Eyestrain or asthenopia is a common problem that is associated with reading, computer use, and other near work.1–5 Symptoms include impaired reading performance, such as slow reading and repetition, headaches and soreness, glare, blurring and diplopia, and text distortions.6,7 Eyestrain can be associated with a variety of conditions, but when it occurs with normal or corrected to normal refraction, dry eye or accommodation/vergence dysfunction are often considered to be the cause.8
A novel treatment approach for eyestrain has involved the use of colored lenses and overlays,9–18 and there are many reports that the use of individually preferred colors reduce symptoms and improve reading speed.19–35 The mechanisms for these improvements are not understood. Recently, Allen et al.36 reported that students with eyestrain who responded to color overlays had larger accommodation lags than controls when viewing a near (40 cm) target. Their lags were significantly reduced by the use of their preferred color overlay, whereas accommodative responses did not change in the asymptomatic control group presumably because responses were normal and lags were within the depth of focus.
Longitudinal chromatic aberration (LCA) contributes to the accommodation response by way of the L/M-cone contrast ratio.37–42 Cone contrast is determined for each cone class separately by way of the Michelson contrast formula, which uses the maximum and minimum cone excitations in the stimulus. The ratio of L-cone contrast to M-cone contrast provides a signal to the required direction of accommodation. If there is a lag of accommodation, long wavelengths have greater defocus than the medium wavelengths. Thus, L-cone contrast is lower than M-cone contrast, signaling a need to increase accommodation. Similarly, if there is a lead of accommodation, L-cone contrast is higher than M-cone contrast, signaling a need to decrease accommodation.
In addition to this cone-contrast mechanism, the color cast of an object can influence the position of optimal focus by way of LCA. For example, a red-hued target has a proportionally greater content of long wavelengths, which are focused more hyperopically in the eye. The red-hued target requires slightly greater accommodation in comparison with a white target. In this way, LCA affects the response of a contrast-maximizing (or blur minimizing) accommodative mechanism.
L/M cone-contrast sensitivity has been reported to correlate with reading; larger ratios were associated with weaker reading performance.43 These results suggest the possibility that performance may suffer for readers with large L/M ratios because of weakened accommodative responses caused by chronically increased demand.
In this study, we examined the relationship between eyestrain, color preferences, and accommodation/vergence function. To maintain balance, these systems are cross-linked so that a change in convergence results in a reciprocal convergence accommodation response (CA), and a change in accommodation drives accommodative convergence as well. Because of studies finding a high association between accommodative dysfunction and vergence anomalies, we included measures of vergence in our data analysis.44,45 Specifically, we tested the hypothesis that symptomatic students might be selecting colors that would reduce accommodation demand based on LCA. Students with eyestrain may choose colors that reduce demand, using the LCA mechanism to stimulate the system, improve focus, and reduce symptoms.
Subjects for this study were undergraduate students recruited from the Claremont Colleges (Claremont McKenna, Pomona, Scripps, Pitzer, and Harvey Mudd) and were the subjects of previously reported studies.4,7 We distributed the Conlon et al.6 Visual Discomfort Survey across all five campuses and received 571 responses. Before completing the survey, students completed an informed consent approved by the institutional review board that adhered to the Declaration of Helsinki principles.
From this sample, 88 participants between the ages of 18 and 22 were invited for further study with the constraint that the sample contained a wide distribution of symptom severity. This was done by sorting the sample into two groups based on their Visual Discomfort Survey scores and recruiting roughly equal numbers of participants from both the high and low groups. The definition of the two symptomatic groups is described below.
At the initial visit, all students were required to pass a screening for visual acuity (of 20/25 or better in each eye at distance and at near), no strabismus (no constant strabismus), stereopsis (appreciation of random dot targets and steroacuity of 70 sec arc or better), uncorrected refractive error (myopia of >0.50, astigmatism and anisometroipa of >1.00 D, and hyperopia ≥1.50 D), and ocular pathology, including color deficiency (pass the Ishihara plates and the L'Anthony D-15 test). Further details are described in Borsting et al.4 All students also underwent a non-cycloplegic subjective refraction or over-refraction, performed by one of the researchers (EB). In subjects wearing contact lenses, the refraction was performed while the students wore the lenses. Screening criteria and attrition reduced the size of the sample to 74. Of this sample, complete optometric and color preference data were available for 47 individuals.
