGB (but not for Latinx LGB): Black LGB had shorter duration and later bedtime than White LGB. For sleep variability indicators, one significant association emerged for Latinx LGB group: Latinx LGB had greater bedtime variability than White LGB. I also conducted the same sets of analyses to examine weekday and weekend sleep in separate models (see Table 4 for the estimates of primary interest; see Table S3 and Table S4 in supplements for the full model estimates). Most of the disparities that were observed for the indicators of overall sleep across weekdays and weekends also emerged for weekday sleep, which provides evidence for the robustness of the findings. As for the contrasts between weekday and weekend sleep, some disparities that were observed for weekday sleep did not emerge for weekend sleep. Specifically, for indicators of sleep average levels, the significant contrast between Black LGB and heterosexual White for bedtime (Black LGB had later bedtime than heterosexual White) only emerged for weekday sleep but not weekend sleep; for sleep variability indicators, each significant disparity observed for duration or bedtime variability did not emerge for weekend sleep. Discussion During early adolescence, youth are at increased risk for insufficient and inconsistent sleep (Carskadon, 2011; Crowley et al., 2018; Moore et al., 2011). Unfortunately, youth are not equally susceptible to those sleep disturbances: Disparities in sleep have been consistently observed among marginalized youth populations, especially those who are ethnic-racial (see Guglielmo et al., 2018 for a review; Giddens et al., 2022) or sexual minorities (Dai et al., 2020; Kann et al., 2016; Kreski & Keyes, 2022). However, existing research on adolescent sleep health 20 disparities has primarily focused on a single type of marginalized identity (i.e., either on ethnic- racial minorities or sexual orientation minorities), neglecting the intersecting nature of adolescents’ multiple social identities. To fill in this gap in literature, guided by intersectionality theory (Crenshaw, 1989), which emphasizes individuals with multiple marginalized identities tend to be at heightened risk of experiencing multiplicative risks of health problems, this study examined sleep disturbances among LGB youth of color. The findings do suggest sleep health disparities, both in average levels and variability of sleep indicators, among this unique group of adolescents. This was observed when comparing them to both the non-marginalized group and groups with a single marginalized identity. To comprehensively evaluate the sleep disparities among LGB youth of color, I compared their sleep to both non-marginalized group (i.e., White heterosexual) and groups with single marginalized identity (i.e., White LGB, Latinx heterosexual, Black heterosexual). LGB youth of color (i.e., Latinx LGB and Black LGB) show clear deficits in sleep than their non- marginalized peers: Latinx LGB and Black LGB have poorer sleep (e.g., shorter duration, later bedtime, and larger variability in both duration and timing) when compared to their heterosexual White peers. This clearly highlights the elevated risks of sleep disturbances that Latinx and Black LGB groups of youth have. It is noteworthy that the disparities in sleep not only occurred for average levels of sleep indicators (e.g., duration and bedtime), but also for sleep variability indicators (e.g., duration, bedtime, and risetime variability). As an increasing body of literature has provided clear evidence for the detrimental impact of inconsistent sleep patterns (i.e., large variability) for a wide range of developmental domains, such as mental health (Asarnow et al., 2014; Fuligni & Hardway, 2006) and physical health (Kjeldsen et al., 2014; Abbott et al., 2019), except for sleep duration and timing, future intervention programs should also consider targeting 21 sleep variability to improve the sleep health among LGB youth of color. I also contrasted the sleep patterns of LGB youth of color to their same ethnic-racial heterosexual peers, which provide more nuanced findings. Latinx LGB youth have greater variability in both sleep duration and bedtime than heterosexual Latinx youth, whereas Black LGB do not differ in any sleep indicators from their heterosexual Black peers. The findings contribute to the small literature on sleep disparities involving individuals with double marginalized identities (Caceres et al., 2019) by extending the focus to adolescent populations. For Latinx group, the current findings suggest that Latinx LGB indeed exhibit poorer sleep health (i.e., greater variability in both duration and bedtime) than their heterosexual counterparts from the same ethnic background. This underscores the heightened susceptibility of Latinx LGB group to developing sleep disturbances. It is evident that treating Latinx adolescents as a homogenous group may be less effective in enhancing sleep health among Latinx LGB youth. Latinx LGB adolescents may encounter unique challenges that adversely affect their sleep health when compared to their heterosexual counter parts within the Latinx community. Notably, specific cultural values prevalent in the Latinx community, such as familismo (i.e., prioritizing family over individual needs) and machismo (i.e., a strong sense of masculinity pride), can contribute to some family members perceiving sexual minority individuals as a source of embarrassment to the family or holding biased attitudes toward sexual minorities (Przeworski & Piedra, 2020). The resulting conflicts between the sexual identity of Latinx LGB youth and these cultural values, coupled with family members’ potential negative attitudes towards LGB individuals, may contribute to heighted distress and worry among Latinx LGB youth, ultimately leading to sleep disturbances. Therefore, intervention programs should be culturally sensitive and specifically tailored to address the unique needs of the Latinx LGB subgroup, necessitating 22 additional efforts and attention. As for Black group, contrary to the Caceres et al. (2019) findings that Black LGB adults are more likely to have very short sleep than their heterosexual Black counterparts, the current study did not find any significant differences in terms of sleep duration between Black LGB adolescents and their heterosexual Black peers. Moreover, there were no differences between the two groups for sleep timing or variability indicators. However, these findings should not be interpreted as indicating that Black LBG youth were at a relatively low risk for sleep disturbances. When examining the descriptive statistics across the examined groups (see Figure 1), it becomes evident that heterosexual Black and Black LGB were the two groups that performed the poorest almost across all the sleep indicators. This suggests that Black adolescents, as a collective group, is the most disadvantaged group for sleep disturbances. These findings align with a substantial body of research, which consistently highlights the high risk of sleep disturbances among Black youth (Yip et al., 2020; see review by Guglielmo et al., 2017). The similar disadvantages observed in both heterosexual Black and Black LGB adolescent groups may indicate common risk factors that compromise the sleep health of both groups. For instance, mental health issues and concerns of community violence have been observed to disproportionately impact Black youth compared to other ethnic-racial groups. Furthermore, these factors have been linked to an increased prevalence of sleep disturbances (Short et al., 2020; Bagley et al., 2016). Furthermore, the current study delved into comparisons between Latinx and Black LGB in relation to their White peers also with LGB identity. Compared to White LGB, Black LGB adolescents generally exhibit poorer sleep health, characterized by shorter duration and later bedtime, while Latinx LGB had poorer sleep in one specific sleep indicator–bedtime variability. 