This work was supported by Natural Science Foundation of Beijing Municipality (7202229).

The present study enrolled consecutive myopia patients in the Department of Ophthalmology of Peking University Third Hospital. The research protocol was approved by the Peking University Third Hospital Ethics Committee, and informed consent was obtained from each participant.
1. Patient preparation
<ol>
	<li>Use the following initial inclusion criteria to enroll subjects: myopia subjects aged 17-45 years old.</li>
	<li>Use the following exclusion criteria: any history of ocular diseases, including keratitis, glaucoma, cataract, retinal and macular diseases, that significantly impact corrected distance visual acuity (CDVA). Evaluate uncorrected distance visual acuity (using the standard logarithmic VA chart), dominant eye, intraocular pressure, slit lamp, corneal topography, fundus photography, automatic computer optometry, cycloplegic subjective refraction, and CDVA. Exclude participants with keratoconus, cloudy cornea, or retinal abnormalities, including retinal breaks, retinal vascular inflammation, congenital retinal and macular diseases, or monocular CDVA worse than zero (based on the standard logarithmic VA chart).</li>
	<li>Set up the DVA test components, including test distance, environment, hardware, software, movement mode, and rules as follows:
	<ol>
		<li>For test distance and environment, set the test distance according to the size of the screen and examination requirements. 
		&#8203;NOTE: Here, DVA was assessed at 2.5 m in a quiet and bright room (luminance 15-30 lux).</li>
		<li>For hardware, present the optotype with a 24 inch in-plane switching (IPS) or twisted nematic (TN) screen (refresh rate, 60 to 144 Hz; response rate less than 5 ms).</li>
		<li>Ensure that the software is designed to display the optotype according to the preset velocity and size. Use the dynamic optotype as the letter E designed according to the standard logarithmic visual chart with four opening directions: upper, left, lower, and right. Ensure that the visual angle of the motion optotype presented at the test distance equals the optotype with the decimal size in the standard logarithmic visual chart. Set the color of the letter E to black, with a white background. Express the velocity of motion as the viewing angle changes per second.</li>
		<li>Movement mode: during the test, ensure that the optotype with a specific size and velocity appears in the middle of the screen&#39;s left side, moves horizontally to the right side, and then disappears.</li>
		<li>Test rule: Ask the subjects to identify the opening direction of the visual target. Test the minimum visual target at a certain speed that the subjects can recognize.</li>
	</ol>
	</li>
</ol>
2. Subjective refraction
NOTE: The result of subjective cycloplegic refraction is the basis for the eyeglasses prescription to correct the refractive error in myopia subjects.
<ol>
	<li>Perform automatic computer optometry as the primary data for subjective cycloplegic refraction and measure the pupil distance.</li>
	<li>Examine one eye at a time and occlude the other eye.
	<ol>
		<li>First, achieve the maximum plus to maximum visual acuity: fogging with +0.75 - +1.0 D lens, inducing a visual acuity of 0.3-0.5 (decimal visual acuity). Next, gradually decrease the positive lens in a 0.25 D step. Use a Lancaster red-green test to tune the accurate spherical diopter. Add more negative/positive lens if the patients report that the letter seen against the red/green background is clearer. 
		NOTE: The primary spherical diopter is obtained after the above step.</li>
	</ol>
	</li>
	<li>Refine the cylinder axis.
	<ol>
		<li>Place the Jackson cross cylinder device in the &#34;axis&#34; position so that the thumb-wheel&#39;s connecting line is parallel to the axis of the astigmatism. Rotate the thumb-wheel and ask the subject to compare the clearness between both sides. Turn the cylinder axis toward the red dots on the cross cylinder in the side with clearer vision. Repeat the binary comparison until the endpoint.</li>
	</ol>
	</li>
	<li>Refine cylinder power.
	<ol>
		<li>Turn the Jackson cross cylinder device so that the thumb-wheel&#39;s connecting line is at 45&#176; to the astigmatism axis. Rotating the thumb-wheel, ask the subject to compare the clearness between both sides. If the patient reports clearer placement of the cross cylinder red/white dots connecting line along the cylinder axis, add a negative/positive lens, respectively. Repeat the binary comparison until the endpoint.</li>
	</ol>
	</li>
	<li>For the second maximum plus to maximum visual acuity, repeat the Lancaster red-green test to tune the accurate spherical diopter.</li>
	<li>For binocular balance, apply a vertical prism of 6&#916; before one eye to dissociate the binocular vision. Balance the clearness of the optotypes between both eyes.</li>
</ol>
3. Dynamic visual acuity test
NOTE: DVA was measured binocularly with refractive errors fully corrected with eyeglasses in the present study.
<ol>
	<li>Test settings
	<ol>
		<li>Adjust the test distance according to the requirements. Adjust the seat to make the subject&#39;s sight at the screen&#39;s midpoint level. Ensure the subject wears the distance vision corrected eyeglasses binocularly.</li>
	</ol>
	</li>
	<li>Test parameter configurations
	<ol>
		<li>Set the optotype moving velocity and the initial optotype size.</li>
	</ol>
	</li>
	<li>For the pretest, display five optotypes with a randomized opening direction to guide the subjects to understand the test mode.</li>
	<li>Formal test
	<ol>
		<li>Start the test at the size 3-4 lines bigger than the best-corrected distance visual acuity. Display the optotype with randomized opening directions.</li>
		<li>Ask the subject to identify the opening direction of the moving optotype. Present the next optotype after the subject&#39;s response. Present eight optotypes for a certain size. If five out of eight optotypes are identified correctly, adjust the optotype to one size smaller. Repeat the above procedures until the size for which the subject can identify less than five optotypes is obtained.</li>
	</ol>
	</li>
	<li>Record the minimum size (A, decimal VA) that subjects can recognize (five out of eight optotypes are identified correctly) and the number (b) of optotypes recognized for one size smaller than A.</li>
	<li>DVA calculation
	<ol>
		<li>Present eight optotypes for each size so that each identified optotype gains 0.1/8 visual acuity. Calculate DVA according to the algorithm of logarithmic visual acuity, as shown by Eq (1); see step 3.5 for an explanation of A and b: 
		<img alt="Equation 1" src="/files/ftp_upload/63864/63864eq01v2.jpg" style="margin: auto;" /> (1) 
		NOTE: In the present study, optotypes of 40 and 80 dps were examined in order. Previous studies have reported that people could apply smooth pursuit when observing dynamic objects moving at 30-60 dps, whereas observing objects moving faster than 60 dps involves head movement and saccade16,17. Thus, two motion speeds of 40 and 80 dps were selected.</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing interests.

