We used G*Power 3.1
15 to perform an a priori power calculation to determine the appropriate sample size. To detect a significant difference between the groups at the two-sided α = 0.05 level with an estimated effect size f of 0.8 based on a previous study
12 and 95% power, we calculated that we needed to recruit 30 subjects. To account for up to 20% drop out, we recruited 36 subjects in total; that is,12 per group. We performed statistics with GraphPad Prism 5.0 (La Jolla, CA, USA) and IBM SPSS Version 18.0 (Armonk, NY, USA). To compare categorical variables, we used the χ
2 test of Independence. For race/ethnicity, we used the Freeman-Halton extension of the Fisher exact test to account for sparse expected cell values. To compare three means, we used the 1-way ANOVA with Tukey HSD post hoc test. To compare the changes in the scores after the task on the questionnaire items, we used the Mann-Whitney
U test. As needed, we assessed the normality of the data graphically and with the Kolmogorov-Smirnov test. To determine whether variables significantly differed from 0, we used the 1-sample
t-test or the 1-sample Wilcoxon signed-rank test. To determine whether wearing low-blocking or high-blocking eyeglasses during the computer task attenuated eye fatigue as measured by the change in CFF after computer use after adjusting for possible confounding variables, we generated a multivariable linear regression model. Our model included forced entry of the following predictor variables: age, sex, contact lens use (dichotomized as yes or no), and lens group assignment (dummy-coded as two dichotomous variables to indicate assignment to one of three groups). We considered
P < 0.05 to be statistically significant.