23 These findings shed light on the ethnic-racial disparities within the LGB community, challenging previous studies that have treated LGB minorities as a homogenous group when examining health disparities, often without considering the significant heterogeneity based on ethnicity and race (Kann et al., 2016; Li et al., 2017; Seo et al., 2014). Taken together, the findings that LGB youth of color are at an elevated risk of encountering sleep disturbances compared to their peers who possess a single marginalized identity (i.e., either their same ethnic-racial heterosexual peers or White peers with LGB identity) provides robust support for the intersectionality framework. This framework underscores the multiplicative health risks faced by individuals at the intersection of multiple marginalized identities (Crenshaw, 1989). However, the tenets of intersectionality theory extend beyond recognizing intersecting identities; they also underscore the importance of identifying the distinct risks encountered by the groups bearing multiple marginalized identities, which may multiplicatively jeopardize their health (Crenshaw, 1989). This perspective offers a promising avenue for future research: There is a compelling need for further investigations to uncover the underlying factors contributing to the heightened sleep health concerns among Black and Latinx LGB youth. To more comprehensively investigate the specific facets of sleep disparities among LGB youth of color, the current study also included two critical timing indicators that constrain the duration–bedtime and risetime–as sleep outcomes (Crowley et al., 2018). The results indicated that sleep disparities among LGB youth of color, especially for average levels of sleep, were more salient for bedtime than risetime. Existing literature has consistently shown that bedtimes exert a greater influence on adolescents’ sleep duration than risetime, mainly due to its regulatory effect on weekday sleep duration (Adam, 2007; Giddens et al., 2022). This was 24 reaffirmed in the current study, where I observed a stronger correlation between average duration and average bedtime (r = -.39), in contrast to risetime (r = .16). Taken together, these pieces of evidence suggest that targeting bedtime represents a more promising avenue for future interventions aimed at mitigating sleep disparities among LGB youth of color. Additionally, as the factors that regulate adolescents’ bedtime and risetime differ substantially, with bedtime often influenced by societal demands, academic schedules, and peer interactions, while risetime largely determined by school start time (Crowley et al., 2018), future research is needed to explore how different risk and protective factors operate on bedtime and risetime within the context of LGB youth of color. LGB youth of color may allocate time before bed for coping mechanisms to deal with stressful encounters associated with their marginalized identities that they experience from the day. These coping strategies can vary, ranging from relatively adaptive approaches, such as seeking support from peers through social media (Gordián-Arroyo et al., 2022), to potentially maladaptive ones, including rumination and bedtime procrastination (De Lise et al., 2023). Both types of coping mechanisms involve an additional time commitment before bedtime, consequently delaying their overall bedtime. Additionally, the anxiety from anticipating negative experiences linked to their marginalized identities may prevent them from falling asleep (Chan & Fung, 2021). Investigations to these potential mechanisms are essential for a deeper understanding of the root causes behind the observed sleep disparities for this group of adolescents. Aiming to provide a more accurate and nuanced picture in terms of the processes of sleep where disparities are more likely to occur, this study carefully distinguished between weekday and weekend sleep to examine sleep disparities among LGB youth of color. The findings reveal that these sleep disparities are more evident during weekdays than weekends. I speculate that, 25 this discrepancy may stem from the negative experiences of discrimination or victimization tied to their ethnicity-race minority identity or sexual orientation from school peers and educators being more likely to happen during school days (e.g., weekdays) than weekends. Such encounters have been linked to emotional distress and physiological arousal (Almeida et al., 2009; Meyer, 2003). Consequently, these adverse effects may contribute to poorer sleep patterns among LGB youth of color (see the meta-analytic review by Bartel et al., 2015). However, given that there are various factors that differently regulate adolescents’ sleep during weekdays vs. weekends (Crowley et al., 2018), it is imperative for future research to delve into those various factors to identify potential causal factors contributing to more pronounced weekday (than weekend) sleep disparities among LGB youth of color. Strength, Limitations, and Future directions A significant strength of this study is its utilization of a national sample of youth in their early adolescence to examine sleep health disparities tied to double marginalized identities of ethnic-racial and sexual minorities. The limited studies only focused on adult community samples to examine this. This study extended this small body of literature by focusing on a national sample of youth in their early adolescence. In addition, the utilization of a national sample of adolescents significantly enhances the potential for broad generalizability of the findings, extending their relevance to youth populations across the U.S. Another strength of the current study is the utilization of actigraphy assessment of sleep across an extended timeframe. Compared to relying solely on adolescents’ self-reported or parent-reported sleep data, especially those based on one-time retrospective report, which are especially susceptible to recall bias, actigraphy-based assessments across multiple days provides more accurate and reliable measures of individual average sleep duration and timing. Furthermore, based on those multiple days of 26 assessment, I can also examine sleep variability indicators. Related to this, compared to other studies that assessed daily sleep within a limited time frame (e.g., 7 days; McHale et al., 2011), ABCD study extended the assessing time to 21 days, which allows more days of data entries separately for weekday vs. weekend sleep. By utilizing this more reliable weekday and weekend sleep data, this study was able to examine sleep disparities among LGB youth of color separately for weekday vs. weekend sleep, providing a more nuanced and comprehensive understanding of this complex issue. The findings, however, should be interpreted in the light of limitations. First, this study relies on a single wave of actigraphy sleep data currently available in ABCD, which precludes the examination of developmental changes in sleep patterns and disparities. Previous research has highlighted drastic changes in youth sleep patterns as they progress from early to middle and late adolescence, attributed to neurocognitive and biophysiological maturation, puberty development, and increasing social demands (Colrain & Baker, 2011; Hagenauer et al., 2009). Along with the developmental changes in general sleep patterns, it is possible that the observed disparities among LGB youth of color in their early adolescence may manifest differently in later developmental stages. As more waves of actigraphy sleep data become available, future investigations can provide insights into the evolving nature of sleep disparities among LGB youth of color from a developmental perspective. Second, the ABCD study was not specifically designed to center on LGB youth, thus the sample size for LGB group is small. This limitation constrained my ability to delve deeper into the potential heterogeneity for sleep patterns within the groups of LGB youth of color. For example, previous studies have found sex differences for sleep disparities related to both ethnicity-race and sexual orientation (James et al., 2020; Kreski et al., 2022). Some other aspects 27 upon which heterogeneity may exist include country of origin, skin color, and race groups. Future research endeavors should intentionally seek to recruit a larger and more diverse cohort of LGB youth of color to examine the potential heterogeneity based on these aspects. This more thorough investigation has the potential to pinpoint more specific subgroups of adolescents who may be most vulnerable to sleep disturbances. Another limitation of this study lies in its measurement of ethnic-racial identity and construction of sexual orientation identity. In terms of ethnic-racial identity, the ABCD study relied solely on caregivers' reports to determine adolescents' race and ethnicity. This approach may not fully capture the nuances of how adolescents themselves perceive and label their ethnic- racial identity. This is particularly important in cases where adolescents come from mixed-race backgrounds, which can result in complex racial identities (Harris & Sim, 2002). To enhance the accuracy and comprehensiveness in the measurement of adolescents’ ethnic-racial background, future research should consider including both self-reports and caregiver reports. Regarding sexual orientation identity, this study categorized individuals who self-identified as LGB at any of the three waves as sexual minorities. However, it’s important to acknowledge that adolescents’ sexual orientation development can be more complex and dynamic, with some individuals consistently identifying as LGB while others may report fluctuating identities across the three waves. While this study did not delve into characterizing the specific patterns of adolescents’ sexual orientation development, future studies can pursue this avenue and delve deeper into various patterns of sexual orientation development and their health implications. Additionally, it is important to note that there is an overrepresentation of White adolescents while underrepresentation of ethnic-racial minority adolescents, especially those from non-Latinx, non-Black background in the ABCD sample. This makes the sample of LGB 28 youth of other ethnic-racial minority groups (e.g., Asian, Other) especially small such that I must exclude them from the analyses. Therefore, the findings derived from this study cannot be generalized to these specific groups of adolescents. To fill in this gap, future research endeavors should focus on investigating whether LGB youth from Asian or Other ethnic-racial background are in elevated risks of sleep disturbances. Conclusions The current study examined adolescents’ sleep health disparities based on double marginalized identities of ethnic-racial minority and sexual orientation minority. The findings add novelty to the literature by showing that LGB youth of color are at excessive risk of sleep disturbances, when either compared to the non-marginalized White heterosexual group, or to the groups with single marginalized identity (i.e., White LGB, Latinx heterosexual, Black heterosexual). Furthermore, by comprehensively including multiple dimensions of sleep, the findings underscore that the sleep disparities among LGB youth of color span across all the examined dimensions (i.e., duration, timing, and variability). This current study also carefully examined weekday and weekend sleep in separate analyses and showed that sleep disparities among LGB youth of color was more salient during weekdays than weekends. Taken together, the study provided valuable insights in terms of for whom and on what sleep dimension future intervention programs are needed to improve sleep health and mitigate sleep health disparities. 