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Understanding emotional regulation and mood of young adults in the context of homelessness using geographic ecological momentary assessment

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Understanding emotional regulation and mood of young adults in the context of homelessness using geographic ecological momentary assessment

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Content Understanding Emotional Regulation and Mood of Young Adults in the Context of Homelessness using Geographic Ecological Momentary Assessment by Sara Semborski, M.S.W. A Dissertation Presented to the FACULTY OF THE USC SUZANNE DWORAK-PECK SCHOOL OF SOCIAL WORK UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (SOCIAL WORK) May 2023 Copyright 2023 Sara Semborski ii Dedication For those without place, for those displaced. Those kicked-out, excluded, overlooked, who aren’t given a place at the table even though we are totally able. For the big feelers and self-healers. For those trying to get ahead but are left without a bed. It shouldn’t be this hard. And it is something for which we are all responsible. iii Acknowledgements The following body of work was certainly a group project. I may have invested the most tears, but many people have poured their time, energy, and care into my development over the last several years. I am deeply grateful for each of you and how you have contributed to the person I am, not only as a researcher and scholar, but how I show up and interact with the world. First and foremost, I acknowledge my mentor and dissertation chair, Dr. Benjamin Henwood, for your continued partnership and guidance throughout my doctoral education. You are a gifted thinker and storyteller and I have grown in my capacity to think and examine problems, construct arguments, and tell compelling data-driven stories because of your mentorship. My development over the past years was shaped largely by my involvement in your projects, namely the Log My Life Study, which provided invaluable experience. Thank you for all you have invested in me, for showing up when I needed support, and for being someone I could put my trust in every step of the way, only due in part to being a fellow Philadelphia sports fan. I look forward to the potential of future collaborations. To staff and faculty at the USC Suzanne Dworak-Peck School of Social Work. To Malinda Sampson, who without I would not have even been in the program because I missed the initial email inviting me to interview. Malinda, since before day one you have been keeping me on track and that is no small task. Thank you for all that you have done for me to ensure my success. Your nurturing spirit is such a gift to the school. To Dr. Michael Hurlburt, who served as the PhD program director for most of my time in the program, you were one of the largest influences in how I experienced my doctoral education. I am incredibly fortunate to have had you as a mentor in several capacities including, but not limited to leadership, teaching, and ethics. Thank you for leading by example and being a trusted ally to your student colleagues. To Dr. Jungeun Olivia Lee, for your leadership in the home stretch. Thank you for your support and encouragement to make it to the finish line. To the rest of my dissertation committee. To Dr. Eric Rice, who also served on my qualifying exam committee and a co-sponsor on my dissertation grant, you began offering mentorship before I even set foot in Los Angeles. You were not available during my interview at USC, so you took the better part iv of an hour out of your day to call me after the fact. During that call we talked about the meaning of this work, its impact, and role in our lives. That call was integral to my decision to uproot my life and begin this journey. Thanks for being one of the real ones. To Dr. Tyler Mason, also a member of my qualifying exam committee and co-sponsor on my dissertation grant, for sharing knowledge on momentary sampling and emotion dynamics. Your guidance has been integral to my success, and it has been a pleasure working with you. To Dr. Jordan Davis, for your willingness to jump in as a mentor and ponder methodological questions. For your attempts at keeping me focused on my research question. Thank you for making yourself available. The love you have for what you do is evident. To the other co-sponsors on my NIH F31 grant and predoctoral fellowship training. To Dr. Jeremy Mennis, thank you for responding to a cold email from a stranger (me) and for your willingness to serve as a co-sponsor. The ways in which you have combined methodologies in transdisciplinary work has been an inspiration. To Dr. Eric Granholm, thank you for your willingness to serve as a co-sponsor on my training grant that served as the foundation of this dissertation. Your expertise in the areas of mental health and emotional liability were instrumental in obtaining funding and training to further develop into the researcher I am today. To other significant mentors. To Dr. Suzanne Wenzel, who served on my qualifying exam committee, taught the grant writing class where I began developing ideas for this dissertation work, served as interim Dean during my time at the USC School of Social Work, and who shares a love of femme-lead rock and roll; thank you for the fire you bring to issues you (and I) care about. Since I met you at the National Alliance to End Homelessness Conference in 2016 your intentionality, integrity, and passion has been an inspiration to me. To Dr. Harmony Rhoades, for your partnership and friendship. Thank you for being one of my most trusted mentors. I cannot express how much your support in navigating the past six years has meant to me. Thank you for sharing your wisdom in a fun and down to earth way. I have learned so much from you and have had so much fun while doing it. Here’s to future collaborations! To Dr. Jeremy Goldbach, for helping me shape my first ideas on my doctoral journey, for v encouraging me to listen to Taylor Swift’s “Shake It Off” in the face of inevitable rejection, and for always looking out for my best interest. Thank you. To individuals who do not know me personally but have offered their support anyway. To Dr. Daniel McNeish, for answering many emails and entertaining many technical questions about dynamic structural equation modeling. Your work has been pivotal in my overall dissertation process and the generosity you had in doing what you could to help me learn is very appreciated. I still have many questions, but that’s for future me to work out. I look forward to crossing paths in the future. To Dr. Sarah Sperry, for your willingness to share your own work and R syntax to contribute to my learning. Not only did it help me wrestle with questions that kept me up at night, but the generosity you extended is something I hope to pay forward. To the OG Hen House, for some of the fondest memories I have over the past years. Our work and friendship took us all over the globe, on many treks, and truly gave me some of the best years of my life. To Brian Redline, for like everything. Early on you showed me the research ropes, but more importantly you showed me care. Your friendship was the glue that held me together during year one and without you I might not be here. To Dr. Danielle Madden and our many adventures. Thank you for your friendship and for making work and life outside of work so fun. To Dr. Eldin Dzubur, for helping me wrap my brain around this stuff. For answering a zillion questions. For being a great teacher, travel companion, and homie. To student colleagues, classmates, friends, and allies. To Dr. Jessica Dodge, for being a rock throughout the program, a calming and reassuring presence, and a constant source of encouragement. I know we are in it for the long haul, and I am thankful my PhD pursuit gave me you. To Dr. Graham DiGuiseppi, for crossing the finish line with me. There were times when you carried me in the past year with your listening ear and sense of humor. Thank you for your support, collaboration, and peer-review. You have had a hand in many of my achievements over the past years. I am thrilled to be concurrently embarking on new chapters and know that this is, in no way, goodbye. To Laura Petry, for you care and passion for young people experiencing homelessness, for your perseverance, and collaboration. You are vi someone who ends up with way too many animals in your apartment because you care (I can relate) and this instinct is evident in your relationships, the questions you ask, and how you consider complex social issues. It has truly been a pleasure working with you. I look forward to the potential of our career paths crossing again – here’s to hoping, anyway. To Rory O’Brien, for your gentle, but powerful presence in our collective experience as student colleagues. Your care and consideration for others, including your peers, is grounded in a deep sense of justice. Thank you for extending that to me, and for the calming fire you bring to the fight. I will fight alongside of you forever. To Amy Lee, for starting this thing with me and ending it with me. I am thankful for your presence during each phase, especially in early days. Your kindness helped make the transition across the country more bearable. To Shaddy Saba, a clinician and researcher, for holding tension between clinical and research worlds and the sunshine you bring to each conversation. To Erika Salinas, for your inspiration as an academic scholar, student, teacher, and mother. To Adriane Clomax, for holding tension between professional and personal happiness, and encouraging me to follow my own dreams. To Leslie Schnyder, for your nurturing presence, for your example of perseverance in the face of challenging obstacles and being a source of momentum for student colleagues as a collective. To Ronna Banada, for your peer leadership and the time you put into organizing us as group of student colleagues. I appreciate all you do. And finally, to my family. To my parents, who rarely questioned (for better or worse) my impulses and pursuit of my own goals and dreams. Thank you for your continued support and unwavering belief in my capacity. You have encouraged me to dream big and go far, even if the distance between us is hard. Thank you for all you have given me. To my grandparents, gram and pap, thank you for your continued enthusiasm and excitement for my achievements. Even though it is likely you have had no idea why I am still in school and on my fourth significant graduation, you have always been so proud of me. And last but certainly not least, to my spouse, Aaron. Sharing the journey with you, including my aspirations and achievements, gives it all deeper meaning. The life we have been building together for quite some time is by far what I have the most pride in. Much of this milestone is owed to you and your care for me. Thank you for listening well and for your guidance. I truly could not have done it without vii your love and support. I love you. And to our beagles, Otis and Beck, and our sweet, late Bean Dip, even though you are dogs and will never understand, thanks for emotionally supporting me every single day. viii Table of Contents Dedication ..................................................................................................................................................... ii Acknowledgements ...................................................................................................................................... iii List of Tables ............................................................................................................................................... xi List of Figures ............................................................................................................................................. xii Abbreviations ............................................................................................................................................. xiii Abstract ...................................................................................................................................................... xiv Chapter 1. Introduction ................................................................................................................................. 1 Conceptual Framework ............................................................................................................................. 2 Paper 1 .................................................................................................................................................. 3 Paper 2 .................................................................................................................................................. 5 Paper 3 .................................................................................................................................................. 6 The Social Ecology Model for Health Promotion ................................................................................. 8 Contribution .............................................................................................................................................. 9 Chapter 2. Modeling emotion dynamics of housed and unhoused young adults using Ecological Momentary Assessment (Paper 1) .............................................................................................................. 11 Abstract ................................................................................................................................................... 12 Introduction ............................................................................................................................................. 13 General Methodology ............................................................................................................................. 15 Participants and procedure .................................................................................................................. 15 Measures ............................................................................................................................................. 16 Analyses .............................................................................................................................................. 17 Study 1 .................................................................................................................................................... 18 Analyses .............................................................................................................................................. 18 Results ................................................................................................................................................. 19 Discussion ........................................................................................................................................... 20 Study 2 .................................................................................................................................................... 22 Analyses .............................................................................................................................................. 22 Results ................................................................................................................................................. 23 Discussion ........................................................................................................................................... 24 General Discussion ................................................................................................................................. 26 EMA design considerations ................................................................................................................ 27 Limitations .......................................................................................................................................... 28 Conclusion .......................................................................................................................................... 28 ix Chapter 3. Navigating risk environments: The relationship between housing and mood for young adults experiencing homelessness (Paper 2) ............................................................................................... 35 Abstract ................................................................................................................................................... 36 Introduction ............................................................................................................................................. 37 The present study ................................................................................................................................ 39 Methods .................................................................................................................................................. 39 Participants and procedures ................................................................................................................ 40 Quantitative procedures ...................................................................................................................... 40 Qualitative participants and procedures .............................................................................................. 41 Measures ............................................................................................................................................. 41 Mixed Methods Analysis .................................................................................................................... 42 Results ..................................................................................................................................................... 44 Geospatial Results ............................................................................................................................... 44 Quantitative Results ............................................................................................................................ 45 Qualitative Results .............................................................................................................................. 47 Discussion ............................................................................................................................................... 53 Limitations and future directions ........................................................................................................ 57 Conclusion .......................................................................................................................................... 58 Chapter 4. Stress lability among housed and unhoused LGBTQ young adults: The role of identity homophily in social networks (Paper 3) ..................................................................................................... 70 Abstract ................................................................................................................................................... 71 Introduction ............................................................................................................................................. 72 The present study ................................................................................................................................ 74 Methods .................................................................................................................................................. 75 Measures ............................................................................................................................................. 76 Analyses .............................................................................................................................................. 78 Results ..................................................................................................................................................... 79 Characteristics associated with like alters ........................................................................................... 79 Location-scale model of homophilous alters by housing status .......................................................... 80 Discussion ............................................................................................................................................... 83 Limitations and future directions ........................................................................................................ 85 Conclusion .......................................................................................................................................... 85 Chapter 5. Conclusion ................................................................................................................................. 91 Introduction ............................................................................................................................................. 91 x Review of Major Findings and Integration with Existing Literature ...................................................... 92 Modeling emotion dynamics of housed and unhoused young adults using ecological momentary assessment (Paper 1) ........................................................................................................................... 92 Navigating risk environments: The relationship between housing and mood for young adults experiencing homelessness (Paper 2) ................................................................................................. 94 Stress lability among housed and unhoused LGBTQ young adults: The role of identity homophily in social networks (Paper 3) ................................................................................................................ 96 Integration and Theoretical Application ................................................................................................. 97 Recommendations ................................................................................................................................... 99 Clinical Implications ........................................................................................................................... 99 Policy Implications ........................................................................................................................... 101 Conclusion ............................................................................................................................................ 102 References ................................................................................................................................................. 103 xi List of Tables Table 2.1. Demographic characteristics. .................................................................................................... 29 Table 2.2. Estimates and 95% credible intervals for multilevel location-scale model with housing status. .......................................................................................................................................................... 30 Table 2.3. Correlation matrix of emotion dynamics................................................................................... 31 Table 2.4. Group-level weighted edges in housed and unhoused networks. .............................................. 31 Table 2.5. Node centrality measures across the EMA week, by group. ..................................................... 32 Table 2.6. Logistic regression analyses predicting housed status. ............................................................. 33 Table 3.1. Sample characteristics by housing status .................................................................................. 59 Table 3.2. Mixed-effects location-scale models for drug activity .............................................................. 60 Table 3.3. Mixed-effects location-scale models for violence concentration .............................................. 62 Table 3.4. Mixed-effects location-scale models for homelessness concentration ...................................... 64 Table 3.5. Mixed methods findings ............................................................................................................ 66 Appendix A. LAPD crime categories utilized for constructing violence hot spots ................................... 69 Table 4.1. Sample Characteristics .............................................................................................................. 87 Table 4.2. Mixed-effects regressions of characteristics associated with homophilous alters at................. 88 Table 4.3. Mixed-effects location-scale models of stress by housing status .............................................. 89 xii List of Figures Figure 1.1. Dissertation Conceptual Framework ....................................................................................... 10 Figure 2.1. Emotion networks of housed and unhoused groups ................................................................ 34 Figure 3.1. Drug activity hot spots ............................................................................................................. 61 Figure 3.2. Violence hot spots.................................................................................................................... 63 Figure 3.3. Homeless hot spots .................................................................................................................. 65 Figure 3.4. EMA points .............................................................................................................................. 65 Figure 4.1. Predicted WS variance functions plotted against random-location effect for the housed group ........................................................................................................................................................... 90 Figure 4.2. Predicted WS variance functions plotted against random-location effect for the unhoused group ........................................................................................................................................................... 90 xiii Abbreviations AR Auto-regression BS Between-subject Cishet Cisgender Heterosexual DSEM Dynamic Structural Equation Modeling DSM Diagnostic and Statistical Manual (of Mental Health Disorders) EMA Ecological Momentary Assessment FDR False Discovery Rate GEMA Geographic Ecological Momentary Assessment HIV Human Immunodeficiency Virus HUD Housing and Urban Development (Department of) LA Los Angeles LAPD Los Angeles Police Department LGBTQ Lesbian, gay, bisexual, transgender, queer MAUP Modifiable Areal Unit Problem NA Negative Affect NAEH National Alliance to End Homelessness NIMH National Institute of Mental Health OR Odds Ratio PA Positive Affect PIT Point in Time PTSD Post-traumatic stress disorder SAMHSA Substance Abuse and Mental Health Services Administration SD Standard Deviation VAR Vector Auto-regression WS Within-subject xiv Abstract Skills in emotional regulation, defined as “strategies used to influence, experience, and modulate emotions” are important for decision-making processes and can help support the navigation of complex and chaotic settings. These skills may be especially important for the estimated one-in-ten young adults, aged 18-25, who experience homelessness over the course of one year as they navigate complex and chaotic environments associated with homelessness. Unfortunately, these environments often introduce traumatic experiences which can result in difficulties with regulation, and in turn, lead to mental health problems which are already higher among young adults who have experienced homelessness. To date few studies have examined emotional regulation skills, a transdiagnostic component underlying psychopathology, among young adults with history of homelessness, a heterogenous population with diverse mental health diagnoses. There is some evidence that skills in emotional regulation may be protective against suicidality and violence within this population; however, we do not have a clear, broad understanding of the role of emotional regulation in navigating the social and physical environments associated with homelessness. To increase understanding of the dynamics of emotional regulation and the role of social and physical environments, the current dissertation employed Geographic Ecological Momentary Assessment (GEMA). GEMA integrates ecological momentary assessment (EMA) and geographic information systems science allowing for cross-validation and enrichment of research on place, well-being, and health. The aim of this work is to better understand how the social and physical contexts of homelessness may be related to emotional regulation. Housing status serves as a main analytic axis, as the sample consists of both formerly homelessness (residing in supportive housing) and currently homeless (street- or shelter-based) young adults who completed questionnaires and GEMA for a period of 7 days. The findings from this work are used to develop recommendations for direct service providers and policymakers regarding how to further adapt service environments to meet the complex needs of at-risk young people, including interventions for young adults experiencing homelessness. 