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The acute and longitudinal associations between sedentary behaviors, affective states, and emotional disorder symptoms among youth

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The acute and longitudinal associations between sedentary behaviors, affective states, and emotional disorder symptoms among youth

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Content THE ACUTE AND LONGITUDINAL ASSOCIATIONS BETWEEN SEDENTARY BEHAVIORS, AFFECTIVE STATES, AND EMOTIONAL DISORDER SYMPTOMS AMONG YOUTH By Jennifer Zink A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (PREVENTIVE MEDICINE [HEALTH BEHAVIOR RESEARCH]) May 2021 Copyright 2021 Jennifer Zink ii Acknowledgments First and foremost, I would like to thank my dissertation committee for their endless guidance and support throughout my graduate studies. I am especially grateful for my committee chair, Dr. Britni Belcher, for her unwavering mentorship since the first day of my training. Thank you to my lab members, who have become my school family over the years. Lastly, I would like to express my sincerest gratitude for my friends, family, and puppy; their love, loyalty, and patience have been a constant, whether we were 3 or 3000 miles apart. iii Table of Contents Acknowledgments ................................................................................................................... ii List of Tables .......................................................................................................................... v List of Figures ......................................................................................................................... vi Abstract ................................................................................................................................... viii Chapter 1: Introduction ........................................................................................................... 1 Background and Significance ..................................................................................... 2 Definition of Sedentary Behaviors Measurement of Sedentary Behaviors Sedentary Behaviors and Health Outcomes Definition of Affective States Measurement of Affective States and Prevalence of Emotional Disorders Emotional Disorder Symptoms and Health Outcomes Sedentary Behavior-Emotional Disorder Symptom Link Gaps in Knowledge on Sedentary Behavior-Emotional Disorder Symptom Associations Chapter 2: An In-Lab Study of the Acute Affective Responses to Reducing Sitting Time in Youth ....................................................................................................................................... 20 Abstract ....................................................................................................................... 21 Introduction ................................................................................................................. 22 Aim and Hypotheses Methods ....................................................................................................................... 24 Participants Procedures Measures Statistical Analysis Results ......................................................................................................................... 29 Negative Affect (aim 1) Positive Affect (aim 1) State Anxiety (aim 1) Mid-Point Analyses Among Subsample of Los Angeles Participants Discussion ................................................................................................................... 33 Strengths and Limitations ........................................................................................... 36 Conclusions ................................................................................................................. 37 Chapter 3: Time-Varying Associations Between Ecological Momentary Assessment-Reported Sedentary Behaviors and Affective States Among Adolescents ............................................ 39 Abstract ....................................................................................................................... 40 Introduction ................................................................................................................. 42 Aims and Hypotheses iv Methods ....................................................................................................................... 44 Participants Procedures Measures Statistical Analysis Results ......................................................................................................................... 54 Ecological Momentary Assessment Compliance Descriptive Statistics Negative Affect (aim 1) Positive Affect (aim 2) Sensitivity Analyses Discussion ................................................................................................................... 62 Strengths and Limitations ........................................................................................... 67 Conclusions ................................................................................................................. 69 Chapter 4: Cross-Lagged Associations Between Patterns of Objectively Measured Sedentary Time and Emotional Disorder Symptoms Across Early Adolescence ................................... 70 Abstract ....................................................................................................................... 71 Introduction ................................................................................................................. 73 Aim and Hypotheses Methods ....................................................................................................................... 75 Participants Procedures Measures Statistical Analysis Results ......................................................................................................................... 81 Data Availability and Descriptive Statistics Cross-Lagged Associations (aim 1) Ancillary Analyses with Average Sedentary Time Discussion ................................................................................................................... 100 Strengths and Limitations ........................................................................................... 105 Conclusions ................................................................................................................. 106 Chapter 5: Discussion ............................................................................................................. 107 Summary of Findings .................................................................................................. 108 Implications ................................................................................................................. 109 Future Directions ........................................................................................................ 113 Strengths and Limitations ........................................................................................... 116 Conclusions ................................................................................................................. 118 References ............................................................................................................................... 119 v List of Tables Table 1: Characteristics of the pooled study sample and stratified by crossover study (study 1) .................................................................................................................................. 25 Table 2: Mean (SD) affective states at pre-test and post-test by experimental condition (study 1) .................................................................................................................................. 29 Table 3: Characteristics of the study sample (study 2) ........................................................... 45 Table 4: EMA prompting schedule for the current study (study 2) ........................................ 46 Table 5: EMA item wording, response options, and formatting for each prompt during the assessment period (study 2) .................................................................................................... 48 Table 6: Prompt compliance by participant characteristics (study 2) ..................................... 53 Table 7: Characteristics of the study sample at baseline (study 3) ......................................... 76 Table 8: Mean (SD) and range of study variables across data collection waves (study 3) ..... 84 vi List of Figures Figure 1: The circumplex model of affect adapted from Feldman Barrett & Russel ............. 8 Figure 2: The tripartite model of anxiety and depression ....................................................... 11 Figure 3: Conceptual model of the three studies to appear in the dissertation ....................... 16 Figure 4: Study participant flow (study 1) .............................................................................. 31 Figure 5: Mean (SD) affective states reported at pre-, mid-, and post-test by experimental condition (study 1) .................................................................................................................. 32 Figure 6: Intercept-only TVEM plot depicting average negative affect from 7am to 8pm (study 2) .................................................................................................................................. 55 Figure 7: TVEM plot depicting the time-varying association between EMA-reported screen- based SB and negative affect from 7am to 8pm (study 2) ...................................................... 56 Figure 8: TVEM plot depicting the time-varying association between EMA-reported non-screen- based SB and negative affect from 7am to 8pm (study 2) ...................................................... 57 Figure 9: Intercept-only TVEM plot depicting average positive affect from 7am to 8pm (study 2) .................................................................................................................................. 58 Figure 10: TVEM plot depicting the time-varying association between EMA-reported screen- based SB and positive affect from 7am to 8pm (study 2) ....................................................... 59 Figure 11: TVEM plot depicting the time-varying association between EMA-reported non- screen-based SB and positive affect from 7am to 8pm (study 2) ........................................... 60 Figure 12: TVEM plot depicting the time-varying association between activPAL-measured sedentary time and negative affect from 7am to 8pm (study 2) ............................................. 61 Figure 13: TVEM plot depicting the time-varying association between activPAL-measured sedentary time and positive affect from 7am to 8pm (study 2) ............................................. 62 Figure 14: Graphical depiction of the RI-CLPM (study 3) ..................................................... 80 Figure 15: Study participant flow (study 3) ............................................................................ 82 Figure 16: RI-CLPM of the association between sedentary alpha and symptoms of major depressive disorder (study 3) .................................................................................................. 85 Figure 17: RI-CLPM of the association between sedentary Gini and symptoms of major depressive disorder (study 3) .................................................................................................. 87 vii Figure 18: RI-CLPM of the association between sedentary breaks and symptoms of major depressive disorder (study 3) .................................................................................................. 89 Figure 19: RI-CLPM of the association between sedentary alpha and symptoms of generalized anxiety disorder (study 3) ....................................................................................................... 91 Figure 20: RI-CLPM of the association between sedentary Gini and symptoms of generalized anxiety disorder (study 3) ....................................................................................................... 93 Figure 21: RI-CLPM of the association between sedentary breaks and symptoms of generalized anxiety disorder (study 3) ....................................................................................................... 95 Figure 22: RI-CLPM of the association between average sedentary time and symptoms of major depressive disorder (study 3) .................................................................................................. 98 Figure 23: RI-CLPM of the association between average sedentary time and symptoms of generalized anxiety disorder (study 3) .................................................................................... 99 viii Abstract Evidence suggests that there is a link between sedentary behavior (SB) and depressive and anxiety symptoms (including poor affective states) among youth, yet inconsistencies in the literature remain as a result of study limitations to date. This dissertation leveraged data from randomized trial, ecological momentary assessment, and longitudinal cohort studies to investigate the SB-emotional health associations across different time scales ranging from hours to years. The overarching objective of this dissertation was to increase our scientific understanding of the SB-emotional health link by taking a nuanced approach towards operationalizing SB, differentiating between different types of SB (screen-based SB, non-screen- based SB) and investigating accumulation patterns of SB (bout length, distribution, and breaks), rather than simply total volume of SB. The specific aims of this dissertation were to (1) test the acute effects of interrupting sitting time on affective and anxiety states across three hours in an in-lab randomized crossover trial, (2) assess changes in the strength of the association between screen-based SB, non-screen-based SB, and affective states across the day (from 7am to 8pm), and (3) examine the cross-lagged associations between patterns of objectively measured sedentary time and emotional disorder symptoms across three years. Findings suggest that (1) interrupting sitting may be an effective strategy for acutely reducing negative affect among certain youth populations (e.g., females), (2) the strength of the associations between SB and affective states differ by time of day and by type of SB, and (3) longitudinal associations appear to be uni-directional such that depressive and anxiety symptoms are predictive subsequent patterns of sedentary time, but not vice versa. Taken together, the timing and type of SB, in addition to directionality, appear to be important factors in determining the strength of the association between SB and emotional health. This dissertation provides a foundation for future ix studies to take a more nuanced approach to examining the SB-emotional health relationship, so that we can begin to understand the mechanisms linking these variables and ultimately optimize preventive intervention strategies for improving emotional and physical health among youth. 1 Chapter 1: Introduction 2 Background and Significance Definition of Sedentary Behaviors Sedentary behaviors (SB) are low-intensity (≤1.5 metabolic equivalents) activities performed in the seated or lying position. 1 Many behaviors that are ubiquitous among youth are considered sedentary, such as sitting in the classroom. On school days, it is estimated that children and adolescents accrue over 70% of their daily sedentary time during school hours, while the remaining 30% of sedentary time is accumulated via passive/motorized school transport (e.g., riding in the car or bus) and during leisure time (e.g., reading, TV viewing). 2 Moreover, certain forms of leisure-time SB have been steadily increasing over the past 15 years. One study of a nationally representative sample of youth estimated that 22.1% of adolescents used the computer for three or more hours daily in 2003 and by 2017, these rates had doubled. 3 Reflecting the above statistics, leisure-time SB is typically defined as screen behaviors such as TV viewing, computer use, and videogame playing. Currently, two-thirds of children and adolescents exceed the recommended amount of time spent engaged in screen-based SB (two hours daily). 4 However, recent evidence indicates that non-screen-based SB, such as reading or homework, may be equally prevalent during leisure-time 5 and may have differential relationships with health outcomes. Therefore, non-screen-based SB should be considered in the operational definition of SB and quantified with methodologically appropriate measures. Measurement of Sedentary Behaviors Given that SB is so pervasive, valid and reliable measurement of these behaviors is necessary for understanding and preventing the negative health outcomes associated with sedentariness. Prior to the development of objective measures of activity, investigators relied on validated self-report assessments, such as the Three-Day Physical Activity Recall and the 3 International Physical Activity Questionnaire, with each requiring participants to recall and report on the mode, duration, and frequency activity (including SB). 6 Recently, additional questionnaires have been developed to specifically assess the type, context, and duration of various types of SB. Self-report measures continue to be a common method of assessment, despite their susceptibility to cognitive errors and recall biases in youth. 7, 8 Preference for self- report measures may be attributed to the tools’ ability to provide insight into the different types of SB and the context surrounding SB, which some research suggests is important for assessing the associations between SB and various health outcomes. 9-12 Real-time self-report measures of behaviors, such as Ecological Momentary Assessment (EMA), are able to reduce the recall biases associated with traditional self-report methods of activity while still providing insight into the types and contexts of SB. EMA is a method whereby participants are prompted to report on recent activities via mobile devices during specified time windows (e.g., every 2 hours) across the day. 13, 14 With this methodology, investigators are able to collect intensive data to better understand the within-day dynamic associations among the antecedents and consequences associated with SB, while still maintaining ecological validity and reducing recall bias. 14 Investigators may also use ecologically valid objective measures of free-living activity such as accelerometers, which quantify frequency, duration, and intensity of activity in youth by detecting accelerations in bodily movement. 15, 16 Recent algorithms for these devices have also allowed investigators to detect patterns in movement (e.g., sedentary bouts, distribution of bout lengths), 17 which appear to contribute to health outcomes independent of average or total levels of activity. 18, 19 Some accelerometers measure body movement on three axes: the vertical, anteroposterior, and lateral planes. 16 Participants may wear accelerometers on their wrist or hip; 16 4 however, hip-worn accelerometers are unable to differentiate sitting from standing behaviors, 20 which are not classified as sedentary. 21 To address this limitation of accelerometry, thigh- mounted monitors such as the ActivPAL (PAL Technologies, UK) may be used objectively capture activity duration and intensity. These validated devices are able to differentiate sitting from standing, and can detect sit-to-stand and stand-to-sit transitions in youth, which clinical studies indicate may be important for health outcomes. 22-24 Therefore, ActivPALs are becoming more frequently used in SB research. Each method of measuring SB poses unique strengths and weaknesses, which must be considered to successfully address different research questions. With the prevalence of SB increasing throughout adolescence 25 and tracking into adulthood, 26 well-informed conceptualization and measurement of SB is pertinent to understanding the immediate and distal health consequences associated with being sedentary. Sedentary Behaviors and Health Outcomes Accumulating evidence indicates that SB is a modifiable risk factor for poor weight- related and cardiometabolic outcomes in youth; those who are sedentary are more likely to be overweight or obese, independent of physical activity levels. 25, 27, 28 SB is also related to poorer body composition, such as increased body fat percentage 29, 30 and increased visceral fat accumulation, 31, 32 which may be particularly detrimental to health because of its metabolic activity and close proximity to vital organs. 33, 34 However, not all studies have found that SB has detrimental health consequences in youth. For example, one study of over 1,300 school-aged children found no association between self-reported weekly screen-based SB and body mass index. 35 A systematic review of over 230 studies of SB and health outcomes among youth highlights that generally, less SB is better for health, with the caveat that there are 5 inconsistencies in findings across individual studies. 36 Conflicting findings may be attributed to methodological differences in quantifying SB. Self-reported behaviors consistently have weaker associations with health outcomes compared to objective measures. The differences in the strength of the associations may be due to recall errors and biases in self-report measures, 37, 38 which can demonstrate weak correlations to objective measures of sedentary time. 39 The associations between SB, obesity, and cardiometabolic health may be attributed to several behavioral and physiological mechanistic pathways. Increased arousal from screen-based SB prior to bedtime may disrupt sleep quality and duration in youth, 40, 41 therefore increasing obesity risk. 42, 43 Reduced sleep quality and duration can cause fatigue and daytime sleepiness, 44 which may perpetuate a preference for low-energy activities and energy-dense snacks. 45 Additionally, screen-based SB before bedtime can result in delayed bedtimes, 46, 47 which relate to a greater consumption of low-nutrient and energy-dense foods 48 and obesity. 49 Similarly, adolescents may also be more likely to consume sweets, soft drinks, and snacks while engaging in SB. 50, 51 A study of over 4,000 adolescents found that SBs such as TV viewing and recreational computer use were associated with increased fast food and sugar consumption, 52 which some believe may be a result of mindless eating during screen time. 53, 54 The resulting combination of the low metabolic energy demand of SB and the increased energy intake via dietary behaviors can lead to excess body weight. Taken together, SB may be particularly dangerous for health due to its link to additional behavioral risk factors for obesity, such as shorter sleep duration, delayed bedtimes, and poorer dietary intake. Prolonged sitting can also cause deleterious physiological changes which may also contribute to the SB-physical health associations. A review of experimental studies indicated that sedentariness results in metabolic inflexibility, 55 or the inability to switch from one fuel source to 6 another in response to changes in nutrient availability, which has been strongly implicated in the pathogenesis of weight gain. 56 SB is also related to biomarkers of cardiovascular disease and poor metabolic health in youth, which track into adulthood and increase risk for cardiovascular- related morbidity and mortality later in life. 57 It is hypothesized that the lack of muscle stimulation during prolonged sitting can suppress the activity of lipoprotein lipase, one of the primary enzymes responsible for controlling blood lipid levels. 58, 59 Consequently, excessive SB is associated with increased triglycerides and decreased high-density lipoprotein cholesterol (dyslipidemia) in adolescents. 31, 60, 61 Over time, this profile may lead to vascular dysfunction and higher risk for cardiometabolic disease later in life. 62 Furthermore, the SB-related metabolic problems later in life have been linked to certain forms of cancer, such as endometrial and colorectal cancers, which are thought to be linked to metabolic biomarker (i.e., insulin, glucose) dysregulation. 63 Sedentariness may also be detrimental to health via its relationship with fitness levels. SB is inversely related to musculoskeletal fitness; 64 which may have long-term implications for chronic disease outcomes such as cardiovascular disease, type 2 diabetes, and metabolic syndrome. 65, 66 Additionally, consistent inverse associations are emerging between leisure-time SB and several markers of cardiorespiratory fitness, such as VO2 max 67 and performance on the shuttle-run 68 and cycle-ergometer 69 tests. Lack of physical activity and excessive SB may also have a synergistic detrimental effect on fitness, 70 making youth at greater risk for health problems such as high blood pressure 71-73 and insulin resistence, 74 independent of adiposity. This is particularly alarming as cardiovascular disease and type 2 diabetes are among the leading causes of mortality United States, accounting for approximately 30% of deaths. 75 As youth continue to spend a majority of their day being sedentary, poorer cardiometabolic health is 7 likely continue into adulthood, putting them at risk for premature morbidity and mortality. 73, 76, 77 Thus, sitting can impact cardiometabolic health, while the behaviors typically performed during sitting (e.g., eating during screen-based SB) can also uniquely contribute health risk. Taken together, sedentariness is detrimental to physical health and functioning, which can be attributed to several mechanistic pathways. However, a greater understanding of how the distal health consequences of sedentariness may be eradicated via reducing SB in youth is still needed. In addition to being detrimental to physical health, time spent in SB may also deleterious to the brain structure, and therefore cognitive functioning in youth. Executive functioning, a set of cognitive skills for goal-directed behavior that is imperative for positive behavioral, academic, and social-emotional development, 78, 79 is inversely related to time spent sedentary. 80, 81 Depending on the content, TV viewing, videogame playing, or computer use may not provide the cognitive stimulation needed to develop executive function skills. 82 Moreover, physical activity is a well-established behavioral factor that relates to brain structures that are related to better top- down cognitive control (executive functioning, inhibition, attention). 83, 84 Significant changes in the prefrontal cortex, a brain region implicated in top-down mental processes, are consistently observed after exposure to aerobic activity interventions, therefore leading to increased speeds in executive functioning skills. 