DVA is a promising indicator to assess visual function, which might better reflect actual vision in daily life. Myopic patients could have corrected, normal SVA, but their DVA might be affected. This study demonstrates a method to examine the DVA in myopic subjects with eyeglass correction accurately and analyzes its correlation with optometric parameters, including refraction, accommodation, and the dominant eye. The results indicated that DVA at 40 dps was superior to that at 80 dps. The closer the refraction state is to emmetropia, the better the eyeglass-corrected DVA at 40 and 80 dps. No correlation was found between DVA and accommodation function parameters and the dominant eye.
In the present study, the SVA was corrected completely with eyeglasses for all subjects. However, the DVA value differs from person to person. The single-factor linear mixed model results indicated that monocular and binocular mean sphere and SE are all significant influential factors for DVA, which means that the closer the refractive state is to emmetropia, the better the DVA at 40 and 80 dps. The results suggested that the decrease in DVA caused by ametropia may be challenging to completely correct with eyeglasses. Several mechanisms might be able to explain the results. The prism effect is stronger in larger diopter eyeglasses, which has a displacement effect on the object image18. Robust DVA depends on an accurate prediction of the target's motion trajectory to form an effective pursuit and saccade16,17. Thus, the prism effect may affect the subjects' prediction of the movement of dynamic visual targets and affect the pursuit, resulting in worse DVA18. Previous research demonstrates no significant difference in DVA among tennis athletes with normal vision or refractive errors with and without correction19. The difference in the results might be attributed to the difference in the test distance. The DVA test in that study was performed at a near distance (45 cm), and the near visual acuity might not have been affected in subjects with refractive error.
Future studies could further apply eye-tracking tools during DVAT to record ocular movements to substantiate this assumption. Moreover, the visual clarity in the peripheral region of eyeglasses is less clear than that in the central region due to peripheral defocus12. While observing moving targets, objects could not image constantly through the central zone20. Thus, unclear vision through the paracentral or peripheral visual field could impact DVA. Moreover, previous research has demonstrated that myopic eyes have a thinner GC-IPL and retinal nerve fiber layer (RNFL) than emmetropic eyes21,22. RNFL thickness and ganglion cell density decrease with increasing myopia diopter22. The decrease in ganglion cell density in myopic eyes may decrease the function of visual signal transmission and management, leading to a decrease in DVA conduction function.
The present study found that the diopter of eyeglasses influenced DVA with SVA correction, and the larger the diopter was, the worse the DVA. A previous study has shown that people wearing eyeglasses tend to experience a higher risk of traffic accidents23, which may be related to the impact of peripheral vision damage of eyeglasses on DVA. Thus, DVAT might better reflect functional vision in daily life to provide information for driving safety and sports performance. Since the diopter of eyeglasses significantly impacts the DVA, highly myopic subjects who have a higher demand for dynamic vision might choose to correct the refractive error with methods other than eyeglasses or have substantial career planning. In the future, the influence of other myopia correction methods on DVA, including contact lenses and refractive surgeries, can be further explored for occupational-based recommendations, including drivers and athletes. Moreover, considering the impact of age and refractive error correction on DVA, different ranges of normal values should be set according to age, and the impact of refractive error diopter should be considered when applying DVAT in the clinical setting.
Certain limitations exist in the present study. First, this study only investigated the impact of myopia on DVA in eyeglass-corrected patients. Other static distance visual acuity correction methods, including contact lenses and surgeries, might also influence DVA, which should be further investigated in the future. Second, only a single mode of optotype movement was applied in the test. More movement directions need to be explored in the future. A DVAT that can change the observation depth of the field can be designed to better reflect real-life scenes such as driving. Third, DVA is associated with eye tracking, including smooth pursuit and saccade. The present research lacks accessibility to eye-tracking devices, which is helpful for these kinds of studies. Further research could collect eye-tracking data during DVAT to substantiate ocular movement during the test. Fourth, compared with parvocellular (P) ganglion cells, magnocellular (M) ganglion cells primarily transmit high temporal frequency signals, which might be responsible for the visualization of the motion optotype in the test, which remains to be explored in future research.
In summary, the study evaluated and analyzed optometric influential factors in binocular DVA in myopic subjects whose vision was corrected to normal with eyeglasses. The results provided the normal values and distributions of DVA at 40 and 80 dps, and demonstrated that the binocular DVA at 40 dps was significantly superior to that at 80 dps. DVA improves first and then declines with aging. With SVA corrected with eyeglasses, the worse the monocular and binocular sphere and SE, the worse the DVA. No correlation was found between the dominant eye, accommodation function and DVA. The present research provides a standard and efficient protocol to examine DVA in eyeglass-corrected myopia patients, and provides the basis for better interpreting DVAT in clinical ophthalmology and evidence on the impact of eyeglass correction on motion-related activities.