29 CHAPTER 3. STUDY TWO: SLEEP DURATION, TIMING, VARIABILITY AMONG LGB YOUTH OF COLOR: UNDERSTANDING THE ROLES OF STRUCTURAL STIGMA AND INTERPERSONAL DISCRIMINATION FROM AN INTERSECTIONAL PERSPECTIVE Introduction LGB youth of color are at heightened risk of experiencing compromised physical and mental health (Poteat et al., 2009; Dettlaff et al., 2018). Study one extended this literature by emphasizing sleep as another critical aspect of health where LGB youth of color encounter disparities, particularly when compared to the non-marginalized heterosexual White youth and the groups with single marginalized identity (i.e., White LGB youth or heterosexual ethnic-racial minority youth). While uncovering theses sleep health disparities is crucial, it is equally important to delve deeper into the underlying factors leading to the heightened level of sleep disturbances experienced by LGB youth of color. This investigation can provide invaluable insights into the processes that future intervention practices and policies should target to mitigate sleep health disparities. Stigma plays a significant role in contributing to adverse health outcomes among marginalized groups (Hatzenbuehler et al., 2013). Exposure to biased attitudes, stereotypes, and discrimination tied to marginalized identities (i.e., ethnic-racial minority, sexual minority) is a common, even daily, experience for LGB people of color (Jackson et al., 2021). As youth’s social world expands rapidly when they enter adolescence, it is likely that LGB youth of color become increasingly susceptible to those negative experiences tied to their marginalized identity (Mills et al., 2014). Understanding how stigma impacts the health of LGB youth of color is critical to identify effective levers of change to mitigate these negative health consequences and 30 improve health equity. Theoretical framework suggested that when studying the health consequences of stigma among marginalized groups, it is crucial to consider systems of oppression at both the structural level (e.g., structural stigma) and at the interpersonal level (e.g., interpersonal discrimination; Cole, 2009). However, existing research has primarily focused on interpersonal level, investigating how encountering discrimination perpetrated by significant others (peers, educators) is associated with health outcomes (see the meta-analytic review Benner et al., 2018). Another limitation is that no studies have examined the role of structural stigma or interpersonal discrimination in sleep health disparities among LGB youth of color. To fill in these gaps, the current study investigated structural stigma and interpersonal discrimination based on one’s ethnicity-race and sexual orientation as contributing factors to sleep disturbances among LGB youth of color. Structural Stigma and Sleep Disturbances among LGB Youth of Color Despite the emerging evidence suggesting the general health consequences of state-level stigma on different marginalized groups, to my knowledge, youth sleep health has never been included as a health outcome. Yet, theoretical work has laid the foundations for this linkage. Marginalized adolescents often anticipate or experience unfair treatment at the macro-level, which can be stressful. For instance, Black and Latinx youth often report worrying about unwarranted police stops due to the ethnic-racial bias of police enforcement portraying youth of color as more suspicious than White youth (Del Toro et al., 2022), and fear of family separation is an everyday challenge for Latinx youth under the current anti-immigration sentiments and policies (Cardoso et al., 2021; Cross et al., 2021). Given that a substantial portion of Black and Latinx families may face financial constraints that impede their potential relocation to more 31 progressive states, the states characterized by elevated levels of structural racism becomes an inescapable macro-level oppressive environment for these communities. This predicament is especially deleterious to the overall well-being of Black and Latinx youth. LGB youth often feel unsafe and worry to be threatened in schools and communities due to a lack of adequate protections from sexual orientation-related antibullying policies (Duncan & Hatzenbuehler, 2014; Kosciw et al., 2020). The stress associated with structural stigma can trigger physiological stress responses (Hatzenbuehler & McLaughlin, 2014), increase arousal and vigilance (Hicken et al., 2013), and elicit negative emotions and mental health problems (see Alvaro et al., 2013 and Lovato & Gradisar, 2014 for reviews), all of which can make it difficult for marginalized youth to fall and stay asleep. Structural stigma can also lead to sleep disturbances by imposing structural barriers to health providers and resources, such as physical and mental health support or counseling institutions, for marginalized youth. For instance, due to the limited primary-care providers in Black-concentrated neighborhoods (White et al., 2012), sleep disorders and mental health problems of adolescents residing in these neighborhoods are less likely to be properly diagnosed and treated (Feagin & Bennefield, 2014; Williamson et al., 2022). As a result, sleep disturbances are more common among Black youth than their White counterparts (Suarez et al., 2015). Sexual minority youth may also utilize healthcare services to a lesser degree than their heterosexual peers due to healthcare providers’ assumptions of patients being heterosexual and judgmental attitudes towards sexual minorities (Arbeit et al., 2016). Despite these theoretical foundations, there is a lack of empirical research on the link between structural stigma based on ethnicity-race and sexual orientation and sleep health among youth. The current study specifically focused on early adolescence, when young people start to 32 actively construct their social identities in multiple dimensions (Meeus, 2011). A toxic, oppressive social context can negatively affect identity development via internalized inferiority, potentially leading to adverse health and developmental outcomes in the long term (Benninger & Savahl, 2017). In addition, because youth are just starting to comprehend social issues related to structural stigma and stereotypes (Seider et al., 2019), their increased awareness and reflection on these issues may make them especially vulnerable to their influences. Previous studies that did examine the health implications of structural stigma have primarily focused on a single dimension, precluding the understanding of how multiple dimensions of structural stigma function conjointly to influence adolescent outcomes. The current study addressed these gaps by examining how intersecting structural stigma may contribute to youth sleep disparities (i.e., the extent to which the associations between youth identities and sleep vary by one’s state-level racism and heterosexism). To my knowledge, only three studies investigated structural stigma on a single dimension and its impact on youth sleep health. Two studies examined police stop as a form of structural racism within law enforcement (as it tends to target ethnic-racial minority youth disproportionately), linking it to short sleep durations or not sleeping the recommended hours among U.S. and U.K. adolescents (Jackson et al., 2020; Jackson & Testa, 2022). Another study examined how concerns about anti-immigration policy, another critical dimension of structural stigma, were associated with sleep disturbances, including short sleep duration, among Latinx adolescents from immigrant families during the Trump era (Eskenazi et al., 2019). However, these studies are limited by their focus on a single aspect of structural racism (i.e., biased police stop, anti-immigration policy), which cannot fully capture the multifaceted nature of structural racism. There is also a lack attention to structural stigma beyond racism, such 33 as structural heterosexis, and its effects on adolescent sleep. Methodologically, these studies have relied on self-reports to assess adolescent sleep, which can be subject to self-reporting biases (Matthews et