April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
A Comparison of Learning Effects for Standard Automated Perimetry, Short-wavelength Automated Perimetry and Frequency-Doubling Technology Perimetry in Healthy Subjects
Author Affiliations & Notes
  • Lorraine Myers
    Department of Ophthalmology, Indiana University, Indianapolis, IN
  • Rongrong Hu
    Department of Ophthalmology, Indiana University, Indianapolis, IN
  • Linda S Morgan
    Department of Ophthalmology, Indiana University, Indianapolis, IN
  • Joni S Hoop
    Department of Ophthalmology, Indiana University, Indianapolis, IN
  • Lyne Racette
    Department of Ophthalmology, Indiana University, Indianapolis, IN
  • Footnotes
    Commercial Relationships Lorraine Myers, None; Rongrong Hu, None; Linda Morgan, None; Joni Hoop, None; Lyne Racette, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 5612. doi:
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      Lorraine Myers, Rongrong Hu, Linda S Morgan, Joni S Hoop, Lyne Racette; A Comparison of Learning Effects for Standard Automated Perimetry, Short-wavelength Automated Perimetry and Frequency-Doubling Technology Perimetry in Healthy Subjects. Invest. Ophthalmol. Vis. Sci. 2014;55(13):5612.

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

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Abstract
 
Purpose
 

To evaluate learning effects for standard automated perimetry (SAP), short-wavelength automated perimetry (SWAP) and frequency-doubling technology (FDT) perimetry in the same sample of perimetrically naive healthy subjects.

 
Methods
 

Thirty-two subjects with healthy eyes based on a complete ocular examination underwent three SAP, three SWAP and three FDT tests in one randomly selected eye. The tests were performed on two separate visits. At the first visit, subjects performed two SAP, two SWAP and two FDT tests. The third tests were performed on the second visit. To minimize fatigue effects, test order was randomized across subjects and rest periods were provided as needed. The Swedish Interactive Thresholding Algorithm was used for SAP and SWAP, and the Zippy Estimation by Sequential Testing was used for FDT. The 24-2 program was used for all tests. Test duration, mean deviation (MD), pattern standard deviation (PSD), mean sensitivity (MS), and the number of points triggered at 5% and 1% on the total (TD) and pattern deviation (PD) were assessed over the test series. ISV for MS at each location was defined as the average threshold sensitivity and its statistical variance across all subjects. Mean TRV was calculated as the average of the statistical variance of the MS across the three tests for each test location.

 
Results
 

For SAP, a statistically significant improvement in MD (0.02) and MS (0.004) was observed over the test series. For SWAP, an improvement in MD (0.01) and MS (p<0.0001) was also observed over the series of test. A reduction in the number of TD points triggered at 1% was also observed. ISV also decreased significantly (p<0.0001). For FDT, a statistically significant improvement in MD (p=0.02) and MS (p<0.0001) was observed. ISV significantly decreased over the test series (p=0.003). Mean TRV was significantly greater for SWAP and FDT compared with SAP (p<0.0001) (Figure 1).

 
Conclusions
 

When tested in the same sample of healthy subjects, we found generally similar learning effects for SAP, SWAP and FDT. Mean test-retest variability was larger for SWAP and FDT compared to SAP.

 
 
Figure 1. Box plots of the difference of mean TRV between SAP, SWAP and FDT.
 
Figure 1. Box plots of the difference of mean TRV between SAP, SWAP and FDT.
 
Keywords: 758 visual fields • 579 learning  
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