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Reliability of a computer-based system for measuring visual performance skills

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Athletes have demonstrated better visual abilities than nonathletes. A vision assessment for an athlete should include methods to evaluate the quality of visual performance skills in the most appropriate, accurate, and repeatable manner. This study determines the reliability of the visual performance measures assessed with a computer-based system, known as the Nike Sensory Station. One hundred twenty-five subjects (56 men, 69 women), age 18 to 30, completed Phase I of the study. Subjects attended 2 sessions, separated by at least 1 week, in which identical protocols were followed. Subjects completed the following assessments: Visual Clarity, Contrast Sensitivity, Depth Perception, Near-Far Quickness, Target Capture, Perception Span, Eye-Hand Coordination, Go/No Go, and Reaction Time. An additional 36 subjects (20 men, 16 women), age 22 to 35, completed Phase II of the study involving modifications to the equipment, instructions, and protocols from Phase I. Results show no significant change in performance over time on assessments of Visual Clarity, Contrast Sensitivity, Depth Perception, Target Capture, Perception Span, and Reaction Time. Performance did improve over time for Near-Far Quickness, Eye-Hand Coordination, and Go/No Go. The results of this study show that many of the Nike Sensory Station assessments show repeatability and no learning effect over time. The measures that did improve across sessions show an expected learning effect caused by the motor response characteristics being measured.
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Reliability of a computer-based system for measuring
visual performance skills
Graham B. Erickson, O.D.,
a
Karl Citek, O.D., Ph.D.,
a
Michelle Cove, O.D.,
b
Jennifer Wilczek, O.D.,
c
Carolyn Linster,
a
Brendon Bjarnason,
a
and Nathan Langemo
a
a
Pacific University College of Optometry, Forest Grove, Oregon;
b
Luxottica Group, Buffalo, New York; and
c
Parker
Optometry, Prince George, British Columbia.
KEYWORDS
Visual performance;
Sports vision;
Visual acuity;
Dynamic visual acuity;
Contrast sensitivity;
Accommodative
facility;
Stereopsis;
Perception span;
Eye-hand coordination;
Visual-motor reaction
time
Abstract
BACKGROUND: Athletes have demonstrated better visual abilities than nonathletes. A vision assessment
for an athlete should include methods to evaluate the quality of visual performance skills in the most
appropriate, accurate, and repeatable manner. This study determines the reliability of the visual perfor-
mance measures assessed with a computer-based system, known as the Nike Sensory Station.
METHODS: One hundred twenty-five subjects (56 men, 69 women), age 18 to 30, completed Phase I of
the study. Subjects attended 2 sessions, separated by at least 1 week, in which identical protocols were
followed. Subjects completed the following assessments: Visual Clarity, Contrast Sensitivity, Depth Per-
ception, Near–Far Quickness, Target Capture, Perception Span, Eye–Hand Coordination, Go/No Go,
and Reaction Time. An additional 36 subjects (20 men, 16 women), age 22 to 35, completed Phase
II of the study involving modifications to the equipment, instructions, and protocols from Phase I.
RESULTS: Results show no significant change in performance over time on assessments of Visual Clar-
ity, Contrast Sensitivity, Depth Perception, Target Capture, Perception Span, and Reaction Time. Perfor-
mance did improve over time for Near-Far Quickness, Eye-Hand Coordination, and Go/No Go.
CONCLUSIONS: The results of this study show that many of the Nike Sensory Station assessments show
repeatability and no learning effect over time. The measures that did improve across sessions show an
expected learning effect caused by the motor response characteristics being measured.
Optometry 2011;82:528-542
Researchers and clinicians have sought to discover the
specific vision skills that correlate to success in sports. The
vision and visual perceptual skills identified as important
include static and dynamic visual acuities, contrast
sensitivity, distance stereopsis, accommodative-vergence
facility, span of perception, central eye-hand reaction and
response speeds, and peripheral eye-hand response speed.
1,2
Two extensive review articles cite numerous studies to con-
clude that athletes have demonstrated better visual abilities
than nonathletes, and that top athletesdthose who are most
successfuldoften have visual abilities that are superior to
lower-level or less successful athletes.
3,4
Some aspects of
these skills commonly are assessed as part of a routine vi-
sion examination, but many are not evaluated for various
reasons. For some vision skills, there is little or no standard-
ization of assessment techniques, and some instrumentation
may be outdated, if available at all.
Disclosure: The authors thank Nike, Inc., for its generous support of
this project and John R. Hayes, Ph.D., for assistance with statistical anal-
yses. Nike provided the Nike Sensory Station and subject compensation to
facilitate this study. Drs. Erickson and Citek frequently provide consulta-
tion services to Nike but have no financial relationship involving the pro-
ducts tested or discussed in this article.
* Corresponding author: Graham B. Erickson, O.D., Pacific University
College of Optometry, 2043 College Way, Forest Grove, OR 97116.
E-mail: ericksog@pacificu.edu
1529-1839/$ - see front matter Ó2011 American Optometric Association. All rights reserved.
doi:10.1016/j.optm.2011.01.012
Optometry (2011) 82, 528-542
To provide specialized vision care to an athlete, the eye
care practitioner should identify the vision factors essential
to successful performance of the sport tasks. The vision
assessment should then include methods to evaluate the
quality of those skills in the most appropriate, accurate, and
repeatable manner. A significant body of research compares
performance on various measures of visual function in
athlete populations to guide the clinician in selecting
appropriate visual measures, as described below. The battery
of tests comprising the Nike Sensory Station (Nike, Inc.,
Beaverton, Oregon) attempts to address this clinical need.
Static visual acuity
Assessment of visual performance skills routinely begins
with measurement of static visual acuity (SVA). Compro-
mised SVA can negatively affect other areas of visual
performance.
1
Previous research has found mixed results re-
garding SVA in athlete populations: when SVA is assessed
using chart systems with 20/20 as the best acuity measure-
able, there is no statistically significant difference in the
visual ability of athletes compared with that of nonathletes.
5,6
Even when a best acuity demand of 20/15 is presented, Laby
et al.
7
found that 81% of professional baseball players could
achieve that level. Laby et al.
7
subsequently modified their
assessment method to achieve acuity demands down to
20/7.5, reporting overall mean SVAs of approximately
20/13 with several athletes attaining SVAs of 20/9.2 or better.
However, several studies in which visual acuity was
degraded with plus addition lenses did not find a detrimen-
tal effect of defocus.
8-10
Review of the study protocols re-
veals that subjects were assessed on predictable, repetitive
motor tasks. One of the goals of the current study is to de-
termine which assessment tasks, if any, are subject to a sim-
ple motor learning effect with repeated measurement.
Dynamic visual acuity
Dynamic visual acuity (DVA) generally is defined as the
ability of the visual system to resolve detail when there is
relative movement between the target and the observer.
1,4,11
Many sports involve extensive movement of an object (e.g.,
ball, puck), competitors, teammates, the athlete, or all simul-
taneously. Often at elite levels of sport, the velocity of move-
ment between the athlete and the target is tremendously high;
therefore, it is essential for athletes to be able to accurately
perceive and identify critical target features during dynamic
situations. Stine et al.,
3
in a review of the literature, found that
athletes show superior DVA abilities compared with nonath-
letes and that elite athletes have better DVA than do amateur
or nonelite athletes. These findings suggest that there is an
important link between elite athletes and DVA ability. On
the other hand, Ward and Williams
12
reported no significant
differences in performance on a DVA test between elite and
subelite youth soccer players. However, their use of a predict-
able rotator device to measure this function may not have
been environmentally appropriate to simulate the visual
task demands of a large-field, dynamic sport, such as soccer.
Although many researchers agree about the importance of
DVA in sport,
4
this visual skill is often not assessed in clinical
practice because of limitations in commercial instruments
available to measure it.
DVA decreases with increasing target velocity.
11
To have
a high DVA requires the ability to resolve targets at a higher
velocity than average. Brown
13
concluded that the relation-
ship between DVA and target angular velocity is approxi-
mately linear for targets moving at velocities up to 90
per second. DVA is affected by both the target parameters
(e.g., target luminance, target/observer velocity, and
exposure time of target) and the physiologic abilities of
the observer (e.g., resolving power of the eye, oculomotor
abilities, peripheral awareness, and psychological abilities
to interpret what is seen).
14
Enhancement of either the tar-
get parameters or physiologic abilities of the subject can
improve DVA abilities.
14-16
Contrast sensitivity
Contrast sensitivity measures the visual system’s ability to
process spatial or temporal information about objects and
their backgrounds under varying lighting conditions.
17
Measuring an athlete’s contrast sensitivity is important be-
cause most sports involve interpreting visual information at
contrast levels below what is measured with a typical visual
acuity chart.
1
Performance of athletes on contrast sensitiv-
ity testing is significantly better than that of nonathletes
across all spatial frequencies evaluated.
1,18,19
Ginsburg
20
showed thatcontrast sensitivity decreases as the
velocity of a target increases and that, as target size decreases,
contrast sensitivity demand increases. Ginsburg
20
also showed
that higher spatial frequencies will be affected before lower
spatial frequencies by changes in target distance, movement
direction, and low illumination. Contrast sensitivity also
may be degradedin contact lens wearers if lensfit or water con-
tent are not optimal for the patient, resulting in corneal edema
even when SVA seems acceptable.
