Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 21 - 30 of 93
Description
Adverse childhood family environments have been found to have long-term effects on a child's well-being. Although no prior studies have examined the direct effects of childhood family adversities on nighttime blood pressure (BP) dip, parental death and divorce in childhood, have been associated with a variety of related psychological problems in adulthood. The current study examined the direct effects of parental death and divorce in childhood and quality of early family relationships on adult nighttime BP dip as well as the mediating role of three psychosocial factors (depression, hostility and social stress). One hundred and forty-three young adults were asked to complete self-reported measures of the three psychosocial factors and quality of family relationships. Study participants wore an ambulatory blood pressure (ABP) monitor over a 24-hr period in order to assess nocturnal BP dip. Although neither childhood family adversity nor quality of childhood family relationships directly predicted nighttime BP dipping, quality of early family relationships predicted all three psychosocial factors, and hostility was found to mediate the relationship between quality of childhood family relationships and nighttime systolic BP dip. Early family experiences play an important role in influencing nighttime cardiovascular functioning by influencing an individual's psychological functioning in young adulthood. Because nighttime non-dipping has been associated with increased risk for cardiovascular disease and other serious health conditions, the results of the present study have important clinical implications and provide specific psychosocial pathways that may be targeted in future programs designed to prevent and treat cardiovascular disease.
ContributorsTanaka, Rika (Author) / Luecken, Linda J. (Thesis advisor) / Wolchik, Sharlene (Committee member) / Davis, Mary (Committee member) / Arizona State University (Publisher)
Created2012
Description
In rehabilitation settings, activity limitation can be a significant barrier to recovery. This study sought to examine the effects of state and trait level benefit finding, positive affect, and catastrophizing on activity limitation among individuals with a physician-confirmed diagnosis of either Osteoarthritis (OA), Fibromyalgia (FM), or a dual diagnosis of OA/FM. Participants (106 OA, 53 FM, and 101 OA/FM) who had no diagnosed autoimmune disorder, a pain rating above 20 on a 0-100 scale, and no involvement in litigation regarding their condition were recruited in the Phoenix metropolitan area for inclusion in the current study. After initial questionnaires were completed, participants were trained to complete daily diaries on a laptop computer and instructed to do so a half an hour before bed each night for 30 days. In each diary, participants rated their average daily pain, benefit finding, positive affect, catastrophizing, and activity limitation. A single item, "I thought about some of the good things that have come from living with my pain" was used to examine the broader construct of benefit finding. It was hypothesized that state and trait level benefit finding would have a direct relation with activity limitation and a partially mediated relationship, through positive affect. Multilevel modeling with SAS PROC MIXED revealed that benefit finding was not directly related to activity limitation. Increases in benefit finding were associated, however, with decreases in activity limitation through a significant mediated relationship with positive affect. Individuals who benefit find had a higher level of positive affect which was associated with decreased activity limitation. A suppression effect involving pain and benefit finding at the trait level was also found. Pain appeared to increase the predictive validity of the relation of benefit finding to activity limitation. These findings have important implications for rehabilitation psychologists and should embolden clinicians to encourage patients to increase positive affect by employing active approach-oriented coping strategies like benefit finding to reduce activity limitation.
ContributorsKinderdietz, Jeffrey Scott (Author) / Zautra, Alex (Thesis advisor) / Davis, Mary (Committee member) / Barrera, Manuel (Committee member) / Okun, Morris (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Characterization and analysis of long term field aged photovoltaic modules and encapsulant materials
Description
Photovoltaic (PV) module degradation is a well-known issue, however understanding the mechanistic pathways in which modules degrade is still a major task for the PV industry. In order to study the mechanisms responsible for PV module degradation, the effects of these degradation mechanisms must be quantitatively measured to determine the severity of each degradation mode. In this thesis multiple modules from three climate zones (Arizona, California and Colorado) were investigated for a single module glass/polymer construction (Siemens M55) to determine the degree to which they had degraded, and the main factors that contributed to that degradation. To explain the loss in power, various nondestructive and destructive techniques were used to indicate possible causes of loss in performance. This is a two-part thesis. Part 1 presents non-destructive test results and analysis and Part 2 presents destructive test results and analysis.