al., 2018). This could potentially mask the adverse effects of structural stigma on adolescent sleep. To address these limitations, the current study utilized state-level indicators of structural stigma on ethnicity-race and sexual orientation, which captures multiple sources of oppression (e.g., aggregated implicit bias, state-level laws, institutional policies and representation of marginalized populations). Additionally, it employed objective assessment of sleep duration based on actigraphy. Intersectional Discrimination and Sleep Disturbances among LGB Youth of Color In addition to investigating the processes at the structural level through which stigma jeopardizes sleep health of LGB youth of color, the current study also seeks to understand the role of interpersonal discrimination, the manifestation of stigma at the interpersonal level, in shaping sleep health of LGB youth of color. Discrimination, defined as “unfair or prejudicial treatment of people and groups based on characteristics such as race, gender, age, or sexual orientation” (American Psychological Association, 2022), is an everyday challenge faced by youth of color and LGB youth. According to U.S. national data, Black and Latinx youth report experiencing discrimination based on their ethnicity-race more frequently than other ethnic-racial groups (Nagata et al., 2021). For sexual minority youth, discrimination based on their sexual orientation is a major source of stress (Almeida et al., 2009). Discrimination has been theorized to contribute to youth sleep disturbances via multiple processes. The stress associated with discrimination can trigger heightened physiological responses (e.g., increased heart rate, cortisol, inflammation, heightened arousal and vigilance), preventing youth from falling and staying asleep and leading to shortened 34 sleep duration (Levy et al., 2016; Matthews & Pantesco, 2016). Discrimination can also lead to psychological distress, such as depression, anxiety, and loneliness, which are associated with shortened sleep duration (Majeno et al., 2018). Discrimination can also activate maladaptive stress responses, such as rumination (Borders & Liang, 2011), unhealthy eating (Brown et al., 2022), and substance use (Pascoe & Smart Richman, 2009), all of which can contribute to delayed sleep time and shortened sleep duration. Indeed, clear evidence exists that perceived discrimination targeting ethnicity-race and sexual orientation are independently associated with shorter nighttime sleep among adolescents both concurrently (Yip, Cham, et al., 2020; Yip, Cheon, et al., 2020; Nagata et al., 2021) and longitudinally (Luk et al., 2019; Yip et al., 2022). However, most literature has exclusively focused on discrimination that targets a single marginalized identity (e.g., either ethnicity-race or sexual orientation). According to intersectionality theory, individuals with multiple marginalized identities likely experience both forms of discrimination (e.g., ethnic-racial discrimination and sexual orientation-based discrimination; Lewis & Van Dyke, 2018). Relative to navigating a single form of discrimination, experiencing both forms of discrimination may explicatively increase stress and, therefore, is especially detrimental for one’s health (the double jeopardy hypothesis; Meyer, 2010). More specifically, two potential hypotheses were proposed regarding how navigating multiple forms of discrimination tied to different marginalized identities could compromise health outcomes: The additive hypothesis and the multiplicative hypothesis (Raver & Nishii, 2010). In an additive way, the negative effects of experiencing multiple forms of discrimination are accumulate, meaning that additional form of discrimination incrementally adds to the overall negative impact on an individual's health. Alternatively, in a multiplicative fashion, the effects of 35 multiple forms of discrimination interactively or multiplicatively lead to a more substantial detrimental impact on health outcomes, compared to the individual effect of each form of discrimination. The empirical investigations of the two hypotheses pertaining to discrimination based on ethnicity-race and sexual orientation has primarily focused on adult population, with mental health as the primary outcome variable of interest. A recent review of these studies has lent support to the multiplicative hypothesis, showing that perceiving more than one form of discrimination is associated with an elevated risk of depressive symptoms among LGB people of color (Vargas et al., 2020). When it comes to exploring these dynamics in LGB youth of color, only two studies have examined the specific way (additive vs. multiplicative) through which experiencing both ethnic-racial discrimination and sexual orientation discrimination affect their mental health (Thoma & Huebner, 2013; Mallory & Russell, 2021). Yet, these two studies have yielded inconsistent findings: Thoma and Huebner (2013) found evidence supporting both additive hypothesis and multiplicative hypothesis for LGB youth of color’s depressive symptoms, but neither hypothesis appeared to hold for suicide ideation. Conversely, the other study supported the multiplicative hypothesis for depressive symptoms and additive hypothesis for suicide ideation (Mallory & Russell, 2021). It seems that the applicability of these hypotheses depends on the specific outcome under consideration, and it is possible that both hypotheses could be true for the same outcome. Informed by both theoretical work and the small body of empirical evidence, the current study tested both additive and multiplicative hypotheses for sleep outcomes among a group of LGB youth of color. Specifically, to test the additive hypothesis, I first included both ethnic- racial discrimination and sexual orientation discrimination simultaneously in the same model, to 36 predict sleep outcomes; then, to test the multiplicative model, I introduced the interaction between these two forms of discrimination, to predict sleep outcomes. Various Sleep Indicators With the aim to provide a comprehensive and complete understanding of the impact of structural stigma and interpersonal discrimination on sleep health among LGB youth of color, adopting a multifaceted concept of sleep health (Buysse 2014; Meltzer, Williamson, et al., 2021), the current study focused on three dimensions of sleep that are important for adolescents: duration, timing (bedtime, risetime), and consistency. Extant literature and reviews have provided consistent evidence supporting the profound implications of insufficient sleep on adolescent adjustment and health (Leproult & Van Cauter, 2010; Galvan, 2019; Killgore, 2010; Palmer & Alfano, 2017; Shochat et al., 2014). A smaller but growing body of literature has begun to examine the health and developmental implications of sleep timing (e.g., bedtime) and inconsistent sleep for adolescents. They provided initial evidence that late bedtime and significant variability in duration or bedtime are linked to various health issues among adolescents. These health concerns span the physical domain, including obesity (Kjeldsen et al., 2014; Hart et al., 2011) and an increased risk of metabolic diseases (Abbott et al., 2019), as well as the emotional domain, such as increased negative mood (Asarnow et al., 2014). Additionally, to provide more nuances in terms of the specific sleep issues that are subject to the negative influences of structural stigma or interpersonal discrimination, the current study also conducted separate analyses to examine weekday sleep. This is informed by previous studies highlighting that weekday sleep is largely regulated by social and school activities among adolescents (Zeringue et al., 2021). Therefore, it is possible that the association between adverse experiences related to stigma and sleep outcomes are more pronounced for weekday sleep than overall sleep. 