85 If SB continues to comprise a large proportion of the daily lives of youth, there may be fewer opportunities for physical activity, and therefore the top-down cognitive control benefits that are associated with it. Similarly, a lack of physical activity can limit the subsequent release of brain-derived neurotrophic factor and vascular endothelial growth factor in the brain, neurochemicals that are needed for optimizing brain structure and executive function. 86, 87 Lastly, SB may increase negative affect, 88, 89 a state that can lead to cognitive biases and therefore negatively impacts executive functioning skills. 90-92 8 Definition of Affective States Affect (or affective state) is a psychological construct that is often used interchangeably with emotion and mood. 93 Although there is no consensus in the psychological community, many believe that affective states can encompass both mood and emotion. 94 Mood is described as a free-floating experience, while emotion is typically attributed to stimuli, events, or motives. 94, 95 Thus, affect is momentary feeling state and can be a response to a stimulus, either positive or negative. Although numerous conceptual models of affect exist, the field commonly describes affective states by two bipolar and independent dimensions, valence (pleasure vs. displeasure), and arousal (high vs. low). 96 Valence represents the hedonistic dimension of affect, while arousal represents the activation dimension of affect, as shown in Circumplex Model of Affect in Figure 1. Figure 1. The circumplex model of affect adapted from Feldman Barrett & Russel. 97 9 Based on this conceptual model of affective states, high positive affect is defined as a state where high pleasantness and high arousal are present 98 which can result in feelings of alertness and happiness. Low positive affect may be characterized by feelings of lethargy and fatigue. 99 A state of high negative affect (high arousal and low pleasantness) can be comprised of aversive feelings such as anger, disgust, or fear, while a state of low negative affect may can be described by feelings of serenity and relaxation. 99, 100 Measurement of Affective States and Prevalence of Emotional Disorders Paralleling the above conceptualization of affective states, investigators often assess self- reported affect by using the Positive and Negative Affect Schedule (PANAS), comprised of two 10-item subscales (one for positive affect and one for negative affect). 99 Participants are asked to report the extent to which they are currently feeling each of the affective characteristics on a 5- point Likert scale ranging from ‘not at all’ to ‘extremely’. The PANAS has consistently demonstrated reliability and validity. 99, 101 A modified version of the PANAS has been developed and validated for use in children and adolescents. 102, 103 The Positive and Negative Affect Scale for Children (PANAS-C) is longer than the original PANAS and includes different item wording to ensure that younger populations are able read and understand the questionnaire. 103 Participant responses are scored on the same Likert scale as the original PANAS. Responses for each subscale are then summed to create a positive affect score and a negative affect score. 103 For more efficient and ecologically valid assessment, a 10-item short version of the PANAS-C can be utilized and applied to EMA studies of affect and health behavior. 104, 105 The Profile of Mood States (POMS) is another commonly used scale used to quantify mood and affective states. The POMS was originally developed for use in psychiatric 10 populations, but has since established reputability for mood research in healthy samples aged 18 years and older. 106 This validated measure consists of 65 items which comprise six subscales: tension-anxiety, depression, anger-hostility, vigor-activity, fatigue, and confusion- bewilderment. 107, 108 Each of the subscale scores are then added (except for vigor) to create a total mood disturbance score. 107, 108 Due to the length of the original scale, the POMS-Short Form (37 items) and POMS-Adolescents (24 items) were developed to accommodate a broader range of populations. 109, 110 As with the original POMS, the POMS-Adolescents asks participants to report the extent to which they are currently feeling different mood states on a Likert scale from “not at all” to “extremely”. 109 The POMS survey is not frequently used in EMA research because the scales are too long for the EMA platform, which requires surveys to be as short as possible in order to reduce participant burden and maximize compliance. 111 Therefore, the PANAS is typically the preferred scale in EMA research of affective states and health behaviors. Affective states are important to study because of their relationship to serious emotional health issues. Low positive affect (e.g., lethargy, fatigue) and high negative affect (e.g., anger, fear) have been long-regarded as prominent symptoms of internalizing disorders such as depression and anxiety. 112 According to the tripartite model of anxiety and depression (Figure 2), generalized anxiety and depression are distinguishable disorders, with overlapping symptoms. Both emotional disorders encompass high negative affect, however low positive affect is a symptom unique to depression, while hyperarousal a symptom specific to anxiety disorders. 113, 114 11 Figure 2. The tripartite model of anxiety and depression. Adolescence is a vulnerable developmental period in which youth are at elevated risk for poorer psychosocial well-being. 115, 116 Depressive and anxiety disorders are among the most common emotional health ailments experienced by youth in the United States, with one study estimating the lifetime prevalence of an anxiety disorder to be approximately 1 in 3 in adolescents 117 and another study indicating that 18% of youth report symptoms of depression. 118 Additionally concerning is that the prevalence of depressive and anxiety disorders continues to increase throughout adolescence, 117, 118 which may be attributed to the substantial co-morbidity observed between depressive and anxiety disorders. 119, 120 Youth who experience anxiety are more likely to subsequently develop depression, therefore amplifying functional impairment characterized by absences from school, hindered interpersonal/social functioning, and poorer cognition, making daily activities such as classroom learning more difficult. 121-125 Moreover, these factors can have implications for long-term adult functioning such as reduced educational, vocational, and financial attainment, resulting in a lifetime of negative consquences. 126-128 Emotional Disorder Symptoms and Health Outcomes In addition to hindering personal functioning from childhood through adulthood, emotional disorders can increase risk for several adverse health outcomes. Those who experience recurrent anxiety or depressive episodes during adolescence demonstrate especially pervasive ANXIETY DEPRESSION Anxious Arousal High Negative Affect Low Positive Affect 12 psychosocial impairment into adulthood. 129-131 Such impairments are related to substance dependence or abuse and the host of negative health outcomes associated with these behaviors in adults; including morbidity and mortality due to certain types of cancers, cardiovascular disease, and type 2 diabetes. 132-136 Therefore, emotional disorders among adolescents can have substantial implications for future disease burden and health care costs, making this health issue an important topic of study for future research. Emotional disorders and their associated symptoms during childhood and adolescence have also been longitudinally associated with negative health outcomes such as overweight, obesity, and increased waist circumference. 137-139 The associations between emotional disorder symptoms and obesity are likely bi-directional, 140 whereby depressive and anxiety disorder symptoms can be both a risk factor for, and a result of obesity. Obesity may be associated with subsequent symptoms of emotional disorders by increasing one’s experiences of weight stigma, 141 and lowering self-esteem 142 and self-efficacy for engaging in health-promoting behaviors. 143 Conversely, depressive and anxiety disorder symptoms may lead to weight gain by increasing appetite for highly palatable foods that individuals may use for coping 144, 145 and by increasing the likelihood of sleep problems, which have been shown to influence weight-related outcomes. 146 Directing research towards understanding and interrupting the bi-directional cycle of emotional disorder symptoms and weight gain can be cumulatively beneficial for both, the physical and mental health of youth. Depressive and anxiety disorders can also increase risk for negative cardiometabolic outcomes, possibly as a result of the aforementioned behavioral consequences of emotional disorders. Higher serum cholesterol can be found adolescents with depression compared to those without depression. 147 Symptoms of depressive and anxiety disorders (e.g., high negative affect 13 and low positive affect) are prospectively related to insulin resistance, 148 type 2 diabetes, 149 and increased blood pressure. 150 Even those with only a parental history of emotional disorders are at increased cardiometabolic risk, as demonstrated by having higher blood pressure and greater arterial stiffness. 151 These findings suggest that there is an interplay between emotional, physiological, and behavioral factors that is currently not well-understood. Thus, future research should focus on disentangling these relationships and identifying intervention points to reduce the risk for negative health outcomes in adulthood. Sedentary Behavior-Emotional Disorder Symptom Link SB and symptoms of emotional disorders are related to one another and are therefore risk factors for similar adverse health outcomes (e.g., overweight/obesity, cardiometabolic risk). 152 Previous studies have demonstrated positive cross-sectional associations between SB and symptoms of depression, 153-156 in addition to associations between SB and symptoms of anxiety disorders such as generalized anxiety 156, 157 and social anxiety 158, 159 among youth. However, longitudinal studies of these associations have yielded conflicting findings. For example, in a year-long prospective study, investigators found that sedentary adolescents were at greater risk for developing depressive symptoms. 160 Similarly, screen-based SB during childhood has been prospectively associated with increased risk for symptoms of anxiety in adulthood. 161 On the other hand, Cho and Park found no longitudinal associations between SB and depressive symptoms in a sample of over 10,000 adolescents, 162 and another study found that TV viewing, but not computer use, was longitudinally related to depressive symptoms. 163 The inconsistencies in findings from prospective studies may be attributed to the potential bi-directional nature of the association between SB and emotional disorder symptoms. Recently, a study in adolescent girls found persistent bi-directional associations between self-reported SB and depressive symptoms 14 across a four-year period, such that SB was a risk factor and a consequence of depressive symptoms in this sample. 164 Similarly, another study in adolescents found that screen-based SB may be bi-directionally associated with depressive symptoms across an 11-year span. 165 A better understanding of the longitudinal and reciprocal associations between SB and emotional health can inform interventions that target the specific mechanisms underlying these associations and reduce the immediate and distal disease burden that results from the SB-emotional disorder symptom linkage. There are several mechanisms that may explain the longitudinal and bi-directional associations between SB and emotional disorder symptoms. Prolonged sitting may reduce the synthesis and metabolism of serotonin—a neurotransmitter believed to play a role in depressive and anxiety disorders. 166 As mentioned previously, arousal of the central nervous system resulting from screen-based SB before bedtime may interrupt sleep quality 40, 41, 167 and correlate with emotional disorder symptoms among adolescents. 146, 168 Therefore, biological underpinnings may partially explain SB-emotional disorder associations, however more research is needed to further elucidate these relationships. On a psychosocial level, SB can relate to subsequent emotional disorder symptoms by negatively impacting interpersonal and social functioning. The social withdrawal theory postulates that screen behaviors are related to poorer emotional health by promoting social isolation. 169 Those who become socially isolated during adolescence may be at heightened risk for emotional disorders as a result of limited access to social support. 170 More recently, evidence suggests that social media may provide a new avenue for maladaptive social comparison and feedback-seeking behaviors to occur, which can increase depressive symptoms in adolescents. 171 Additionally, social media and the Internet have provided new opportunities for cyberbullies to 15 access their victims; evidence suggests that cyberbullying can be just has harmful as traditional bullying for adolescent emotional well-being. 172 Alternatively, individuals with symptoms of depression and anxiety disorders are more likely to engage in SB; 173 adolescents may seek opportunities for SB as an attempt to alleviate symptoms of emotional disorders. Depression-related fatigue may promote engagement in SB, which requires little-to-no energy expenditure. 174 Additionally, those suffering from symptoms of social anxiety may fear in-person social interactions and create a preference for computer use as means of communication with peers. 175, 176 However, online communication does not promote social skill development or comfort with in-person interactions, 177, 178 and a cycle of social anxiety and computer use can persist. Thus, adolescents with symptoms of emotional disorders may experience difficulties in drawing the motivation, energy, and social skills necessary to engage in activities other than SB. There are many potential mechanisms which may explain the positive and potentially bi- directional SB-emotional disorder symptom associations observed, however critical gaps in the literature limit our ability to elucidate the primary mechanisms at hand. Therefore, more research is needed to develop effective intervention strategies aimed at disentangling SB and emotional symptoms as means of preventing the distal health consequences. Gaps in Knowledge on Sedentary Behavior-Emotional Disorder Symptom Associations This dissertation proposal will address three pertinent gaps in the SB-emotional health literature in attempt to aid our understanding of these associations in youth: (1) a lack of evidence from controlled in-lab experimental studies; (2) a limited conceptualization of self- reported and objective SB; and (3) little use of EMA methodology to capture dynamic 16 relationships between SB and affective states. See Figure 3 for a conceptual model of the dissertation. Figure 3. Conceptual model of the three studies to appear in the dissertation. Currently there is a dearth of controlled experimental evidence on the effects of SB on acute symptoms of emotional disorders, such as negative affective states, among youth. Previous experimental studies have focused on the cardiometabolic consequences of prolonged sitting time, 23, 179 however no in-lab studies have investigated the relationship between SB and affect. Therefore, there is a need for experimental studies of the acute effects of reducing sitting time on 17 affective states. Without experimental evidence, causal inferences cannot be made regarding SB- emotional disorder symptom associations. If findings suggest that interrupting sitting can reduce depressive and anxiety symptoms in a controlled laboratory environment, then we will provide support that prolonged sitting is a significant contributing factor to acute affective states. On the other hand, if findings cannot support the efficacy of interrupting sitting for reducing the acute affective symptoms of depression and anxiety, then this may be an indication that psychosocial mechanisms (e.g., cyberbullying, social isolation), or behaviors done while sitting, are important drivers of SB-emotional disorder symptom associations. In addition to a lack of experimental evidence, the field thus far has relied on a limited conceptualization of SB. Most studies assessing the associations between SB and symptoms of emotional disorders have limited the definition of SB to only include self-reported time spent engaged in TV viewing, computer use, and videogame playing. 180 Evidence within these studies indicates that these forms of SB may differentially influence symptoms of depression and anxiety disorders. For example, TV viewing appears to have weaker associations with depressive symptoms as compared to computer use or videogame playing cross-sectionally and longitudinally. 181, 182 Other studies assessing SB and anxiety symptoms have shown similar results. 156, 183 In the few studies that have compared screen-based to non-screen-based SB, there were gender differences such that screen-based SB had worse emotional health consequences for girls, while non-screen-based SB was associated with worse emotional outcomes for boys. 161, 184 Therefore, the conceptualization of SB must be expanded to encompass activities beyond TV viewing, computer use, and videogame playing, including as social media use, homework, and reading. The potential for differential associations between various forms of SB and symptoms of emotional disorders must be understood to elucidate the mechanisms linking these variables to 18 one another. Further, if only certain forms of SB are related to symptoms of emotional disorders, then intervention strategies can be designed to only target the SBs that appear impact emotional health among youth the most. In addition to a limited conceptualization of self-reported SB, previous studies have relied on a limited definition of objectively measured sedentary time. Only total volume of daily sedentary time has been investigated with relation to risk for symptoms of emotional disorders in youth. 185, 186 However, recent evidence suggests that the manner in which sedentary time is accumulated across the day, such as the duration of sedentary bouts, the number of breaks from sedentary to non-sedentary activities, and the distribution of sitting events, may be stronger predictors of acute and longitudinal physical health outcomes compared to total volume of sedentary time. 18, 187 Therefore, it is plausible that various patterns of accumulation of sitting throughout the day may differentially influence distal emotional health outcomes. Thus, it is necessary to understand these sedentary patterns as they longitudinally relate to symptoms of emotional disorders because can they inform school policy (e.g., reducing sedentary bouts during the school day) as well as tailored intervention strategies. Lastly, there are a limited number of studies assessing the acute affective antecedents and consequences of SB in real-world settings. The use of EMA can be beneficial for understanding the coupling of these behaviours across the day in youth. 13 The repeated-measures nature of data collection via EMA allows investigators assess within-day variations in behavior and affective states. 14 Because the coupling of SB and affective states is likely to be acute, dynamic, and vary across the day, intervention strategies would be optimized if they targeted the specific time periods where SB and affective states are most likely to be associated with one another. Additionally, capturing within-day variations can aid in the assessment of the person-level trait 19 of fluctuation, which may have differential associations with affective outcomes compared to average amounts of SB across a week or several months. 188 Thus, the EMA methodology can be leveraged to aid our understanding of the complex acute coupling of SB and affective states. In sum, there are several methodological weaknesses in the literature assessing the associations between SB, affective states, and additional, more chronic, symptoms of depressive and anxiety disorders; these gaps in our understanding are continuing to perpetuate a limited understanding of the mechanisms linking these variables. This dissertation will address each of the literature gaps stated above and will progress a program of research dedicated to creating effective intervention strategies for preventing and reducing SB and emotional disorder symptom-related morbidity among youth. 20 Chapter 2: An In-Lab Study of the Acute Affective Responses to Reducing Sitting Time in Youth 21 Abstract Objective: Sedentary time may be acutely related to poorer affective states among youth. This study assessed whether interrupting sitting over three hours was sufficient to influence negative affect, positive affect, and state anxiety in children and adolescents. Methods: These preplanned secondary analyses were part of two larger randomized crossover trials from which our sample was pooled. Youth (N=93; mean [SD] age=11.2 [2.5] years; 50.5% female; 51.6% healthy weight) completed two experimental conditions in random order: continuous sitting for three hours and sitting for three hours interrupted with three minutes of walking every 30 minutes. Negative affect, positive affect, and state anxiety were reported at pre-test and post-test. Multilevel models assessed whether post-test scores were dependent on the experimental condition; participant age and sex were tested as moderators. Results: The experimental condition was unrelated to post-test positive affect and state anxiety in the full sample. However, sex moderated how the experimental condition influenced post-test negative affect (interaction p=0.02); among males, the experimental condition was unrelated to negative affect at post-test (β=-0.2, 95%CI -0.4-0.1, f 2 =0.01, p=0.19), but among females, the continuous sitting condition was marginally related to higher post-test negative affect compared to the interrupted sitting condition (β=0.5, 95%CI -0.1-1.2, f 2 =0.03, p=0.08). Conclusion: This study provides preliminary evidence that interrupting sitting across three hours with moderate-intensity walking may be sufficient for reducing negative affect in females. Further research is needed to test interrupting sitting at different frequencies with different intensities and durations to gain a better understanding of the potential emotional benefits of reducing sitting time among all youth. 22 Introduction Americans spend a majority of their waking hours in activities that require very little energy expenditure. 189 Consequently, sedentary behaviors (SB; low-energy behaviors occurring in the sitting or lying position) are becoming increasingly common among youth 190 and the rates of SB typically rise as children transition into adolescence. 191, 192 In addition to the relatively immediate cardiometabolic consequences of excessive sitting, 61, 193, 194 SB is also associated with negative affective or mood consequences that are reflective of symptoms of depressive and anxiety disorders in youth, including sadness and unhappiness. 195-197 One strategy to reduce risk of the negative outcomes associated with SB is to interrupt prolonged sitting bouts with short activity breaks. Experimental studies suggest that interrupting sitting with moderate-intensity walking is sufficient for inducing acute physiologic responses that improve short-term metabolic outcomes among youth and young adults. 23, 179, 198 There may also be underlying physiological mechanisms explaining the SB-affective state link. Excessive sitting can acutely increase circulating pro-inflammatory biomarkers such as C-reactive protein, 199, 200 which have been associated with poorer affective states and an increased risk for symptoms of emotional disorders. 201-203 Similarly, SB-related metabolic biomarkers, such as circulating glucose, may be related to affective states among youth. 204 Therefore, interrupting sitting may an effective strategy for improving acute affective states in addition to acute metabolic outcomes. One study among adults found that interrupting sitting acutely improved self-rated mood, 205 while another study among children aged 7 to 11 found that reducing sitting time was related to less negative affect among those of healthy weight. 206 Despite adolescence being a vulnerable developmental period when affective states becomes more variable 207, 208 and evidence suggesting that interrupting sitting can be an efficacious strategy for improving 23 affective states acutely, there are no studies to date that experimentally test the influence of interrupting sitting on affective and anxiety states among adolescents. The affective consequences of excessive SB during vulnerable developmental periods such as childhood and adolescence can have a longstanding impact across the lifespan. Poorer academic performance, decreased social functioning, and increased risk for substance use have all been observed in youth experiencing symptoms of depression and anxiety, 209, 210 and are related to negative outcomes such as lower vocational attainment and substance use in adulthood. 211-213 Given that a majority of the school day is comprised of sitting 214 and that SB is one of the most prevalent leisure-time activities among adolescents, 215 a greater understanding of the efficacy of interrupting sitting for affective improvements is needed among youth. Therefore, this investigation had one aim and three hypotheses: Aim 1: To test the acute effects of interrupting sitting time on affective and anxiety states in an in-lab randomized crossover trial among youth. H1A: Youth will report lower negative affect after exposure to the interrupted sitting condition, compared to the continuous sitting condition. H1B: Youth will report higher positive affect after exposure to the interrupted sitting condition, compared to the continuous sitting condition. H1C: Youth will report lower levels of state anxiety after exposure to the interrupted sitting condition, compared to the continuous sitting condition. Additionally, exploratory analyses were conducted to assess whether age and sex moderate the association between interrupting sitting and affective and anxiety states, as older adolescents and girls may be more likely to experience symptoms of emotional disorders, including worse affect. 216-218 24 Methods Participants This study pooled data from two randomized crossover studies utilizing the same experimental protocol; both lab-based studies had the primary aim of examining the effects of interrupting youth’s sitting time on metabolic outcomes. 