21
Finally, contrast sensitiv-
ity can increase or decrease after refractive surgery.
22
Many commercial systems are available to measure
contrast sensitivity.
23
Several use linear grating patterns
that vary in spatial frequency, contrast level, and, possibly,
orientation. Others use letters or numbers of varying con-
trast levels or size. Contrast sensitivity measurements usu-
ally involve determination of a threshold contrast level at
specific spatial frequencies.
Stereopsis
Determining distance and spatial localization of an object is
a necessity for athletes in many sports. These judgments
can be made using monocular depth cues, but it is
suggested that superior binocular depth perception (stere-
opsis) can be advantageous for the athlete.
24
The research
Erickson et al Clinical Research 529
results on assessment of stereopsis have produced mixed re-
sults: some studies found no difference between athlete and
nonathlete populations, whereas other studies found better
performance in athletes.
1,7
It has been suggested that the
difference in findings may be because of the lack of stan-
dardized testing procedures, the use of simulated depth tar-
gets, and the limitations of the instruments to measure
threshold stereoacuity.
4
Previous studies used near stereo
tests or testing at far with the American Optical Vecto-
graphic Projection Slide (Southbridge, Massachusetts) or
a Howard-Dolman apparatus.
1
As many sports are dynamic,
athletes possibly would perform better with a dynamic ster-
eopsis assessment because static testing may not reveal any
difference between athletes and nonathletes.
25-27
Accommodative-vergence facility
Competitive sports rarely occur at one distance. Most
athletes need to look between far, intermediate, and near
distances quickly, requiring rapid accommodative-vergence
responses. This visual skill can be assessed with the Haynes
distance rock test. A study using this test presented
normative data for a population of elite athletes but did
not compare performance with that of nonathletes.
1
Perception span
Perception span, or central visual recognition accuracy, uses
tachistoscopic presentation to measure the speed and span of
recognition. Several studies have investigated speed of
recognition abilities in athletes. Most studies have found
that experienced athletes can evaluate sport-relevant infor-
mation more rapidly than inexperienced observers; sport
situations studied include cricket, volleyball, tennis, and
‘fast ball’’ sports.
28-32
Other studies have investigated both
speed and span of recognition by evaluating the ability to re-
call a sequence of numbers presented tachistoscopically for
1/50 of a second and found no difference in athletes com-
pared with nonathletes.
5,33
However, Melcher and Lund
34
did find significant differences in performance both for
span and speed of recognition, which were also present
when distraction factors were added to the task to simulate
competition conditions. In consideration of these differences
in research results, the authors conclude that it is the use of
numerical stimuli confounding the assessment of speed of
recognition in athletes; the use of target parameters that
more closely simulate the visual information processed in
sport situations can yield better discrimination of perception
span abilities that correlate with sports performance.
Central eye-hand reaction and response time
Visual-motor reaction and response speeds are critical to
performance. Reaction time is the elapsed time between the
onset of a visual stimulus and the initiation of a motor
response. Response time is the total time required by the
visual system to process a stimulus plus the time needed to
complete the motor response. Several studies report that
athletes in various sports have faster reaction times com-
pared with nonathletes and that reaction time is a discrim-
inator between expertise levels.
35-43
However, other studies
have not found this difference.
44-46
A gender bias also has
been reported, with men achieving faster times than women
on average.
1
A previously available instrument for measur-
ing this performance skill was the Reaction Plus timer (WR
Medical Electronics, Stillwater, Minnesota).
1
Following
brief training regimens, eye-hand reaction time could be
improved, making this a potentially valuable assessment
and goal for the athlete.
1
Peripheral eye-hand response
Overall ability to process and respond to visual stimuli
strongly enhances an athlete’s eye-hand coordination.
4
The
typical instrumentation used for evaluating eye-hand coor-
dination has been a 2-dimensional panel with an array of
lights mounted on a wall, such as the Wayne Saccadic Fix-
ator (Wayne Engineering, Skokie, Illinois). The athlete is
required to press a randomly lit button as rapidly as possi-
ble with one hand, then another button is lit in a random po-
sition on the instrument, and the reaction time reflex cycle
is repeated for the selected test time period. The panel is set
at the athlete’s arm length and is larger than the central vi-
sual field, thus assessing a peripheral eye-hand response.
The instrument typically is programmed to test in 2 primary
modes: visual proaction time, a self-paced mode for a set
time period in which each light stays lit until the button
is pressed, then the next random light is lit; and visual re-
action time, an instrument-paced stimulus presentation in
which each light stays lit for a preset amount of time (typ-
ically 0.75 seconds) before automatically switching to an-
other light regardless of whether the button is pressed.
One study found better visual proaction times in youth ath-
letes than nonathletes,
47
whereas another study found no
such difference between adult athletes and nonathletes.
5
Visual reaction time has been compared in athletes and
nonathletes in only 1 study, with athletes performing better
than nonathletes.
5
The level of ambient room lighting af-
fects test outcomes: performance improves significantly
as room illumination is decreased.
48-50
The Nike Sensory Station is designed to test vision skills
that previously have been identified as important for sports,
including static and dynamic visual acuities, contrast
sensitivity, distance stereopsis, accommodative-vergence
facility, span of perception, central eye-hand reaction and
response speeds, and peripheral eye-hand response speed. It
is designed to provide a visual performance profile that
graphically represents the athlete’s visual strengths and
weaknesses. The purpose of this study is to determine the
test-retest reliability of the visual performance measures
assessed with the Nike Sensory Station.
530 Optometry, Vol 82, No 9, September 2011
Methods
Phase I
Subjects. An Institutional Review Board proposal for the
use of human subjects in research was submitted and
approved. One hundred thirty-four subjects were recruited
from the Pacific University student body and surrounding
community to participate in Phase I of this study. All
subjects signed an informed consent form at the time of the
initial screening, and all were required to pass a vision
screening with a minimum visual acuity (VA) of 20/50 in
each eye. One hundred twenty-five subjects (56 men, 69
women), age 18 to 30, completed the study. Subjects who
completed the study were compensated with passes to the
Nike employee store located in Beaverton, Oregon.
Test protocol. The Nike Sensory Station consists of a single
computer controlling 2 high-resolution liquid crystal display
monitors (both 0.28 mm dot pitch): one 22-inch diagonal
display and one 42-inch diagonal touch-sensitive display. A
handheld Apple iPod touch
Ò
(Apple Corporation, Cupertino,
California), connected via wireless input to the computer, is
used in several assessments as described below. Custom soft-
ware controls the displays, input acquisition, and test proce-
dures based on subject responses. Prerecorded instructions
are automatically played at the start of each assessment
(see Appendix); if a subject has questions concerning a test
procedure, the prerecorded instructions are repeated with
no additional coaching from the researcher.
Subjects attended 2 sessions in which identical protocols
were followed. At the end of session 1, subjects were asked
to return in 1 to 2 weeks to repeat the assessments. This
time interval was established in an attempt to minimize the
potential learning effects for the assessments. Most subjects
returned within 10 to 14 days, with the longest duration
between tests being 30 days. Monocular and binocular VAs
were measured at the start of each session under normal
room illumination (w250 lux) with a printed logMAR
chart at 6 m. Subjects then were assessed on each section of
the Sensory Station in order as described below. Ambient
illumination at the 22-inch display was w80 lux; at the 42-
inch display, w140 lux; and at the iPod touch, w100 lux.
Subjects completed the Sensory Station assessments
adhering to a standardized protocol: Visual Clarity, Contrast
Sensitivity, Depth Perception, Near-Far Quickness, and
Target Capture at 16 feet (4.9 m) from the respective displays.
Subjects were then moved to within arm’s length of the
42-inch display and performed the Perception Span, Eye–
Hand Coordination, Go/No Go, and Reaction Time assess-
ments. Each session was completed in about 20 minutes. For
clarification, when referring to a specific assessment on the
Nike Sensory Station, the proper name the manufacturer has
given the assessment will be used and capitalized.
Nike Sensory Station assessments. Visual Clarity (visual
acuity). SVA was measured at 16 feet (4.9 m) with the
22-inch display. Black Landolt rings, with gaps at the top,
bottom, left, and right, were presented on a white back-
ground in random order at preset acuity demands. Subjects
were instructed to swipe the screen of the iPod touch in the
direction of the gap in the ring as soon as it was identified.
Animation examples were shown, followed by 3 practice
trials. If the gap direction was not easily discriminated, the
subject was encouraged to guess, per the instructions. Final
threshold acuity was measured between the demands of
20/8 and 20/99 using a staircase reversal algorithm.
51
The
algorithm begins with a large (20/50 equivalent) stimulus,
decreasing in size until the subject does not correctly iden-
tify the stimulus. When this occurs, the stimulus increases
in size until it is identified correctly. This procedure con-
tinues until several reversal points are achieved; the exact
number of reversal points for the algorithm is proprietary
and not available for publication. SVA is based on the num-
ber of correct responses, with consideration for guessing.
An occluder was held by the examiner in front of the sub-
ject’s nontested eye during the monocular measurements.
The sequence of the SVA assessment was always right
eye (O.D.), left eye (O.S.), and both eyes (OU).
