Older Driver Mobility: The Impact of Visual and Cognitive Decline on Safe Driving
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Published: 29th May 2020
Older Driver Mobility: The Impact of Visual and Cognitive Decline on Safe Driving
Older adults aged 65 and older account for every one out of six drivers on the road and are continuing to drive longer than previous generations (Mizenko, Tefft, Arnold & Grabowski, 2014). As individuals age, there are unique changes in their physical, visual, and cognitive ability that makes driving more difficult. The U.S Department of Highway and Safety found per mile crash rates increase greatly starting at age 70 (Insurance Institute for Highway Safety, 2017). In order to drive safely, older adults need to visually see and scan the roadway as well as cognitively interpret driving situations in order react quickly and appropriately. Visual and cognitive age related changes in older adults preclude safe driving. Therefore, it is important that older drivers receive adequate assessment through a combination of visual and cognitive functioning tests as well as obtain proper interventions to delay driving cession live an enhanced quality of life.
Out of all the senses used in driving, vision is the most dominant (Ball & Rebok, 1994). As individuals age, there is a decline in visual acuity, or the sharpness of vision, due to anatomical changes of the eye and increased occurrences of diseases such as glaucoma, macular degeneration, and cataracts (American Medical Association (AMA), 2003). The anatomical changes that occur in the eye are a decrease in pupil size, the lens becoming less transparent and more yellow, a decrease in the number of rods and cones, and changes in the refraction of light rays (Saxon, Etten & Perkins, 2015).While the central assessment used by state licensing departments to check eye ability is visual acuity is, there has long been a poor correlation between reduced visual acuity and crash rate (Cross, West, Rubin, Ball, McGwin, Owsley & Roenker, 2009; Owsley, Sloane, Bruni, Ball, Roenker, 1991).
A more accurate predictor of crash incidence is contrast sensitivity (AMA, 2003). Contrast sensitivity also referred to as visual threshold is the minimum amount of light that will activate the visual receptors and spark a nerve impulse to be sent to the brain via the central nervous system (Saxon et al., 2015). Older adults require more light to see a stimulus, therefore they are more likely to monitor their driving and strop driving in low light situations such as thunderstorms, fog, or night driving (Freeman, Munoz, Turano & West, 2005).
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In continuation, contrast sensitivity can be measured by recording how much contrast an individual requires to see letters on a Pelli-Robson eye chart (Owsley et al. 1991). Letters on the chart become more transparent as one moves down the test chart, therefore an individual with low contrast sensitivity would be able to read the bolded letters at the top but not the faint letters at the bottom. In regards to crash rates, California drivers who scored low contrast sensitivity on the Pelli-Robson chart were involved in a far greater number of accidents when driving in heavy traffic compared to those with high contrast sensitivity scores (Hennessy, 1995).
Another key visual change in older adults that impacts driving is the loss of visual fields. Similar to visual acuity, visual fields may decline due to anatomical changes of the eye from aging or from the occurrence of ocular diseases (AMA, 2003). Older adults can lose central and peripheral visual field vision. Central visual field loss weakens an individual’s capability to scan the entire roadway in front and peripheral visual field loss weakens an individual’s ability to see traffic, pedestrians, and road signs in the periphery (AMA, 2003). Visual field loss proves to be indicative of increased crash rates across all age groups as a research study of ten thousand drivers found those with restricted visual field were twice as likely to get in an accident than individuals without field loss (Johnson & Keltner1983).
Multiple studies have proven there are higher correlations of visual field loss and crash rates among older adults (Ball, Owsley, Sloane & Roenker, 1993, Anstey et. al 2005 & Freeman et. al, 2005). Owsley, Ball & McGwin (1998) determined older drivers with field of view loss greater than 40% were had a 20 times greater chance of being in a car accident than those with insignificant visual field losses. To further, older adults with major restrictions in visual field had a six times greater chance of being in a car accident than older adults who had minimal visual field restrictions (Ball, Owsley, Sloane & Roenker, 1993)
To contrast visual skills involved in driving, there are a great deal of cognitive skills an older adult must utilize. The cognitive abilities of executive functioning, memory, and visual attention have proven to be crucial when planning and executing driving responses (Anstey et al., 2005). It is important to stress, each of these abilities are reduced by normal aging and further reduced in cases of mild cognitive impairments (MCI) and dementia (AMA, 2003).
As part of the normal cognitive aging, most older adults have increased difficulty storing or retrieving material from their memory (Saxon et. al, 2015). The stored memories remain largely present and this helps older adults remember how to operate a car, navigate to the destination, and understand what road signs mean. However, declines in working memory means it takes older adults longer to take in information from roadway and process it (AMA, 2003). According to Pyun et al. (2018), driving is correlated with an increased demand on working memory. Therefore, older adults with cognitive impairments have increased difficulty changing lanes and have increased response latency when braking. In regards to crash rates, Hu, Trumble, Foley, Eberhard, & Wallace (1998) found, older men who scored poorly on the word recall working memory test had a 50% increased crash risk.