1 Chapter 1. Introduction This dissertation is composed of four studies presented across three chapters. The overarching goal of this dissertation is to better understand the implications of socio-environmental factors in emotional dynamics and regulation for two samples of young adults: those currently experiencing homelessness and formerly homeless young adults who have transitioned into supportive housing. Skills in emotional regulation, defined as “strategies used to influence, experience, and modulate emotions,” are important for decision-making processes and can help support the navigation of complex and chaotic settings (Henwood et al., 2019a; Rice et al., 2007, 2008; Rice, 2010, 2010). These skills may be especially important for the estimated one-in-ten young adults, aged 18-25, who experience homelessness over the course of one year as they navigate frequently reported traumatic experiences related to homelessness (Morton, Dworsky, et al., 2018a). Unfortunately, these traumatic experiences themselves can result in difficulties with emotional regulation, which in turn, can lead to mental health problems which are already higher among young adults who have experienced homelessness (Hodgson et al., 2013). A key feature of many mental health conditions, difficulty with emotional regulation is a transdiagnostic process underlying psychopathology (Anestis et al., 2009; Gross, 1998, 1998; Gross & Jazaieri, 2014; Werner & Gross, 2010, 2010) and is prominent among young people who experience homelessness (Hodgson et al., 2013), but have been understudied (Barr et al., 2017; Duval & Vincent, 2009; Petering et al., 2018; Wong et al., 2013a). These difficulties occur when individuals are not able to modify their emotions in response to rapidly altering demands in the environment (Kashdan, 2010). Because the environment and emotional experience are dynamic and not static, there is a need to examine emotions at a granular level over time and across a variety of situations. Such analytic methods have historically been neglected in psychopathology research (Henwood et al., 2012; Molenaar & Campbell, 2009; Rice, 2010; Rice et al., 2007, 2008, 2012) but have received new attention over the last decade. Emotion dynamics, which are dynamic patterns of emotions drawn from real-time within-subjects data, describe shifts in emotions across the day and are suggestive of real-time emotional regulation 2 (Hollenstein, 2015; Trull & Ebner-Priemer, 2014). Emotion dynamics can be thought of as “emotional trajectories” and largely focus on the fluctuation of emotions, including variability, emotional inertia (i.e., temporal dependency), and instability (i.e., the combination of variability and temporal dependency) (Trull et al., 2015). To increase understanding of the dynamics of emotional regulation among young adults with history of homelessness and the role of social and physical environments, the current dissertation uses data from the Log My Life Study, a dataset funded by the National Institute of Mental Health (1R01MH110206), which employed Geographic Ecological Momentary Assessment (GEMA) to examine HIV risk among currently homeless and formerly homeless young adults (Henwood et al., 2019a). GEMA integrates Ecological Momentary Assessment (EMA) and geographic information systems science, allowing for cross-validation and enrichment of research on place, well-being, and health (Kirchner & Shiffman, 2016). Given housing is a key structural factor in overall health (A. C. Baker et al., 2018; Bambra et al., 2010; Forchuk et al., 2016; D. K. Padgett, 2020; Singh et al., 2019), the socio- environmental contexts associated with the experience of homelessness and supportive housing are likely to differently impact how emotions unfold over time. As the sample contained both currently and formerly homeless young adults, housing status served as a primary axis of analysis. The specific aims guiding this dissertation are a) to examine whether emotion dynamics differ by housing status (Paper 1), b) to investigate the relationship between risky locations and emotional trajectories (Paper 2), and c) to identify social network characteristics associated with increased emotional regulation (Paper 3). Conceptual Framework To date there has been limited research investigating the association between social/physical environments and emotion dynamics among young adults with history of homelessness. Further, no previous work has examined these processes at a granular, momentary level. Guided by the Social Ecology Model for Health Promotion (Best et al., 2003; Stokols, 1992), this work assumes: 1) health behavior is influenced by physical environments, social environments, and personal attributes; 2) environments are multidimensional, such as social or physical, discrete attributes (spatial arrangements) 3 or constructs (social climate); 3) human-environment interactions occur at varying levels of aggregation (individuals and groups); and 4) people influence their settings, and these settings can then influence health behaviors (Glanz et al., 2015). Figure 1.1 displays the socio-ecological model that guides this proposal. Each paper presented in this work considers how individual, relational, community, and societal factors relate to and influence momentary emotions and mood. Paper 1 Paper 1 addresses the first aim of this dissertation and begins with considering the basics of emotion dynamics and examining differences by housing status through two studies. This paper does this through two studies that focus on the importance of the individual level in the Social Ecology Model, hypothesizing that young adults residing in supportive housing will have better outcomes in terms of emotional dynamics and regulatory capacities. Difficulties with emotional regulation underpin many diagnoses and are implicated in most psychological disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM), fifth edition (Gross & Jazaieri, 2014; Werner & Gross, 2010). It is true that previous studies of young adults experiencing homelessness found as many as 24% met the criteria for Post-Traumatic Stress Disorder (PTSD) (Bender et al., 2010), 53% for Conduct Disorder or Oppositional Defiant Disorder, 32% for Attention Deficit Hyperactivity Disorder (ADHD), 21% for mood disorders, 21% for mania or hypomania, and 10% for Schizophrenia (Edidin et al., 2012). Individuals who consistently experience rapidly changing emotions may exhibit impaired coping skills and may be more vulnerable to engaging in risky behavior when upset (Anestis et al., 2009). Currently, supportive housing is the primary intervention used to counter the effects of homelessness and has been advanced as a solution to homelessness among young adults (Gaetz, 2014). An array of specific housing models fall under the umbrella of supportive housing for young adults (e.g., Permanent Supportive Housing and Transitional Housing) (Semborski, Redline, et al., 2021). These models are tailored to meet the unique developmental needs of young adults and often include mental health services in programming geared toward promoting independence and self-sufficiency (Semborski et al., 2020). Consequently, successful service delivery of supportive housing with young adults has been 4 linked to improved mental health outcomes (Henwood, Redline, Semborski, et al., 2018; Kisely et al., 2008) and lower rates of inpatient admission among adults (Gilmer, 2016). This may also mean improvements in momentary affect dynamics and regulation among those living in supportive housing compared to their unhoused peers. Study 1. Affect and emotion, often used interchangeably, represent the experiential and behavioral components of emotion. Although regulation can include the increase, maintenance, or decrease of positive or negative emotions, affect regulation models of psychopathology typically propose that maladaptive behaviors function to mitigate negative emotions (Gross & Thompson, 2007; Werner & Gross, 2010). Study 1 presented in Paper 1 examines positive affect (PA) and negative affect (NA) broadly, measuring emotional inertia and instability. These constructs relate to the temporal ordering and variability of emotions. Emotional inertia refers to a temporal dependency of an emotion from one time point to the next. Heightened emotional inertia, particularly inertia of negative affect, has been linked to symptoms indicative of clinical depression and neuroticism (Trull et al., 2015). Additional connections between emotional inertia and borderline personality disorder, PTSD, and eating disorders have been identified (Koval et al., 2021). Emotional instability, on the other hand, refers to heightened variability of emotions over time. A greater range or variability may be indicative of a sensitive individual who responds with greater intensity to events; whereas lower levels may indicate systems that are less sensitive to events (Kuppens & Verduyn, 2015; Trull et al., 2015). We expect those in supportive housing will have less emotional dysregulation (i.e., lower emotional inertia and less severe instability), indicating better outcomes in affect dynamics, compared to those who are currently homeless. Study 2. Study 2 uses emotional network density metrics to examine inter-connections between specific emotions included in PA (happy, calm, excited) and NA (sad, stressed, irritated). Using a network approach, emotional network density builds on the concept of inertia by considering not only the temporal dependency of an emotion on itself, but temporal dependency of all emotions in the “network.” Increased density may reflect a tendency for emotional activation of many emotions with the activation of a single emotion. High connectivity points toward increased fragility and higher levels of psychopathology 5 (Bringmann et al., 2013, 2022; Lydon-Staley et al., 2019; Pe et al., 2015a; Shin et al., 2022a; Wichers et al., 2015; Wigman et al., 2015). Likewise, we expect those who are currently experiencing homelessness to have denser emotional networks than those residing in supportive housing, indicating more fragility in their emotional network. Paper 2 The second paper presented in this dissertation uses mixed methods to examine association between risky geographical locations and mood of currently homeless (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed). The physical locations examined exist as parts of neighborhoods in the larger city of Los Angeles, California. Neighborhoods are considered a part of the community factor of the Social Ecology Model. Areas with concentrated homelessness are often associated with higher rates of violence, crime, and drug activity. Unsheltered individuals themselves experience heightened rates of violence and victimization (Tong et al., 2021). Further, drug activity has been linked to increased rates of aggravated assault; and drug activity’s spatial relationship to violence also extends to the surrounding areas (Contreras & Hipp, 2020). Stressors, such as these, can trigger emotion responses, including mental health symptoms. In these moments there often is an important exchange happening between the person and the environment. The built environment is defined as the physical form of communities, including the human-made and other physical surroundings that provide the settings for human activity (R. S. Lazarus & Cohen, 1977). In an attempt to grasp the scope of the homelessness problem, the Department of Housing and Urban Development (HUD) requires communities that receive funding from HUD conduct a biennal Point-In- Time (PIT) count to track the size, composition, and location of the homeless population (Narendorf et al., 2016; Troisi et al., 2015). As a result of PIT Counts, we have been able to identify outdoor locations where two or more individuals are gathered without a structure, often referred to as “homeless hot spots” (Goldfischer, 2019). Hot spots are commonly used in epidemiology for their ability to describe the relative prevalence of a given disease or condition across different geographic areas (Wand & Ramjee, 2010) within the built environment. Paper 2 combines geographic data, including the United States 6 Department of Housing and Urban Development’s Point In Time count, an annual count of the prevalence of homelessness across the U.S., and crime data from the Los Angeles Police Department, with GEMA surveys to examine the relationship between one’s risk environment (i.e., concentration of risk based on hot spots) and mood. Given the reality of risk associated with homeless hot spots, it is unsurprising that unstably housed young adults may attempt to avoid these areas (SAMHSA, 2019). In fact, many young people will utilize their social network, couch surfing to avoid using adult shelters (Curry et al., 2017; Morton, Dworsky, et al., 2018a). However, how young adults navigate and respond to risk environments associated with homelessness has yet to be explored. Broadly, it is understood that the number of nights spent indoors correlates with increased health (Anderson et al., 2021) and as structural housing interventions remain the primary intervention being applied to the challenge of homelessness, including young adult homelessness (Aubry et al., 2020; Munthe-Kaas et al., 2018; Wickham, 2020), we hypothesize housing will be protective, leading to better outcomes in positive and negative affect, and improved dynamics (i.e., less erratic/variable). Paper 3 Finally, to better understand the emotional experience of young adults with a history homelessness, there is a need to examine their social context. Research has indicated that young adults who experience homelessness report diverse sources of social support including family members, peers from both home and the street (Barman-Adhikari et al., 2016; de la Haye et al., 2012; Milburn et al., 2009), and romantic partners or people whom they are sexually involved (Wenzel et al., 2012a). Social networks are not only a pathway to better understand young adults experiencing homelessness, but they also offer a highly viable platform for intervention work. Homelessness researchers have increasingly focused on the social environment of young people experiencing homelessness (Barman-Adhikari et al., 2016; Barman-Adhikari & Rice, 2014; Blair & Pukall, 2015; Gray et al., 2016; Henwood et al., 2017; Mercken et al., 2009; D. K. Padgett et al., 2008; Petering et al., 2018; Rhoades et al., 2017; Rice et al., 2011; Winetrobe et al., 2017) due to the unique value of social relationships during the period of young 7 adulthood (Anda, 2013). Social networks have been found to be a primary facilitator for engagement in risk or protective behaviors during this developmental stage (Barman-Adhikari et al., 2015; Rice, 2010; Rice et al., 2008, 2011). Therefore, social networks have been recommended for use in prevention and intervention planning (Edidin et al., 2012) and considered a practical next step in intervention work with young adults experiencing homelessness (Petering et al., 2016; Rice & Rhoades, 2013). The third paper presented in this dissertation focuses on the relational aspects of the Social Ecology Model within a specific sub-population of young people experiencing homelessness: lesbian, gay, bisexual, transgender, and/or queer (LGBTQ) youth. Among the one-in-ten young adults that experience homelessness over the course of one year (Morton, Dworsky, et al., 2018b), an estimated 20- 40% are LGBTQ despite being only 7-10% of the general population (Maccio & Ferguson, 2016; Norman-Major, 2017). LGBTQ-related health disparities, including mental health disparities, are linked to minority stressors (Meyer, 1995, 2003) which can include the trauma experienced prior to entering into homelessness related to rejection of LGBTQ identities (Castellanos, 2016; Robinson, 2021). Previous research has identified stigma as a contributing factor to being kicked out and experiencing homelessness (Bruce et al., 2014). In the face of minority stress, social support can mitigate poor mental health outcomes (Choukas- Bradley & Thoma, 2022; Goldbach et al., 2014; Hatzenbuehler, 2011; Scardera et al., 2020) while promoting positive mental health and wellness (Henderson et al., 2022). For LGBTQ young people this often includes relationships with other LGBTQ individuals (Goldbach & Gibbs, 2015). Research has shown LGBTQ individuals tend to have peer groups that include LGBTQ people and cisgender, heterosexuals (Cishet) people tend to have peer groups that include other Cishets (Grudniewicz et al., 2016; Jones et al., 2022). The idea that people form ties with individuals who are like them is referred to as homophily (McPherson et al., 2001), and research indicates that homophilous relationships are often perceived as supportive. In fact, several studies found that social support played a role in perceived stress (Haslam et al., 2005), such that a greater sense of shared identity was associated with more perceived 8 support and less perceived stress (Haslam & Reicher, 2006). However, in some cases the buffering effects of social support on stress was only seen in the context of shared social identity (Frisch et al., 2014). Using momentary data, this final study investigated the role of homophilous relationships, an example of a community-level factor in the social-ecological model, based on gender and sexual orientation in the regulation of stress. Previous studies identified relationships between gender homophily and greater support (Lee et al., 2018), shared LGBTQ identity and greater closeness and emotional intimacy (Paceley et al., 2017), and shared sexual orientation and everyday providers of social support (Frost et al., 2016). From the social ecology perspective, we are considering how individual factors moderate the relationship between relational factors and perceived stress. Given previous research, it remains unclear if shared identity provides additional regulatory benefits above and beyond that of perceived support. Therefore, this study explores if homophilous social interactions support in regulating stress (i.e., stress reduced and less variable) above and beyond the effects of perceived support due to increased intimacy associated with shared identity. Like other studies presented in this work, housing status served as a primary axis of analysis which is particularly important to consider here, given the lack of safe spaces for LGBTQ young people experiencing homelessness (Choi et al., 2015; Coolhart & Brown, 2017; Ormiston, 2022). The Social Ecology Model for Health Promotion The three papers presented in this dissertation bring together all factors outlined in the Social Ecology Model for Health Promotion, presented in Figure 1.1. Study participants held distinct characteristics, including but not limited to social identities, housing status, and emotional responses, considered in the individual level of the model. Thus, the individual level is considered throughout this work, and is the primary predictor of mood and emotional regulation presented in Paper 1. Paper 2 introduces the community level, considering risk hot spots and their relationship with emotional dynamics. Risk is measured by concentration of homelessness, violence, and drug activity and are environmental factors that impact the overall social well-being of individuals who meet these factors. Finally, Paper 3 examines the relationship between social interactions at the relational level and emotion 9 dynamics captured through stress variability. Individual factors (i.e., gender and sexual orientation) are considered in this relationship and modeled as an effect modifier. While societal factors are not measured and directly accounted for, previous research indicates societal factors, including stigma, discrimination, and stereotyping may provide insight into the relationships between the other socio-environmental factors and mood. Contribution This dissertation examines emotion dynamics using momentary data collected in real-time to understand emotional regulation at a granular level, as impacted by the real-world socio-environmental contexts associated with homelessness. Broadly, findings from this work can provide an empirical basis to guide both clinical work and policy related to mental health service delivery, including homeless services, and support in further adapting service environments to meet the complex needs of at-risk young people. Specifically, accomplishing these aims will produce evidence regarding emotional regulation capacities of both formerly and currently homeless young adults. While literature focused on young adults experiencing homelessness has increased over recent years, there remains a dearth of information on 1) skills in emotional regulation and/or emotion dynamics among this population and 2) the experiences and mental health outcomes of formerly homeless young adults after they transition to supportive housing. Study results contribute a better understanding of social networks of housed and unhoused young people, how they may differ based on housing status, and the role of specific socio-environmental factors in emotion dynamics. Taken together, this dissertation examines the social ecology of homelessness among young adults, guided by the assumption that health behavior is influenced by physical environments, social environments, and personal attributes. With a better understanding of these three facets, we will be on track for improved solutions, interventions, and ideally, health outcomes for marginalized and often forgotten young people. 10 Figure 1.1. Dissertation Conceptual Framework 11 Chapter 2. Modeling emotion dynamics of housed and unhoused young adults using Ecological Momentary Assessment (Paper 1) Highlights • Examined emotion dynamics in two samples of young adults (currently homeless and formerly homeless residing in supportive housing) using Dynamic Structural Equation Modeling. • Housed young adults were found to experience lower inertia of NA, relative to unhoused participants. • Unhoused young adults were found to have denser emotion networks, compared to their housed counterparts, particularly regarding negative affect. • This may indicate a tendency for unhoused young people to get caught in cycles of negative emotions, indicating a greater struggle to down-regulate, compared to housed peers. 12 Abstract Objective: This paper examined mental health among young adults currently experiencing homelessness (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed) through modeling emotional dynamics in two studies. Study 1 examined associations between housing status and positive affect (PA) and negative affect (NA) emotion dynamic indices, including emotional inertia and instability. Study 2 used emotional network density indices to examine how inter-relations between specific emotions included in PA (happy, calm, excited) and NA (sad, stressed, irritated) were associated with housing status. Method: Data was derived from seven days of EMA with 221 housed (n=118) and unhoused (n=103) young adults with history of homelessness in Los Angeles, CA between June 2017 and January 2019. Results: Study 1 identified increased inertia of NA among unhoused participants, relative to those in housing. In study 2 unhoused participants were found to have denser emotion networks compared to their housed counterparts, particularly regarding negative emotions. Additionally, “happy” was identified as an emotion most impacted by and impactful on other emotions for the housed group and “sad” emerged as this emotion for the unhoused group. Discussion: Given the reverberating impact negative emotions appear to have on an unhoused young adult’s entire emotional experience, interventions targeting emotional regulation may be increasingly important for young adults coping with the experience of homelessness and are discussed. 13 Introduction Approximately 10% of 18 to 25-year-olds experience at least one night of homelessness over the course of a year in the United States (Morton, Dworsky, et al., 2018b). The pathways into homelessness for young people stem from a combination of individual, familial, and systemic issues, often experienced as trauma. After entering into homelessness, previous trauma interacts with experiences associated with street life to create vulnerability to psychopathology (Davies & Allen, 2017). Cumulative trauma `exposure can lead to emotional dysregulation (Macia et al., 2020), which is implicated in most mental health disorders (Gross & Jazaieri, 2014; Werner & Gross, 2010). This is the basis for the large body of literature demonstrating a connection between trauma, mental health, and high-risk behaviors such as substance use, aggression, and risky sex; all of which strongly correlate with the experience of homelessness (Ayano et al., 2020; Davies & Allen, 2017; Heerde & Hemphill, 2016a, 2016b, 2019; Meade & Sikkema, 2005). Maladaptive behaviors associated with risk-taking often function as a means to decrease negative or distressing emotions or increase positive emotions (Gross & Thompson, 2007; Werner & Gross, 2010). Thus, emotional dysregulation is a psychological mechanism through which mental illness promotes self-destructive behaviors in homelessness (Powell & Maguire, 2018). Currently, supportive housing is the primary intervention used to counter the effects of homelessness and has been advanced as a solution to homelessness among youth and young adults (Gaetz, 2014). An array of specific housing models fall under the umbrella of supportive housing for young adults (e.g., Permanent Supportive Housing and Transitional Housing) (Semborski, Redline, et al., 2021). These models are tailored to meet the unique developmental needs of young adults and often include mental health services and programming geared toward promoting independence and self- sufficiency (Semborski et al., 2020). Consequently, successful service delivery of supportive housing with young adults has been linked to improved mental health outcomes (Henwood, Redline, Semborski, et al., 2018; Kisely et al., 2008) and lower rates of inpatient admission (Gilmer, 2016). However, not all research highlights a positive impact of supportive housing on mental health. Henwood et al. (2018) highlighted ongoing mental health concerns expressed by young people residing in 14 supportive housing related to feelings of loneliness and worry of entering back into homelessness. Additional evidence suggests that while supportive housing improves mental health initially, improvements become marginal over time and prevalence of mental illness remains high (Harris et al., 2019). This is increasingly problematic, as emotional dysregulation contributes to difficulties with remaining housed (Macia et al., 2020). Though studies have shown that homelessness experiences are related to emotion dysregulation, studies have yet to look at housing status. Emotion dysregulation is a complex construct, and most research has focused on self-report questionnaires of emotion dysregulation. However, it is important to look at novel, momentary emotion dynamic metrics. Emotion dynamics, which are dynamic patterns of emotions drawn from real-time within-subjects data, describe shifts in emotions across the day and are suggestive of emotional patterns that underpin psychopathology (Hollenstein, 2015; Trull & Ebner- Priemer, 2014). However, momentary analytic methods have historically been neglected in psychopathology research. Because emotion dysregulation is a complex construct and there have been a host of ways developed to characterize real-time emotion regulation, this study used a two-study approach to analyze differences in real-time emotion regulation by housing status (i.e., housed vs unhoused), with the first study examining PA and NA affective dynamic indices and the second study using network analysis to study inter-relations between specific affective states. Study 1 examined associations between housing status and positive affect (PA) and negative affect (NA) emotion dynamic indices, including emotional inertia and instability. These constructs relate to the temporal ordering and variability of emotions. Emotional inertia refers to a temporal dependency of an emotion from one time point to the next. Heightened emotional inertia, particularly inertia of negative affect, has been linked to symptoms indicative of clinical depression and neuroticism (Trull et al., 2015), borderline personality disorder, PTSD, and eating disorders (Koval et al., 2021). Emotional instability, on the other hand, refers to heightened variability of emotions over time. A greater range or variability may be indicative of a sensitive individual who responds with greater intensity to events; whereas lower levels may indicate 15 systems that are less sensitive to events (Kuppens & Verduyn, 2015; Trull et al., 2015). We expect those supportive housing will have less emotional dysregulation (i.e., lower emotional inertia and less severe instability), indicating better outcomes in emotion dynamics, compared to those who are currently homeless. While study 1 looks at aggregated PA and NA, study 2 uses emotional network density to examine specific emotions included in PA (happy, calm, excited) and NA (sad, stressed, irritated). Using a network approach, emotional network density builds on the concept of inertia by considering not only the temporal dependency of an emotion on itself, but temporal dependency of all emotions in the “network.” Increased density may reflect a tendency for emotional activation of many emotions with the activation of a single emotion. High connectivity points toward increased fragility and higher levels of psychopathology (Bringmann et al., 2013, 2022; Lydon-Staley et al., 2019; Pe et al., 2015a; Shin et al., 2022a; Wichers et al., 2015; Wigman et al., 2015). Likewise, we expect those who are currently experiencing homelessness to have denser emotional networks than those residing in supportive housing, indicating more fragility in their emotional network. Methodology applying to both studies is now discussed, followed by the presentation of study 1 and 2, concluding with a general discussion of findings. General Methodology Participants and procedure Both studies consisted of secondary data analysis of momentary data from seven days of EMA of young adults actively experiencing homelessness or formerly homeless residing in supportive housing. A total of 251 young adults were enrolled in the study; however, 12 opted out of the EMA portion of the study prior to the start of the EMA week. During the week, an additional eight people opted out of the study (i.e., did not complete the EMA week). Among those who did complete the study week, ten people had low compliance; meaning, they completed greater than one standard deviation below the average amount of prompts and were thus excluded, as is common in the modeling of intensive longitudinal data 16 (Edmondson et al., 2013; Granholm et al., 2008, 2020; Hartley et al., 2014; Wen et al., 2017), leaving an analytic sample of 221 participants (103 unhoused and 118 housed). Individuals were enrolled in the study between June 2017 and January 2019 in Los Angeles County and were recruited via drop-in centers or supportive housing programs using stratified convenience sampling. At drop-ins, participants were screened and considered to be unhoused if lacking a consistent living situation that they could rely on for more than 30 days. All young adults were eligible if they were between the ages of 18 and 25, or up to 27 for those in housing, provided they enrolled in the housing program prior to turning 25. Furthermore, to be eligible, individuals had to be able to read and understand English without assistance and provide written consent. Prior to initiating the week of EMA, participants completed a baseline survey that gathered demographic information and other relevant details related to housing instability and mental health. Participants received $20 for completion of the baseline survey. Following this, participants were set up for the EMA portion of the study, complete with a custom-designed mobile app to deliver momentary surveys via a smartphone. Participants had the option to use a study-provided phone, usually a third generation MotoG (Motorola, USA) smartphone with unlimited data or use their personal phone, provided it was compatible with the study app. Those who opted to use their own phones (8.6% of study participants) received $10 to offset the cost of cellular data. Throughout the EMA week, prompts were delivered approximately every two hours during waking hours, which were programmed into the app to accommodate individual schedules. Compensation for completing the study was compliance-based, incentivizing participation, with a maximum compensation of $130. Measures Affect ratings Items assessing momentary affect were constructed from the circumplex model of affect (Posner et al., 2005). In attempt to minimize reactivity to being prompted, these questions asked participants how they were feeling “just before the phone went off” and were measured on a 5-point Likert-type scale ranging from Not at all (1) to Extremely (5). Negative affect (NA) was derived from items associated with 17 negative valence: “stressed,” “sad,” and “irritated” (α = 0.88 in this sample) across 6,683 total prompts (mean = 30.2, SD = 9.9 per person). Likewise, positive affect (PA) was composed of “happy,” “calm,” and “excited” (α = 0.83 in this sample). Mental health Mental health symptomatology for depression, anxiety, and post-traumatic stress disorder (PTSD) were assessed at baseline, along with childhood trauma and skills in emotional regulation. Depression was assessed by the Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001), anxiety by the 7-item GAD- 7 that assesses symptoms of generalized anxiety disorder (Spitzer et al., 2006), and PTSD by the Primary Care PTSD Screener (Prins et al., 2004). Additional adverse childhood experiences were measured using the UCLA PTSD Reaction Index Part II which captures a trauma history profile (Steinberg et al., 2004). Finally, baseline difficulties in emotional regulation was captured by the Difficulties in Emotional Regulation Scale (DERS-18) which assesses how individuals perceive their regulatory skills (Gratz & Roemer, 2004). Housing status Housing status was determined by enrollment site and screener. Participants enrolled via supportive housing programs as residents of those programs were considered “housed.” Those enrolled from drop-in centers and emergency shelter took a short screener. Unhoused status was determined by an initial question asking, “where did you sleep last night?” This was followed up by a question inquiring about length of stay. Individuals lacking a place of stay which they could not remain at for longer than 30 days were considered unhoused and offered participation in the larger study. Analyses Both studies utilized Dynamic Structural Equation Modeling (DSEM), which is a multilevel modeling approach that combines time series, structural equation modeling, and time-varying effects modeling. DSEM is flexible to allow multiple outcome variables and latent variables, while decomposing between- and within-person variance. DSEM as it is estimated in present analyses using Mplus version 8.7 employs Bayesian estimation, allowing for a large number of random effects (Hamaker et al., 2018). 