Contrast Sensitivity. Four black circles, each of which
subtended 0.82, were presented on a light gray background
in a diamond configuration covering 2.35on the 22-inch
display at 16 feet (4.9 m) (see Figure 1). One circle at
random contained a pattern of concentric rings that varied
sinusoidally in brightness from the center to the edge. Sub-
jects were instructed to swipe the screen of the iPod touch
in the direction of the circle with the pattern. Animation ex-
amples were shown, followed by 3 practice trials. If the pat-
terned circle was not easily discriminated, the subject was
encouraged to guess, per the instructions. Contrast sensitiv-
ity was measured binocularly at 2 spatial frequencies, 6 and
18 cycles per degree (cpd), using a staircase reversal algo-
rithm similar to that described previously. Final threshold
contrast sensitivity was measured between 10% and 1.0%
Figure 1 Concentric ring target for Contrast Sensitivity assessment.
Erickson et al Clinical Research 531
(1.0 to 2.0 log units) contrast at 6 cpd, and between 32%
and 2.5% (0.5 to 1.6 log units) contrast at 18 cpd.
Depth Perception (stereopsis). The subject wore a pair of
liquid crystal goggles (NVIDIA
Ò
3D VisionÔ, Santa Clara,
California), connected via wireless link to the computer,
while viewing the 22-inch display at 16 feet (4.9 m). The
liquid crystal shutter system created simulated depth in
1 of 4 black rings presented on a white background, such
that the ring should appear to float in front of the screen
(see Figure 2). The sizes and arrangement of the rings
were identical to those of the circles used in Contrast Sen-
sitivity. The width of the lines defining each ring was 12
mm, subtending 0.14. Subjects were instructed to swipe
the screen of the iPod touch in the direction of the floating
ring and were encouraged to respond as quickly as possible.
Animation examples were shown, followed by 3 practice
trials. If the floating ring was not easily discriminated, the
subject was encouraged to guess, per the instructions.
Threshold stereopsis was measured between 237 and 12
arc seconds using a staircase reversal algorithm similar to
that described previously. In addition, response time for
the first 2 stimulus presentations at the subject’s threshold
was recorded, and an average response time for the testing
was calculated.
Near-Far Quickness (accommodative-vergence facility).
The subject remained at 16 feet (4.9 m) aligned with the
22-inch display. The subject also held the iPod touch at 16
inches (40 cm) from the eyes, with the top edge positioned
just below the bottom of the far screen (see Figure 3). Po-
sitioning and instructions were presented with an animation
example; if needed, the examiner helped the subject with
the positioning adjustment. In alternating style, a 20/80-
equivalent black Landolt ring was presented in a box on
the handheld screen, and a black Landolt ring 0.1 log unit
above the threshold determined with the Visual Clarity as-
sessment was presented on the far screen. The subject was
instructed to swipe the screen of the iPod touch in the
perceived direction of the gap in the ring presented on
each display; incorrect responses would not change the tar-
get presentation. The assessment began with 3 practice
trials. The first Landolt ring was always presented on the
far screen. After the correct response was recorded, the
Landolt ring appeared on the handheld screen. The
subject then continually switched focus between far and
near for 30 seconds, trying to correctly identify as many
rings as possible. The number of correct responses deter-
mined the score.
Target Capture (DVA). The subject now was aligned with
the 42-inch display at 16 feet (4.9 m) and instructed to
fixate a central white dot until a yellow-green Landolt ring
(dominant wavelength about 555 nm at maximum satura-
tion possible on the display) appeared briefly in 1 of the 4
corners of the screen (see Figure 4). The size of the Landolt
ring was 0.1 log unit above the threshold determined with
the Visual Clarity assessment, and the angular distance
along the diagonal from the fixation dot to the center of
the Landolt ring was approximately 6.1. Because of the re-
duction in VA away from the fovea,
52
individuals with VA
of 20/50 or better would need to saccade from the fixation
dot to the Landolt ring to correctly discriminate the direc-
tion of the gap. This is a method of assessing DVA. As be-
fore, subjects indicated the perceived direction of the gap
by swiping the screen of the iPod touch. Animation exam-
ples were shown followed by 3 practice trials. If the subject
could not see the orientation of the gap in the ring, guessing
Figure 2 Depth Perception target on screen.
Figure 3 Positioning of iPod touch for Near-Far Quickness.
Figure 4 Target Capture stimulus on screen.
532 Optometry, Vol 82, No 9, September 2011
was encouraged. The duration of the Landolt ring presenta-
tion started at 500 milliseconds and was progressively
shortened after correct responses. The threshold stimulus
exposure duration was determined using a staircase reversal
algorithm.
Perception Span. The subject was positioned within arm’s-
length of the 42-inch touch-sensitive display, with the
center of the screen at about eye level. Automated instruc-
tions directed the subject to focus on a shrinking white dot
in the center of a grid pattern composed of up to 30 circles
(see Figure 5). When the dot disappeared, a pattern of
yellow-green dots (same color parameters as above) flashed
simultaneously for 100 milliseconds within the grid. The
subject then touched the screen to recreate the pattern of
dots. If the subject achieved a passing score (R75% cor-
rect), the grid pattern increased in size with an increasing
number of dots. The first 2 levels had 6 circles in the grid
pattern with 2 and 3 dots, the next 5 levels had 18 circles
with 3 to 7 dots, and the last 4 levels had 30 circles with
7 to 10 dots. Each circle was 19 mm in diameter, and the
largest grid pattern was 18 cm in diameter. The grids and
dot patterns were preset to maintain standardization. The
dot patterns at each level were pseudorandomized to main-
tain equivalent spatial distribution of the dots for each pre-
sentation and to eliminate ‘‘clustering’’ of dots and easily
recognizable patterns or shapes. Animation examples
were shown, followed by 2 practice trials. The overall score
for this assessment was based on the cumulative number of
correct responses; missed responses and extra guesses were
subtracted from the cumulative score. If the subject did not
achieve a passing score on a level, that level was repeated.
If the subject again failed to pass that level, the assessment
was terminated. If the subject achieved a passing score on
the second attempt, only the higher score was used for
the overall score, and testing continued. The maximum
score possible on this assessment was 64.
Eye-Hand Coordination (peripheral eye-hand response).
For this assessment, subjects held their arms at shoulder
height within easy reach of a grid of circles presented on
the 42-inch touch-sensitive display (see Figure 6). The grid
consisted of 8 columns (68.6 cm) and 6 rows (44.5 cm) of
equally spaced circles, with each circle 48 mm in diameter.
During the assessment, a yellow-green dot (same color pa-
rameters as above) appeared within 1 circle of the grid. Au-
tomated instructions directed the subject to touch the dot as
quickly as possible using either hand. As soon as the dot
was touched, a subsequent dot would be presented. A se-
quence of 96 dots was pseudorandomized to maintain
equivalent spatial distribution within each presentation
and to eliminate ‘‘clustering’’ of dots and easily recogniz-
able patterns. Animation examples were shown, followed
by 1 full practice trial. The score was the total time to touch
all 96 dots.
Go/No Go. The positioning of the subject and the grid
pattern on the display for this assessment were identical to
those for Eye-Hand Coordination. However, the dot stim-
ulus could be either yellow-green (same color parameters
as above) or red (dominant wavelength about 620 nm at
maximum saturation possible on the display). Although
these colors could be confused by some color-deficient
individuals, the difference in apparent brightnesses of the
dots is sufficient to allow easy discrimination. If the dot was
yellow-green, the subject was directed to touch it as before.
But if the dot was red, the subject was instructed not to
touch it. Both the red and yellow-green dots appeared at
random locations for only 450 milliseconds, with no time
gap between dot presentations. If a yellow-green dot was
not touched within this time, no point was awarded for that
presentation; if a red dot was touched, a point was
subtracted from the overall score. Again, subjects were
encouraged to touch as many yellow-green dots as possible.
Automated instructions and animation examples were
shown, but there was no practice trial for this assessment.
Ninety-six total dots (64 yellow-green, 32 red) were
presented in a pseudorandomized sequence to maintain
equivalent spatial distribution within each presentation and
Figure 5 Thirty-circle grid for Perception Span. Figure 6 Grid for Eye-Hand Coordination and Go/No Go.
Erickson et al Clinical Research 533
to eliminate ‘‘clustering’ of dots and easily recognizable
patterns. The overall score was the cumulative number of
yellow-green dots touched minus any red dots touched.
Reaction Time (central eye-hand reaction and response
time). For the final assessment, subjects remained at
arm’s length from the 42-inch touch-sensitive display.
Two annular patterns appeared on the screen with centers
30.5 cm apart; each annulus consisted of 2 concentric
circles, 11.4 cm and 3.2 cm in diameter (see Figure 7). Au-
tomated instructions directed the subject to place the finger-
tips of the dominant hand on the inner circle of the annulus
on that side of the screen, with no portion of the hand ex-
tending across the boundary line marked on the screen. If
the hand was aligned correctly, this control annulus would
change color to yellow-green (same color parameters as
above). The subject then was instructed to center the
body in front of the opposite (test) annulus and focus atten-
tion on the center of that annulus. After a randomized delay
of 2, 3, or 4 seconds, the test annulus turned yellow-green,
and the subject moved the hand to touch its inner circle as
quickly as possible. Animation examples were shown, fol-
lowed by 2 practice trials. Five trials were conducted per
subject to calculate average reaction and response times.
