Contrast Sensitivity and Visual Acuity in Low-Vision Students Thesis

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Contrast Sensitivity and Visual Acuity in Low-Vision Students

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the

Graduate School of The Ohio State University

Steve Murimi Mathenge Njeru, BS

Graduate Program in Vision Science

The Ohio State University

2020

Thesis Committee

Angela M. Brown, PhD, Advisor

Bradley E. Dougherty, OD, PhD

Deyue Yu, PhD

Copyrighted by

Steve Murimi Mathenge Njeru

2020

Abstract

Purpose: This study primarily compared the test-retest reliability of the Pelli-Robson chart (PR) and Ohio Contrast Cards (OCC) amongst testers. The secondary goal of this study was to examine the impact on contrast sensitivity if the testing distance for the Pelli-Robson chart were to be changed. An additional goal was to evaluate the relationship between visual acuity

(VA) and contrast sensitivity (CS) when using letter-based charts and grating cards.

Methods: Thirty low-vision students were tested, ranging from 7-20 years old. Each student was tested with both VA and CS tests in randomized order, which included: the Bailey-

Lovie chart (BL), Pelli-Robson chart, Teller Acuity Cards (TAC), and Ohio Contrast Cards. Each student repeated both the PR chart and OCC in separate rooms, but neither the BL chart nor TAC was repeated. The PR chart was also tested at closer testing distance, based on the student’s logMAR acuity from the BL chart. For the letter charts, a letter-by-letter scoring method was used. For grating cards, these were both scored as preferential looking tests.

Results: The Limits of Agreement for the OCC and PR chart were +/- 0.451 and +/-

0.536, respectively. There was no statistically significant difference between tester or order of testers for the PR chart and OCC. Using the PR chart at a closer distance yielded an improvement in contrast threshold performance when compared to the standard testing distance. Students performed better on grating cards than letter charts, regardless of VA testing or CS testing.

Conclusions: The Ohio Contrast Cards are a promising diagnostic tool for evaluating CS on low-vision patients and other patients who cannot be tested using letter-based charts.

Clinicians using the Pelli-Robson chart for low-vision patients should consider testing closer than the standard testing distance.

iii

Dedication

This is dedicated to my family, friends, and mentors, who have always supported me in all my

endeavors.

Acknowledgments

Ohio State School for the Blind

Dr. Gregory R. Hopkins III, OD, MS

Faustina O. Opoku, BS

Vita

May 2012……………High School Diploma: P.K.Yonge Developmental Research School

May 2016……………Bachelor of Science: Biomedical Sciences, University of Central Florida

Publications

Waddell, J. C., Njeru, S. M., Akhiyat, Y. M., Schachner, B. I., Correa-Roldán, E. V., &

Crampton, W. (2019). Reproductive life-history strategies in a species-rich assemblage of

Amazonian electric fishes. PloS one, 14(12).

Fields of Study

Major Field: Vision Science

Table of Contents

Abstract ...... iii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vi

List of Tables ...... viii

List of Figures ...... ix

Chapter 1. Introduction ...... 1

Chapter 2. Methods ...... 14

Chapter 3. Results ...... 22

Chapter 4. Discussion ...... 34

Chapter 5. Conclusions ...... 39

References...... 40

Appendix A. Consent Form for Ages 18 and Older ...... 43

Appendix B. Consent Form for Ages 12-17 ...... 44

Appendix C. Room Checklist for Setup ...... 46

Appendix D. Data Collection Sheet...... 48

Appendix E. Gift Card Receipt ...... 50

vii

List of Tables

Table 1 Subject Demographics and Visual Disorders ...... 16

Table 2 Randomized Testing Sequence ...... 21

viii

List of Figures

Figure 1 Bailey-Lovie Chart ...... 3

Figure 2 Teller Acuity Cards ...... 4

Figure 3 Pelli-Robson Chart ...... 5

Figure 4 Lighthouse Letter Contrast Sensitivity Test (Mars chart) ...... 6

Figure 5 Arden Grating System ...... 8

Figure 6 Vistech Vision Contrast System ...... 9

Figure 7 Functional Acuity Contrast Test ...... 10

Figure 8 Melbourne Edge Test ...... 11

Figure 9 Ohio Contrast Cards ...... 12

Figure 10 Testing Materials in Each Testing Room ...... 20

Figure 11 Bailey-Lovie Chart vs. Pelli-Robson Chart ...... 23

Figure 12 Teller Acuity Cards vs. Ohio Contrast Cards ...... 24

Figure 13 Bailey-Lovie Chart vs. Teller Acuity Cards ...... 25

Figure 14 Pelli-Robson Chart vs. Ohio Contrast Cards ...... 27

Figure 15 Repeatability of Pelli-Robson Chart ...... 29

a) Tester 1 vs. Tester 2

b) AMB vs. SMN

Figure 16 Repeatability of Ohio Contrast Cards ...... 31

a) Tester 1 vs. Tester 2

b) AMB vs. SMN

Figure 17 Pelli-Robson (one meter) vs. Pelli-Robson (closer distance) ...... 33

Chapter 1. Introduction

Purpose

Many eye practitioners use visual acuity (VA) to determine the overall visual abilities of their patients. Visual acuity is extensively used in both comprehensive and problem focused eye exams where visual defects from ocular disease (e.g. diabetic retinopathy) or refractive error (e.g. myopia) are suspected. However, VA does not always reveal the full story of a patient’s visual abilities. Other vision tests such as contrast sensitivity (CS) are often used in combination with visual acuity to gain a better understanding on quality of life (Marron & Bailey, 1982).

Contrast sensitivity is an individual’s ability to distinguish an object from its background using luminance as a cue. This is often used to help with everyday mobility tasks like navigating through a room or telling the difference between the sidewalk and street (Marron & Bailey,

1982). Many practitioners test both VA and CS on their low-vision patients, but only test VA on their normal-sighted patients. Contrast sensitivity testing used to have a reputation for being too unreliable, too difficult to test, and unrelated to quality of life (Moseley & Hill, 1994). However, past and current research has established standardized methods for testing that are easier for examiners to use, provide more reliable data from patients, and show how changes in contrast sensitivity can affect quality of life.

The Pelli-Robson and Snellen letter charts are easy to use on most verbal normal-sighted or low-vision patients. However, for non-verbal, disabled, or developmentally delayed patients, the Pelli-Robson and Snellen letter charts tend to be ineffective methods of testing. This problem led to the development of grating-based tests such as the Teller Acuity Cards (TAC) and Ohio

Contrast Cards (OCC). Unlike letter-based charts, the TAC and OCC do not require patients to recognize and report letters. The Teller Acuity Cards provided a suitable alternative for clinicians

1 and researchers to assess visual acuity on a wide range of patients (McDonald et al., 1985). The

Ohio Contrast Cards are a new grating-based contrast sensitivity test designed for use on similar testing populations (Hopkins, Dougherty, & Brown, 2017). Here, we further develop the OCC by testing the repeatability across testers and test-retest reliability between the PR chart and OCC.

