A Scoping Review of Psychological Interventions for PTSD in Military Personnel and Veterans.

Post-traumatic stress disorder (PTSD) has emerged as a key concern for military and veteran populations. This article describes what is being done programmatically and therapeutically to treat PTSD in military personnel and veterans returning from deployment. This scoping review demonstrates that (1) research published in this area has been rapidly increasing since its inception in the 1980s; (2) the vast majority of articles focus on cognitive-behavioral approaches to treatment, and this area of the literature presents strong evidence for these approaches; and (3) there is a lack of randomized controlled trials for treatments, such as art therapies and group therapies.

SOCIAL, EMOTIONAL, COGNITIVE, AND PHYSIOLOGICAL CORRELATES OF ELECTRONIC SOCIAL BEHAVIOR

There has been very little research that has studied the capacities that can be fostered to mitigate the risk for involvement in electronic bullying or victimization and almost no research examining positive electronic behavior. The primary goal of this dissertation was to use the General Aggression Model and Anxious Apprehension Model of Trauma to explore the underlying cognitive, emotional, and self-regulation processes that are related to electronic bullying, victimization, and prosocial behavior. In Study 1, we explored several potential interpretations of the General Aggression Model that would accurately describe the relationship that electronic self-conscious appraisal, cognitive reappraisal, and activational control may have with electronic bullying and victimization. In Study 2, we used the Anxious Apprehension Model of Trauma to explore rejection cognitions as the mediator of the relationships among emotionality (emotionality, shame, state emotion responses, and physiological arousal) and electronic bullying and victimization using structural equation modelling. In addition, we explored the role of rejection cognitions in mediating the relationship of moral disengagement with electronic bullying. In Study 3, we examined predictors of electronic prosocial behavior, such as bullying, victimization, time online, electronic proficiency, electronic self-conscious appraisals, emotionality, and self-regulation. All three studies supported the General Aggression Model as a framework to guide the study of electronic behavior, and suggest the importance of cognitive, emotional, and behavioral means of regulation in shaping electronic behavior. In addition, each study has implications for the development of high quality electronic bullying prevention and intervention research.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.