Current visual assessment mainly focuses on static vision, including static visual acuity (SVA), visual field, and contrast sensitivity. In daily life, either the object or the observer is often in motion rather than being stationary. Therefore, SVA may not sufficiently reflect visual function in daily lives, especially when objects are moving at high speeds, such as during sports and driving1. DVA defines the ability to identify the details of moving optotypes1,2, which might reflect real-life situations better and be more sensitive to visual disturbance and improvement3,4. Moreover, as magnocellular (M) ganglion cells located mainly in the peripheral retina primarily transmit high temporal frequency signals, DVA might reflect visual signal transmission differently from SVA5,6. The DVA test (DVAT) can be mainly divided into two types: static- and moving-object DVATs. While the static-object DVAT demonstrates the vestibule-ocular reflex7,8,9,10, the moving-object DVAT is commonly applied in clinical ophthalmology to detect visual acuity in the identification of moving targets3,4.
The prevalence of myopia has rapidly increased in recent decades, especially in Asian countries11. Myopia has an essential impact on static uncorrected distance visual acuity, which could be corrected with various lenses. Eyeglasses are mostly used among myopia patients due to accessibility and convenience. However, eyeglasses, especially high myopia lenses, have obvious peripheral defocus and prism effects that cause unclear and skewed images to be observed through the peripheral region12,13,14,15. For a static optotype, the subject commonly uses the central area of eyeglasses that could obtain a clear vision. However, the moving target could easily move out of the eyeglasses&#39; clearest point. Thus, with eyeglass correction, myopic subjects might have normal SVA and affected DVA. However, no research has been performed to investigate the impact of myopia diopter on DVA in populations with eyeglasses.
This study demonstrates a method to examine DVA in eyeglass-corrected myopia patients and aimed to explore the impact of myopia diopter on moving-object binocular DVA in eyeglass-corrected patients. The research provides a basis for accurately interpreting DVAT in clinical ophthalmology considering the impact of eyeglasses and evidence on the influence of corrected myopia on motion-related activities.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Automatic computer optometry</td>
			<td>TOPCON</td>
			<td>KR8100</td>
			<td></td>
		</tr>
		<tr>
			<td>Corneal topography</td>
			<td>OCULUS</td>
			<td>Pentacam</td>
			<td></td>
		</tr>
		<tr>
			<td>Dynamic visual acuity test design software</td>
			<td>Mathworks</td>
			<td>matlab 2017b</td>
			<td></td>
		</tr>
		<tr>
			<td>Fundus photography</td>
			<td>Optos</td>
			<td>Daytona</td>
			<td></td>
		</tr>
		<tr>
			<td>Matlab</td>
			<td>Mathworks</td>
			<td>2017b</td>
			<td></td>
		</tr>
		<tr>
			<td>Noncontact tonometry</td>
			<td>CANON</td>
			<td>TX-20</td>
			<td></td>
		</tr>
		<tr>
			<td>Phoropter</td>
			<td>&#160;NIDEK</td>
			<td>RT-5100</td>
			<td></td>
		</tr>
		<tr>
			<td>scientific statistical software</td>
			<td>IBM</td>
			<td>SPSS 26.0</td>
			<td></td>
		</tr>
		<tr>
			<td>Slit lamp</td>
			<td>Koniz</td>
			<td>IM 900</td>
			<td></td>
		</tr>
	</tbody>
</table>

binocular dynamic visual acuity in eyeglass-corrected myopic patients

Current clinical visual assessment mainly focuses on static vision. However, static vision may not sufficiently reflect real-life visual function as moving optotypes are frequently observed daily. Dynamic visual acuity (DVA) might reflect real-life situations better, especially when objects are moving at high speeds. Myopia impacts static uncorrected distance visual acuity, conveniently corrected with eyeglasses. However, due to peripheral defocus and prism effects, eyeglass correction might affect DVA. The present research demonstrates a standard method to examine eyeglass-corrected DVA in myopia patients, and aimed to explore the influence of eyeglass correction on DVA. 
Initially, standard subjective refraction was performed to provide the eyeglass prescription to correct the refractive error. Then, binocular distance vision-corrected DVA was examined using the object-moving DVA protocol. Software was designed to display the moving optotypes according to the preset velocity and size on a screen. The optotype was the standard logarithmic visual chart letter E and moves from the middle of the left to the right side horizontally during the test. Moving optotypes with randomized opening direction for each size are displayed. The subjects were required to identify the opening direction of the optotype, and the DVA is defined as the minimum optotype that subjects could recognize, calculated according to the algorithm of logarithmic visual acuity.
Then, the method was applied in 181 young myopic subjects with eyeglass-corrected-to-normal static visual acuity. Dominant eye, cycloplegic subjective refraction (sphere and cylinder), accommodation function (negative and positive relative accommodation, binocular cross-cylinder), and binocular DVA at 40 and 80 degrees per second (dps) were examined. The results showed that with increasing age, DVA first increased and then decreased. When myopia was fully corrected with eyeglasses, a worse binocular DVA was associated with more significant myopic refractive error. There was no correlation between the dominant eye, accommodation function, and binocular DVA.

