Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 Oct 21.
Published in final edited form as: J Glaucoma. 2012 Apr-May;21(4):221–227. doi: 10.1097/IJG.0b013e3182071832

Driving Simulation as a Performance-based Test of Visual Impairment in Glaucoma

Felipe A Medeiros 1, Robert N Weinreb 1, Erwin Boer 1, Peter N Rosen 1
PMCID: PMC3804259  NIHMSID: NIHMS515708  PMID: 21467952

Abstract

The fundamental goal of glaucoma management is to prevent patients from developing visual impairment sufficient to produce disability in their daily lives and impair their quality of life. Ultimately, patients are interested in how their vision will impact their ability to perform daily activities, such as driving. Although technological advancements such as automated perimetry and devices for optic nerve imaging have resulted in great improvement in our ability to quantify structural and functional damage in glaucoma, the impact on vision-related quality of life of some of the information acquired from these tests remain elusive. On the other hand, performance-based measures may be better correlated to traditional measures of vision health and, more importantly, they provide a more direct measure of disability. Driving simulators can be used as a performance-based test for evaluation of functional impairment in glaucoma. Their use can potentially help the evaluation of driving safety and performance of diseased subjects and provide insight into the different mechanisms involved in causing driving impairment in this disease. The ability to do this in an experimentally controlled and standardized setting enables testing of a much larger number of hypotheses compared to on-road evaluations. Besides evaluating driver fitness, simulators could also potentially be used as a sophisticated test to evaluate cognitive impairment in the context of an everyday task (driving) that has not been available through traditional neuropsychological assessment.


Glaucoma is a progressive optic neuropathy that may result in significant visual impairment. The loss of vision affects the quality of life and also has economic consequences to the patient and to society.1 With an ageing population, it is estimated that over 58 million people will have open angle glaucoma by the year 2020. With approximately 10% of affected individuals being bilaterally blind, glaucoma is a leading cause of irreversible blindness.2 In the United States, the management of glaucoma costs about $2.5 billion per year and the disease is one of the most frequently reported reasons for a visit to the physician.3 Furthermore, impaired health, both physical and mental, and decreased vision-related quality of life add significantly to the burden of the disease.

Clinical evaluation of glaucomatous damage is routinely based on visual field testing and assessment of damage to the optic disc and retinal nerve fiber layer (RNFL).1 Several tests have been introduced into clinical practice to improve functional and structural evaluation in glaucoma, including automated perimeters and devices for imaging of the optic disc and RNFL.4-10 Although these tests may have improved diagnosis and detection of disease progression, it is still unclear how abnormalities detected in these tests impact the ability to perform activities of daily living, such as driving. Further, although glaucoma has traditionally been thought as a disease restricted to the eye, recent studies have suggested that glaucomatous damage may affect cortical and subcortical neuronal populations which may result in further impairment in visual and task performance for diseased patients.11-15 It is unclear whether conventional tests currently used for glaucoma evaluation can adequately measure and quantify the impairment associated with a broader loss of neuronal activity within the entire visual pathway of glaucomatous patients.

Driving is the primary mode of transportation in many countries including the United States and the ability to drive is intimately associated with health-related quality of life.16-19 Several eye diseases have been associated with increased risk for motor vehicle accidents, including glaucoma.20-29 Although it is clear that vision is essential for driving, it is not clear what vision skills and tests are actually more closely related to the ability of driving safely. Despite visual acuity being the most commonly tested visual parameter for licensure by motor vehicle departments, most studies have found no or only a weak association between visual acuity and automobile crash rates. This clearly indicates the need for a more comprehensive evaluation of visual performance as it relates to driving ability.30-34

Driving simulators have been increasingly used to evaluate driving safety due to their efficiency, convenience, expense and ability to assess well-controlled and repeatable driving situations that facilitate the identification of cause-effect relationships.35-42 Because naturalistic driving data provides no experimental control and every event is unique, establishing causality based on records of crashes or from self-reported data will always be challenging. In contrast, driving simulators make it possible to replicate scenarios as often as needed under a variety of controlled settings. Besides assessing driving safety, simulators have also been used as a comprehensive method for the assessment of complex visual functioning in diseases such as Parkinson's and Alzheimer's and may offer an improved method of evaluating cognitive functions in the context of everyday tasks that has not been available through traditional neuropsychological assessment.43-46

In this paper, we review the rationale behind the use of driving simulators for assessment of driving safety and visual disability. In addition, we discuss its potential role as a performance-based test for cognitive impairment in glaucoma.

Glaucoma and Fitness to Drive

Glaucoma has been associated with an increased risk of motor vehicle accidents.20-29,47-50 Patients with visual field defects with a bilateral horizontal field width of less than 100 degrees are more than twice as likely to have an automobile accident as those without field defects.47 McGwin et al51 found that glaucoma patients had a three-fold increase in the odds of a state-reported accident compared to controls without the disease. In another study, Haymes et al52 found an even higher association between glaucoma and motor vehicle collisions, although the control group used in the study was generally healthier and had better measures of visual acuity than glaucoma subjects. Alternatively, Owsley et al.48 showed that people involved in injurious accidents are 3.6 times more likely to have glaucoma than those without any record of accidents. Although glaucoma seems to be associated with increased risk of automobile accidents, no information about stratification of accident risk in relation to the size, shape, location or depth of visual field defects or amount of structural damage is currently available.

