Optometry and Vision Department, Facultad de Óptica y Optometría, Avda, Arcos de Jalón 118, 28037 Madrid, Spain.
Neuro-Computing and Neuro-Robotics Research Group, Universidad Complutense de Madrid, Avda, Arcos de Jalón 118, 28037 Madrid, Spain.
Int J Environ Res Public Health. 2021 Apr 29;18(9):4733. doi: 10.3390/ijerph18094733.
(1) Background: In mesopic lighting conditions, or under adverse environmental circumstances, visual information is reduced, which increases the risk of traffic accidents. This effect could be reduced with a precise evaluation of the visual function under mesopic conditions, but it is difficult to replicate in clinics. This study aims to develop an easy-to-adopt method to evaluate mesopic visual acuity (VA) in drivers. (2) Methods: Prospective and observational study in drivers. logMAR mesopic VA was compared with photopic VA measured under different combinations of contrast charts and filters to find the combination that responds best to mesopic conditions. (3) Results: Fifty-six drivers were examined. The best correlation was found with an 80% density filter and a Weber contrast chart of 20%. The logMAR VA for this combination was 0.01 ± 0.11, which was close to the mesopic VA values (0.01 ± 0.12). The difference between both logMAR VA was 0.00 ± 0.06 (R = 0.86; ≤ 0.001; ICC = 0.86). (4) Conclusions: The use of 20% contrast optotypes and the interposition of an 80% filter under photopic conditions provide VA values similar to those measured under mesopic lighting conditions, making this simple system a good predictor of mesopic VA values.
(1) 背景:在中间视觉照明条件下或在不利的环境情况下,视觉信息会减少,这增加了交通事故的风险。通过对中间视觉条件下的视觉功能进行精确评估,可以降低这种影响,但在临床实践中很难复制。本研究旨在开发一种易于采用的方法来评估驾驶员的中间视觉视力(VA)。
(2) 方法:对驾驶员进行前瞻性和观察性研究。比较了 logMAR 中间视力 VA 与不同对比度图表和滤光片组合下测量的明视觉 VA,以找到对中间视觉条件反应最佳的组合。
(3) 结果:检查了 56 名驾驶员。发现 80%密度滤光片和 20%韦伯对比度图表的组合相关性最佳。该组合的 logMAR VA 为 0.01 ± 0.11,接近中间视力 VA 值(0.01 ± 0.12)。两种 logMAR VA 之间的差异为 0.00 ± 0.06(R = 0.86; ≤ 0.001;ICC = 0.86)。
(4) 结论:在明视觉条件下使用 20%对比度视标并插入 80%滤光片可提供与中间视觉照明条件下测量的 VA 值相似的值,因此,这种简单的系统是中间视觉 VA 值的良好预测指标。