Subject examinations 
For the enrolled subjects, accommodation function, including negative relative accommodation (NRA), accommodation response (binocular cross-cylinder (BCC)), and positive relative accommodation (PRA), were examined in the mentioned order. Binocular DVA at 40 dps and 80 dps was tested with distance visual acuity-corrected eyeglasses based on subjective refraction.
Statistical analysis 
Statistical analysis was performed using scientific statistical software. Descriptive statistics of continuous variables were reported as the mean and standard deviation, and numbers and proportions were applied for categorical variables. The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes, and the binocular difference (D/ND) was calculated as the value of the nondominant eye subtracted from that of the dominant eye.
A paired t-test was used to compare the DVA at 40 dps and 80 dps. Curve estimation, including linear, quadratic, and cubic models, was used to fit the correlation between DVA and age. To analyze the potentially influential factors, linear mixed models were established to fit with DVA as the dependent variable and included the random effect at the subject level. First, single-factor linear mixed models were applied to estimate the effect of each variable as a covariant or factor according to the type of the variable. The following variables were tested as potential influential factors for DVA: refraction parameters, including the monocular and mean binocular sphere; cylinder and spherical equivalent (SE); and the absolute value of the difference in the binocular sphere; cylinder and SE; dominant-eye parameters, including dominant and nondominant-eye sphere; cylinder and SE; and the difference in the sphere, cylinder, and SE between the dominant and nondominant eye and accommodation function parameters, including NRA, BCC, and PRA.
Next, a multifactor linear mixed model was established to include several potential influential factors in one model. For a preparatory step, collinearity analysis was conducted with the included variables. A variance inflation factor greater than 10 was considered to indicate multicollinearity. Redundant variables were excluded based on clinical significance. Based on the influential factors used, two different models were fitted: the full and dominant-eye models. For the full model, the following variables were included: age; sex; accommodation function parameters (NRA, BCC, and PRA); mean binocular SE and the absolute value of the difference in the binocular cylinder and SE, dominant eye, dominant-eye cylinder, and the difference in cylinder and SE between the dominant and nondominant eyes following the preparatory collinearity analysis. For the dominant-eye model, only dominant-eye parameters were included as influential factors. P &#60; 0.05 denotes a significant difference.
The demographic and main clinical data of the included subjects are shown in Table 1. This study included 181 subjects, with an average age of 27.1 &#177; 6.3 years old, and males accounted for 37.6% of the subjects. The right eye was the dominant eye for 60.2% of subjects. The mean binocular sphere and cylinder were -5.26 &#177; 2.06 D and -0.99 &#177; 0.82 D, respectively. The absolute values of the difference in the binocular sphere and cylinder were 0.85 &#177; 0.91 D and 0.39 &#177; 0.34 D, respectively.
The cumulative LogMAR visual acuity of DVA at 40 and 80 dps and the histogram are presented in Figure 1. The cumulative results demonstrated that 75% of subjects possessed better than 0.2 LogMAR DVA for 40 dps and 62% for 80 dps DVA. The percentage of the subjects with better than 0.1 logMAR 40 dps binocular DVA was 22%, and for 80 dps, the percentage was 12%. The average binocular DVA values at 40 dps and 80 dps were 0.161 &#177; 0.072 and 0.189 &#177; 0.076, respectively, and the 40 dps DVA was significantly better than the 80 dps DVA (P &#60; 0.001).
The results of curve estimation between DVA and age are demonstrated in Figure 2. Significant results were obtained fitting an age-DVA of 40 dps with a quadratic (R2 = 0.38, P = 0.031) and cubic curve (R2 = 0.38, P = 0.030), but not a linear model (R2 = 0.21, P = 0.051). For 80 dps DVA, all the linear (R2 = 0.24, P = 0.035), quadratic (R2 = 0.43, P = 0.019), and cubic (R2 = 0.43, P = 0.020) curves could appropriately fit the age-DVA scatter plot.
Figure 3 demonstrates the effect of each potential influential factor for 40 and 80 dps DVA in single-factor linear mixed models, and the statistical results are summarized in Table 2 and Table 3. Larger right (estimate, -0.012), left (estimate, -0.010), dominant (estimate, -0.010), and nondominant (estimate, -0.010) eye spheres; larger right (estimate, -0.012), left (estimate, -0.010), dominant (estimate, -0.010) and nondominant (estimate, -0.010) eye SEs; and larger mean binocular spheres (estimate, -0.012) and SEs (estimate, -0.012) were significant negative influential factors of 40 dps DVA (P &#60; 0.001 for each variable). For DVA of 80 dps, larger monocular sphere and SE (estimate, -0.012, -0.010, -0.010, -0.010 for right, left, dominant, and nondominant eye, respectively; P &#60; 0.001 for each variable), larger left eye cylinder (estimate, -0.013; P = 0.04), larger nondominant eye cylinder (estimate, -0.016; P = 0.01), smaller binocular cylinder difference between dominant and nondominant eye (estimate, 0.027; P = 0.015), larger mean binocular sphere (estimate, -0.012; P &#60; 0.001) and SE (estimate, -0.012; P &#60; 0.001) were significant negative influential factors. Accommodation function parameters, including NRA, BCC, and PRA, were not significant influential factors for either 40 or 80 dps DVA.