Driving is critically important to most people as they depend on maintaining driving privileges for their independence and mobility. In fact, mobility outside home is cited as one of the two most important visual functions by patients with glaucoma.53 Elderly subjects who stop driving are nearly five times more likely to move to a long-term care facility54. They also have higher rates of depression and report a lower quality of life.17-19 Patients with glaucoma frequently report cessation of driving activity as they perceive more difficult driving associated with visual field damage. The Salisbury Eye Evaluation Project reported that patients with bilateral glaucoma were almost 3 times more likely to stop driving compared to individuals without the disease.20 Similar results were found by the Blue Mountains Eye Study.55 Additionally, some glaucoma patients restrict their driving activities, without completely stopping it, by avoiding driving at night or under foggy conditions, for example.25 Further, glaucoma is most prevalent in older drivers who may have other coexisting ocular (e.g., cataract, age-related macular degeneration), and neurologic conditions (e.g., Alzheimer's disease, stroke), which may further impair functional vision and driver fitness.

Although some glaucoma patients stop driving due to perceived difficulties, a large number of patients with very advanced visual field loss continue to drive even after a previous collision.21 This imposes a large risk to themselves and to the society. Further, many unsafe drivers lose insight regarding their driving performance because they have slowly adapted to their declining abilities. Current licensure requirements by Motor Vehicle Departments in most states are mainly based on visual acuity measures and, in some cases, assessment of the visual field. However, mounting evidence suggests that visual acuity is a poor predictor of driving safety and cannot reliably identify high-risk drivers. In a large study of 17,500 California drivers, visual acuity was only weakly correlated with risk of automobile crashes. This lead the authors to conclude that poor visual acuity could not be taken as a causal factor in accidents.56 In another cohort study of 3158 subjects, a 20/40 or worse visual acuity was not associated with risk for motor vehicle collisions.33 Although the presence of bilateral visual field defects has been more consistently associated with an increased risk for accidents, some studies have failed to report a clear association between visual field loss and motor vehicle collision rates.31,57 Further, there is no current agreement on the type and severity of visual field defect that would be associated with an increase in driving risk. In fact, the specific visual field requirements mandated for licensing purposes are highly variable among different states in the United States. It is also interesting to note that glaucoma patients may have a higher risk for motor vehicle collision rates that cannot be completely attributed to visual field defects. This was suggested by a recent study by Hymes and colleagues52 that found a higher rate of collisions among glaucoma subjects even after adjustment for visual field impairment. This suggests that other factors could be responsible for the increased rates.

Driving is a highly complex and often visually and cognitively demanding activity involving simultaneous use of central and peripheral vision, attention switching, kinetic depth perception, gap estimation, dynamically changing spatial orientation and evaluation of hazardous occurrences, all unfolding under the “pressure of time”.26 Conventional visual sensory tests such as Snellen acuity, contrast sensitivity or visual field assessment seek to minimize distractions and secondary task demands. Therefore, it is not surprising that they may not be strong predictors of driving safety. Additionally, stationary visual acuity test targets may not accurately evaluate the vision skills necessary to perform well in the motion-based driving environment. Previous studies have evaluated the Useful Field of View (UFOV; Visual Awareness, Inc, Chicago, IL) as a test to evaluate driving fitness. UFOV is a computer administered test of visual processing speed and visual attention.58 UFOV results have been shown to be a powerful predictor of the risk of motor vehicle crashes in longitudinal studies.31,59 However, although previous studies have evaluated and detected UFOV differences between glaucoma and healthy subjects,50,52,60,61 no comprehensive evaluation of UFOV and its relationship with different structural and functional tests of glaucomatous damage has been performed.

Eyecare specialists are often asked to assess driver fitness in patients with glaucoma or other diseases who have significant visual field defects and are attempting to renew their driver's licenses. In some situations, they feel ill equipped to make these assessments because it is not clear how to correlate visual field defects to specific driving impairments. Additionally, although they can usually reliably estimate loss of visual acuity due to cataracts, macular degeneration, glaucoma or diabetic retinopathy, they feel ill equipped to assess disorders of vision involving visual memory, visual processing speed, visual attention, and other dimensions of vision at the level of the brain. Dynamic visual, cognitive and information processing skills needed for driver fitness are not usually measured or assessed clinically, despite being recommended by the American Academy of Ophthalmology Driving Assessment Policy Statement and by the American Medical Association Physician's Guide to Assessing and Counseling Older Drivers.62 However, as no single, perceptual, cognitive or psychomotor test captures all these domains, eyecare practitioners usually simply document the presence of ocular disease, visual acuity and visual field impairment and do not routinely evaluate dynamic perceptual and cognitive abilities associated with driving. It is clear that estimating functional vision for driving goes beyond static measures of vision and structural alterations caused by eye diseases, and involves dynamic cognitive processing of visual information, which controls driving decisions and psychomotor behaviors. These observations highlight the need for more comprehensive tests of driving performance and safety that can be applied in research and clinical settings, such as driving simulators.

Driving Simulators to Assess Driving Safety and Visual Function Impairment

Driving simulators have become widely used to assess driving safety and performance under a variety of conditions. They have been used to assess general driving behavior in young and old populations63 and in diseases such as stroke,64 traumatic brain or spinal injury,65 Parkinson's,46,66,67 Alzheimer,44,45,68 and attention deficit disorders.69 Also, simulators have been used to study the influence of distracting situations (e.g., cell phone usage),70 and alcohol or drug use on driving behavior.71,72 The gain in their popularity stems from the increase in design sophistication which now enables realistic simulations of complex on-road situations with commercially available low-cost systems. Their primary advantage compared to on-road assessment is the ability to perform controlled experiments, that is, different types of driving scenarios can be standardized and tested repeatedly in order to assess a particular task or behavior. Although still considered the “gold-standard”, on-road assessment of driving safety can be technically difficult and may be associated with prohibitive costs, such as those related to liability insurance, track rental or course development. Furthermore, on road assessments correlate poorly with crash involvement and may carry unacceptable risks for the subjects.