Methods of Visual Acuity Assessment

Until the development of the Snellen Chart in 1862, there were multiple ineffective and non-standardized methods of determining visual acuity. During this time, Herman Snellen, a

Dutch ophthalmologist, developed a new letter-based optotype chart to standardize visual testing, which was reviewed by (Cole, 2014). This chart is arranged with the largest letters at the top and the smallest letters at the bottom. The typical testing distance is 20 feet or 6 meters. Each letter subtends an angle of five minutes of arc when tested at the appropriate testing distance and the patient is given credit for identifying at least three out of the five letters per line. The patient’s

“Snellen Acuity” is then defined as the testing distance from the chart divided by the size of the smallest line that the patient was able to identify letters. It is the most commonly used testing method for visual acuity. However, clinical researchers found that examiners were using different sized rooms and non-standardized letters on their charts which made the Snellen chart a non- standardized method of testing visual acuity (Bailey & Lovie-Kitchin, 2013).

Louise Sloan designed a set of standardized letters that included only 10 capitalized non- serifed optotypes, such as “C, D, H, K, and Z.” These were created to have equal legibility and difficulty, and to have a consistent letter height to width (5:1) ratio (Sloan, 1959). These letters are now commonly used on several types of charts, including the Bailey-Lovie and Pelli-Robson charts.

The continued work on creating an improved VA chart lead to the development of the

Bailey-Lovie chart (BL). Visual acuity charts that use LogMAR are now considered to be the

“gold standard” for visual acuity testing in clinical research and low-vision testing (Raasch,

Bailey, & Bullimore, 1998). With this new chart, Ian Bailey and Jan Lovie focused on six main areas of improvement: the number of letters per line, legibility, spacing between letters/rows, letter size progression, scale scoring, and range of letter sizes (Bailey & Lovie, 1976). This chart

(Figure 1) uses ten Sloan letters, which as reviewed above, are non-serifed and have a height to width ratio of 5:4. It is mostly commonly tested at 6 meters or 20 feet for normal-sighted patients.

The chart has five letters per row a total of 14 rows of letters and 70 letters on the chart. These letters have approximately equal legibility, letter spacing of about four-stroke widths, and row spacing that is equal to the height of the letters in the smaller row. Even more importantly, each row of letters differs from the one above it by 0.10 log10 units. By focusing on these main areas of improvement, Bailey and Lovie allowed testing to occur at any distance by appropriately log- scaling the VA to the testing distance (Bailey & Lovie-Kitchin, 2013). With the development of the Bailey-Lovie chart, clinicians and researchers were finally able to use a chart that provided repeatable data without compromising the validity of the results due to environmental changes.

Figure 1. Bailey-Lovie Chart

Although the BL chart helped solve many problems associated with the Snellen chart, it did not solve the problem of obtaining reliable data with non-verbal patients. During the 1980s,

Davida Y. Teller developed a new grating-based acuity test called the Teller Acuity Cards which was redesigned in 2003 as TAC II. These are a set of 16 (55.5cm x 25.5cm) cards that are most commonly tested at a distance of 55cm (McDonald et al., 1985). Each card is set up with one side of the card’s face being blank and the other side having a 12cm x 12cm black/white stripped pattern, and a small peephole sitting in the middle. As seen in Figure 2, the spatial frequency of each card is different from its predecessor by a factor of the square root of 2 (i.e. one-half octave step) as the examiner flips through each card (Clifford, Haynes, & Dobson, 2005). Therefore, as the examiner presents each subsequent card, it becomes harder for the patient to see and identify where the grating appears on the card. The examiner observes the “looking behavior” of the individual and determines the visual acuity as the last seen card. With the development of the

Teller Acuity Cards, practitioners were now able to obtain useable information about the visual capabilities of pediatric populations and adult populations where letters could not be reported.

Figure 2. Teller Acuity Cards

Methods of Contrast Sensitivity Assessment

Before the development of a consistent testing method for contrast sensitivity, it was challenging to measure a patient’s contrast sensitivity outside the laboratory. However, the development of the Pelli-Robson chart (PR) in the 1980s changed how clinicians and researchers could test for contrast sensitivity (Pelli, Robson, & Wilkins, 1988). Denis Pelli and John Robson designed an 83.8cm x 56.7cm chart that contained 48 Sloan letters in groups of three. Each letter is roughly 4.9cm x 4.9cm. As seen in Figure 3, each group of letters varies in contrast from the preceding group by 0.15 log10 units (Pelli & Bex, 2013), which allows practitioners to test a wide range of contrast sensitivities from 100% to 0.56%. Each letter subtends at an angle of 2.8 degrees, which is equivalent to a Snellen acuity of “20/672” at a testing distance of one meter

(Arditi, 2005; Dougherty, Flom, & Bullimore, 2005). Originally, a distance of three meters was recommended for those who have relatively normal-sighted vision (Mantyjarvi & Laitinen, 2001;

Pelli & Bex, 2013). However, for both normal-sighted patients and low-vision patients, a distance of one meter is recommended, as described by the manual (Arditi, 2005; Chung & Legge, 2016).

Figure 3. Pelli-Robson chart

Similar to the Pelli-Robson chart, the Mars chart (or Lighthouse Letter Contrast

Sensitivity Test) is a letter-based contrast sensitivity chart. As seen in Figure 4, It measures at a size of 35.6cm x 22.8cm and was intended for testing at 0.5 meters so that each letter subtends at an angle of 2 degrees for a Snellen equivalent of “20/480” (Dougherty et al., 2005). However, in clinical settings, it is commonly tested at a near distance of 40cm so that each letter subtends at

2.5 degrees (Arditi, 2005). While the Pelli-Robson chart must be mounted on a wall or placed on a stand, the Mars Chart can be held by the patient or the practitioner. This makes the Mars chart less restricted by the size of the exam room, reduces storage space, and allows for portable use outside the exam room. The near testing distance makes for improved uniform lighting of the chart at 85 cd/m2 (Arditi, 2005). Each subsequent letter on the Mars chart changes in contrast by

0.04 log10 units, whereas the Pelli-Robson chart has each subsequent letter changing by 0.05 log10 units. Both charts are performed with similar procedures of stopping the patient when two letters are missed in a row. The Mars chart was found to be a reliable alternative to the Pelli-Robson chart in normal-sighted and low-vision populations (Dougherty et al., 2005).

Figure 4. Lighthouse Letter Contrast Sensitivity Test (Mars chart)

Another method of testing contrast sensitivity includes the Arden Grating Contrast

(AGT). This method was primarily developed to be used as a screening test for retinal diseases.

