Visual acuity in children: The development of crowded and single letter acuities
DOI:
https://doi.org/10.5384/sjovs.vol4i2p21Keywords:
Visual acuity, crowding, single letter, development, childrenAbstract
Previous research has explored the development of visual acuity in babies and children, and it has been established that the measured acuities are dependent on the methods used to test visual function. In the present study the developmental trends in crowded versus single letter visual acuities were explored. 343 children 5 to 11 years of age, was tested using Glasgow Acuity Cards (GAC), and a significant correlation between binocular visual acuity and age was found using simple regression analysis (y=0.023x + 0.81, r2 = 0.32). Mean values on crowded visual acuity varied between 0.95 ± 0.6 in 5 year olds to 1.08 ± .09 logMAR in 11 year olds. One way ANOVA confirmed the developmental trend (F(1, 341)=160.45, p<0.0001). In a second study, this result were replicated in a smaller group of fully corrected children (regression analysis showed significant development with age, y=0.025x+.86, r2 = 0.429, one way ANOVA: F(1,72) = 54.11, p<0.0001). The repeatability of the GAC was tested using a test-retest strategy, 6 months apart. A significant improvement of monocular visual acuities at the second retest may be explained, at least in part, by the expected development during this period. A single letter acuity test was constructed by matching GAC optotypes. Fiftyt two emmetropic children (mean age 8.21±1.76 yrs) were tested on both tests. Developmental trends were then compared for single and crowded acuities. Linear regressions were F(1,50)=23.87, p=0.0001, y=0.019x+0.915, r2=0.31 for crowded letters and F(1,50)=5.81, p<0.05, y=0.009x+1.07, r2=0.104 for single letters. Repeated measures ANOVA (test * age) showed reliable differences for both main effects. Single letter acuity was found to display a very slight developmental trend, and was better than crowded acuity. When deciding normal values and cut-off limits, one have to take into consideration which test is being used as normative data will not directly translate from one test to another.Metrics
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