Estimating the Impact of Aging on Visual Function Using Useful Field of View (UFOV) with a Focus on the Population of Gangwon-do in Korea

Abstract

Background/objectives: There is a need in Korea for research estimating the impact of aging using the Useful Field Of View (UFOV) test, which can evaluate visual function for elderly drivers.

Methods: This observational study involved young people in their twenties and thirties, later-middle-aged people in their fifties or older, and elderly people 65 or older recruited from the Gangwon-do region. UFOV testing was conducted on the participants where the participants completed a questionnaire about general and driving-related characteristics. A one-way analysis of variance (ANOVA) was performed to analyze the mean difference by age group, and a Pearson correlation analysis was carried out to evaluate the correlation between age and visual function. In addition, a simple linear regression analysis was conducted to verify UFOV subdomains that can confirm changes according to age increasing.

Results: Findings after analyzing UFOV subtest differences by age group revealed significant differences in the visual function index of the young, later-middle-aged, and elderly in all three tests, and the difference between the later-middle-aged and old groups was only found in divided attention. The correlation between age and visual function was significant in all three subtests. And all three subtests were confirmed to be indicators that can verify changes according to increasing age.

Conclusions: This study showed that visual function significantly decreases with age. Selective attention was confirmed as a visual function type that changes sensitively according to increasing age.

Keywords: aging; driving; elderly people; later-middle-aged; visual function.

Figure 1

This figure is a graph explaining the factorial ANOVA of the UFOV subtest by increasing age and driving license states: (a) estimated marginal means of visual processing speed; (b) estimated marginal means of divided attention; and (c) estimated marginal means of selective attention.

Figure 2

This figure is a graph explaining the simple linear uni-variable regression of the UFOV subtest by increasing age: (a) normal P–P plot of regression standardized residual with subtest 1; (b) normal P–P plot of regression standardized residual with subtest 2; (c) normal P–P plot of regression standardized residual with subtest 3; and (d) scatter plot between UFOV subtests and age.

Figure 3

This figure is a graph explaining the simple linear uni-variable regression of the UFOV subtest by increasing age: (a) normal P–P plot of regression standardized residual with subtest 1 log data; (b) normal P–P plot of regression standardized residual with subtest 2 log data; (c) normal P–P plot of regression standardized residual with subtest 3 log data; and (d) scatter plot between UFOV subtests’ log data and age.

Figure 4

This figure is a graph explaining the simple linear uni-variable regression of the UFOV subtest by increasing age: (a) normal P–P plot of regression standardized residual with subtest 1; (b) normal P–P plot of regression standardized residual with subtest 2; (c) normal P–P plot of regression standardized residual with subtest 3; and (d) scatter plot between UFOV subtests and driving experience.