23, 179 For the first randomized crossover study, children aged seven to 11 years in good general health were recruited between June 2013 and January 2017 via flyers, listservs, world of mouth, and social media in the Washington, D.C. region (Clinicaltrials.gov registration no. NCT01888939). The second randomized crossover study (Clinicaltrials.gov registration no. NCT03153930) was conducted among youth aged 11 to 15 who were a subsample of the Mothers’ and Their Children’s Health (MATCH) cohort study of maternal stress and child obesity risk in the greater Los Angeles area. 219 MATCH participants were recruited via flyers and in-person research staff visits at public elementary schools and community events. The inclusion criteria for mother-child dyads of the MATCH study were (1) the child is in 3 rd -6 th grade at baseline, (2) more than half of the child’s custody belongs to the mother, and (3) both the mother and child can read English or Spanish. Dyads were excluded from the MATCH cohort if the mother or the child (1) was taking medications for thyroid function of psychological conditions, (2) had a health condition that limited physical activity, (3) was enrolled in a special education program, (4) was currently using oral or inhalant corticosteroids for asthma, (5) was pregnant, (6) the child was classified as underweight by a body mass index percentile of <5% adjusted for sex and age, or (7) the mother worked more than two weekday evenings (between 5- 9pm) per week or more than eight hours on any weekend day. The MATCH study data collection was completed in 2018. To be eligible for the present study, participants were required to be 25 enrolled in the MATCH cohort across all six waves (three years). Table 1 presents the demographic characteristics of each of the study samples. Table 1. Characteristics of the pooled study sample and stratified by crossover study (N=93) Characteristic Mean (SD) or N (%) Washington, D.C. (N=59) Los Angeles (N=34) Pooled (N=93) Age (mean [SD]) 9.5 (1.3) 14.1 (1.0) 11.2 (2.5) Sex, % Girls 27 (45.8%) 20 (58.8%) 47 (50.5%) Race, % White 30 (50.8%) 12 (35.3%) 42 (45.2%) Ethnicity, % Hispanic 5 (8.5%) 17 (50.0%) 22 (23.7%) Weight Status Healthy Weight 25 (42.4%) 23 (67.6%) 48 (51.6%) Overweight/Obese 34 (57.6%) 11 (32.4%) 45 (48.4%) Procedures Both randomized crossover studies from which the data were pooled used the following protocol: Baseline Screening Visit At the first study appointment, participants provided assent, while their parents provided informed consent. Participants were then screened for study eligibility. Children with cardiac or pulmonary disease, allergies to metals, evidence of type 2 diabetes, presence of endocrinologic disorders leading to obesity, or taking medications for ADHD were excluded. Demographic characteristics and anthropometric measurements (height and waist circumference in centimeters, weight in kilograms) were collected in duplicate and averaged to classify participants as healthy weight (body mass index below the 85 th percentile) or overweight/obese (body mass index greater than or equal to the 85 th percentile), according to Centers for Disease Control and Prevention recommendations. 220 Younger participants (Washington, D.C. trial) then completed a V02max fitness test using a modified Balke continuous ramp protocol. A Bruce protocol was used for older participants (Los Angeles trial) 26 to accommodate more advanced fitness levels. Ventilatory threshold was estimated by gas exchange using the V-slope method and dual criteria graphs. 221 Results from the fitness tests were then used to determine individualized moderate-intensity walking speed and treadmill grade for each participant during the interrupted sitting condition. Experimental Visits Participants returned to the lab for two more visits to receive each of the following experimental conditions in random order: 1. Uninterrupted sitting (SIT): Participants were seated for three hours with limited movement, and only rising for bathroom use. 2. Sitting interrupted with walking (SIT+WALK): Participants walked on a treadmill at their individualized walking speed and treadmill grade (60% of VO2max) for three minutes every 30 minutes (e.g., minutes 27-30, 57-60, etc.), totaling 18 minutes of walking across the three hours. At pre-test, participants reported on affective and anxiety states. Children then completed the experimental condition (SIT or SIT+WALK) assigned for that test visit. Immediately following the three-hour condition, participants reported on affective and anxiety states again. This procedure was repeated at the second experimental visit; therefore, each child has pre-test and post-test measures of affect and anxiety for each experimental condition. A subsample (N=11) of the Los Angeles trial participants also had mid-point measures of affect and anxiety, which were completed 90 minutes after the pre-test measures. The research team was present throughout the experimental visits to assist participants. All study procedures were approved by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the University of Southern California Institutional Review Boards. 27 Measures Affective States: Participants reported on affective states via the 10-item Positive and Negative Affect Schedule for Children (PANAS-C), which contains five questions to measure negative affect and five questions to measure positive affect “right now.” Negative affect items asked participants to report the extent to which they were feeling sadness, being scared, miserable, afraid, and mad. Positive affect items captured the extent to which participants were feeling happy, cheerful, proud, joyful, and lively. Response options ranged from “not at all” (1) to “extremely” (5) and were summed for each affective subscale. Therefore, scores ranged from five to 25 for each affective subscale and were treated as continuous variables. The PANAS-C has been previously validated in youth. 103 Cronbach’s alpha for positive affect and negative affect was 0.91 and 0.72, respectively among our Washington, D.C. subsample; it was 0.88 for positive affect and 0.83 for negative affect among our Los Angeles subsample. State Anxiety: Participants reported on state anxiety via the State Trait Anxiety Inventory for Children (STAIC). 222 The STAIC consists of two 20-item scales, one intended to measure state anxiety, and the other to measure trait anxiety. Participants only reported on state anxiety for the present study; reporting on the extent to which they felt nervous, jittery, scared, etc. “at this very moment.” Response options ranged from “not at all” (1) to “very” (3) and were summed. Therefore, scores for this scale can range from 20 to 60 and were treated as continuous for the present analyses. Cronbach’s alpha was 0.83 among our Washington, D.C. subsample and was 0.81 among our Los Angeles subsample. Covariates: Covariates were selected a priori based on past research on associations between sitting time and symptoms of emotional disorders. 156, 165 In addition to weight status (dichotomous; healthy vs. overweight/obese) determined by the body mass index measure 28 described above, a priori covariates included in all models were self-reported age (continuous), sex (dichotomous; male vs. female), race (dichotomous; white vs. not), ethnicity (dichotomous; Hispanic vs. not), and test visit site (e.g., Washington, D.C. vs. Los Angeles). Self-reported hunger reported at pre-test on a visual analogue scale from “not at all hungry” (0) to “greatest imaginable hunger” (100) was also tested as a covariate and retained in all models if significant confounding was observed. Statistical Analysis Frequencies and/or means were calculated for all participant characteristics. Paired t-tests stratified by experimental condition assessed differences in mean pre-test and post-test affective and anxiety scores. Repeated-measures ANOVA stratified by experimental condition assessed differences in mean affective and anxiety scores at pre-, mid-, and post-test among the subsample of Los Angeles youth with mid-point measures. Aim 1 Separate random-intercept linear mixed models assessed the association between experimental condition, SIT vs. SIT+WALK (predictor) and each post-test affective or anxiety state score (outcome), totaling three models. Each model was adjusted for the a priori covariates above, randomization order, pre-test scores, and hunger ratings (if significant confounding was present). Cohen’s f 2 , a measure of effect size for mixed models, was calculated using established methods. 223 Exploratory Analyses Linear mixed models were also used to test age (continuous), sex (dichotomous), and test visit site (dichotomous) as moderators of the association between experimental condition and post-test affective and anxiety state scores. After testing the hypotheses from aim 1, an 29 interaction term between experimental condition and age was then entered into each of the three models to test age as a moderator. Paralleling the strategy for testing age as a moderator, an interaction term between experimental condition and sex was then entered into each of the three models to test for the significance of sex as a moderator. Lastly, the interaction term between experimental condition and test visit site was then entered into each model and tested for significance. Corrections for multiple testing were not applied to exploratory moderation analyses. All analyses were conducted using SAS v9.4. Results Figure 4 presents a diagram of the study participant flow. A total of 93 (level 2) participants completed both experimental conditions, yielding a level 1 sample size of 186. Mean (SD) affective states at pre- and post-test stratified by experimental condition can be found in Table 2. Negative affect and state anxiety were significantly lower at post-test compared to pre- test following the SIT+WALK condition (Table 2). State anxiety was also significantly lower at post-test compared to pre-test following the SIT condition, and positive affect was higher at post- test compared to pre-test following the SIT condition (Table 2). Table 2. Mean (SD) affective states at pre-test and post-test by experimental condition (N=93) SIT SIT+WALK Pre-test Post-test Difference P Pre-test Post-test Difference P Negative Affect 6.0 (1.5) 5.7 (1.5) 0.3 (1.7) 0.12 6.2 (2.2) 5.6 (1.4) 0.5 (2.2) 0.02 Positive Affect 14.2 (5.3) 14.8 (5.7) -0.6 (2.7) 0.04 14.1 (5.5) 14.6 (5.5) -0.5 (4.2) 0.25 State Anxiety 29.7 (4.3) 28.8 (4.0) 0.9 (3.1) 0.01 30.6 (5.0) 29.2 (3.8) 1.4 (3.7) 0.001 Note. P-values derived from paired samples t-test. Negative affect (hypothesis 1A). The experimental condition (SIT vs. SIT+WALK) was unrelated negative affect at post-test after controlling for a priori covariates and hunger ratings (β=0.2, 95%CI -0.2-0.5, f 2 =0.00, p=0.39) among the entire sample. Age and test site were not modifiers of the association between experimental condition and negative affect, while sex was a 30 significant modifier (interaction β=-0.8, 95%CI -1.4- -0.1, p=0.02). Among males, the experimental condition was unrelated to negative affect at post-test (β=-0.2, 95%CI -0.4-0.1, f 2 =0.01, p=0.19), but among females, the SIT condition was marginally related to higher post- test negative affect compared to the SIT+WALK condition (β=0.5, 95%CI -0.1-1.2, f 2 =0.03, p=0.08). Positive affect (hypothesis 1B). The experimental condition was unrelated positive affect at post-test after controlling for a priori covariates and hunger ratings (β=0.1, 95%CI -0.9-1.0, f 2 =0.00, p=0.91) among the entire sample. Age, sex, and test site were not modifiers of the association between experimental condition and post-test positive affect. State anxiety (hypothesis 1C). The experimental condition was unrelated state anxiety at post-test after controlling for a priori covariates and hunger ratings (β=0.02, 95%CI -0.7-0.7, f 2 =0.00, p=0.96) among the entire sample. Age, sex, and test site were not modifiers of the association between experimental condition and post-test state anxiety. Mid-point analyses among subsample (n=11) of Los Angeles participants. Figure 5 presents mean affective states at pre-, mid-, and post-test stratified by experimental condition and repeated-measure ANOVA results. 31 Figure 4. Study participant flow. 32 SIT SIT+WALK Note. P-values derived from repeated-measures ANOVA. Figure 5. Mean (SD) affective states reported at pre-, mid-, and post-test by experimental condition. 33 Discussion The aim of the present study was to assess the effects of experimentally manipulating sitting time (via short, moderate-intensity walking breaks) on affective and anxiety states across three hours among youth. Among our sample, we did not find evidence that interrupting sitting every 30 minutes across three hours was sufficient for improving affective and anxiety states, contrary to what we had hypothesized and contrary to previous studies. Similar randomized trials among adults and children have demonstrated the efficacy of interrupting sitting with walking for acutely improving mood and affective states. 205, 206 Additionally, a free-living observational study combining ecological momentary assessment and accelerometry among adults found that sedentary breaks (sitting interruptions) were related to improved subsequent mood states. 224 Although our findings are inconsistent with previous experimental and observational studies of sedentary breaks and mood, our exploratory analyses were suggestive that moderation by sex may have been present; interrupting sitting may reduce negative affect among girls, but not boys. In other words, SB may be more tightly linked with negative affect among girls compared to boys, which has been demonstrated in a previous longitudinal study among adolescents. 225 To our knowledge, this is the first study to experimentally assess the effects of interrupting sitting on affective and anxiety states among a sample that contained adolescents. Our findings indicate that the potential beneficial effects of interrupting sitting on affect (as previously found among other studies with younger children and adults) may not generalize to adolescents. Adolescence is a unique developmental period marked by rapid biopsychosocial changes (i.e. biological, cognitive, and social changes that occur during puberty) that may confer emotional vulnerability. 226, 227 As such, emotional health declines during the adolescent developmental period, reflected by increases in the prevalence depressive and anxiety symptoms 34 similar to those investigated in the present study. 115, 116 However, our participants reported relatively low levels of negative affect and state anxiety, which may have contributed to our null findings. Future studies should consider testing the effects of interrupting sitting on affective and anxiety states among a sample of adolescents experiencing more variable and negative affect, in order to gain a clearer understanding of the efficacy (or lack thereof) of sitting interruptions for improving mood among adolescents. Another reason for our contradictory findings could be that the intensity of the walking breaks was not sufficient to produce affective changes. The opponent-process theory postulates that a stressor can invoke an initial psychological response, followed by a compensatory psychological response of the opposite valence (pleasure or displeasure). 228 Previous studies among young adolescents (~13 years old) have supported this theory, with findings suggesting that moderate-to-vigorous physical activity invokes a negative affective response during the activity and is followed by rapid affective improvements after the physical activity has been completed. 229, 230 Similarly, acute affective improvements following moderate-to-vigorous intensity activity have been observed among adolescent populations aged 13 to 18 years old in the lab 231 and free-living settings. 232 In the context of the opponent-process theory, our walking breaks may not have been of adequate intensity to invoke a negative affective response, and thus a positive compensatory response following the conclusion of the walking break was not observed. Therefore, it is still plausible that sitting interruptions may still be an important strategy for acutely improving mood. However, future studies are still needed to test sedentary breaks of different intensities to gain a better understanding of what intensity of activity is needed for inducing affective improvements, to ultimately create feasible interventions that have a long-standing impact on health. 35 In addition to our intervention possibly being comprised of sedentary breaks that were not of adequate intensity, our null findings may also be attributed to our intervention duration being too short (three hours) to induce affective responses. A previous randomized crossover trial among adults found that sitting interruptions consisting of mini-bouts of walking across six hours improved self-rated mood. 205 Though nonsignificant, our estimates of the associations between experimental condition, negative affect, and state anxiety were in the expected directions. Therefore, we may have observed further improvements in affective and anxiety states if the intervention duration had continued beyond three hours. Future studies should consider testing acute sedentary breaks interventions lasting six hours, similar to the aforementioned study among adults, 205 or eight hours to reflect the school day. Similarly, it is possible that the affective responses to the intervention could have persisted beyond our post-test measure, which was taken immediately following the end of the experimental condition. Evidence from exercise trials suggest that mood can continue to improve up to one day following the completion of the activity. 233 On the other hand, our null findings could mean that the behaviors one does while sitting may be as important for affective outcomes as the sitting itself. Previous studies of SB and emotional health suggest that screen-based SBs differentially relate to affect compared to non- screen-based SBs, 184 and even within the screen-based SB construct, device-specific associations with emotional outcomes can be observed. 234 It has been hypothesized that these device-specific associations with depressive and anxiety symptoms can be attributed to psychosocial mechanisms, rather than physiological ones. For example, computer and social media use may foster opportunities for negative online social interactions and upward social comparison, 235, 236 which would not otherwise occur while engaged in other screen-based SBs, such as TV viewing. 36 It is important to note that we did not control what types of SBs participants were engaged in during the experimental conditions, as long as the participants were limiting their physical movement. Participants were required to view age-appropriate content (without commercials) when watching Netflix, however, a majority of participants brought their own smartphones to study visits, from which we did not monitor content. Therefore, our findings may be explained by a combination of both, the experimental conditions themselves, and the type of SB engaged in during the conditions. Strengths and Limitations Strengths of this study include the rigorous experimental design and the use of previously validated instruments for assessing affective outcomes. Exploratory analyses investigating potentially important moderators, such as sex, is also a strength of the current study. The lab setting and the use of clinical space allowed us to control for the context and dose of the sitting interruptions. There were also study limitations. First, the lab setting may also be considered a limitation of this study because lab studies have limited the ecological validity. Similarly, the sitting condition was only three hours long, which is not reflective of the amount of time youth spend sitting in the real-world. For example, the Los Angeles trial participants of the present study accumulated on average, 548.5 minutes (~nine hours) of sedentary time per day during wave 6 of the MATCH cohort study. Therefore, in-lab findings may differ from those that may be observed in the real-world setting. Second, our sample size and composition limits the generalizability to other populations, such as those with clinical levels of depressive and anxiety symptoms. Future studies should aim to gain a better understanding sedentary break interventions and affective responses among samples with a broader range of depressive and 37 anxiety levels (including those above the clinical threshold). Similarly, future studies should consider gaining a better understanding of pubertal variables, such as status and timing, and how they may influence the efficacy of a sedentary breaks intervention; our third limitation is that we were unable to assess the potential confounding and moderating role of pubertal status and timing, which may be important for activity levels and affective outcomes. 237-240 Fourth, only pre- and post-test measures of affect and anxiety were taken among the majority of our sample. Future studies should take a more microtemporal approach than the current study by measuring affect and anxiety more frequently (e.g., every 30 minutes following the walking bout); microtemporal approaches may be important in the context of the present study because affect and anxiety can change over the course of minutes (versus hours). Future studies should also consider measuring affect beyond immediately following the conclusion of the last walking bout. Lastly, we did not rigorously control what the participants were doing while sitting, which may also influence affective outcomes independent of the sedentary interruptions. Future studies can address this limitation by controlling what the participants do while sitting (i.e., limiting content to only a few pre-selected movies and shows during the experimental conditions), or by testing the independent and interactive effects of the sitting interruptions and device/content-type on affective and anxiety outcomes. Conclusions This study provides preliminary evidence that interrupting prolonged bouts of sitting with moderate-intensity walking may be sufficient for reducing negative affect among girls across three hours. Additional research using rigorous controlled experimental designs can aid our understanding of the potential emotional benefits of reducing sitting time via short sitting interruptions among all youth. Based on the current study, we make recommendations for future 38 high-quality investigations that can contribute to a larger body work aimed at informing sedentary break intervention development for improving emotional outcomes among youth. 39 Chapter 3: Time-Varying Associations Between Ecological Momentary Assessment- Reported Sedentary Behaviors and Affective States Among Adolescents 40 Abstract Objective: Free-living evidence of acute sedentary behavior (SB)-affective state associations among youth is inconsistent; this may be attributed to a limited operationalization of self- reported SB and the failure to account for potential time-varying associations across the day. This study investigated the within-day, time-varying associations between ecological momentary assessment (EMA)-reported screen-based SB, non-screen-based SB, and affective states (positive and negative) among adolescents. Methods: Participants (N=15, mean[SD] age=13.1[1.0] years, 66.7% female, 40.0% Hispanic, 66.7% healthy weight) reported on screen-based SBs, non-screen based SBs, and positive and negative affect “just before the phone went off” via EMA up to 7 times/day for 7-14 days. EMA prompts occurred randomly within pre-selected 2-hour time windows between 7am and 8pm. Four separate time-varying effect models (varying slopes across the day) assessed how EMA- reported screen-based SB and non-screen-based SB were each related to positive and negative affective states. Results: Participants completed 636 EMA surveys (257[40.4%] reports of screen-based SB, 110[17.3%] reports of non-screen-based SB, and 269[42.3%] where no SB was reported). Screen-based SB was related to negative affect from 7am to about 9am (beta range: 0.3 to 0.5). Screen-based SB and positive affect demonstrated positive associations from 7am to 9:30am (beta range: 0.3 to 1.1) and from about 2:30pm to about 6:30pm (beta range: 0.3 to 0.4). Non- screen-based SB was inversely related to negative affect from about 11:30am to 3:30pm (beta range: -0.2 to -0.1). Non-screen-based SBs were also inversely related to positive affect, from approximately 7am to 9am (beta range: -0.9 to -0.4) and after about 2:30pm for the remainder of the day (beta range: -0.5 to -0.3). 41 Conclusion: The strength of acute associations between SB and affective states differs across the day and by operationalization of SB (screen-based vs. non-screen based); highlighting that there are potentially critical intervention windows during the day that specific types of SBs may be beneficial or detrimental for affective states among adolescents. 42 Introduction In recent decades, health conditions that were once considered unlikely to affect youth are becoming increasingly prevalent among the adolescent population. Rates of obesity, 241, 242 type 2 diabetes, 243, 244 and hypertension 245 are rising among youth, and the notable prevalence of each is expected to persist. Combating and preventing these non-communicable diseases must be prioritized, as diabetes alone is responsible for nearly $100 billion per year in healthcare costs in the United States. 246 Sedentary behaviors (SB) are typically defined as screen-based activities such as TV viewing and computer use, and are a risk factor for such non-communicable diseases. 180, 247 SB is highly prevalent in the adolescent population, which can be attributed to factors such as urbanization, 248 technological advancements, 249 and the development of social media. 250 Because opportunities for being sedentary will continue to surround youth, interventions targeting health-promoting decision-making skills are critically needed—however, interventions aimed at reducing SB have thus far been relatively ineffective in younger populations. 251 Interventions targeting SB may be unsuccessful among adolescents because they do not take factors such as affective states into account. Studies indicate that positive affect is related to salubrious behaviors such as more physical activity, 105 while negative affect is related risky behaviors such as poorer dietary intake 252 and increased cigarette smoking. 253 Therefore, it is plausible that SB and affective states may be related to one another during adolescence, a developmental period when time spent sedentary increases and affect becomes less positive and more variable. 254, 255 Further, despite evidence suggesting that nighttime-specific SB may be linked to emotional health, including worse affect, 168, 256 studies continue to rely on retrospective self-report measures that amalgamate levels of SB over the past week or month. 160, 257 Therefore, 43 there is a need for a more nuanced understanding of the associations between SB and affective states, especially if they may be variable and dynamic across the day. Ecological momentary assessment (EMA), a real-time data collection method whereby participants can report on their recent behaviors and affective states multiple times across the day, is an ideal method for assessing the acute changes or fluctuations in SB and affective states. 