Figure 4 illustrates the effects of factors and covariates for the full variables linear mixed model for 40 and 80 dps DVA, and the results are summarized in Table 4. When 40 dps DVA was used to measure variability, only a larger binocular mean SE (estimate, -0.012; 95% CI, -0.017 to -0.006; P &#60; 0.001) was a significant negative influential factor. Larger mean binocular SE (estimate, -0.011; 95% CI, -0.016 to -0.005; P &#60; 0.001) and older age (estimate, 0.002; 95% CI, 0.00002 to -0.004; P &#60; 0.048) were significant negative influential factors for 80 dps DVA.
Figure 5 shows the effect of factors and covariates for the dominant-eye multifactor linear mixed model, and the results are summarized in Table 5. Variables selected in the dominant-eye model included the dominant eye, dominant eye SE, dominant eye cylinder, binocular cylinder, and SE difference between the dominant and nondominant eyes based on collinearity analysis. When 40 and 80 dps DVA were used to measure variability, only larger dominant-eye SE (estimate, -0.010; 95% CI, -0.015 to -0.005; P &#60; 0.001 for 40 and 80 dps analysis) was a significant negative influential factor.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63864/63864fig01.jpg" /> 
Figure 1: Dynamic visual acuity distribution. (A) Histogram of DVA at 40 dps; (B) Histogram of DVA at 80 dps; (C) Cumulative percentage of DVA at 40 and 80 dps H. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/63864fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63864/63864fig02.jpg" /> 
Figure 2: Scatter plots and fitting curves showing the curve estimation between age and DVA. (A) Linear model for 40 dps DVA; (B) Quadratic model for 40 dps DVA; (C) Cubic model for 40 dps DVA; (D) Linear model for 80 dps DVA; (E) Quadratic model for 80 dps DVA; (F) Cubic model for 80 dps DVA. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/63864fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63864/63864fig03.jpg" /> 
Figure 3: Forest plot showing the single-factor model. The central short stick indicates the estimates; bars indicate the 95% confidence interval. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/63864fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63864/63864fig04.jpg" /> 
Figure 4: Forest plot showing the full model. The central short stick indicates the estimates; bars indicate the 95% confidence interval. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/63864fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63864/63864fig05.jpg" /> 
Figure 5: Forest plot showing the dominant-eye model. The central short stick indicates the estimates; bars indicate the 95% confidence interval. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/63864fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Demographic and main clinical data of the study population. The demographic data, refraction parameters, dominant eye parameters, and accommodation function of the study population are shown. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second; BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/Table 1 (18).xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Results of single-factor linear mixed model for 40 dps DVA variability. The statistical results of a linear mixed model are demonstrated with DVA of 40 dps as the dependent variable. The refraction, dominant eye, and accommodation function parameters serve as independent variables. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second; BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/Table 2 (6).xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Results of single-factor linear mixed model for 80 dps DVA variability. The statistical results of a linear mixed model are demonstrated with DVA of 80 dps as the dependent variable. The refraction, dominant eye, and accommodation function parameters serve as independent variables. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second; BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/Table 3 (3).xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Results of full model for 40 and 80 dps DVA variability. The statistical results of a multifactor linear mixed model are demonstrated with DVA of 40 or 80 dps as the dependent variable. The variables include age, sex, accommodation function parameters, mean SE, and the absolute value of the difference in the binocular cylinder and SE, dominant eye, dominant-eye cylinder, and the difference in cylinder and SE between the dominant and nondominant eyes following the preparatory collinearity analysis. *The binocular difference (OD/OS) was the absolute value of the difference between the right and left eyes. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second; BCC = binocular cross-cylinder; NRA = negative relative accommodation; PRA = positive relative accommodation; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/Table 4 (1).xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Results of dominant-eye model for 40 and 80 dps DVA variability. The statistical results of a linear mixed model are demonstrated with DVA of 40 or 80 dps as the dependent variable. The variables include dominant eye parameters. #The binocular difference (D/ND) was calculated subtracting the nondominant eye value from the dominant eye value. Abbreviations: DVA = dynamic visual acuity; dps = degrees per second; CI = confidence interval; DVA = dynamic visual acuity; SE = spherical equivalent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63864/Table 5 (1).xlsx" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients at JoVE.com

