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Driving and elderly primes in a simulated driving environment

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ABSTRACT Research studies have demonstrated that stereotypes can elicit a priming response. An experiment was conducted to test the effects of priming elderly and young stereotypes on driving behavior. Participants drove in a driving simulator while navigating through two driving routes. Participants were guided by a neutral voice similar to…

ABSTRACT Research studies have demonstrated that stereotypes can elicit a priming response. An experiment was conducted to test the effects of priming elderly and young stereotypes on driving behavior. Participants drove in a driving simulator while navigating through two driving routes. Participants were guided by a neutral voice similar to "Siri" that informed them where to turn. Each route primed the participants with names that were deemed "old" or "young" as determined by a survey. The experiment yielded slower driving speeds in the elderly condition than in the young consistent with previous research regarding elderly stereotypes (Bargh et al, 1996; Branaghan and Gray, 2010; Taylor, 2010; Foster, 2012). These findings extend research on priming and behaviors elicited by participants in a simulated driving environment.
ContributorsThew, Lisa (Author) / Branaghan, Russell (Thesis advisor) / Song, Hyunjin (Committee member) / Kuzel, Michael (Committee member) / Arizona State University (Publisher)
Created2014
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A statistical clinical decision support tool for determining thresholds in remote monitoring using predictive analytics

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Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring…

Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning of a threshold classifier. Disease severity also impacted the model. Forecasting future FEV1 data points is possible with a complex ARIMA (45, 0, 49), but variation and sources of error require tight control. Validation was above average and encouraging for clinician acceptance. These statistical algorithms provide for the patient's own data to drive reduction in variability and, potentially increase clinician efficiency, improve patient outcome, and cost burden to the health care ecosystem.
ContributorsFralick, Celeste (Author) / Muthuswamy, Jitendran (Thesis advisor) / O'Shea, Terrance (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Shea, Kimberly (Committee member) / Arizona State University (Publisher)
Created2013