ContributorsChicca, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this work, different methods for fabrication of flexible sensors and sensor characterization are studied. Using materials and equipment that is unconventional, it is shown that different processes can be used to create sensors that behave like commercially available sensors. The reason unconventional methods are used is to cut down on cost to produce the sensors as well as enabling the manufacture of custom sensors in different sizes and different configurations. Currently commercially available sensors are expensive and are usually designed for very specific applications. By creating these same types of sensors using new methods and materials, these new sensors will show that flexible sensor creation for many uses at a fraction of the cost is achievable.
ContributorsCasanova, Lucas Montgomery (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Spousal loss is a common, significant life event that can negatively affect multiple facets of individual health and psychological adjustment. Social support is one factor that is shown to improve adjustment following spousal loss, but much less is known regarding which facet of social support is most predictive of positive adjustment outcomes following spousal loss. This study examined the course of changes in mental health and well-being following spousal loss and which facets of social support are associated with better outcomes following spousal loss. Latent growth curve modeling was applied to data from 265 widowed individuals, ages 65 and older, across four assessments (baseline, and 6-, 18-, and 48- months following spousal loss). I examined the following research questions: (1) adjustment following spousal loss will follow a trajectory of an increase in depressive symptoms and anxiety and decrease in well-being with a leveling-off over time, with between-person differences, and (2) emotional support and instrumental support given will lead to more positive adjustment outcomes over time. Depressive symptoms followed the hypothesized trajectory but anxiety and well-being showed relative stability before and after spousal loss. Instrumental support was the most beneficial facet of social support, such that receiving more instrumental support was associated with lower levels of depressive symptoms and anxiety 6-months following spousal loss. Giving more instrumental support led to an increase in well-being following spousal loss. Instrumental support given and received led to increases in well-being as a function of spousal loss. The discussion focuses on whether and how these findings can help to identify ways through which support and help can be given to individuals to improve adjustment to spousal loss and fully recover.
ContributorsSullivan, Colleen Elizabeth (Author) / Infurna, Frank (Thesis director) / Luthar, Suniya (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Effortful Control (EC) is a person's ability to self-regulate when presented with an environmental stimulus (Rothbart, et al., 2003). It has been well-established that high levels of EC are associated with multiple positive social and academic outcomes in adolescence (Spinrad et al., 2009). Research suggests that parents have a strong impact on numerous child outcomes, such as EC, through both genetic and environmental pathways. Past research has also examined how parents diagnosed with psychopathology contribute to maladaptive outcomes in their children, including poor regulation, through both genetic and environmental processes (Ellis, et al., 1997). However, less is known about the longitudinal effects of parent dysfunction on the child's environment and regulatory abilities and potential mediators of those effects. The current study tested the hypotheses that parent Alcohol Use Disorder (AUD) would specifically predict early adversity, biological mother conscientiousness, and child EC longitudinally and that early adversity and biological mother conscientiousness would predict child EC. Participants were from a longitudinal study of familial alcoholism (N = 195). Regression analyses indicated that parent AUD was not specifically associated with child EC or with biological mother conscientiousness. However, parent AUD was related to higher levels of early adversity. Additionally, biological mother conscientiousness was associated with higher levels of child EC and early adversity was associated with lower levels of child EC when controlling for earlier EC. Given these findings, future research should test mediation models in which parent AUD predicts child EC indirectly through early adversity.
ContributorsRuof, Ariana Kelsey (Author) / Chassin, Laurie (Thesis director) / Elam, Kit (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The rates of anxiety, depression, and attempted suicide for transgender individuals are extremely elevated relative to the general population. Yet, little research has been conducted about the transgender population regarding social transition (an individual presenting as their authentic/true gender, one different than the gender they were assigned at birth, in the context of everyday life) and parental acceptance. Both of which have been shown to impact the mental health of transgender individuals. The purposes of this study were: (1) To characterize a sample of transgender adults on their age of awareness of their authentic gender identity and their age of social transition. (2) Examine whether age of social transition, (3) parental acceptance, and (4) the gap in time between age of awareness and age of social transition (awareness-transition gap) were related to mental health. (5) Examine whether parental acceptance was related to age of social transition or to awareness-transition gap. (6) Examine whether age of social transition or awareness-transition gap interact with parental acceptance as correlates of mental health. The sample consisted of 115 transgender adults, ages 18 to 64. Measures were separated into 7 subheadings: demographics, transgender
on-cisgender identity, age of awareness, age of social transition, primary caregiver acceptance, secondary caregiver acceptance, and mental health. Hypotheses were partially supported for age of social transition with mental health, parental acceptance with mental health, and awareness-transition gap with parental acceptance. This study investigated under studied concepts of social transition and parental acceptance that appear to have an effect on the mental health of transgender adults.