258 Additionally, time-varying effect modelling (TVEM) is becoming increasingly utilized for analyzing EMA data. TVEM optimizes the repeated-measures data structure of EMA in order to model dynamic associations over time, allowing investigators to pinpoint specific time windows where associations may be strongest or weakest between two variables, and therefore identifying critical intervention periods. 259, 260 To our knowledge, the only EMA study to assess the acute within-day relationship between affective states and self-reported SB among adolescents found no associations. 261 However, TVEM was not used, which may contribute to the null findings because significant associations during certain time points within the day may not have been detected by the traditional modelling methods employed. 261 Moreover, the conceptualization of SB in the aforementioned study was limited to small-screen recreational activities (e.g., TV viewing, videogame playing), which may not have captured other highly prevalent SB in adolescents, such as social media use, homework, and reading. 262 Therefore, this operationalization of SB was unlikely to identify true levels of sedentary time and limited the investigators’ ability assess if affective states differed depending on the type of SB the participant engaged in; which has been suggested by other studies of SB and emotional health in youth. 181, 183, 196 For example, screen- based SBs (vs. non-screen based SBs) appear to be particularly detrimental for symptoms of emotional disorders. 195 Taken together, the acute associations between SB and affective states 44 are not well-understood; it is still unknown if the associations between SB and affective states differ by time of day and by the type of SB reported by the participant. Intervention strategies may be optimized if findings indicate that only certain forms of SB during critical time points within the day tend to co-occur with affective states. Therefore, this study has the following aims: Aim 1: To assess changes in the strength of the association between screen-based SB, non- screen-based SB, and negative affect across the day. H1A: The direct association between EMA-reported screen-based SB (e.g., TV viewing, texting, social media use, videogame playing, or computer use) and negative affect will be strongest in the evening hours. H1B: EMA-reported non-screen-based SB (e.g., homework/reading, hanging out, art/painting, car/bus transport) will be unrelated to negative affect across the day. Aim 2: To assess changes in the strength of the association between screen-based SB, non- screen-based SB, and positive affect across the day. H2A: The inverse association between EMA-reported screen-based SB (e.g., TV viewing, texting, social media use, videogame playing, or computer use) and positive affect will be strongest in the evening hours. H2B: EMA-reported non-screen-based SB (e.g., homework/reading, hanging out, art/painting, car/bus transport) will be unrelated to positive affect across the day. Methods Participants The participants in this study were a subsample (N=15) of participants in the Mothers’ and Their Children’s Health (MATCH) cohort study of maternal stress and their children’s 45 obesity risk. 219 MATCH participants were recruited via flyers and in-person research staff visits at public elementary schools and community events. The inclusion criteria for mother-child dyads of the MATCH study were (1) the child is in 3 rd -6 th grade at baseline, (2) more than half of the child’s custody belongs to the mother, and (3) both mother and child are able to read English or Spanish. Dyads were excluded from the MATCH cohort if the mother or the child (1) was taking medications for thyroid function of psychological conditions, (2) had a health condition that limited physical activity, (3) was enrolled in a special education program, (4) was currently using oral or inhalant corticosteroids for asthma, (5) was pregnant, (6) the child was classified as underweight by a body mass index percentile of <5% adjusted for sex and age, or (7) the mother worked more than two weekday evenings (between 5-9pm) per week or more than eight hours on any weekend day. Participants enrolled in the MATCH study were recruited for the Sedentary Behavior and Outcomes Study, a randomized-crossover trial investigating the metabolic effects of interrupting sitting (ClinicalTrials.gov registration no. NCT03153930). To be eligible for the Sedentary Behavior and Health Outcomes Study (and therefore the present pilot sub-study), participants were required to be enrolled in the MATCH cohort across all six waves (three years) and be in good general health (e.g., from study 1; youth with cardiac or pulmonary disease, allergies to metals, evidence of type 2 diabetes, presence of endocrinologic disorders leading to obesity, or taking medications for ADHD were excluded). All study procedures were approved by the University of Southern California Institutional Review Board. Table 3 presents the demographic characteristics of the study sample. Table 3. Characteristics of the study sample (N=15) Characteristic Mean (SD) or N (%) Age 13.1 (1.1) Sex, % Female 10 (66.7%) 46 Ethnicity, % Hispanic 6 (40.0%) Highest Maternal Ed., % College + 11 (73.3%) Weight Status, % Healthy Weight 10 (66.7%) BMI Percentile 55.4 (32.1) Note. BMI=body mass index Procedures Participants and their parents provided assent and consent, respectively. Participants reported on demographic characteristics, including age, sex, ethnicity, and highest maternal education achieved, which was used as a proxy for socioeconomic status. Anthropometric measurements (height in centimeters, weight in kilograms) were collected in duplicate by study staff and used to calculate body mass index (BMI) percentile using the Centers for Disease Control and Prevention EpiInfo tool. Participants were provided a Moto G mobile phone at a study visit (Motorola Mobility, Chicago, IL) with the Movisens EMA app pre-downloaded to use for the duration of the study. Immediately after this appointment, each participant received random EMA prompts within the specified time windows shown in Table 4 for the next seven complete days. Thus, participants may have received up to 34 EMA prompts across the seven-day assessment period. Table 4. EMA prompting schedule for the current study Daily Measurement Schedule 7am- 8am 9am- 10am 11am- 12pm 1pm- 2pm 3pm- 4pm 5pm- 6pm 7pm- 8pm Weekdays X X X X Weekend days X X X X X X X After the initial seven-day assessment period, participants were asked to return the mobile devices to the study team and to complete the same seven-day EMA protocol again (ranging from one week to approximately month later), totaling 14 assessment days. Depending on randomization order from the Sedentary Behavior and Health Outcomes randomized trial, participants were given a wrist-worn Fitbit-like device (Lycos Life), which was programmed to 47 prompt participants to interrupt SB (e.g., with walking) every 30 minutes with vibrations, for either the first or second EMA assessment week. Seven participants received the LYCOS Life during the first assessment week, and eight participants received the LYCOS Life during the second observational week. During the assessment periods when the Fitbit-like device was not assigned to the participants, the participants were instructed by study staff to proceed with their normal daily routines. Randomization order (whether the participant received the LYCOS Life during the first assessment week or during the second assessment week) did not differ by participant characteristics (data not shown). The EMA mobile device chimed and/or vibrated to prompt the participant to stop his/her current activity and answer the EMA survey, which took approximately two minutes to complete. At each prompt, participants were asked for report on their current affective states and SBs. All EMA prompt responses were date- and time- stamped. Measures Sedentary Behaviors: Via EMA, participants were asked to report the primary SB that he/she was currently engaged in at the time of the EMA prompt. Response options were TV/movies/videos, social media (Facebook, Snapchat, Instagram, Tumblr), videogames, computer/tablet use, homework/reading, hanging out/chatting, art/painting/coloring, riding in the car/bus, or none of these things. The SB item at any given prompt was dummy-coded into two separate variables, any screen-based SB (yes vs. no) and any non-screen-based SB (yes vs. no). Affective States: EMA questions prompted participants to report on their current affective states based on five items of the PANAS-C short form: stressed, mad, sad, happy, and joyful. The response options ranged from zero to three; “0= Not at all,” “1=A little,” “2=Quite a bit,” and “3=Extremely.” The responses for stressed, mad, and sad (three items) were averaged to 48 create a composite score for negative affect (within-subject internal consistency reliability, 𝜔 =0.81); and the responses for happy and joyful (two items) were averaged to create a composite score for positive affect (𝜔 =0.90). Therefore, positive and negative affect could each range from 0 to 3 at any given EMA prompt. Covariates: Time-invariant covariates were selected a priori based on previous work showing associations with SB and symptoms of emotional disorders and include baseline BMI (kg/m 2 ), age (continuous; years), sex (dichotomous; female, yes vs. no), ethnicity (dichotomous; Hispanic, yes vs. no), and socioeconomic status (dichotomous; maternal education college or higher, yes vs. no). 156, 165 Additionally the time-varying covariate of day of week (dichotomous; weekend, yes vs. no) was included in all models. The time-varying covariates of EMA-reported physical activity (dichotomous; any physical activity, yes vs. no), environmental context (dichotomous; indoors, yes vs. no), social context (dichotomous; alone, yes vs. no), and experimental condition (Lycos Band; yes vs. no) were each tested one at a time as covariates as time-invariant effects and were retained in the models if they were significantly associated with the outcome at the p<0.05 level. See Table 5 for a more detailed description of each of the EMA items presented above. Table 5. EMA item wording, response options, and formatting for each prompt during the assessment period Construct Items Response Options Format Positive and Negative Affect (PANAS-C short) Right before the phone went off, how (HAPPY, JOYFUL, STRESSED, MAD, SAD) were you feeling? Not at all A little Quite a bit Extremely Separate screen for each item Sedentary Behavior Please choose the ONE main sedentary activity you were doing just before the phone went off. TV/Movies/Videos Texting Single screen 49 Social Media (Facebook, Snapchat, Instagram, Tumblr, etc.) Video Games Computer Use/Tablet Use Homework/Reading Hanging Out/Chatting Art/painting/coloring Riding in the car/bus None of these things Covariates Physical Activity Please choose the ONE main physical activity you were doing just before the phone went off. Exercise Sports Walking/Biking for Transportation Active House Chores None of These Things Single screen Environmental Context Were you inside or outside just before the phone went off? Inside Outside Single screen Social Context Who were you with just before the phone went off? (Choose all that apply) Mom Dad Sister(s) or Brother(s) Other Family Members (cousins, uncles) Friend(s) Classmate(s) People You Don’t Know Single screen 50 Other I Was Alone Statistical Analysis Frequencies and/or means were calculated for participant characteristics, affective states, and EMA-reported SBs. Cross-tabulations were used to calculate mean affective state score by yes/no report of screen-based and non-screen-based SBs. EMA prompt compliance was calculated as the proportion of prompts completed out of the total number prompts sent to the participants. Separate multilevel logistic regression models tested if participant age, sex, ethnicity, weight status (healthy weight vs. overweight/obese), maternal education, day of week, week-level positive affect, and week-level negative affect predicted momentary EMA prompt compliance (prompt completed; yes vs. no). See Table 6 for EMA prompt compliance descriptive statistics. TVEMs, which are uniquely suited for the analyses of intensive, repeated measures (e.g., time-stamped EMA data), were used to model the associations between SB and affective states across the day. TVEMs are designed to test changes in the strength of the association between the predictors and the outcome over time, which is modeled non-parametrically. Moreover, they can accommodate an unequal temporal spacing of observations and an unequal number of observations per participant due to missing observations. 260 TVEM results are presented graphically in which time is on the X-axis and the magnitude of the association is presented on the Y-axis. The solid line of the TVEM graph represents the estimated regression coefficient over time, whereas the dashed lines reflect the corresponding 95% confidence intervals for the regression coefficient. A confidence interval (dashed lines) that does not overlap with zero at any moment in time is indicative of a significant association between the predictor (SB) and outcome 51 (affective states) during that specific time interval. All models in the current study presented results from 7am to 8pm, reflective of the EMA sampling protocol. Models were fit using the P-spline technique for its computational flexibility and efficiency. Contrary to the B-spline technique, the P-spline method uses an automated model selection procedure, making it the preferred method when fitting TVEMs, which can have complex coefficient functions. 259, 260 Therefore, the P-spline approach is more appropriate for modelling momentary within-day changes captured in EMA studies of health behavior. 263 To describe average levels of negative affect and positive affect across the day, TVEMs will be used to predict either outcome variable (negative affect and positive affect) with only an intercept function and an error term specified. Aim 1 Two separate TVEMs assessed the strength of the association between each dichotomous SB variable (dummy-coded screen-based SB and non-screen-based SB) and negative affect across the day. Aim 2 Paralleling the statistical approach above, two TVEM models assessed the strength of the associations between screen-based and non-screen-based SBs and positive affect across the day. Sensitivity analyses Two additional analyses were included: (1) a sensitivity analysis removing observations from participants (N=2) with no prior smartphone ownership to assess how providing a study smartphone may have influenced findings; and (2) two TVEMs assessing the associations between objectively measured sedentary time (measured with activPAL accelerometers) in the past 15-minutes and EMA-reported current negative and positive affect across the day, as 52 previous studies have indicated differential associations between self-reported and objective measures of SB in relation to other outcomes. 264, 265 Understanding how SB measurement-type may influence the strength of the associations observed with affective states may elucidate the previous inconsistencies in the literature and increase our understanding of the mechanisms at hand. All analyses were conducted using SAS v9.4. 53 Table 6. Prompt compliance by participant characteristics (Level 1 N=1030 prompts, Level 2 N=15) Entire Sample < 13 years old >=13 years old Boys Girls Non- Hispanic Hispanic Maternal Ed. <College Maternal Ed. College + Healthy Weight Overweight/ Obese Weekdays Weekend Days Missed Prompts 394 82 312 122 272 222 172 92 302 292 102 213 181 Answered Prompts 636 118 518 231 405 389 247 189 447 414 222 362 274 Compliance 61.8% 59.0% 62.4% 65.4% 59.8% 63.7% 59.0% 67.3% 59.7% 58.6% 68.5% 63.0% 60.2% 53 54 Results EMA compliance. The multilevel logistic regression analyses of EMA compliance at the prompt level indicated that demographic characteristics (age: OR=1.1, 95%CI 0.8-1.7, p=0.53; sex: OR=0.8, 95%CI 0.3-1.9, p=0.62; ethnicity: OR 0.7, 95%CI 0.3-1.7, p=0.45; maternal education: OR 0.7, 95%CI 0.3-2.0, p=0.64), weight status (OR 0.3, 95%CI 0.7-3.6, p=0.26), and day of week (OR 0.9, 95%CI 0.7-1.1, p=0.28) were each unrelated to momentary EMA prompt compliance (prompt completed; yes vs. no). Person-mean negative affect (OR 0.6, 95%CI 0.2- 1.4, p=0.19) and positive affect (OR 0.9, 95%CI 0.5-1.6, p=0.71) were also unrelated to EMA prompt compliance. Momentary EMA prompt compliance was related to the time of day such that participants were more likely to complete EMA prompts later in the day (OR=1.1, 95%CI 1.0-1.1, p=0.02). Descriptive statistics. Of the 636 completed EMA prompts, screen-based SBs were reported on 257 (40.4%) occasions and non-screen-based SBs were reported on 110 (17.3%) occasions (and no SBs were reported during the remaining 269 [42.3%] occasions). Mean (SE) negative affect was 0.3 (0.02) across the observational period. On occasions when screen-based SBs were reported, mean (SE) negative affect was 0.4 (0.05), while it was 0.2 (0.05) on occasions when non-screen-based SBs were reported. Mean (SE) positive affect was 1.6 (0.04) across the observational period. On occasions when screen-based SBs were reported mean (SE) positive affect was 1.7(0.06), while it was 1.5 (0.08) on occasions when non-screen-based SBs were reported. Negative affect (aim 1). The intercept-only TVEM plot for negative affect is presented in Figure 6. The mean level of EMA-reported negative affect remained steadily around 0.3 across the daily EMA prompting period (7am to 8pm). The highest levels of negative affect were 55 reported around 10am (mean negative affect=0.4 [95%CI 0.1-0.6]) and the lowest levels of negative affect were reported around 5pm (mean negative affect=0.3 [95%CI 0.1-0.4]). Figure 7 presents the time-varying associations between EMA-reported screen-based SB and negative affect from 7am to 8pm and indicates that there was a significant association from 7am to about 9am (beta range: 0.3 to 0.5) after adjusting for a priori covariates, social context, and environmental context. Figure 8 presents the time-varying associations between EMA-reported non-screen-based SB and negative affect across the day; significant inverse associations were observed from about 11:30am to 3:30pm (beta range: -0.2 to -0.1) after adjustment for the same covariates stated above. Physical activity and the Lycos Life were not significant predictors and were therefore not retained in the models. Figure 6. Intercept-only TVEM plot depicting average negative affect from 7am to 8pm. 56 Figure 7. TVEM plot depicting the time-varying association between EMA-reported screen-based SB and negative affect from 7am to 8pm. 57 Figure 8. TVEM plot depicting the time-varying association between EMA-reported non- screen-based SB and negative affect from 7am to 8pm. Positive affect (aim 2). The intercept-only TVEM plot for positive affect is presented in Figure 9. The mean level of EMA-reported positive affect slightly increased across the day from 7am (mean positive affect=1.2 [95%CI 0.8-1.6]) to 8pm (mean positive affect=1.8 [95%CI 1.2- 2.3]). Figure 10 presents the time-varying associations between EMA-reported screen-based SB and positive affect across the day; significant associations were observed from 7am to 9:30am (beta range: 0.3 to 1.1) and from about 2:30pm to about 6:30pm (beta range: 0.3 to 0.4). Figure 11 presents the time-varying associations between EMA-reported non-screen-based SB and positive affect; significant inverse associations were observed from 7am to about 9am (beta range: -0.9 to -0.4) and after about 2:30pm for the remainder of the day (beta range: -0.5 to -0.3). Each of the models above adjusted for a priori covariates but were not adjusted for the additional 58 covariates (e.g., physical activity, environmental context, social context, and Lycos Life) that were tested because they were not statistically significant. Figure 9. Intercept-only TVEM plot depicting average positive affect from 7am to 8pm. 59 Figure 10. TVEM plot depicting the time-varying association between EMA-reported screen-based SB and positive affect from 7am to 8pm. 60 Figure 11. TVEM plot depicting the time-varying association between EMA-reported non- screen-based SB and positive affect from 7am to 8pm. Sensitivity Analyses. After the removal of participants with no prior smartphone ownership (N=2 participants, 92 completed EMA prompts), the time-varying associations between screen-based SB, non-screen-based SB, and affective states remained comparable to those presented above (sensitivity plots not shown), therefore these participants were retained in the final models. Figure 12 presents the time-varying associations between activPAL-measured sedentary time in the 15-minute window prior the EMA prompt and EMA-reported negative affect across the day. Sedentary time was unrelated to negative affect until about 6:30pm, when sedentary time was related to more negative affect for the rest of the day (until 8pm) (beta range: 0.02 to 0.03). Figure 13 presents the time-varying associations between activPAL-measured 61 sedentary time and positive affect, demonstrating that these associations were nonsignificant across the day (from 7am-8pm). Figure 12. TVEM plot depicting the time-varying association between activPAL-measured sedentary time and negative affect from 7am to 8pm. 62 Figure 13. TVEM plot depicting the time-varying association between activPAL-measured sedentary time and positive affect from 7am to 8pm. Discussion To our knowledge, this is the first study to demonstrate that the acute associations between SB and affective states may differ by time of day and type of SB using EMA and TVEM. Contrary to our hypothesis (H1A), screen-based SBs (e.g., TV viewing, computer use, videogame playing) were related to negative affect only in the morning hours; similarly, inconsistent with our hypothesis (H2A), screen-based SBs were related to more positive affect in the morning hours and during the late afternoon hours. We also found that non-screen-based SBs (e.g., reading, doing homework) were inversely related to negative affect only during the afternoon (H1B), with the caveat that these associations were approaching non-significance and should be interpreted with caution. Lastly, non-screen-based SBs were inversely related to 63 positive affect in the morning hours and after 2:30pm, contrary to what we had hypothesized (H2B). Although most of the findings of the current study were inconsistent with what we had expected to observe, our results demonstrate promising evidence that the acute associations between SB and affective states are likely to vary across the day. In addition to being time- varying in nature, the associations also appear to differ by SB-type. Taken together, there may be critical windows during the day that specific SBs can be beneficial or detrimental for affective states. We demonstrated that the association between screen-based SBs and negative affect can be observed acutely within the day, but that the association appears to be significant only during the morning hours. It is plausible that the repetitive, daily screen-based SB-negative affect associations in the morning may accumulate over time into longitudinal associations; one study among adolescents found that screen-based SBs such as computer use and videogame playing were associated with more negative affect one year later. 225 It is worth noting, however, that we did not observe nighttime-specific associations between screen-based SB and negative affect as we had hypothesized. This may be because the most commonly-reported screen-based SB among our sample was TV viewing, and prior research suggests that compared to other forms of screen- based SB, TV viewing is not as strongly related to emotional outcomes among adolescents. 234 Previous research suggests that evening-time engagement in other forms of screen-based SB, such as computer and mobile phone use, may be more detrimental for adolescent emotional health; 256, 266, 267 because these behaviors are less passive than TV viewing 268 and can increase mental and emotional arousal during the time of day when the body would otherwise be preparing to relax. 269 Therefore, nighttime-specific associations may not have been observed 64 among our sample due to the sub-type of screen-based SB (TV viewing) that was most- commonly reported by our sample in the evening, including during the last prompt of the day. Although screen-based SBs can pose negative affective consequences for adolescents, our study also demonstrated that engagement in screen-based SBs was acutely related to more positive affect in the morning and afternoon hours. Adolescents may view screen-based SBs as pleasurable leisure-time activities, and evidence suggests that youth cope with stressors by engaging in screen-based SBs. 270, 271 The participants in our sample may have engaged in screen- based SBs in the morning hours to temporarily heighten mood in preparation for the upcoming day at school (weekdays) or day of structured organized activities (weekend days). Similarly, our sample may have engaged in screen-based SBs in the afternoon hours in attempt to alleviate stress from academics on weekdays and unwind from overscheduling on weekend days, which can be common during adolescence. 272-274 Despite our findings that there may be acute affective benefits of engagement in screen-based SBs, longitudinal evidence indicates that screen-based SBs are unrelated to positive affect. 225 It has been suggested that youth may choose obesogenic behavioral coping strategies, such engagement in screen-based SBs, to manage or improve mood following a stressor; however, these coping strategies are considered maladaptive because while affective states may acutely improve, emotional and physical health are more likely worsen long- term as a result of these behavioral coping strategies. 275 Therefore, the acute association between engagement in screen-based SBs and higher positive affect may be transitory and likely does not accumulate into longer-term emotional benefits. Compared to screen-based SBs, less is known about non-screen-based SBs in relation to symptoms of emotional disorders, and to our knowledge there are no EMA studies of the acute within-day associations between non-screen-based SBs and affective states among adolescents. 65 One cross-sectional study found that the average time spent in non-screen-based SBs was unrelated to mental well-being, while engagement in screen-based SBs was related to poorer mental well-being. 184 Another study among a nationally-representative sample of adolescents found that engagement in non-screen-based SBs may even be protective against poorer emotional health. 276 Acutely, we found that non-screen-based SBs were related to less positive affect in the morning and evening hours, but not during midday; given this timing, it is possible that the associations we observed were driven by homework. Homework was among the most commonly-reported non-screen-based SBs in our sample, and some evidence suggests that homework can be related poorer affect among youth. 