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

The present research demonstrates a method to accurately examine dynamic visual acuity (DVA) in myopic subjects with eyeglass correction. Further analysis indicated that the closer the refraction state to emmetropia, the better the eyeglass-corrected binocular DVA is at both 40 and 80 degrees per second.

Current clinical visual assessment mainly focuses on static vision. However, static vision may not sufficiently reflect real-life visual function as ...

binocular-dynamic-visual-acuity-in-eyeglass-corrected-myopic-patients

Nanyang Technological University

Research

JoVE Journal

Medicine

2.4K Views.  Peking University Third Hospital. The present research demonstrates a method to accurately examine dynamic visual acuity (DVA) in myopic subjects with eyeglass correction. Further analysis indicated that the closer the refraction state to emmetropia, the better the eyeglass-corrected binocular DVA is at both 40 and 80 degrees per second.

Video: Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Dissection of Human Vitreous Body Elements for Proteomic Analysis

The vitreous is an optically clear, collagenous extracellular matrix that fills the inside of the eye and overlies the retina. 1,2 Abnormal interactions between vitreous substructures and the retina underlie several vitreoretinal diseases, including retinal tear and detachment, macular pucker, macular hole, age-related macular degeneration, vitreomacular traction, proliferative vitreoretinopathy, proliferative diabetic retinopathy, and inherited vitreoretinopathies. 1,2 The molecular composition of the vitreous substructures is not known. Since the vitreous body is transparent with limited surgical access, it has been difficult to study its substructures at the molecular level. We developed a method to separate and preserve these tissues for proteomic and biochemical analysis. The dissection technique in this experimental video shows how to isolate vitreous base, anterior hyaloid, vitreous core, and vitreous cortex from postmortem human eyes. One-dimensional SDS-PAGE analyses of each vitreous component showed that our dissection technique resulted in four unique protein profiles corresponding to each substructure of the human vitreous body. Identification of differentially compartmentalized proteins will reveal candidate molecules underlying various vitreoretinal diseases.

This video shows an effective technique for differentiating and dissecting the various semi-transparent structures of the human vitreous body in post mortem eyes.

The vitreous is an optically clear, collagenous extracellular matrix that fills the inside of the eye and overlies the retina. 1,2 Abnormal interactions ...

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily life, despite similar visual deficits. We assume this could be due to variable development of compensatory strategies such as altered visual scanning behavior. Scanning compensatory therapy (SCT) is studied as part of the visual training after infarction next to vision restoration therapy. SCT consists of learning to make larger eye movements into the blind field enlarging the visual field of search, which has been proven to be the most useful strategy1, not only in natural search tasks but also in mastering daily life activities2. Nevertheless, in clinical routine it is difficult to identify individual levels and training effects of compensatory behavior, since it requires measurement of eye movements in a head unrestrained condition. Studies demonstrated that unrestrained head movements alter the visual exploratory behavior compared to a head-restrained laboratory condition3. Martin et al.4 and Hayhoe et al.5 showed that behavior demonstrated in a laboratory setting cannot be assigned easily to a natural condition. Hence, our goal was to develop a study set-up which uncovers different compensatory oculomotor strategies quickly in a realistic testing situation: Patients are tested in the clinical environment in a driving simulator. SILAB software (Wuerzburg Institute for Traffic Sciences GmbH (WIVW)) was used to program driving scenarios of varying complexity and recording the driver's performance. The software was combined with a head mounted infrared video pupil tracker, recording head- and eye-movements (EyeSeeCam, University of Munich Hospital, Clinical Neurosciences).
The positioning of the patient in the driving simulator and the positioning, adjustment and calibration of the camera is demonstrated. Typical performances of a patient with and without compensatory strategy and a healthy control are illustrated in this pilot study. Different oculomotor behaviors (frequency and amplitude of eye- and head-movements) are evaluated very quickly during the drive itself by dynamic overlay pictures indicating where the subjects gaze is located on the screen, and by analyzing the data. Compensatory gaze behavior in a patient leads to a driving performance comparable to a healthy control, while the performance of a patient without compensatory behavior is significantly worse. The data of eye- and head-movement-behavior as well as driving performance are discussed with respect to different oculomotor strategies and in a broader context with respect to possible training effects throughout the testing session and implications on rehabilitation potential.

Patients with visual deficits after stroke report about different constraints in daily life most likely due to variable compensatory strategies, which are difficult to differentiate in clinical routine. We present a clinical set-up which allows measurement of different compensatory head- and eye-movement-strategies and evaluating their effects on driving performance.

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily ...

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind. Using only audio based cues and set within the context of a video game metaphor, users gather relevant spatial information regarding a building's layout. This allows the user to develop an accurate spatial cognitive map of a large-scale three-dimensional space that can be manipulated for the purposes of a real indoor navigation task. After game play, participants are then assessed on their ability to navigate within the target physical building represented in the game. Preliminary results suggest that early blind users were able to acquire relevant information regarding the spatial layout of a previously unfamiliar building as indexed by their performance on a series of navigation tasks. These tasks included path finding through the virtual and physical building, as well as a series of drop off tasks. We find that the immersive and highly interactive nature of the AbES software appears to greatly engage the blind user to actively explore the virtual environment. Applications of this approach may extend to larger populations of visually impaired individuals.

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind.

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind. Using only audio ...

Detecting Abnormalities in Choroidal Vasculature in a Mouse Model of Age-related Macular Degeneration by Time-course Indocyanine Green Angiography

Indocyanine Green&#160;Angiography (or ICGA) is a technique performed by ophthalmologists to diagnose abnormalities of the choroidal and retinal vasculature of various eye diseases such as age-related macular degeneration (AMD). ICGA is especially useful to image the posterior choroidal vasculature of the eye due to its capability of penetrating through the pigmented layer with its infrared spectrum. ICGA time course can be divided into early, middle, and late phases. The three phases provide valuable information on the pathology of eye problems. Although time-course ICGA by intravenous (IV) injection is widely used in the clinic for the diagnosis and management of choroid problems, ICGA by intraperitoneal injection (IP) is commonly used in animal research. Here we demonstrated the technique to obtain high-resolution ICGA time-course images in mice by tail-vein injection and confocal scanning laser ophthalmoscopy. We used this technique to image the choroidal lesions in a mouse model of age-related macular degeneration. Although it is much easier to introduce ICG to the mouse vasculature by IP, our data indicate that it is difficult to obtain reproducible ICGA time course images by IP-ICGA. In contrast, ICGA via tail vein injection provides high quality ICGA time-course images comparable to human studies. In addition, we showed that ICGA performed on albino mice gives clearer pictures of choroidal vessels than that performed on pigmented mice. We suggest that time-course IV-ICGA should become a standard practice in AMD research based on animal models.

