Matching Items (6)
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- All Subjects: Epilepsy
- All Subjects: Mindfulness-based cognitive therapy
- Creators: Karoly, Paul
- Creators: Helms Tillery, Stephen
Description
The use of bias indicators in psychological measurement has been contentious, with some researchers questioning whether they actually suppress or moderate the ability of substantive psychological indictors to discriminate (McGrath, Mitchell, Kim, & Hough, 2010). Bias indicators on the MMPI-2-RF (F-r, Fs, FBS-r, K-r, and L-r) were tested for suppression or moderation of the ability of the RC1 and NUC scales to discriminate between Epileptic Seizures (ES) and Non-epileptic Seizures (NES, a conversion disorder that is often misdiagnosed as ES). RC1 and NUC had previously been found to be the best scales on the MMPI-2-RF to differentiate between ES and NES, with optimal cut scores occurring at a cut score of 65 for RC1 (classification rate of 68%) and 85 for NUC (classification rate of 64%; Locke et al., 2010). The MMPI-2-RF was completed by 429 inpatients on the Epilepsy Monitoring Unit (EMU) at the Scottsdale Mayo Clinic Hospital, all of whom had confirmed diagnoses of ES or NES. Moderated logistic regression was used to test for moderation and logistic regression was used to test for suppression. Classification rates of RC1 and NUC were calculated at different bias level indicators to evaluate clinical utility for diagnosticians. No moderation was found. Suppression was found for F-r, Fs, K-r, and L-r with RC1, and for all variables with NUC. For F-r and Fs, the optimal RC1 and NUC cut scores increased at higher levels of bias, but tended to decrease at higher levels of K-r, L-r, and FBS-r. K-r provided the greatest suppression for RC1, as well as the greatest increases in classification rates at optimal cut scores, given different levels of bias. It was concluded that, consistent with expectations, taking account of bias indicator suppression on the MMPI-2-RF can improve discrimination of ES and NES. At higher levels of negative impression management, higher cut scores on substantive scales are needed to attain optimal discrimination, whereas at higher levels of positive impression management and FBS-r, lower cut scores are needed. Using these new cut scores resulted in modest improvements in accuracy in discrimination. These findings are consistent with prior research in showing the efficacy of bias indicators, and extend the findings to a psycho-medical context.
ContributorsWershba, Rebecca E (Author) / Lanyon, Richard I (Thesis advisor) / Barrera, Manuel (Committee member) / Karoly, Paul (Committee member) / Millsap, Roger E (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study examined whether cognitive behavioral therapy and mindfulness interventions affect positive (PA) and negative affect (NA) reports for patients with rheumatoid arthritis (RA) before, during, and after stress induction. The study also investigated the effects of a history of recurrent depression on intervention effects and testing effects due to the Solomon-6 study design utilized. The 144 RA patients were assessed for a history of major depressive episodes by diagnostic interview and half of the participants completed a laboratory study before the intervention began. The RA patients were randomly assigned to 1 of 3 treatments: cognitive behavioral therapy for pain (P), mindfulness meditation and emotion regulation therapy (M), or education only attention control group (E). Upon completion of the intervention, 128 of the RA patients participated in a laboratory session designed to induce stress in which they were asked to report on their PA and NA throughout the laboratory study. Patients in the M group exhibited dampened negative and positive affective reactivity to stress, and sustained PA at recovery, compared to the P and E groups. PA increased in response to induced stress for all groups, suggesting an "emotional immune response." History of recurrent depression increased negative affective reactivity, but did not predict reports of PA. RA patients who underwent a pre-intervention laboratory study showed less reactivity to stressors for both NA and PA during the post-intervention laboratory study. The M intervention demonstrated dampened emotional reactions to stress and lessened loss of PA after stress induction, displaying active emotion regulation in comparison to the other groups. These findings provide additional information about the effects of mindfulness on the dynamics of affect and adaptation to stress in chronic pain patients.
ContributorsArewasikporn, Anne (Author) / Zautra, Alex J (Thesis advisor) / Davis, Mary C. (Committee member) / Karoly, Paul (Committee member) / Arizona State University (Publisher)
Created2012
Description
Abstract
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.
ContributorsCorallo, Kelsey Lynn (Author) / Lanyon, Richard (Thesis director) / Knight, George (Committee member) / Karoly, Paul (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / Department of Psychology (Contributor)
Created2015-05
Description
Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in view of a fixed camera limiting their freedom of movement. The goal of this project is to design an automatic patient monitoring system with software to track patient movement in order to increase a patient's mobility. This report discusses the impact of an automatic patient monitoring system and the design steps used to create and test a functional prototype.
ContributorsBui, Robert Truong (Author) / Frakes, David (Thesis director) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
Neurological disorders are the leading cause of physical and cognitive declineglobally and affect nearly 15% of the current worldwide population. These disorders
include, but are not limited to, epilepsy, Alzheimer’s disease, Parkinson’s disease,
and multiple sclerosis. With the aging population, an increase in the prevalence of
neurodegenerative disorders is expected. Electrophysiological monitoring of neural
signals has been the gold standard for clinicians in diagnosing and treating neurological
disorders. However, advances in detection and stimulation techniques have paved the
way for relevant information not seen by standard procedures to be captured and
used in patient treatment. Amongst these advances have been improved analysis of
higher frequency activity and the increased concentration of alternative biomarkers,
specifically pH change, during states of increased neural activity. The design and
fabrication of devices with the ability to reliably interface with the brain on multiple
scales and modalities has been a significant challenge.
This dissertation introduces a novel, concentric, multi-scale micro-ECoG array
for neural applications specifically designed for seizure detection in epileptic patients.
This work investigates simultaneous detection and recording of adjacent neural tissue
using electrodes of different sizes during neural events. Signal fidelity from electrodes
of different sizes during in vivo experimentation are explored and analyzed to highlight
the advantages and disadvantages of using varying electrode sizes. Furthermore, the
novel multi-scale array was modified to perform multi-analyte detection experiments
of pH change and electrophysiological activity on the cortical surface during epileptic
events. This device highlights the ability to accurately monitor relevant information
from multiple electrode sizes and concurrently monitor multiple biomarkers during
clinical periods in one procedure that typically requires multiple surgeries.
ContributorsAkamine, Ian (Author) / Blain Christen, Jennifer (Thesis advisor) / Abbas, Jimmy (Committee member) / Muthuswamy, Jitendran (Committee member) / Goryll, Michael (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2024
Description
Mindfulness is a concept derived from the Buddhist discourses of the Satipattana. Interventions that draw on mindfulness have been shown to reduce psychologically distressing symptoms in clinical settings. It has become widely used as a therapeutic technique in counseling, so it is important to develop an instrument measuring mindfulness-related constructs. This study presents a new instrument measuring the importance of mindfulness-related constructs. Results from an exploratory factor analysis revealed a clear two-factor structure, with the factors named "Present Moment Awareness", and "Compassion and Ethical Behavior." These items were positively correlated with each other and, as expected, negatively correlated with depression. Finally, hours of meditation moderated this association such that the association was stronger among participants who reported higher levels of meditation practice.
ContributorsMeer, David (Author) / Santos, Carlos (Thesis advisor) / Homer, Judith (Committee member) / Kemer, Gulsah (Committee member) / Arizona State University (Publisher)
Created2014