277, 278 Although non-screen-based SBs may be acutely related to lower positive affect in our sample, the association may be transitory and not necessarily translate into long-term poorer emotional outcomes. Rather, the nature of non- screen-based SBs (e.g., homework, reading, art) may promote cognitive function among youth; 265, 279, 280 therefore potentially improving the cognitively-demanding task of emotion regulation and subsequently improving the emotional health of youth. 281-284 Altogether, our findings highlight that non-screen-based SBs are important for acute affective states and should continue to be investigated in future studies. Because of the lack of evidence on non-screen- based SBs and acute affective states, more research is needed to understand why the associations are time-varying across the day and if the acute associations observed may translate into long- term emotional outcomes. The sensitivity analyses assessing accelerometer-measured sedentary time in relation to affective states yielded differential findings compared to our analyses with EMA-reported screen-based and non-screen-based SBs. Objectively measured sedentary time in the past 15 minutes was related to negative affect in the evening hours. These findings are consistent with 66 what we had hypothesized for the analyses using the EMA-reported SB construct and are consistent with a recent in-lab study that found that interrupting sitting time can reduce negative affect among healthy youth. 206 However, a previous free-living study of the acute (e.g., past 30 minutes) associations between objectively measured sedentary time and negative affect did not find that within-person deviations from one’s usual time spent sedentary were related to subsequent negative affect. 285 Our use of TVEM provides insight into a possible source of inconsistencies across study findings. Other modelling methods typically used for multilevel data (i.e. EMA data), are parametric and may impose linearity on the treatment of conceptual time. 286 In cases where associations may be non-linear, nonparametric functions of time, such as the case with SB and affective states according to our study, TVEMs may be more appropriate to use. 287 Taken together, in instances where it is plausible that the strength of the association of interest may non-parametrically differ as a function time, TVEMs may reveal associations that other (i.e. parametric) modelling methods may not. The differential findings between screen-based SB, non-screen-based SB, and objectively measured sedentary time highlight that each are distinct, yet interrelated, nuances of a larger behavior broadly referred to as SB. A previous study among youth found that EMA-reported screen-based SBs were highly correlated with objectively measured sedentary time, 288 and ancillary analyses that we conducted among our sample support this finding. Therefore, the differences in associations by operationalization of SB (e.g., screen-based SBs vs. objectively measured sedentary time) observed in our study were likely not due our EMA items insufficiently capturing time spent sedentary. Alternatively, our study supports the notion that what one does while being sedentary may be as important for understanding affective states as one’s total time spent sitting. Altogether, future investigations of SB-affective state associations 67 should take more nuanced approaches to conceptualizing SB beyond objectively measured sedentary time or engagement in screen-based SBs, which are currently the most commonly- utilized operational definitions of SB in the emotional health literature. 289 Strengths and Limitations The real-time, repeated measures data collected from our EMA surveys allowed us to model complex temporal associations and within-day changes in the relationship between SB and affective states using TVEM. Since EMA is a data-capture strategy that collects data in the naturalistic setting, the present study was highly ecologically valid. The EMA surveys used were also less susceptible to recall errors compared to other retrospective self-report measures of activity behaviors in youth. 14 Lastly, this was the first study to combine EMA and objectively measured SB to assess how the operationalization of SB (screen-based, non-screen-based, and sitting time) may have influenced the strength of the associations with affective states observed. There are also limitations of this study. Our EMA prompting period spanned from 7am to 8pm each day, therefore our findings cannot be generalized beyond these times of day. The EMA prompt compliance among our sample was slightly below what has been previously reported among other non-clinical samples of youth. 290 Similarly, participants were less likely to complete EMA prompts during the morning hours, perhaps because the EMA prompting schedule started too early in the day. Because TVEM for EMA data is a relatively new area of research, there is no consensus on how to handle missing data for these models and it is currently unknown how to adjust for systematic missing data points which often occur in EMA research. To gain a better understanding of how missing data may influence findings, it has been suggested that models can be rerun stratified by participant compliance (i.e. in those above and in those below the sample mean compliance). 291 However, stratified analyses by participant compliance could not be 68 conducted among our sample due to the small sample size; instead, models were rerun after removal of the participant with the lowest EMA prompt compliance (~32%) and results were comparable to those presented above. Future studies among larger samples should attempt to gain a better understanding of how missing data may impact study findings by stratifying analyses by participant compliance. Additionally, the current study only used two to three items to capture affective states, which may not be ideal for measuring this construct. However, using two to three items for capturing affective states in consistent with previous EMA studies, 105, 292 and is meant to reduce the number of items within a given EMA prompt. Limiting the amount of time to complete each survey can maximize prompt compliance as evidence suggests survey completion time is inversely related to prompt compliance. 293 Our sample size of 15 individuals limits the generalizability of our findings to other populations. However, the present study warrants future confirmatory studies among larger samples (at the EMA-prompt level and at the person-level) to increase the generalizability of findings and to allow for the investigation of a more nuanced operationalization of the SB construct (e.g., sub-types of screen-based and non-screen-based SB) in relation to acute affective states across the day. A better understanding of how the different sub-types of screen-based SB and non-screen-based SB relate to affective states will aid the development of tailored intervention strategies targeting the forms of SB that appear to be most important for affective states. Due to our EMA item wording (i.e. SB and affective states “right now”), this study did not assess temporality or the potential bi-directionality of the associations between SB and acute affective states, which previous studies suggest is plausible. 285, 294 Furthermore, the present study captured the main SB that participants were engaging in at the time of the EMA prompt, but 69 didn’t capture additional SBs that may have occurred simultaneously, which could have influenced our findings. Lastly, because our EMA prompting schedule did not ask participants about their SBs and affective states in the middle of the day on weekdays (due to school schedules), models included weekend day vs. weekday as a covariate, but stratified analyses by weekend day vs. weekday could not be conducted. Similarly, mid-day estimates were driven by the weekend day prompts, as participants were not prompted during mid-day on weekdays. Future studies should attempt to address this limitation by prompting participants across the entire day on both, weekend days and weekdays. Identifying if the time-varying associations between SB and affective states differ by day of week will aid our understanding of the mechanisms linking these variables, ultimately leading to the development of tailored intervention strategies that target the most vulnerable periods of times when the associations are the strongest. Conclusions This study demonstrated that the acute associations between SB and affective states differ by time of day and operationalization of SB, highlighting that there may be critical windows during the day that specific types of SB can be beneficial or detrimental for affective states. We therefore provide preliminary evidence suggesting that future confirmatory studies investigating the SB-affective state relationship should take into account the time-varying nature of the associations and consider more nuanced conceptualizations of the SB construct beyond screen- based SBs to better understand the complex relationships at hand. 70 Chapter 4: Cross-Lagged Associations Between Patterns of Objectively Measured Sedentary Time and Emotional Disorder Symptoms Across Early Adolescence 71 Abstract Objective: Evidence suggests that the manner in which sedentary time is accumulated across the day is important for physical health. It remains unknown if the way in which sedentary is accumulated also relates to emotional health, which is important to understand during the transition from childhood to adolescence. We explored the longitudinal and bi-directional associations between novel sedentary time accumulation metrics, alpha (bout length), Gini (bout length distribution), and sedentary breaks, and symptoms of major depressive disorder and generalized anxiety disorder during the adolescent transition among Los Angeles youth. Methods: Youth (N=167, 10.1[0.9] years old at baseline, 54.5% female, 59.3% Hispanic, 35.9% overweight/obese at baseline) participated in a three-year longitudinal study that consisted of six assessments of sedentary time, and depressive and anxiety symptoms at six-month intervals. At each assessment, participants wore waist-worn accelerometers (Actigraph GT3X) and completed the Revised Child Anxiety and Depression Scale. Those who had one or more days of valid accelerometer data for two or more assessments and self-reported emotional disorder symptoms were included. Separate random intercept cross-lagged panel models (RI-CLPM) estimated the within-person uni-directional and bi-directional associations between the sedentary time accumulation metric variables, and symptoms of major depressive disorder and generalized anxiety disorder across all temporally adjacent assessments (i.e., assessment one to assessment two, assessment two to assessment three, etc.). Results: The RI-CLPMs did not reveal bi-directionality of associations between any of the study variables. However, within-person uni-directional associations were observed across some assessments. Within-person variation in depressive and anxiety symptoms most-consistently predicted sedentary time accumulation. Typically, higher-than-usual depressive or anxiety 72 symptoms were associated with longer, less evenly distributed bouts and fewer breaks than usual six months later, independent of average levels of depressive or anxiety symptoms. Conclusion: Deviations from one’s usual level of depressive or anxiety symptoms, even at subclinical levels, may contribute to the allocation and distribution of sedentary time accumulation six months later, but not vice versa. Future studies should attempt to replicate findings and establish causality, as just-in-time adaptive intervention strategies targeting occasions when depressive or anxiety symptoms are higher-than-usual may promote a healthier accumulation of sedentary time among youth, thereby having a potentially longstanding impact on emotional and physical health. 73 Introduction Major depressive disorder and generalized anxiety disorder are among the two most common emotional problems in youth, 117, 118, 295 with each increasing in prevalence throughout adolescence. 117, 118 Estimates from representative samples of adolescents in the United States indicate that the prevalence of anxiety and depressive disorders are approximately 30% and 18%, respectively. 117, 118 Both types of emotional disorders, even at subclinical levels, during adolescence can substantially increase risk for a host of other negative outcomes such as cigarette and alcohol use, 296-298 and obesity. 299, 300 Further, symptoms of depression and generalized anxiety during adolescence track into adulthood, 301-303 and can confer similar health risk later in life. 304-308 One method for ameliorating the longstanding impact of emotional problems among adolescents is to gain a better understanding of the modifiable behavioral risk factors associated with depressive and anxiety symptoms in order to optimize preventive intervention strategies. Time spent sedentary (i.e. time spent sitting or lying down) parallels the rise of major depressive disorder and generalized anxiety disorder across adolescence, 192 and accumulating evidence indicates that sedentary time is a risk factor for both of these emotional disorders among youth, both at clinical and subclinical levels. 156, 186, 309 Moreover, recent studies of the sedentary time-emotional health link show that the association may be bi-directional, whereby each is a risk factor and consequence of one another. 165, 310 Excessive sitting during screen-based sedentary behaviors such as TV viewing, can increase social isolation and risk of experiencing symptoms of emotional disorders. 161, 311 On the other hand, depression-related fatigue may create a preference for a sedentary lifestyle; 185 and adolescents may gravitate towards sedentary behaviors to attempt to cope with or alleviate generalized anxiety symptoms. 312 Given the 74 possibility of a perpetuating cycle of sedentariness and poor emotional health among adolescents, a greater understanding of the linkage between these variables is needed. Accelerometers, small devices that detect accelerations in movement, are commonly used among sedentary behavior researchers to objectively quantify physical (in)activity. 15, 16, 313 Moreover, recent advancements in accelerometer technology have led to the development of algorithms that can detect patterns of sedentary time across the day (e.g., breaks in bouts and bout length), in addition to the traditional metric of average levels of sedentariness. 17 This is important because previous studies of the patterning of sedentary time across the day and physical health outcomes indicate that the manner in which sedentary time is accumulated may contribute to health risk, independent of average levels of sedentariness. 314 For example, sedentary time accumulated in longer bouts, with fewer breaks, is associated with poorer cardiometabolic and fitness outcomes among youth. 19, 315, 316 Further, recent evidence suggests that as children age into adolescence, they begin to accumulate sedentary time in unhealthier patterns (e.g., fewer sedentary breaks and more prolonged bouts of sedentary time). 317 However, longitudinal and bi-directional studies of objectively measured sedentary time and emotional health during adolescence have yet to assess if various accumulation patterns of sedentary time may be differentially related symptoms of emotional disorders such as major depressive disorder and generalized anxiety disorder. 165, 185, 310, 318, 319 Therefore, this study has one aim in order to fill this critical gap in knowledge: Aim 1: To examine the cross-lagged associations between patterns of objectively measured sedentary time and emotional disorder symptoms across three years. 75 H1A: Sedentary time accumulated in longer bouts will be positively bi-directionally associated with symptoms of major depressive disorder and symptoms of generalized anxiety disorder. H1B: Sedentary time that is not evenly distributed across the day will be positively bi- directionally associated with symptoms of major depressive disorder and symptoms of generalized anxiety disorder. H1C: Fewer breaks in sedentary time across the day will be positively bi-directionally associated with symptoms of major depressive disorder and symptoms generalized anxiety disorder. Methods Participants Participants were youth with complete covariate data who contributed at least one valid day of accelerometer data for two of the six waves of the MATCH cohort study of maternal stress and child obesity risk in the Los Angeles area (N=167). 219 This minimum level of protocol compliance was utilized to maximize the sample size for statistical analyses and is consistent with previous longitudinal investigations among this cohort. 317 MATCH participants were recruited via flyers and in-person research staff visits at public elementary schools and community events. The inclusion criteria for mother-child dyads of the MATCH study were (1) the child is in 3 rd -6 th grade at baseline, (2) more than half of the child’s custody belongs to the mother, and (3) both mother and child are able to read English or Spanish. Dyads were excluded from the MATCH cohort if the mother or the child (1) was taking medications for thyroid function of psychological conditions, (2) had a health condition that limited physical activity, (3) was enrolled in a special education program, (4) was currently using oral or inhalant 76 corticosteroids for asthma, (5) was pregnant, (6) the child was classified as underweight by a body mass index percentile of <5% adjusted for sex and age, or (7) the mother worked more than two weekday evenings (between 5-9pm) per week or more than eight hours on any weekend day. See Table 7 for demographic characteristics of the study sample. Table 7. Characteristics of the study sample at baseline (N=167) Characteristic Mean (SD) or N (%) Age 10.1 (0.9) Sex, % Female 91 (54.5%) Ethnicity, % Hispanic 99 (59.3%) Income Quartile, % Less $35,000/year 43 (25.7%) $35,001-$74,999/year 52 (31.1%) $75,000-$104,999 36 (21.6%) $105,000 or above/year 36 (21.6%) Weight Status, % Overweight/Obese 60 (35.9%) Procedures Six in-person assessments took place approximately every six months (baseline, six months, 12 months, 18 months, etc.), over three years. At the initial assessment appointment, mothers provided parental consent and their children provided written assent. Mothers also provided their children’s demographic information via paper-and-pencil questionnaires at the baseline appointment. During each of the six assessments, trained study staff took anthropometric measures (height in centimeters, weight in kilograms) of participants and calculated body mass index percentile using the Centers for Disease Control and Prevention EpiInfo tool. Participants completed paper-and-pencil surveys at each time point reporting on symptoms of major depressive disorder and generalized anxiety disorder. They were also provided a waist-worn accelerometer, which they were instructed to wear for the next seven consecutive days on the 77 right hip. The University of Southern California Institutional Review Board approved all study procedures. Measures Symptoms of Major Depressive Disorder and Generalized Anxiety Disorder: The Revised Child Anxiety and Depression Scale (RCADS), a validated instrument for use among youth, was used to measure symptoms of major depressive disorder and generalized anxiety disorder. 320, 321 The major depressive disorder subscale consists of 10 items such as “I feel sad or empty,” with response options ranging from never (zero) to always (three). Responses for each of the ten items were summed to create a continuous major depressive disorder subscale score, which ranged from zero to 30, with higher values indicating more symptoms (baseline Cronbach’s alpha=0.82). Similarly, the generalized anxiety subscale consists of six items such as “I worry about things,” with response options ranging from never (zero) to always (three). The sum of the individual responses for the six items were calculated to create a continuous generalized anxiety subscale score, ranging from zero to 18, with higher values indicating more symptoms (baseline Cronbach’s alpha=0.84). Sedentary Time and Accumulation: Sedentary time was measured using the Actigraph, Inc. GT3X model accelerometer, which has been validated for use in youth. 322, 323 Participants were instructed to wear the accelerometers (which were attached to an adjustable belt) on their right hip, at all times of the day except when sleeping, bathing, and swimming. Activity data were collected in 30-second epochs; non-wear was defined as 60 continuous minutes of zero activity counts and valid days were defined as days with 10 or more hours of wear time. Non- wear and non-valid days were removed prior to analyses. Sedentary time was defined as <100 activity counts per minute. 324 78 Alpha, an accelerometer activity pattern metric that can be used to describe the duration of sedentary bouts accumulated within the day, was calculated as follows: 325 𝐴𝑙𝑝 ℎ𝑎 = 1 + 1 𝑀 , where 𝑀 = 𝑚𝑒𝑎𝑛 log ( 𝑠𝑒𝑑𝑒𝑛𝑡𝑎𝑟𝑦 𝑒𝑣𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡 ℎ 𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑠𝑒𝑑𝑒𝑛𝑡𝑎𝑟𝑦 𝑒𝑣𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡 ℎ ) Thus, participants with lower alphas tended to accumulate a large proportion of his/her sedentary time in long bouts. Gini index, a standardized statistic for describing patterns of accumulation, 326 was also calculated for sedentary time at the day-level. Gini can range from zero to one, with smaller values indicating that sedentary bout lengths were accumulated (or distributed) more evenly across the day. Lastly, the number of breaks in sedentary bouts in a given day were summed; a break in sedentary time was defined as any accelerometer-captured activity exceeding 100 activity counts per minute, lasting for at least one minute. 317 If a participant contributed more than one valid day of accelerometer data in a given wave, then the day-level sedentary alpha, sedentary Gini, sedentary breaks variables were each averaged to create wave-level variables, to be consistent with emotional disorder symptoms, which were also measured at the wave-level. Covariates: Participant sociodemographic characteristics at baseline (sex, age, ethnicity) and baseline weight status (as determined by the body mass index percentile measure described above) were tested as between-person, time-invariant covariates and were retained in the models if they were significant. Mean daily valid accelerometer wear-time (minutes) was tested as a within-person, time-variant covariate and retained in the models if significant. Across all waves and all participants, there were only nine occasions where a weekend day was not among one of the valid days that comprised the wave-level activity variables, therefore, day of week (weekday vs. weekend day) was not tested as a time-variant covariate in the models. 79 Statistical Analysis Frequencies and/or means were calculated for all participant demographic characteristics at baseline, and emotional disorder symptoms and accelerometer-derived variables at each wave. Independent samples t-tests were used to assess if those included in the analytic sample (vs. excluded) differed in age. Chi-square tests were used to assess if those included (vs. excluded) from analyses differed by sex, ethnicity, weight status (healthy weight vs. overweight/obese), or income quartile. Random intercept cross-lagged panel models (RI-CLPM) assessed the bi-directional associations between the sedentary time accumulation metric variables and major depressive disorder and generalized anxiety disorder symptoms across six waves (three years). RI-CLPMs are an extension of traditional cross-lagged panel models that apply a multilevel modelling approach to allow investigators to assess the within-person dynamics. 327 The random intercept term included in all RI-CLPMs partials out between-person variance and represents stable, trait- like differences between participants. In addition to the random intercept, trait-like time-invariant covariates (e.g., sociodemographic characteristics) are entered into the model at the between- person level. The autoregressive and cross-lagged parameter estimates generated from RI- CLPMs reflect within-person dynamics. 327 In other words, the RI-CLPM determines how an individual’s deviation from their own mean in the predictor (e.g., sedentary alpha) over time predicts that same individual’s subsequent change in the outcome (e.g., major depressive disorder symptoms), and vice versa. See Figure 14 for graphical representation of the RI-CLPM. 80 Figure 14. Graphical depiction of the RI-CLPM. 327 Squares reflect observed variables (i.e. self-reported major depressive disorder symptoms at each wave) and circles reflect latent constructs. The two random intercepts reflect stable, between-person processes. Within- person processes over time are reflected by the autoregressive and the cross-paths. Paths are constrained to 1 for random intercept specification. Models were estimated using full information maximum-likelihood estimation (FIML) to account for missing data (i.e., participants dropping in and out of waves due to accelerometer noncompliance). 328 Further, it has been demonstrated that FIML can mitigate model estimation problems that may stem from non-normality of variables (i.e., the emotional disorder symptom in the present study). 328 Because RI-CLPMs require linear relationships between predictors and outcomes, cross-sectional associations between the sedentary time accumulation variables and depressive and anxiety symptoms by wave were assessed, to ensure that a linear relationship was a reasonable assumption. 152 Aim 1 Three separate RI-CLPMs assessed the reciprocal associations between (1) sedentary alpha and major depressive disorder symptoms, (2) sedentary Gini and major depressive disorder symptoms, and (3) sedentary breaks and major depressive disorder symptoms over three years. 81 Similarly, three additional RI-CLPMs were used to assess the reciprocal associations between sedentary alpha, sedentary Gini, and sedentary breaks, and generalized anxiety disorder symptoms over three years. Two separate ancillary RI-CLPMs were also used to assess the reciprocal associations between average sedentary time and symptoms of both emotional disorders over three years. Preliminary analyses were conducted using SAS v9.4 and RI-CLPMs were fitted with MPlus, Version 8. Results Data availability and descriptive statistics. Figure 15 presents the study participant flow. There were 202 children enrolled in the MATCH study at baseline. Those included (N=167) vs. excluded (N=35) from analyses did not differ by age, ethnicity, income quartile, or weight status (all p’s>0.14). However, those excluded were more likely to be male (p=0.02). Participants contributed an average of 4.4 valid days of accelerometer data per study wave. Table 8 presents the mean (SD) and range of all accelerometer-derived and emotional disorder symptom variables across all six waves of the MATCH study. Average time spent sedentary increased from 474 minutes/day at baseline to 533.9 minutes/day at wave six. Simultaneously, participants interrupted their sedentary time less across the study period, averaging 91.3 sedentary breaks/day at wave one compared to 77.7 sedentary breaks/day at wave six. All other study variables remained relatively stable across all six waves of the study. 