The booklet consists of seven plates with different sine wave grating patterns but similar ranges of contrast threshold. The first plate is an instructional screening plate, while plates two through six are the testing plates (Weatherhead, 1980). The examiner begins by showing the patient the bottom of the plate, so the patient understands the task at hand since the bottom of each plate contains the highest contrast value. The examiner sets the AGT plate in front of the patient at about 50cm and presents the lowest-contrast grating area of the plate, which is the top of the plate. If the patient is unable to see the grating bars, the examiner then slowly reveals the rest of the plate until the patient can see the grating bars. The patient is then given a score between 1-20, as marked on the side of each plate, which is based on how much of the plate had to be revealed before the patient could identify the grating bars. The Y dimension of each plate varies in contrast while the X dimension of each plate varies in spatial frequency with each successive plate showing grating periods of 0.20, 0.40, 0.8, 1.60, 3.20, and 6.40 cycles per degree (Arden &

Jacobson, 1978). The change in contrast for each of the proceeding grating bars on the testing plates is approximately 0.088 log10 units. Some issues with the AGT is that the required illumination is very high (130 to 150 cd/m2) and the patient must also be able to understand the directions of verbally reporting when the grating is seen (Arden & Jacobson, 1978).

Figure 5. Arden Grating Contrast

Another grating contrast sensitivity is the Vistech Vision Contrast Test System (Vistech).

This test was introduced in 1984 as a way of testing contrast sensitivity over a wide range of spatial frequencies with different orientations of the grating plates. There are five rows with subsequent spatial frequencies of: 1.5, 3, 6, 12, and 18 cycles per degree (Ginsburg, 1984). As the patient goes across each row, the contrast value of each plate differs from its predecessor and the patient must correctly identify the orientation of the grating until directed by the examiner to stop.

These plates are reported as having a change in contrast value that ranges from 0.25 log10 units to

1.75 log10 units. Each grating plate is either vertically oriented or tilted at a 15-degree angle to the left or right. The patient stands at roughly 3 meters or 10 ft from the chart in Figure 6 and reports the orientation of each grating (e.g. “up,” ”left,” “right”). When the patient can no longer identify the orientation, he or she can report the plate as being blank. In previous studies, using cataract and refractive surgery patients, this chart was found to have a poor reliability (Pesudovs, Hazel,

Doran, & Elliott, 2004).

Figure 6. Vistech Vision Contrast System

To improve the repeatability of the Vistech Vision Contrast System, a second chart called the Functional Acuity Contrast Test (FACT) was designed. As seen in Figure 7, the FACT uses circular grating plates with similar spatial frequencies and grating orientations like the Vistech

Vision Contrast System. The log progression was changed to 0.15 log10 units from 0.25 log10 units in order to improve the precision of the test, which would hopefully increase the reliability of the test. Previous studies have found the FACT to be slightly more reliable than the Vistech vision contrast system, when comparing the test-retest agreement in normal-sighted patients (+/-

0.42 log10 units vs. +/- 0.57 log10 units) (Pesudovs et al., 2004). This improvement in reliability was believed to be due to the smaller-step progression scale, which reduced the variability of patient data. However, this smaller scale also reduced the maximum contrast sensitivity score for normal-sighted patients. By using a smaller scale, this lowered the maximum contrast sensitivity score that could be tested by examiners and artificially reduced the range of contrast sensitivity for patients. The FACT was also designed to be a three-alternative forced choice method for each plate rather than allowing the patient to respond with “not seen” as a response. By using a three- 9 alternative forced choice method, this allowed patients to achieve lower thresholds because the patient had a 33% chance of answering correctly even if they are unable to identify the orientation of the plates.

Figure 7. Functional Acuity Contrast Test (FACT)

The Melbourne Edge Test (MET) is a portable contrast sensitivity test (30cm x 25cm) with a total of 20 disks that are arranged in four rows of five disks on a gray background, as depicted in Figure 8. Each disk has a diameter of 20mm. The patient sits at a distance of 40 cm with background lighting ranging from 18 to 80 cd/m2 and reports the direction of the edge within each disk (e.g. 45, 90, 135 or 180 degrees) (Haymes & Chen, 2004). Each succeeding disk differs in contrast with the top row of disks changing at a 2db step scale while disks on rows 2-4 change at a 1db step scale. Each disk is oriented in one direction and the patient chooses from four directions. The ability to detect the edges and identify their orientation is evidently related to the peak contrast sensitivity. The range of contrast values is from 1 to 24db (-0.10 to -2.40 log10 units). For every change in 1db, this is approximately equivalent to 0.10 log10 units (Michelson contrast). When compared against the Pelli-Robson chart, previous studies found the MET to be 10 less reliable in both normal-sighted and low-vision subjects (Haymes & Chen, 2004). This was reportedly due to the difference in the size of the step scales. Also, many indices of visual perception, such as contrast sensitivity or visual acuity, are slightly worse for oblique gratings than vertical or horizontal gratings. Therefore, patients may have a harder time identifying the edge at an oblique angle vs. a horizontal or vertical angle, which can influence the maximum contrast sensitivity value achieved by the patient. Studies have found that cells in the cortex are selectively sensitive to orientation with a preference for horizontal or vertical stimuli rather than oblique stimuli (Appelle, 1972). However, the MET is still considered to be a valuable diagnostic tool to test the contrast sensitivity of low-vision patients.

Figure 8. Melbourne Edge Test (MET)

These contrast sensitivity tests generally have one main characteristic in common. They require the patient to be able to say what they see. However, what if the patient is unable to speak? To provide better testing for non-verbal or developmentally delayed populations, researchers at The Ohio State University (OSU) developed a new grating-based acuity test known as the Ohio Contrast Cards. This is a collection of 15 (56cm x 25cm) cards, Figure 9, that are presented at a distance of 57cm. On the face of each card there is a blank area on one side and a

“20/4000” sized grating (22cm x 20cm), on the other side, with a small peephole in the center

(Hopkins et al., 2017). Similar to the Pelli-Robson chart, each subsequent card changes in contrast by 0.15 log10 units. These grating cards are capable of testing a contrast range from 100% to 1%. The goal for the examiner is to observe the “pointing behavior” of the patient or the

“looking behavior” if the patient is unable to point. The examiner will then determine if the patient was able to see and identify the location of the stimulus. In a pilot study, the OSU researchers used the OCC on low-vision students attending the Ohio State School of the Blind

(OSSB) and compared the results of each student on the OCC to their results on the PR chart

(Hopkins et al., 2017).

Figure 9. Ohio Contrast Cards

Project Goals

In this project, our main goal was to determine the test-retest reliability of the Ohio

Contrast Cards compared to test-retest reliability of the Pelli-Robson chart. Our secondary goal was to determine how testing the Pelli-Robson chart at a closer distance would influence contrast threshold performance. Our tertiary goal was to further analyze the relationship between VA and

CS, using letter-based chart and grating cards.