Participants were assessed for eyestrain symptoms by completing the Conlon et al. survey. This questionnaire uses a four-point scale that rates symptom frequency (never occurs, occasionally a couple of times a year, often every few weeks, almost always). The 23 items include a variety of symptoms: reading performance, glare, headache and soreness, blur/diplopia, and text distortions. Respondents receive a score between 0 and 69. On the basis of a large random college sample,46 students with scores one SD above the mean (≥24) were described as high discomfort.
Color Preferences and LCA
Optimal colored light preferences were assessed for each participant using a Mark 2 Intuitive Colorimeter (Cerium Technologies, Kent, UK). Participants looked at black text on a white background from a viewing distance of 40 cm. Participants were shown a gamut of hues and indicated the change in comfort or discomfort of a specific colored light in comparison with the standard white light condition using a five-point rating scale. Preferred hue and saturation were obtained following the manufacturer's procedures.47 Several studies have shown the Intuitive Colorimeter system consistently can identify small chromaticity areas preferred by an individual where eyestrain symptoms disappear and reading speed improves,48–50 but some individuals are inconsistent in their color preferences.50,51
Absolute irradiance measurements were made for each student's preferred color in the Mark 2 Colorimeter using an USB2000 spectrometer with a spectral range of 350 to 850 nm, a peak wavelength efficiency of 500 nm, and resolution of 1.3 nm. The spectrometer was calibrated using an LS-1-CAL tungsten halogen NIST-traceable light source. Light was sampled using a 74-VIS collimating lens and a 50 μm diameter optical fiber cable. Recordings were made using SpectraSuite 2.0151 software, and spectral measurements were converted to CIE Lu'v' coordinates for a C illuminant and 2 deg observer (Ocean Optics, Dunedin, FL). The gamut for the Mark 2 Colorimeter was assessed by making spectral measurements at the maximum luminance and saturation every 30° of hue. The Colorimeter gamut is presented in Fig. 1. Luminance measurements were made for each color condition using a Minolta LS-100 photometer (Konica Minolta, Windsor, CT). Three recordings were made for each color condition and averaged.
Because participants' color preferences were chosen by comparing a new color to the standard light source, LCA values were calculated as the change in accommodative demand relative to the standard light. LCA for each color was modeled using the Chromatic Eye52 and the spectral power density data. For a spatial frequency of 4 cpd and pupil diameter of 5 mm, we calculated the polychromatic optical transfer function using the geometrical optical method of Smith (1982).53 To find the value that would maximize luminance contrast of the retinal image, the accommodation of the chromatic eye was determined by an iterative procedure in Excel (Microsoft, Bellevue, WA) using the Solver program (Frontline Systems, Incline Village, NV). Photopic luminance was based on the familiar V(lambda) function.54 Changes in accommodative demand or refractive error have only small effects on LCA,55,56 and therefore this model is reasonably robust to assess LCA for the experimental conditions.
Comprehensive evaluations of accommodation and vergence were performed following standard procedures57 by one of the researchers (EB) who was masked to the participant's symptom score on the Conlon et al. survey. Accommodation was evaluated by five tests: (1) amplitude (push up method); (2) facility using ±2.00 flippers at 40 cm (OD, OS, & OU); (3) amplitude-scaled facility;58 (4) posture using the monocular estimation method (MEM); and (5) negative (NRA) and positive (PRA) relative accommodation. Four vergence tests were used: (1) a cover test at 3 m and 40 cm with prism neutralization to assess phoria; (2) negative fusional vergence and positive fusional vergence at 40 cm with a prism bar; (3) nearpoint of convergence using an Astron Rule with nearpoint Snellen letters; and (4) facility (12 base-out/3 base-in). A mean of three measurements were used, except for facility tests, cover test, and MEM. Because of time constraints, only one measurement was made for these tests. For facility testing, we demonstrated the procedure to the patient before the administration of the test.
Four sets of analyses were conducted for 47 participants with complete data. First, uncorrected refractive error was examined as a potential factor related to eyestrain symptoms. Second, descriptive statistics were made for the three measures compared in this study: eyestrain symptoms, color preferences, and accommodation/vergence function. Then two different correlational analyses were made to examine the relationship between color preferences and eyestrain symptoms or accommodation/vergence function. Previous studies have reported our results on the relationship between eyestrain symptoms and accommodation/vergence function.46
Uncorrected Refractive Error
The uncorrected refractive error was determined by comparing a subjective refraction to the student's habitual correction. The mean spherical equivalent was calculated as the difference between the habitual correction and the subjective refraction. In students who did not wear a refractive error correction, the habitual correction was plano.