37 This multifaceted approach to sleep disparities also has important practical implications: The findings can serve as valuable resources for researchers and healthcare providers seeking to tailor interventions to address specific sleep issues. For example, if the analyses reveal that weekday variability is a primary concern, and that it is especially susceptible to negative experiences related to stigma, interventions can be designed to target this specific facet of sleep. These interventions may prioritize strategies to enhance the consistency of weekday sleep patterns and work to reduce relevant stigma experience that contributes to sleep disturbances. The Present Study The current study drew data from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org/), a prospective longitudinal study designed to understand child health and brain development in the United States. Using actigraphy assessed sleep duration data in early adolescence assessed at two-year follow-up and state-level structural stigma on racism and heterosexism at baseline, the current study 1) demonstrated the levels of state-level structural stigma across the 17 assessed states in the U.S., and 2) examined, for LGB youth of color, how state-level structural stigma was associated with sleep health, and whether ethnic-racial discrimination and sexual orientation discrimination were associated with sleep health in additive or multiplicative (i.e., interactive) way. As the first aim is for demonstration purpose and, for the second aim, empirical evidence exists supporting both additive and multiplicative hypothesis, no specific hypotheses were provided for this study. Methods Participants Data was drawn from the ABCD study. This study collects data of 11,876 individuals aged about 9 to 11 years old across 21 cites in the United States at baseline and will follow this 38 sample for 10 years annually. The data collection is still going on. The baseline data was collected between September 2016 and November 2018. Within each study site, stratified probability was used to select schools, both public and private. Within each school, eligible youths were invited to participate in the study. Then participants were followed up each year. The time interval between each wave of data collection was approximately one year. The current study focused on the subsample of LGB youth of color, which was selected based on the following criterion: 1) Black and Latinx youth (because ABCD study only assessed state-level structural racism targeting race group of Blacks and ethnic group of Latinx, only relevant ethnic-racial minority groups are included); 2) youth who self-identified as sexual minority (responding “Yes” or “Maybe” to the question “Are you gay or bi-sexual?” at any wave from Baseline to Y2). Therefore, from the analytic sample of study one, youth with non- marginalized identity (i.e., White heterosexual) and with single marginalized identity (i.e., Black heterosexual, Latinx heterosexual, and White LGB) were excluded. Details for each step excluding participants are presented in Figure 1. Detailed participant characteristics for the analytic sample are presented in Table 5. Measures Actigraphy sleep This was the same as study one. Specifically, two facets (i.e., average level and variability) of three sleep indicators (i.e., duration, bedtime, and risetime) were examined in the current study. For sleep duration, I used the interval that participants were identified as in the actual state of asleep (i.e., light, deep, and rapid eye movement) each night. Bedtime refers to the first minute that the participant was identified as staying in bed, and risetime refers to the first minute that the participant was identified as being out of bed. Both bedtime and risetime were 39 provided by the ABCD study. Both average and variability of the three sleep indicators were created based on the daily assessments. An individual’s average level was created based on the mean values across the available studied days. An individual’s sleep variability was created based on the variances across the available studied days. Both average level and variability were created for each sleep indicator. To address the high positive skewness in the original scores of variances, I conducted natural log transformation for all the three sleep variability indicators. Structural stigma. Structural stigma was created based on three variables at baseline. To assess structural stigma towards marginalized populations at the state level, the ABCD Study team compiled publicly available data from surveys that were administrated across the nation across multiple years. For example, Project Implicit is a research project that has collected measures of racial bias and bias against sexual minorities over the Internet for more than 20 years (https://implicit.harvard.edu/implicit/). American National Election Survey, conducted before and after every presidential election, collected data about voters’ attitudinal race bias in the U.S. State-level stigma related to race captured people’s attitudes and prejudice toward Blacks aggregated at state level. The ABCD Study drew data from projects that assessed peoples’ general attitudes toward Black people, the impact of discrimination on the lives of Black people, the existence of racial prejudice, and endorsement of racial stereotypes. State-level structural stigma related to the ethnicity of Latinx captured 1) people’s attitudes and prejudice toward immigrants and Hispanics, 2) state-level policies related to immigration (e.g., whether immigrants were granted access to health services). Immigration policies and attitudes were incorporated because they affect the health of Latinx communities 40 regardless of nativity status (see Crookes et al., 2022 for a review). State-level structural stigma related to sexual orientation was created based on 1) state- level scores from national surveys assessing peoples’ biased attitudes towards homosexuality and same-sex marriage legality, 2) institutional representation of sexual minority groups (e.g., the proportion of openly LGBTQ elected officials in government, the proportion of public high schools with Gay-Straight Alliances, the state-level of proportions of LGBT adults, the state- level density of same-sex couples), and 3) state-level legislations and policies protecting or denying LGB group rights (i.e., from Movement Advancement Project). All the three dimensions of state-level structural stigma were constructed as latent factor scores indicated by different data sources (Hatzenbuehler et al., 2022). To create structural racism, I first created a composite structural stigma toward ethnicity-race by taking the average score of state-level structural stigma towards Blacks and Latinx. As structural racism and structural heterosexism was highly correlated (r = .89, p < .001), which indicated that states that were high on structural racism was also high on structural heterosexism, I created the overall structural stigma by taking the average of the two dimensions. Two forms of discrimination. Discrimination targeting ethnicity-race and sexual orientation were assessed at two-year follow-up. Adolescents reported on two questions, assessing their perceptions of discrimination based on race-ethnicity (“in the past 12 months, have you felt discriminated against because of your race, ethnicity, or color?”) and sexual orientation (“felt discriminated against because someone thought you were gay, lesbian, or bisexual”), respectively. The original response scale was 0 (No), 1 (yes), 777 (Don’t know), and 999 (Refused to answer). Both 777 and 999 were coded missing. Covariates. Covariates included sociodemographic factors (i.e., grade, sex assigned at 41 birth, felt gender congruence, socioeconomic status [SES], immigration status, and family structure), psychosocial factors (i.e., youth psychopathology, parental warmth, peer support), neighborhood factors (i.e., safety, crime), as well as factors found to affect sleep duration (i.e., school start time, weight status, screen time, substance use, caffeine intake, physical activity). Due to the relatively small sample size, to maintain power, I excluded non-significant covariates. The covariates included in the final model included three sociodemographic factors (i.e., grade, sex assigned at birth, socioeconomic status) and another contextual factor that has significant effect on adolescent sleep pattern (i.e., school start time). Parents reported their children’s grade at school, assigned sex at birth (0 = Male, 1 = Female) at baseline. Family SES was created as a latent factor score with three indicators assessed by parental reports at baseline: parental education (λ = .71), employment status (λ = .48), and family economic hardship (λ = -.39). Data Analysis Plan All primary analyses were conducted in path analysis models in a structural equation framework using Mplus 8.8 (Muthén & Muthén, 2017). Maximum likelihood estimator with robust standard errors (MLR) was used to provide robust estimates with non-normal data. CLUSTER option with TYPE = COMPLEX function in Mplus was used to handle the non- independence in the data by study sites. To examine Aim 1, I visualized the levels of structural stigma (i.e., the average of structural racism and structural heterosexism) across the 17 states in the U.S. Specifically, on the U.S. map, I colored the 17 states for which the state-level stigma was provided in the ABCD study, with darker colors indicating higher levels of state-level stigma (grey color indicating the statues for which state-level stigma was not assessed in the ABCD study). To examine Aim 2, I first examined how state-level structural stigma and the two forms 42 of interpersonal discrimination (i.e., ethnic-racial discrimination, sexual orientation discrimination) were associated with sleep (termed as “main effect model”). In the model, sleep indicators were predicted by state-level structural stigma and both forms of interpersonal discrimination. To assess the unique effect of each form of interpersonal discrimination and state-level structural stigma above and beyond each other on sleep, they were examined in the same model. Each sleep indicator was examined in separate models. Next, I examined how the interaction between the two forms of discrimination (i.e., ethnic-racial discrimination, sexual orientation discrimination) was associated with sleep (termed as “interaction effect model”). Based on the main effect model, the interaction term between ethnic-racial discrimination and sexual orientation discrimination was included in the model to predict sleep indicator. To account for the multicollinearity