<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>How Baseline Testing Can Predict Walking Tasks</dc:title><dc:creator>Friedman, Ethan </dc:creator><dc:subject>Walking</dc:subject><dc:subject>Contrast Sensitivity</dc:subject><dc:coverage>Kinesiology</dc:coverage><dc:relation>B S</dc:relation><dc:description>The risk of falling and associated injuries increases with age. Thus, researchers investigate changes in gait mechanics, including fall mechanics and the role of visual input in motor control. This study determined how altering path visual color contrast affects walking performance and if common clinical baseline tests can predict task success. 20 older and 20 younger healthy adults were analyzed and compared after qualifying for the study. Each performed the following baseline assessments: the Iconographical Falls Efficacy Scale (Icon-FES), FrACT visual contrast sensitivity test, 5-meter walk test (5MWT), Timed Up and Go (TUG), and Four-Square Step Test (FSST). Participants then walked on four different virtual paths that were projected onto a treadmill within a virtual reality system. These paths varied in shape (straight and winding) and color contrast (high and low). We recorded steps off the path, or stepping errors, for each condition. Baseline tests showed older adults walked with similar preferred speeds to young adults (p=0.844), but slower TUG (p=0.024) and FSST (p=0.008) times, and decreased contrast sensitivity (p=0.033). During the walking task older adults made more stepping errors than younger adults on the low contrast winding path (p=0.027). FSST had significant correlation with the stepping errors made on the Low Contrast Windy path (p=0.009). By evaluating correlations between baseline assessments and step error percents while walking, this study aimed to identify predictors for walking performance. The reported findings offer a semi quantitative way healthcare professionals can assess patient's walking abilities.   </dc:description><dc:contributor>Jonathan Bates Dingwell, Thesis Supervisor</dc:contributor><dc:contributor>Mark Dyreson, Thesis Honors Advisor</dc:contributor><dc:rights>open_access</dc:rights><dc:date>2025-04-22T21:13:08Z</dc:date><dc:identifier>https://honors.libraries.psu.edu/catalog/9797edf5156</dc:identifier></oai_dc:dc>