The mean spherical equivalent refraction error was small, averaging about a quarter diopter (OD: mean = ±0.25 D, SD = 0.27 D; OS: mean = ±0.29 D, SD = 0.28 D). Comparisons by t-test showed no significant differences between the high and low discomfort groups [OD: t(45) = −0.20, p = 0.85; OS: t(45) = 0.74, p = 0.46]. The types of refraction errors were divided into myopic, emmetropic, or hyperopic categories with errors in the range of −0.25 to +0.25 D considered to be emmetropic. Sixty-two percent of the high group and 69% of the low group had no refraction error. A Freeman-Halton extension of the Fischer exact test showed that there were no differences between groups in the types of refraction errors (p = 0.82).
The mean Conlon et al. symptom score was 20.7 (SD = 12.4) with a range from 3 to 52. This mean was higher than in a previous college sample,7 because there were 21 students in the high discomfort group and 26 in the low group.
Color and LCA Preferences
The standard reference light condition in the Colorimeter had CIE Lu'v' coordinates of 0.225 and 0.526, respectively. The distribution of participants' color preferences is described in Fig. 1. Mean luminance was 23.1 cd/m2 with SD of 4.2. The reference lighting condition had a luminance of 31.36 cd/m2.
Fig. 1 describes the relationship between color space and the LCA accommodative stimulus demand in a three-dimensional figure. Participants selected colors along three axes: one that increased demand from the standard light source to yellows and reds, another that decreased demand using green, and a third that decreased demand using blue. Some participants also selected desaturated hues that were quite close in color space to the standard reference light.
The mean change in LCA accommodative demand was −0.02 D and a SD of 0.05 with a range from −0.13 to 0.07 D. Several participants selected colors on the edge of the Colorimeter gamut, and we do not know if they would have preferred a more saturated color than the one available.
Accommodation/vergence testing was completed on all participants. Stratifying the sample by symptom severity produced a broad range of function. Table 1 presents the descriptive statistics.
Eyestrain and Color Preferences
The color preferences of the high and low discomfort groups are shown in Fig. 1. The low discomfort group selected colors that varied along the u' axis with little change in v' axis, whereas the high group chose colors that for the most part were below the standard light coordinates (0.225, 0.526) along both axes.
The CIE v' axis negatively correlated with Conlon et al. symptom scores (r = −0.51, p = 0.0002), but the CIE u' coordinates were not significant (p = 0.10). As symptom frequency increased, students preferred greens and blues compared with the standard lighting condition. Symptom severity did not correlate with luminance values of the color condition or with changes in luminance relative to the reference lighting condition.
Eyestrain and LCA
A correlation between LCA accommodation demand values and eyestrain showed a significant negative relationship between Conlon et al. symptom severity and the change in accommodation demand produced by the preferred color (r = −0.31, p = 0.03). As symptoms increased, students preferred colors that reduced accommodation demand.
Color preferences were categorized into three groups based on the LCA accommodation demand value: colors that reduced demand (LCA change ≤−0.01 D), did not change demand (−0.01 D <LCA change <0.01 D), and that increased demand (LCA change ≥0.01 D). Table 2 presents the proportion of students from the high and low discomfort groups by their preferred color type. A Freeman-Halton extension of the Fisher exact probability test showed that color preference distribution was significantly different between the high and low groups (p = 0.0009). Two thirds of the high discomfort subjects preferred colors that reduced demand, one third selected neutral colors (LCA ± 0.01 D), and none selected a color that increased demand. In contrast, the low discomfort group was more evenly distributed in their color choices.
Accommodation/Vergence Function and Color Preferences
To examine if accommodation/vergence functions correlated with color preferences, a forward, stepwise regression analysis was performed for each CIE u' and v' coordinate and luminance values as the dependent measure and 12 of the accommodation/vergence test results described in Table 1 as the independent variables. Accommodation facility measures were highly correlated with each other, so Facility OS and OU were not used in the regression model to avoid problems of multicollinearity. Variance inflation factors for the independent variables used in the model were low, ranging from 1.31 to 2.17. Stepwise regression F-to-enter ratios were set at 4.0 with F-to-remove ratios at 3.996. R2 or explained variances was defined by the ratio of the sum of squares as a result of regression and the total sum of squares.