18 Considerations specific to intensive longitudinal data include unequal time intervals, sometimes a result of missing data. From a Bayesian approach missing data are treated similarly to random effects and other parameters whereby a missing value for individual i at prompt t is conditional on the neighboring values and individual i’s autoregressive parameter (Hamaker et al., 2018). Mplus allows for unevenly spaced time to be accounted for, rather than assuming observations occur at equal time intervals, through the TINTERVAL option. In present analyses, the time between each prompt was rounded to the nearest integer such that the time variable represented the number of hours participants were in the study (range=39-143; housed x=127.8; unhoused x=125.1). It is recommended that a 1 to be specified using TINTERVAL for model parsimony (Asparouhov & Muthén, 2020); however, an interval of 2 was chosen for present analyses as prompting occurred approximately every two hours during waking hours, further increasing parsimony. Prior to analysis, data stationarity was assessed as modeling techniques assume stationarity (i.e., the means, variance, and autocorrelations of the outcome do not change systematically over time). Failure to account for linear trends in longitudinal data can artificially inflate correlations between two longitudinally measured variables (Falkenström et al., 2017). Stationarity was assessed with a simple linear regression of time as the only predictor of PA and NA. Slight trends were detected for both PA and NA, as well as for every individual affect variable (i.e., happy, calm, excited, stressed, sad, irritated) independently. Prior to analyses data were preprocessed by applying linear detrending (McNeish & Hamaker, 2019). Study 1 Study 1 hypothesized that those supportive housing will have improved emotion dynamics (i.e., lower emotional inertia and less severe instability), compared to those who are currently homeless. Analyses Study 1 included an autoregressive (AR) model with a lag of one [AR(1)] from the DSEM framework to assess emotional inertia and instability. The AR(1) model was set to 10,000 iterations and regressed a person’s emotion at prompt t onto that same emotion at the previous prompt (t-1). This was 19 completed for both PA and NA, controlling for baseline differences by housing status and mental health measures. Emotional inertia The AR slope (φ) represents the degree to which emotions persist over time (i.e., inertia) and was allowed to be person specific (i.e., random). As with autocorrelation, more positive values of inertia indicate greater persistence or carryover of emotion from one occasion to the next (Koval et al., 2021). However, if the AR slope is close to zero, the value is not as dependent on its previous value. With less inertia, emotions are likely to return to baseline relatively quickly (Koval et al., 2021; Kuppens et al., 2010; Schuurman et al., 2016). The autoregressive slope of PA and NA was then regressed on housing status, a time-invariant covariate. Emotional instability Also captured in the AR(1) model is the residual variance, sometimes referred to as innovation variance, which captures deviations from an individual’s current emotion independent from their emotion at the previous event. These innovations measure differences in sensitivity or exposure to factors not explicitly modeled. The within-person innovation variance was also allowed to be person specific, and the model included covariates for both the mean structure and the residual variance. Because variance cannot be negative, the log of the variance is presented, as is common (McNeish, 2021; Sperry et al., 2020; Sperry & Kwapil, 2022); and thus must be exponentiated to obtain the true variance. Like inertia, DSEM estimated innovation variance as latent such that it was able to then become a between-level outcome, examined by housing status. Results Descriptive statistics of the analytic sample are displayed in Table 2.1. Housed participants were slightly older (p=.01), included more men (p=.02), and young adults with post high school education (p=.009). Unhoused young adults were disproportionately Black (p<.001) and more had been arrested as adults (p<.001). Anxiety, depression, and PTSD symptoms did not differ by housing status; nor did average trauma scores derived from the UCLA Trauma Index or baseline emotional regulation skills via 20 the Difficulty in Emotional Regulation Scale (DERS-18). Finally, regarding the EMA week, more unhoused participants opted to use their personal smartphone device (p=.01). Housing did not meet the significance threshold of 0.05 for PA inertia or instability (both PA and NA); however, housed participants had lower NA inertia over the EMA week (γ=-0.12). See Table 2.2 for estimates and 95% credible intervals for the AR(1) model with housing status as a time-invariant predictor. As for the relationship between mental health items and emotion dynamics, the relationships between anxiety, depression, and PTSD symptoms and emotion dynamics were null. However, a higher trauma index was associated with lower PA inertia (γ=-0.28) and higher NA instability (ω=0.14). Likewise, those who perceived emotional regulation to be more difficult (i.e., had higher DERS score) experienced decreased PA inertia (γ=-0.22). Correlations of mean affect over the week, instability, and inertia are displayed in Table 2.3. Higher PA over the week was associated with increased instability of PA (r=0.24, p<0.001) and higher NA was associated with increased PA instability (r=0.21, p=0.002), NA instability (r=0.72, p<0.001), NA inertia (r=0.65, p<0.001), but lower PA inertia (r=-0.20, p=0.003). PA inertia also had an inverse correlation with PA instability (r=-0.28, p<L0.001) and NA instability (r=-0.33, p<0.001); as did PA instability with NA inertia (r=-0.21, p=0.002). Positive correlations were identified between NA instability and PA instability (r=0.54, p<0.001). Discussion Partial support for our hypothesis was found regarding NA inertia, such that those who were housed experienced lower NA inertia. This indicates NA at one time point had a larger influence on NA at the next time point for those who were unhoused, compared to those who were housed, suggesting that NA was less resistant to change and likely persisted longer for those who were unhoused. This could mean that when unhoused young people in the study experienced NA they were less sensitive to environmental cues (Koval et al., 2016, 2021; Kuppens et al., 2010), compared to their housed peers, and may experience greater difficulty in regulating NA. 21 Beyond NA inertia, differences in emotion dynamics by housing status were not founded. It is noteworthy that the DERS-18 was also not associated with emotion dynamics, apart from PA inertia where greater perceived difficulty with emotional regulation was associated with less carryover of PA from one time point to the next. Although those who were less inert in PA did not necessarily rate their PA higher over the week, the fact that less PA carryover was associated with greater perceived difficulty in emotional regulation is understandable. Aside from this finding, the null relationships between the DERS and other emotion dynamics in the present study, particularly regarding instability, are unexpected, as previous research found predictive validity of the DERS, such that greater perceived difficulty with emotional regulation was associated with increased emotional instability captured using the mean squared successive difference (MSSD) (Semborski, Henwood, et al., 2021). Partial null findings regarding housing are supported by the lack of mental health differences by housing status. Emotion dynamics have previously shown specificity for anxiety and depression (Bosley et al., 2019), which did not differ by housing status in this sample. However, symptoms of anxiety, depression, and PTSD were also not associated with emotion dynamics, which stands in contradiction to previous literature that portrays inertia and instability as features of these diagnoses (Koval et al., 2021; Trull et al., 2015). Despite this, increased childhood aversity (i.e., experiencing a greater number of childhood traumas) per the UCLA trauma index, was associated with lower PA inertia, but higher NA instability. Childhood trauma is a key risk factor for psychopathology (Danese & Baldwin, 2017) and previous research has connected childhood trauma with later mental illness featuring emotional instability or lability (Aas et al., 2014; Marwaha et al., 2016). The negative relationship between PA inertia and childhood trauma suggests those who have experienced a greater number of trauma events in their life also experienced less carryover of PA from one time point to the next during the EMA week. DiPierro et al. (2018) identified intrusive NA and concurrent deficits in PA as features of traumatic response and noted that individuals with increased trauma exposure tend to draw their attention to NA in otherwise positive contexts. Though this aligns with present findings, these results also seem to contradict previous 22 literature that has established a positive correlation between PA inertia and mental health symptoms, much like NA inertia (Kuppens et al., 2012). However, literature has also established that NA inertia is almost twice as strongly correlated with well-being as the inertia of PA (Houben et al., 2015; Koval et al., 2021), and therefore NA inertia may be better suited to measure adverse mental health outcomes. Across the full sample, those with greater instability of one valence also experienced increased instability of the other valence, while inertia of PA was not associated with NA inertia. Further, negative relationships between inertia and instability were identified, in line with previous findings (R. J. Thompson et al., 2012); though it is not impossible to be highly inert and also highly instable. Just as consideration of average affect levels is important, so too is the role of within-person variance (i.e., standard deviation) in the interpretation of affect dynamics (Koval et al., 2021). Previous research also indicates that the effects of emotion dynamics may be negated by mean levels (Brose et al., 2020; Dejonckheere et al., 2019; Kalokerinos et al., 2020). Present analyses briefly touched on this and identified higher mean NA was associated with increased instability of NA for both housed and unhoused subgroups and instability of PA for the unhoused sample, possibly an artifact of the common floor effect of assessing NA (Leckie, 2014). Higher mean NA was also found to have a positive correlation with NA inertia, regardless of housing status. Average PA was only associated with increased instability of PA among those who were housed, indicating those who are happier on average also experience larger fluctuations or emotion swing. Thus, future efforts to examine emotion dynamics ought to consider the implications of average affect levels in assessing emotion dynamics (Koval et al., 2021) Study 2 Study 2 hypothesized that those who are currently experiencing homelessness will have denser emotional networks than those residing in supportive housing, indicating greater connectivity among individual emotions which makes regulation more difficult. Analyses Emotion network density considers how strongly emotions impact each other. Thus, density indicates the strength of emotional ties. Using a multilevel vector autoregressive model [VAR(1)] the 23 density of individuals’ emotional networks (overall, positive, and negative) were estimated. Here, the average temporal connection strengths were assessed following the Bringmann et al. (2013) framework but as a multivariate model that a Dynamic Structural Equation Modeling (DSEM) framework easily accommodates (McNeish & Hamaker, 2019). This required a series of autoregressive and cross-lagged models with each emotion that composes positive (i.e., happy, calm, excited) and negative (i.e., sad, stressed, irritated) affect at prompt t being predicted by all other emotions (including itself) at prompt t-1. This process was completed for each subgroup and then for everyone to obtain both the group and individual networks. Group-level weighted edges sensitive to directionality were comprised of the 36 slope estimates. Daily standardized coefficients of person-specific slopes were retained to generate centrality measures for each participant. However, because the interest is the strength and not necessarily directionality, absolute values were used in the calculation of centrality measures. Thus, a higher coefficient represents a stronger connection between emotions Present analyses focus only degree and strength, as other centrality measures are not recommended for psychological network analysis (Bringmann et al., 2019). Degree indicates the proportion of edges a node (i.e., emotion) shares with other nodes. Strength not only considers the presence of the edge, but the weight (i.e., slope) and is referred to as “density” in emotion network literature. Results Figure 2.1 depicts average patterns between emotions for the full sample, housed, and unhoused subsamples. Weighted edges provided in Table 2.4 were then used to calculate centrality measures, shown in Table 2.5. Network patterns in the housed subsample indicated that “irritated” had the most connections to other emotions in the network, regardless of directionality. However, “happy” emerged as the emotion with the strongest overall connection to other affect measures for those residing in housing when considering edge weights. For the unhoused group, both “happy” and “sad” had equally the most ties to other nodes, but the ties of “sad” were found to be stronger. The extent to which emotions are impacted by other emotions is captured by in-degree and in- strength. For those residing in supportive housing, “irritated” was found to have the highest degree (i.e., 24 most in-ties), but when considering strength of ties, “excited” was identified as the node most impacted by other emotions with an in-strength of 0.26, slightly higher than the in-strength of “irritated” at 0.24. For the unhoused subgroup, “happy” and “excited” had the same number of in-connections (0.50), but in- connections for “happy” were stronger, with an in-strength of 0.25. Conversely, out-degree and out-strength indicate the amount of impact an emotion has on other emotions. For the housed sample, “happy” was found to have the greatest impact on surrounding emotions with an out-degree of 0.50 and out-strength of 0.30. The node with the largest out-degree among the unhoused sample was “sad” with an out-degrees of 0.83 and an out-strength of 0.50. To further test the second hypothesis, logistic regression was used to predict the likelihood of being housed. Table 2.6 displays unadjusted and adjusted odd ratios, considering baseline differences in the two groups. Due to collinearity, in- and out-strengths were examined separately. After controlling for age, race, gender, being arrested as an adult, and post high school education, a one-unit increase in in- strength of NA was associated with a 96% decrease in odds of being housed (aOR=0.04, p<0.001). Similarly, out-strength of NA was also associated with significantly lower odds of being housed (aOR=0.03, p<0.001). Likewise, PA in-strength was associated was associated with a significant increase in the odds of being housed (aOR=3.74, p=0.04), as was PA out-strength (aOR=6.61, p=0.01). However, overall network density (i.e., considering both PA and NA) remained a significant predictor of housing status after controlling for group differences, such that a one-unit increase in emotion network density was associated with a 65% decrease in the odds of being housed (aOR=0.35, p<0.001). Discussion The second hypothesis expected differences in network density by housing status. Here, unhoused participants were found to have denser emotion networks compared to their housed counterparts, particularly regarding negative emotions. Controlling for baseline differences by housing status, greater overall density and greater NA density predicted lower odds of being housed, while greater PA density predicted higher odds of being housed. This may indicate increased emotional vulnerability for young adults actively experiencing homelessness. Previous research that indicated denser emotion networks, 25 particularly density of negative emotions, to be associated with neuroticism (Bringmann et al., 2016), major depressive disorder (Pe et al., 2015b), anxiety disorders (Shin et al., 2022b), and depression specifically among adolescents (Lydon-Staley et al., 2019). However, the present study contradicts this literature in that there was no significant difference in anxiety, depression, or PTSD symptoms by housing status. Despite this contradiction, results highlight differing emotional experiences among young adults by housing status, such that it may be easier for housed young adults to regulate and “feel better” compared to unhoused young adults who may get stuck in cycles of negative emotion. When examining individual emotions among participants in the present study, “sad” was found to have the largest impact on other emotions in the network for the unhoused subsample, while “happy” had the largest impact for housed participants. This aligns with findings that suggest greater PA density was associated with higher odds of being housed, whereas greater NA density was associated with lower odds of being housed. Bringmann and colleagues (2016) identified emotional inertia or spillover of the emotion “sad” to be higher among individuals with greater neuroticism and negative emotions to co-occur more across time. This may indicate that when negative emotions arise, housed young adults may be able to regulate and “feel better” more quickly. Though there were no statistically significant differences in weekly averages of affect (see Table 1), the unhoused group consistently had higher average affect, regardless of valence. Despite similar intensity of affect (i.e., mean), the impact of perceived emotions was greater in emotion networks of unhoused young people such that it seems likely that the activation of a single node (i.e., emotion) could trigger the activation of other nodes. This may indicate a tendency for unhoused young people to get caught in cycles of negative emotions, indicating a greater struggle to down-regulate, compared to housed peers. This is problematic, as previous work show skills in emotional regulation to be protective against substance use (Wong et al., 2013b), violence (Petering et al., 2018), suicidality (Barr et al., 2017), and other maladaptive behaviors (Maguire et al., 2017) among young adults experiencing homelessness. This speaks to the importance of interventions such as mindfulness which have proven efficient in facilitating emotional regulation (Desrosiers et al., 2013; Teper et al., 2013; Trosper et al., 2009) with an unique 26 impact on dynamics of negative emotions (Keng et al., 2021). In fact, previous research indicates mindfulness-based interventions have successfully improved outcomes for young adults experiencing homelessness (Bender et al., 2015; Brown & Bender, 2018; Chavez et al., 2020) and goes further to demonstrate the benefits of peer-led mindfulness interventions (Barr et al., 2022; Petering et al., 2021). Aside from mindfulness, Acceptance and Commitment Therapy (ACT), Cognitive Behavioral Therapy (CBT), and Dialectical Behavioral Therapy (DBT) are common therapeutic approaches employed to improve skills in emotional regulation. Aspects of these modalities may prove beneficial when working with this population of young adults, as well as group therapy focused on emotional regulation (Moore et al., 2022). Further, life skills development is often employed with young people who have faced a disproportionate amount of adversity, including in many supportive housing programs (Semborski, Redline, et al., 2021). Life skills training focuses on self-management and has potential as an emotional regulation intervention (Zawadzka, 2019). Present findings reinforce the need for an increased focus on emotional regulation which could include any of these or a combination of these interventions. General Discussion The present study examined affect dynamics and emotion network density in housed and unhoused young adults with histories of homelessness. Results from Study 1 showed increased NA inertia among unhoused young adults, relative to housed young adults in this sample, in partial support of the first hypotheses. Further analyses of emotion network density, presented in Study 2, identified differences in the structure of emotional networks between groups, supporting the second hypothesis. Specifically, Study 2 identified denser emotion networks among the unhoused group, suggesting increased emotional vulnerability to psychopathology, relative to the housed group. Network density was largely driven by negative emotions for unhoused individuals, aligning with increased inertia of NA in the unhoused group identified in Study 1. Overall, we see the central role NA plays in the emotional experience of unhoused young adults. Both studies suggest that NA at one time point has a larger effect on emotions at the following time point for those who were unhoused, compared to those who were housed. This both may make it more difficult to regulate negative emotions for this group, but also makes 27 them more vulnerable to adverse mental health outcomes, as NA is a prominent factor in both internalizing and externalizing psychopathology (Stanton & Watson, 2014). Although findings cannot speak to causation, they potentially highlight a more positive emotional experience for those residing in supportive housing, compared to their unhoused counterparts. While findings did not identify improved outcomes in emotion dynamics among those who transitioned from homelessness into housing, “happy” was identified as an emotion most impacted by other emotions for the housed group and “sad” emerged as this emotion for the unhoused group. Further, feeling sad appeared to impact the overall emotional experience of unhoused young adults above and beyond that of feeling happy for their housed peers. This may speak to the difficulty of coping with the complex and chaotic environments associated with homelessness (Srinivasan, 2021) which are often traumatic (Gilmoor et al., 2020; Pope et al., 2020; Tsai et al., 2020). EMA design considerations Given these findings, it is important to consider how decision-making regarding study design and analysis may impact interpretation. Previously mentioned was the 7-day study duration. Other considerations regarding time scale focus on prompting scheme. The present study utilized a prompting scheme that delivered prompts approximately every two hours during waking hours. Recent meta-analytic work indicated time scale does not significantly moderate observed relationships with psychological well- being; however, this work focused largely on affect across hours and days, rather than minutes or seconds (Houben et al., 2015). A study with a wider prompting window may be more likely to miss momentary shifts in affect, but a prompting scheme of greater intensity increases participant burden (G. Lazarus et al., 2021). Time scale concerns may also be applied to the analyses of emotional lags, as different processes may unfold at different time scales and thus require differing lag lengths to be captured. The length of the lag is likely to influence the magnitude and shape of lagged connections and time-dependent dynamics (Adolf et al., 2021; Dormann & Griffin, 2015). When the same lag length is applied to all participants within a study, individual differences in psychological and affective trajectories are likely disregarded to 28 some degree (G. Lazarus et al., 2021). Individual, n=1 models for intensive longitudinal data which are usually applied before extending the model to a multilevel structure where measurement occasions are nested within people (McNeish & Hamaker, 2019) may be helpful in identifying individual lag lengths. Limitations Many limitations have been discussed in tandem with interpretation, however, several remain. Bringmann et al. (2016) pointed out the potential for spurious relationships. Six emotion variables were used in present analyses, and it is likely that these six emotions are insufficient to capture one’s full range of momentary emotional states and it may be necessary to include additional emotions to get a more complete snapshot of the complexity of emotions. Finally, it must be noted that present analyses compare across two groups and cannot speak to causation regarding the impact of housing on emotional wellness and mental health. Conclusion The present study expands on existing literature by examining emotion dynamics among a lesser studied group of young adults with histories of homelessness. Emotional regulation as a focus of intervention with this population has been growing in popularity, previously without inquiry into underlying emotion dynamics. Current analyses utilized two established measures of emotion dynamics (e.g., instability and inertia) and emotion network density, also growing in popularity. Housing status served as a primary axis of analysis and indicated differences in how emotions are experienced. Given the lack of significant differences regarding mental health, including symptomatology, among the housed and unhoused groups, it is reasonable to construe findings considering the socio-environmental differences between active homelessness and the supportive housing environments. Most importantly, given the reverberating impact negative emotions appear to have on an unhoused young adult’s entire emotional experience, incorporating interventions to target emotional regulation in service planning may be especially important for young adults coping with the experience of homelessness. 29 Table 2.1. Demographic characteristics. n / x % / SD n / x % / SD p Age 21.8 2.06 22.6 2.45 0.01 Gender Man 63 61.8 54 46.2 0.02 Woman 31 30.4 44 37.6 0.26 Trans*, non-binary, gender non- conforming, or expansive 8 7.8 19 16.2 0.06 Sexual Orientation 0.60 Heterosexual 56 54.4 62 52.5 Gay/Lesbian 16 15.5 21 17.8 Bi/Pan-sexual 24 23.3 22 18.6 Something else 7 6.8 13 11 Sexual Minority 47 45.6 56 47.5 0.79 Race/Ethnicity Black 49 47.6 29 24.6 <0.001 Latino 17 16.5 31 26.3 0.08 Bi/Multi-racial or ethnic 22 21.4 34 28.8 0.20 White 9 8.7 11 9.3 0.88 Something else 6 5.8 13 11 0.17 Ever in foster care 47 45.6 59 50 0.52 Juvenile justice involved 34 33 43 36.4 0.59 Arrested as an adult 52 50.5 28 23.7 <0.001 Ever gang involved 16 15.7 16 13.7 0.67 Education completed Less than high school 31 30.1 30 25.4 0.44 High school or GED 60 58.3 58 49.2 0.18 Post high school 12 11.7 30 25.4 0.009 Age of first homelessness 17.7 3.3 17.4 3.9 0.53 Lifetime homelessness 0.50 Less than one year 43 41.8 44 37.3 One year or more 60 58.3 74 62.7 Mental Health Depression (PHQ-9) score 8.85 7.05 9.23 6.86 0.69 moderate/severe indicator 43 41.8 52 44.4 0.69 Anxiety (GAD-7) score 7.70 6.23 8.49 6.33 0.35 moderate/severe indicator 40 38.9 52 44.4 0.40 PTSD score 1.86 1.53 2.08 1.60 0.32 PTSD indicator 38 36.9 50 42.7 0.37 UCLA trauma index 5.57 3.21 5.62 3.47 0.90 DERS score 38.90 15.89 37.70 14.51 0.57 EMA week Used study phone 14 13.6 5 4.2 0.01 Had a typical week 70 68 83 70.3 0.33 Positive week mean 2.78 1.00 2.54 0.94 0.07 Happy week mean 3.06 1.08 2.77 1.05 0.05 Calm week mean 2.86 0.99 2.65 0.94 0.11 Excited week mean 2.43 1.11 2.21 1.01 0.14 Negative week mean 1.90 0.84 1.77 0.71 0.25 Sad week mean 1.83 0.87 1.71 0.75 0.26 Stressed week mean 1.97 0.82 1.87 0.76 0.35 Irritated week mean 1.88 0.09 1.74 0.06 0.18 Unhoused (n=103) Housed (n=118) 30 Table 2.2. Estimates and 95% credible intervals for multilevel location-scale model with housing status. Effect Notation Posterior Median 95% Credible Interval Positive Inertia (Phi PA ) housed γ 11 -0.001 [-0.15, 0.14] moderate/severe depression (PHQ- 9) γ 12 0.08 [-0.12, 0.29] moderate/severe anxiety (GAD-7) γ 13 0.15 [-0.12, 0.37] PTSD indicator (PC Screener) γ 14 -0.07 [-0.30, 0.19] UCLA trauma score γ 15 -0.28 [-0.46, -0.08] * DERS-18 score γ 16 -0.22 [-0.42, -0.03] * Negative Inertia (Phi NA ) housed γ 21 -0.12 [-0.24, -0.01] * moderate/severe depression (PHQ- 9) γ 22 -0.04 [-0.24,0.18] moderate/severe anxiety (GAD-7) γ 23 0.21 [-0.02, 0.43] PTSD indicator (PC Screener) γ 24 -0.11 [-0.33, 0.12] UCLA trauma score γ 25 -0.02 [-0.19, 0.18] DERS-18 score γ 26 0.01 [-0.18, 0.21] Positive Instability (LogV PA ) housed ω 11 -0.03 [-0.13, 0.07] moderate/severe depression (PHQ- 9) ω 12 -0.07 [-0.21, 0.10] moderate/severe anxiety (GAD-7) ω 13 -0.06 [-0.25, 0.09] PTSD indicator (PC Screener) ω 14 0.001 [-0.14, 0.17] UCLA trauma score ω 15 0.06 [-0.09, 0.22] DERS-18 score ω 16 0.13 [-0.03, 0.27] Negative Instability (LogV NA ) housed ω 21 -0.03 [-0.12, 0.05] moderate/severe depression (PHQ- 9) ω 22 0.02 [-0.14, 0.17] moderate/severe anxiety (GAD-7) ω 23 0.11 [-0.05, 0.29] PTSD indicator (PC Screener) ω 24 0.04 [-0.12, 0.17] UCLA trauma score ω 25 0.14 [0.01, 0.27] * DERS-18 score ω 26 0.04 [-0.08, 0.17] Note: Model controlled for differences by housing status (age, race, gender, arrests, and post high school education) 31 Table 2.3. Correlation matrix of emotion dynamics. Table 2.4. Group-level weighted edges in housed and unhoused networks. Happy Calm Excited Sad Stressed Irritated Housed Happy 0.12 -- 0.1 -- -- -- Calm 0.09 -- -- -- -- -- Excited 0.09 -- 0.12 -0.05 -- -- Sad -- -- -- 0.1 -- -- Stressed -- -- -- -- 0.08 0.09 Irritated -- 0.04 -- 0.05 0.06 0.09 Unhoused Happy 0.1 -- 0.08 -0.09 -- -- Calm 0.08 -- -- -- -- -- Excited 0.09 -- 0.07 -0.04 -- -- Sad -0.08 -- -- 0.15 -- -- Stressed -- -- -- 0.13 0.08 -- Irritated -- -- -- 0.11 0.06 -- Note: values correspond to weighted edges in Figure 1. 1 2 3 4 5 6 1. Positive mean 1.00 2. Negative mean -0.004 0.95 1.00 3. Positive inertia 0.02 0.74 -0.20 H 0.003 1.00 4. Positive instability 0.24 H <0.001 0.21 UH 0.002 -0.28 UH <0.001 1.00 5. Negative inertia -0.06 0.40 0.65 <0.001 0.02 0.80 -0.21 H 0.002 1.00 6. Negative instability -0.07 0.031 0.72 <0.001 -0.33 <0.001 0.54 <0.001 0.17 0.08 1.00 Note: H = significant for only housed group; UH = significant only for unhoused group. 32 Table 2.5. Node centrality measures across the EMA week, by group. Happy Calm Excited Sad Stressed Irritated Housed Degree 0.36 0.18 0.36 0.27 0.27 0.46 Strength (i.e., Density) 0.40 0.13 0.36 0.20 0.23 0.33 In-degree 0.33 0.17 0.33 0.17 0.33 0.67 In-strength 0.22 0.09 0.26 0.10 0.17 0.24 Out-degree 0.50 0.17 0.33 0.50 0.33 0.33 Out-strength 0.30 0.04 0.22 0.20 0.14 0.18 Unhoused Degree 0.55 0.09 0.36 0.55 0.27 0.18 Strength (i.e., Density) 0.53 0.08 0.28 0.58 0.27 0.17 In-degree 0.50 0.17 0.50 0.33 0.33 0.33 In-strength 0.25 0.08 0.20 0.23 0.21 0.17 Out-degree 0.67 0.00 0.33 0.83 0.33 0.00 Out-strength 0.35 0.00 0.15 0.50 0.14 0.00 Note: degree is the proportion of edges; strength is the sum of the absolute values of weighted edges, corresponding with Table 4 and Figure 1; bold values indicate highest degree and strength per group. 33 Table 2.6. Logistic regression analyses predicting housed status. unadj. OR (se) adj. OR (se) unadj. OR (se) adj. OR (se) unadj. OR (se) adj. OR (se) Overall density 0.37 (0.10)*** 0.35 (0.11)*** Positive in-strength 3.94 (2.21)* 3.74 (2.45)* Negative in-strength 0.05 (0.03)*** 0.04 (0.02)*** Positive out-strength 8.86 (5.82)*** 6.61 (4.83)** Negative out-strength 0.03 (0.02)*** 0.03 (0.02)*** Demographics Age 1.28 (0.10)** 1.23 (0.10)** 1.25 (0.10)** Race (black) 0.29 (0.11)*** 0.29 (0.12)** 0.27 (0.11)*** Gender (man) 0.68 (0.22) 0.67 (0.23) 0.70 (0.24) Arrested as an adult 0.28 (0.10)*** 0.26 (0.10)*** 0.29 (0.12)** Post high school education 1.91 (0.87) 2.07 (1.00) 1.96 (0.96) Mental Health Moderate/Severe Depression 0.85 (0.35) 0.82 (0.37) 0.70 (0.32) Moderate/Severe Anxiety 1.30 (0.58) 1.44 (0.68) 1.63 (0.78) PTSD indicator 1.25 (0.48) 1.15 (0.46) 1.17 (0.48) UCLA trauma index 0.97 (0.05) 1.01 (0.06) 1.03 (0.06) DERS score 0.99 (0.01) 1.00 (0.01) 1.00 (0.01) Note: *p≤0.05, **p≤0.01, ***p≤0.001 Overall Density In-strength Out-strength 34 34 Figure 2.1. Emotion networks of housed and unhoused groups This figure shows the emotion networks of the housed subsample (left), and unhoused subsample (right). Green edges indicate positive relationships, while red indicates negative relationships. Only edges that surpassed the significance threshold are shown (i.e., p-values less than 0.05). Dashed edges represent slope values 1 SD below the edge mean (weak connections), bold solid lines represent slope values 1 SD above the mean (strong connections), and the remaining lighter, solid lines correspond to slope values around the mean (values between 1 SD below and above the mean; moderate connections). 35 35 Chapter 3. Navigating risk environments: The relationship between housing and mood for young adults experiencing homelessness (Paper 2) Highlights • This mixed methods study utilized Geographic Ecological Momentary Assessment and qualitative interviews to examine the relationship between risky geographical locations and mood of currently homeless (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed). • Qualitative results focused on the impact of locations with increased drug activity and violence for both housed and unhoused participants, but housed participants explicitly discussed homelessness in reference to these risk factors. • Areas of concentrated risk were associated with increased emotional erraticism among unhoused participants, but the inverse or no relationship was identified for the housed group. • Results point to the protective nature of housing and highlight how obtaining housing may be helpful in mental health recovery among people experiencing homelessness. 36 36 Abstract Objective: This mixed methods study examined the relationships between risky geographical locations and mood of currently homeless (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed). Housing status was examined as an effect modifier in the relationship between risk environments and mood. Method: Data come from a larger mixed-methods, Geographic Ecological Momentary Assessment study investigating HIV risk behaviors in a sample of young adults with history of homelessness. Study participants included individuals currently experiencing homelessness (i.e., unhoused; n=72) and formerly homeless individuals recruited from supportive housing programs (i.e., housed; n=87) in Los Angeles, California between June 2017 and January 2019. First, Getis-Ord Gi* Hot Spot Analysis was used to measure and construct variables indicating the concentration of homelessness, violent crime, and drug activity (i.e., risk hot spots). These variables were then used in the mixed-effects location scale models, followed by qualitative analysis whereby semi- structured interviews were used to explain and expand upon the quantitative findings. Results: Overall, findings suggested areas of concentrated risk were associated with increased emotional erraticism among unhoused participants, but the inverse or no relationship was identified for the housed group. Qualitative results focused on the impact of locations with increased drug activity and violence. Mixed methods findings were expansive in that the qualitative results in many cases confirmed and often expanded upon quantitative findings. Discussion: Results point to the protective nature of housing and highlight how obtaining housing may be helpful in mental health recovery among people experiencing homelessness. 