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- Creators: Gray, Robert
- Creators: Mayyas, AbdRaouf
- Creators: Robinson Kurpius, Sharon E.
Description
Decades of research and empirical studies support the belief that traumatic life events lead to a multitude of negative outcomes (Tedeschi & Calhoun, 1996), however, new research suggests that some survivors of trauma experience significant psychological growth, known as posttraumatic growth (PTG) (Tedeschi, Park, & Calhoun, 1998). The current study focused on the trauma of a traumatic brain injury (TBI) and its relation to the development of PTG. A TBI is both a psychological trauma and a type of acquired brain injury that occurs when physical injury causes damage to the brain (National Institutes of Health [NIH], 2013). Empirical studies examining TBIs and PTG are minimal. The current study focused on survivors who have sustained a TBI from a motor vehicle accident to help control for contextual factors of the injury that are known to affect outcomes. The aim of this study was to elucidate the physical, sociodemographic, contextual, and psychological factors that helped predict the development of PTG among a population of TBI survivors. In addition, another aim of this study was to gain a better understanding of the relationship between PTG and posttraumatic stress disorder (PTSD) symptomatology. Cross-sectional data from self-identified TBI survivors of motor vehicle accidents (n = 155) were used to construct a model of prediction of PTG. Preliminary analyses revealed a reliability issue with the measure that assessed participants’ personality, and these variables were not used in planned analyses. Results revealed that the majority of participants were female, Caucasian, highly educated, and unemployed. Overall, the sample indicated significant injury severity, disability, and lower than average mental and physical functioning. The final model accounted for approximately 15% of the variance in PTG and significant predictors included: gender, time since injury, and the interaction between PTSD symptoms and time since injury. The findings of this research can help inform treatment programs and rehabilitation services as well as funding that can aim to improve outcomes from survivors of TBI. Study limitations included the use of cross-sectional data, a homogenous and unrepresentative sample of TBI survivors, recruitment concerns, and low reliability observed in one of the integral measures of the study.
ContributorsGildar, Natalie J (Author) / Bernstein, Bianca L (Thesis advisor) / Lavoie, Michael (Committee member) / Robinson Kurpius, Sharon E. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.
ContributorsAlomari, Jamil (Author) / Mayyas, AbdRaouf (Thesis advisor) / Cooke, Nancy J. (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017