between the interaction term and the two predictors, before creating the interaction term, both predictors were grand-mean centered. When significant effects emerged for interaction term, I used “MODEL CONSTRAINT” feature in Mplus to examine if the sleep mean level when adolescents report both forms of discrimination was significantly different from the sleep mean levels when they only report ethnic-racial discrimination, only report sexual orientation discrimination, or report no discrimination at all. Results I first examined the descriptive statistics of primary variables (see lower portion in Table 6). In the overall sample, 14% participants reported ethnic-racial discrimination and 19% participants reported sexual orientation discrimination; 19% participants reported single form of interpersonal discrimination (i.e., either ethnic-racial or sexual orientation discrimination) and 5% participants reported experiencing both forms of interpersonal discrimination (i.e., both 43 ethnic-racial and sexual orientation discrimination). In the overall sample, the average sleep duration was 7.29 hours every day; the average bedtime was 11.58 (11:35pm); the average risetime is 7.56 (7:34am). The standard deviation for duration, bedtime, and risetime is 1.33, 1.63, and 2.34, respectively, indicating that, a participant would usually sleep between 5.96 to 8.62 hours, go to bed at between 9.95pm (9:57pm) and 01.21 (01:13am), and get out of bed between 05.22am (5:13am) and 08.90 (8:54am). The bivariate correlations among primary variables were presented in the upper portion in Table 6. Higher state-level structural stigma was correlated with shorter sleep duration average at moderate magnitude. The two forms of interpersonal discrimination (i.e., ethnic racial discrimination and sexual orientation discrimination) were positively, moderately correlated with each other. For the indicators of sleep average level: shorter duration was correlated with earlier bedtime at moderate magnitude; the positive correlation between bedtime and risetime was also moderate. The correlations among the variability in sleep duration, bedtime, and risetime were positive and from moderate to strong. The correlation between the mean level and variability of a specific sleep indicator was not significant for duration, while moderate for bedtime and risetime, suggesting that they were likely to capture unique aspects of sleep. I also examined state-level structural stigma for each of the 17 assessed states in the U.S. The visualization was presented in Figure 3. South Carolina, Oklahoma, and Missouri (in descending order) were the three states that had the highest level of structural stigma; while Illinoi, California, Oregon, New York, Colorado, Connecticut, and Maryland (in ascending order) were the states that had the lowest levels or structural stigma; the other states were in the middle. For the primary analyses, I first examined the main effect model where each sleep 44 indicator was predicted by state-level structural stigma and the two forms of interpersonal discrimination (i.e., ethnic-racial discrimination, sexual orientation discrimination; see the “main effect model” column in Table 7 for the estimates of primary interest and in Table S5 in supplements for the full model estimates). For the associations between state-level structural stigma and indicators of sleep average levels, significant effects emerged for duration and bedtime: adolescents who lived in states where the structural stigma was higher had shorter sleep duration and later bedtime. The association between structural stigma and average risetime was not significant. For the associations between state-level structural stigma and sleep variability indicators, significant effect emerged for one sleep indicator – duration: adolescents who lived in states with higher levels of structural stigma had greater sleep duration variability. None of the associations between interpersonal discrimination (either for ethnic-racial discrimination or sexual orientation discrimination) and sleep indicators (regardless of average or variability) was significant. Next, I examined the interaction effect model where, based on the main effect model, the interaction of the two forms of interpersonal discrimination (i.e., ethnic-racial discrimination × sexual orientation discrimination) was included in the model to predict sleep (see the “interaction effect model” column in Table 7 for the paths of primary interest and in Table S5 in supplements for the full model estimates). For indicators of sleep average levels, the interaction between ethnic-racial discrimination and sexual orientation discrimination was significant for risetime, marginally significant (p = .064) for duration, but not significant for bedtime. The interaction effect between ethnic-racial discrimination and sexual orientation discrimination was not significant for any sleep variability indicators. For the two interaction effects that were either significant or marginally significant, I 45 probed the interactions to examine the extent to which risetime and sleep duration differed between adolescents who reported both forms of discrimination and adolescents who reported ethnic-racial discrimination only, sexual orientation discrimination only, or no discrimination at all. For average duration (see Figure 4), adolescents who reported both forms of interpersonal discrimination (mean = 6.90 hours) slept significantly shorter than adolescents who only reported ethnic-racial discrimination (mean = 7.34 hours) but did not differ from adolescents reporting no discrimination (mean = 7.18 hours) or only reporting sexual orientation discrimination (mean = 7.22 hours). For average rise time (see Figure 5), adolescents who reported both forms of interpersonal discrimination (mean = 7:27am) slept shorter than adolescents who only reported sexual orientation discrimination (marginally significant, mean = 7:56am), but did not differ from adolescents reporting no discrimination (mean = 7:30am) or only reporting ethnic-racial discrimination (mean = 7:47am) I also conducted separate analyses to examine weekday sleep (see Table 8 for the estimates of primary interest; see Table S6 in supplements for the full model estimates). Because the available sample size for reliable (more than four valid days) weekend sleep was too small (n = 97), I did not examine weekend sleep. Either state-level structural stigma or two forms of interpersonal discrimination (i.e., ethnic-racial discrimination and sexual orientation discrimination) was not significantly associated with any sleep indicators. For the interaction effect between two forms of interpersonal discrimination on sleep, similar to the primary analyses of overall sleep, the interaction between ethnic-racial discrimination and sexual orientation discrimination was significant for average duration and average risetime. For average duration (see Figure 6), adolescents who reported both forms of interpersonal discrimination (mean = 6.87 hours) slept significantly shorter than adolescents who only reported ethnic-racial 46 discrimination (mean = 7.35 hours) or reported no discrimination (mean = 7.24 hours) but did not differ from adolescents who only reported sexual orientation discrimination (mean = 7.35 hours). For average risetime (see Figure 7), adolescents who reported both forms of interpersonal discrimination (mean = 6:58am) slept shorter than adolescents who report no discrimination (marginally significant, mean = 7:19am), only reported sexual orientation discrimination (mean = 7:33am), or only reported ethnic-racial discrimination (mean = 7:44am) Discussion LGB youth of color are at elevated risk for sleep disturbances (Study one). Stigma related to their ethnicity-race and sexual orientation has been theorized as fundamental risk factor compromising health, including sleep health, among LGB youth of color (Hatzenbuehler, 2013). A large body of literature has focused on stigma to investigate health disparities. However, they are limited in primarily focusing on interpersonal processes of stigma while neglecting its manifestation at structural level. Moreover, many studies concentrate on examining stigma related to a single dimension of identity (either ethnicity-race or sexual orientation), and often concentrate solely on mental health as outcomes. To address those gaps in existing literature, adopting an intersectionality perspective, the current study investigated how stigma targeting both ethnicity-race and sexual orientation at the structural level (structural intersectionality) and at the interpersonal level (intersectional discrimination) were associated with sleep health among a group of Black and Latinx LGB adolescents. The current findings suggest that both state-level structural stigma and interpersonal discrimination are detrimental for sleep health among LGB youth of color. State-level structural stigma, as the average of state-level structural racism and heterosexism, was directly linked to overall shorter duration, later bedtime, and greater duration variability. The two forms of interpersonal discrimination (ethnic-racial discrimination and 47 sexual orientation discrimination) did not have direct additive effects on sleep outcomes. Instead, their