Indocyanine Green Angiography (or ICGA) performed by tail vein injection provides high quality ICGA time course images to characterize abnormalities in mouse choroid.

Indocyanine Green&#160;Angiography (or ICGA) is a technique performed by ophthalmologists to diagnose abnormalities of the choroidal and retinal ...

Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication

Computerized dynamic posturography with the EquiTest is an objective technique for measuring postural strategies under challenging static and dynamic conditions. As part of a diagnostic assessment, the early detection of postural deficits is important so that appropriate and targeted interventions can be prescribed. The Sensory Organization Test (SOT) on the EquiTest determines an individual&#39;s use of the sensory systems (somatosensory, visual, and vestibular) that are responsible for postural control. Somatosensory and visual input are altered by the calibrated sway-referenced support surface and visual surround, which move in the anterior-posterior direction in response to the individual&#39;s postural sway. This creates a conflicting sensory experience. The Motor Control Test (MCT) challenges postural control by creating unexpected postural disturbances in the form of backwards and forwards translations. The translations are graded in magnitude and the time to recover from the perturbation is computed.
Intermittent claudication, the most common symptom of peripheral arterial disease, is characterized by a cramping pain in the lower limbs and caused by muscle ischemia secondary to reduced blood flow to working muscles during physical exertion. Claudicants often display poor balance, making them susceptible to falls and activity avoidance. The Ankle Brachial Pressure Index (ABPI) is a noninvasive method for indicating the presence of peripheral arterial disease and intermittent claudication, a common symptom in the lower extremities. ABPI is measured as the highest systolic pressure from either the dorsalis pedis or posterior tibial artery divided by the highest brachial artery systolic pressure from either arm. This paper will focus on the use of computerized dynamic posturography in the assessment of balance in claudicants.

Individuals with intermittent claudication exhibit poor balance compared to healthy controls. Computerized dynamic posturography is an objective method for measuring an individual's postural responses to balance disturbances. This provides an objective reflection of the person&#x2019;s ability to respond to situations under which sensory stimuli are altered and unexpected perturbations occur.

Computerized dynamic posturography with the EquiTest is an objective technique for measuring postural strategies under challenging static and dynamic ...

Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients

In order to follow optic neuritis patients and evaluate the effectiveness of their treatment, a handy, accurate and quantifiable tool is required to assess changes in myelination at the central nervous system (CNS). However, standard measurements, including routine visual tests and MRI scans, are not sensitive enough for this purpose. We present two visual tests addressing dynamic monocular and binocular functions which may closely associate with the extent of myelination along visual pathways. These include Object From Motion (OFM) extraction and Time-constrained stereo protocols. In the OFM test, an array of dots compose an object, by moving the dots within the image rightward while moving the dots outside the image leftward or vice versa. The dot pattern generates a camouflaged object that cannot be detected when the dots are stationary or moving as a whole. Importantly, object recognition is critically dependent on motion perception. In the Time-constrained Stereo protocol, spatially disparate images are presented for a limited length of time, challenging binocular 3-dimensional integration in time. Both tests are appropriate for clinical usage and provide a simple, yet powerful, way to identify and quantify processes of demyelination and remyelination along visual pathways. These protocols may be efficient to diagnose and follow optic neuritis and multiple sclerosis patients.
In the diagnostic process, these protocols may reveal visual deficits that cannot be identified via current standard visual measurements. Moreover, these protocols sensitively identify the basis of the currently unexplained continued visual complaints of patients following recovery of visual acuity. In the longitudinal follow up course, the protocols can be used as a sensitive marker of demyelinating and remyelinating processes along time. These protocols may therefore be used to evaluate the efficacy of current and evolving therapeutic strategies, targeting myelination of the CNS.

Object From Motion (OFM) and Time-constrained stereo protocols are sensitive tools to identify monocular and binocular dynamic visual function deficits, which are uniquely affected in optic neuritis patients. Furthermore, these tests may be used as quantitative noninvasive tools to assess the extent of myelination along visual pathways.

In order to follow optic neuritis patients and evaluate the effectiveness of their treatment, a handy, accurate and quantifiable tool is required to ...

Assessment of Vascular Function in Patients With Chronic Kidney Disease

Patients with chronic kidney disease (CKD) have significantly increased risk of cardiovascular disease (CVD) compared to the general population, and this is only partially explained by traditional CVD risk factors. Vascular dysfunction is an important non-traditional risk factor, characterized by vascular endothelial dysfunction (most commonly assessed as impaired endothelium-dependent dilation [EDD]) and stiffening of the large elastic arteries. While various techniques exist to assess EDD and large elastic artery stiffness, the most commonly used are brachial artery flow-mediated dilation (FMDBA) and aortic pulse-wave velocity (aPWV), respectively. Both of these noninvasive measures of vascular dysfunction are independent predictors of future cardiovascular events in patients with and without kidney disease. Patients with CKD demonstrate both impaired FMDBA, and increased aPWV. While the exact mechanisms by which vascular dysfunction develops in CKD are incompletely understood, increased oxidative stress and a subsequent reduction in nitric oxide (NO) bioavailability are important contributors. Cellular changes in oxidative stress can be assessed by collecting vascular endothelial cells from the antecubital vein and measuring protein expression of markers of oxidative stress using immunofluorescence. We provide here a discussion of these methods to measure FMDBA, aPWV, and vascular endothelial cell protein expression.