We tested a pediatric low-vision population and compared the reliability of the Ohio

Contrast Cards between testers and compared the results of the Ohio Contrast Cards against the

12 results of the Pelli-Robson chart. We compared the results of the Ohio Contrast Cards to the Pelli-

Robson data, since the Pelli-Robson chart is considered to be the “gold standard” for contrast sensitivity testing due to its proven reliability (Arditi, 2005; Dougherty et al., 2005; Elliott,

Sanderson, & Conkey, 1990). If proven to have statistical test-retest reliability comparable to the

Pelli-Robson chart, the Ohio Contrast Cards could be implemented into the clinical setting as an additional diagnostic tool in helping to assess the contrast sensitivity of low-vision patients, normal-sighted patients, and non-verbal patients. Due to varying visual abilities of low-vision patients, the standard testing distance for the Pelli-Robson does not always effectively predict the peak contrast sensitivity for each patient. Therefore, we evaluated whether changing the testing distance of the Pelli-Robson chart, based on a student’s visual acuity, would significantly influence their ability to detect contrast. If the contrast sensitivity is better at a closer testing distance, we can work to create a new standard for determining the best test distance for the Pelli-

Robson chart in low-vision patients.

Chapter 2. Methods

Overview:

This study tested 30 students at the Ohio State School for the Blind (OSSB). We tested each student twice using the PR chart and OCC, by the two testers (AMB, SMN) in random order. We also tested the PR chart at two different distances, and VA using TAC and the BL chart.

Participants:

Thirty low-vision students from the Ohio State School of the Blind (15 males and 15 females) participated in this study. Their ages ranged from 7-20 years (mean = 15.067, +/- SD of

3.713) and, were in the 1st to the 12th grade (mean = 8.00, +/- SD of 3.87). The demographic data and visual disorders for each student appear in Table 1. A recruitment package containing information about the purpose of the study and informed consent forms were provided for OSSB to distribute to the parental guardians of students or students over the age of 18 that were interested in participating in the study. For students under the age of 18, parental consent was obtained by mail and assent was obtained from the student before data collection started. Verbal assent was obtained using age-appropriate assent forms for those under 12 and for those between

12-17 if necessary, based on mental competence. For competent students over the age of 18, signed informed consent was obtained verbally before data collection started. Mental competence was based on information provided by the chaperone assigned to the student through OSSB. All students received $5.00 gift cards for participating in this study.

In planning this project, statistical power analyses were performed, relying on the statistics from previous measurements of CS and VA that were performed on a similar population of OSSB students (Hopkins et al., 2017), expressed in log10 units (Michelson contrast). The level

14 of performance was estimated to be composed of two components: his or her level of visual function and measurement error. The test-retest reliability of these tests will depend only on the measurement error, so it was important to base our statistical power analysis from the measurement error observed in the Hopkins data set.

The levels of the performance on the OCC and PR chart from (Hopkins et al., 2017) were modeled using all the other measurements in that data set through the nonlinear regression program in SPSS. By using this analysis, the OCC and PR chart measurements were predicted

(pred) to be:

OCCpred = 0.88* PRscore - 0.55*TACscore - 0.147*BLscore

PRpred = 0.993*OCCscore + 0.209*TACscore + 0.255*BLscore.

Next, the measurement error associated with each student’s OCC or PR score was estimated:

OCCerror = OCCobserved - OCCpred

PRerror = PRobserved - PRpred

The standard deviation of the OCCerror estimates was 0.298 and the standard deviation of the PRerror estimates was 0.307. The standard deviation of the test-retest differences was predicted to be the square root of two times the error scores, or 0.422 for the OCC and 0.435 for the PR chart. To achieve a statistical power of 80% with a clinically significant test-retest difference of 0.30 log10 units, the study needed 16 students to be tested with the OCC and 17 students needed to be tested with the PR chart. This was determined by using the website: powerandsamplesize.com. The standard error of the difference (OCCpred - PRpred) was 0.584, which indicated that a difference of 0.30 log10 units would be statistically significant at p < 0.05 with a probability over 80% if at least 19 students were tested.

At the beginning of the project it was not clear how many of the students would be able

to perform all the tests, especially the PR chart. If all 30 students had measurements from both the

OCC and PR chart, the statistical power for these differences would have all been greater than

95%.

Table 1. Subject Demographics and Vision Disorders

Subject Eye Number Age Grade Gender Race Vision Disorder Tested 1 19 12 male white Blue cone monochromat OS 2 19 12 female white Retinopathy of prematurity OD 3 18 12 male white Congenital cataracts OS 4 11 3 male white Septo-optic dysplasia OU 5 15 9 male white Blue cone monochromat OD 6 20 12 male white Leber's congenital amaurosis OS 7 15 9 male white Albinism OD 8 16 10 female white Cortical blindness OD 9 19 11 female black Optic nerve hypoplasia OD 10 9 2 male white Central areolar macular dystrophy OS 11 9 2 male white Dominant optic atrophy OD 12 13 4 female white Congenital nystagmus OD 13 16 9 male white Cortical blindness OS 14 19 11 female white Stickler syndrome OU 15 14 8 male white Congenital cataracts OS 16 14 7 male white Leukemia OS 17 15 9 female white Septic-optic dysplasia OD 18 16 10 female black Optic atrophy OD 19 17 11 female white Axenfeld-Rieger Syndrome OD 20 17 11 female white Rod cone dystrophy OS 21 18 12 male black Retinitis pigmentosa OS 22 10 3 female white Septic-optic dysplasia OU 23 18 11 female hispanic Optic atrophy OD 24 17 10 male white Septic-optic dysplasia OU 25 15 8 female black Optic atrophy OD 26 7 1 female black Rod-cone dystrophy OU 27 13 7 female asian Optic atrophy (wolfram syndrome) OS 28 7 1 female white Congenital glaucoma, Aniridia OD 29 18 9 male black Retinoblastoma OS 30 18 12 male black Retinopathy of prematurity OD

Vision Tests

The visual function of the student was measured using four tests. Visual acuity and contrast sensitivity were measured using two types of tests: letter charts and cards. Those who were able to read, and report letters were tested with both types of tests, whereas those who could not were tested only with the cards. The letter charts were the Bailey-Lovie chart and Pelli-

Robson chart. The grating cards were the Teller Acuity Cards and Ohio Contrast Sensitivity

Cards.

Charts

The Bailey-Lovie chart is a recognition acuity test that typically measures visual acuity at

100% contrast. The tester used the descending method of limits by having each student begin with the top row of letters on the chart at a testing distance of 2 meters. However, the chart was moved closer for students unable to identify the top row of letters. When the student was unable to correctly recognize at least three out of the five letters, he or she was instructed to stop testing and the total correct responses and misses were recorded. The equation: VA = 1.1 - 0.02 * N was used to determine the final logMar acuity with “N” being the number of correct letters (Vanden

Bosch & Wall, 1997).