interaction was associated with shorter sleep duration and earlier risetime. Structural Stigma and Sleep Disturbances among LGB Youth of Color First, the findings show that structural stigma is associated with sleep health of LGB color of youth, such that, Black and Latinx LGB youth who reside in states that are high on structural stigma had shorter sleep, later bedtime, and greater duration variability. This finding adds novelty to the adolescent sleep health literature by clearly highlighting structural stigma as a risk factor for sleep health among LGB youth of color. To our knowledge, this is the first study that has established the linkage between structural stigma and adolescent sleep. There are several potential mechanisms that can explain why living in a state that is oppressive toward their ethnic-racial and sexual orientation identities may compromise the sleep health among LGB youth of color. One key mechanism involves the stress induced by living in an environment characterized by high structural stigma. This stress may arise from anticipation or actual experience of unfair treatment, feelings of unsafe and worrying (Del Toro et al., 2022, Cardoso et al., 2021, Cross et al., 2021). Such stressors can activate physiological stress responses (Hatzenbuehler & McLaughlin, 2014), heighten arousal and vigilance (Hicken et al., 2013), and elicit emotional distress (see Alvaro et al., 2013 and Lovato & Gradisar, 2014 for reviews), all of which can make it difficult for individuals to fall and stay asleep. In addition, structural stigma can adversely impact the sleep of LGB youth of color by creating barriers to accessing safe and affirming healthcare and mental health services (Feagin & Bennefield, 2014; Williamson et al., 2022; Arbeit et al., 2016). The lack of these crucial resources can make their sleep or mental health problems less likely to be properly treated, thereby contributing to, or exacerbating their sleep disturbances. Also, being LGB youth of color in a high structural stigma 48 environment often means multiple exclusions both by their ethnic-racial group and the broader LGBTQ+ community. The feelings of loneliness and emotional disturbance due to this isolation have been associated with youth sleep insufficiency and inconsistency (Harris et al., 2013; Kelly et al., 2022). However, despite the various theorized processes that link structural stigma to sleep disturbances among LGB youth of color, very few have been examined by empirical studies. The observed effect sizes for the association between structural stigma and sleep indicators were modest, yet relatively higher compared to other identified psychosocial protective or risk factors for adolescent sleep duration and timing (correlation coefficients |r|s < .15 in the meta-analytic reviews by Bartel et al., 2015 and Khor et al., 2021). My speculation is that, in contrast to the protective and risk factors investigated in earlier studies, which pertained to more specific domains (e.g., negative family environment, pre-sleep worry) and thus only accounted for a small portion of variance in adolescents’ sleep health, the focal risk factor in the current study, structural stigma, takes on a more broad and complex form. In the current study, structural stigma was operationalized as a general construct integrating people’s biased attitudes toward ethnic-racial and sexual minority groups aggregated at the state level, state-level legislations and policies denying LGB group rights group, representation of minority groups in governmental and different levels of organization. It encompasses multiple processes and is positioned to explain larger variance in youth sleep. Future research should focus on disentangling those underlying constructs and exploring those various processes to provide a clearer picture in terms of how and why state-level structural stigma compromises sleep health among LGB youth of color. Those investigations can also identify potential intervention targets to break this link and improve sleep health among LGB youth of color. 49 Notably, the current study found a very high correlation (r = .89) between the two forms of structural stigma. In other words, states that are high on structural racism is also high on structural heterosexism. The correlation coefficient between structural racism and structural heterosexism observed in the current study contradicts the finding in another study focusing on the correlation between structural racism and sexism (r = -.32; Homan et al., 2021). This discrepancy might be due to the different sources of data that the Homan et al. (2021) study and the ABCD study used to create those state-level structural stigma indicators. For instance, ABCD study drew data from Project Implicit aggregated at the state-level to create both structural racism and heterosexism. The high correlation between those two forms of structural stigma may also suggest that some similar historical and political legacies fostered state-level structural racism and heterosexism, such as more conservative social values and political climate (Updegrove et al., 2020), as well as the lack of comprehensive public education on diversity and inclusion (Orr et al., 2023). The visualization of the overall structural stigma, created as the average of the two forms, in the US map clearly underscore the states where the issue of structural stigma is most pronounced. Those states represent risky broader environment that are hostile and oppressive for both groups of ethnic racial minority and sexual minority. More research efforts should be dedicated to those identified states to investigate the specific aspects and policies contributing to health disparities. Intersectional Discrimination and Sleep Disturbances among LGB Youth of Color This study also paid attention to stigma at the interpersonal level. However, different from previous research, which has often overlooked the examination of both forms of interpersonal discrimination relevant to LGB youth of color, the current study takes an intersectional perspective. It delved into how ethnic-racial discrimination and sexual orientation 50 discrimination were additively or multiplicatively (interactively) associated with sleep indicators among LGB youth of color. The findings did not support the additive hypothesis (Raver & Nishii, 2010): When both ethnic-racial discrimination and sexual orientation discrimination were included in the model simultaneously, none of the associations was significant. However, the results did support the multiplicative hypothesis (Raver & Nishii, 2010), showing that the interaction between ethnic-racial discrimination and sexual orientation discrimination was significantly associated with shorter sleep duration and earlier bedtime. To better disentangle how the interaction of ethnic-racial discrimination and sexual orientation discrimination was associated with sleep, as the interpersonal discrimination was assessed as binary variables (i.e., no vs. yes), the current study conducted mean differences analyses to interpret those significant effects. The results showed that perceiving both forms of discrimination (when compared to perceiving no discrimination or perceiving a single form of discrimination), was associated with shorter duration or earlier risetime. The results align with the intersectional theory, which posits that for individuals situated at the intersection of multiple marginalized identities, they confront multiplicative challenges, leading to their compromised health conditions (Crenshaw, 1989). This study highlights intersectional discrimination as one of the unique challenges that are detrimental for the sleep health of LGB youth of color. Notably, these contrasts between perceiving both forms of discrimination and perceiving no or a single form of discrimination were more salient for weekday sleep than overall sleep across weekday and weekends. This is not surprising given that previous studies have shown adolescents reported encountering most frequent and influential discriminatory experiences from school peers and personnel (Benner & Graham, 2013; Ryan & Rivers, 2010). It is concerning that the observed weekday sleep deficits among LGB youth of color is substantial (29 to 22 51 minutes for sleep duration, 21 to 46 minutes for risetime). Experiences of encountering both forms of discrimination are not uncommon for LGB youth of color, as indicated by the medium correlation coefficient between the two forms of discrimination. Considering the severe sleep disturbances observed in the current study and the well-established literature highlighting the concurrent and long-term implications of such sleep disturbances for the development of mental health disorders (see the reviews by Gregory & Sadeh, 2012 and Gregory & Sadeh, 2015), LGB youth of color who experience both forms of discrimination should be closely monitored for both their sleep disturbances and mental health status. These findings also have important policy implications. Given the substantial constraining effect of school start time on youth’s risetime and the subsequent sleep duration (Carskadon, 2011; Crowley et al., 2018), the observed deficits in earlier risetime and shorter sleep duration among the LGB youth of color who perceived both forms of