Two measures correlated with the u' coordinates: accommodative amplitude (standard coefficient = 0.34) and vergence facility (standard coefficient = 0.31). Together, these two variables accounted for 24% of the variance [F(2,44) = 6.91, p = 0.002]. For the v' coordinate, NRA produced a significant correlation with a standard coefficient of 0.37 [F(1,45) = 7.22, p = 0.01], accounting for 14% of the variance. None of the accommodation/vergence measures correlated with luminance or change in luminance relative to the reference light.
Accommodation/Vergence Function and LCA
A similar stepwise regression was performed with LCA accommodation demand values as the dependent measure. Results were quite similar to the u' coordinate correlations described above. Accommodative amplitude (standard coefficient = 0.32) and vergence facility (standard coefficient = 0.34) together accounted for 25% of the variance [F(2,44) = 7.24, p = 0.002]. The similarity of these two results can be explained by an examination of Fig. 1 where u', v', and LCA accommodation demand are plotted together. LCA demand varies systematically with the u'-axis, and the two variables are highly correlated (r = 0.97).
Participants were grouped into strong or weak accommodation/vergence function based on a median split of accommodative amplitude and vergence facility. If a student performed in the upper one-half the sample on both tests, they were placed in the high functioning group (n = 12), and if their score was in the lower one-half the sample on either test, they were placed in the low functioning group (n = 35). The color choices of the two groups were then compared using the same LCA accommodative demand categories as were used in Table 2 above: increase, no change, or decrease demand. The proportion of students in the high and low functioning groups is presented in Table 3. A Freeman-Halton extension of the Fisher exact probability test showed that the high and low functioning groups had significantly different distributions (p = 0.003). Only one high functioning student selected a color to reduce accommodative demand, whereas most low functioning students preferred decreasing (57%) or not changing (31%) accommodation demand.
A comparison between the high and low discomfort groups produced different accommodation/vergence correlates. Using the same type of stepwise regression, the high discomfort group showed a strong correlation between LCA demand and NRA (standard coefficient = 0.69) accounting for 47% of the variance [F(1,19) = 17.26, p = 0.0005]. The low discomfort group had no significant correlations.
Figs. 2 to 4 show scatterplots between the LCA accommodation demand values and measures of accommodation amplitude, NRA, and vergence facility. As accommodation/vergence function decreased, students preferred colors that reduced accommodative demand.
This study found that color preferences correlated not only with eyestrain severity but also with accommodation/vergence function. Three patterns were found that are shown in Fig. 1. First, college students that experienced greater eyestrain in terms of symptom frequency selected colors that reduced accommodative demand along the v' axis in CIE Lu'v' color space toward saturated blue. Second, those with weaker accommodation/vergence systems reduced demand by selecting colors that varied along the u' axis, preferring saturated green. Third, individuals with normal accommodation/vergence function and no eyestrain chose colors that either had no effect on demand or increased it along the u' axis toward deeper yellow, orange, and red. Luminance preferences did not correlate with eyestrain or accommodation/vergence function. In addition, uncorrected refractive error was small, comparable in the high and low discomfort groups, and therefore unlikely to be a factor in the severity of eyestrain symptoms.
These findings suggest that accommodation/vergence function systematically influenced the color preferences of individuals. Students with weak function who are symptomatic tended to choose different colors from those who were asymptomatic. Students with frequent symptoms and weak function preferred blue, whereas those who were asymptomatic with weak function preferred green. The blue color reduced LCA demand more than green, so more frequent symptoms may reflect a more pronounced functional impairment. Chase et al.46 found a strong positive correlation between autorefraction measures of accommodation lag and symptom severity.