The Pelli-Robson chart was placed at two different test distances with one distance being at one meter, which is the standard distance, and a closer testing distance which was calculated by using this equation: Distance (meters) = 1.0 - logMAR acuity. This equation was determined in a pilot study by (Hopkins et al., 2017). Each student was instructed to read each letter in the group with every correct response and missed response being marked down, with only O for C or C for

O being accepted as substitutions (Arditi, 2005). The student was encouraged to continue the test until he or she could no longer correctly identify at least two out of three letters in the triplet. The lowest contrast threshold was determined using a letter-by-letter scoring method since each

17 proceeding group differs by a contrast value of 0.15 log10 units. However, each letter counts as

0.05 log10 units. The equation used to determine the final contrast threshold was: 0.05 * N – 0.15, where “N” is the number of correct letters and 0.15 represents that the first three letters (or first triplet of letters) on the Pelli-Robson chart are at 100% contrast sensitivity (Mantyjarvi &

Laitinen, 2001).

The Teller Acuity Cards are recognized as a valid behavioral assessment of grating acuity that was tested at 57cm. Generally, the tester started by presenting the card with the lowest spatial frequency in the set to establish the student’s understanding of the test. Each tester then used the descending method of limits to determine the visual acuity each student. The student was instructed to identify the side containing the grating by pointing to it with a “magic wand.” The tester then judged whether the student had seen the grating by observing the student’s behavior as he or she pointed to it. The first missed card and last seen card by each student was confirmed by repeating the presentation of that card with a randomized variation of the grating on the left or right side.

Similarly, to the TAC, the OCC was tested at 57cm. The scoring and stopping method of the OCC was the same as the TAC, as described above.

Testing Procedures

Data collection was divided into two phases, with one (randomly chosen) tester administering the first phase and the other tester administering the second phase. The process of randomization was determined prior to the testing any students. Each phase took place in a different room, as noted below in Figure 10.

1. Each room was illuminated with two fluorescent lamps near the testing materials and the

overhead fluorescent lights to provide uniform illumination on all testing materials. Due

to the structure of the room, window lighting was also present. The average illuminance 18

falling on the BL chart was 558 lux and 721 lux for the PR chart. As for the grating cards,

the average illuminance for the OCC and TAC was 744 lux.

2. Each tester was randomized to the first phase (room A) or the second phase (room B) of

the testing procedure. The tester in room B was not present in room A during the testing

procedures.

3. If student had vision in both eyes, the student was tested with their preferred eye;

however, when necessary, neither eye (OD or OS) was patched if the student had

difficulties with patching. If the student only had vision in one eye, the student was tested

with their sighted eye (OD or OS).

a. Students wore their habitual refractive correction during testing.

4. Four tests were administered in room A: Bailey-Lovie chart, Pelli-Robson chart, Teller

Acuity Cards, and Ohio Contrast Cards. Each of these tests was timed.

a. Testing was performed using the Bailey-Lovie and Pelli-Robson charts in

randomized order. The Bailey-Lovie chart was placed at the standard distance of

2 meters but could be moved closer for students with reduced visual acuity. The

Pelli-Robson chart was tested at the standard distance of one meter.

b. Testing was also performed using the Teller Acuity Cards and Ohio Contrast

Cards. The Pelli-Robson chart was also tested at a closer testing distance. The

order of these three tests was randomized as well. While performing the grating

card tests, the student and tester were seated face-to-face for a test distance of

57cm. The second closer distance for the Pelli-Robson chart was determined by

using the student’s logMar acuity from the Bailey-Lovie chart, as described

above.

5. After the student completed the testing in room A, he or she moved to Room B for the

second phase of testing.

6. Testing in room B consisted of two tests: the Pelli-Robson chart and the Ohio Contrast

Cards. These two tests were performed in randomized order. Each student used the same

eye that was tested in room A and each test was timed.

a. The Pelli-Robson chart was tested at the standard distance of on4 meter with a

chart that consisted of different letters than the chart used in room A. Testing

with the Ohio Contrast Cards was performed at 57 cm.

Bailey-Lovie chart, Pelli-Robson chart (one meter)

Room A Teller Acuity Cards, Ohio Contrast Cards, Pelli-Robson chart (closer distance)

Room B Pelli-Robson chart (one meter), Ohio Contrast Cards

Figure 10. Testing Materials in Each Testing Room

Table 2. Randomized Testing Sequence

Subject First PR (one PR (closer Second PR (one Number Tester BL meter) distance) OCC TAC Tester meter) OCC 1 AMB 1 2 1 2 3 SMN 1 2 2 AMB 1 2 1 2 3 SMN 1 2 3 AMB 1 2 1 2 3 SMN 1 2 4 AMB 1 2 1 2 3 SMN 1 2 5 AMB 2 1 2 1 3 SMN 2 1 6 SMN 1 2 1 3 2 AMB 1 2 7 AMB 1 2 1 3 2 SMN 1 2 8 SMN 1 2 2 1 3 AMB 1 2 9 AMB 2 1 3 1 2 SMN 1 2 10 SMN 2 1 3 1 2 AMB 2 1 11 SMN 1 2 1 3 2 AMB 2 1 12 SMN 1 2 2 1 3 AMB 1 2 13 AMB 2 1 3 2 1 SMN 2 1 14 AMB 2 1 3 2 1 SMN 2 1 15 AMB 2 1 2 3 1 SMN 1 2 16 SMN 2 1 1 3 2 AMB 2 1 17 SMN 1 2 2 3 1 AMB 1 2 18 SMN 1 2 2 1 3 AMB 2 1 19 AMB 2 1 2 1 3 SMN 2 1 20 AMB 1 2 2 3 1 SMN 1 2 21 SMN 2 1 3 1 2 AMB 1 2 22 AMB 2 1 3 1 2 SMN 2 1 23 SMN 2 1 3 2 1 AMB 2 1 24 AMB 2 1 3 2 1 SMN 2 1 25 AMB 2 1 2 1 3 SMN 1 2 26 AMB 2 1 3 2 1 SMN 2 1 27 SMN 1 2 1 2 3 AMB 2 1 28 SMN 2 1 3 2 1 AMB 1 2 29 SMN 1 2 2 3 1 AMB 2 1 30 SMN 1 2 3 1 2 AMB 2 1

Chapter 3. Results

We were able to enroll 30 students through the Ohio State School for the Blind. We obtained useable data from 28 students. Both of the two students who were not tested successfully were diagnosed with septo-optic dysplasia, which results in a significant reduction of visual and neurological abilities.

Visual Acuity vs. Contrast Threshold

Bailey-Lovie Chart vs. Pelli-Robson Chart

Figure 11 shows the contrast threshold data obtained from the Pelli-Robson chart, graphed as a function of the logMAR acuity that was obtained from the Bailey-Lovie chart. Note that the logMAR performance is graphed as negative so that bigger numbers on the graph represent better performance for both the Pelli-Robson and Bailey-Lovie chart. The positive slope of the regression line indicates a positive correlation between the Bailey-Lovie and Pelli-Robson chart. A majority of the data points fit reasonably close to the regression line, which indicates a strong association between the two tests (r = +0.800, p < 0.0001). Students with a lower logMAR acuity tended to have a lower contrast threshold, which means that their performance on the Pelli-

Robson chart was associated with their performance on the Bailey-Lovie chart.