Reaction Time was measured as the elapsed time between
onset of the test annulus and release of the control annulus.
Response time was measured as the elapsed time between
onset and touching of the test annulus. After 5 trials, the
computer calculated the averages and standard deviations
for the reaction and response times. If any single measure
differed from the mean by more than 2 standard deviations
in either direction, another trial was conducted to replace
the outlying measure for that trial. The software was pro-
grammed such that no more than 2 trials could be repeated
for any subject.
Phase II
Subsequent to subject feedback and analysis of Phase I
data, modifications were made to the equipment, instruc-
tions, and protocols in an effort to improve the reliability of
the assessments. Ambient illumination was identical to that
in Phase I. Likewise, data collection was conducted in the
same manner as in Phase I.
Subjects. Thirty-six subjects (20 men, 16 women), age 22
to 35, were recruited from the Pacific University student
body and surrounding community to participate in Phase II
of this study; none of these subjects participated in Phase I.
As in Phase I, all subjects signed an Informed Consent
Form at the time of the initial screening, and all were
required to pass a vision screening with a minimum VA of
20/50 in each eye.
Subjects attended 2 sessions in which identical protocols
were followed. At the end of session 1, subjects were asked
to return within about 1 week to repeat the assessments,
with the shortest interval being 3 days and the longest being
10 days. Subjects were compensated with passes to the
Nike employee store located in Beaverton, Oregon.
Test protocol modifications. During Phase I, researcher
observation and subject feedback consistently indicated
that multiple swipes on the iPod touch occasionally were
necessary to register a response. Although this may have
been a minor annoyance and of no consequence for the
outcome on most assessments, it could have impacted the
results on Depth Perception response time and Near-Far
Quickness. Thus, for Phase II the iPod touch had a plastic
sleeve placed on it to improve the subject’s accuracy when
swiping a response.
Because of observed ceiling effects, Contrast Sensitivity
upper limits were increased to 2.4 log units (0.4% contrast)
for 6 cpd and 2.0 log units (1.0%) contrast for 18 cpd. The
instructions for Near-Far Quickness and Target Capture
were improved to minimize confusion, and Go/No Go
instructions reflected the addition of a practice trial (see
Appendix). The practice trial protocols were altered for
Near-Far Quickness (increased the practice trials to 6)
and Eye-Hand Coordination (reduced the length of the
practice trial by one half: 48 targets instead of 96). The
‘touch zones’ for Reaction Time were increased to include
any portion of the control and test annuli. Finally, program-
ming changes were made to the protocols of some of the
assessments. Analysis of individual subject data sets found
a minor error in the staircase reversal algorithm used for
scoring Visual Clarity, Contrast Sensitivity, Depth Percep-
tion, Target Capture, and Perception Span. The software
programming for the staircase reversal algorithms was not
consistently applied as intended, necessitating this second
phase of data collection to assess repeatability. A separate
software error in Perception Span resulted in an unexpected
bimodal distribution of the scores during Phase I.
Statistical analyses
Results were analyzed primarily using repeated measures
analysis of variance (ANOVA) and Student’s-paired ttests,
as appropriate. Difference-versus-means plots and 95%
limits of agreement (LOA) are reported to demonstrate
test-retest reliability.
53
Subsequent to the changes to the in-
strumentation and protocol described above, results for
Figure 7 Reaction Time display (with subject properly positioned).
534 Optometry, Vol 82, No 9, September 2011
Contrast Sensitivity, Depth Perception, Target Capture, Per-
ception Span, and Reaction Time are reported only for
Phase II data. Changes to the remaining assessments did
not result in significantly different distributions or variances
for data measured in Phase II with respect to Phase I.
Therefore, results for Visual Clarity, Near-Far Quickness,
Eye-Hand Coordination, and Go/No Go are pooled across
phases and reported together for all subjects.
Results
Visual Clarity
Repeated-measures ANOVA found significant differences
in binocular versus monocular acuity assessment, (F
2,320
5
31.93, P,0.001). However, there are no significant differ-
ences between sessions 1 and 2, (F
1,160
50.32, P50.57),
and no interaction effect of session and eye used, (F
2,320
5
0.50, P50.61). On average, binocular acuity is better than
either monocular acuity: mean (SD) [Snellen equivalent]
OU 20.205 (0.146) [20/12.5] versus O.D. 20.117 (0.167)
[20/15.3] and O.S. 20.132 (0.171) [20/14.8].
Figure 8 plots the difference in binocular acuities
between sessions versus mean binocular acuity for all
subjects. Because multiple results have the same differ-
ence-mean combination, some of the symbols on the figure
are ‘‘jiggled’’ in either or both directions to show the actual
number of datapoints. Monocular difference-mean plots are
not shown for the sake of brevity but are similar to those of
the binocular results. The average differences are OU
0.006, O.D. 20.005, and O.S. 0.016. The average differ-
ences are not significantly different from zero, OU
(t
160
50.40, P50.69), O.D. (t
160
50.32, P50.75),
and O.S. (t
160
51.02, P50.31). However, the slopes of
the linear regressions are significantly different from zero,
OU 0.328, (t
160
520.78, P,0.001); O.D. 0.159,
(t
160
59.51, P,0.001); and O.S. 20.152, (t
160
59.43,
P,0.001).
Contrast Sensitivity
Repeated measures ANOVA found a significant difference
in sensitivities between spatial frequencies, (F
1,35
5
298.08, P,0.001). However, there are no significant dif-
ferences in sessions, (F
1,35
51.11, P50.30), nor the inter-
action effect of spatial frequency and session, (F
1,35
50.21,
P50.65).
Figure 9 plots the difference in log sensitivities between
sessions versus mean log sensitivity for both spatial fre-
quencies assessed. Because multiple results have the same
difference-mean combination, some of the symbols on each
figure are ‘‘jiggled’’ in either or both directions to show the
actual number of datapoints. The average difference is
20.02 for 6 cpd and 20.04 for 18 cpd. Neither of the av-
erage differences are significantly different from zero, (t
35
50.70, P50.49) for 6 cpd and (t
35
50.96, P50.35)
for 18 cpd. However, the slopes of the linear regressions,
0.240 and 0.159, are significantly different from zero (t
35
57.59, P,0.001) and (t
35
53.42, P50.002),
respectively.
Depth Perception
The Student’s t-test shows no significant difference between
sessions for either threshold (t
35
50.83, P50.413), or
mean response time (t
35
51.32, P50.20). Figure 10A plots
the difference in thresholds between sessions versus mean
threshold. Because multiple results have the same
difference-mean combination, some of the symbols on the
figure are ‘‘jiggled’’ in either or both directions to show
the actual number of datapoints. The average difference
(LOA) is 25.58 (284.89, 79.31) arc seconds. The average
difference is not significantly different from zero (same
statistic as above). The slope of the linear regression,
20.489, is not significantly different from zero (t
35
50.07,
P50.94).
Figure 8 Difference-versus-mean plot of binocular (OU) acuity for Vi-
sual Clarity. Average difference (solid black line), 95% limits of agreement
(dashed lines), linear regression (solid red line). Some of the symbols are
‘‘jiggled’’ in either or both directions to show the actual number of
datapoints.
Figure 9 Difference-versus-mean plot of log Contrast Sensitivity for 6
cpd: data (blue 1’s), average difference (solid black line), 95% limits of
agreement (solid double lines), linear regression (solid red line); and for
18 cpd: data (blue squares), average difference (short-dashed black line),
95% limits of agreement (long-dashed black lines), linear regression
(dashed red line). Some of the symbols are ‘‘jiggled’’ in either or both di-
rections to show the actual number of datapoints.
Erickson et al Clinical Research 535
Figure 10B plots the difference in mean response times
between sessions versus average mean response time. The
average difference (LOA) is 2159.82 (21584.75,
1424.93) milliseconds. The average difference is not signif-
icantly different from zero (same statistic as above). The
slope of the linear regression, 20.275, is not significantly
different from zero, (t
35
50.002, P51).
Near-Far Quickness
The score for 1 subject was not recorded by the software in
Phase I, session 1; therefore, that subject’s score for session
2 also is not included for analysis. There is an overall
significant improvement in score from session 1, mean
(SD), 23.1 (5.30), to session 2, 25.7 (5.74), (t
159
56.20,
P,0.001). Figure 11 plots the difference in correct re-
sponses between sessions versus mean correct response.
The average difference (LOA) is 2.68 (28.04, 13.40).
The average difference is significantly different from zero
(same statistic as above). The slope of the linear regression,
0.104, is not significantly different from zero, (t
159
50.24,
P50.81).
Target Capture
The Student’s t-test found no significant difference in
threshold between sessions, (t
35
50.70, P50.49).
Figure 12 plots the difference in thresholds between
sessions versus mean threshold. The average difference
(LOA) is 18.75 (2295.63, 333.31). The average difference
is not significantly different from zero (same statistic as
above). The slope of the linear regression, 20.545, is not
significantly different from zero, (t
35
50.02, P50.98).
Perception Span
The Student’s t-test found no significant difference in
scores between sessions, (t
35
51.68, P50.10).
Figure 13 plots the differences in scores between ses-
sions versus mean score. The average difference
(LOA) is 2.69 (216.21, 21.60). The average difference
is not significantly different from zero (same statistic
as above). The slope of the linear regression, 0.147,
is not significantly different from zero, (t
35
50.09,
P50.93).
