July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Novel computer-based tests for assessing performance in visually guided tasks in people with age-related macular degeneration: searching for everyday objects and detecting road signs
Author Affiliations & Notes
  • Bethany Elora Higgins
    Optometry and Visual Sciences, City, University of London, London, United Kingdom
  • Deanna J Taylor
    Optometry and Visual Sciences, City, University of London, London, United Kingdom
  • Wei Bi
    Optometry and Visual Sciences, City, University of London, London, United Kingdom
  • Alison M Binns
    Optometry and Visual Sciences, City, University of London, London, United Kingdom
  • David P Crabb
    Optometry and Visual Sciences, City, University of London, London, United Kingdom
  • Footnotes
    Commercial Relationships   Bethany Higgins, None; Deanna Taylor, None; Wei Bi, None; Alison Binns, None; David Crabb, Allergan (R), Roche (F), Santen (R)
  • Footnotes
    Support  This study was funded as part of an unrestricted investigator initiated research grant from Roche Products Ltd UK
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 5922. doi:https://doi.org/
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      Bethany Elora Higgins, Deanna J Taylor, Wei Bi, Alison M Binns, David P Crabb; Novel computer-based tests for assessing performance in visually guided tasks in people with age-related macular degeneration: searching for everyday objects and detecting road signs. Invest. Ophthalmol. Vis. Sci. 2019;60(9):5922. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To test the hypothesis that the performance in novel computer-based tasks of everyday visual function worsens with disease severity in people with non-neovascular (dry) age-related macular degeneration (AMD).

Methods : Participants with and without dry AMD (≥60 years, minimum logMAR binocular visual acuity 0.7) performed two computer-based (touch screen) tasks of everyday visual function. In a search task, involving multiple trials, participants had to locate an image of a single real-world object within a rectangular array of 49 distractor images. Next, in a series of simulated moving driving scenes, participants were asked to identify one or two approaching real-world road signs and then select these road signs from four options (See Figure 1). Outcome measures for all tests were average response time (RT) and total correct responses.

Results : Forty-nine participants had no AMD (n=11), early/intermediate AMD (n=16) or geographic atrophy (GA)(n=22). Groups were age-similar with median (interquartile range [IQR]) age of 75 (63, 76), 78 (61, 87) and 76 (65, 86) years respectively. Median (IQR) logMAR visual acuity was 0.00 (-0.08, 0.12), 0.13 (-0.08, 0.7) and 0.32 (0.12, 0.7) respectively. Median (IQR) visual search RTs were 2.7 (1.7, 3.3), 2.7 (2.1, 4.6) and 3.3 (2.1, 12.2) seconds respectively. Median (IQR) road sign identification times (double road signs) were 2.0 (1.6, 2.7), 2.7 (1.5, 3.7) and 3.0 (2.1, 8.0) seconds respectively. Participants with severe AMD (GA) recorded slower RTs in all tasks (Kruskal-Wallis, p=0.01). There were no statistically significant differences between groups in total correct responses in any of the tasks.

Conclusions : In a novel computer-based assessment, people with increasing severity of AMD take longer to perform visual search for everyday objects and take longer to identify road signs than those with no AMD. These novel assessments could be useful as patient-relevant, secondary outcomes for clinical trials. Moreover, the current lab-based tests have potential to be adapted into portable tablet-based formats.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Figure 1 illustrates screenshots of the novel computer-based tests utilised in the study. One is a visual search test of everyday objects while the other two are road sign detection tests.

Figure 1 illustrates screenshots of the novel computer-based tests utilised in the study. One is a visual search test of everyday objects while the other two are road sign detection tests.

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