37 37 Introduction The most recent estimate of the prevalence of homelessness in the United States suggests that over 580,000 people were experiencing homelessness on the streets and in shelters in January 2020 (NAEH, 2022). The nation has a system of temporary, emergency shelters that can reach approximately 354,000 people on a given night. This means that there remains a large population of people experiencing unsheltered homelessness that sleep in locations not meant for human habitation (e.g., sidewalks, public transit, parks, vehicles, abandoned structures, etc.) (NAEH, 2022). Of those who experience unsheltered homelessness, most are single adults (i.e., not experiencing homelessness as members of a household with children) or unaccompanied children or young adults (i.e., under the age of 25 and experiencing homelessness without a family member) (Batko et al., 2020b). Research indicates that homelessness among young adults is growing, and now accounts for 30% of all homeless populations (SAMHSA, 2020) and prevalence rates suggest that one-in-ten young adults aged 18 to 25 experience homelessness over the course of one year (Morton, Dworsky, et al., 2018b). Youth and young adult homelessness is particularly damaging as it interferes with their developmental needs and there could be life-long implications of experiencing homelessness at a young age (NAEH, 2022). Historically, it has been challenging to understand the prevalence of youth and young adult homelessness due to a diversity of sleeping arrangements, a major distinction between young adult and older adult experiences of homelessness. In addition to street-based locations, common sleeping arrangements among young adults who are unstably housed also include doubling up, couch surfing, and the use of motels (Curry et al., 2017; Morton, Dworsky, et al., 2018b). Many young people may prefer doubling up rather than utilizing public or service spaces located in areas of concentrated homelessness, often referred to as “homeless hot spots,” because they contain risks associated with homelessness such as drugs and violence (SAMHSA, 2019). Areas with concentrated homelessness are often associated with higher rates of violence and crime. Unsheltered individuals themselves experience heightened rates of violence, victimization, and other traumatic events. Persistent homelessness, in particular, is associated with higher odds of 38 38 victimization (Tong et al., 2021). Specifically, unsheltered individuals are more likely to experience distinct categories of common crime, including assault, robbery, and theft (Ellsworth, 2019). In fact, changes in visible homelessness have effects on local crime. One study found that visible homelessness was associated with increases in misdemeanor assault within 100 feet of the location of unsheltered individuals and violent crime (i.e., robbery, felony assault, rape, murder, and manslaughter) increased in nearby areas (Corinth & Finely, 2018). Another study identified prior drug arrests, homeless shelters, and bus stops as the three largest factors predicting homeless related crime, including both victimization and perpetration (Yoo & Wheeler, 2019). Many individuals experiencing homelessness often participate in risk behaviors as means of survival and coping in dangerous environments. Some studies suggest that people enduring unsheltered homelessness are more likely than those in shelter to use drugs, including intravenous drugs. However, people experiencing homelessness who received treatment for substance use were also more likely to be sheltered (Batko et al., 2020b). Overall, drug overdose is responsible for one out of four deaths among people experiencing homelessness (Fine et al., 2022); and in some areas it is the leading cause of death (Doran et al., 2016). In examining the physical locations of drug dealing, homeless shelters were identified as common drug dealing locations, specifically for opioid transactions; and authors identified a three block influence of shelters on drug activity (Barnum et al., 2017). Likewise, drug activity has been linked to increased rates of aggravated assault; and drug activity’s spatial relationship to violence also extends to the surrounding areas (Contreras & Hipp, 2020). Given the reality of risk associated with homeless hot spots and homelessness in general, it is unsurprising that unstably housed young adults may attempt to avoid these areas (SAMHSA, 2019; Curry et al., 2017; Hail-Jares, 2023; Perez & Romo, 2011; Petry et al., 2022). However, how young adults navigate and respond to risk environments associated with homelessness has yet to be explored. Broadly, it is understood that the number of nights spent indoors correlates with increased health (Anderson et al., 2021). Thus, structural housing interventions, often referred to as supportive housing, remain the primary intervention being applied to the challenge of homelessness, including young adult homelessness (Aubry 39 39 et al., 2020; Munthe-Kaas et al., 2018; Wickham, 2020). A qualitative inquiry into the experience of supportive housing among young adult residents found housing generally supported self-identified improved mental health (Henwood, Redline, Semborski, et al., 2018), but the degree to which supportive housing impacts how young adults navigate and experience environmental risks is not known. The present study To better understand this, the present mixed-methods study examines the physical locations and mood of currently homeless (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed). Mental health, often measured by mood and emotional dysregulation (Gross & Jazaieri, 2014; Werner & Gross, 2010), can be greatly impacted by one’s environment and one’s mood can lead to events that alter their environment. Affect regulation theory suggests mood is an iterative process of attending to, appraising, and responding to the environment by which individuals identify their emotional experience and then decide what specific actions to take to attend to their emotional state (Gross et al., 2019). The availability of strategies to manage emotions is somewhat dependent on available resources, which likely differs greatly by housing status. Given the protective nature of housing, it is expected that young adults residing in supportive housing in this study will spend less time in areas of concentrated risk (i.e., homeless, drug, and violence hot spots) compared to those who are unhoused. Additionally, because unhoused individuals are constantly exposed to environmental elements, it is also expected that the emotional impact of hot spots and areas with greater risk will be greater for the unhoused group, leading to more variable and erratic mood. Methods To test this, the present study utilized one week of Geographic Ecological Momentary Assessment (GEMA), coupled with semi-structured interviews. GEMA offers geocoded locations of study participants in addition to the momentary mood data collected from Ecological Momentary Assessment (EMA), which is considered to be a gold-standard for capturing ecologically valid self-report information (J. T. Mitchell et al., 2014; Stone et al., 2007). Specifically, momentary sampling is uniquely 40 40 well-suited for capturing mood and psychological symptoms while eliminating recall bias and the need for informant reports (Harvey et al., 2021; Wenze & Miller, 2010). Participants and procedures Data come from a larger mixed-methods, GEMA study investigating HIV risk behaviors in a sample of young adults with history of homelessness. Study participants included individuals currently experiencing homelessness (i.e., unhoused) recruited from drop-in centers and emergency shelter, and formerly homeless individuals recruited from supportive housing programs (i.e., housed) in Los Angeles, California between June 2017 and January 2019. Eligible young adults were 18–25 years old, spent the prior night in a location that meets federal definitions for transition age youth experiencing homelessness (U.S. Administration for Children and Families, 2017), and were able to complete English‐language surveys without assistance. A total of 251 young adults were enrolled in the study; however, 12 opted out of the EMA portion of the study prior to the start of the EMA week. During the week, an additional eight people opted out of the study and did not complete the EMA week. Seven people were missing location data due to malfunction, leaving 224 participants with 5,557 prompts. However, prompts included in the present study were restricted to those that were completed in Los Angeles city due to availability of publicly available location data, leaving 202 people with 4,323 prompts. Finally, people with less than 10 observations for the entire week were excluded from analyses for optimal modeling, as is common when modeling intensive longitudinal data (Edmondson et al., 2013; Granholm et al., 2008, 2020; Hartley et al., 2014; Wen et al., 2017). The final analytic sample included 159 people (72 unhoused and 87 housed) with a total of 4,070 prompts. Participants received an average of 25.9 prompts during the week (SD=9.7, range=10-49) with no statistically significant difference by housing status. Quantitative procedures Prior to the week of EMA, participants completed a baseline survey where they provided information regarding demographics and their histories of homelessness. Upon completion, participants 41 41 had the option to enroll in 7 days of momentary sampling. A custom application installed on personal phones (n=12, 7.6%) or borrowed study phones delivered EMA prompts approximately every two hours during waking hours. Usual sleeping hours were identified by participants and programmed into the app. Participants received on average five prompts each day and prompts took an average of 60 seconds to complete. Prompts were geocoded at the location in which the prompts were initiated. Participants were compensated up to $130 for participation in the full study. Full study protocol are available elsewhere (Henwood et al., 2019b). Qualitative participants and procedures Following the EMA week, semi-structured interviews were completed with a subsample of 42 participants (22 housed, 20 unhoused). Most qualitative participants were bi- or multi-racial or ethnic, assigned male at birth, and LGBTQ with a greater proportion of males and LGBTQ being unhoused compared to housed (p=0.03 and p=0.001, respectively). Participants were offered participation in the qualitative portion based on high compliance and mobility over the week. Qualitative interviews lasted approximately one hour and relied upon geocoded maps of EMA responses as for elicitation and focused on identifying important places and events that impacted mood over the week. If participants reported substance use during the week, questions focused on gaining insight into the socio-environmental context of the use. Additional consent for qualitative interview participation was required. Interviews were conducted in‐person, audio‐recorded, and transcribed verbatim. Participants received $20 to complete the approximately one-hour interview. Measures Baseline measures Information regarding gender, sex assigned at birth, sexual orientation, and race and ethnicity was gathered at baseline. Options for gender identity included “man,” “woman,” “transman.” “transwoman,” “genderqueer/gender non-conforming,” and “different identity” which they were allowed to write in. Sex assigned at birth offered “male” or “female” and “I don’t know.” Participants selected between “gay/lesbian,” “bisexual,” “heterosexual/straight,” “questioning/unsure,” or “another sexual orientation” 42 42 which they were allowed to write in. Race was offered as a check-all question including the following options: “American Indian or Alaska Native,” “Asian,” “Black or African American,” “I am Hispanic/Latino and do NOT identify with any of the above racial categories,” “Native Hawaiian or Pacific Islander,” “South Asian,” “White,” “Bi- or Multi-racial or ethnic,” “Other (please specify),” and “I don’t know.” Ethnicity was assessed by asking: “Do you identify as Hispanic or Latino?” All people who responded affirmatively to more than one racial category and/or ethnicity were considered Bi- or Multi-racial/ethnic. Homeless history was captured by asking about the age in which individuals first experienced homelessness and the total length of time they had experienced homelessness over their life course, ranging from less than 1 month to 9 or more years. Ecological Momentary Assessment Current mood was sampled by asking: “Just before the phone went off, how [HAPPY] did you feel?” derived from the Circumplex Model of Affect (Posner et al., 2005; Tseng et al., 2014). Positive affect was taken as the average of responses from five-point Likert scales assessing “happy,” “excited,” and “calm” from “not at all” (1) to “extremely” (5). At each EMA prompt, past two-hour drug use was assessed by asking participants, “over the past two hours, have you used any drugs?” Drug types included marijuana, meth, ecstasy, synthetic marijuana, hallucinogens, prescription drugs not as prescribed, heroin, and other. Mixed Methods Analysis The present study used qualitative interviews to help explain quantitative findings. First, spatial analysis was used to measure and construct variables indicating the concentration of homelessness, violent crime, and drug activity (i.e., risk hot spots). These variables were then used in the quantitative models. This was followed by qualitative analysis whereby semi-structure interviews were used to explain and expand upon the quantitative findings. Geospatial analysis Publicly available data was accessed for prevalence rates of homelessness, violent crime, and drug-related arrests in Los Angeles city for the time period of the study (June 1, 2017 to March 31, 2019). 43 43 To track the size, composition, and location of people experiencing homelessness, the Department of Housing and Urban Development (HUD) requires communities that receive funding from HUD conduct a biennial Point-In-Time (PIT) count (Narendorf et al., 2016; Troisi et al., 2015). PIT count data from 2018 was utilized (LAHSA, 2018) and organized by census tract such that each tract contained the 2018 count of unsheltered individuals in each tract. Crime and arrest data from the Los Angeles Police Department was accessed from an online catalogue of Los Angeles open data (LAPD, 2022a; 2022b) and organized by census tracts. Crime data was used to compile a count of the number of violent and/or sexually motivated crimes per census tract. The number of drug-related arrests per census tract was derived from arrest data. Arrests related to “narcotic drug laws,” “liquor laws,” and “drunkenness” were included as drug-related arrests. Appendix A includes the crimes coded as violent crime. Datasets were imported into ArcGIS Pro for analysis. Hot spot analysis (Getis-Ord Gi*) was conducted for each spatial variable separately (i.e., homelessness, violence, and drug activity) using a fixed distance band of 1,600. The distance band was calculated using the “calculate distance band from neighbor count” tool where 10 neighbors were specified for census tract polygons and indicated an average distance of 1,595.72. The False Discovery Rate (FDR) Correction was used to correct for multiple testing. The “hot spot analysis” tool calculated z- scores and p-values for each census tract indicating where high or low values clustered spatially by considering each value within the context of neighboring values. This was accomplished by identifying high value tracts surrounded by other tracts with high values. The local sum was then compared proportionally to the sum of all tracts. Areas where the local sum was higher than the expected local sum and the difference was too large to be the result of random chance, a statistically significant z- score resulted (>1.96). Quantitative analyses A series of mixed-effects location-scale models examined the relationship between physical risk environments and mean mood and mood variability using RUNMIXREGLS (Hedeker & Nordgren, 2013) in Stata 16.1. Mixed-effects location scale models extend traditional mixed-effects models by modeling 44 44 the between-subject (BS) and within-subject (WS) variances as log-linear functions of the covariates. The BS variance function measures variability in mean responses, having adjusted for covariates; and the WS variance function measures variability in the responses about subjects’ adjusted mean responses (Hedeker & Nordgren, 2013; Leckie, 2014). Z-scores generated from hot spot analyses functioned as independent variables in the models measuring the concentration of the environmental risk. Housing status served as an effect modifier in models. Due to collinearity, separate location-scale models assessed implications of environmental risk factors (i.e., homelessness, violence, and drug activity) on positive affect (PA) and mood variability. Additional models are presented for the lagged and lead effects of risk on mood to investigate the carry-over effect of locations, making for a total of nine models. All models controlled for the effect of time, significant differences between groups regarding race/ethnicity (i.e., Black and Latino), and momentary drug use. Qualitative analysis Interviews were initially analyzed using an immersive approach in which multiple investigators read and reread each transcript to develop a code book that would then by systematically applied across all transcripts (Crabtree & Miller, 1999). Codes utilized for the present study were derived from interview questions and included positive and negative mood, contextual influences, and safety. Interview transcripts were then co-coded by two independent reviewers and reviewed for agreement. The research team then met to compare the coded material and address any discrepancies, which were resolved through consensus (Padgett, 2016). Quotes were then organized by code and housing status for analysis. Results Geospatial Results Figure 3.1 displays drug-related hot spots in Downtown and Hollywood. Violence hot spots were identified in Hollywood, Downtown, and South LA, shown in Figure 3.2. Violence hot spots were the most prevalent with the Downtown hot spots extending into Westlake and areas in Chinatown surrounding Union Metro Station. South LA was not identified as an area with statistically significant concentrations of homelessness or drug activity, but nearly the entirety of South LA was identified as 45 45 areas with a statistically significant concentration of violence. Finally, the largest concentrations of homelessness, shown in Figure 3.3, are in Downtown Los Angeles in and around Skid Row. Other concentrations were noted near Santa Monica, near Hollywood, and in the San Fernando Valley. Quantitative Results Participant characteristics from baseline and the EMA week are displayed in Table 3.1 by housing status. In terms of demographics, the housed and unhoused groups differed on race and ethnicity, where more unhoused participants identified themselves as Black or African American (47% of unhoused compared to 24% of those housed, p=0.002) and more housed participants identified as Latino (25% compared to about 13% of unhoused, p=0.04). Otherwise, nearly half of the combined sample identified as a man (49.7%), heterosexual (50.3%), and LGBTQ (52.2%). Unhoused participants, on average, had higher mean PA over the EMA week (p=0.02), but were in homeless and violence hotspots for a greater proportion of prompts (p=0.02, p=0.03, respectively). Likewise, when measuring the concentration of risk (i.e., the amount of risk activity relative to the average in the study area), unhoused individuals, on average, spent time in locations with a greater concentration of homelessness (p=0.02), violence (p=0.003), and theft and/or vandalism (p=0.002). Additionally, the unhoused group spent time in areas with a greater concentration of drug activity, but this did not meet the 0.05 threshold for significance (p=0.06). A map of the locations where EMA surveys were completed in the city of Los Angeles may be seen in Figure 3.4. Drug activity After controlling for the effect of time, ethno-racial differences by housing status, and drug use, mean model results suggest housed residents experienced decreased PA over the EMA week, compared to those who were unhoused (β=-0.25, p=0.01). The BS variance function also identified that PA of unhoused participants was more homogenous at prompts (i.e., responded more similarly) when they were in areas with a greater concentration of drug activity than their average (α=-0.02, p=0.02). Those in housing had decreased WS variance (τ=-0.43, p=0.009) of PA, but increased NA variance (τ=1.01, p=0.006), compared to their unhoused peers. Unhoused individuals when in locations with a greater 46 46 concentration of drug activity were more erratic in their PA (τ=0.05, p=0.01), such that their variability increased by a factor of 5%. Similarly, unhoused participants who were in areas with more drug activity than the average over the week were more variable in NA (τ=0.62, p<0.001); whereas, housed participants who were in areas with more drug activity than the average over the week were less variable in NA (τ=-0.39, p=0.02). See Table 3.2 for the mixed-effects location-scale models for drug-related risk. Violence Participants residing in supportive housing again had decreased PA compared to those who were unhoused (β=-0.25, p=0.02). However, when in locations with a greater concentration of violence than their own average, housed individuals reported higher PA (β=0.03, p=0.006), relative to unhoused participants who experienced a decrease in PA (β=-0.02, p=0.001). However, housed participants who were in more violent areas overall across the week also reported increased mean NA (β=0.15, p=0.001). BS variance of PA was decreased for unhoused participants both at the between- and within- subject levels. This means that unhoused individuals who, on average, were in more violent areas across the EMA week were more homogenous in their PA response (α=-0.30, p=0.02). Likewise, when in locations with a greater concentration of violence than their average, unhoused participants were more consistent in their PA, though just meeting the 0.05 threshold (α=-0.01, z=-1.96, p=0.05). Additionally, housed participants who were, on average, in more violent areas across the EMA week were more heterogenous in their reported PA (α=0.29, z=1.96, p=0.05). The WS variance identified that housed participants were less erratic in their PA (τ=-0.41, p=0.01) as well as in their NA (τ=-0.55, p=0.01). Additionally, housed individuals who were in more violent areas than others (i.e., above the group mean) across the EMA week were also more variable in their reported NA across the week (τ=0.57, p<0.001). See Table 3.3 for the mixed-effects location-scale models for violence risk. Homelessness Mean model results indicate housed participants, on average, experienced decreased PA (β=-0.24, p=0.02) and increased NA (β=0.87, p=0.003), compared to their unhoused counterparts. Also, participants 47 47 reported slightly higher PA at prompts where individuals used more drugs than their average (β=0.09, p=0.004). The BS variance function identified that the responses of unhoused participants were more homogenous in their PA (i.e., responded more similarly) at prompts when they were in areas with a greater concentration of homelessness than their average (α=-0.02, p=0.03). This means group-level variance of PA decreased by a factor of two percent when in areas with a greater concentration of homelessness. Finally, the WS variance function revealed that housed individuals were less variable in their PA compared to those who were unhoused (τ=-0.42, p=0.01), even when in areas with a greater concentration of homelessness than their own average (τ=-0.08, p=0.004). Conversely, unhoused participants became more variable in their PA when in locations with a greater concentration of homelessness than their average (τ=0.09, p<0.001), such that individual PA variance increased by an average of nine percent. See Table 3.4 for the mixed-effects location-scale models for homelessness risk. Qualitative Results Qualitative analysis of in-depth interviews resulted in the identification of two overarching risk factors associated with the physical environments of housed and unhoused young adults: 1) drug activity and 2) violence. While there were some natural differences in the perception and impact of these risk factors on mood by housing status, both housed and unhoused participants discussed the relevance of drug activity and violence in their daily lives. However, risk factors were explicitly linked to the lived experience of homelessness by housed residents only. The two risk factors will now be discussed in order and by housing status. Drug activity Housed: “I stay away from places that look like there’s homeless people.” Both young adults actively experiencing homelessness and residing in supportive housing identified substance use as an environmental factor with large impacts on their mood. For housed young adults, this was largely related to their identity and behaviors associated with their experience of homelessness and fears of backsliding. Resident 1126 stated, “I definitely don’t go to places I used to 48 48 hang out, not like I would use drugs and stuff. I definitely stay away from those places, so I just try to keep myself busy with as many productive things as I can during the day.” Resident 1103 elaborated: “It’s still raw, I can’t just go to a certain street or I just can’t hit up a certain friend. Especially if they are obviously drinking. They are always going to be there, maybe a different location, they are still doing the same thing. Or always running from the cops, you get me? Well yeah, that’s cool for a bit but it’s every day. I don’t wanna wake up early just to go drink. It gets boring, it gets old and it’s just, what are you going to do. You know the ‘hood isn’t your life … just astonishes me now. They really wanna wake up early just to go drink, be drunk by 11:00am, what is that? Where do you even get your money? Do you not want to go shopping? Do you not want to work for yourself? Do you not want to work something for yourself, do you not you know, aspire to be something or do something?” For several residents, this translated into using home as a means of avoiding negative environments, such as housed participant 1126: “No, I mean I have [thought about it] a couple of times, maybe if I get really stressed out or something. But I don’t ever go because I just have too much to lose. I already lost my daughter from using drugs and stuff like that, so I can’t go back. So I just will go home. If I wanna go over there, I’ll just go home instead.” Another resident (1140) equated these factors explicitly with homelessness: “I stay away from places that look like there’s homeless people. It’s just a real negative vibe for me. I don’t want to know about it. I’m not being cruel. If somebody asks me for help, okay, I’ll try, but I don’t want to look at it, I don’t want to see it. It kind of upsets me. I don’t want to have the feeling of that could happen. I see it, I pass by, and I turn the other way, I go on other streets. There are a lot of people that are just high, and you can tell they’re homeless because they’re high. They’re walking around crazy, or they have bags at bus stops, and all that. They’re living literally on the street.” Resident 1116 goes on to describe her perspective of the relationship between homelessness and drug use and how the two are intertwined: “I noticed that they didn’t become homeless because they’re hooked on drugs necessarily, it’s that after you become homeless then people resort to drugs. Or like within my first 49 49 week of being on the streets I was exposed to meth, and I tried crystal meth, and thankfully it wasn’t for me.” Unhoused: “It’s there, of course I want it.” While housed participants discussed negative impacts of drug activity related to their progress, some unhoused participants appeared to think of it more in terms of safety, such as participant 2050 who shared about a specific park they try to avoid: “I just didn’t want to deal with all those people. These are all boys from the shelter life and street life that I came in contact with. This one drug boy over there, he’s been there for three years… he’s drunk and he starts talking like a fucking maniac. So I did not feel safe, I thought I was going to be shot. Oh yeah, and they’re peeing the corner. It’s just not safe and it’s not sanitary either.” In fact, another unhoused participant described the guidelines for drug use that their small encampment community follows to keep themselves safe. Described as a “small homeless apartment complex,” participant 2059 stated that individuals have their own tents but generally have common agreements about substance use: “We have a few people at the encampment that are potheads through and through, but, on occasion, they do cocaine, or on occasion, they do acid. That’s kind of the life of a pothead. We’re potheads through and through, but on occasion, we have our personal drug of choice that we like to add, but mostly we don’t tolerate meth. We don’t tolerate crack. We don’t tolerate anything that requires shooting up like heroin. If it requires a needle or it requires smoking outside of tobacco and marijuana, it’s not tolerated on the block and it’s not tolerated in the neighborhood, and we made it very, very clear that violators will be punished severely, only because we’re potheads, but we’re not horrible people.” Beyond safety, there were several unhoused participants that fell in line with thinking about drug use in terms of their own progress, much like the housed group. Participant 2049 said, “Just being around it. Seeing it there, it’s right there, of course I want it. But just the fact of just having, how do you put it, like a guilty conscience.” 2049 was not the only participant that shared feeling bad about substance use. Participant 2043 shared a bit about why they feel bad about it: “That place isn’t the best because it’s 50 50 people just getting high all the time. And you can see I was there for a minute. ... it’s so easy to just make friends with people that are not really ... no plan or anything. It’s ‘just let me just get high for the day.’ And yeah, it’s ruining my life.” Likewise, unhoused participant 2125 shared that even though they haven’t arrived at their goal, they are still doing what they can to continue moving in the direction they want to go: “I recently have been avoiding more the places that I know that people who I was with at [the shelter] are. Not because they’re bad influences or anything like that, but just because it’s a part of my life that I’m glad to be over with. That even though I’m in temporary housing, so it’s still not fully done, at least I’m out of the part where I’m in a shelter with the people who live there.” Violence Unhoused: “My heart was racing just from watching someone in a fight.” Violence was another topic that surfaced frequently in discussions of environmental impacts on mood, particularly among the unhoused participants. Some locations were cited as risky areas based on past experiences and memories, such as for participant 2091 who stated they avoided one area because he “lost a lot of people in that area.” Multiple people discussed violence around service environments and shelters. Participant 2095 described avoiding the area where their shelter is located due to violence: “Sometimes, when the shelter’s closed or something, sometimes we’ll avoid going over ‘cause sometimes, stuff will happen over there. It’s not really the best area. Sometimes if something happened the night before, like a shooting or just something crazy happened over there. Sometimes we’ll go somewhere else, just to stay away from the trouble. Yeah, whenever somebody calls, “Oh, you know what happened last night?” They were shooting or something like that or the police are riding around looking for somebody or something like that. She’s like, ‘Okay, we won’t go over there today.’” Five other unhoused participants described violence they witnessed in service environments, including participant 2096 who described getting “cut” in a shelter and participant 2074 who mentioned experiencing biological responses to violence including a racing heart. 51 51 As a result, many unhoused young people are hypervigilant. Participant 2049’s hypervigilance manifests as distrust of others: “Just sleeping on the street is a little more risky than where I’m from. Here you don’t know who you can run into or what you can run into. There’s a lot of scandalous people that do whatever they can do to survive. So you run into those people or you run into the more helpful people that have been on the streets for a while and they come off as easy going and whatever but at the same time you’ve got to remember that everyone’s not as they seem. So being on the street by yourself is kind of fearful, I would say. It’s scary.” Another manifestation of the hypervigilance brought about by the environments associated with homelessness was violence in response to violence. This was outlined by participant 2094: “I wouldn’t lie and say it haven’t made me abusive because it did, but I will be abusive if someone really disrespects me, like put their hands on me or just threaten me so you know.” Several young people reported substance use to cope with these threats. Specifically, when physical safety was threatened, at times in reference to social service locations, young people chose to use substances. Participant 2074 shared: “Yeah, to be honest, because something at the [shelter] happened. Yeah, I felt threatened or harassed. And I had to go smoke some weed. Because I was just so stressed.” Likewise, participant 2095 recounted a similar experience: “The shelter is stressful sometimes. Yeah, that’s right by the shelter, so probably there was a lot going on that night, or something and just decided to have a drink.” As a result of these experiences, some unhoused participants expressed feeling numb. Participant 2049 details this: “I just think I don’t feel one or the other, because I just learned to numb myself really. I learned to just not put any emotion in anything. So you know, it’s like even if I’m safe, I don’t feel safe. Even if I’m unsafe, I don’t feel unsafe. I just don’t feel. Like I just, you know, I’m just like whatever. The times I felt unsafe was probably when somebody tried to rob me, so yeah. I felt a little bit unsafe, which was downtown.” 