Eye-Hand Coordination
The score for 1 subject was not recorded by the software in
Phase I, session 1; therefore, that subject’s score for session
2 also is not included for analysis. There is an overall
significant improvement (reduction) in total time from
session 1, mean (SD) 49.7 (4.66), to session 2, 47.0
(4.58), (t
159
511.76, P,0.001). Figure 14 plots the dif-
ference in total times between sessions versus mean total
time. The average difference (LOA) is 22.70 (28.04,
2.99) seconds. The average difference is significantly dif-
ferent from zero (same statistic as above). The slope of
the linear regression, 20.020, is not significantly different
from zero, (t
159
50, P51).
Figure 10 A, Difference-versus-mean plot of Depth Perception thresholds, in arc seconds. Average difference (solid black line), 95% limits of agreement
(dashed lines), linear regression (solid red line). Some of the symbols are ‘‘jiggled’’ in either or both directions to show the actual number of datapoints. B,
Difference-versus-mean plot of Depth Perception mean response times, in milliseconds. Average difference (solid black line), 95% limits of agreement (dashed
lines), linear regression (solid red line).
Figure 11 Difference-versus-mean plot of Near-Far Quickness correct
responses. Average difference (solid black line), 95% limits of agreement
(dashed lines), linear regression (solid red line).
536 Optometry, Vol 82, No 9, September 2011
Go/No Go
There is an overall significant improvement in score from
session 1, mean (SD) 26.6 (12.85), to session 2, 34.1
(11.27), (t
160
59.90, P,0.001). Figure 15 plots the dif-
ference in scores between sessions versus mean score.
The average difference (LOA) is 7.58 (211.46, 26.61).
The average difference is significantly different from zero
(same statistic as above). The slope of the linear regression,
20.155, is not significantly different from zero, (t
160
5
0.20, P50.84).
Reaction Time
Response time for 1 subject in session 1 was longer than
1.1 seconds, more than 5 standard deviations greater than
the mean response time. Consequently, all data for this
subject were removed for analysis.
For Reaction Time, the Student’s t-test found no signif-
icant difference between sessions, (t
34
51.11, P50.27).
Figure 16A plots the difference in reaction times between
sessions versus mean reaction time. The average
difference (LOA) is 24.69 (253.69, 44.30). The average
difference is not significantly different from zero (same
statistic as above). The slope of the linear regression,
0.302, is not significantly different from zero, (t
34
5
0.07, P50.94).
For response time, the Student’s t-test found no signifi-
cant difference between sessions, (t
34
50.52, P50.61).
Figure 16B plots the difference in response times between
sessions versus mean response time. The average difference
(LOA) is 4.47 (295.15,104.09). The average difference is
not significantly different from zero (same statistic as
above). The slope of the linear regression, 0.353, is not sig-
nificantly different from zero, (t
34
50.04, P50.97).
Discussion
The purpose of this study is to determine the test-retest
reliability of the visual performance measures assessed with
the Nike Sensory Station. To establish repeatability, the
assessments were conducted over 2 sessions separated by
about 1 week. Results show no significant change in
performance over time on assessments of Visual Clarity,
Contrast Sensitivity, Depth Perception, Target Capture,
Perception Span, and Reaction Time. This demonstrates
no learning effect over time and establishes repeatability of
these measures.
Figure 12 Difference-versus-mean plot of Target Capture thresholds, in
milliseconds. Average difference (solid black line), 95% limits of agreement
(dashed lines), linear regression (solid red line).
Figure 13 Difference-versus-mean plot of Perception Span scores. Av-
erage difference (solid black line), 95% limits of agreement (dashed lines),
linear regression (solid red line).
Figure 14 Difference-versus-mean plot of Eye-Hand Coordination total
time, in seconds. Average difference (solid black line), 95% limits of agree-
ment (dashed lines), linear regression (solid red line).
Figure 15 Difference-versus-mean plot of Go/No Go scores. Average
difference (solid black line), 95% limits of agreement (dashed lines), linear
regression (solid red line).
Erickson et al Clinical Research 537
Performance did improve from session 1 to session 2 for
Near-Far Quickness, Eye-Hand Coordination, and Go/No
Go. These measures that improved across sessions show an
expected learning effect caused by the motor response
characteristics being measured.
As expected, binocular measures on Visual Clarity are
about 0.08 log unit, or about 4 letters, better than monoc-
ular measures. Because the sequence of this assessment was
always O.D., O.S., and OU, the results could have been
influenced by a practice effect. Nonetheless, in absolute
terms, our result differences may not be of practical
significance. Interestingly, performance also varied slightly
across subjects between sessions for Visual Clarity and
Contrast Sensitivity based on mean threshold levels, as
evidenced by the shallow but significant linear regression
slopes. For Visual Clarity, binocular acuity decreases by
about 0.03 log unit, or about 1.5 letters, for each 1-line
decrease in mean binocular acuity. Similarly, right eye
acuity decreases by about 0.016 log unit, or less than
1 letter, for each 1-line decrease in mean O.D. acuity, but
left eye acuity increases by about 0.015 log unit, or less
than 1 letter, for each 1-line decrease in mean O.S. acuity.
For Contrast Sensitivity, threshold improves for either
spatial frequency by about 0.02 log unit for each 0.1-log
unit improvement in mean contrast sensitivity. Presumably,
these effects result from an increased likelihood of response
variability when initial measurements are less than maxi-
mum values. However, additional study investigating these
phenomena, as well as validation against similar estab-
lished instruments, is warranted.
In addition, for Contrast Sensitivity results, the ceiling
effects evident in Phase I data are eliminated for 18 cpd
measures but remain for 6 cpd measures in Phase II. Contrast
of 2.4 log units (0.4%) is the minimum capable of being
displayed on the monitor used, thus limiting the ability to
assess true threshold at 6 cpd. This ceiling effect may mask
performance differences between testing sessions. Nonethe-
less, contrast sensitivity beyond 2.0 log units at any spatial
frequency may be only of academic interest, because the
application of such high sensitivity has yet to be established.
Addition of the plastic sleeve on the iPod touch reduced
the occurrence of the necessity for multiple swipes, as
observed and reported by the individual researchers. This is
one possible reason for the difference in Depth Perception
results between Phase I and Phase II. Depth Perception
results also show an apparent ceiling effect at the resolution
limit of the instrument (12 arc seconds). This is surprisingly
good stereoacuity, especially for a far measure. The results
suggest that a study to determine validity of the assessment
is needed.
For Near-Far Quickness, on average, subjects completed
about 1 additional cycle of target presentations during
session 2, which equates to about a 10% improvement. The
iPod touch multiple-swipe problem did not have a signif-
icant effect on test performance, as evidenced by the similar
distributions and variances of the data for the 2 study
phases. For Eye-Hand Coordination, the reduction in
practice trial time instituted in Phase II did not significantly
decrease the motor learning effect. For Go/No Go, on
average, scores improved by about 28% between sessions.
However, the addition of a practice trial in Phase II did not
result in improved test performance. Further study is
needed to determine if these improvements measured on
repeated testing are clinically relevant.
Figure 16 A, Difference-versus-mean plot of Reaction Time reaction times, in milliseconds. Average difference (solid black line), 95% limits of agreement
(dashed lines), linear regression (solid red line). B, Difference-versus-mean plot of Reaction Time response times, in milliseconds. Average difference (solid
black line), 95% limits of agreement (dashed lines), linear regression (solid red line). RT 5Reaction Time.
Figure 17 Example of screen glare from the glossy glass surface of the
42-inch display during Perception Span assessment.
538 Optometry, Vol 82, No 9, September 2011
A factor that may have affected the results is screen
glare from the glossy glass surface of the 42-inch display.
Because of the reflective nature of the display surface,
direct light sources facing the screen create reflections that
can be distracting to the user. Measures were taken to
minimize the impact of avoidable sources of reflected glare
from varying light conditions during the data collection: the
windows in the research room were draped with heavy
black cloth, and the overhead lighting was adjusted to
minimize reflectance on the monitors. The remaining
unavoidable reflections on the display primarily arose
from individual subject appearance and the clothing they
may have been wearing (see Figure 17). These reflections
could have affected performance on Target Capture, Per-
ception Span, Eye-Hand Coordination, Go/No Go, and Re-
action Time. However, because we did not record or control
what subjects were wearing, we have no method of deter-
mining whether this actually was a significant factor in sub-
ject performance.
This study was limited to the assessments on the Nike
Sensory Station. Although some of the assessments are
similar to those made with other previously or currently
available instruments, the measurements made in this study
are not intended to be normative. Likewise, the subjects in
this study were not assessed with any other instrumentation.
Therefore, a direct comparison of the current study data with
results from other instruments is not appropriate. Future
studies could conduct such comparisons to determine the
validity of the individual assessments, although standard-
ized and validated assessment tools are not available for all
of the assessment areas measured with the Nike Sensory
Station. It also remains to be determined whether these
assessments measure visual performance skills that reflect
the requisite skills for athletes or differentiate athletes by
skill level. Future studies also could investigate athlete
populations as subjects and develop normative performance
data for specific ages, skill levels, and sports applications.