Simulators can range in complexity from off-the-shelf desktop computer systems to high-fidelity complex systems with motion platforms. Although the latter would presumably provide the highest level of face or physical validity and be able to replicate most accurately real driving situations, there is evidence that far less complex systems may provide comparable or even better validated measures of driving behavior.73,74 In fact, decreased computer costs have enabled development of simulators that can largely replicate the most advanced ones in rendering highly representative driving scenes; and this is at a fraction of the cost of simulators used previously. Lower cost driving simulation systems with lower resolution and smaller vertical fields of view have been shown to be sufficient to evaluate various aspects of driver fitness and may reduce unwanted side effects such as simulator sickness. Such capabilities have made it possible for many researchers to benefit from the availability of driving simulators.

Several driving scenarios may be replicated in a driving simulator in order to test different tasks such as regulation of vehicle speed and direction, lane positioning, obeying traffic signs and signals, negotiating curves, merging into traffic, passing into oncoming traffic, maintaining safe following distance, and response to errors by others, such as sudden appearance of pedestrians or cars in the central or peripheral field of view. These scenarios may be customized depending on the driving behavior that is under investigation. Additionally, changing weather and lighting conditions may impose additional difficulties that can be more sensitive for detection of driving impairment.75,76

When used as a tool to evaluate driving safety, it is essential that the performance data obtained from a driving simulator can be demonstrated to have predictive ability for driving behavior on the road. Several studies have compared the performance measured on driving simulators to that obtained on on-road tests.63,74,77-85 Lee63 conducted a study of 129 elderly subjects that underwent evaluation by a low-cost driving simulator and subsequent on-road performance tests. A high correlation (r = 0.72) was found between overall measures of performance in the driving simulator and the on-road assessment. In another study, Boydstun and colleagues83 also found a strong correlation (r = 0.88) between performance on a simulator and driving tests on the road in a group of healthy subjects and individuals with motor handicap. Blaauw82 found that performance measured on a driving simulator was able to discriminate experienced from inexperienced drivers better than an assessment conducted on the road. Schechtman et al81 found that the types of errors most commonly made by drivers in a driving simulator were very similar to those made during on-road testing when performing complex driving maneuvers. Lew et al39 concluded that automatic assessment of simulator performance in patients with traumatic brain injury provided valid measures that could be more sensitive predictors of future driving performance than traditional on-road assessments with a human judge. The authors suggested that this was because the simulator exposed the driver to a wider range of demands than it is safely possible on the road.

Driving simulators have also been validated using surrogate safety measures and self-reported data about motor vehicle accidents. Reimer et al86 showed that measures of performance obtained in a driving simulator correlated well with self-reported driving history. For example, history of total speeding tickets showed a significant correlation to speeding during the driving simulator test. Also, five-year accident history was correlated with number of crashes during the simulation.86

Driving Simulation as a Performance-based Test in Glaucoma

The fundamental goal of glaucoma management is to prevent patients from developing visual impairment sufficient to produce disability in their daily lives and impair their quality of life. Ultimately, patients are interested in how their vision will impact their ability to read, write, eat, dress, use a computer, interact socially or drive a car, for example. This has led to the application of so-called performance-based measures for evaluation of visual impairment in glaucoma in which the individual is asked to perform a specific task commonly encountered in daily living while being evaluated using standardized criteria.87-90 Although technological advancements such as automated perimetry and devices for optic nerve imaging have resulted in great improvement in our ability to quantify structural and functional damage in glaucoma, the clinical significance and impact on vision-related quality of life of some of this information remain elusive. On the other hand, performance-based measures are, in general, better correlated to traditional measures of vision health and, more important, seem to directly measure disability.

Driving simulators can be used as a performance-based test for evaluation of functional impairment in glaucoma. Their use can potentially help the evaluation of driving safety and performance of diseased subjects and provide insight into the different mechanisms involved in causing driving impairment in this disease. The ability to do this in an experimentally controlled and standardized setting enables testing of a much larger number of hypotheses compared to on-road evaluations. For example, a large number of glaucoma patients with different degrees of disease severity and types of visual field defect could be evaluated using driving simulators to help elucidate risk factors for unsafe driving in this population. Studies of the association between patterns and rates of progressive visual field loss and simulator measures may help us understand the impact of adaptations to visual impairment in the ability to drive. Also, investigations of the relationships between driving simulator measures and other clinically performed tests, such as optic disc and retinal nerve fiber layer imaging assessment and function-specific perimetry, could help validate these tests as predictive measures for development of disability from glaucoma.

A few studies have conducted driving simulation in relatively small samples of patients with glaucoma. Szlyk et al24 found that glaucoma patients had a higher number of accidents during driving simulation compared to normal control subjects and that these measures were related to self-reported history of higher number of real life accidents. Additionally, they showed that the number of accidents was correlated to the horizontal extension of visual field measured by Goldmann kinetic perimetry. In another study of glaucoma patients with mild or no visual field loss, only contrast sensitivity measures were related to driving simulator performance.91 In a study of subjects with central and peripheral visual field loss, including patients with glaucoma, age-related macular degeneration and retinitis pigmentosa, Coeckelbergh and colleagues92 showed that patients with different types of defect showed differential performance on several driving simulator measures. For example, subjects with peripheral field loss made more lane boundary crossings than subjects with central or mild visual field defects. Further, some of the driving simulator measures were predictive of performance during on-road test driving, such as minimum time to collision and driving speed.