82 Figure 15. Study participant flow. 83 Cross-lagged associations. Figures 16-21 present the standardized estimates for each of the RI-CLPMs. Additionally, the Akaike and Bayesian information criteria (AIC and BIC; smaller is better), the comparative fit index (CFI; optimal value >0.90), the Tucker-Lewis index (TLI; optimal value >0.90), and the root-mean-square error of approximation (RMSEA; optimal value <0.06) were reported to indicate how well the hypothesized models fit the observed data. 329 84 Table 8. Mean (SD) and range of study variables across data collection waves Wave 1 (N=160) Wave 2 (N=148) Wave 3 (N=128) Wave 4 (N=141) Wave 5 (N=136) Wave 6 (N=136) Mean (SD) Range Mean (SD) Range Mean (SD) Range Mean (SD) Range Mean (SD) Range Mean (SD) Range Sedentary Time, Minutes/Day 474.0 (86.0) 226.0- 860.1 480.2 (69.1) 312.5- 762.4 503.2 (74.5) 325.0- 814.4 499.0 (82.2) 237.5- 695.9 516.4 (74.0) 327.0- 751.5 533.9 (89.2) 301.0- 907.0 Sedentary Alpha a 1.9 (0.2) 1.6-3.5 1.8 (0.1) 1.6-2.2 1.8 (0.1) 1.6-2.1 1.8 (0.1) 1.4-2.3 1.8 (0.1) 1.5-2.0 1.8 (0.1) 1.4-2.3 Sedentary Gini b 0.6 (0.03) 0.5-0.7 0.6 (0.03) 0.5-0.7 0.6 (0.04) 0.5-0.7 0.6 (0.03) 0.5-0.7 0.6 (0.03) 0.5-0.7 0.6 (0.03) 0.5-0.7 Sedentary Breaks, Number per Day 91.3 (13.9) 59.0- 139.3 87.0 (13.5) 50.5- 113.7 83.3 (15.9) 42.0- 132.5 80.5 (17.1) 21.9- 114.2 77.3 (16.4) 29.3- 112.8 77.7 (15.5) 37.0- 113.2 Major Depressive Disorder c 5.1 (4.3) 0-24 4.6 (4.0) 0-19 4.7 (3.8) 0-17 4.3 (4.1) 0-21 4.0 (4.0) 0-21 4.3 (4.6) 0-26 Generalized Anxiety Disorder d 4.4 (3.5) 0-16 3.8 (3.0) 0-14 4.2 (3.4) 0-16 4.1 (3.6) 0-18 3.8 (3.8) 0-18 4.0 (3.7) 0-17 a Sedentary bout duration/length, smaller values indicate that sedentary time is accumulated in longer bouts b Sedentary bout length distribution, ranges from 0 to 1 with smaller values indicating equal distribution of bout lengths across the day c Continuous RCADS subscale score, ranges from 0 to 30 with higher values indicating more symptoms d Continuous RCADS subscale score, ranges from 0 to 18 with higher values indicating more symptoms 84 85 Figure 16. RI-CLPM of the association between sedentary alpha (“Alpha”) and symptoms of major depressive disorder (“Dep.”) across six waves, with six-month time lags (Hypothesis 1A; N=167). Cross-paths reflect the within-person bi- directional relationship between sedentary alpha and symptoms of major depressive disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non- significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=3553.0; BIC=3737.0; CFI=0.95; TLI=0.93; RMSEA=0.056. *p<0.05 **p<0.01 ***p<0.001 85 86 The model testing the association between sedentary alpha and symptoms of major depressive disorder (Figure 16) revealed good model fit to the data. At the between-person level, sedentary alpha and symptoms of major depressive disorder did not significantly covary with one another. However, participant age was inversely associated with sedentary alpha at the between- person level, suggesting that older participants accumulated their sedentary time in longer bouts compared to younger participants. Weight status was positively associated symptoms of major depressive disorder at the between-person level, indicating that participants with overweight/obesity reported more symptoms of major depressive disorder compared to their healthy weight counterparts. At the within-person level, the autoregressive paths for sedentary alpha were not consistently significant, revealing that within-person deviations from one’s usual sedentary alpha did not consistently predict within-person deviations in sedentary alpha at the next wave. On the other hand, the autoregressive paths for within-person symptoms of major depressive disorder were consistently significant; meaning that within-person deviations in symptoms of major depressive disorder were positively predicted by deviations from participants’ own levels of symptoms of major depressive disorder at earlier timepoints. Lastly, the cross-lagged paths reveal consistent marginal associations, with within-person symptoms of major depressive disorder being inversely related to within-person sedentary alpha, but not vice versa. Therefore, when one experienced greater symptoms of major depressive disorder than their own usual at a given wave, he/she subsequently accumulated sedentary time in longer bouts than his/her own usual at the next wave (starting from wave two onward). 87 Figure 17. RI-CLPM of the association between sedentary Gini (“Gini”) and symptoms of major depressive disorder (“Dep.”) across six waves, with six-month time lags (Hypothesis 1B; N=167). Cross-paths reflect the within-person bi-directional relationship between sedentary Gini and symptoms of major depressive disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non- significance and poorer model fit when included. Sedentary Gini was rescaled by multiplying by 10 for model convergence. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=5349.2; BIC=5533.2; CFI=0.88; TLI=0.82, RMSEA=0.075. *p<0.05 **p<0.01 ***p<0.001 87 88 The model presenting the associations between sedentary Gini and symptoms of major depressive disorder is shown in Figure 17. At the between-person level, sedentary Gini and symptoms of major depressive disorder did not significantly covary with one another. However, participant age was associated with sedentary Gini at the between-person level; sedentary bout lengths were less evenly distributed among older participants compared to younger participants. As with the model presented above (Figure 16), weight status was associated with symptoms of major depressive disorder at the between-person level such that participants with overweight/obesity reported more symptoms of major depressive disorder than their healthy weight counterparts. The autoregressive paths indicate that sedentary Gini was not predicted by itself at most timepoints; deviations from one’s usual sedentary Gini did not predict future within-person sedentary Gini. On the other hand, within-person symptoms of major depressive disorder were associated with within-person symptoms of major depressive disorder at the next wave at all timepoints. The cross-paths reveal that at only one time-point (from wave two to wave three), symptoms of major depressive disorder were predicted by sedentary Gini. At wave two, when sedentary Gini was higher than one’s own usual (e.g., a less even distribution of sedentary bout lengths), he/she subsequently reported fewer symptoms of major depressive disorder than usual. Conversely, sedentary Gini was predicted by symptoms of major depressive disorder at two timepoints at the within-person level. When one reported more symptoms of major depressive disorder than his/her own usual, he/she subsequently accumulated sedentary bout lengths less evenly than usual. 89 Figure 18. RI-CLPM of the association between sedentary breaks (“Breaks”) and symptoms of major depressive disorder (“Dep.”) across six waves, with six-month time lags (Hypothesis 1C; N=167). Cross-paths reflect the within-person bi- directional relationship between sedentary breaks and symptoms of major depressive disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non- significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=11671.8; BIC=11855.8; CFI=0.93; TLI=0.90, RMSEA=0.07. *p<0.05 **p<0.01 ***p<0.001 89 90 The model testing the association between sedentary breaks and symptoms of major depressive disorder is presented in Figure 18. At the between-person level, sedentary breaks and symptoms of major depressive disorder did not significantly covary with one another. However, sex and age were significantly related to sedentary breaks at the between-person level such that boys had fewer sedentary breaks compared to girls, and older participants had fewer sedentary breaks compared to younger participants. As with the models above (Figures 16 and 17), participants with overweight/obesity reported more symptoms of major depressive disorder compared to their healthy weight counterparts. The autoregressive paths show that within-person sedentary breaks were predicted by within-person sedentary breaks from the previous wave at almost all timepoints. Similarly, within-person symptoms of major depressive disorder were also predicted by within-person symptoms of major depressive disorder from the previous wave at all timepoints. Three of five cross-lagged paths showed that within-person symptoms of major depressive disorder were associated with subsequent within-person sedentary breaks; during waves when a participant had higher than usual symptoms of major depressive disorder, he/she subsequently had fewer sedentary breaks at the next wave. Within-person sedentary breaks were not predictive of within-person symptoms of major depressive disorder at any timepoint. 91 Figure 19. RI-CLPM of the association between sedentary alpha (“Alpha”) and symptoms of generalized anxiety disorder (“Anx.”) across six waves, with six-month time lags (Hypothesis 1A; N=167). Cross-paths reflect the within-person bi- directional relationship between sedentary alpha and symptoms of generalized anxiety disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non- significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=3261.0; BIC=3445.0; CFI=0.96; TLI=0.94, RMSEA=0.05. *p<0.05 **p<0.01 ***p<0.001 91 92 The model testing the association between sedentary alpha and symptoms of generalized anxiety disorder (Figure 19) revealed good model fit. At the between-person level, sedentary alpha and symptoms of generalized anxiety disorder did not significantly covary with one another. As with the model presented in Figure 16, age was related to sedentary alpha at the between-person level; participants who were older accumulated their sedentary time in longer bouts compared to younger participants. Moreover, sex, age, and weight status were each related to symptoms of generalized anxiety disorder at the between-person level. Boys reported fewer symptoms of generalized anxiety disorder than girls, older participants reported fewer symptoms of generalized anxiety disorder compared to younger participants, and those with overweight/obesity reported more symptoms of generalized anxiety disorder than those of healthy weight. At the within-person level, the autoregressive paths for sedentary alpha were not consistently significant; within-person deviations from one’s usual sedentary alpha did not consistently predict within-person deviations in sedentary alpha at the next wave. However, the autoregressive paths for generalized anxiety disorder were consistently significant starting at wave two; within-person deviations in symptoms of generalized anxiety disorder were positively predicted by deviations from participants’ own levels of symptoms of generalized anxiety disorder at earlier timepoints. The cross-lagged paths indicate that within-person symptoms of generalized anxiety disorder predicted within-person sedentary alpha at the next wave during the earlier timepoints of the study; however, the directions of the associations were not consistent, and associations were no longer significant starting at wave four. Within-person sedentary alpha was not associated with subsequent within-person symptoms of generalized anxiety disorder at any timepoint. 93 Figure 20. RI-CLPM of the association between sedentary Gini (“Gini”) and symptoms of generalized anxiety disorder (“Anx.”) across six waves, with six-month time lags (Hypothesis 1B; N=167). Cross-paths reflect the within-person bi- directional relationship between sedentary Gini and symptoms of generalized anxiety disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non- significance and poorer model fit when included. Sedentary Gini was rescaled by multiplying by 10 for model convergence. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=5056.2; BIC=5240.2; CFI=0.92; TLI=0.87, RMSEA=0.061. *p<0.05 **p<0.01 ***p<0.001 93 94 The model testing the association between sedentary Gini and symptoms of generalized anxiety disorder (Figure 20) revealed good model fit for the data. At the between-person level, sedentary Gini and symptoms of generalized anxiety disorder did not significantly covary with one another. Consistent with the model presented in Figure 17, age was associated with sedentary Gini at the between-person level; older participants accumulated sedentary bout lengths less evenly compared to younger participants. As with the generalized anxiety model presented in Figure 19, age and weight status were related to symptoms of generalized anxiety disorder at the between-person level. The autoregressive paths indicate that within-person sedentary Gini was only predicted by previous within-person sedentary Gini at one timepoint. Alternatively, symptoms of generalized anxiety disorder consistently predicted itself over time at the within-person level. The cross-paths show that symptoms of generalized anxiety were predictive of sedentary Gini and two timepoints; when one reported more symptoms of generalized anxiety compared to his/her own usual, then he/she subsequently accumulated sedentary bout lengths less evenly than usual. No other significant associations were found. 95 Figure 21. RI-CLPM of the association between sedentary breaks (“Breaks”) and symptoms of generalized anxiety disorder (“Anx.”) across six waves, with six-month time lags (Hypothesis 1C; N=167). Cross-paths reflect the within-person bi- directional relationship between sedentary breaks and symptoms of generalized anxiety disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non-significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=11379.0; BIC=11563.0; CFI=0.96; TLI=0.94, RMSEA=0.05. *p<0.05 **p<0.01 ***p<0.001 95 96 Figure 21 presents the model testing the association between sedentary breaks and symptoms of generalized anxiety disorder. At the between-person level, sedentary breaks and symptoms of generalized anxiety disorder did not covary with one another. Age and sex were related to sedentary breaks at the between-person level; and sex, age, and weight status were related to symptoms of generalized anxiety disorder at the between-person level. At the within- person level, the autoregressive paths for sedentary breaks were not consistently significant; within-person deviations from one’s usual number of sedentary breaks did not consistently predict within-person deviations in sedentary breaks at the next wave. The within-person autoregressive paths were relatively consistently significant for symptoms generalized anxiety disorder, however. The cross-paths revealed a significant association between within-person sedentary breaks and within-person symptoms of generalized anxiety disorder; when one accumulated more sedentary breaks than his/her own usual, he/she reported fewer symptoms of generalized anxiety disorder than usual at the subsequent wave. However, this association only appeared from wave one to wave two, and at no other timepoints in the study. Two additional cross-paths indicated a significant association between within-person symptoms of generalized anxiety disorder and within-person sedentary breaks; when one reported more symptoms of generalized anxiety than their own usual, he/she subsequently had fewer sedentary breaks than usual at the next timepoint. It is worth noting, however, that although this model revealed associations between within-person sedentary breaks and within-person symptoms of generalized anxiety disorder in both directions, reciprocal associations between these variables at the same timepoint were not observed. Ancillary analyses with average sedentary time. The standardized estimates for the RI- CLPMs assessing the association between average sedentary time and symptoms of major 97 depressive disorder (Figure 22) and generalized anxiety disorder (Figure 23) are presented below. At the between-person level, both models show that average sedentary time did not significantly covary with symptoms of major depressive disorder or generalized anxiety disorder. Participant age was related to average sedentary time at the between-person level, indicating that older participants accumulated more sedentary time on average, compared to younger participants. Both models also showed that at the within-person level, the autoregressive paths for average sedentary time were not consistently significant; within-person deviations from one’s usual time spent sedentary did not consistently predict within-person deviations in sedentary time at the next wave. The cross-paths in Figure 22 demonstrate only one significant within-person association; at wave one, when one reported more symptoms of major depressive disorder than his/her own usual, then he/she subsequently (at wave two) accumulated more average sedentary time than his/her own usual. Similarly, the cross-paths in Figure 23 demonstrated only one marginally significant association; at wave three, when one reported more symptoms of generalized anxiety disorder than his/her own usual, then he/she subsequently (at wave four) accumulated more average sedentary time than his/her own usual. All other within-person cross- paths in the both models were null, providing little evidence of the association between average sedentary time and symptoms of emotional disorders in either direction. 98 Figure 22. RI-CLPM of the association between average sedentary time (“Sed.”) and symptoms of major depressive disorder (“Dep.”) across six waves, with six-month time lags (Ancillary Analyses; N=167). Cross-paths reflect the within-person bi- directional relationship between average sedentary time and symptoms of major depressive disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within-person level due to non-significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non-significant paths. AIC=14508.3; BIC=14692.2; CFI=0.94; TLI=0.90, RMSEA=0.063. *p<0.05 **p<0.01 ***p<0.001 98 99 Figure 23. RI-CLPM of the association between average sedentary time (“Sed.”) and symptoms of generalized anxiety disorder (“Anx.”) across six waves, with six-month time lags (Ancillary Analyses; N=167). Cross-paths reflect the within- person bi-directional relationship between average sedentary time and symptoms of generalized anxiety disorder. Sex (male=1), age, and weight status (overweight/obese=1) at baseline were added to the model as time-invariant covariates at the between-person level. Valid accelerometer wear-time was not retained in the model as a time-variant covariate at the within- person level due to non-significance and poorer model fit when included. Solid black lines indicate significant paths at the p<0.05 level, dashed black lines indicate marginally significant paths at the p<0.10 level, and dashed gray lines indicate non- significant paths. AIC=14218.8; BIC=14402.7; CFI=0.96; TLI=0.93, RMSEA=0.05. *p<0.05 **p<0.01 ***p<0.001 99 100 Discussion To our knowledge, this is the first longitudinal and bi-directional study of objectively measured sedentary time and emotional disorder symptoms that investigated metrics of sedentary time beyond average time spent sedentary, including sedentary alpha, sedentary Gini, and number of sedentary breaks. This is important because when comparing our novel sedentary accumulation metric results to those for average sedentary time, the associations were more consistent for the novel accumulation metrics; suggesting that emotional disorder symptoms may relate to the ways in which one accumulates sedentary time, but not his/her overall time spent sedentary. Previous studies of the bi-directional associations between sedentary time and emotional disorder symptoms among youth have yielded inconsistent results, with some suggesting the plausibility of bi-directionality, and others yielding null findings. 165, 185, 225, 310, 330 Contrary to our hypotheses, we did not observe bi-directionality at the same timepoint in our study. Rather, we observed uni-directional associations, with emotional disorder symptoms typically predicting subsequent sedentary time approximately six months later; a finding that has been previously demonstrated among a sample of adolescent girls. 331 It is important to note that the uni-directional associations that we observed were consistent with our hypothesized directions; emotional disorder symptoms were generally related unhealthier sedentary accumulation (i.e., longer sedentary bouts, less even distribution of bout lengths, and fewer sedentary interruptions/breaks). In general, our between-person findings regarding sedentary time and accumulation are consistent with previous study findings. The sedentary accumulation pattern metrics, alpha and Gini, were both associated with age at baseline such that older participants accumulated their sedentary time in longer bouts and less evenly compared to younger participants in our study, 101 paralleling investigations that suggest that older youth accumulate their sedentary time in unhealthier ways. 317, 332, 333 Similarly, older participants broke up their sedentary time less often than younger participants in the current study, consistent with what has been previously demonstrated among this age group. 317, 334 Unexpectedly, however, we found that boys broke up their sedentary time less often than girls. While previous findings suggest that adolescent girls typically break up their sedentary time less than adolescent boys, 317, 334, 335 it is plausible that we did not have similar observations because the present study occurred prior to when sex differences in age-related declines in sedentary breaks are thought to emerge. 317, 334 Lastly, we found that older participants accumulated more average sedentary time compared to younger participants, contributing to a well-established body of literature that indicates that average sedentary time steadily increases as youth age into adolescence. 336, 337 Taken together, these findings consistently highlight the importance of interventions targeting sedentary time prior to adolescence, to prevent age-related increases in average sedentary time and to mitigate the transition into unhealthful accumulations of sedentary time. While most of the abovementioned between-person findings were reflective of age differences in sedentary time and accumulation, this was not the case for symptoms of emotional disorders. Rather, age, sex, and weight status were each related to symptoms of emotional disorders at the between-person level in the present study. We found that participants with overweight or obesity at baseline reported more symptoms of major depressive disorder than their healthy weight counterparts. A previous study among 1490 adolescents had similar findings, reporting that youth with obesity reported more depressive symptoms than their peers of overweight and healthy weight. 338 Paralleling these findings, another study among adolescent girls found that obesity was longitudinally associated with depressive symptoms across four 102 years. 339 There are a number of potential explanations, including weight-related bullying and peer victimization, 340 body dissatisfaction, 341 and weight-related concerns 342 each plausibly increasing depressive symptoms among youth with overweight and obesity. We also found that those with overweight or obesity and girls reported more symptoms of generalized anxiety compared to their peers of healthy weight and boys, respectively; both of which have been previously demonstrated in youth. 117, 306, 343-345 Among the many possible reasons, 346 girls may have more difficulty regulating negative emotions compared to boys, leading to greater feelings of worry. 345 Lastly, age was related to symptoms of generalized anxiety such that older participants reported fewer symptoms of generalized anxiety compared to younger participants. One study among over 400 youth aged nine to 12 years old found that younger participants were more likely to cope with daily stressors with internalization and “keeping the problem to oneself” compared to older participants, 347 possibly contributing to observed age differences in symptoms of anxiety. Taken together, the present study replicates previous between-person findings of age, sex, and weight status differences in emotional disorder symptoms. It is worth noting that sedentary time/patterns of accumulation and emotional disorder symptoms were not correlated with one another at the between-person level in any of our models. However, significant within-person associations did emerge in our study, highlighting a possible source of heterogeneity in findings from previous between-person investigations of objectively measured sedentary time and emotional disorder symptoms. 185, 348, 349 With the exception of one path, all significant cross-paths (longitudinal) were in the direction of depressive symptoms predicting subsequent sedentary time/accumulation. Specifically, we found that starting at wave 2 (~10.5 years old) and at each timepoint thereafter, within-person depressive symptoms predicted subsequent within-person alpha; when a participant reported 103 more depressive symptoms than usual at a given wave, he/she accumulated sedentary time in longer bouts than usual at the next wave. Paralleling these results, during roughly half of the observational waves, when one reported more depressive symptoms than usual, he/she subsequently broke up his/her sedentary time less than usual at the next wave. Less consistently, we saw that depressive symptoms were related to subsequent sedentary Gini in the hypothesized direction—when one reported more depressive symptoms than usual, he/she accumulated his/her sedentary bouts less evenly than usual at the next wave. While our cross-paths were inconsistently significant across timepoints (possibly due to a lack of statistical power), our findings highlight that deviations from one’s usual amount of depressive symptoms, even at levels well-below the clinical threshold, can be one factor that potentially contributes to an unhealthier accumulation of sedentary time. A possible explanation for these findings is that when one experiences somatic depressive symptoms, such as fatigue or sleep difficulties, he/she may proceed to accumulate their sedentary time in longer bouts with fewer breaks in order to cope with such symptoms. For example, one study among youth with chronic fatigue highlights that many cope with their fatigue by engaging in sedentary behaviors for prolonged amounts of time. 350 We also observed within-person associations between symptoms of generalized anxiety disorder and sedentary time/accumulation. All except one of the significant cross-paths found were in the direction of symptoms of generalized anxiety disorder predicting sedentary accumulation patterns at the next observational wave. Among the significant cross-paths, when one reported more symptoms of generalized anxiety than their own usual, he/she generally subsequently accumulated his/her sedentary time in longer and less evenly distributed bouts, with fewer breaks/interruptions than usual. Symptoms of anxiety, especially at subclinical levels, 104 are common among youth, as children and adolescents are learning emotion regulation skills, 351 are navigating physical and social developmental changes, 352, 353 and are perhaps facing increasing academic pressure. 