Conclusions
The results of this study show that many of the Nike
Sensory Station assessments demonstrate repeatability and
no learning effect over time. The measures that did improve
across sessions demonstrate an expected learning effect
caused by the motor response characteristics being
measured.
References
1. Coffey B, Reichow A. Optometric evaluation of the elite athlete: the
pacific sports visual performance profile. Problems in Optometry
1990;2:32-59.
2. Erickon GB. Sports Vision: Vision Care for the Enhancement of Sports
Performance. St. Louis: Butterworth (Elsevier); 2007:45-83.
3. Stine CD, Arterburn MR, Stern NS. Vision and sports: A review of the
literature. J Am Optom Assoc 1982;53:627-33.
4. Hitzeman SA, Beckerman SA. What the literature says about sports
vision. Optom Clin 1993;3:145-69.
5. Christenson GN, Winkelstein AM. Visual skills of athletes versus non-
athletes: Development of a sports vision testing battery. J Am Optom
Assoc 1988;59:666-75.
6. Beckerman SA, Hitzeman S. The ocular and visual characteristics of
an athletic population. Optometry 2001;72:498-509.
7. Laby DM, Rosenbaum AL, Kirschen DG, et al. The visual function of
professional baseball players. Am J Ophthalmol 1996;122:476-85.
8. Applegate RA, Applegate RA. Set shot shooting performance and vi-
sual acuity in basketball. Optom Vis Sci 1992;69:765-8.
9. Bulson RC, Ciuffreda KJ, Hung GK. The effect of retinal defocus on
golf putting. Ophthalmic Physiol Optics 2008;28:334-44.
10. Mann DL, Abernethy B, Farrow D. The resilience of natural intercep-
tive actions to refractive blur. Hum Mov Sci 2010;29:386-400.
11. Burg A. Visual acuity as measured by dynamic and static tests: A
comparative evaluation. J Appl Psychol 1966;50:460-6.
12. Ward P, Williams AM. Perceptual and cognitive skill development in
soccer: the multidimensional nature of expert performance. J Sport
Excerc Psychol 2003;25:93-111.
13. Brown B. Dynamic visual acuity, eye movements and peripheral acu-
ity for moving targets. Vision Res 1972;12:305-21.
14. Hoffman LG, Rouse M, Ryan JB. Dynamic visual acuity: A review.
J Am Optom Assoc 1981;52:883-7.
15. Coffey B, Buchholz J, Miller K, et al. Test-retest reliability for a new
device to measure dynamic visual acuity, abstract. Optom Vis Sci
2005. E-abstract 055184:82.
16. Coffey B, Richards L, Olmschenk S, et al. Preliminary normative data
for a new device to measure dynamic visual acuity, abstract. Optom
Vis Sci 2004;81(suppl):127.
17. Hoffstetter HW, Griffin JR, Berman MS, et al., eds. Dictionary of Vi-
sual Science and Related Clinical Terms. 5th ed. Boston: Butterworth-
Heinemann; 2000:111.
18. Kluka DA, Love PA, Sanet R, et al. Contrast sensitivity function pro-
filing: By sport and sport ability level. Int J Sports Vision 1995;2:
5-16.
19. Love PA, Kluka DA. Contrast sensitivity function in elite women and
men softball players. Int J Sports Vision 1993;1:25-30.
20. Ginsburg AP. Visual Information Processing based on Spatial Filters
Constrained by Biological Data. Springfield: National Technical In-
formation Service; 1978:97-98.
21. Grey CP. Changes in contrast sensitivity when wearing low, medium
and high water content soft lenses. J Br Contact Lens Assoc 1986;9:
21-5.
22. Ginsburg AP. Contrast sensitivity: determining the visual quality and
function of cataract, intraocular lenses and refractive surgery. Curr
Opin Ophthalmol 2006;17:19-26.
23. Citek K. Contrast sensitivity function. In: Duckman RH, ed. Visual
Development, Diagnosis, and Treatment of the Pediatric Patient. Phil-
adelphia: Lippincott Williams & Wilkins; 2006:52-68.
24. Boden LM, Rosengren KJ, Martin DF, et al. A comparison of static
near stereo acuity in youth baseball/softball players and non–ball
players. Optometry 2009;80:121-5.
25. Solomon H, Zinn HJ, Vacroux R. Dynamic stereoacuity: a test for hit-
ting a baseball? J Am Optom Assoc 1988;59:522-6.
26. Hofeldt AJ, Hoefle FB. Stereophotometric testing for Pulfrich’s phe-
nomenon in professional baseball players. Percept Mot Skills 1993;
77:407-16.
27. Laby DM, Kirschen DG. Dynamic stereoacuity: Preliminary results
and normative data for a test for the quantitative measurement of mo-
tion in depth. Bin Vis Eye Muscle Surg Q 1995;10:191-200.
28. Deary IJ, Mitchell H. Inspection time and high-speed ball games. Per-
ception 1989;18:789-92.
29. Isaacs LD, Finch AE. Anticipatory timing of beginning and interme-
diate tennis players. Percept Mot Skills 1983;57:451-4.
30. Nettleton B. Flexibility of attention and elite athletes’ performance in
‘fast ball games.’Percept Mot Skills 1986;63:991-4.
Erickson et al Clinical Research 539
31. Goulet C, Bard M, Fleury M. Expertise differences in preparing to re-
turn a tennis serve: a visual information processing approach. J Sport
Psychol 1989;11:382-98.
32. Wright DL, Pleasants F, Gomez-Meza M. Use of advanced visual cue
sources in volleyball. J Sport Exerc Psychol 1990;12:406-14.
33. Milne DC, Lewis RV. Sports vision screening of varsity athletes.
Sports Vision 1993;1:8-14.
34. Melcher MH, Lund DR. Sports vision and the high school student ath-
lete. J Am Optom Assoc 1992;63:466-74.
35. Hughes PK, Blundell NL, Walters JM. Visual and psychomotor perfor-
mance of elite, intermediate and novice table tennis competitors. Clin
Exp Optom 1993;76:51-60.
36. Whiting HTA, Sanderson FH. Dynamic visual acuity and performance
in a catching task. J Mot Behav 1974;6:87-94.
37. Rombouts SA, Barkhof F, Sprenger M, et al. The functional basis of
ocular dominance: functional MRI findings. Neurosci Lett 1996;221:
1-4.
38. Ridini LM. Relationship between psychological functions tests and se-
lected sport skills of boys in junior high. Res Q Am Assoc Health Phys
Educ 1968;39:674-83.
39. Mont
es-Mic
o R, Bueno I, Candel J, et al. Eye-hand and eye-foot visual
reaction times of young soccer players. Optometry 2000;71:775-80.
40. Knapp BN. Simple reaction times of selected top-class sportsmen and
research students. Res Q 1961;32:409-11.
41. Blundell NL. Critical visual-perceptual attributes of championship
level tennis players. In: Howell ML, Wilson BD, eds. Kinesiological
Sciences. Proceedings of the VII Commonwealth and International
Conference on Sport, Physical Education, Recreation and Dance.
Brisbane: University of Queensland; 1984:51-9.
42. Kioumourtzoglou E, Kourtessis T, Michalopoulou M, et al. Differ-
ences in several perceptual abilities between experts and novices in
basketball, volleyball, and water-polo. Percept Mot Skills 1998;86:
899-912.
43. Harbin G, Durst L, Harbin D. Evaluation of oculomotor response in
relationship to sports performance. Med Sci Sports Exerc 1989;21:
258-62.
44. Runninger J. Vision requirements of competitive sports. So J Optom
1975;17:13-5.
45. Sanderson FH, Holton JN. Relationships between perceptual motor
abilities and cricket batting. Percept Mot Skills 1980;51:138.
46. Classe JG, Semes LP, Daum KM, et al. Association between visual re-
action time and batting, fielding, and earned run averages among
players of the Southern Baseball League. J Am Optom Assoc 1997;
68:43-9.
47. Vogel GL, Hale RE. Does participation in organized athletics increase
a child’s scoring ability on the Wayne Saccadic Fixator? J Behav Op-
tom 1992;3:66-9.
48. Appler DV, Quimby CA. The effect of ambient room illumination
upon Wayne Saccadic Fixator performance. J Am Optom Assoc
1984;55:818-21.
49. Beckerman SA, Zost MG. Effect of lighting levels on performance on
the Wayne Computerized Saccadic Fixator and Wayne Peripheral
Awareness Trainer. J Behav Optom 1994;5:155-8.
50. Beckerman S, Fornes AM. Effects of changes in lighting level on per-
formance with the AcuVision 1000. J Am Optom Assoc 1997;68:
243-7.
51. Cornsweet TN. The staircase method in psychophysics. Am J Psycho-
phys 1962;75:485-91.
52. Westheimer G. Visual acuity. In: Kaufman PL, Alm A, Adler FH, eds.
Adler’s Physiology of the Eye.10th ed. St Louis: Mosby; 2003:462.
53. Altman DG, Bland JM. Measurement in medicine: the analysis of
method comparison studies. The Statistician 1983;32:307-17.