Glaucoma may affect several other aspects of visual function besides those related to the presence of peripheral field loss on perimetric tests. Deficits in contrast sensitivity,93 color and shape detection,94,95 visual processing speed,50,52,61 motion detection,96-98 divided and selective attention, spatial orientation and visual search23 can result in reduced task performance on activities of daily living, even when visual acuity is still good. The mechanism for image quality degradation and reduced task performance in glaucoma could be related to neural under-sampling as a result of retinal ganglion cell death or dysfunction.99 Retinal ganglion cell death results in loss of neurons in the lateral geniculate nucleus and in cortical and subcortical areas of the brain which may result in further degradation of image quality and impairment in complex visual tasks.12 Visual and task performance measures that go beyond high contrast acuity and include dimensions of vision that tap into the neural transfer function may provide new insights into glaucoma early detection, progression and task performance.

Besides evaluating driver fitness, simulators could also be used as a sophisticated test to evaluate cognitive impairment. According to Lengenfelder et al43, virtual reality technologies such as driving simulators potentially offer an improved method of evaluating cognitive functioning in the context of everyday tasks that has not been available through traditional neuropsychological assessment. In fact, the driving simulator can be thought of as an treadmill-based exercise tolerance test for the brain65 integrating component level visual functions (color, shape, contrast, motion, visual processing speed, divided attention, kinetic depth perception, reaction time, peripheral motion detection, spatial orientation and others) into a coordinated, system level performance task. Component level visual functions (visual performance) are tested in the context of dynamically changing task performance (driving). Like the treadmill-based exercise tolerance test that evaluates cardiac function under increasing exercise difficulty, the driving simulator evaluates visual function under increasing task performance demands. This may uncover impairments that would otherwise go undetected if the patient is only tested using conventional measures. For example, increasingly challenging visual tasks on the simulator, under low contrast, low luminance conditions and performed under the pressure of time, could potentially reveal functional impairments that would not be detected by standard visual field assessment. This hypothesis remains to be investigated.

Driving simulators have several potential limitations. They evaluate only a representation of the reality. The driving environment is simpler in a simulator than in the real world and the amount of overall information processing required by the driver is less in the simulator. However, this relative simplicity is essential in order to enable testing of specific associations or hypothesis of cause-effect relationship. Driving simulators have also been associated with simulator adaptation syndrome (SAS), characterized by autonomic symptoms such as drowsiness, vertigo or nausea during testing.100,101 The discomfort is thought to occur due to a mismatch between visual cues of movement and imperfect or non-existing inertial cues. SAS has been reported to occur in 5% to 15% of subjects undergoing testing, although the prevalence varies according to certain risk factors such as age and type of simulator.101,102 Questionnaires have been proposed to evaluate propensity to SAS and may help identify patients who are not suitable for testing.100

Conclusion

Glaucoma is a progressive optic neuropathy that may significantly impair visual function and the ability to perform activities of daily living, such as driving. However, little is currently known about the relationship between the different measures of structural and functional damage in the disease and the risk of vision-related disability. Driving simulation can play a significant role in linking visual performance to task performance, providing a standardized way to assess driver fitness and safety, and an improved method for evaluating cognitive functioning in the context of everyday tasks. As a performance-based test, driving simulators can provide data concerning the risk of functional disability and can potentially serve as endpoints that have direct clinical impact in glaucoma clinical trials.

Acknowledgments

Supported in part by the National Eye Institute grant EY021818 (FAM)