52 52 Desensitization, another strategy to cope with risk environments, was shared by participant 2048: “That I’ve adjusted to feeling neither safe or unsafe like yeah that’s about the least safe that I would ever feel anymore.” Of course, environmental risks are not static. Two unhoused participants elaborated on how their perception of their environment changes over the course of the day. Participant 2049 said, “And during the day, actually, would be when I feel more safe. And when I was actually out and about at these other, these ones on the outer area. Because it was during the daytime and usually during the day you can actually see other people even though some people are crazy out there. But it’s just more safer than it is at night time.” Likewise, participant 2091 noted he feels uneasy as perceived risk increases: “Yeah, just in some areas. And then most of the time what areas, especially when it gets nighttime, because I stay out late. So, when it gets real late, that’s when you kind of feel uneasy.” Housed: “You’re not hearing police sirens to put you to sleep” Sometimes housing is in an area where there is a threat for violence, too. Five residents noted they were hypervigilant in their neighborhood because they identified elements, such as resident 1080: “A lot of temptations. It’s a super bad area to live in. Right across the street is full of gang bangers and everything. So it is bad.” Another resident, participant 1140, related feeling unsafe around her apartment because of her past: “I don’t feel safe if I’m around the place that I moved into and I’m walking around by myself, because I know I’m by myself. I knew so many people in the past. What if they see me? These people are really violent. I don’t want them to try to force me to do something, or even disrespect me in any type of way. I don’t know. When that happens, I usually call my friend. I tell her okay, I’m in a new place, I’m letting you know where I’m at, in case something happens.” Additionally, three residents talked about police presence as an emotional trigger. Resident 1140 elaborated: 53 53 “What triggers me the most is hearing a bunch of police sirens, or fire. I right away remember how many times I’ve been in the fire, like in an ambulance, how many times I’ve been arrested, and it’s traumatizing for me. Most of the time, it felt like I was just being drugged or abused and used, and I didn’t have a choice, and I was staying out in the streets, and they would just arrest me, or they would just take me, give me drugs just to calm down, and I didn’t have a say so. I’m really traumatized, and when I hear other close sirens to me, I feel threatened, we’re watching you, you better be doing good, or we’re always around you.” As with this resident, police activity may signal a threat of violence. Another housed participants reported getting “spooked” when they thought they saw a cop car outside of a friend’s house (participant 1116). Conversely, not hearing police sirens and violence was cited as a marker of why participant 1103’s housing was a positive environment: “But it’s comforting here, it’s new but I like it. Here, you know you’re not hearing police sirens, ‘freakin, to put you to sleep. You don’t hear gunshots, you don’t hear just ruckus, partying, music, it’s just quiet and it’s calming and it’s just soothing.” Discussion This mixed-methods study highlights the impact of physical environments on mood through momentary sampling and qualitative interviews with a sample containing young adults both currently experiencing homelessness and those who had previously experienced homelessness but were residing in supportive housing. Geospatial analysis reinforces literature that has established spatial relationships between homelessness, violent crime, and drug activity (Barnum et al., 2017; Doran et al., 2022; Ellsworth, 2019; Fine et al., 2022; Tong et al., 2021; Yoo & Wheeler, 2019). Quantitative results revealed unhoused individuals were in homeless and violence hot spots for a greater proportion of prompts and spent more time in riskier locations (i.e., areas with a greater concentration of risk) throughout the EMA week. Housed and unhoused also had different emotional experiences in relation to risky physical contexts by housing status and qualitative interviews help explain the differences. Table 3.5 provides a summary of mixed methods findings and comparisons between quantitative and qualitative findings. Qualitative analysis reinforced drug activity and violence as two important environmental risk factors that 54 54 influenced mood and even influenced choices about where young people in the study traveled over the course of their days. Discussions of drug activity and violence were not surprising, given the connection between both drug activity and violence with homelessness (Barnum et al., 2017; Doran et al., 2022; Ellsworth, 2019; Fine et al., 2022; Tong et al., 2021; Yoo & Wheeler, 2019). Mixed methods findings for each contextual risk are now presented, followed by a discussion of the role of housing in navigating environmental risks. Environmental risk factors Drug Activity Quantitative results suggested unhoused participants were more erratic (i.e., variable) in their PA and homogenous as a group when in locations with a greater concentration of drug activity. Unhoused individuals who were in areas with more drug activity than the average participant over the entire EMA weeks were also more variable in their reported NA, compared to housed participants who were less variable in their NA. These finding were further explained by qualitative findings where unhoused participants related their feelings to issues of safety, sanitation, and desires to change their circumstances and get ahead. However, mixed methods findings were discrepant for housed participants as they reported actively avoiding areas associated with drug activity, but drug-related hot spots were not found to be significantly associated with emotional patterns. Violence In terms of average mood, unhoused participants reported decreased PA when in areas with a greater concentration of violence, compared to housed individuals. Unhoused individuals also became more alike as a group in their PA response with increasing levels of violence in their environment. This was discussed by many unhoused participants in qualitative interviews whereby they described continual hypervigilance in relationship to violence in their environment (e.g., constantly on alert and/or feeling numb). On the other hand, housed participants were generally less erratic in both PA and NA, but residents who were in more violent areas than others on average were more heterogenous as a group in their reported PA and erratic in their reported NA. Additionally, housed individuals who were in more 55 55 violent areas than others on average reported higher levels of NA. These results indicated, in part, that housed participants as a group had an array of responses to more violent areas, making the group more heterogenous in their variability. This was seen in qualitative results where some housed participants reflected on their experience with violence when unhoused and cited police sirens as triggering while others described their housing as a safe buffer. Homelessness Mixed methods findings about unsheltered homelessness were generally discrepant. Risk factors were explicitly linked to the lived experience of homelessness by housed residents only, despite being less erratic in areas with greater concentrations of homelessness. Conversely, homelessness was not specifically named by unhoused participants, but they experienced increased erraticism in areas with more homelessness. Housing as a protective factor in navigating risk environments Key findings from qualitative interviews included the use of housing as a protective factor against risk. Several residents discussed going home when they considered going somewhere they knew would be risky for them. This reinforces quantitative findings that suggested unhoused individuals were more erratic in the presence of more visible homelessness and drug activity, while housed residents were more consistent in areas with a greater concentration of homelessness. When faced with the possibility of encountering a risky environment that could be triggering, housed participants described using their housing as a tool to avoid risk. This most often was in relationship to areas associated with drug activity related to past street life. It is noteworthy that substance use remained a concern for individuals even after transitioning to housing. Housed individuals, at times, seemed temped by environments of their pasts and their associated risks, and the fear of backsliding was a large deterrent to engaging in these locations. In fact, housing was often used as a strategy to avoid temptation. Residents reported relying on their housing as a place to go rather than engage in known risk environments. This may speak to the protective nature of housing and the emotional benefits of knowing one has a safe, secure place to return to at the day’s end. Similar 56 56 sentiments have previously been recorded via qualitative inquiry into supportive housing with older adults where residents described housing as a place of solitude, particularly among those who are more socially engaged (Henwood, Lahey, et al., 2018). Another important factor about housing mentioned my numerous housed participants was the lack of police presence when inside of their apartment. Housed residents reflected on how violence was a major concern when experiencing homelessness. While they reported that police presence is still unnerving, the takeaway was a sense of gratitude to not be constantly hearing sirens. Previous literature has documented fear of law enforcement among young people experiencing homelessness, particularly among LGBTQ young people (McCandless, 2017) and youth of color (Ivanich & Warner, 2019). Additional distrust of law enforcement is likely due to the fact that law enforcement often carries out street sweeps and “move along” orders (Batko et al., 2020a). The focus of violence in the discussions with unhoused participants is fitting, as they experienced decreased PA when in more violent areas, whereas violence was not associated with erraticism, other than prospectively. The qualitative material reinforces this, as discussions of mood tended to describe valence and intensity, rather than variability. Navigating risk environments without housing The unhoused group tended to respond more similarly to risk environments as a group than their housed counterparts. We identified within-person effects in the BS variance function for homelessness, drug activity, and violence. This means at prompts where unhoused participants were in risker locations than their average, they were more alike as a group in their individual responses. Like housed residents, unhoused participants talked about trying to avoid risk environments to the best of their ability. While some spoke of avoiding areas with police presence, violence, and drug activity; others talked about avoiding locations where specific people who may be poor influences frequent. Increased erraticism among unhoused participants associated with concentrated drug activity was backed by qualitative interviews where unhoused participants spoke of trying to avoid areas with drugs to change their circumstances. Of course, the motive of substance use impacts the emotional response (Glodosky & Cuttler, 2020; Hamilton et al., 2020) which may partly explain the erraticism. There seemed 57 57 to be an essence of a struggle among the unhoused participants: on one hand they want out of their situation, they want better, and to avoid drug activity. On the other hand, they talked about drug use almost as an inevitable part of homelessness: one participant talked about how people around them were getting high all the time and how easy it is to “just make friends with people that have no plan.” This person said, “it’s ruining my life.” Another person said, “it’s there, of course I want it.” Using substances to cope with the trauma of homeless has been documented among young people in particular (Johnson & Chamberlain, 2008), and was confirmed by a housed resident who said they don’t believe people become homeless because of substances, but rather it is more likely they become addicts as a result of homelessness. Limitations and future directions This study has several limitations to acknowledge. First, it is important to note that environmental hazards are magnified for unhoused individuals along lines of race, gender, sexual orientation, and disability status (Goodling, 2020), which this study did not specifically address due to the already lengthy analyses. However, future work may want to take an intersectional approach. In terms of methods, using this hot spot analysis has strengths, but also weaknesses. In terms of its strength, it likely was the most accurate way to capture these risks during the specific time frame of the study. However, participants were only enrolled in the study for one week. Certainly, trends by block could have shifted during that time particularly regarding the concentration of homeless outside of Skid Row, as these analyses rely on counts from single point in time. However, despite the limitations of time, one study found spatial accuracy of GEMA 88% was within a half mile (Mennis et al., 2017). Still, hot spot analyses rely on limiting inquiry to a single geographic region, known as the study area. The study area in this study was the city of Los Angeles and the city was further analyzed by census tract. Thus, this study is limited by the Modifiable Areal Unit Problem (MAUP), a common limitation in spatial analysis. MAUP can be a source of statistical bias (Dark & Bram, 2007), particularly when point estimates are aggregated, as they were in this study. Aggregating to smaller areal units can be helpful, and census tracts are comparatively small units. Finally, low rates of drug use were reported in EMA surveys, and it is 58 58 presumed some drug use was not reported. The amount of error in measuring momentary drug use cannot be estimated, as drug use did not serve as inclusion or exclusion for study participation and the sample was obtained via convenience sampling. Conclusion The present study examined the relationship between physical risk environments and mood among young adults with history of homelessness. Additionally, this study explored housing status as an effect modifier and identified different emotional patterns based on housing status. Quantitative analyses showed areas of concentrated risk were associated with increased erraticism among unhoused participants, but the inverse or no relationship was identified for the housed group. Qualitative analysis identified drug activity and violence as risky areas associated with homelessness that participants generally tried to avoid, regardless of housing status, though with some nuance. Similarities in how housed and unhoused participants appraised risks in their environments and attempted to respond to those risks by trying to avoid certain locations illuminates the capacity of unhoused young adults. Throughout this study we see housing as an incredible asset for these young people and evidence that, if provided the opportunity, there are many unhoused young people who would also utilize housing as a tool to avoid risks and achieve their personal goals. This is noteworthy as treatment first approaches and the philosophy of “housing readiness” lingers in homeless service provision (Padgett et al., 2016), including among funders. The idea that people are “ready” to receive housing when they have satisfied a check list is challenged by present findings. Results highlight risk assessment and critical thinking among unhoused young adults and point to the protective nature of housing as a key factor in mental health recovery among people young people experiencing homelessness. 59 59 Table 3.1. Sample characteristics by housing status p Demographic characteristics Gender Man 0.76 Woman 0.92 Trans*, gender non-conforming, or expansive 0.30 Sex assigned at birth, female 0.35 Sexual Orientation Heterosexual 0.70 Gay/Lesbian 0.89 Bi/Pan 0.41 Another orientation 0.51 LGBTQ 0.90 Race/Ethnicity Black 0.002 Latino 0.04 Bi/Multi-racial or ethnic 0.47 White 0.88 Another racial identity 0.19 Homeless for more than 1 year 0.39 Drug use reported during EMA week 0.09 p Age of first homelessness 0.39 Proportion of prompts with reported drug use 0.10 Mean PA 0.02 Risk Hot Spots* Pr(T > t) Homeless 0.02 Violence 0.03 Drug 0.61 Concentration of Risk^ Homeless 0.01 Violence 0.003 Drug 0.06 * proportion of prompts in hot spot ^ average risk z-score over EMA week 70 (44.0) 33 (37.9) 37 (51.4) 0.14 (0.25) 0.11 (0.22) 0.18 (0.27) x (sd) x (sd) x (sd) 0.37 (0.42) 0.38 (0.46) 0.36 (0.41) 2.51 (2.77) 1.04 (1.67) 0.77 (1.22) 1.37 (2.05) 3.20 (1.85) 2.84 (1.83) 3.64 (1.80) 2.16 (2.56) 1.86 (2.35) 0.23 (0.37) 0.18 (0.36) 0.30 (0.37) 0.65 (0.37) 0.60 (0.42) 0.71 (0.30) 14 (8.8) 10 (11.5) 4 (5.6) 2.70 (0.97) 2.54 (0.91) 2.89 (1.00) 105 (66.0) 60 (69.0) 45 (62.5) 17.67 (3.54) 17.86 (3.57) 17.40 (3.51) 42 (26.4) 25 (28.7) 17 (23.6) 17 (10.7) 9 (10.3) 8 (11.1) 55 (34.6) 21 (24.1) 34 (47.2) 31 (19.5) 22 (25.3) 9 (12.5) 83 (52.2) 45 (51.7) 38 (52.8) 35 (22.0) 17 (19.5) 18 (25.0) 16 (10.1) 10 (11.5) 6 (8.3) 28 (17.6) 15 (17.2) 13 (18.1) 66 (41.5) 39 (44.8) 27 (37.5) Total (n=159) Housed (n=87) Unhoused (n=72) n (%) n (%) n (%) 55 (34.6) 29 (33.3) 26 (36.1) 23 (14.5) 16 (18.4) 7 (9.7) 79 (49.7) 41 (47.1) 38 (52.8) 80 (50.3) 45 (51.7) 35 (48.6) 60 60 Table 3.2. Mixed-effects location-scale models for drug activity estimate se z p estimate se z p Mean Intercept β 0 2.77 0.11 24.34 <0.001 1.74 0.16 10.87 <0.001 Housed β 1 -0.25 0.10 -2.45 0.01 0.05 0.05 1.16 0.25 Drug BS β 2 -0.03 0.04 -0.68 0.49 -0.01 0.02 -0.58 0.56 Drug WS β 3 -0.01 0.007 -1.41 0.16 0.004 0.004 1.14 0.26 BS Drug * Housed β 4 0.02 0.05 0.36 0.72 0.01 0.03 0.32 0.75 WS Drug * Housed β 5 0.01 0.01 1.11 0.27 -0.001 0.008 -0.18 0.86 BS Variance Intercept α 0 -0.19 0.14 -1.33 0.18 -0.06 0.18 -0.35 0.73 Housed α 1 0.05 0.17 0.32 0.75 0.14 0.25 0.58 0.56 Drug BS α 2 0.03 0.06 0.56 0.58 -0.06 0.08 -0.68 0.49 Drug WS α 3 -0.02 0.008 -2.31 0.02 0.01 0.01 0.94 0.35 BS Drug * Housed α 4 -0.06 0.08 -0.80 0.42 0.08 0.11 0.77 0.44 WS Drug * Housed α 5 0.008 0.01 0.64 0.52 -0.01 0.02 -0.60 0.55 WS Variance Intercept τ 0 -0.10 0.13 -0.71 0.48 -4.61 0.31 -14.80 <0.001 Housed τ 1 -0.42 0.16 -2.60 0.009 1.01 0.36 2.78 0.006 Drug BS τ 2 0.03 0.04 0.72 0.47 0.62 0.12 5.10 <0.001 Drug WS τ 3 0.05 0.02 2.59 0.01 0.02 0.02 0.96 0.34 BS Drug * Housed τ 4 0.003 0.06 0.05 0.96 -0.39 0.16 -2.39 0.02 WS Drug * Housed τ 5 -0.03 0.03 -1.21 0.23 -0.04 0.03 -1.31 0.19 Scale σ 2 ω 0.78 0.05 14.55 <0.001 3.25 0.26 12.38 <0.001 Covariance σ υω 0.37 0.11 3.3 0.002 0.96 0.39 2.46 0.01 Covariance σ υω 2 -0.51 0.08 -6.33 <0.001 Note: models controlled for the effect of time, race, and momentary substance use. Positive (n=159, k=4,070) Negative (n=159, k=4,071) 61 61 Figure 3.1. Drug activity hot spots 62 62 Table 3.3. Mixed-effects location-scale models for violence concentration estimate se z p estimate se z p Mean Intercept β 0 2.74 0.12 23.55 <0.001 1.93 0.11 16.99 <0.001 Housed β 1 -0.25 0.11 -2.27 0.02 0.01 0.01 0.08 0.94 Violence BS β 2 -0.007 0.03 -0.22 0.53 0.05 0.03 1.87 0.06 Violence WS β 3 -0.02 0.006 -3.27 0.001 0.01 0.01 0.92 0.35 BS Violence * Housed β 4 0.008 0.05 0.16 0.88 0.15 0.04 3.46 0.001 WS Violence * Housed β 5 0.03 0.01 2.73 0.006 -0.02 0.02 -1.13 0.26 BS Variance Intercept α 0 0.10 0.15 -0.65 0.52 -0.61 0.18 -3.32 0.00 Housed α 1 -0.03 0.18 -0.18 0.85 0.06 0.41 0.16 0.87 Violence BS α 2 -0.30 0.13 -2.34 0.02 -0.23 0.13 -1.80 0.07 Violence WS α 3 -0.01 0.01 -1.96 0.05 0.02 0.02 1.04 0.30 BS Violence * Housed α 4 0.29 0.15 1.96 0.05 0.33 0.32 1.05 0.30 WS Violence * Housed α 5 0.003 0.02 0.20 0.84 -0.04 0.04 -1.02 0.31 WS Variance Intercept τ 0 -0.11 0.13 -0.85 0.39 -0.96 0.15 -6.36 <0.001 Housed τ 1 -0.41 0.16 -2.52 0.01 -0.55 0.22 -2.53 0.01 Violence BS τ 2 0.03 0.06 0.53 0.59 0.07 0.08 0.95 0.34 Violence WS τ 3 0.007 0.02 0.32 0.74 -0.04 0.02 -0.19 0.85 BS Violence * Housed τ 4 0.05 0.09 0.56 0.58 0.57 0.10 5.58 <0.001 WS Violence * Housed τ 5 -0.001 0.03 0.99 0.98 0.02 0.04 0.44 0.66 Scale σ 2 ω 0.77 0.05 14.58 <0.001 0.94 0.07 12.59 <0.001 Covariance σ υω 0.39 0.11 3.51 <0.001 0.71 0.11 6.35 <0.001 Covariance σ υω 2 -0.49 0.08 -6.27 <0.001 Note: models controlled for the effect of time, race, and momentary substance use. Positive (n=159, k=4,070) Negative (n=159, k=4,071) 63 63 Figure 3.2. Violence hot spots 64 64 Table 3.4. Mixed-effects location-scale models for homelessness concentration estimate se z p estimate se z p Mean Intercept β 0 2.76 0.11 24.5 <0.001 1.95 0.13 15.53 <0.001 Housed β 1 -0.24 0.10 -2.34 0.02 0.87 0.30 2.95 0.003 Homelessness BS β 2 -0.02 0.05 -0.41 0.68 0.03 0.12 0.23 0.82 Homelessness WS β 3 -0.01 0.008 -1.27 0.21 0.02 0.01 1.54 0.12 BS Homelessness * Housed β 4 0.07 0.08 0.83 0.41 -0.20 0.24 -0.85 0.40 WS Homelessness * Housed β 5 0.01 0.01 0.92 0.36 -0.01 0.02 -0.70 0.49 BS Variance Intercept α 0 -0.23 0.15 -1.59 0.11 -0.66 0.19 -3.49 <0.001 Housed α 1 0.11 0.18 0.64 0.52 -0.65 1.06 -0.62 0.54 Homelessness BS α 2 -0.03 0.11 -0.23 0.82 0.02 0.24 0.07 0.95 Homelessness WS α 3 -0.02 0.01 -2.23 0.03 0.03 0.020 1.52 0.13 BS Homelessness * Housed α 4 -0.03 0.16 -0.2 0.84 0.60 0.7 0.85 0.40 WS Homelessness * Housed α 5 0.004 0.02 0.24 0.81 -0.01 0.03 -0.40 0.69 WS Variance Intercept τ 0 -0.09 0.14 -0.61 0.54 -0.87 0.14 -6.22 <0.001 Housed τ 1 -0.42 0.17 -2.49 0.01 0.74 0.54 1.36 0.17 Homelessness BS τ 2 0.02 0.06 0.27 0.79 0.02 0.17 0.13 0.89 Homelessness WS τ 3 0.09 0.02 4.23 <0.001 0.03 0.02 1.62 0.11 BS Homelessness * Housed τ 4 0.17 0.11 1.56 0.12 -0.30 0.38 -0.78 0.44 WS Homelessness * Housed τ 5 -0.08 0.03 -2.90 0.004 -0.07 0.04 -1.90 0.06 Scale σ 2 ω 0.81 0.05 14.66 <0.001 0.86 0.08 11.25 <0.001 Covariance σ υω 0.34 0.11 2.95 0.004 0.66 0.11 5.74 <0.001 Covariance σ υω 2 -0.51 0.08 -6.24 <0.001 Note: models controlled for the effect of time, race, and momentary substance use. Positive (n=159, k=4,070) Negative (n=159, k=4,071) 65 65 Figure 3.3. Homeless hot spots Figure 3.4. EMA points 66 66 Table 3.5. Mixed methods findings Risk Category Quantitative Finding Qualitative Finding Conclusion Drug-activity Unhoused individuals were more erratic in their PA when in locations with a greater concentrations of drug activity than their own average. PA of unhoused participants was also more homogenous at prompts when they were in areas with a greater concentration of drug activity than their average. Unhoused participants who were in areas with more drug activity than the average over the week were also more variable in NA; whereas, housed participants who were in areas with more drug activity than the average over the week were less variable in NA, comparatively. Housed: “I definitely don’t go to places I used to hang out, not like I would use drugs and stuff. I definitely stay away from those places, so I just try to keep myself busy with as many productive things as I can during the day.” “No, I mean I have [thought about it] a couple of times, maybe if I get really stressed out or something. But I don’t ever go because I just have too much to lose.” Unhoused: “…This one drug boy over there, he’s been there for three years… he’s drunk and he starts talking like a fucking maniac. So I did not feel safe, I thought I was going to be shot. Oh yeah, and they’re peeing the corner. It’s just not safe and it’s not sanitary either.” “…outside of tobacco and marijuana, it’s not tolerated on the block and it’s not tolerated in the neighborhood, and we made it very, very clear that violators will be punished severely, only because we’re potheads, but we’re not horrible people.” Discrepant: Although housed participants reported actively avoiding areas associated with drug activity, drug-related hot spots were not found to be significantly associated with emotional patterns. Expansive: Unhoused participants were identified as being more erratic in their mood, but more alike as a group in their emotional responses when in areas with a greater concentration of drug activity. Qualitatively, unhoused participants related their feelings to issues of safety, sanitation, and desires to change their circumstances and get ahead. 67 67 “That place isn’t the best because it’s people just getting high all the time. And you can see I was there for a minute. ... it’s so easy to just make friends with people that are not really ... no plan or anything.” Violence Housed participants reported higher PA, relative to unhoused participants who reported a decrease in PA, when in locations with a greater concentration of violence. However, housed participants who were in more violent areas on average across the week also reported higher levels NA on average. Housed participants who were, on average, in more violent areas across the EMA week were more heterogenous in their reported PA. Housed participants were also less erratic in both PA and NA. However, housed individuals who were in more violent areas than others (i.e., above the group mean) across the EMA week were also more variable in their reported NA across the week. Housed: “What triggers me the most is hearing a bunch of police sirens, or fire. I right away remember how many times I’ve been in the fire, like in an ambulance, how many times I’ve been arrested, and it’s traumatizing for me.” “But it’s comforting here, it’s new but I like it. Here, you know you’re not hearing police sirens, ‘freakin, to put you to sleep. You don’t hear gunshots, you don’t hear just ruckus, partying, music, it’s just quiet and it’s calming and it’s just soothing.” Unhoused: “Sometimes, when the shelter’s closed or something, sometimes we’ll avoid going over … It’s not really the best area. Sometimes if something happened the night before, like a shooting or just something crazy happened over there. Sometimes we’ll go somewhere else, just to stay away from the trouble.” Expansive: Quantitative results indicated that housed participants as a group had an array of responses to more violent areas, making the group more heterogenous in their variability. Some housed participants reflected on their experience with violence when unhoused and cited police sirens as triggering but described their housing as a buffer. Expansive: Unhoused participants, on the other hand, responded similarly as a group to more violent areas. They were also less erratic when in areas with more violence, but more erratic prior to being in a location with more violence. This anticipation was evident in qualitative interviews. Hypervigilance was also expressed, which could explain decreased variability when in areas with more violence. 68 68 “It triggered a little PTSD, and I started…my heart was racing, just from watching someone in a fight.” “I’ve adjusted to feeling neither safe or unsafe like yeah that’s about the least safe that I would ever feel anymore.” Homelessness Housed individuals were less erratic in PA in their mood compared to those who were unhoused, even when in areas with a greater concentration of homelessness than their own average. Conversely, unhoused participants became more erratic in PA when in locations with a greater concentration of homelessness than their average. Unhoused participants were also more homogenous in PA at prompts when they were in areas with a greater concentration of homelessness. Housed: “I stay away from places that look like there’s homeless people ... If somebody asks me for help, okay, I’ll try, but I don’t want to look at it, I don’t want to see it. It kind of upsets me. I don’t want to have the feeling of that could happen. I see it, I pass by, and I turn the other way, I go on other streets.” Discrepant: Risk factors were explicitly linked to the lived experience of homelessness by housed residents only, despite being less erratic in areas with greater concentrations of homelessness. Discrepant: Although homelessness was not specifically named by unhoused participants, they experienced increased erraticism in areas with more homelessness. 69 69 Appendix A. LAPD crime categories utilized for constructing violence hot spots Violence Hot Spots Aggravated assault Battery Brandishing weapon Criminal homicide Human trafficking Intimate partner aggrevated and simple assault Manslaughter Lynching Other assault Rape, attempt and forcible Pimping Shots fired at dwelling or vehicle Violation of restraining order Attempted robbery Robbery 70 70 Chapter 4. Stress lability among housed and unhoused LGBTQ young adults: The role of identity homophily in social networks (Paper 3) Highlights • This study explores if homophilous social interactions based on gender and sexual orientation support in regulating stress (i.e., stress reduced and less variable) above and beyond the effects of perceived support due to increased intimacy associated with shared identity, and whether differences emerge by housing status. • Interactions with like alters proved beneficial in regulating stress among unhoused, LGBTQ participants, such that erraticism was decreased following interactions with alters holding a greater number of shared identities. • The opposite was found for housed, LGBTQ participants who became more erratic in their stress responses following interactions with alters with more shared identities. 71 71 Abstract Objective: Young adults who experience homelessness are disproportionately LGBTQ. Previous research has established that homophilous relationships based on gender and sexual orientation are often perceived as more supportive and thus, can be helpful in regulating stress, including stressors related to the experience of homelessness. This study explores if homophilous social interactions based on gender and sexual orientation support in regulating stress (i.e., stress reduced and less variable) above and beyond the effects of perceived support due to increased intimacy associated with shared identity, and whether differences emerge by housing status. Methods: Data come from an Ecological Momentary Assessment (EMA) study of young adults with history of homelessness who were either actively experiencing homelessness (i.e., unhoused; n= 90), or had formerly experienced homelessness residing in supportive housing (i.e., housed; n= 115). An egocentric network inventory was used to elicit five people in participants’ social networks using interaction-based elicitation and was embedded into EMA prompts. A mixed-effects model examined the amount of support associated with homophilous relationships; and two location-scale models, one for the housed and one for the unhoused group, investigated the relationship between LGBTQ identity, homophilous relationships, and momentary stress. Results: Interactions with like alters was associated with lower stress variability (i.e., erraticism) among unhoused, LGBTQ participants, such that erraticism was lower following interactions with alters holding a greater number of shared identities. However, the opposite was found for housed, LGBTQ participants who became more in their stress responses following interactions with alters with more shared identities. Discussion: Results are interpreted through the lens of stigma-related theories as experienced by LGBTQ individuals. 72 72 Introduction Among the one-in-ten young adults that experience homelessness over the course of one year (Morton, Dworsky, et al., 2018b), an estimated 20-40% are lesbian, gay, bisexual, transgender, and/or queer (LGBTQ) despite being only 7-10% of the general population (Maccio & Ferguson, 2016; Norman- Major, 2017). Sources indicate LGBTQ young people are 2-13 times more likely to experience homelessness compared to cisgender, heterosexual (Cishet) peers (Coolhart & Brown, 2017; Morton, Samuels, et al., 2018). Literature overwhelmingly suggests LGBTQ homelessness stems from family conflict and breakdown, particularly when young people ‘come out’ to their families (Durso & Gates, 2012; McCann & Brown, 2019; Rhoades et al., 2018; Robinson, 2018; Shelton & Bond, 2017; Whitbeck et al., 2004). In fact, when examining the social networks of LGBTQ young people, those with experience of homelessness had more often disclosed their LGBTQ status to parents compared to those who had not experienced homelessness (Semborski, Srivastava, et al., 2021). These experiences may be more pronounced for transgender young people who experience even higher rates of homelessness risk factors (e.g., bullying, substance use, partner violence), as well as higher rates of being forced out by their family compared with LGBQ youth (Choi et al., 2015). Additionally, these factors contribute to LGBTQ youth being overrepresented in the child welfare system (McCormick et al., 2017), an additional risk factor for homelessness (Kelly, 2020). LGBTQ young people often experience homelessness longer than Cishet young adults (Choi et al., 2015), despite often having greater service needs (Prock & Kennedy, 2017) attributed to increased vulnerability (McCann & Brown, 2019), heightened rates of substance use, and mental health challenges (Bruce et al., 2014; Choukas-Bradley & Thoma, 2022; Delozier et al., 2020; Felner et al., 2020; Fish, 2020; Goldbach et al., 2014; Keuroghlian et al., 2014; Kidd et al., 2017). LGBTQ-related health disparities, including mental health disparities, are linked to minority stressors (Meyer, 1995, 2003). Broadly, minority stress theory suggests that health disparities between LGBTQ and cisgender, heterosexual (Cishet) individuals are related to the chronic stress experienced from identifying as a member of a socially disadvantaged group. It is important to note that general stressors can be 73 73 exacerbated through minority stress (Goldbach & Gibbs, 2017). Generally, any individual experiencing homelessness likely experiences homelessness as a stressor. However, if one has entered into homelessness as a result of rejection of their LGBTQ identity, as is not uncommon for LGBTQ young people experiencing homelessness (Castellanos, 2016; Robinson, 2021), the stress of the experience of homelessness may be exacerbated. Other stressors included in minority stress theory include external prejudice events, such as discrimination or victimization as a result of sexual orientation and/or gender identity, and internal processes including expectations of rejection, internalized homophobia or transphobia, and concealment of one’s LGBTQ identity (Douglass & Conlin, 2022). Previous research has identified stigma as a contributing factor to being kicked out and experiencing homelessness (Bruce et al., 2014). Once on the streets, LGBTQ young people experience more bullying and harassment (Keuroghlian et al., 2014), higher rates of violent victimization, and are more often the victims of crime (Ormiston, 2022; Ventimiglia, 2011) while commonly fearing law enforcement (McCandless, 2017). Further minority stressors are often experienced while seeking services. Previous studies identified that LGBTQ young adults experiencing homelessness perceived less safety in service environments compared to community settings (DiGuiseppi et al., 2022) and experienced negative stereotyping by staff (Robinson, 2021). Additional minority stress among transgender and gender-expansive young people has been documented regarding bathroom use during the experience of homelessness, as well as gender-based violence in shelter settings (Robinson, 2021). In the face of minority stress, social support can mitigate poor mental health outcomes (Choukas- Bradley & Thoma, 2022; Goldbach et al., 2014; Hatzenbuehler, 2011; Scardera et al., 2020) while promoting positive mental health and wellness (Henderson et al., 2022). Social support is particularly important during the years of young adulthood (Anda, 2013), as relationships play a key role in positive development into adulthood (O’Connor et al., 2011). For LGBTQ young people this often includes relationships with other LGBTQ individuals (Goldbach & Gibbs, 2015). Research has shown LGBTQ individuals tend to have peer groups that include LGBTQ people and Cishet people tend to have peer groups that include other Cishets (Grudniewicz et al., 2016; Jones et al., 2022). The idea that people form 74 74 ties with individuals who are like them is referred to as homophily (McPherson et al., 2001), and research indicates that homophilous relationships are often perceived as supportive. In fact, several studies found that social support played a role in perceived stress (Haslam et al., 2005), such that a greater sense of shared identity was associated with more perceived support and less perceived stress (Haslam & Reicher, 2006). However, in some cases the buffering effects of social support on stress was only seen in the context of shared social identity (Frisch et al., 2014). The present study Hatzenbuehler (2009) proposed a framework that described the mechanisms through which minority stress impacts mental health, theorizing stigma-related stress negatively effects health of minoritized populations by causing emotional dysregulation (Hatzenbuehler, 2009). Using momentary data from a sample of young adults with history of homelessness, the present study seeks to examine the correlation between homophily based on gender identity and sexual orientation and perceived stress among currently and formerly homeless young adults. For LGBTQ young people, we expect perceived homophily regarding sexual orientation and/or gender identity to be associated with greater perceived support. Previous studies identified relationships between gender homophily and greater support (Lee et al., 2018), shared LGBTQ identity and greater closeness and emotional intimacy (Paceley et al., 2017), and shared sexual orientation and everyday providers of social support (Frost et al., 2016). However, it is unclear if shared identity provides additional regulatory benefits above and beyond that of perceived support. Therefore, this study explores if homophilous social interactions support in regulating stress (i.e., stress lower and less variable) above and beyond the effects of perceived support due to increased intimacy associated with shared identity. Additionally, given the lack of safe spaces for LGBTQ young people experiencing homelessness (Choi et al., 2015; Coolhart & Brown, 2017; Ormiston, 2022), it is thought that the effects of shared identity would be more pronounced for those experiencing homelessness. 75 75 Methods Data come from an intensive longitudinal study of young adults with history of homelessness who were either actively experiencing homelessness (i.e., unhoused) recruited from drop-in centers and emergency shelter or had formerly experienced homelessness and living in supportive housing at the time of the study (i.e., housed). To be eligible to participate in this study, which took place in Los Angeles County between June 2017 and March 2019, young adults were required to be between the ages of 18 and 25, or up to the age of 27 if in housing and they had entered the program prior to turning 25; able to read and understand surveys in English and delivered via smartphone; and willing and able to provide written informed consent. Young adults were unhoused if they lacked a fixed, regular, and adequate nighttime residence in which they could stay longer than 30 days (United States Congress, 2009). Upon enrollment, participants received an iPad (Apple, USA) to complete a self-administered questionnaire consisting of two parts: an assessment of demographics and historical experiences, as well as an egocentric network inventory (subsequently described). Following the baseline questionnaire, participants enrolled in the EMA portion of the study and had the option to use a study-provide smartphone, usually a third generation MotoG (Motorola, USA), complete with an unlimited data plan or their own personal phone if it was Andriod-based (Google, USA) and compatible with the study smart phone app. Those who utilized their own personal phone (n=19; 8.6%) were compensated an additional $10 to offset the cost of data usage. Throughout the seven-day study period, momentary surveys were delivered approximately every two hours during waking hours via a custom software app designed for the study. Sleep and wake hours were programmed into the app at time of enrollment to best fit individual, anticipated schedules. Research staff assisted in phone set up and allowed participants to engage in a demonstration of the momentary surveys. Participants earned up to $130 for completing the main study components and were incentivized by compliance with momentary prompts. Full study procedures are available elsewhere (Henwood et al., 2019b). 76 76 Measures Baseline questionnaire Demographic information collected by the baseline questionnaire included in the present study consisted of sexual orientation, sex assigned at birth, gender identity and race. Participants selected which sexual orientation best described them from gay or lesbian, bisexual, heterosexual or straight, questioning or unsure, asexual, another orientation. Sex assigned at birth indicated the sex issued on their original birth certification: male, female, or don’t know. Gender identity selections, offered as a check-all, included male, female, transman, transwoman, genderqueer/gender non-conforming, and a different identity, with a write-in option. LGBTQ individuals included a selection of any sexual orientation besides heterosexual and/or gender of male or female. A cross comparison of gender and sex at birth was done to ensure all gender minorities were captured. Racial-ethno identity was assessed by asking if participants identified as Hispanic or Latino, followed by racial identity offering American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, South Asian, White, Bi/Multi-racial or ethnic, Hispanic/Latino only, and other. An egocentric network inventory followed the demographic survey. The focal point of a social network is referred to as an ego (Burt, 1984; Perry et al., 2018), represented by study participants in the present study. Social network members, also referred to as alters, were elicited using an interaction-based elicitation that asked egos to “name five people [they] interacted with or talked to the most in the past three months.” This method has been employed in numerous studies with young people experiencing homelessness (Barman-Adhikari et al., 2018; Petering et al., 2016; Rice & Yoshioka-Maxwell, 2015; Semborski et al., 2023; Tyler, 2013; Wenzel et al., 2012b; Yoshioka-Maxwell et al., 2015). Aliases or nicknames could be used to increase confidentiality and comfort. The five alters were referred to as their “top five.” Thus, the present study contains 205 egos with five alters, making for a total of 1,025 alters. Following elicitation, participants provided information about each alter. Sexual orientation and gender identity were assessed in the same manner as with egos. Homophilous alters (i.e., like alters) are network members that share characteristics, in this case sexual and/or gender identities, with the ego. To 77 77 accomplish this, sexual orientation was dichotomized into LGBQ and heterosexual for both ego and their alters. Gender identity was reduced to three categories: cis-man, cis-woman, and gender expansive comprised of transmen, transwomen, genderqueer/non-conforming, and other identities. A dichotomous variable was then created for each alter for both sexual orientation and gender identity indicating shared identity. For sexual orientation a value of 1 was given to hetero alters of a hetero ego and to LGBQ alters of a LGBQ ego. A value of 1 was also assigned for shared gender identities: to cis-men alters of cis-men egos, cis-women alters of cis-women egos, and gender expansive alters of gender expansive egos. At each prompt, the presence of like alters were summed for both sexual orientation and gender identity. Thus, like alters were measured by a continuous variable ranging from 0-10, a possible maximum of five alters with two shared identities at each prompt. Additional information gathered about alters included unhoused status, reflecting alters who were actively experiencing homelessness at the time of the study. Other information focused on the nature of the relationship between the ego and alter. Participants were asked to select alters that provided support. Emotional support included those who in the past three months provided help or advice when “in crisis, feeling depressed, or dealing with drama and major issues.” Material support was assessed by asking, “in the past three months, who have you borrowed money or other material things from when you needed it?” Likewise, alters were considered to provide service support if they were someone who participants “talked to about where to get social services (help with housing, food, clothes, casework, etc.).” A continuous variable was then created indicating the amount of support, summed from the three support types (range: 0-3), for each alter. Ecological momentary assessment Momentary surveys delivered to smartphones over the EMA week focused on collecting socio- environmental data and began with asking participants to select which of their top five alters they had interacted with over the past two hours. Names elicited during the baseline survey were piped into momentary surveys. Baseline and EMA data were linked via participant identification number and alter characteristic was linked to alter number (1-5). 78 78 Momentary surveys also assessed perceived stress by asking, “just before the phone went off, how stressed did you feel?” Participants answered based on a five-point Likert scale ranging from “not at all” (1) to “extremely” (5) (Posner et al., 2005; Tseng et al., 2014). Analyses A total of 251 young adults were enrolled in the study; however, 12 opted out of the EMA portion of the study prior to the start of the EMA week. During the week, an additional eight people opted out of the study and did not complete the EMA week. Among those who did complete the study week, ten people had low compliance; meaning, they completed greater than one standard deviation below the average amount of prompts (i.e., had completed less than 10 prompts during the week) and were thus excluded, leaving 221 participants. Of these, 16 people did not answer any social network questions in the momentary surveys. Little’s chi-squared test was used to assess the missing social network data in momentary prompts and identified these data were not missing completely at random (x 2 12<0.001). Using mixed effects modeling, predictors were used to examine missingness (k=1,887), and identified missingness could be explained, at least in part, by housing status. Seventy-five percent (n=12) of the 16 people with complete missingness on momentary social network data were unhoused (p=0.002). As analyses consider housing status as the primary axis of analysis, missing values were not imputed bringing the final analytic sample to 4,766 prompts across 205 people (115 housed and 90 unhoused). Initial data cleaning was done in R, data merged, and matched EMA with baseline for analysis in Stata 16.1. Chi-square and t-tests were used for testing differences between housed and unhoused subsamples. Three mixed effects models examined characteristics associated with homophilous alters within the full sample and housed and unhoused subgroups. The purpose of these models was to identify traits associated with like alters; therefore, models were run at the alter level. As each ego listed five alters, the model for the full sample included a sample size of 1,025 (205 egos x 5 alters). Location-scale models (Hedeker et al., 2009) using the MIXREGLS function (Hedeker & Nordgren, 2013) assessed the association of shared identity based on gender and sexual orientation, and stress. Mixed-effects location scale models extend traditional mixed-effects models by modeling the 79 79 between-subject (BS) and within-subject (WS) variances as log-linear functions of the covariates. The BS variance function measures variability in mean responses, having adjusted for covariates; and the WS variance function measures variability in the responses about subjects’ adjusted mean responses (Hedeker & Nordgren, 2013; Leckie, 2014). Two location-scale models were conducted: one for the housed group (n=115) and one the unhoused group (n=90). Models controlled for sex assigned at birth, race, reported momentary stress events, time, and other relevant alter characteristics including the amount of support offered and unhoused status. Results Table 4.1 displays participant (i.e., ego) characteristics, as well as relevant characteristics of their five alters. LGBTQ and Cishet individuals significantly differed in their average stress levels reported over the course of the EMA week, with LGBTQ participants reporting higher stress (p<0.001). Additionally, more individuals who were assigned female at birth were LGBTQ (p=0.02). Likewise, more participants who identified as male were Cishet (p<0.001). Data from momentary prompting showed LGBTQ participants reported a stress event at 10% of prompts, compared to Cishets who reported stress events at an average of 5% of prompts (p=0.006). For LGBTQ participants, a greater proportion of prompts also featured alters who were reported to be the same sexual orientation (p=0.009) and currently experiencing homelessness (p=0.03). Gender and sexual orientation characteristics of alters gathered at baseline also differed significantly between LGBTQ and Cishet participants. Unsurprisingly, LGBTQ egos had more LGB and Trans*, gender non-conforming, or expansive alters (p’s<0.001). Likewise alters elicited from LGBTQ egos were more often currently experiencing homelessness (p<0.001). Finally, Cishet egos had significantly more egos with shared sexual and/or gender identities (85.8%), compared to LGBTQ egos (47.7%; p<0.001). Characteristics associated with like alters Mixed-effect regression models of characteristics associated with homophilous alters are presented in Table 4.2. Across the EMA week, LGBTQ participants, on average, had 0.43 less shared identities present at any given prompt, compared to Cishet participants (β=-0.43, p=0.001). However, 80 80 each additional supportive quality present was associated with an average increase of 0.49 shared identities (β=0.49, p<0.001) and each additional unhoused alter was associated with an average increase of 0.71 shared identities (β=0.71, p<0.001). Because these statistics refer to the qualities of the social network present at each prompt and not necessarily indicative of the characteristic of the specific social network member, a mixed-effects logistic regression at the alter level is also presented in Table 4.2. The alter level regression utilized social network data gathered at baseline, rather than the network reported present over the EMA week. Each participant (n=205) listed five alters, making for a total of 1,025 alters nested within study participants (i.e., ego). Similarly, LGBTQ egos had 94% lower odds of having LGBTQ alters (OR=0.06, p<0.001), compared to Cishet participants who reported 85.8% of their elicited alters as Cishet. Support at the alter level was marginally significant and indicated that with each additional supportive characteristic reported, alters had 22% higher odds of sharing sexual orientation and/or gender identity with the ego (OR=1.22, p=0.05). Likewise housed egos had 88% higher odds of alters sharing these identities, though again, marginally significant (OR=1.88, p=0.05). Unhoused status was not significantly associated with shared identity at the alter level. Location-scale model of homophilous alters by housing status Table 4.3 displays location-scale models stratified by housing status. As evident from the scale parameters, there is considerable heterogeneity of scale between subjects regardless of housing status, as estimates of σ 2 ω substantially exceed their standard error estimates. Meaning, subjects differ in terms of their stress variation. For both housed and unhoused, the positive linear covariance indicates that subjects who are high in their mean also experienced greater variation in stress. However, a likelihood ratio test compared linear and quadratic relationships, identifying a quadratic model was a better fit for those in housing (x 2 1=19.72, p<0.001) and as well as those who were unhoused (x 2 1=64.67, p<0.001). The quadratic relationship suggests those who had high stress were less varied across prompts in their average reported stress. See Figure 4.1 for WS variance functions plotted against random-location effect for the housed group, and Figure 4.2 for the unhoused group. The positive linear covariance identified could mean that better moods (i.e., less stress) may be more “trait-like” and not as reactive to environmental 81 81 cues. Additionally, this positive association might reflect a floor effect of measurement, also captured in quadratic relationships (Hedeker & Nordgren, 2013; Leckie, 2014). Mean model results After controlling for sex assigned at birth, race, the effect of time, and momentary stress events that could impact affect, mean model results showed housed, LGBTQ participants with the average amount of like alters experienced greater stress, on average, compared to Cishet participants (β=0.30, p=0.02). However, housed participants who saw more unhoused alters than the group average experienced lower stress overall (β=-0.42, p=0.009). In terms of shared identity, housed, Cishets who saw more like alters than the group average over the EMA week reported lower average stress levels (β=-0.28, p<0.001), whereas LGBTQ participants in housing who saw more LGBTQ alters over the course of the week than the group average reported higher average stress levels (β=0.60, p<0.001). The significant relationships identified in the housed group were not identified in the unhoused group. Mean model findings were null for the unhoused group. Between-subject (BS) variance function BS variance model results for the housed subsample revealed that homophilous alters had differing impact among LGBTQ and Cishet participants. Overall, LGBTQ young adults residing in supportive housing were, on average, more heterogenous in their reported stress across the EMA week (α=0.69, p=0.02). Heterogeneity further increased for LGBTQ participants who interacted with a greater number of alters with LGBTQ identities, relative to those who interacted with less homophilous alters across the week, indicating the unexplained differences in mean stress levels were more pronounced for LGBTQ housed residents who saw more like alters (α=1.55, p<0.001). The inverse relationship was identified for Cishet participants (α=-0.79, p=0.002), such that Cishets who interacted with a greater number of Cishet alters over the course of the week were more homogenous as a group in their stress response. BS variance model results for the unhoused subsample indicated that homophilous alters for LGBTQ participants and supportive alters had differing impact on group-level stress variability. 82 82 Unhoused, participants who reported interacting with a more supportive network over the course of the week were more homogenous than those who reported a less supportive social environment over the EMA week (α=-0.31, p=0.01). However, like the housed group, heterogeneity increased for unhoused, LGBTQ participants who interacted with a greater number of alters with LGBTQ identities, relative to those who interacted with less homophilous alters across the week (α=0.61, p=0.03). Within-subject (WS) variance function The WS variance function for the housed group identified four significant associations. First, at prompts with more perceived support than one’s average, participants experienced lower moment-to- moment variability in stress (τ=-0.07, p=0.04). Further, housed, Cishet young adults who saw more alters with shared identities than the group average over the course of the week experienced lower moment-to- moment variability in stress (τ=-0.52, p=0.01). Conversely, housed LGBTQ participants who saw more LGBTQ alters over the course of the week had greater stress variability (τ=0.55, p=0.02) and they also experienced greater stress variability, indicating more erraticism, following interactions with alters holding a greater number of LGBTQ identities (τ=0.18, p=0.007). This means housed LGBTQ participants’ stress variability increased by a factor of 18% with each additional shared identity reported in the preceding two hours. The WS variance function indicated that unhoused LGBTQ participants experienced decreased moment-to-moment variability, indicating more consistent affect, following interactions with alters holding a greater number of shared identities, relative to the within-subject effect of homophilous alters on Cishet variability (τ=-0.30, p<0.001). Unlike housed LGBTQ participants who experience increased variability following interactions with LGBTQ alters, unhoused LGBTQ participants’ stress variability decreased by a factor of 26% with each additional shared identity reported in the preceding two hours. Alters’ unhoused status only appeared to have associations with within-subject variability for unhoused participants. Here, unhoused participants who saw more unhoused alters over the course of the week were less variable overall (τ=-0.57, p=0.04), but were more variable in their stress at prompts where more unhoused alters were reported compared to their own average (τ=0.33, p<0.01). 83 83 Discussion The present study investigated the importance of social interactions with persons of shared identities for LGBTQ young adults drawing on a body of literature that has established connections between identity homophily and perceived support for LGBTQ individuals (Frost et al., 2016; Lee et al., 2018; Paceley et al., 2017). Results from the present study echoed this literature. In this study, social network members who were the same sexual orientation and/or gender identity as the study participant were also reported as providing a greater degree of support to the study participant, for both LGBTQ and Cishet participants alike. As a result of this support, research has also recognized the emotional benefits of homophilous relationships, including a buffering effect on stress (Frisch et al., 2014; Haslam & Reicher, 2006) and the specific effects of minority stress (Goldbach & Gibbs, 2015). However, in previous research, it was unclear whether identity homophily provides additional stress buffering effects beyond what is provided by perceived support. Given this, the present study explored if identity homophily is associated with lower stress above and beyond the effects of perceived support. Literature on the emotional effects of homophily historically has focused on valence or mean levels. The present study identified decreased average stress levels among housed, Cishet participants who had interacted with more Cishet social network members, but increased average stress levels for housed, LGBTQ participants who had interacted with more LGBTQ network members; indicating shared identity based on sexual orientation and/or gender identity had differing effects on stress based on LGBTQ status. This relationship was only identified for housed participants. For unhoused participants, alters had no statistically significant impacts on average stress levels. The present study also extended beyond examining the mean and examined the relationship between shared identity and stress variability. In terms of variability, we identified different stress responses following interactions with homophilous alters by housing status, indicating partial support of our exploratory aims. Interactions with like alters was associated with lower stress variability among unhoused, LGBTQ participants, such that variability was decreased following interactions with alters holding a greater number of shared identities. 84 84 However, the opposite was found for housed, LGBTQ participants who became more variable in their stress responses following interactions with alters with more shared identities. Taken with results from the mean model, this may seem counterintuitive; however, increased stress variability among LGBTQ young adults residing in supportive housing is conceivable considering LGBTQ young people often do not perceive service environments as LGBTQ-friendly spaces (Coolhart & Brown, 2017; Ormiston, 2022; Robinson, 2021). In fact, LGBTQ young adults residing in supportive housing in one study identified their housing environment as phobic to the point where they felt it could possibly risk their eviction (DiGuiseppi et al., 2022). Thus, it is thought that interactions with other LGBTQ individuals in and/or around the housing environment could be associated with increased stress and stress variability. Further, perceived hostility in the housing environment, combined with changes that occur in the transition from homelessness to housing, which often include shifting priorities (Henwood, Redline, Semborski, et al., 2018) and restructuring of social ties to align with priorities (Henwood et al., 2017; Henwood, Redline, & Rice, 2018), may also trigger increased stress. In the transition from homelessness into housing, individuals may attempt to distance themselves from aspects associated with street life, including social ties. As LGBTQ participants endorsed more unhoused alters than Cishets, and reported unhoused alters present at a greater proportion of prompts, it may be that shared identity is conflated with unhoused status for LGBTQ individuals in this study. Since housed young adults may worry about re- entering homelessness, the threat posed by the possibility of identifying with aspects associated with homelessness, including LGBTQ identities, may contribute to increased erraticism among LGBTQ residents in supportive housing following interactions with other LGBTQ individuals. Additionally, this could simply reflect the fact that, when stressed, young adults in housing sought out their closest social connections, which happen to be those sharing LGBTQ identities. Regardless, these findings suggest homophilous relationships have additional importance above and beyond providing support and the impacts of unhoused alters. However, both housed and unhoused LGBTQ individuals who saw more like alters than the group average over the week were more heterogenous in their stress response. This indicates a varying responses to homophilous relationships for 85 85 those identifying as LGBTQ, an important consideration when interpreting findings overall, as LGBTQ participants who interacted with more LGBTQ alters were also less alike as a group in their stress response. Limitations and future directions Several study limitations must be addressed. Additional theoretical considerations regarding the interpretation of effects associated with homophily point out the confounding nature of homophily with social contagion (Shalizi & Thomas, 2011). This combined with the nature of how the study was designed makes it impossible for us to rule out the possibility that stress variability was “passed” from one individual to another through a contagion effect. Likewise, alter attributes likely intersect in complex ways. Therefore, this study examined alter characteristics (i.e., shared identity, support, and unhoused status) present at each time point. We do not examine if the characteristics present were from one or multiple alters. Further, there is much work to be done to understand specific experiences of unique subgroups featured in this sample. LGBTQ youth of color face compounded risks related to homelessness and intersectional discrimination that impact stress exposure and overall health (Serpas & García, 2021). Perhaps most obvious when considering LGBTQ populations is the aggregation of sexuality and gender. Research on stressors for transgender and gender expansive individuals cite specific experiences, such as gender dysphoria (Hunter et al., 2021) and a host of other gender-specific minority stressors (Delozier et al., 2020). One of the few studies that disaggregates transgender participants examined the experiences of transgender youth compared to the full LGBTQ sample and found that transgender youth reported higher frequencies of running away or being ejected from their family home, foster home, or relatives’ home (Shelton et al., 2018). Conclusion This study further highlights the effects of minority stress on LGBTQ young people who have experienced homelessness. Overall, these findings show a complex relationship between LGBTQ identity and mental health. Though LGBTQ network members were associated with increased stress variability 86 86 for LGBTQ participants residing in supportive housing, network members of shared sexual and/or gender identities were also more likely to be considered as emotionally supportive regardless of housing status. For the young adults in this sample, this may speak to the importance of shared identity and the broader socio-political landscape that is ultimately harmful to LGBTQ people. To this regard, findings reinforce a large, continued need for a LGBTQ-specific service agenda within the broader programming efforts for homeless youth and young adults. This work echoes previous recommendations to expand federal initiatives and funding to include LGBTQ youth. Given the complexity of these relationships, it becomes more imperative that service spaces for young people are designed for inclusion and to foster relationships, both within and beyond the LGBTQ community. 