# Assessment of visual function under various lighting conditions in a cohort of active older drivers: dimensionality and principal metrics

## TL;DR

This study explores how different visual functions affect driving in older adults under various lighting conditions, identifying key metrics for better evaluation.

## Contribution

The study introduces a PCA-based approach to identify minimal visual function metrics for assessing older drivers under different lighting.

## Key findings

- Mesopic VA was the most informative measure, accounting for 47% of total variance.
- Adding a VFM measure increased explained variance to 70%.
- Four metrics were needed to explain 90% of the variation in visual function data.

## Abstract

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 (ps < 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.