discrimination may be mitigated to some extent by the implementation of delaying school start time. Moreover, future research should investigate potential coping strategies and protective factors across multiple levels (e.g., family, peers, school, neighborhood; Huynh & Gillen-O’Neel, 2016; Chen et al., 2022) that may serve to mitigate the adverse effects of perceiving both forms of discrimination on sleep health among LGB youth of color. Strength, Limitations and Future Directions The current study contributes to literature in several critical ways. First, focusing a unique group of LGB youth of color, the current study was able to take a close look at the experiences of this group of adolescents and explore how structural stigma and interpersonal discrimination tied to their double marginalized identities affect their sleep health. The results demonstrated both state-level structural stigma and interpersonal discrimination as risk factors for their health. Second, taking an intersectional perspective, the current study elucidated that 52 ethnic-racial discrimination and sexual orientation discrimination were interactively associated with sleep disturbances of LGB youth of color. In addition, by including various sleep indicators and conducting separate analyses for weekday sleep indicators, the current study clearly showed the facets of sleep health among LGB youth of color that were susceptible to the detrimental effects of structural stigma and interpersonal discrimination. The current findings provide valuable insights in terms of the specific dimensions of sleep that intervention programs should target to mitigate the detrimental effect of structural racism and interpersonal discrimination on the sleep health of LGB youth of color. The current study has several limitations that should be acknowledged. First, the ABCD study only collected data across 17 out of the 50 states in the U.S., thus limiting the generalizability of the current findings to other social contexts. Future large-scale studies may consider collecting data from more states in the U.S. This approach is likely to yield findings that possess greater generalizability. The restricted number of states also limited the statistical analyses that we can utilize to capture the characteristics in the data that individuals were nested within states. The common statistical modeling for nested data (e.g., multilevel structural equation modeling; MSEM) necessitates a minimum cluster size of 20 for model convergence (McNeish & Stapleton, 2016). While we carefully addressed this nesting by employing the CLUSTER option with TYPE = COMPLEX in Mplus, with more states included, future studies can employ MSEM to replicate the findings observed in the current study. With more statistical power provided by including more states, expect for examining the main effect of structural stigma on adolescents’ sleep, future studies can explore more complex models that are theoretically valid. For instance, how stigma at the structural state-level impact adolescents’ sleep health through intersectional discrimination at the interpersonal level (i.e., cross-level 53 mediation model); or how stigma at the structural state-level may modify the association between intersectional discrimination at the interpersonal level and sleep health (i.e., cross-level interaction model) among LGB youth of color. Additionally, the assessment of structural stigma was limited in two ways in the ABCD study. First, when compiling indicators of structural racism, the ABCD study did not consider dimensions that may be more relevant to youth population, such as ethnic-racial disproportional funding allocation for youth mental health and sleep disturbance treatment (Alvarez et al., 2022), racism at law enforcement (Del Toro et al., 2022). These dimensions may have more salient implications for adolescent sleep health. Incorporating those more proximal indicators for youth’s life and well-being, future studies may reveal stronger effects between structural stigma and sleep health among LGB youth of color. Additionally, because ABCD study only assessed state-level structural racism towards Black and Latinx populations, which prevented me from examining experiences of LGB youth from other ethnic-racial groups, such as Asian Americans, native American, Pacific Islander, and other ethnic-racial groups. Future research should aim to incorporate more comprehensive indicators of structural stigma that may be applicable to a wider range in terms of ethnicity-race of LGB youth. Third, discrimination targeting ethnicity-race and sexual orientation was only assessed based on a single item respectively in the ABCD study, which tend to underestimate adolescents’ actual discrimination experience (Carter et al., 2017), thus biasing the findings (Benner et al., 2022). Indeed, the prevalence observed in the current study was relatively lower (14% for ethnic- racial discrimination, 19% for sexual orientation discrimination) than previous studies that use multiple items (e.g., 33% to 70% for sexual orientation discrimination in Mallory & Russell, 2021). Despite the relatively low prevalence of reporting ethnic-racial discrimination and sexual 54 orientation discrimination, the current study still found the interaction effect between those two forms of discrimination on adolescents’ sleep health. However, future studies could benefit from utilizing measures that can comprehensively capture adolescents’ discrimination experiences to assess the role of both forms of discrimination for youth sleep health disparities. Conclusions The current study took an intersectionality approach in investigating the specific ways in which state-level racism and heterosexism at the structural level and ethnic-racial discrimination and sexual orientation discrimination at the interpersonal level influence sleep health among a group of LGB youth of color. The current study provided initial evidence that state-level structural stigma directly affects sleep health, while the two forms of interpersonal discrimination impact sleep in a multiplicative way. The results highlight both state-level structural stigma and interpersonal discrimination as sleep health risk factors for this group of adolescents. The study also highlights the unique challenge of intersectional discrimination, revealing that perceiving both forms of discrimination, as opposed to none or just one, is associated with increased sleep disturbances among LGB youth of color. Future research efforts should be dedicated to further exploring the underlying mechanisms through which structural stigma and intersectional discrimination compromise sleep health. Additionally, efforts should also be made to identify protective factors that can mitigate those negative effects of structural stigma and intersectional discrimination on sleep health of LGB youth of color. 55 CHAPTER 4. GENERAL CONCLUSION Sleep health has critical implications for adolescents’ healthy development (El-Sheikh et al., 2022; Matricciani et al., 2019). However, adolescence, especially from early adolescence, is marked by heightened sleep disturbances, encompassing sleep insufficiency, delayed bedtime and early risetime, as well as large variability (Carskadon, 2011; Crowley et al., 2018). Unfortunately, marginalized youth groups, such as ethnic-racial or sexual minorities are even at higher risk for developing sleep disturbances. Drawing data from the ABCD study, a national prospective longitudinal study on youth in the Unites State, this dissertation investigated sleep disparities among youth with double marginalized identities – specifically, LGB youth of color, compared to both their non-marginalized peers and peers with a single marginalized identity. In addition, this dissertation comprehensively examined the role of stigma, addressing both marginalized identities of ethnicity-race and sexual orientation, at both the structural level and the interpersonal level, in contributing to sleep disturbances among LGB youth of color. The findings of the first study significantly adds to the existing literature on sleep health disparities among youth. Primarily, the findings underscore the elevated sleep disturbances experienced by LGB youth of color. Going beyond a simple comparison, the study delves into multiple dimensions of sleep and distinguishes between weekday vs. weekend sleep, emphasizing that sleep deficits among LGB youth of color are pervasive across multiple dimensions and are more salient during weekdays than weekends. The second study, focusing specifically on the unique experience of LGB youth of color, illustrated state-level structural stigma and perceiving both forms of interpersonal discrimination targeting their both marginalized identities as risk factors for their sleep health. Together, these findings provide crucial insights for future intervention programs, offering directions on whom to target and what 56 aspects to address in order to mitigate sleep health disparities based on ethnicity-race and sexual orientation. 57 BIBLIOGRAPHY Abbott, S. M., Weng, J., Reid, K. J., Daviglus, M. L., Gallo, L. C., Loredo, J. S., Nyenhuis, S. M., Ramos, A. R., Shah, N. A., Sotres-Alvarez, D., Patel, S. R., & Zee, P. C. 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