References

  • 1.Weinreb RN, Khaw PT. Primary open-angle glaucoma. Lancet. 2004 May 22;363(9422):1711–1720. doi: 10.1016/S0140-6736(04)16257-0. [DOI] [PubMed] [Google Scholar]
  • 2.Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006 Mar;90(3):262–267. doi: 10.1136/bjo.2005.081224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Schappert SM. Office visits for glaucoma: United States, 1991-92. Adv Data. 1995 Mar 30;(262):1–14. [PubMed] [Google Scholar]
  • 4.Girkin CA, McGwin G, Jr, Long C, DeLeon-Ortega J, Graf CM, Everett AW. Subjective and objective optic nerve assessment in African Americans and whites. Invest Ophthalmol Vis Sci. 2004 Jul;45(7):2272–2278. doi: 10.1167/iovs.03-0996. [DOI] [PubMed] [Google Scholar]
  • 5.Medeiros FA, Zangwill LM, Bowd C, Weinreb RN. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. Arch Ophthalmol. 2004 Jun;122(6):827–837. doi: 10.1001/archopht.122.6.827. [DOI] [PubMed] [Google Scholar]
  • 6.Medeiros FA, Alencar LM, Zangwill LM, Bowd C, Sample PA, Weinreb RN. Prediction of functional loss in glaucoma from progressive optic disc damage. Arch Ophthalmol. 2009 Oct;127(10):1250–1256. doi: 10.1001/archophthalmol.2009.276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Medeiros FA, Sample PA, Weinreb RN. Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. Am J Ophthalmol. 2004 May;137(5):863–871. doi: 10.1016/j.ajo.2003.12.009. [DOI] [PubMed] [Google Scholar]
  • 8.Sample PA, Bosworth CF, Blumenthal EZ, Girkin C, Weinreb RN. Visual function-specific perimetry for indirect comparison of different ganglion cell populations in glaucoma. Invest Ophthalmol Vis Sci. 2000 Jun;41(7):1783–1790. [PubMed] [Google Scholar]
  • 9.Girkin CA, Sample PA, Liebmann JM, et al. African Descent and Glaucoma Evaluation Study (ADAGES): II. Ancestry differences in optic disc, retinal nerve fiber layer, and macular structure in healthy subjects. Arch Ophthalmol. 2010 May;128(5):541–550. doi: 10.1001/archophthalmol.2010.49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Garway-Heath DF. Early diagnosis in glaucoma. Prog Brain Res. 2008;173:47–57. doi: 10.1016/S0079-6123(08)01105-9. [DOI] [PubMed] [Google Scholar]
  • 11.Gupta N, Greenberg G, de Tilly LN, Gray B, Polemidiotis M, Yucel YH. Atrophy of the lateral geniculate nucleus in human glaucoma detected by magnetic resonance imaging. Br J Ophthalmol. 2009 Jan;93(1):56–60. doi: 10.1136/bjo.2008.138172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Gupta N, Yucel YH. What changes can we expect in the brain of glaucoma patients? Surv Ophthalmol. 2007 Nov;52(2):S122–126. doi: 10.1016/j.survophthal.2007.08.006. [DOI] [PubMed] [Google Scholar]
  • 13.Gupta N, Yucel YH. Glaucoma as a neurodegenerative disease. Curr Opin Ophthalmol. 2007 Mar;18(2):110–114. doi: 10.1097/ICU.0b013e3280895aea. [DOI] [PubMed] [Google Scholar]
  • 14.Yucel Y, Gupta N. Glaucoma of the brain: a disease model for the study of transsynaptic neural degeneration. Prog Brain Res. 2008;173:465–478. doi: 10.1016/S0079-6123(08)01132-1. [DOI] [PubMed] [Google Scholar]
  • 15.Yucel YH, Zhang Q, Weinreb RN, Kaufman PL, Gupta N. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res. 2003 Jul;22(4):465–481. doi: 10.1016/s1350-9462(03)00026-0. [DOI] [PubMed] [Google Scholar]
  • 16.DeCarlo DK, Scilley K, Wells J, Owsley C. Driving habits and health-related quality of life in patients with age-related maculopathy. Optom Vis Sci. 2003 Mar;80(3):207–213. doi: 10.1097/00006324-200303000-00010. [DOI] [PubMed] [Google Scholar]
  • 17.Ragland DR, Satariano WA, MacLeod KE. Driving cessation and increased depressive symptoms. J Gerontol A Biol Sci Med Sci. 2005 Mar;60(3):399–403. doi: 10.1093/gerona/60.3.399. [DOI] [PubMed] [Google Scholar]
  • 18.Fonda SJ, Wallace RB, Herzog AR. Changes in driving patterns and worsening depressive symptoms among older adults. J Gerontol B Psychol Sci Soc Sci. 2001 Nov;56(6):S343–351. doi: 10.1093/geronb/56.6.s343. [DOI] [PubMed] [Google Scholar]
  • 19.Marottoli RA, Mendes de Leon CF, Glass TA, et al. Driving cessation and increased depressive symptoms: prospective evidence from the New Haven EPESE. Established Populations for Epidemiologic Studies of the Elderly. J Am Geriatr Soc. 1997 Feb;45(2):202–206. doi: 10.1111/j.1532-5415.1997.tb04508.x. [DOI] [PubMed] [Google Scholar]
  • 20.Ramulu PY, West SK, Munoz B, Jampel HD, Friedman DS. Driving cessation and driving limitation in glaucoma: the Salisbury Eye Evaluation Project. Ophthalmology. 2009 Oct;116(10):1846–1853. doi: 10.1016/j.ophtha.2009.03.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ramulu P. Glaucoma and disability: which tasks are affected, and at what stage of disease? Curr Opin Ophthalmol. 2009 Mar;20(2):92–98. doi: 10.1097/ICU.0b013e32832401a9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Janz NK, Musch DC, Gillespie BW, Wren PA, Niziol LM. Evaluating clinical change and visual function concerns in drivers and nondrivers with glaucoma. Invest Ophthalmol Vis Sci. 2009 Apr;50(4):1718–1725. doi: 10.1167/iovs.08-2575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Haymes SA, LeBlanc RP, Nicolela MT, Chiasson LA, Chauhan BC. Glaucoma and on-road driving performance. Invest Ophthalmol Vis Sci. 2008 Jul;49(7):3035–3041. doi: 10.1167/iovs.07-1609. [DOI] [PubMed] [Google Scholar]