354 Our study suggests that regardless of overall levels of generalized anxiety, deviations from one’s usual amount of symptoms were related to subsequently unhealthier accumulations of sedentary time. Youth experiencing more feelings of worry than usual may cope with these symptoms by engaging in sedentary behavior. For example, academic-related anxiety may lead to longer amounts of time spent doing homework, 355 potentially contributing to the accumulation of sedentary time in prolonged and uninterrupted bouts. Additionally, youth may turn to leisure sedentary behaviors for prolonged periods of time to cope with non-academic-related worries, as evidence suggests that youth may engage in more screen-based sedentary behaviors than usual during stressful and uncertain times. 356 Understanding the factors that contribute to sedentary accumulation patterns among youth is important because sedentary time accumulated in longer bouts with fewer breaks can relate to poorer metabolic health, 19, 314 potentially increasing risk for serious diseases such as type 2 diabetes and certain forms of cancer later in life. 357, 358 Interestingly, in the present study, the sedentary accumulation pattern variables were not consistently predictive of future sedentary accumulation patterns across the study period; and emotional disorder symptoms were similarly predictive of sedentary accumulation patterns. Therefore, deviations in emotional disorder symptoms may be just as important as previous sedentary accumulation patterns in predicting how sedentary time is accumulated in the future. Thus, if future studies can establish causality, then targeting deviations (e.g., when more symptoms are reported than usual) in depressive and 105 generalized anxiety symptoms with intervention strategies during adolescence may, in turn, improve physical health by influencing how sedentary time is accumulated. Strengths and Limitations A notable strength of the present study is that it analyzed repeated measures (across six time points) during a vulnerable developmental period for youth. The objective measure of sedentary time and the assessment of novel sedentary accumulation metrics were also strengths of the current study. Further, we used a novel analytic strategy that leveraged multilevel modelling approach to cross-lagged panel models in order to differentiate between- and within- person associations. The novel sedentary accumulation metrics assessed, hand-in-hand with our statistical methods, allowed us to take a more nuanced approach to our investigation of the bi- directional associations between sedentary time and emotional disorder symptoms compared previous studies; ultimately increasing our understanding of the relationships at hand. A limitation of the current study is that only one valid day of accelerometer was required for at least two of the six waves of the MATCH study. Therefore, the activity levels included in the analyses may not have reflected the usual activity levels of our participants. Additionally, our relatively small sample size limits the generalizability of our findings to other samples and limited our power for the structural equation models. Future studies should consider attempting to replicate our study findings using a similar modelling approach among larger samples of youth. Our analyses were limited symptoms of major depressive disorder and generalized anxiety disorder, which may be differentially related to sedentary time compared to other forms of emotional disorder symptoms commonly found in youth (i.e. social anxiety). 330 Therefore, future studies should attempt to gain an understanding of whether our findings can be extrapolated to symptoms of other emotional disorders in addition to those studied in the present 106 investigation. Lastly, on average, our participants reported very few emotional disorder symptoms; therefore, future studies should also attempt to replicate and generalize our findings among clinical samples of youth. Conclusions This study demonstrated that the associations between symptoms of major depressive disorder, symptoms of generalized anxiety disorder, and sedentary accumulation patterns are likely to be uni-directional during late childhood and early adolescence. Deviations from usual depressive and anxiety symptoms were generally predictive of longer and less evenly distributed sedentary bouts, and fewer sedentary breaks than usual six months later; however, deviations from usual emotional disorder symptoms were not as strongly related to average time spent sedentary. Therefore, depressive and anxiety symptoms may relate to how one subsequently accumulates sedentary time, but not overall time spent sedentary. Future studies should attempt to replicate and extend these findings, as intervention strategies aimed at promoting a healthier accumulation of sedentary time among youth may be optimized if they target occasions when depressive and anxiety symptoms are higher than usual. 107 Chapter 5: Discussion 108 Summary of Findings The overall goal of this dissertation was to investigate the associations between sedentary behaviors (SB), affective states, and emotional disorder symptoms across different study designs and time scales to increase our understanding of the possible associations at hand among youth. The current dissertation addresses several methodological limitations found in the extant literature to date, including the lack of experimental study designs and the limited conceptualization of SB that typically only encompasses behaviors such as screen-based SB or average time spent sedentary. The purpose of Study 1 was to examine the efficacy of experimentally interrupting sitting time for improving acute affective and anxiety states across three hours among a sample of children and adolescents in a randomized crossover trial. Although more evidence of a causal association between interrupting sitting and acute affective responses is needed due to a majority of null Study 1 findings, our results provide a preliminary indication that interrupting sitting time may acutely reduce negative affect among certain groups (e.g., girls). Building upon Study 1, the purpose of Study 2 was to assess the co-occurrence of different types of ecological momentary assessment (EMA)-reported SBs (e.g., screen-based SB, non-screen-based SB) and affective states across the entire day (7am-8pm). We found that the strength of the association between SB and affective states differs by time of day and operationalization of self-reported SB. Further, ancillary analyses revealed that objectively measured sedentary time was unrelated to affective states across most of the day, which may serve as a potential explanation for the primarily null Study 1 findings. Lastly, in Study 3, we were interested in understanding the potential for bi- directional associations between SB, conceptualized as patterns of sedentary time accumulation, and depressive and anxiety symptoms across three years from late childhood to early 109 adolescence. In this study, we found only uni-directional associations across six-month increments, with depressive and anxiety symptoms typically predicting subsequent prolonged and uneven sedentary time accumulation, with fewer breaks in sedentary time, but not vice versa. Paralleling findings from Study 1 and Study 2, emotional disorder symptoms were weakly or unrelated to average time spent sedentary in Study 3. Implications By leveraging data from randomized trial, EMA, and longitudinal cohort studies, we were able to investigate the associations of interest across different study designs and time scales, ranging from hours to years. Results across studies identified associations both within the day and across months. These findings suggest that acute day-level associations between SB and affective states may accumulate over time to contribute to longer-term associations between SB and more chronic depressive and anxiety symptoms. In addition to assessing these associations across different time scales, it is noteworthy that we examined a broad range of conceptualizations of SB that included sitting, patterns of sitting accumulation, and behaviors done while sitting (screen-based SB vs. non-screen-based SB). Importantly, we found differences in the strength of associations with affective states/emotional health by conceptualization of SB, highlighting that these associations are likely vary and are therefore complex. Thus, our results provide a strong basis of support for the need for future studies to consider temporal characteristics of associations and clear definitions of type of SBs, rather than relying on cross- sectional associations and global measures of general SB. This will advance the field by providing a deeper understanding of the mechanisms that are most likely to be driving the SB- emotional health link among youth and therefore provide a basis for intervention targets. 110 Pinpointing the specific mechanisms that are most likely to be underlying the SB- emotional health link is necessary so that we can optimize intervention strategies, such as those that target only the specific types of SBs that are most likely to relate to emotional outcomes. Psychosocial mechanisms may explain, in part, why certain types of SB may be related to poorer emotional outcomes compared to other types of SB. For example, negative online social interactions with peers 236 or cyberbullying 172 may explain why specific forms of SB, such as computer use, have strong associations with depressive and anxiety symptoms. Similarly, there are growing concerns about other types of SB, such as social media use, and how it may be contributing to the emotional ill-being of youth. For example, upward social comparison and passive social media use may be particularly deleterious for emotional health. 235, 359 Therefore, it is imperative that future studies continue to clearly differentiate types of SB when linking them to emotional health and further investigate specific behaviors done within each form of SB (e.g., cyberbullying within computer or social media use) to understand the potential psychosocial mechanisms at hand. Additionally, factors such as sleep, fatigue, and cognition pose further explanations for why emotional disorder symptoms may predict subsequently accumulated SB, as observed in this dissertation. For example, depression or anxiety-related sleep problems may increase fatigue 360 and impair cognitive performance, 361 thereby creating a preference for leisure-time behaviors that require little energy expenditure and that are not cognitively demanding, including prolonged and uninterrupted SB (e.g., TV viewing). 350 Similarly, it may also take longer to complete academic-related SBs, such as reading or homework, when one is experiencing depressive and anxiety-related reductions in cognitive performance, 362 therefore contributing to prolonged amounts of time spent sedentary. Future studies should attempt to further understand 111 the role of sleep and fatigue in linking emotional health to certain types/accumulation of SB; intensive longitudinal data capture strategies, such as EMA, may be especially useful to understand how these complex and likely dynamic associations unfold in real-time. Taken together, there are several hypothesized reasons why SB and emotional health are linked to one another among youth, postulating a wide range of explanations including, but not limited to, psychosocial, somatic, and cognitive factors. With additional evidence rooted in nuanced studies that are informed by those presented in this dissertation, we can begin to understand which mechanisms may be the key drivers of the SB-emotional health link, allowing us to develop targeted intervention strategies. In addition to mechanistic implications, this dissertation also has measurement implications for future studies of SB and emotional health. Our findings suggest that the associations between SB and emotional health can differ depending on how SB is operationalized in a given study, indicating that average time spent sedentary may not be as important for emotional outcomes compared to the activities performed while sedentary/how sedentary time is accumulated. This is a key finding that provides insight into specific behaviors that should be targeted for future research and intervention. Accordingly, this finding highlights that studies utilizing self-report measures of SB should include detailed items on time spent in different subtypes of SBs, including screen-based and non-screen-based SB. Furthermore, a single variable that amalgamates time spent in each different type of SB with one another should no longer be derived from self-report measures of SB. Rather, separate variables should be generated based on SB-type and analyses should be conducted separately for each. To date, however, most studies fail to differentiate between SB-types in their measurement and analytic strategies. 186, 309 Investigations that continue to use variables that reflect overall time spent 112 sedentary, rather than time spent in specific SBs, will further contribute to inconsistencies in the literature and limit our understanding of the associations at hand. Similarly, observational studies that leverage devices to objectively measure sedentary time, yet only quantify total volume of SB, will perpetuate our limited understanding of how and why movement behaviors relate to emotional health. It is essential that future studies with objective measures of sedentary time investigate metrics beyond total volume of time spent sedentary because our findings suggest that the way in which sedentary time is accumulated across time can be important for emotional health. Objective monitors present the unique opportunity to capture bodily movement beyond total volume of (in)activity and investigate accumulation metrics such as sedentary bout lengths and frequency of breaks in sedentary bouts, and therefore should be leveraged as such if we are to truly understand SB and emotional disorder symptom associations. This dissertation has additional methodological implications, highlighting that time of day and temporality/directionality should be carefully incorporated into future investigations of SB and emotional health. In our within-day study of the acute associations between SB and affective states, we found that time of day is an important moderator. The strength of the associations at hand differed by time of day, with associations being null during certain hours of the day and significant during other hours of the day. Therefore, future studies should continue to incorporate time of day into their analyses to reflect the dynamic nature of these relationships, otherwise null conclusions may be erroneously drawn. Consequently, an understanding of when associations may be the strongest or weakest can inform just-in-time adaptive intervention strategies that are aimed at improving mood and decreasing time spent sedentary. Similarly, intervention strategies could be optimized if the temporality/directionality of SB-emotional health associations were clearer. Many of the longitudinal studies to date assume that SB is 113 predictive of subsequent emotional disorder symptoms, but not vice versa; 234 however, Study 3 presented in this dissertation found that the reverse directionality may be more likely. Because few studies have examined if emotional health may be predictive subsequent SB, the possible reasons for this directionality remain unclear. Therefore, more studies of SB-emotional health (or affective state) associations are warranted, both, acutely (within the day), and longitudinally (across months or years), to establish temporality/directionality. Future work that is informed by this dissertation and leverages the abovementioned measurement/methodological considerations will have important policy implications. Once we have a better understanding of how, when, and why SB and emotional health are linked to one another, we can begin to develop national SB recommendations for youth. To date, the United States has not established guidelines recommending a safe amount of time spent in SB for youth, despite having clear guidelines for other energy balance behaviors, such as physical activity. 363 Other countries recommend that youth should not exceed two hours per day of time spent in leisure SBs, 363 however this dissertation suggests that not all types of leisure-time SBs relate to health in the same way. Therefore, national guidelines should go beyond overall leisure-time SB and establish recommendations by SB-type. Research informed by this dissertation can also have policy implications for the school setting. With continuing evidence suggesting that prolonged and uninterrupted sedentary may be detrimental to health, policies that aim to restructure the school day to weave sedentary breaks throughout the day may benefit the emotional and physical health of youth, perhaps having a longstanding impact on distal health outcomes. Future Directions There are additional behavioral factors that are important to consider in future acute and longitudinal investigations of SB and emotional health. With objective activity monitoring 114 becoming increasingly available and utilized, the field is shifting towards taking a 24-hour movement approach to understanding movement behaviors and health. In the context of the 24- hour approach, everyday movement behaviors fall on a continuum of intensity and encompass physical activity, SB, and sleep. 364 Traditionally, these three behaviors have been investigated independently with regards to health implications among youth, including emotional health. 36, 365, 366 However, emerging evidence across age ranges and health outcomes suggests that the mix of each, physical activity, SB, and sleep, may be especially important to consider. 364 To date, however, few studies of movement behaviors and emotional health among youth have taken a 24-hour approach. 70 To better understand the SB and emotional health link among youth, the field may benefit from incorporating the additional 24-hour movement behaviors (e.g., physical activity, sleep) into future studies. While this dissertation did not investigate physical activity, it may be an important moderator of SB-emotional health associations. Previous studies suggest that physical activity may act as a buffer of the associations between SB and depressive and anxiety symptoms. 234 In other words, SB and emotional disorder symptoms may only be associated with one another among youth who are simultaneously physically inactive. 330 During childhood and adolescence, physical activity is usually accumulated with peers, and oftentimes in the form of organized sports or team sports. 367 Therefore, physical activity may be an important source of social support and increased self-esteem, 368, 369 whereby a protective effect against emotional disorder symptoms is rendered. 370, 371 Evidence suggests that sports may also be protective against emotional disorder symptoms among youth by increasing opportunities for spending time outside. 372 This is important because evidence suggests that time spent outside and exposure to nature can relate to better mood among adolescents, 373 perhaps by providing opportunities for 115 reducing stressors in one’s built environment (i.e., crowding, noise exposure). 374 Additionally, regular physical activity can be related to certain structural and functional changes during brain development that have been implicated in mental well-being among youth. 375 For example, neuroimaging studies suggest that physical activity may be related to structural changes in the prefrontal cortex and can improve self-regulation skills (including emotion regulation), 375 conferring resilience against emotional problems. In sum, there are several potential mechanisms by which physical activity may mitigate the negative impact that SB may have on emotional health. Therefore, future studies should aim to understand the possible interaction between SB and physical activity when linking these behaviors to emotional disorder symptoms. Investigators should also consider incorporating sleep into their future studies of SB and emotional health. Compared to physical activity, less is known about sleep and how it may influence SB-emotional health associations. For example, separate research threads have linked screen-based SB to poorer sleep health, 376 and poorer sleep health to depressive symptoms. 377 Yet, a recent study of over 2800 adolescents bridged these two lines of inquiry and found that problems falling asleep, problems staying asleep, and sleep duration may act as mediators of the association between screen-based SBs and depressive symptoms. 378 Screen-based SBs may displace sleep, increase physiological arousal prior to bedtime, and delay the circadian rhythm, 379 in turn resulting in sleep problems. This is important because sleep problems can act as a precursor for certain symptoms of emotional disorders, including irritable mood, loss of energy, and inability to concentrate during the day. 377 However, more research is needed to support the hypothesized mediating role of sleep health in SB-emotional disorder symptom associations before the development of intervention strategies can take place. 116 Beyond taking a 24-hour movement approach by investigating the mix of physical activity, SB, and sleep in relation to emotional outcomes, future investigations should also aim to understand how pubertal development may influence the associations at hand. Puberty is the developmental transition from childhood to adolescence that is marked by rapid biopsychosocial changes that may increase risk for depressive and anxiety symptoms, particularly among girls. 380 Biologically, the fluctuations in ovarian hormones, such as estrogen, that occur during pubertal development and throughout the monthly menstrual cycle may be related to emotional dysregulation and changes in emotion processing whereby increased risk for depressive and anxiety symptoms may arise. 381, 382 Psychosocially, the rapid changes in body composition (e.g., increased accumulation and changes in distribution of adipose mass) that arise during puberty can also lower self-esteem and increase risk for emotional disorder symptoms for girls in Western societies that have thinness ideals. 383-386 In addition to emotional changes that may arise during the pubertal transition, changes in activity levels have also been observed during this developmental period. 387 Girls have demonstrated steeper declines in physical activity and similar increases in SB during the transition from childhood into adolescence compared to boys, 317, 387 yet the reasons for these observations remain unclear. 388 Taken together, the associations between pubertal development, SB, and emotional disorder symptoms are likely to be complex and may confer vulnerability specifically for pubertal girls. However, more research is needed to understand the role of pubertal development in SB-emotional health associations in order to inform tailored intervention strategies that target those who are most likely to experience rises in SB and declines in emotional health. Strengths and Limitations 117 There are several notable strengths of this dissertation. First, we leveraged three different study designs to assess the associations between SB and affective states/emotional disorder symptoms across different time scales (ranging from hours to years). Second, SB was operationalized differently in each study so that we could gain an understanding of how the conceptualization and measurement of SB may have influenced the strength of the observed associations. Third, we were able to apply novel statistical modelling techniques to our intensive longitudinal data to understand complexities and nuances in the temporality of the relationship between SB and emotional health, such as time-varying effects and within-person associations. However, there are also limitations of this dissertation that warrant discussion. Two of the three studies presented in this dissertation were observational, limiting our ability to draw causal conclusions. Each of the studies presented were also conducted among relatively small, non-representative samples of youth. Therefore, our findings cannot be extrapolated to other age groups, those located in different geographic regions, those of different racial/ethnic backgrounds, or clinical samples. However, this dissertation provides a strong foundation for future similar investigations among larger, more diverse samples than those presented here. Additionally, the small number of participants (N=15) who contributed data to each of the three studies prevented ancillary analyses only among these participants to understand if day-level findings can truly be extrapolated to month/year-level findings. We also did not investigate an exhaustive set of affective experiences or emotional disorder symptoms, therefore future studies should aim to understand if similar associations are observed between SB and symptoms of other emotional disorders, such as social anxiety disorder. Lastly, this dissertation did not test mechanisms that link SB and symptoms of emotional disorders to one another, however our findings can inform future mechanistic studies. 118 Conclusions SB appears to be related to affective states and symptoms of emotional disorders, both, within the day and longitudinally across months/years. However, the operationalization of SB may influence the strength of the relationships at hand, highlighting that what one does while sitting may be as important for emotional health as the sitting itself. Additionally, timing (e.g., time of day) and directionality appear to be important factors in determining the strength of the relationships between SB and emotional health. Taken together, this dissertation provides a strong foundation for future studies in the field that aim to take a nuanced approach to understanding why SB and emotional health may be related to one another among youth. Once additional high-quality studies are conducted using similar methodologies to those presented in this dissertation, we can begin to the understand the mechanisms linking these variables and therefore optimize preventive intervention strategies that aim to improve emotional and physical health across childhood and adolescence. 119 References 1. 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Abstract (if available)