Measures of accommodation or vergence with the strongest correlation to LCA demand were vergence facility and accommodative amplitude. Clinical amplitude of accommodation is a measure of the maximum accommodation response that may be exerted and sustained within a short time interval, typically several seconds. Clinical vergence facility with prism flippers is a measure of the speed at which a person can alternately converge and diverge in the presence of a fixed and conflicting accommodative demand. It therefore represents complex interactions between Maddox components of vergence and Heath's components of accommodation. Selection of colors with less LCA demand may help the system compensate for weak accommodative function. A third accommodation/vergence measure (NRA) had a strong and positive correlation with LCA demand but only for high symptomatic subjects. Perhaps individuals with a greater ability to relax their accommodative system are comfortable with colors that stimulate the accommodative system more. Colors such as reds and yellow are acceptable choices because the system is able to sufficiently relax the accommodative system to adjust for greater demand. Individuals with a weaker ability, however, will make color selections that correspond with less demand.
Clinical methods for assessing accommodation and vergence can produce variable results when assessing symptoms. For example, Chase et al.46 found that objective methods for measuring accommodation function using autorefraction produced strong correlations with symptom severity, whereas clinical accommodation measurements generally did poorly with the exceptions of amplitude-scaled facility and amplitude of accommodation. In this study, the push-up amplitude test was the only accommodation measure to correlate with LCA demand. Compared with autorefraction, push-up amplitude overestimates accommodative function by about 2 D,59 but amplitude scores from the two methods significantly correlate with each other. Therefore interpretation of results from this study probably should be limited to a correlation analysis that assesses relative degrees of accommodative/vergence function. In future studies, autorefraction would provide a more objective method to determine which individual would benefit from color treatment and identify the specific hues and saturations that maximally reduce accommodation lag.
The size of LCA accommodative demand in the color conditions of this experiment was quite small and varied from −0.13 to 0.07 D compared with the standard light condition. Therefore, one might expect minimal effect on the accommodative system. However, other studies have found large shifts in accommodation lag in response to LCA. Rucker and Kruger41 reported more than a 2 D increase in lag with increased L-cone weighting of a luminance signal (see reference 44, Fig. 7), and using a low-pass filter, Kroger and Binder reduced demand by 0.50 D.60 In addition, transparencies used by Allen et al.36 have less saturation than the color space used in this experiment, and these colors induced a decrease in accommodative lag that ranged from 0.25 to 0.50 D for symptomatic individuals. The size of these changes in lag in these other studies suggests the possibility that the gain ratio between the LCA stimulus and the accommodative response may vary individually and can be larger than a one-to-one correspondence.
LCA values were based on individual color preferences determined using the Intuitive Colorimeter, and subjective judgment procedures may hamper interpretation of these results. Other studies have reported that some symptomatic individuals do not have consistent color preferences,50,51 so their LCA values could vary from one Colorimeter test to another, and weaken any correlation analysis that uses their responses. In this study, however, correlations were reasonably strong, and so results are not likely confounded by too many participants with inconsistent preferences. Those with inconsistent color preferences may not benefit from color treatment. Other conditions, such as dry eye or migraine, could be the cause of symptoms or gain ratios from the LCA stimulus may be too low to affect the accommodation response.
Future studies need to look more closely at individual differences in the gain ratio from the LCA stimulus and examine symptomatic and asymptomatic individuals on the same set of color to provide a better comparison. As can be seen in Fig. 1, the gamut of available colors was restrictive relative to full color space. Within this gamut, a number of participants selected colors that were at the most extreme saturation available, and their accommodative responses were measured at these points. Their color choices may not have been optimal for reducing lag and given the choice might have preferred more saturated colors that further reduced accommodative demand. Perhaps there are additional colors that provide similar LCA demand values and would have effects that are analogous to the colors selected by our participants. A greater spectrum of testable colors would allow us to better examine individual color preferences.
L- and M-cone contrast ratios also should be examined in future studies. L/M-cone contrast has been shown to influence accommodative responses over a 1.5 D range37 and to affect reading performance.43 Individual differences in L/M ratios may contribute to symptoms and accommodative function.
These results are based on correlational findings and should be interpreted cautiously. Although preliminary, findings may have significant implications for the clinical treatment of accommodation/vergence dysfunction, but further clinical study is needed to assess the effectiveness of color as an adjunct to standard treatment approaches.
Stefanie A. Drew
College of Optometry
Western University of Health Science
309 E 2nd Street
Pomona, California 91766
We thank William Ridder III for help and advice during the course of this study.
This research was supported by grant number R15EY015922 from the National Eye Institute, National Institute of Health.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Eye Institute or the National Institutes of Health.
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