  • 24.Szlyk JP, Mahler CL, Seiple W, Edward DP, Wilensky JT. Driving performance of glaucoma patients correlates with peripheral visual field loss. J Glaucoma. 2005 Apr;14(2):145–150. doi: 10.1097/01.ijg.0000151686.89162.28. [DOI] [PubMed] [Google Scholar]
  • 25.McGwin G, Jr, Mays A, Joiner W, Decarlo DK, McNeal S, Owsley C. Is glaucoma associated with motor vehicle collision involvement and driving avoidance? Invest Ophthalmol Vis Sci. 2004 Nov;45(11):3934–3939. doi: 10.1167/iovs.04-0524. [DOI] [PubMed] [Google Scholar]
  • 26.Owsley C, McGwin G., Jr Vision and driving. Vision Res. 2010 May 23;May 23; doi: 10.1016/j.visres.2010.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.McGwin G, Jr, Xie A, Mays A, et al. Visual field defects and the risk of motor vehicle collisions among patients with glaucoma. Invest Ophthalmol Vis Sci. 2005 Dec;46(12):4437–4441. doi: 10.1167/iovs.05-0750. [DOI] [PubMed] [Google Scholar]
  • 28.McGwin G, Jr, Owsley C. Risk factors for motor vehicle collision-related eye injuries. Arch Ophthalmol. 2005 Jan;123(1):89–95. doi: 10.1001/archopht.123.1.89. [DOI] [PubMed] [Google Scholar]
  • 29.Owsley C, McGwin G., Jr Vision impairment and driving. Surv Ophthalmol. 1999 May-Jun;43(6):535–550. doi: 10.1016/s0039-6257(99)00035-1. [DOI] [PubMed] [Google Scholar]
  • 30.Owsley C, Stalvey BT, Wells J, Sloane ME, McGwin G., Jr Visual risk factors for crash involvement in older drivers with cataract. Arch Ophthalmol. 2001 Jun;119(6):881–887. doi: 10.1001/archopht.119.6.881. [DOI] [PubMed] [Google Scholar]
  • 31.Owsley C, Ball K, McGwin G, Jr, et al. Visual processing impairment and risk of motor vehicle crash among older adults. JAMA. 1998 Apr 8;279(14):1083–1088. doi: 10.1001/jama.279.14.1083. [DOI] [PubMed] [Google Scholar]
  • 32.McCloskey LW, Koepsell TD, Wolf ME, Buchner DM. Motor vehicle collision injuries and sensory impairments of older drivers. Age Ageing. 1994 Jul;23(4):267–273. doi: 10.1093/ageing/23.4.267. [DOI] [PubMed] [Google Scholar]
  • 33.Cross JM, McGwin G, Jr, Rubin GS, et al. Visual and medical risk factors for motor vehicle collision involvement among older drivers. Br J Ophthalmol. 2009 Mar;93(3):400–404. doi: 10.1136/bjo.2008.144584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Rubin GS, Ng ES, Bandeen-Roche K, Keyl PM, Freeman EE, West SK. A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE study. Invest Ophthalmol Vis Sci. 2007 Apr;48(4):1483–1491. doi: 10.1167/iovs.06-0474. [DOI] [PubMed] [Google Scholar]
  • 35.Bedard MB, Parkkari M, Weaver B, Riendeau J, Dahlquist M. Assessment of driving performance using a simulator protocol: validity and reproducibility. Am J Occup Ther. 2010 Mar-Apr;64(2):336–340. doi: 10.5014/ajot.64.2.336. [DOI] [PubMed] [Google Scholar]
  • 36.de Winter JC, de Groot S, Mulder M, Wieringa PA, Dankelman J, Mulder JA. Relationships between driving simulator performance and driving test results. Ergonomics. 2009 Feb;52(2):137–153. doi: 10.1080/00140130802277521. [DOI] [PubMed] [Google Scholar]
  • 37.Yan X, Abdel-Aty M, Radwan E, Wang X, Chilakapati P. Validating a driving simulator using surrogate safety measures. Accid Anal Prev. 2008 Jan;40(1):274–288. doi: 10.1016/j.aap.2007.06.007. [DOI] [PubMed] [Google Scholar]
  • 38.Fildes B, Charlton J, Muir C, Koppel S. Driving responses of older and younger drivers in a driving simulator. Annu Proc Assoc Adv Automot Med. 2007;51:559–572. [PMC free article] [PubMed] [Google Scholar]
  • 39.Lew HL, Poole JH, Lee EH, Jaffe DL, Huang HC, Brodd E. Predictive validity of driving-simulator assessments following traumatic brain injury: a preliminary study. Brain Inj. 2005 Mar;19(3):177–188. doi: 10.1080/02699050400017171. [DOI] [PubMed] [Google Scholar]
  • 40.Lee HC, Lee AH. Identifying older drivers at risk of traffic violations by using a driving simulator: a 3-year longitudinal study. Am J Occup Ther. 2005 Jan-Feb;59(1):97–100. doi: 10.5014/ajot.59.1.97. [DOI] [PubMed] [Google Scholar]
  • 41.Lee HC, Lee AH, Cameron D, Li-Tsang C. Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes. J Safety Res. 2003;34(4):453–459. doi: 10.1016/j.jsr.2003.09.007. [DOI] [PubMed] [Google Scholar]
  • 42.Godley ST, Triggs TJ, Fildes BN. Driving simulator validation for speed research. Accid Anal Prev. 2002 Sep;34(5):589–600. doi: 10.1016/s0001-4575(01)00056-2. [DOI] [PubMed] [Google Scholar]
  • 43.Lengenfelder J, Schultheis MT, Al-Shihabi T, Mourant R, DeLuca J. Divided attention and driving: a pilot study using virtual reality technology. J Head Trauma Rehabil. 2002 Feb;17(1):26–37. doi: 10.1097/00001199-200202000-00005. [DOI] [PubMed] [Google Scholar]
  • 44.Rizzo M, McGehee DV, Dawson JD, Anderson SN. Simulated car crashes at intersections in drivers with Alzheimer disease. Alzheimer Dis Assoc Disord. 2001 Jan-Mar;15(1):10–20. doi: 10.1097/00002093-200101000-00002. [DOI] [PubMed] [Google Scholar]