Abstract Evidence suggests that there is a link between sedentary behavior (SB) and depressive and anxiety symptoms (including poor affective states) among youth, yet inconsistencies in the literature remain as a result of study limitations to date. This dissertation leveraged data from randomized trial, ecological momentary assessment, and longitudinal cohort studies to investigate the SB-emotional health associations across different time scales ranging from hours to years. The overarching objective of this dissertation was to increase our scientific understanding of the SB-emotional health link by taking a nuanced approach towards operationalizing SB, differentiating between different types of SB (screen-based SB, non-screen-based SB) and investigating accumulation patterns of SB (bout length, distribution, and breaks), rather than simply total volume of SB. The specific aims of this dissertation were to (1) test the acute effects of interrupting sitting time on affective and anxiety states across three hours in an in-lab randomized crossover trial, (2) assess changes in the strength of the association between screen-based SB, non-screen-based SB, and affective states across the day (from 7am to 8pm), and (3) examine the cross-lagged associations between patterns of objectively measured sedentary time and emotional disorder symptoms across three years. Findings suggest that (1) interrupting sitting may be an effective strategy for acutely reducing negative affect among certain youth populations (e.g., females), (2) the strength of the associations between SB and affective states differ by time of day and by type of SB, and (3) longitudinal associations appear to be uni-directional such that depressive and anxiety symptoms are predictive subsequent patterns of sedentary time, but not vice versa. Taken together, the timing and type of SB, in addition to directionality, appear to be important factors in determining the strength of the association between SB and emotional health. This dissertation provides a foundation for future studies to take a more nuanced approach to examining the SB-emotional health relationship, so that we can begin to understand the mechanisms linking these variables and ultimately optimize preventive intervention strategies for improving emotional and physical health among youth.

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Asset Metadata

Creator Zink, Jennifer (author)

Core Title The acute and longitudinal associations between sedentary behaviors, affective states, and emotional disorder symptoms among youth

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior Research)

Publication Date 04/08/2021

Defense Date 02/23/2021

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

Tag anxiety,Depression,ecological momentary assessment,OAI-PMH Harvest,randomized trial,screen time,sitting time

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Belcher, Britni R. (committee chair), Dunton, Genevieve F. (committee member), Huh, Jimi (committee member), Page, Kathleen A. (committee member), Pentz, Mary Ann (committee member)

Creator Email jen.zink.54@gmail.com,jennifaz@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-437606

Unique identifier UC11668613

Identifier etd-ZinkJennif-9426.pdf (filename),usctheses-c89-437606 (legacy record id)

Legacy Identifier etd-ZinkJennif-9426.pdf

Dmrecord 437606

Document Type Dissertation

Rights Zink, Jennifer

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA