This study examined the associations between video gaming, sleep, and neuropsychological functioning. A total of 143 mother-children dyads were included in the study. The children’s neurocognitive functions were measured using the Wechsler Intelligence Scale for Children– Fourth Edition (Hong Kong), the Test of Everyday Attention for Children (TEA-Ch), and the Grooved Pegboard Test. Sleep quality was measured by the Children's Sleep Habits Questionnaire (CSHQ) and the Pittsburgh Sleep Quality Index (PSQI). Problematic behaviors were measured using the Child Behavior Checklist (CBCL). It was found that (1) more video gaming was associated with poorer subjective sleep quality and shorter total bed time, but not any actual reported sleep time or any domains of problematic sleep in children, (2) playing video games before bed was not associated with more sleep problems in children, (3) children with more sleep problems were perceived to have more internalizing and externalizing behaviors, (4) sleep problem was negatively associated with tests of perceptual reasoning abilities, and had a moderating effect on the relationship between video-gaming and a hand-eye coordination task. Results implied video gaming might not be predominantly bad for children, and the use of it as a training tool must target specific cognitive skills in order to be effective. Children’s sleep problems should be part of a clinical computation and adequately addressed. / published_or_final_version / Clinical Psychology / Master / Master of Social Sciences

published_or_final_version / Clinical Psychology / Master / Master of Social Sciences

Sleep quality is an aspect of sleep on which there is currently a lack of research and, in particular, there is little published data related to the quality of sleep obtained by athletes. Sleep quality is difficult to define because it includes quantitative aspects of sleep such as sleep duration, sleep latency and number of arousals as well as the subjective aspects such as ‘depth’ and ‘restfulness’ of sleep. Athletes require more sleep than the non-athlete population in order to recover from their exertions and, although evidence suggests that athletes are concerned about the impact that inadequate sleep has upon performance, there is a paucity of literature examining how poor sleep patterns affect the athletes’ psychological states during training and competition. Due to the scarcity of literature examining how the quality of sleep affects the psychological states of athletes, very little research has focused on athletes’ sleep and mood within the South African (SA) context. A quantitative research method with a combination of an exploratory and descriptive approach was employed. The sample of this study comprised of 87 athletes. The researcher made use of a purposive, non-randomized sampling technique. A self-report biographical questionnaire, the Profile of Mood States 2 – Adults (POMS 2), the State-Trait Anxiety Inventory-Adults (STAI-A) and the Pittsburgh Sleep Quality Index (PSQI) was administered once a week on the same day for the duration of 4 weeks. Descriptive and inferential statistics, Pearson R correlation and One-sample t-test were used to analyse the data. The researcher obtained the necessary authorisation to conduct the study and followed the ethical rules of conduct. Results indicated that there were certain relationships between the quality of sleep and mood states among athletes such as fatigue-inertia, anxiety (state and trait), depression-dejection, anger-hostility, vigour-activity and confusion-bewilderment. There was sufficient evidence to conclude that there was a significant between-week (week 1 and 2, 2 and 3, 3 and 4) difference in fatigue-inertia and sleep quality. Apart from differences in fatigue-inertia and sleep quality scores over the four weeks, all other results indicate a constant outcome over the four weeks for mood states and sleep quality. The results also indicated sufficient evidence to conclude that although there was evidence of a relationship between quality of sleep and mood states, it was not possible to make a definitive conclusion regarding whether sleep quality, anxiety, and depression were bi-directionally related. Despite some of the limitations to the study, the findings were thought to contribute in a valuable way to furthering knowledge regarding the quality of sleep and mood states among athletes. The current data suggest that monitoring athletes’ sleep and mood states may be a determining factor for satisfactory performance; therefore, the reasons for poor sleep quality should be identified in order to implement any coping strategies needed.

Despite the growing need, prior research on how the civilian work domain may affect posttraumatic stress is scarce. Moreover, few if any studies have investigated how perceptions of one's job and insomnia may shape traumatic stress symptoms, post-combat. Presently, about 4 million Americans have served in the "Global War on Terrorism," including nearly 1 million reservists. By contrast, 8.7 million Americans served in the Vietnam War: over twice the number of U.S. military personnel who have fought since 9/11. Surprisingly, combat experiences alone do not explain the majority of posttraumatic stress disorder cases, even after multiple deployments, suggesting the presence of moderators of the stressor-strain conceptual model. Based in occupational stress theories of allostatic load, job demands-resources, strain, and social support this thesis applied frequentist and Bayesian analytical strategies to investigate the psychological experiences and occupational health of three subgroups of combat veterans. In the present study, sleep quality and a job situation characterized by isolated, demanding civilian work with low decision authority (i.e., "iso-strain") were hypothesized to moderate the effect of combat experiences on posttraumatic stress. As part of the larger SERVe reintegration project, survey data were collected among a baseline sample (N=382) of post-9/11 veterans living in the Pacific Northwest. The research question of whether greater perceived psychosocial stressors among active reservists driving that group's higher perceived levels of poor sleep, iso-strain, and posttraumatic stress was unfounded. Overall, however, the central hypotheses of the simple effects of iso-strain and sleep quality on posttraumatic stress following combat were significant.

School readiness incorporates children's academic abilities and their ability to self-regulate in the classroom. Prior research shows that sleep is related to children's development of these skills, although the mechanisms through which sleep affects school readiness are not well understood. Research also indicates that economically disadvantaged children and children of color may have poorer academic and regulatory skills at school entry and may sleep less and sleep less well on a regular basis. The current study explores the role of sleep quantity and quality in young children's development of two skills critical for school success: self-regulation and academic abilities. This study intentionally focuses on a predominantly African-American, economically disadvantaged population, who may be at risk for greater sleep-related difficulties and lower school-related skills at kindergarten entry. It was hypothesized that a) young children with higher quantity and quality of sleep would show greater development of academic skills and self-regulation across one calendar year, b) the role of sleep in the development of these abilities would be relatively stronger among kindergarteners than among 1st graders, and c) the role of sleep quality and quantity in young children's development of academic abilities would be partially explained by the relation between sleep and self-regulation. Results provide mixed support for the hypotheses, indicating that sleep quality and quantity relate differentially to different school-related skills among kindergarteners and 1st graders. This study contributes research to help explain how and why sleep affects young children and may offer insights for caregivers and educators working to help children develop school-related skills.

Background. Caregivers of individuals with Autism Spectrum Disorder (ASD) are a highly stressed group and their parenting stress has been linked to increased depression, anxiety, and reduced parenting self-efficacy (Osborne & Reed, 2008; Rezendes & Scarpa, 2011; Weiss & Lunsky, 2011). Given these areas of concern that parents of children with autism experience, there is a need to look more closely at modifiable factors that improve parental well-being. As such, health habit behaviors, including sleep, diet, exercise, and substance use are modifiable factors demonstrated to be causally related to well-being in adults. The present dissertation consists of two studies that explored how parents’ engagement in health habits (sleep, diet, exercise, and substance use) related to their well-being (stress and depression) and explored the possible determinants of health habit engagement. Specifically, how parental characteristics (both psychological and demographic), social support (both relationship-based and resource-related), and child factors (child behavior and demographic variables), relate to parents’ engagement in these habits. And, in study two, these exploratory analyses went further by examining how parent’s engagement in healthy habits relates to the observed parenting quality. Methods and Results: Study 1. Study one was a needs assessment completed by mothers and fathers (n=68) of children in an Applied Behavior Analysis (ABA) early intervention program in the northeastern United States, all at-risk for a developmental disability, many of whom were at risk for developing ASD. Parents completed questionnaires that included questions about their healthy habit engagement adapted from the Promise Neighborhoods RFA Indicators and the Promise Neighborhoods Research Consortium [PNRC] Measurement System (Promise Neighborhoods Research Consortium: Measures, 2001) as well as those related to parent, social support, and child characteristics. Parental characteristics included demographic characteristics of caregiver age and education level along with questionnaires on the parent’s psychological functioning; a measure of well-being (WHO-5; Topp, Østergaard, Søndergaard, & Bech, 2015), parental stress (PSI-4; Abidin, 2012), and caregiver depressive symptoms (PHQ-9; Kroenke, Spitzer, & Williams, 2001). Questionnaires covering the second domain of social support included a measure of perceived social support (Interpersonal Support Evaluation List (ISEL-12); Cohen & Hoberman, 1983), marital satisfaction (Kansas Marital Satisfaction Scale; Schumm et al., 1986a), household income, and caregiver nativity. Questionnaires covering the third domain of child factors included a measure of child sleep problems (Children’s Sleep Habits Questionnaire; Owens, Spirito, & McGuinn, 2000), the ratio of children to adults in the home, child age, and child gender. Overall, about half of the caregivers reported an insufficient amount of sleep (less than 7 hours on average). A third of caregivers reported they did not exercise at all. Only a third reported eating breakfast every day, half ate only one serving of fruit most days and one serving of vegetables a day, half ate family meals prepared at home almost every day, a third ate fast food regularly, about half were told to lose weight. Most did not smoke or drink alcohol regularly. Additionally, about 30% reported elevated levels of depressive symptoms and elevated levels of parental stress. Significant relationships were found between parental characteristics, social support, child factors, and healthy habit engagement. Of note, caregiver stress, depression, and well-being were related adversely to mother’s sleep, diet, and substance use. Perceived social support was positively related to sleep, marital satisfaction negatively to smoking, household income negatively to diet and alcohol consumption, and nativity positively to sleep, diet, and alcohol consumption. No correlations were found with child factors and healthy habits. Examining a regression model of the facilitators and barriers to healthy habit engagement, caregiver well-being positively related (t=4.015, p<.001) while child sleep disruptions negatively related to healthy habit engagement (t=-2.344, p=.026). Additionally, depression was found tomediate the relationship between healthy habit engagement and parental stress using PROCESS (CI= (-1.811, -.324), R2=.274). Methods and Results: Study 2. Study two aimed to narrow in on a specific population of mothers of preschool-aged children with autism. Participants were 46 mother-child dyads, with children ages 2-6 to 5-6 recruited from a preschool utilizing an Applied Behavior Analysis (ABA) approach to schooling. Children had a classification of ASD, verified by the Autism Diagnostic Observation System – Two (ADOS-2) (Lord, Rutter, DiLavore, Risi, Gotham, & Bishop, 2012). Parenting behaviors, categorized as positive and harsh parenting, were observed across three tasks and coded using the Psychological Multifactor Care Scale — ASD Adapted Preschool Version (Brassard, Donnelly, Hart, & Johnson, 2016). Mothers completed the same questionnaires as study one for measures of healthy habit engagement, parental characteristics (excluding the WHO-5), and social support, There were additional child factor measures; however, including the child sleep problems and child externalizing behavior subscales from the CBCL (Achenbach & Rescorla, 2000), child language functioning (Vineland-III Communication subscale; Sparrow, Cicchetti, & Saulnier, 2016), ASD severity (ADOS-2), along with ratio of children to adults in the home, child age, and child gender. Overall, more than half of the mothers reported an insufficient amount of sleep (less than 7 hours on average). Almost half of mothers reported they did not exercise at all. Almost half reported eating breakfast every day, a third ate only one serving of fruit most days, and half ate one serving of vegetables a day, a third ate family meals prepared at home almost every day, a third ate fast food regularly, and a third were told to lose weight. Most did not smoke. Additionally, 11% of the sample had elevated depressives symptoms and 20% had elevated levels of parental stress. Similar significant relationships were found between parental characteristics, social support, child factors, and healthy habit engagement in study two. Of note, caregiver stress and depression were related negatively to caregiver’s sleep, diet, exercise, and being overweight. More perceived social support was related to better diet, household income to not being overweight, and nativity to smoking. Correlations were also found with child factors and healthy habits; child sleep with mother’s sleep, externalizing behavior problems withsmoking, and high child to adult ratio with mother’s sleep. Examining a regression model of the facilitators and barriers to healthy habit engagement, caregiver depressive symptoms related negatively to healthy habit engagement (t=-.380, p=.049). ASD severity (t=-.511, p=.045) and child age (t=-.523, p=.014) came out as negatively related to mother’s diet in a similar model analysis. Additionally, mothers sleep directly related to both positive (R2=.213) and harsh (R2=.165) observed parenting quality. Conclusion. The results from study one and study two suggest that sleep, diet, exercise and substance use are important for parent’s well-being in both parents of children in early intervention and mothers of preschool-aged children with autism. Furthermore, parental wellbeing was the most predictive of engagement in healthy habits when examining possible facilitators and barriers. Child sleep was an important potential barrier in parents of children in early intervention and autism severity and child age were important potential barriers to mother’s diet in mothers of preschool-aged children with ASD. Furthermore, in study two, mothers sleep was an important factor not only for well-being but also for an objective measure of parentingquality, further strengthening the importance and value of sleep for a highly stressed population.

Data from smartphones and wearable devices provide rich longitudinal information on participants and allow for causal inference for daily exposures and outcomes. However, informative missingness, latent variables, unmeasured confounding, and uneven data collection rates are common in mobile health studies and may introduce bias. In addition, there are likely violations of stationarity, a key assumption for traditional longitudinal methods. To overcome these challenges, we first propose an expectation maximization algorithm to adapt the conventional test for unit root non-stationarity to a context with missing data, and develop a sensitivity analysis for data missing not at random. Using our method, we identify a patient with bipolar spectrum disorder who has a unit root in their daily negative mood score data. We hypothesize the non-stationarity may result from the underlying latent disease states such as mania or depression, and thus we additionally develop a model to identify and control for latent modification and confounding. Specifically, we propose a hidden Markov model for individual causal inference which handles missing data in the outcome through marginalization and multiple imputation. We compare the performance of our proposed model with a frequentist and a Bayesian implementation to a naive approach in a simulation and application to a multi-year smartphone study of bipolar patients. We employ the approach to evaluate the individual effect of digital social activity on sleep duration across different latent disease states. Lastly, we employ functional data analysis methods to summarize overnight wrist actigraph data, to evaluate the role of sleep as a mediator between stress and positive mood. We demonstrate that functional principal component analysis identifies key information about sleep that is otherwise lost using a scalar representation of sleep duration.