540 Optometry, Vol 82, No 9, September 2011
Appendix
Nike Sensory Station Instruction Set (Phase I with
strikethrough, Phase II with underline)
Visual Clarity
Let’s begin with visual clarity, how clearly you see a
stationary object. Stand facing the screen with your toes on
the line. For this assessment we’re we are going to use the-a
C-shaped symbol called a Landolt ring. The gap in the ring
will face in 1 of 4 directions - up, down, right or left. As
soon as you are able to see the direction of the gap, swipe
the hand heldhandheld screen in the same direction as the
gap. So, ifIf the gap is at the left of the ring, swipe the
screen from right to left. If the gap is at the top of the
ring, swipe the screen from bottom to top. The ring will
change in size after each response. If you can notcannot
see the gap, take your best guess. Now let’s begin with a
practice run. If you have any questions now is a good
time to ask your trainer. Remember to swipe the screen
as soon as you see the gap.
Contrast Sensitivity
Our Your next assessment will be contrast sensitivity, how
well you see subtle differences in brightness. For this as-
sessment you will need to focus on the 4 gray circles.
One of these circles contains a pattern comprised of light
and dark shaded rings while the other 3 are solid gray.
Swipe the screen in the direction that corresponds with
the circle that containscontaining the ring pattern. So, iflf
the pattern appears in the top circle, swipe the screen up.
Swipe the screen as soon as you identify the correct circle.
If you can notcannot see the ring pattern, take your best
guess. Let’s begin with a practice run. If you have any ques-
tions ask your trainer now.
Depth Perception
Our Your next assessment is depth perception, how quickly
and accurately you judge target distances. Your trainer will
have you wear a special pair of glasses once we begin. On
the screen you will see 4 completecircles. One of these cir-
cles will appear closer then the other 3. Swipe the screen in
the direction of the circle that appears closer to you. So ifIf
the bottom circle appears closer to you thenthan the others,
quickly swipe the screen down. If you are unsure, make
your best guess. We will begin with a practice round. The
trainer will now hand you the glasses. Remember, respond
as quickly as you can.
Near Far Quickness
Next is near far focusquickness, how quickly and accurately
your eyes shift focus between near and far targets. For this
assessment the trainer will show you how to properly hold
and respond to the hand held display. You will hold the display
16 inches away from your face, with the top edge just below
the far screen. While looking at the hand heldfar screen, a
ring with a gap will appear. inside the box, as As before,
quicklyswipe in the direction of the gap. As soon as you
have responded correctly to the near far ring, a ring will ap-
pear inside the box on the farnear screen. Again swipe the
screen in the direction ofthe gap. Continue this back and forth,
focusing between each target as fast as you can. This assess-
ment is timed, you have 30 seconds to correctly identify as
many rings as you can. Before we start the assessment, let’s
do a practice run to get you warmed up. Remember this is
timed, so push yourself to change focus as quickly as you can.
Target Capture
Moving now to target capture, how quickly you identify a
peripheral target. Keep your eyes on the white dot in the
center of the screen. A ring with a gap will appear for a
split second in 1 of the 4 corners of the display.screen.
Quickly move your eyes to focus on the ring. Identify the
gap direction in the ring and swipe the screen in thatthe di-
rection of the gap. We will start with 36 practice trials. Re-
member keep your eyes on the dot until the ring appears,
then quickly move your eyes to focus and determine the di-
rection of the gap on the ring.
Perception Span
Next is perception span - how much information you
process in a split second. Keep your eyes on the shrinking
circledot in the center of the gridcircles. When the circle
completely shrinksanddot disappears, green dots will flash
inside some of the surrounding circles. Remember the pat-
tern of green dots, and using the touch screen in front of
you, tap on the matching circlecircles to recreatere-create
the same pattern. When you have recreatedre-created the
pattern as best as you can, tap enter. As you progress, the
number of green dots and the size of the grid may increase.
We will start with 2 practice runs. Work at your own pace,
and remember, accuracy is important, speed is not.
Eye-Hand Coordination
Next is eye-hand coordination - how accurately and quickly
your hands move to a visual target. For this assessment you
will stand in front of a large grid of circles. Position
yourself directly in front of the screen so that with your
arms extended your fingertips just touch the screen. Once
you are in correct position, take your hands away from the
screen and hold at shoulder height to begin testing. A single
light will appear in a circle at random locations on the grid.
Using either hand hit the lights as quickly as you can with
your fingertips. Once hit another light will immediately
Erickson et al Clinical Research 541
appear somewhere else on the grid. Continue as fast as you
can until you hear the whistle. You will have 1 practice
round. Remember, push yourself to hit as many lights as
you can as quicklyas you can.
Go/No Go
Next is go/no go - how quickly you make decisions. This
assessment is similar to the last, but now there are 2 types of
lights, green and red. If the light is green, use the fingertips
of either hand to hit the light as quickly as you can. If the
light is red, do not hit the light. You will be penalized for
hitting any red light. Both the red and green lights will only
appear for only a short period, so you must react quickly.
Continue as fast as you can until you hear the whistle. For
this test youYou will not have a practice round. Remember
to hit as many green lights as quickly as you can.
Reaction Time
Moving to reaction time - a measure of how quick you are.
On athe screen in front of you are 2 buttons. When we be-
gin testing, if If you are right handed you will rest the fin-
gertips of your right hand on the start button on the right, if.
If you are left handed you will rest the fingertips of your left
hand on the start button on the left. If you are ambidextrous,
pick the hand you are most comfortable with. No portion of
your hand should extend across the boundary line marked
on the screen. Now stand directly in front of the opposite
button, with your eyes fixed on its center. When ready,
place your fingertips on the start button. After a random
period of time, the light directly in front of you will turn
on. Move your hand over and hit the light as quickly as
you can. You will practice twice before starting the assess-
ment. Remember, this assessment is all about quickness.
542 Optometry, Vol 82, No 9, September 2011
... Christensen and Winkelstein stated that athletes require superior and advanced visual ability to succeed in their sports activities [4]. Moreover, it has been reported that athletes have better visual abilities than non-athletes [5,6]. ...
... According to Wilson and Falkel, the saccadic movements of the eye are among the motor skills of the visual sense that are very important both in sports and in daily life [22]. Moreover, the eye saccadic movements are vital in table tennis [5]. Researchers have demonstrated that the motor skills of the visual sense are improved through practice, and this improvement results in appropriate motor functions during the performance of sport exercises [5]. ...
... Moreover, the eye saccadic movements are vital in table tennis [5]. Researchers have demonstrated that the motor skills of the visual sense are improved through practice, and this improvement results in appropriate motor functions during the performance of sport exercises [5]. Through practice, the control system can produce faster saccadic movements regarding the predictable direction. ...
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Introduction: The present study was designed to investigate the effect of visual and skill training on learning forehand drive in table tennis and motor-perceptual abilities (reaction time, coincidence-anticipation timing, eye-hand coordination, and depth perception).Materials and Methods: Forty volunteer female students (Mean±SD age: 21.50±0.78 years) were selected and randomly assigned to one of four groups (each group had 10 participants): visual and tennis training group, visual training group, tennis training group, and control group. Motor perceptual abilities (reaction time, coincidence-anticipation timing, eye-hand coordination, and depth perception) and forehand drive performance were measured before and after the training period, and also after 24 h retention period. After the pretest, including the accuracy of the kicks test for assessment of forehand drive in table tennis and motor-perceptual test, the experimental groups underwent four weeks (three sessions per week) of visual training, table tennis forehand training, or both. The control group followed their normal daily life for the whole study period. Then, they participated in the posttest and 24 h later in the retention test of kick accuracy.Results: The results revealed that visual and table tennis training, visual training, and table tennis training had a significant effect on the reaction time (P=0.001), coincidence-anticipation timing (P=0.001) and eye-hand coordination (error time) (P=0.01). Moreover, visual and tennis training and table tennis training had a significant effect on the acquisition (P=0.001) and retention of forehand drive (P=0.005). Besides, the post hoc LSD (Least Significant Difference) test showed that visual and tennis training had a more significant impact on the learning forehand drive. Visual training and tennis training had a significant effect on eye-hand coordination (number of errors). The three types of training programs were not effective in the depth of perception.Conclusion: Visual training can be used as a supplementary program in the athletes’ training schedule.
... In addition, modulation of attention is presumably important for the majority of competitive sports (Di Russo et al., 2003), as most sports are not exclusively played at a distance but involve rapid target shifts between far, intermediate, and near distances requiring rapid accommodative-vergence responses (Erickson et al., 2011). Ciuffreda and Wang (2004) went further to suggest that visual attentional training (e.g., dynamically shifting or weighting one's visual attentional focus from one region of the visual field to another) should be incorporated into any sports vision-training paradigm irrespective of a given sport. ...
... These tests include Perception Span, Eye-Hand Coordination (Peripheral Eye-hand response), Go/No Go and Hand Reaction Time (central eye-hand reaction and response time). Reliability and validity information of the Nike SST output parameters can be found in Erickson et al. (2011). The Nike SST also includes 4 training modules to improve decision making (Go/ No Go), split attention, depth perception and eye-hand coordination. ...
... Since some of the software training drills (e.g., dynamic depth perception, eye-hand coordination, decisionmaking, and split attention) directly mimicked test stimuli, it is of no particular surprise that all athletes improved their performance on the Nike/Senaptec measures by the end of their 10 week visual training. It has also been previously reported that improvements on some of the Nike/Senaptec measures can be explained by practice effects within the test-retest paradigm (Erickson et al., 2011;Liu et al., 2020). We were more interested in a possible treatment order-by-time interaction effect on the Nike/Senaptec measures that could indicate greater utility of a particular vision training approach. ...