87 87 Table 4.1. Sample Characteristics Demographic characteristics p Housing status (housed) 0.71 Gender Man <0.001 Woman 0.59 Trans*, gender non-conforming, or expansive -- Sex assigned at birth, female 0.02 Sexual Orientation Hetero <0.001 Gay/Lesbian -- Bi/Pan -- Another orientation -- Race/Ethnicity Black 0.49 Latino 0.11 Bi/Multi-racial or ethnic 0.08 White 0.43 Another racial identity 0.43 Mean stress (x (sd)) <0.001 Momentary characteristics * Alter same gender 0.61 Alter same sexual orientation 0.009 Alter LGBTQ status match 0.78 Alter currently homeless 0.03 Stress event 0.006 Baseline alter characteristics p LGB <0.001 Gender Man <0.001 Woman 0.10 Trans, gender expansive, non- conforming, or non-binary <0.001 LGBTQ <0.001 Ego-alter LGBTQ status match <0.001 Currently homeless <0.001 *proportion of prompts ^ 205 participants, 5 alters each = 1,025 687 (67.0) 241 (47.7) 446 (85.8) 0.42 (0.49) 0.47 (0.50) 257 (25.1) 158 (31.3) 99 (19.0) 0.16 (0.40) 0.19 (0.40) 0.12 (0.33) 0.49 (0.50) 315 (30.7) 241 (47.7) 74 (14.2) 0.36 (0.48) 0.07 (0.26) 0.10 (0.30) 0.05 (0.22) 0.33 (0.47) 0.32 (0.47) 0.34 (0.47) 19 (9.3) 0.49 (0.50) 0.50 (0.50) 461 (45.0) 227 (45.0) 234 (45.0) 71 (6.9) 63 (12.5) 8 (1.5) 488 (47.6) 215 (42.6) 273 (52.5) n=1,025 ^ n=575 n=450 283 (27.6) 217 (43.0) 66 (12.7) 1.88 (1.19) 2.04 (1.25) 1.75 (1.11) 52 (25.4) 31 (30.7) 21 (20.2) 19 (9.3) 11 (10.9) 8 (7.7) 11 (10.9) 8 (7.7) 82 (40.0) 38 (37.6) 44 (42.3) 44 (21.5) 17 (16.8) 27 (25.7) 43 (21.0) 43 (42.6) -- 19 (9.3) 19 (18.8) -- 109 (53.2) 5 (4.9) 104 (100.0) 34 (16.6) 34 (33.7) -- 79 (38.5) 47 (46.5) 32 (30.8) 66 (32.5) 34 (34.3) 32 (30.8) 25 (12.3) 25 (25.3) -- 57 (54.8) 112 (55.1) 40 (40.4) 72 (69.2) Total (n=205) LGBTQ (n=101) Cishet (n=104) n (%) n (%) n (%) 115 (56.1) 58 (57.4) 88 88 Table 4.2. Mixed-effects regressions of characteristics associated with homophilous alters at the EMA and alter levels β se z p OR se z p Housed ego 0.08 0.11 0.72 0.47 1.88 0.61 1.95 0.05 LGBTQ ego -0.43 0.11 -4.02 <0.001 0.06 0.02 -8.23 <0.001 Support count × 0.49 0.01 43.32 <0.001 1.22 0.13 1.93 0.05 Currently homeless count ~ 0.71 0.04 18.72 <0.001 1.26 0.33 0.90 0.37 Constant 0.60 0.10 6.16 <0.001 8.23 2.73 6.36 <0.001 * Outcome is number of shared identities present at each prompt (max 5 alters with 2 shared identities = range of 0-10) ^ Outcome is dichotomous indicator of homophilous relationship × Total number of supportive qualitites (range: 0-3) ~ Total number of alters currently experiencing homelessness (range: 0-5) EMA level (n=205; k=4,766) * Alter level (n=1,025) ^ 89 89 Table 4.3. Mixed-effects location-scale models of stress by housing status estimate se z p estimate se z p Mean Intercept β 0 1.80 0.09 20.37 <0.001 1.94 0.10 19.62 <0.001 BS Like alter β 1 -0.28 0.06 -4.37 <0.001 0.06 0.07 0.78 0.44 WS Like alter β 2 -0.002 0.02 -0.12 0.90 -0.01 0.03 -0.40 0.67 BS Like alter * LGBTQ β 3 0.60 0.16 3.74 <0.001 0.11 0.10 1.10 0.27 WS Like alter * LGBTQ β 4 -0.01 0.03 -0.35 0.73 -0.02 0.05 -0.43 0.67 LGBTQ identity β 5 0.30 0.13 2.36 0.02 0.01 0.09 0.14 0.88 BS supportive alter β 6 -0.05 0.04 -1.32 0.19 -0.02 0.04 -0.41 0.57 WS supportive alter β 7 -0.002 0.02 -0.13 0.90 0.01 0.02 0.50 0.62 BS homeless alter β 8 -0.42 0.16 -2.61 0.009 -0.11 0.15 -0.69 0.49 WS homeless alter β 9 -0.03 0.05 -0.65 0.52 0.04 0.06 0.65 0.52 Sex assigned at birth, female β 10 -0.04 0.06 -0.66 0.51 -0.21 0.06 -3.80 <0.001 Race, Black β 11 -0.04 0.07 -0.55 0.59 0.03 0.06 0.56 0.57 Momentary stress event β 12 0.27 0.07 4.14 <0.001 0.32 0.06 5.78 <0.001 Time β 13 0.009 0.009 0.97 0.33 -0.008 0.007 -1.14 0.26 BS Variance Intercept α 0 -0.25 0.22 -5.68 <0.001 -0.78 0.23 -3.4 <0.001 BS Like alter α 1 -0.79 0.25 -3.17 0.002 0.10 0.19 0.54 0.59 WS Like alter α 2 -0.14 0.07 -1.92 0.06 -0.10 0.11 -0.94 0.35 BS Like alter * LGBTQ α 3 1.55 0.34 4.59 <0.001 0.61 0.27 2.21 0.03 WS Like alter * LGBTQ α 4 0.04 0.10 0.38 0.71 0.12 0.12 0.96 0.33 LGBTQ identity α 5 0.69 0.28 2.42 0.02 -0.19 0.23 -0.82 0.41 BS supportive alter α 6 -0.18 0.12 -1.50 0.13 -0.31 0.13 -2.47 0.01 WS supportive alter α 7 0.09 0.05 1.92 0.06 -0.03 0.04 -0.77 0.45 BS homeless alter α 8 -0.61 0.61 -1.02 0.31 0.25 0.46 0.56 0.58 WS homeless alter α 9 -0.11 0.17 -0.65 0.52 0.09 0.16 0.56 0.57 WS Variance Intercept τ 0 -0.16 0.17 -0.97 0.33 0.19 0.15 1.05 0.30 BS Like alter τ 1 -0.52 0.20 -2.56 0.01 0.17 0.15 1.17 0.24 WS Like alter τ 2 -0.02 0.05 -0.36 0.72 0.003 0.05 0.06 0.95 BS Like alter * LGBTQ τ 3 0.55 0.24 2.27 0.02 0.07 0.21 0.34 0.73 WS Like alter * LGBTQ τ 4 0.18 0.07 2.69 0.007 -0.30 0.08 -3.81 <0.001 LGBTQ identity τ 5 0.35 0.18 1.95 0.05 0.22 0.16 1.36 0.17 BS supportive alter τ 6 0.01 0.12 0.13 0.90 0.01 0.11 0.11 0.91 WS supportive alter τ 7 -0.07 0.03 -2.00 0.04 0.08 0.05 1.68 0.09 BS homeless alter τ 8 -0.59 0.33 -1.79 0.07 -0.57 0.28 -2.02 0.04 WS homeless alter τ 9 0.23 0.12 -1.88 0.06 0.33 0.13 2.53 0.01 Scale σ 2 ω 0.40 0.07 5.90 <0.001 0.28 0.11 2.43 0.02 Covariance σ υω 0.75 0.14 5.45 <0.001 1.21 0.20 6.18 <0.001 Covariance σ υω 2 -0.46 0.10 -4.40 0.001 -0.74 0.14 -5.34 <0.001 Deviance Housed (n=115; k=2,757) Unhoused (n=90; k=2,009) 20120.29 8904.41 90 90 Figure 4.1. Predicted WS variance functions plotted against random-location effect for the housed group Figure 4.2. Predicted WS variance functions plotted against random-location effect for the unhoused group 91 91 Chapter 5. Conclusion Introduction This dissertation aimed to better understand the implications of socio-environmental factors in emotional dynamics and regulation for two samples of young adults: those currently experiencing homelessness and formerly homeless young adults who have transitioned into supportive housing. Prior to this work, literature has demonstrated the importance of emotional regulation for young people navigating complex and often chaotic setting associated with the experience of homelessness. Interventions targeting emotional regulation behavior with this group have been found to promote protective factors against substance use (Wong et al., 2013b), violence (Barr et al., 2022; Petering et al., 2018, 2021), suicidality (Barr et al., 2017), and other maladaptive behaviors (Maguire et al., 2017). Additionally, skills in emotional regulation foster resilience among at-risk young adult populations (Dias & Cadime, 2017; Mestre et al., 2017). Despite the unique value of emotional regulation skills for this population, no previous work has examined emotion dynamics to understand the underlying processes of regulation for young people experiencing homelessness. The empirical studies depicted in this dissertation are the first of their kind. Drawing on the parent study, NIHM Grant No. 1R01MH110206 and NIMH Grant No. F31MH126641, this body of work utilized novel mixed methods including data from momentary sampling coupled with geographic information systems science to examine emotions at a granular level over time and across a variety of socio-environmental situations. As a result, this dissertation added to the literature by 1) modeling emotion dynamics to gain insight into components that comprise emotional regulation for a population proven to benefit from emotional regulation interventions (Paper 1); 2) utilizing geospatial analysis to better understand the connection between risky locations and momentary affect (Paper 2); and 3) examining the implications of time-varying social interactions with momentary affect through a social network inventory embedded within Ecological Momentary Assessment (EMA) (Paper 3). Combining EMA, geospatial analysis, and social network methods permitted the Social Ecological Model to come alive and exist within time and space in the daily lives of these young adults. The value of these 92 92 transdisciplinary data will now be illustrated in the summation of findings and their application in practice, policy, and future research. Review of Major Findings and Integration with Existing Literature This dissertation identified differences in emotional processes by housing status, particularly when considering multiple socio-ecological levels. This work demonstrates that these levels often work together to influence mental health outcomes. This is a common assumption in social ecological theory, which often has a health focus. In fact, the Social Ecology Model has previously been utilized as a tool to promote adaptive functioning, recovery, and participation in community life for persons living with serious mental illness (Kloos & Shah, 2009). Findings presented in this dissertation identify factors at the individual, relational, community, and societal levels that impact moment-to-moment mood and uncover emotional patterns related to mental health and emotional regulation. The subsequent subsections review the findings of Chapters 2-4 in relation to existing literature followed by a crosscutting discussion of key findings that add depth to our understanding of emotional trajectories of currently and formerly homeless young adults and the impact it has in daily life. Modeling emotion dynamics of housed and unhoused young adults using ecological momentary assessment (Paper 1) The first paper presented in this dissertation examined mental health among young adults currently experiencing homelessness (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed) through modeling emotional dynamics in two studies. Study 1 examined positive affect (PA) and negative affect (NA) broadly, measuring emotional inertia and instability. These constructs relate to the temporal ordering and variability of emotions. Emotional inertia refers to a temporal dependency of an emotion from one time point to the next and emotional instability refers to heightened variability of emotions over time. Inertia of NA was found to be associated with housing status in that unhoused participants had greater NA inertia, signaling that, when activated, NA persists for a longer period, compared to those in housing. This may indicate that unhoused young adults in this sample experienced greater difficulty in regulating their NA and/or are less sensitive to environmental 93 93 stimuli. Other null findings from this study are supported by the lack of mental health differences by housing status. Affect dynamics have previously shown specificity for anxiety and depression (Bosley et al., 2019), which did not differ by housing status in this sample. While Study 1 looked at PA and NA, Study 2 used emotional network density to examine specific emotions included in PA (happy, calm, excited) and NA (sad, stressed, irritated). Using a network approach, emotional network density builds on the concept of inertia by considering not only the temporal dependency of an emotion on itself, but temporal dependency of all emotions in the “network.” Emotion network density considers how strongly emotions impact each other. Thus, density indicates the strength of emotional ties. Results indicated that unhoused participants had denser emotion networks compared to their housed counterparts, particularly regarding negative emotions. Controlling for baseline differences by housing status, greater overall density and greater negative density predicted lower odds of being housed. Previous research indicates denser emotion networks, particularly density of negative emotions, are associated with neuroticism (Bringmann et al., 2016), major depressive disorder (Pe et al., 2015b), anxiety disorders (Shin et al., 2022b), and depression specifically among adolescents (Lydon-Staley et al., 2019). Therefore, despite no major differences in anxiety, depression, or PTSD scores by group, the present study still highlights the potential for increased emotional vulnerability for young adults actively experiencing homelessness. Though there were no statistically significant differences in weekly averages of affect (i.e., intensity), the unhoused group consistently had higher average affect, regardless of valence. Despite similar intensity, the impact of individual emotions on other emotions was greater in emotion networks of unhoused young people such that it seems likely that the activation of a single node (i.e., emotion) could trigger the activation of other nodes. This may indicate a tendency for unhoused young people to get caught in cycles of negative emotions, indicating a greater struggle to down-regulate, compared to housed peers. This is problematic, as previous work show skills in emotional regulation to be protective against substance use (Wong et al., 2013b), violence (Petering et al., 2018), suicidality (Barr et al., 2017), and 94 94 other maladaptive behaviors (Maguire et al., 2017) among high risk young adults, including those experiencing homelessness. Although findings cannot speak to causation, they potentially highlight a more positive emotional experience for those residing in supportive housing, compared to their unhoused counterparts. While findings did not identify improved outcomes in emotion dynamics among those who transitioned from homelessness into housing, “happy” was identified as an emotion most impactful on other emotions for the housed group and “sad” emerged as this emotion for the unhoused group. Further, feeling sad appeared to impact the overall emotional experience of unhoused young adults above and beyond that of feeling happy for their housed peers. This speaks to the difficulty of coping with the complex and chaotic environments associated with homelessness (Srinivasan, 2021) which are often traumatic (Gilmoor et al., 2020; Pope et al., 2020; Tsai et al., 2020). Navigating risk environments: The relationship between housing and mood for young adults experiencing homelessness (Paper 2) The second paper presented in this dissertation is a mixed methods study that examined the physical locations and mood of currently homeless (i.e., unhoused) and formerly homeless young adults residing in supportive housing (i.e., housed) over one week through use of Geographic Ecological Momentary Assessment (GEMA) and semi-structured interviews. Specifically, we were interested in housing status as an effect modifier in the relationship between risk environments and mood. Geospatial analysis reinforced literature that established spatial relationships between homelessness, violent crime, and drug activity (Barnum et al., 2017; Doran et al., 2022; Ellsworth, 2019; Fine et al., 2022; Tong et al., 2021; Yoo & Wheeler, 2019). Quantitative results revealed unhoused individuals were in homeless and violence hot spots for a greater proportion of prompts and spent more time in riskier locations (i.e., areas with a greater concentration of risk) throughout the EMA week. Qualitative analysis reinforced drug activity and violence as two important environmental risk factors that impacted mood and even influenced choices about where young people in the study traveled over the course of their day. Mixed methods findings were expansive in many cases in that the qualitative results in many cases confirmed and often 95 95 elaborated quantitative findings; however, there were occasions where qualitative findings were discrepant with quantitative findings. Overall, housed participants generally had lower PA over the EMA, which aligns with findings from Study 2 of Paper 1, where unhoused participants had higher averages across all measured emotions, regardless of valence. Further, findings suggested increased erraticism was associated with areas of concentrated risk for unhoused participants, but the inverse or no relationship was identified for the housed group. Mixed methods findings were expansive in that the qualitative results in many cases confirmed and often expanded upon quantitative findings. In terms of the relationship between violence in the environment and mood, quantitative results indicated that housed participants as a group had an array of responses to more violent areas, making the group more heterogenous in their variability. Some housed participants reflected on their experience with violence when unhoused and cited police sirens as triggering. However, several also described their housing as a buffer. Unhoused participants, on the other hand, responded similarly as a group to more violent areas. They were also less erratic when in areas with more violence, but more erratic prior to being in a location with more violence. This anticipation was evident in qualitative interviews. Unhoused young people described states of hypervigilance, which could explain decreased variability when in areas with more violence. Drug activity was discussed by both housed and unhoused participants in interviews, and although housed participants reported actively avoiding areas associated with drug activity, drug-related hot spots were not found to be significantly associated with emotional patterns for housed young adults. Meanwhile, unhoused participants were identified as being more erratic in areas associated with more drug activity, but more alike as a group in their emotional responses when in these areas. Qualitatively, unhoused participants related their feelings to issues of safety, sanitation, and desires to change their circumstances and get ahead. Finally, environmental risk factors, such as drug activity and violence, were explicitly linked to the lived experience of homelessness by housed residents only. Despite this, housed individuals were less erratic in their mood compared to those who were unhoused, even when in areas with a greater 96 96 concentration of homelessness than their average. Conversely, unhoused participants became more erratic when in locations with a greater concentration of homelessness than their average. Unhoused participants were also more homogenous at prompts when they were in areas with a greater concentration of homelessness. Overall, results pointed to the protective nature of housing and highlights how obtaining housing may be helpful in mental health recovery among people experiencing homelessness. Stress lability among housed and unhoused LGBTQ young adults: The role of identity homophily in social networks (Paper 3) The third paper presented in this dissertation focused on a specific sub-population of young adults that experience homelessness at disproportionate rates: LGBTQ young people. Previous research has established that homophilous relationships based on gender and sexual orientation are often perceived as more supportive (Frost et al., 2016; Lee et al., 2018; Paceley et al., 2017) and thus, can be helpful in regulating stress (Frisch et al., 2014; Haslam et al., 2005; Haslam & Reicher, 2006). However, it was unclear if shared identity provided additional regulatory benefits above and beyond that of perceived support. Results from this study echoed the literature. In this study, social network members who were the same sexual orientation and/or gender identity as the study participant were also reported as providing a greater degree of support to the study participant, for both LGBTQ and Cishet participants alike. Literature on the emotional effects of homophily historically has focused on valence or mean levels, and we found that Cishet participants who interacted with more like alters over the course of the week generally reported lower levels of stress, whereas LGBTQ participants who interacted with more like alters over the course of the week tended to report higher stress levels. The present study then extended beyond this and examined the relationship between homophilous relationships and stress variability and identified different stress responses following interactions with homophilous alters by housing status. Interactions with like alters proved beneficial in regulating stress among unhoused, LGBTQ participants, such that erraticism was decreased following interactions with alters holding a greater number of shared identities. However, the opposite was found for housed, LGBTQ participants who 97 97 became more erratic in their stress responses following interactions with alters with more shared identities. Initially, this may seem counterintuitive; however, increased stress variability among LGBTQ young adults residing in supportive housing is conceivable considering LGBTQ young people often do not perceive service environments as LGBTQ-friendly spaces (Coolhart & Brown, 2017; Ormiston, 2022; Robinson, 2021). In fact, LGBTQ young adults residing in supportive housing in one study identified their housing environment as phobic to the point where they felt it could possibly risk their eviction (DiGuiseppi et al., 2022). Perceived hostility in the housing environment, combined with changes that occur in the transition from homelessness to housing, which often include shifting priorities (Henwood, Redline, Semborski, et al., 2018) and restructuring of social ties to align with priorities (Henwood et al., 2017; Henwood, Redline, & Rice, 2018), may also trigger increased stress. In the transition from homelessness into housing, individuals may attempt to distance themselves from aspects associated with street life, including social ties, as found in Paper 2. Further, LGBTQ participants endorsed more unhoused alters than Cishets, and reported unhoused alters present at a greater proportion of prompts, it may be that shared identity is conflated with unhoused status for LGBTQ individuals in this study. Since housed young adults may worry about re-entering homelessness, the threat posed by the possibility of identifying with aspects associated with homelessness, including LGBTQ identities, may contribute to increased erraticism among LGBTQ residents in supportive housing following interactions with other LGBTQ individuals. Additionally, this could simply reflect the fact that, when stressed, young adults in housing sought out their closest social connections, which happen to be those sharing LGBTQ identities. Integration and Theoretical Application In total, this dissertation highlights differences in emotional processes by housing status. In three studies we see ways in which unhoused young adults may be more vulnerable than their housed counterparts. These differences, however, are nuanced. When only considering housing status, differences in intensity (i.e., average), variability, and inertia of emotions do not reach statistical significance. However, when considering moment-to-moment fluctuations socio-environmental context, differences 98 98 emerge. This speaks to the complexity of the experience of homelessness and the transition from homelessness into supportive housing. The work presented here focuses on health outcomes at the individual level (i.e., emotions). Thus, Paper 1 offers two studies that examine the relationship between characteristics at the individual level (the relationship between housing status and mood). Housing status alone did not predict differences in emotion dynamics of positive or negative affect. However, Study 2 presented in Paper 1 identified denser emotion networks in unhoused participants in reference to housed participants, particularly regarding negative emotions. This indicates that although facets of positive and negative affect, such as intensity, variability, and inertia, do not vary by housing status, the relationships between the individual emotions that comprise positive (happy, calm, excited) and negative affect (sad, stressed, irritated) are distinct between housed and unhoused young adults in the sample. This shows unhoused young adults are potentially more likely to get caught in cycles of emotions and may struggle to down-regulate negative emotions more than their housed peers. Housing status, an individual factor in the Social Ecology Model, continued to be a primary interest in Chapters 3 and 4. Chapter 3 considered how factors at the community level, including the presence of homelessness, violence, and drug activity, relate to emotional trajectories, and the implications of housing as an effect modifier. In contrast to Study 1 presented in Paper 2, there was a statistically significant effect of housing status on mood when considering community-level factors in the environment (i.e., concentration of risk related to homelessness, violence, and drug activity). Not only were unhoused participants more often in locations with more risk, but they were also more erratic at any given time point, and even more so when in areas with increased homelessness and drug activity. In this study, the individual level of the Social Ecology Model moderated the association between community level factors and individual health outcomes. Finally, Paper 3 presented in Chapter 4 models the Social Ecological Model with slightly more complexity. Here, the primary individual factors of interest were sexual orientation and gender. Based on individual characteristics related to these social identities, we were interested in varying effects of the 99 99 relational level (i.e., shared social identity in the context of relationship) on emotion dynamics at the individual level. Modeling was further stratified by housing status. Results suggested LGBTQ individuals had differing responses to interactions with others of the same sexual orientation and/or gender identity, but this relationship trended in different directions based on housing status. This means that two individual factors (housing status and LGBTQ status) interact with relational factors in complex ways with ramifications for health outcomes at the individual level. To contextualize findings from this study, societal factors from the Social Ecology Model are discussed. Although societal factors are not directly modeled, they most certainly are confounders in the analyses presented. Without the understanding of minority stressors and stereotype threat (Aronson & McGlone, 2009; Meyer, 1995, 2003; Thorpe et al., 2022), findings that suggest social network connections with other LGBTQ individuals are helpful in stress regulation for unhoused individuals, but unhelpful for regulation for housed, LGBTQ individuals simply do not make sense. Recommendations Understanding key components of health and the impact of environments on health are prerequisites for the development of effective public policy to create healthful surroundings (Stokols, 1992). Findings from this dissertation draw inferences that cut across socioecological levels and have implications for practice and policy, which will now be addressed. Clinical Implications Most notably, this body of work reinforces previous literature that calls attention to emotional regulation as a viable and much needed intervention with young adults experiencing homelessness (Barr et al., 2017, 2022; Maguire et al., 2017; Petering et al., 2018, 2021) and expands it to include young adults who have transitioned into supportive housing. A well-known example is mindfulness which has proven efficient in facilitating emotional regulation (Desrosiers et al., 2013; Teper et al., 2013; Trosper et al., 2009) with an unique impact on dynamics of negative emotions (Keng et al., 2021). Previous research indicates mindfulness-based interventions have successfully improved outcomes for young adults experiencing homelessness (Bender et al., 2015; Brown & Bender, 2018; Chavez et al., 2020) and goes 100 100 further to demonstrate the benefits of peer-led mindfulness interventions (Barr et al., 2022; Petering et al., 2021). As young people transition from homelessness into supportive housing, Paper 2 illuminates the continued challenges that face young adults after exiting homelessness. Certainly, housed individuals can avoid risks in ways unhoused people cannot. However, housed young adults remain activated by risk environments associated with homelessness. In fact, the fear of backsliding was a large deterrent to engaging in these locations and housing was often used as a strategy to avoid temptation, as residents reported relying on their housing as a refuge from engagement with known risk environments. Previous work with adults transitioning from homelessness to housing also suggests a tension between moving forward and leaving the past behind (Henwood et al., 2017). It is noteworthy that substance use remained a concern for individuals even after transitioning to housing, indicating a need for continued targeted intervention. Providers may want to emphasize social support in substance use recovery, as there is evidence of decreased drug use and positive housing outcomes with increase social support (Davidson et al., 2014; Gutman & Raphael-Greenfield, 2017; Henwood et al., 2012; Tan et al., 2021). Likewise, police activity including hearing sirens was mentioned by numerous participants as triggering. Evidence suggests that LGBTQ young people and youth of color are more likely to report police harassment (Ivanich & Warner, 2019; McCandless, 2017). Police activity may therefore activate mental health symptoms and pose setbacks to recovery. Direct service providers may want to engage residents on these topics to support mental health recovery and the development of positive coping strategies. Finally, for LGBTQ young adults who have transitioned in housing, interactions with other LGBTQ individuals in their social network was associated with increased erraticism in perceived stress, despite often being considered supportive compared to others who do not share these identities. This may speak to the importance of shared identity and the broader socio-political landscape that is ultimately harmful to LGBTQ people. Previous research has indicated that service spaces, including supportive housing, have been perceived as unsafe and even discriminatory of LGBTQ individuals (Coolhart & Brown, 2017; DiGuiseppi et al., 2022; Ormiston, 2022; Robinson, 2021). LGBTQ competency needs to 101 101 be a non-negotiable training for staff working with young people experiencing homelessness. Additionally, service planning ought to include specific programming that addresses the unique and diverse needs of LGBTQ young people. Given the complexity of these relationships, it becomes more imperative that service spaces for young people are designed for inclusion and to foster relationships, both within and beyond the LGBTQ community. This, of course, often requires resources which will now be addressed. Policy Implications To develop LGBTQ competency and specific programming, there remains a large, continued need for a distinct LGBTQ agenda in policy focused on youth and young adult homelessness. within the broader programming efforts for homeless youth and young adults. This work echoes previous recommendations to expand federal initiatives and funding to include LGBTQ youth (Keuroghlian et al., 2014). Certainly, policies like the Runaway and Homeless Youth Act of 1974 provide services designed to aid young people experiencing homelessness, but the truth is that not all young people benefit from it equally. This has contributed to LGBTQ young people being excluded from youth service spaces. Additionally, youth of color and LGBTQ youth of color, in particular, have been disproportionately affected by homelessness (Page, 2017) and, at times, experience prolonged homelessness due to kinship structure and housing discrimination (Shelton et al., 2018). Therefore, an intersectional framework is needed to provide the necessary insights to design effective programs and interventions for young people experiencing homelessness that address the complexity and nuance of their needs. Finally, this body of work speaks to the protective nature of housing. Housing improves health and mental health for the same reasons that homelessness is deleterious. Chapter 3 provides a snapshot of how housing can support the navigation of high-risk environments and support overall mental well-being. This aligns with the vast body of work demonstrating the benefits access to housing has on the entire social ecological system at the individual level (e.g., less disease (World Health Organization, 2018)), relational level (e.g., social connectedness and sense of belonging (La Motte‐Kerr et al., 2020)), community level (e.g., prevents violence (Niolon et al., 2017) and reduces crime (Cohen, 2022)), and 102 102 societal level (e.g., reduces healthcare costs (Gordon et al., 2021; Whittaker et al., 2016) and workforce participation (Lopes et al., 2022)). Research has demonstrated the relationship homelessness (i.e., people who lack housing) has with mental illness, drug abuse, and poverty (D. K. Padgett et al., 2012; R. G. Thompson et al., 2013). However, housing itself is complicated by money, and increasingly so if there is no recognized right to housing that ensures access to all. Housing has become thoroughly engulfed in the creation of wealth which has fueled the astronomical rise in the cost of housing. Certainly, high housing costs are negatively impacting health outcomes through precipitating homelessness (Fullilove, 2010). Inability to pay rent also contributes to young adult homelessness (Shelton et al., 2018). Housing has become so directly linked to health that recent research has called for expanding Medicaid to include housing waivers with priority given to those with serious mental illness (Mathis, 2021). Likewise, this body of work suggests increasing access to housing, including supportive and affordable housing, can protect against negative mental health outcomes and facilitate recovery. Conclusion This dissertation takes a transdisciplinary approach in examining emotion dynamics, and ultimately mental health outcomes, of young people with history of homelessness and explores emotional trajectories through the lens of their social ecological environment that is largely dictated by housing status. This dissertation added to the literature by 1) modeling emotion dynamics to gain insight into components that comprise emotional regulation for a population proven to benefit from emotional regulation interventions; 2) utilizing geospatial analysis to better understand the connection between risky locations and momentary affect; and 3) examining the implications of time-varying social interactions with momentary affect through a social network inventory embedded within Ecological Momentary Assessment. Results highlight individual, relational, community, and societal factors that contribute to individual mental health outcomes. 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Abstract (if available)