  • 45.Rizzo M, Reinach S, McGehee D, Dawson J. Simulated car crashes and crash predictors in drivers with Alzheimer disease. Arch Neurol. 1997 May;54(5):545–551. doi: 10.1001/archneur.1997.00550170027011. [DOI] [PubMed] [Google Scholar]
  • 46.Klimkeit EI, Bradshaw JL, Charlton J, Stolwyk R, Georgiou-Karistianis N. Driving ability in Parkinson's disease: current status of research. Neurosci Biobehav Rev. 2009 Mar;33(3):223–231. doi: 10.1016/j.neubiorev.2008.08.005. [DOI] [PubMed] [Google Scholar]
  • 47.Johnson CA, Keltner JL. Incidence of visual field loss in 20,000 eyes and its relationship to driving performance. Arch Ophthalmol. 1983 Mar;101(3):371–375. doi: 10.1001/archopht.1983.01040010371002. [DOI] [PubMed] [Google Scholar]
  • 48.Owsley C, McGwin G, Jr, Ball K. Vision impairment, eye disease, and injurious motor vehicle crashes in the elderly. Ophthalmic Epidemiol. 1998 Jun;5(2):101–113. doi: 10.1076/opep.5.2.101.1574. [DOI] [PubMed] [Google Scholar]
  • 49.Owen VM, Crabb DP, White ET, Viswanathan AC, Garway-Heath DF, Hitchings RA. Glaucoma and fitness to drive: using binocular visual fields to predict a milestone to blindness. Invest Ophthalmol Vis Sci. 2008 Jun;49(6):2449–2455. doi: 10.1167/iovs.07-0877. [DOI] [PubMed] [Google Scholar]
  • 50.Crabb DP, Fitzke FW, Hitchings RA, Viswanathan AC. A practical approach to measuring the visual field component of fitness to drive. Br J Ophthalmol. 2004 Sep;88(9):1191–1196. doi: 10.1136/bjo.2003.035949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.McGwin G, Jr, Owsley C, Ball K. Identifying crash involvement among older drivers: agreement between self-report and state records. Accid Anal Prev. 1998 Nov;30(6):781–791. doi: 10.1016/s0001-4575(98)00031-1. [DOI] [PubMed] [Google Scholar]
  • 52.Haymes SA, Leblanc RP, Nicolela MT, Chiasson LA, Chauhan BC. Risk of falls and motor vehicle collisions in glaucoma. Invest Ophthalmol Vis Sci. 2007 Mar;48(3):1149–1155. doi: 10.1167/iovs.06-0886. [DOI] [PubMed] [Google Scholar]
  • 53.Aspinall PA, Johnson ZK, Azuara-Blanco A, Montarzino A, Brice R, Vickers A. Evaluation of quality of life and priorities of patients with glaucoma. Invest Ophthalmol Vis Sci. 2008 May;49(5):1907–1915. doi: 10.1167/iovs.07-0559. [DOI] [PubMed] [Google Scholar]
  • 54.Freeman EE, Gange SJ, Munoz B, West SK. Driving status and risk of entry into long-term care in older adults. Am J Public Health. 2006 Jul;96(7):1254–1259. doi: 10.2105/AJPH.2005.069146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Gilhotra JS, Mitchell P, Ivers R, Cumming RG. Impaired vision and other factors associated with driving cessation in the elderly: the Blue Mountains Eye Study. Clin Experiment Ophthalmol. 2001 Jun;29(3):104–107. doi: 10.1046/j.1442-9071.2001.00411.x. [DOI] [PubMed] [Google Scholar]
  • 56.Hills BL, Burg A. A reanalysis of California driver vision data: General findings. Crowthorn, England: Transport and Road Research Laboratory; 1977. [Google Scholar]
  • 57.Decina LE, Staplin L. Retrospective evaluation of alternative vision screening criteria for older and younger drivers. Accid Anal Prev. 1993 Jun;25(3):267–275. doi: 10.1016/0001-4575(93)90021-n. [DOI] [PubMed] [Google Scholar]
  • 58.Ball K, Owsley C. The useful field of view test: a new technique for evaluating age-related declines in visual function. J Am Optom Assoc. 1993 Jan;64(1):71–79. [PubMed] [Google Scholar]
  • 59.Ball K, Owsley C, Sloane ME, Roenker DL, Bruni JR. Visual attention problems as a predictor of vehicle crashes in older drivers. Invest Ophthalmol Vis Sci. 1993 Oct;34(11):3110–3123. [PubMed] [Google Scholar]
  • 60.Crabb DP, Smith ND, Rauscher FG, et al. Exploring eye movements in patients with glaucoma when viewing a driving scene. PLoS One. 2010;5(3):e9710. doi: 10.1371/journal.pone.0009710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Chisholm CM, Rauscher FG, Crabb DC, et al. Assessing visual fields for driving in patients with paracentral scotomata. Br J Ophthalmol. 2008 Feb;92(2):225–230. doi: 10.1136/bjo.2007.129726. [DOI] [PubMed] [Google Scholar]
  • 62.Rizzo M, Kellison IL. Eyes, brains, and autos. Arch Ophthalmol. 2004 Apr;122(4):641–647. doi: 10.1001/archopht.122.4.641. [DOI] [PubMed] [Google Scholar]
  • 63.Lee HC. The validity of driving simulator to measure on-road driving performance of older drivers. Transport Engineering in Australia. 2003;8:89–100. [Google Scholar]
  • 64.Akinwuntan AE, De Weerdt W, Feys H, et al. Effect of simulator training on driving after stroke: a randomized controlled trial. Neurology. 2005 Sep 27;65(6):843–850. doi: 10.1212/01.wnl.0000171749.71919.fa. [DOI] [PubMed] [Google Scholar]
  • 65.Lew HL, Rosen PN, Thomander D, Poole JH. The potential utility of driving simulators in the cognitive rehabilitation of combat-returnees with traumatic brain injury. J Head Trauma Rehabil. 2009 Jan-Feb;24(1):51–56. doi: 10.1097/HTR.0b013e3181956fe3. [DOI] [PubMed] [Google Scholar]