on-cisgender identity, age of awareness, age of social transition, primary caregiver acceptance, secondary caregiver acceptance, and mental health. Hypotheses were partially supported for age of social transition with mental health, parental acceptance with mental health, and awareness-transition gap with parental acceptance. This study investigated under studied concepts of social transition and parental acceptance that appear to have an effect on the mental health of transgender adults.
ContributorsRosenberg, Beth Ann (Author) / Gonzales, Nancy (Thesis director) / Saenz, Delia (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The goal of my study is to test the overarching hypothesis that art therapy is effective because it targets emotional dysregulation that often accompanies significant health stressors. By reducing the salience of illness-related stressors, art therapy may improve overall mood and recovery, particularly in patients with cancer. After consulting the primary literature and review papers to develop psychological and neural mechanisms at work in art therapy, I created a hypothetical experimental procedure to test these hypotheses to explain why art therapy is helpful to patients with chronic illness. Studies found that art therapy stimulates activity of multiple brain regions involved in memory retrieval and the arousal of emotions. I hypothesize that patients with chronic illness have a reduced capacity for emotion regulation, or difficulty recognizing, expressing or altering illness-related emotions (Gross & Barrett, 2011). Further I hypothesize that art therapy improves mood and therapeutic outcomes by acting on the emotion-processing regions of the limbic system, and thereby facilitating the healthy expression of emotion, emotional processing, and reappraisal. More mechanistically, I propose art therapy reduces the perception or salience of stressors by reducing amygdala activity leading to decreased activation of the hypothalamic-pituitary-adrenal (HPA) axis. The art therapy literature and my hypothesis about its mechanisms of action became the basis of my proposed study. To assess the effectiveness of art therapy in alleviating symptoms of chronic disease, I am specifically targeting patients with cancer who exhibit a lack of emotional regulation. Saliva is collected 3 times a week on the day of intervention: morning after waking, afternoon, and evening. Stress levels are tested using one-hour art therapy sessions over the course of 3 months. The Perceived Stress Scale (PSS) assesses an individual's perceived stress and feelings in past and present situations, for the control and intervention group. To measure improvement in overall mood, 10 one-hour art sessions are performed on patients over 10 weeks. A one-hour discussion analyzing the participants' artwork follows each art session. The Spielberger State-Trait Anxiety Inventory (STAI) assesses overall mood for the intervention and control groups. I created rationale and predictions based on the intended results of each experiment.
ContributorsAluri, Bineetha C. (Author) / Orchinik, Miles (Thesis director) / Davis, Mary (Committee member) / Essary, Alison (Committee member) / School of Life Sciences (Contributor) / School for the Science of Health Care Delivery (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
While previous research has investigated the influence parenting styles have on child behavior, there has not been consistent findings on how child behavior in return influences parenting. This study goes beyond the literature by examining bidirectional influences of combined dyad for emotional availability and early problem behaviors (composited across 12 and 30 months) predicting parental warmth, authoritarian parenting, internalizing, externalizing and ADHD symptoms at age eight. This study also examined whether genetic or environmental factors were driving these behaviors. Participants were from the ongoing Arizona Twin Project (N=340 twin children). 25% of the twins were monozygotic, 35% were same-sex dizygotic, and 35% were opposite-sex dizygotic twins. Preliminary correlations showed bidirectional effects between early emotional availability, problem behaviors and parental warmth, authoritarian parenting, internalizing, externalizing and ADHD symptoms at age eight; however, once twin dependence and covariates were controlled for, the bidirectional effects were no longer significant. One important finding emerged: early problem behaviors were predictive of later problem behaviors at eight years. The study also found that externalizing and ADHD symptoms were more heritable than emotional availability, early problem behaviors, and internalizing symptoms. Therefore, interventions should be developed addressing the environmental influences that contribute to early problem behaviors.
ContributorsKaur, Navneet (Author) / Lemery-Chalfant, Kathryn (Thesis director) / Miadich, Samantha (Committee member) / Davis, Mary (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05