(Michelson Contrast) (Michelson Robson Chart Contrast Threshold Robson Threshold ChartContrast

- 0

Pelli

10 Log y = 1.1915x + 2.252 r = 0.800 -1 -2 -1 0 -LogMAR Acuity Bailey-Lovie Chart

Figure 11. Performance on two letter charts: the Pelli- Robson chart and the Bailey-Lovie chart. Log10 contrast threshold as a function of negative-logMAR visual acuity. Equation of solid regression line fitted to the data and correlation coefficient.

Ohio Contrast Cards vs. Teller Acuity Cards

Figure 12 displays the association between the contrast threshold performance on the

Ohio Contrast Cards and the logMAR acuity performance on the Teller Acuity Cards. As in

Figure 11, the logMAR acuity in Figure 12 is negative, so that bigger numbers indicate better performance for both the Ohio Contrast Cards and Teller Acuity Cards. The contrast threshold performance from the Ohio Contrast Cards was also statistically significantly correlated with the logMAR acuity performance from the Teller Acuity Cards (r = +0.493, p = 0.008). However, this

23 positive correlation is not a strong association since a majority of the data points do not fit closely to the regression line.

2.5

1.5

1 (Michelson Contrast) (Michelson

Ohio Contrast Cards Contrast Threshold Threshold Cards Contrast Contrast Ohio 0.5 10 10 y = 0.7702x + 2.2707 Log r = 0.493 0 -1.5 -1 -0.5 0 -LogMAR Teller Acuity Cards

Figure 12. Performance on two grating cards: the Ohio Contrast Cards and Teller Cards. Log10 contrast threshold as a function of negative-logMAR visual acuity. Equation of solid regression line fitted to the data and correlation coefficient.

Letter Charts vs. Grating Cards

Visual Acuity

Figure 13 shows the LogMar acuity performance of the Bailey-Lovie chart vs. Teller

Acuity Cards. Most of the data points sit closely to the solid regression line, which indicates a highly positive correlation between these two tests (r = +0.664, p < 0.0001). Again, the logMAR performance is graphed as negative so that bigger numbers indicate better performance on both the Bailey-Lovie chart and Teller Acuity Cards. A majority of the data points sit above the dotted

24 equality line, which means a better performance on the Teller Acuity Cards compared to the

Bailey-Lovie chart. The overall performances on the Teller Acuity Cards showed a statistically significantly lower logMAR acuity when compared to the Bailey-Lovie chart (mean of the differences = -0.395, t = -7.223, p < 0.0001).

-1

LogMAR Teller Acuity CardsLogMAR Teller -

y = 0.5872x - 0.1044 r = 0.664 -2 -2 -1 0 -LogMAR Bailey-Lovie Chart

Figure 13. Performance on two methods of testing visual acuity: the Bailey-Lovie chart and Teller Acuity Cards. Performance of the Teller Acuity Cards compared against the performance of the Bailey-Lovie chart. Equation of solid regression line fitted to the data and correlation coefficient. Dashed line represents the equality lined fitted to the graph.

Contrast Sensitivity

Here, in Figure 14, we compare the contrast threshold performance of the Pelli-Robson chart (one meter) vs. Ohio Contrast Cards when used by tester 1 (squares) and tester 2 (triangles).

We also compare the performance of the Pelli-Robson chart at the closer testing distance vs. Ohio

Contrast Cards. There is a statistically significant relationship between the PR chart and OCC when used by both tester 1 and tester 2 (Tester 1: r = +0.541, p = 0.005, Tester 2: r = +0.503, p =

0.001). For tester 1 and tester 2, the average difference between contrast threshold performance when comparing the Pelli-Robson chart vs. Ohio Contrast Cards showed a statistically significant difference for each tester (Tester 1: mean of the differences = 0.948, t = 9.360, p < 0.0001, Tester

2: mean of the differences = 1.001, t = 10.00, p = <0.0001). For both tester 1 and tester 2, the

OCC showed a better contrast threshold performance of roughly six groups of three letters when compared to the PR chart . When comparing the OCC against the PR chart at the closer distance, there was no statistically significant difference in contrast threshold performance (mean of the differences = 0.49, t = 0.136, p = 0.893). Note that in Figure 14, for both panels A and B, a majority of the data points are above the equality line, which indicates a better performance on the OCC.

A. 3

Ohio Ohio Contrast CardsContrast

Threshold Contrast) (Michelson Threshold Log 0 0 1 2 3

Log10 Pelli-Robson Chart Contrast Threshold (One Meter) (Michelson Contrast)

B. 3

Ohio Contrast Cards Contrast Contrast CardsContrast Ohio y = 0.6664x + 0.9148 10

Threhold Contrast) Threhold (Michelson r = 0.655

Log 0 0 1 2 3

Log10 Pelli-Robson Chart Contrast Threshold (Closer Distance) (Michelson Contrast)

Figure 14. Log10 Contrast Threshold Performance on two methods of testing contrast sensitivity: the Pelli-Robson chart and Ohio Contrast Cards. Panel A shows the performance on the Ohio Contrast Cards compared against the performance on the Pelli- Robson chart tested at one meter. Panel B shows the performance of the Ohio Contrast Cards against the performance of the Pelli- Robson at the closer distance. Lines: solid - regression line fitted to the data, dashed – equality line. Symbols: squares – tester 1, triangles – tester 2. 27

Contrast Threshold Performance

Repeatability of the Pelli-Robson Chart

We examined test order as factor 1 (tester 1 vs. tester 2) and tester as factor 2 (AMB vs.

SMN) using a two-way mixed ANOVA of the Pelli-Robson chart data set. This allowed us to analyze the within-subjects interaction between factor 1 and 2 on the contrast threshold results obtained from students being tested on the Pelli-Robson chart. After removing a non-significant interaction between test order and testers (F1, 26 = 1.804, p = 0.186, NS), we found that neither test order (F1, 26 = 0.024, p = 0.878, NS) or tester (F1, 26 = 0.095, p = 0.759, NS) was statistically significant. The average difference was 0.017 log10 units for tester 1 vs. tester 2 and -0.049 log10 units for AMB vs. SMN. The standard deviation for tester 1 vs. tester 2 was 0.272, while the standard deviation for AMB vs. SMN was 0.273.

The results are shown on a Bland-Altman plot, in Figure 15, which compares the differences of the Pelli-Robson contrast thresholds at one meter between the two testers as a function of their averages which helps analyze the repeatability of the test. Panel A in Figure 15 shows test order (tester 1 vs. tester 2), while Panel B in Figure 15 shows tester (AMB vs. SMN).

The dashed lines enclose 95% of the data (+/- 1.96 x SD), as marked on the graphs, which are the

Limits of Agreement for the PR chart.

A. 1

0.5

0.536

Contrast Threshold) Contrast 0 10

-0.5 y = -0.0233x + 0.0349 r = 0.048 Difference (Log Difference -1 -0.5 0 0.5 1 1.5 2 2.5

Average (Log10 Contrast Threshold)

B. 1

0.5 0.536

Contrast Threshold) Contrast 0 10

-0.5 y = -0.0205x - 0.0314 r = 0.042 Difference (Log Difference -1 -0.5 0 0.5 1 1.5 2 2.5

Average (Log10 Contrast Threshold)

Figure 15: Contrast Threshold Performance with test order (tester 1 vs. tester 2) and tester (AMB vs. SMN) as the factors: the Pelli-Robson chart (one meter). Both panel A and B are Bland-Altman plots comparing the contrast threshold performance with the difference between the testers plotted as a function of their average. Solid line represents the averages of the differences fitted to the graph. Dashed lines contain 95% (+/- 1.96 x SD) of the data.

Repeatability of the Ohio Contrast Cards

We examined test order as factor 1 (tester 1 vs. tester 2) and tester as factor 2 (AMB vs.

SMN) by using a two-way mixed ANOVA of the Ohio Contrast Cards data set. A two-way mixed

ANOVA was used to analyze the interaction between test order and tester on the results obtained from students tested with the Ohio Contrast Cards. After removing a non-significant interaction between test order and testers (F1, 28 = 0.044, p = 0.835, NS), we found that neither test order (F1,

28 = 0.234, p = 0.631, NS) or tester (F1, 28 = 0.023, p = 0.879, NS) was statistically significantly associated with contrast sensitivity as assessed by the OCC. The average difference was -0.065 log10 units for tester 1 vs. tester 2 and 0.020 log10 units for AMB vs. SMN. The standard deviation for testers 1 vs. tester 2 was 0.210, while the standard of deviation for AMB vs. SMN was 0.220.

The results are shown on a Bland-Altman plot, Figure 16, which compares the differences of the contrast threshold results obtained from the Ohio Contrast Cards between the two testers as a function of theirs means. Panel A in Figure 16 shows test order as a factor, while

Panel B in Figure 15 shows tester as a factor. The dashed lines enclose 95% of the data (+/- 1.96 x SD), as marked on the graphs, which are the Limits of Agreement for the OCC.

0.451

Contrast Contrast Threshold) 0 10

y = -0.0044x - 0.0573

Difference (Log Difference r = 0.01 -1 0 1 2 3

Average (Log10 Contrast Threshold)

0.431

Contrast Threshold) Contrast Threshold) 10

y = -0.0705x + 0.1398 r = 0.154 Difference (Log Difference -1 0 1 2 3

Average (Log10 Contrast Threshold)

Figure 16: Log10 Contrast Threshold Performance with test order (tester 1 vs. tester 2) and tester (AMB vs. SMN) as the factors: Ohio Contrast Cards. Both panel A and B are Bland-Altman plots comparing the contrast threshold performance with the difference between the testers plotted as a function of their average. Solid line represents the averages of the differences fitted to the graph. Dashed lines contain 95% (+/- 1.96 x SD) of the data.

Pelli-Robson chart (one meter) vs. Pelli-Robson chart (closer distance)

The original paper on the Pelli-Robson chart suggests a testing distance of three meters for individuals who are presumed to have near normal contrast threshold (Pelli et al., 1988).

However, low-vision patients and even normal-sighted patients are typically tested at a closer distance of one meter, as suggested by the manual for the PR chart. Utilizing the equation:

Distance (meters) = 1.0 − logMARacuity we used the student’s logMAR acuity results from the BL chart to estimate an even closer testing distance for the PR chart. In Figure 17, note how all the data points sit above or at the equality line, which shows that most students performed better at a closer distance than at one meter with the PR chart. Performance on the PR chart at the two distances was highly correlated (r = 0.788, p<0.0001). Students’ contrast threshold performance improved by an average difference of about

0.42 log10 units (p < 0.0001, t = 5.694), when moving the PR chart to a closer testing distance. On average, students performed better by roughly three groups of three letters on the PR chart when tested at the closer testing distance (i.e. three cards on the OCC).

These data points are based on contrast threshold results obtained by tester 1, regardless of AMB or SMN, due to tester 1 presenting the Pelli-Robson chart at the standard one meter and closer testing distance in room A.

2.5

1.5

Closer Closer ) Distance 0.5

(

(Michelson Contrast) (Michelson

Robson Chart Contrast Threshold Robson Threshold ChartContrast -

Pelli 0 10 y = 0.6073x + 0.7342

Log r = 0.788 -0.5 -0.5 0 0.5 1 1.5 2

Log10 Pelli-Robson Chart Contrast Threshold (One Meter) (Michelson Contrast)

Figure 17: Log10 Contrast Threshold Performance: the Pelli-Robson chart at a closer distance against the Pelli- Robson chart at one meter. Equation of solid regression line fitted to the data and correlation coefficient. Dashed line represents the equality lined fitted to the graph.

Chapter 4. Discussion

Here, I examine each aspect of our study’s goals by discussing the results of our study.

Relationship of Visual Acuity and Contrast Sensitivity

During this study, we compared the relationship between visual acuity and contrast sensitivity amongst low-vision students tested at the Ohio State School of the Blind. We paired the Bailey-Lovie chart against the Pelli-Robson chart and the Ohio Contrast Cards against the

Teller Acuity Cards. By pairing these diagnostic tests against each other, we were able to analyze the role visual acuity has in predicting a student’s contrast sensitivity when using letter-based charts versus grating cards. When we tested students with the BL and PR charts, there was a statistically significant positive correlation between both tests. This means that if a student tested poorly on the BL chart then he/she was more likely to perform poorly on the PR chart as well.

Similarly, the OCC and TAC showed a statistically significant correlation, but this association was not as strong as the association between the BL chart and PR chart. Therefore, the logMAR acuity obtained from TAC may not be a good predictor of the contrast threshold performance on the OCC. This difference in the correlation between VA and CS for letter-based charts and grating cards may be due to the method of testing. Letter-based charts rely on the student’s ability to recognize and self-report letters, whereas grating cards are dependent on the tester to decide if the student looked at the grating stimulus.

This difference in the strength of the relationship between VA and CS, when comparing letter-based charts to gratings cards, suggests that clinicians should test both visual acuity and contrast sensitivity independently, as both measurements assess different visual functions. Visual acuity is a good measure of a patient’s ability to recognize shapes or letters, such as large printed letters on street signs or the fine details of reading material. However, contrast sensitivity is more

34 helpful in helping patient’s distinguish objects from their background, whether it be bright or dark light. Contrast sensitivity plays a major role in everyday activities such as mobility. These differences are important because a patient could be normally sighted based on visual acuity but still have significant struggles with quality of life due to reduced contrast sensitivity.

