July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Predictors of driving performance in older adults with and without visual impairment
Author Affiliations & Notes
  • Joanne M Wood
    School of Optometry, Queensland Univ of Technology, Brisbane, Queensland, Australia
  • Alex A Black
    School of Optometry, Queensland Univ of Technology, Brisbane, Queensland, Australia
  • Kerry Mallon
    School of Optometry, Queensland Univ of Technology, Brisbane, Queensland, Australia
  • Kaarin Anstey
    School of Psychology, UNSW, Sydney, New South Wales, Australia
    Neuroscience Research Australia, UNSW, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Joanne Wood, None; Alex Black, None; Kerry Mallon, None; Kaarin Anstey, None
  • Footnotes
    Support  NHMRC Grant 1008145; NHMRC Grant 1045024
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4259. doi:https://doi.org/
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    • Get Citation

      Joanne M Wood, Alex A Black, Kerry Mallon, Kaarin Anstey; Predictors of driving performance in older adults with and without visual impairment. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4259. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To evaluate the ability of visual, motor and cognitive tests to predict performance on a standardized on-road driving assessment for older drivers with and without visual impairment.

Methods : 438 participants were tested including 234 drivers with a range of visual impairment (mean age = 71.6 ± 5.6 years) and 204 visually normal drivers (72.6 ± 7.2 years). Participants completed a battery of vision, physical and cognitive function tests. Driving exposure (distance driven in the past year) was also recorded. On-road driving ability was assessed in a dual-brake vehicle along a 19.4 km urban route. Driving performance and safety was rated on a 10-point scale by a driver-trained occupational therapist, masked to the ocular status of the drivers. Univariate and stepwise multiple regression analysis identified factors significantly associated with driving performance.

Results : Univariate logistic regression models identified the significant predictors of unsafe driving performance from each domain (vision, motor, and cognitive). The best predictors were central motion and contrast sensitivity from the vision tests, postural sway (eyes closed on foam) and Timed Up and Go (TUG) from the motor tests, and colour choice reaction time (CCRT) and Trail-Making Test part A (TMT-A) (p<0.01) from the cognitive tests. A stepwise multi-domain model using these predictors revealed significant independent contributions for motion sensitivity, sway, TMT-A, CCRT and driving exposure (full model R2 =36.1%, F(1, 71) = 13.3, p<0.001), with sensitivity and specificity of 81% and 67% for the whole sample and 80% and 73% for the visually impaired sample. A model including only the standard vision licensing tests (visual acuity and visual fields) had poor sensitivity and specificity: 64% and 57% for the total sample and 60% and 54% for the visually impaired sample.

Conclusions : Driving is a complex task, and several cognitive, motor and vision measures, such as novel tests of central motion sensitivity, were significantly associated with driving performance among older adults with and without visual impairment. Standard vision licensing tests, were not, however, strongly predictive of driving performance. Further work is needed to refine a performance-based model for identifying older drivers with unsafe driving abilities, particularly in those with visual impairment.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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