Abstract Skills in emotional regulation, defined as “strategies used to influence, experience, and modulate emotions” are important for decision-making processes and can help support the navigation of complex and chaotic settings. These skills may be especially important for the estimated one-in-ten young adults, aged 18-25, who experience homelessness over the course of one year as they navigate complex and chaotic environments associated with homelessness. Unfortunately, these environments often introduce traumatic experiences which can result in difficulties with regulation, and in turn, lead to mental health problems which are already higher among young adults who have experienced homelessness. To date few studies have examined emotional regulation skills, a transdiagnostic component underlying psychopathology, among young adults with history of homelessness, a heterogenous population with diverse mental health diagnoses. There is some evidence that skills in emotional regulation may be protective against suicidality and violence within this population; however, we do not have a clear, broad understanding of the role of emotional regulation in navigating the social and physical environments associated with homelessness. To increase understanding of the dynamics of emotional regulation and the role of social and physical environments, the current dissertation employed Geographic Ecological Momentary Assessment (GEMA). GEMA integrates ecological momentary assessment (EMA) and geographic information systems science allowing for cross-validation and enrichment of research on place, well-being, and health. The aim of this work is to better understand how the social and physical contexts of homelessness may be related to emotional regulation. Housing status serves as a main analytic axis, as the sample consists of both formerly homelessness (residing in supportive housing) and currently homeless (street- or shelter-based) young adults who completed questionnaires and GEMA for a period of 7 days. The findings from this work are used to develop recommendations for direct service providers and policymakers regarding how to further adapt service environments to meet the complex needs of at-risk young people, including interventions for young adults experiencing homelessness.

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Asset Metadata

Creator Semborski, Sara (author)

Core Title Understanding emotional regulation and mood of young adults in the context of homelessness using geographic ecological momentary assessment

School Suzanne Dworak-Peck School of Social Work

Degree Doctor of Philosophy

Degree Program Social Work

Degree Conferral Date 2023-05

Publication Date 04/04/2023

Defense Date 02/27/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag emotion dynamics,emotional regulation,geographic ecological momentary assessment,Homelessness,OAI-PMH Harvest,supportive housing,Young adults

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Henwood, Benjamin (committee chair), Davis, Jordan (committee member), Mason, Tyler (committee member), Rice, Eric (committee member)

Creator Email semborsk@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC112922938

Unique identifier UC112922938

Identifier etd-SemborskiS-11561.pdf (filename)

Legacy Identifier etd-SemborskiS-11561

Document Type Dissertation

Format theses (aat)

Rights Semborski, Sara

Internet Media Type application/pdf

Type texts

Source 20230404-usctheses-batch-1015 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

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