Executive functioning is dependent on working memory to hold driving information in the brain so correct decisions and subsequent sensory and motor responses can be carried out (Anstey et. al, 2005). Older adults have marked decline in executive functioning with age (AMA, 2003). An example of poor executive functioning is when an older adult has a poor reaction time to a stop sign, as it took them longer to process the stimulus and execute an appropriate response.
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The Trail Test Part B relies on executive functioning as an individual must take in visual information, process it, plan, and then perform proper motor movements (Anstey et al., 2005). In relation to driving outcomes, Ball et al. (2005) found drivers aged 78+ who took greater than 147 seconds to finish the Trails B were two times more likely to cause a motor vehicle accident. Additionally, in a research study which compared older adult drivers with no previous car accidents to older adult drivers with a previous car accident(s), those with a car accident record took more time to complete the Trail B test and made a greater number of mistakes (Anstey et. al, 2005).
A crucial skill that interconnects visual and cognitive domains is visual attention. Visual attention is the capability to focus attention on important roadway information while ignoring distracting or unimportant roadway information. (Anstey et. al, 2005). Moreover, selective attention is the ability to prioritize more crucial information like traffic lights and filter out unimportant stimuli such as roadside advertisements (AMA, 2003). Divided attention is the capability to focus on multiple stimuli simultaneously (AMA, 2003). An example of divided attention is focusing on stop lights and the traffic pattern of the cars while driving (AMA, 2003). In older adults, there is a decline in attention with age and a greater decline in divided attention over selective attention (AMA, 2003).
UFOV Assessment and Cognitive Training
A key assessment to measure visual attention is the Useful Field of View test (UFOV Test). The UFOV test measures visual attention to focus attention on a central task, divided attention by making an individual focus on a central stimulus and the periphery, and measures the ability to focus on central and peripheral stimuli when interfering objects are present (Anstey et. al, 2005). In Owsley et al. (1991) research study, older adults who failed the UFOV test were 4.2 times more likely to be in an accident and individuals who had multiple accidents recorded all failed the UFOV test. In a succeeding study, Owsley et al. (1998) found older adults who had poor performance on the divided attention UFOV tests were 2.3 times more likely to be involved in a car crash. Overall, these studies demonstrate older adults’ visual attention and visual fields is a powerful predictor of car accidents.
Moreover, the UFOV test has proven to be a powerful predictor of car accidents in older adults because it encompasses multiple visual and cognitive domains beyond visual attention (Owsley et al. 1991; Wood & Owsley, 2014; Freeman et. al, 2005 & Anstey et. al, 2005). These additional domains include visual field, visual processing speed, and reaction time (Wood & Owsley, 2014; Anstey et. al, 2005). Aside from assessment, the UFOV is also used as training for older adults to improve the many visual and cognitive domains UFOV encompasses.
Several researchers have concluded that cognitive training using the UFOV has yielded successful outcomes on older adults cognitive functioning in relation to driving (Roenker, Cissell, Ball, Wadley & Edwards, 2003; Ball, Edwards, Ross & McGwin, 2010). In Roenker et al. (2003) study, older adults driving ability measured, through the completion of an on-road and stimulator driving assessment, were compared before and after UFOV cognitive training. After an average of 4.5 hours of cognitive training spread over two weeks, older adults in the cognitive training group had improvements in speed of processing, visual attention, and visual scanning when compared to the control group that remained consistent after eighteen months (Roenker et al., 2003). In relation to a driving situation, a modest improvement of 227 ms in an older adults’ speed of processing translates to a car stopping 22 feet shorter when traveling 55 miles per hour (Roenker et al., 2003). Therefore, even moderate gains from cognitive training have the potential to mitigate crash rates among older adults. To further, Ball, Edwards, Ross & McGwin (2010), found older adults who received 10 hours of cognitive speed of processing training yielded a 50% lower rate of at-fault car accidents than the control group who received no training. The rate of at-fault accidents was taken from sate-recorded data over a six-year period following the training, thus proving the results were durable over this time frame only (Ball et al., 2010). Overall, this shows there is a relationship between cognitive training and driving improvements among older adults yet further research should be completed to assess the durability of these results over longer periods of time.
Driving is an instrumental daily living activity that allows older adults to be independent and connected with the community (Arbesman, Lieberman, & Berlanstein, 2014). Older adults that stop driving are found to have higher rates of depression, decreased social connectedness, and are at a higher risk of residing in a long term care facility (AMA, 2003, Edwards et al. 2008). Therefore, as the population of older adults aged 65+ is expected to rise to 70 million in 2030 it is important to be aware of the various visual and cognitive changes that affect driving as well as the impact these changes may have on older adults driving privileges and overall well-being (AMA, 2003).
- American Medical Association. (2003). Physician’s Guide to Assessing and Counseling Older Drivers. Washington, D.C.: U.S. Dept. of Transportation, National Highway Traffic Administration
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- Roenker, D. L., Cissell, G. M., Ball, K. K., Wadley, V. G., & Edwards, J. D. (2003). Speed-of-Processing and Driving Simulator Training Result in Improved Driving Performance. HUMAN FACTORS -NEW YORK THEN SANTA MONICA-, (2), 218.
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