Purpose: To determine the impact of visual impairment on health-related quality of life (QoL) measures in adolescents.
Design: School-based, cross-sectional study.
Methods: Adolescents aged 11 to 18 years from the Singapore Cohort Study of the Risk Factors for Myopia were analyzed. QoL scores were determined using parallel child-self and parent proxy-report of PedsQL 4.0 Generic Core Scales. Refractive error was measured using the table-mounted autorefractor (model RK5 Canon Inc, Ltd, Tochigiken, Japan) and habitual distance logarithm of the minimal angle of resolution (logMAR) visual acuity charts were used.
Results: Data on 1,249 adolescents and 948 parents were analyzed. The prevalence of better eye presenting visual impairment > 0.3 logMAR was 5.7%. The mean (standard deviation) total, physical, and psychosocial health scores of all adolescents were 83.6 (11.8), 89.9 (11.8), and 80.3 (13.7). Healthy adolescents with visual impairment reported statistically but not clinically lower total (-3.8; 95% confidence interval [CI], -7.1 to -0.5; P = .03), psychosocial (-4.2; 95% CI, -8.1 to -0.3; P = .03), and school functioning scores (-5.5, 95% CI, -10.2 to -0.9; P = .02) than those with normal vision. However, no significant difference was observed in the parent proxy-reported scores between the two groups. Differences in total scores between high (1.9; 95% CI, -0.6 to 4.4) and low-myopes (0.2; 95% CI, -1.3 to 1.6) compared with nonmyopes were not significant. Comparable scores were also reported by hyperopes, astigmatism, and their counterparts, as well as their parents. Concordance between child and parent proxy-report was < 0.07.
Conclusion: Healthy adolescents with visual impairment experienced statistically though not clinically impaired health related QoL, but refractive errors did not appear to have an impact on QoL.
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http://dx.doi.org/10.1016/j.ajo.2008.09.025 | DOI Listing |
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Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong, 999077, China.
Optical edge detection is a crucial optical analog computing method in fundamental artificial intelligence, machine vision, and image recognition, owing to its advantages of parallel processing, high computing speed, and low energy consumption. Field-of-view-tunable edge detection is particularly significant for detecting a broader range of objects, enhancing both practicality and flexibility. In this work, a novel approach-adaptive optical spatial differentiation is proposed for field-of-view-tunable edge detection.
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