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In the present study we combined popular methods of sports vision training (SVT) with traditional oculomotor protocols of Optometric Vision Therapy (OVT) and electrophysiological indexes of EEG and VEP activity to monitor training progress and changes in performance of youth ice hockey players without the history of concussion. We hypothesized that administration of OVT protocols before SVT training may result in larger performance improvements compared to the reverse order due to the initial strengthening of visual hardware capable of handling greater demands during training of visuomotor integration and information processing skills (visual software). In a cross-over design 53 youth ice hockey players (ages 13–18) were randomly assigned to one of the two training groups. Group one (hardware-software group) completed 5 weeks of oculomotor training first followed by 5 weeks of software training. For group 2 (software-hardware) the order of procedures were reversed. After 10 weeks of training both groups significantly improved their performance on all but one measure of the Nike/Senaptec Sensory station measures. Additionally, the software-hardware training order resulted in significantly lower frontal theta-to-gamma amplitude ratios on the Nike/Senaptec test of Near-Far Quickness as well as in faster P100 latencies. Both training orders also resulted in significant decreases in post-treatment P100 amplitude to transient VEP stimuli as well as decreased theta-gamma ratios for perception span, Go/No-Go and Hand Reaction time. The observed changes in the electrophysiological indexes in the present study are thought to reflect greater efficiency in visual information processing and cognitive resource allocation following 10 weeks of visual training. There is also some evidence of the greater effectiveness of the software-hardware training order possibly due to the improved preparedness of the oculomotor system in the youth athletes for administration of targeted protocols of the Optometric Vision Therapy.
... The specific vision factors important for sports performance vary depending on the task demands of the sport, but common vision skills include static and dynamic visual acuity, contrast sensitivity, depth perception, central and peripheral hand-eye reaction, and response time. 1 Among these, visual acuity, contrast sensitivity, and depth perception are often considered most important. 2,3 Dynamic visual acuity has been defined as the ability to resolve detail when relative movement exists between the observer and the test object. ...
... 5 Research studies conducted using the Nike Sensory Station showed that certain tasks in the battery are reliable in measuring many visual performance skills. 1 A cross-validated measure to investigate sensory motor abilities among collegiate male and female hockey players found that better performance on measures of dynamic visual acuity and visual-motor control accounted for 70% of variability in goals scored. 5 An extensive study of visual-motor skills in a large sample of athletes using the Nike Sensory Station found that athletes who play interceptive sports (e.g., baseball and softball) perform better on measures of visual acuity, contrast sensitivity, and simple reaction time. ...
... 9 Senaptec Sensory Station, a successor of the Nike Sensory Station, has a battery of 10 sensorimotor tasks deemed important for sports performance, such as dynamic visual acuity, contrast sensitivity, depth perception, multiple object tracking, and tasks that rely on ocular-motor coordination such as near-far quickness, target capture (dynamic visual acuity), perception span, eye-hand coordination, go/no-go, and hand reaction/ response times. 1,5 Research has shown improvements in baseball offensive statistics after sports vision training, so there is some precedent in using training to improve dynamic visual acuity. For instance, a study conducted by Klemish et al. 10 showed that visual acuity, depth perception, and contrast are the important aspects of vision that aid baseball athletes and that sports vision training programs with visual, perceptual, and oculomotor tasks can improve sports performance. ...
Article
Significance: Dynamic reactive sports involve visual abilities such as visual acuity, depth perception, contrast sensitivity, and visual-motor reaction speed. This randomized, double-blinded control design showed no significant improvement in the visual parameters among athletes after training on a digital sports vision training program. Purpose: There is a need for evidence supporting the efficacy of recently developed digital training programs. Methods: Thirty-two athletes from National Collegiate Athletic Association Division III softball and baseball teams were randomly divided into experimental and placebo training groups, undergoing three 20-minute training sessions per week for 3 weeks. The experimental group trained on procedures designed to improve dynamic visual acuity and depth perception, and the placebo group trained on procedures designed to have no direct impact on those same parameters. All measures were recorded at baseline, post-training, and after a month of no training. The athletes also completed a questionnaire to determine the efficacy of the placebo effect. Results: There was no significant effect of evaluation type (post-training and follow-up) and condition (experimental and placebo) on any of the visual parameters. However, stereoacuity, contrast sensitivity, depth perception, and dynamic visual acuity showed minimum effect sizes of 0.5. Fifteen of 16 athletes in the placebo group thought they trained on experimental procedures. Conclusions: No significant improvement differences were seen between experimental and placebo training groups. However, stereoacuity, contrast sensitivity, and depth perception achieved minimum clinical relevance.
... (ii) contrast sensitivity: the ability to perceive temporal or spatial information about objects and their backgrounds under changing light conditions; (iii) near-far quickness: the ability to quickly change gaze focus between far and near distances (Erickson et al., 2011). ...
... The Senaptec Sensory Station was used to assess contrast sensitivity, near-far quickness, and hand-eye coordination, with proven reliability (for a detailed description of the procedure and reliability; see Erickson et al., 2011). Visual clarity was measured with a Snellen chart accordingly to the standard procedure (Azzam and Ronquillo, 2022). ...
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Visual and cognitive skills are key to successful functioning in highly demanding settings such as elite sports. However, their mutual influence and interdependencies are not sufficiently understood yet. This cross-sectional study examined in a cross-sectional study design the relationship of between visual skills and executive functions in elite soccer players. Fifty-nine male elite soccer players (age: 18-34 years) performed tests assessing visual clarity (left-, right- and both eyes), contrast sensitivity, near-far quickness, and hand-eye coordination. Executive functions measures included working memory capacity, cognitive flexibility, inhibition and selective attention. Overall, visual abilities were largely correlated with the executive functions. Near-far quickness performance showed a large correlation with an executive function total score as well as with cognitive flexibility, working memory, and especially selective attention. Visual clarity and contrast sensitivity were moderately correlated with the cognition total score. Most consistent correlations of the executive functionswith the visual functions were present for working memory. , specifically with the visual clarity of the right- and both eyes and near-far quickness. These findings present an overall vision-cognition relationship but also very specific linkages among subcategories of these functions, especially by indicating meaningful relations between near-far quickness, selective attention and cognitive flexibility. Further studies are needed to investigate the neuropsychological mechanisms accounting for the correlations and possible improvements of the executive functions by training specific visual skills.
... 55,122 Another peripheral eye-hand response option has been referred to as a go/no-go task. 123,124 The test setup is similar to other visual-motor reaction time measures of peripheral eye-hand response; however, the light is either a "go" stimulus (e.g., green color) or a "no-go" stimulus (e.g., red color). Athletes are instructed to hit the go stimulus lights and to not hit the no-go stimulus. ...
... Research with the Nike version of this system has demonstrated that many tasks in the battery are reliable and cross-validated. 123,143 Sports Vision Performance by M&S technologies offers several computer-based visual performance assessments, including visual acuity, contrast sensitivity, eye alignment, depth perception, fusional ability, and developmental eye movements. The system allows for the comparison of performance with an athlete database. ...
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... The computer randomly illuminates points of light around the bowl-shaped perimeter. All participants were told to keep looking at the target point and press the button whenever they saw a light, no matter how bright or dim (15). The right eye was examined first, followed by the left eye after a few minutes of rest ( Figure 1). ...
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: The current study investigates the relationship between simple reaction time, choice reaction time, and eye-hand coordination with peripheral vision in elite female table tennis players. Ten female table tennis players of the Iranian national team with a mean age of 19.7 ± 5.964, in the 18th Asian Games of 2018, Jakarta, participated via convenience sampling. The visual field was evaluated with the Humphrey automated perimetry. Choice and simple reaction time were assessed using Deary-Liewald reaction time tester software. In order to measure eye-hand coordination, the manual test of alternate-hand wall toss was used. Data were analyzed in statistical package for the social sciences using Pearson’s correlation. The results show that there was no significant relationship between simple and choice reaction time with peripheral vision in the left and right eyes. Also, results show that there was no significant relationship between eye-hand coordination with peripheral vision in the left and right eyes (P ≤ 0.05). The findings of this study show that experts in an activity visually searched their environment and located essential information more effectively and efficiently than novices. Therefore, we know that this visual feature is more a function of expertise than visual acuity.
... Nike Sensory Stations, with moderate to good reliability. 150,151 Researchers have also 154 manipulated field of view through gaze-contingent displays, 152 where observers watch videos 155 through an aperture that moves with the eyes, revealing only part of the scene (a central mask 156 ...
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... The same year, Burris and colleagues 17 made use of a naturalistic sample of data collected on the Nike (Beaverton, OR) SPARQ Sensory Station, a normative battery of nine validated tasks, [38][39][40] to evaluate the links between visual-motor performance and batting performance in a sample of 252 professional baseball players (141 batters with >30 at bats and 111 pitchers with >30 innings pitched). Using a Bayesian hierarchical modeling approach that allowed for comparison across players from different leagues and contrast to a baseline model, task performance on the Sensory Station was compared with subsequent season game statistics for on-base, walk, strikeout, and slugging percentages for batters and fielder-independent pitching for pitchers. ...
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