The degree of vascular dysfunction and contributing physiological mechanisms can be assessed in patients with chronic kidney disease by measuring brachial artery flow-mediated dilation, aortic pulse-wave velocity, and vascular endothelial cell protein expression.

Patients with chronic kidney disease (CKD) have significantly increased risk of cardiovascular disease (CVD) compared to the general population, and this ...

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with distinct functions and gene expression profiles. The roles of microglial activation in health, injury and disease remain incompletely understood due to their dynamic and complex regulation in response to changes in their microenvironment. Thus, it is critical to non-invasively monitor and analyze changes in microglial activation over time in the intact organism. In vivo studies of microglial activation have been delayed by technical limitations to tracking microglial behavior without altering the CNS environment. This has been particularly challenging during chronic neurodegeneration, where long-term changes must be tracked. The retina, a CNS organ amenable to non-invasive live imaging, offers a powerful system to visualize and characterize the dynamics of microglia activation during chronic disorders.
This protocol outlines methods for long-term, in vivo imaging of retinal microglia, using confocal ophthalmoscopy (cSLO) and CX3CR1GFP/+ reporter mice, to visualize microglia with cellular resolution. Also, we describe methods to quantify monthly changes in cell activation and density in large cell subsets (200-300 cells per retina). We confirm the use of somal area as a useful metric for live tracking of microglial activation in the retina by applying automated threshold-based morphometric analysis of in vivo images. We use these live image acquisition and analyses strategies to monitor the dynamic changes in microglial activation and microgliosis during early stages of retinal neurodegeneration in a mouse model of chronic glaucoma. This approach should be useful to investigate the contributions of microglia to neuronal and axonal decline in chronic CNS disorders that affect the retina and optic nerve.

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Microglia activation and microgliosis are key responses to chronic neurodegeneration. Here, we present methods for in vivo, long-term visualization of retinal CX3CR1-GFP+ microglial cells by confocal ophthalmoscopy, and for threshold and morphometric analyses to identify and quantify their activation. We monitor microglial changes during early stages of age-related glaucoma.

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with ...

A Simple Mechanical Procedure to Create Limbal Stem Cell Deficiency in Mouse

Limbal stem cell deficiency (LSCD) is a state of malfunction or loss of limbal epithelial stem cells, after which the corneal epithelium is replaced with conjunctiva. Patients suffer from recurrent corneal defects, pain, inflammation, and loss of vision.
Previously, a murine model of LSCD was described and compared to two other models. The goal was to produce a consistent mouse model of LSCD that both mimics the phenotype in humans and lasts long enough to make it possible to study the disease pathophysiology and to evaluate new treatments. Here, the technique is described in more detail.
A motorized tool with a rotating burr has been designed to remove the rust rings from the corneal surface or to smooth the pterygium bed in patients. It is a suitable device to create the desired LSCD model. It is a readily available, easy-to-use tool with a fine tip that makes it appropriate for working on small eyes, as in mice. Its application prevents unnecessary trauma to the eye and it does not result in unwanted injuries, as often is the case with chemical injury models. As opposed to a blunt scraper, it removes the epithelium with the basement membrane. In this protocol, the limbal area was abraded two times, and then the whole corneal epithelium was shaved from limbus to limbus. To avoid stroma injury, care was taken not to brush the corneal surface once the epithelium was already removed.

The following article provides an easy, reproducible technique to effectively create a sustainable mouse model of limbal stem cell deficiency (LSCD). This animal model is useful in testing and comparing the efficacy of treatments for limbal stem cell diseases.

Limbal stem cell deficiency (LSCD) is a state of malfunction or loss of limbal epithelial stem cells, after which the corneal epithelium is replaced with ...

Mechanical and Controlled PRP Injections in Patients Affected by Androgenetic Alopecia

23 patients (18 male and 5 female) aged 21-70 years who displayed male pattern hair loss (MPHL) in Stage 1 to Stage 5 as determined by the Norwood-Hamilton classification scale, and female pattern hair loss (FPHL) in Stage 1 to Stage 2 as determined by the Ludwig classification scale, were treated with non-activated autologous platelet-rich plasma (A-PRP). Autologous blood (55 mL) was harvested using sodium citrate as an anticoagulant. A-PRP (23 mL) was produced for all cases using a closed system according to the transfusion service protocol. Following centrifugation (260 x g for 10 min) the A-PRP was inserted in a laser light selector device, and after the centrifugation, 9 mL of A-PRP was collected.
The scalp of the patients affected by androgenetic alopecia (AGA) was divided into four areas (frontal, parietal, vertex, and occipital); local anesthesia was not performed. Interfollicular A-PRP injections (0.2 mL x cm2) were performed by controlled and mechanical injections scheduled at a depth of 5 mm using a medical injector gun. Treatment sessions were performed with a 30-day interval. For each patient, three treatment sessions were performed.
PRP was injected in the androgen-related areas of scalp affected by hair loss. Placebo (normal saline solution) was loaded in another syringe (10 mL) and injected on the adjacent side in a similar fashion.

This paper aims to describe the interfollicular autologous platelet-rich plasma injections (0.20 mL x cm2) in the scalp of 23 patients affected by androgenetic alopecia at a depth of 5 mm using a medical injector gun equipped with a 30 gauge needle, in three sessions.