Letters-Based Charts vs. Grating Cards

In addition to the main goal of this study, we also wanted to compare a student’s performance on letter-based charts vs. grating cards when testing visual acuity and contrast sensitivity. We found that students performed better on both grating card tests when compared to letter-based charts. When comparing the BL chart vs. TAC, a majority of students performed better on the grating cards by a statistically significant average of a four-line difference (0.394 log10 units). During contrast sensitivity testing, we found a statistically significant difference between a student’s score on the PR chart vs. OCC. Regardless of tester, on average, we found that each student scored higher on the OCC by roughly a six-card difference or six groups of three letters when compared to the PR chart. 0.948 log10 units). This difference between the performances on letter-based charts vs. grating cards could be attributed to the difficulties of having to recognize and read letters. During this study, no reading assessment was performed.

However, participants ranged from 1st grade to 12th grade, with varying visual and intellectual impairments. The two testers did not have any prior knowledge of a student’s academic performance in the classroom setting.

Repeatability of the Pelli-Robson Chart and Ohio Contrast Cards

The main goal of this study was to analyze the test-retest repeatability of the OCC between AMB and SMN, while also comparing the test-retest repeatability between the OCC and the PR chart. We found no significant difference across testers in the scores of students when using the OCC, regardless of the order of testers. The size of the confidence interval (+/- 1.96 x

SD) was found to be +/- 0.451 log10 units. This is smaller than the 95% confidence interval (+/-

1.96 x SD) for the PR, which was +/- 0.536 log10 units.

In general, the outliers in both Figures 15 and 16 show a large difference in the responses obtained from the testers when using the PR chart and OCC. For the outliers in Figure 15, one student was a seven-year-old who was cooperative with responses but had difficulty with staying on task while being tested and leaned forward during testing. The other student, in Figure 15, was a sixteen-year-old that was diagnosed with optic atrophy and the difference between testers was most likely due to a lack of engagement during testing. This is based on the time difference in testing between the testers, with the PR chart, which was two minutes and six seconds for tester 1 and thirty seconds for tester 2. For the outlier in Figure 16, this student was diagnosed with Wolfram syndrome and required additional assistance during testing due to significant hearing loss. An interpreter, provided by OSSB, was used to assist in explaining the testing procedures which reduced the direct communication between the student and testers.

By comparing the standard error of the means for both tests, we found there was no statistically significant difference between the PR test-retest and OCC test-retest amongst testers.

The standard error of the means (SEM) for the PR was 0.054 and 0.042 for the OCC (t = 0.79, p =

0.436, NS). This is important considering the poor reliability of previous grating-based tests when compared against the Pelli-Robson chart in previous studies. The OCC shows good promise for researchers and clinicians who seek an alternative method of testing that is not a letter-based chart such as the Mars chart or Pelli-Robson chart.

As previously described, there are several grating-based tests that have been developed prior to the OCC which include the Vistech, FACT, MET, and AGT. In previous studies when comparing the reliability of the PR chart against these grating tests, the PR chart has been found to be more reliable in both normal-sighted and low-vision patients. In a low-vision based study,

36 the MET was found to be less reliable than the PR chart (Limits of Agreement: +/- 0.37 log10 units vs. +/- 0.25 log10 units) (Haymes & Chen, 2004). The difference in reliability for the MET against the PR chart was attributed to the progression of the scale and having to identify the orientation of the grating cards on the MET. Furthermore, when evaluating the reliability of the

Vistech vs. the FACT, researchers described both grating-based tests to be less reliable than the

PR chart due to the size of the step scale for the PR chart which reduced the variability of measurements obtained from patients (Pesudovs et al., 2004).

When comparing the reliability of the PR chart against other studies using low-vision subjects, we found that the Limits of Agreement (+/- 1.96 x SD) was +/- 0.536 log10 units, compared to +/- 0.20 log10 units (Dougherty et al., 2005) or +/-0.25 log10 units (Haymes & Chen,

2004). The difference in the confidence intervals is most likely due to our study having no restriction on the VA or CS for a student to participate in the study. Furthermore, our pediatric population varied in different academic levels depending both on grade level and cognitive function. Despite these differences, we still found the results from the PR chart to be reliable amongst testers during our study.

Testing Distance for the Pelli-Robson Chart

As directed by the testing manual, the Pelli-Robson chart is used at a standard testing distance of one meter when testing low-vision patients. During this study, one of our secondary goals was to evaluate the effect on performance with the Pelli-Robson chart when determining the testing distance based on a student’s visual acuity on the Bailey-Lovie chart. As previously described, we found a significant correlation between visual acuity and contrast sensitivity when comparing the BL chart and the PR chart at one meter. There was also a significant positive correlation between the BL chart and the PR chart at the closer distance ( r = +0.566, p = 0.005).

We found that on average a student improved their contrast threshold scoring by 0.42 log10 units

37 when using a closer testing distance. On average, the closer testing distance was performed at

0.39 meters. This improvement in performance shows that testing at a closer distance should be used when evaluating low-vision patients. However, when comparing the scores of the Pelli-

Robson chart at the closer distance to the Ohio Contrast Cards, we found that students still performed better on the Ohio Contrast Cards by 0.49 log10 units, or a difference of roughly three groups of three letters on the Pelli-Robson chart.

When testing contrast sensitivity in a clinical setting, examiners should start with testing from the standard test distance for the Pelli-Robson chart, as directed by the manual, but also perform additional testing at a closer distance based on the patient’s visual acuity. Testing at a closer distance becomes more relevant for patients who have a visual acuity worse than 20/480 due to the size of the letters on the PR chart. The use of the Ohio Contrast Cards can be an additional diagnostic tool to help with evaluating contrast sensitivity in individuals that have difficulty with the Pelli-Robson charts due to significantly reduced acuity, developmental-delays or due to being non-verbal.

Chapter 5. Conclusions

• In this study, we found no statistically significant difference between the repeatability of

the OCC when compared to the PR chart.

• There was no statistically significant difference across the two testers in how students

performed on the OCC.

• Overall, a majority of students performed better on the PR chart at a closer testing

distance than at the standard distance of one meter, by approximately three groups of

three letters.

• A student’s contrast threshold performance was still better on the OCC, even with the PR

chart at a closer testing distance. Student’s performed better on the OCC by roughly three

groups of three letters on the PR chart or three cards on the OCC.

• Visual acuity and contrast sensitivity are highly correlated with each other on both the

letter-based charts and grating cards. However, in this study, they are more strongly

correlated on letter-based charts (BL chart vs. PR chart).

• Strengths

o The OCC was found to be reliable, amongst testers, even with such a large

variability in the participant pool (e.g. age, gender, visual disorder, cognitive

function).

• Weaknesses

o There were no minimum criteria for age or grade level that prevented a student

from participating in the study. This meant there was variability in the success of

achieving usable data from letter-based charts like the BL and PR chart due to

varying academic levels.

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Appendix A. Consent Form for Ages 12-17

Appendix B. Consent Form for Ages 18 and Older

Appendix C. Checklist for Room Setup

Appendix D. Data Collection Sheet

Appendix E. Gift Card Receipt