Yu Deyue, Perry Landon, Kerwin Thomas, Yang Jingzhen, Lu Zhong-Lin
College of Optometry, The Ohio State University, Columbus, OH, United States.
Driving Simulation Laboratory, The Ohio State University, Columbus, OH, United States.
Front Neurosci. 2025 Apr 15;19:1511366. doi: 10.3389/fnins.2025.1511366. eCollection 2025.
While traditional driving ability evaluations typically assess visual acuity (VA) under photopic conditions, visual functions other than photopic VA also play a crucial role in driving. For older individuals, age-related vision change can impact driving abilities, particularly under mesopic lighting conditions with glare during nighttime driving. This study aims to investigate how visual functions vary across different lighting conditions, examine their correlations, and identify the principal visual function metrics that enable a more comprehensive assessment of active older drivers.
Twenty active older drivers (aged 63 to 87 years; mean = 70 years) participated. All possessed valid driver's licenses, drove at least once per week, and did not use any low vision aids for driving. Six participants had undergone cataract surgery. Participants completed a battery of visual tasks with their habitual correction for daily driving. VA, contrast sensitivity function (CSF) and visual field map (VFM) were measured under photopic and mesopic conditions using the qVA, qCSF and qVFM procedures. Additionally, VA and CSF were assessed in the presence of glare under mesopic condition. Correlations and principal component analysis (PCA) were conducted to identify principal visual function metrics.
VA and CSF exhibited variation across lighting conditions (s < 0.005), with significant correlations observed between multiple pairs of visual functions. A trend of stronger correlations was found in participants who had undergone cataract surgery. PCA suggested that four metrics are necessary to explain most of the nonrandom variation in the data. Mesopic VA was the most informative measure, accounting for 47% of the total variance. Adding a measure of VFM increased the explained variance to 70%. To explain approximate 80% of the total variation, three measures were required, while four measures were needed to achieve 90%.
Using a PCA-based selection approach, the minimal set of visual function metrics for evaluating visual function in active older drivers was identified. These findings provide valuable insights for establishing optimal clinical outcome measures for this population.
传统的驾驶能力评估通常在明视觉条件下评估视力（VA），但除明视觉VA之外的视觉功能在驾驶中也起着至关重要的作用。对于老年人而言，与年龄相关的视力变化会影响驾驶能力，尤其是在夜间驾驶时的中间视觉照明条件下且伴有眩光的情况下。本研究旨在调查视觉功能在不同照明条件下如何变化，检查它们之间的相关性，并确定能够更全面评估活跃老年驾驶员的主要视觉功能指标。
20名活跃的老年驾驶员（年龄63至87岁；平均年龄 = 70岁）参与了研究。所有参与者都持有有效驾照，每周至少驾驶一次，且在驾驶时不使用任何低视力辅助设备。6名参与者接受过白内障手术。参与者戴着日常驾驶时习惯佩戴的矫正眼镜完成了一系列视觉任务。使用qVA、qCSF和qVFM程序在明视觉和中间视觉条件下测量VA、对比敏感度函数（CSF）和视野图（VFM）。此外，在中间视觉条件下有眩光存在时评估VA和CSF。进行相关性分析和主成分分析（PCA）以确定主要视觉功能指标。
VA和CSF在不同照明条件下表现出变化（s < 0.005），多对视觉功能之间观察到显著相关性。在接受过白内障手术的参与者中发现了更强相关性的趋势。PCA表明，需要四个指标来解释数据中大部分的非随机变化。中间视觉VA是最具信息量的测量指标，占总方差的47%。添加一项VFM测量指标可将解释方差提高到70%。要解释约80%的总变化，需要三项测量指标，而要达到90%则需要四项测量指标。
使用基于PCA的选择方法，确定了用于评估活跃老年驾驶员视觉功能的最小视觉功能指标集。这些发现为为该人群建立最佳临床结局指标提供了有价值的见解。