  • 66.Uc EY, Rizzo M, Anderson SW, Dastrup E, Sparks JD, Dawson JD. Driving under low-contrast visibility conditions in Parkinson disease. Neurology. 2009 Oct 6;73(14):1103–1110. doi: 10.1212/WNL.0b013e3181bacf6e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Devos H, Vandenberghe W, Nieuwboer A, Tant M, Baten G, De Weerdt W. Predictors of fitness to drive in people with Parkinson disease. Neurology. 2007 Oct 2;69(14):1434–1441. doi: 10.1212/01.wnl.0000277640.58685.fc. [DOI] [PubMed] [Google Scholar]
  • 68.Reinach SJ, Rizzo M, McGehee DV. Driving with Alzheimer disease: the anatomy of a crash. Alzheimer Dis Assoc Disord. 1997 Jun;11(1):21–27. [PubMed] [Google Scholar]
  • 69.Biederman J, Fried R, Monuteaux MC, et al. A laboratory driving simulation for assessment of driving behavior in adults with ADHD: a controlled study. Ann Gen Psychiatry. 2007;6:4. doi: 10.1186/1744-859X-6-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Strayer DL, Drews FA, Crouch DJ. A comparison of the cell phone driver and the drunk driver. Hum Factors. 2006 Summer;48(2):381–391. doi: 10.1518/001872006777724471. [DOI] [PubMed] [Google Scholar]
  • 71.Hughes DT, Cramer F, Knight GJ. Use of a racing car simulator for medical research the effects of marzine and alcohol on driving performance. Med Sci Law. 1967 Oct;7(4):200–204. doi: 10.1177/002580246700700407. [DOI] [PubMed] [Google Scholar]
  • 72.Gawron VJ, Ranney TA. The effects of alcohol dosing on driving performance on a closed course and in a driving simulator. Ergonomics. 1988 Sep;31(9):1219–1244. doi: 10.1080/00140138808966764. [DOI] [PubMed] [Google Scholar]
  • 73.Lee JD. [Accessed July 31, 2010];Simulator fidelity: how low can you go? 2004 www.uiowa.edu/neuroerg/Simulator%20Users%20Group/HFES_SimPanel.htm. Available at.
  • 74.Reed MP, Green PA. Comparison of driver performance on-road and in a low-cost simulator using a concurrent telephone dialing task. Ergonomics. 1999;42:1015–1037. [Google Scholar]
  • 75.Horswill MS, Plooy AM. Reducing contrast makes speeds in a video-based driving simulator harder to discriminate as well as making them appear slower. Perception. 2008;37(8):1269–1275. doi: 10.1068/p5821. [DOI] [PubMed] [Google Scholar]
  • 76.Reimer B, D'Ambrosio LA, Coughlin JF. Secondary analysis of time of day on simulated driving performance. J Safety Res. 2007;38(5):563–570. doi: 10.1016/j.jsr.2007.07.002. [DOI] [PubMed] [Google Scholar]
  • 77.Lee HC, Lee AH, Cameron D. Validation of a driving simulator by measuring the visual attention skill of older adult drivers. American Journal of Occupational Therapy. 2003 May-Jun;57(3):324–328. doi: 10.5014/ajot.57.3.324. [DOI] [PubMed] [Google Scholar]
  • 78.Lee HC, Lee AH, Cameron D, Li-Tsang C. Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes. J Safety Res. 2003;34(4):453–459. doi: 10.1016/j.jsr.2003.09.007. [DOI] [PubMed] [Google Scholar]
  • 79.Lee JD, McGehee DV, Brown TL, Reyes ML. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum Factors. 2002 Sum;44(2):314–334. doi: 10.1518/0018720024497844. [DOI] [PubMed] [Google Scholar]
  • 80.Lee HC, Lee AH. Identifying older drivers at risk of traffic violations by using a driving simulator: A 3-year longitudinal study. American Journal of Occupational Therapy. 2005 Jan-Feb;59(1):97–100. doi: 10.5014/ajot.59.1.97. [DOI] [PubMed] [Google Scholar]
  • 81.Schechtman O, Classen S, Awadzi K, Mann W. Comparison of driving errors between on-the-road and simulated driving assessment: A validation study. Traffic Injury Prevention. 2009;10:379–385. doi: 10.1080/15389580902894989. [DOI] [PubMed] [Google Scholar]
  • 82.Blaauw GJ. Driving Experience and Task Demands in Simulator and Instrumented Car - a Validation-Study. Hum Factors. 1982;24(4):473–486. [Google Scholar]
  • 83.Boydstun LE, Kessel DS, Miller JM. Assessment of perceptually disabled individuals driving skills using a driving simulator; Proceedings of the Human Factors Society 24th Annual Meeting; Santa Monica, CA. 1980; pp. 111–113. [Google Scholar]
  • 84.de Winter JCF, de Groot S, Mulder M, Wieringa PA, Dankelman J, Mulder JA. Relationships between driving simulator performance and driving test results. Ergonomics. 2009;52(2):137–153. doi: 10.1080/00140130802277521. [DOI] [PubMed] [Google Scholar]
  • 85.Godley ST, Triggs TJ, Fildes BN. Driving simulator validation for speed research. Accident Anal Prev. 2002 Sep;34(5):589–600. doi: 10.1016/s0001-4575(01)00056-2. [DOI] [PubMed] [Google Scholar]
  • 86.Reimer B, D'Ambrosio LA, Coughun JF, Kafrissen ME, Biederman J. Using self-reported data to assess the validity of driving simulation data. Behav Res Methods. 2006 May;38(2):314–324. doi: 10.3758/bf03192783. [DOI] [PubMed] [Google Scholar]
  • 87.Richman J, Lorenzana LL, Lankaranian D, et al. Relationships in glaucoma patients between standard vision tests, quality of life, and ability to perform daily activities. Ophthalmic Epidemiol. 2010 Jun;17(3):144–151. doi: 10.3109/09286581003734878. [DOI] [PubMed] [Google Scholar]
  • 88.Warrian KJ, Altangerel U, Spaeth GL. Performance-based measures of visual function. Surv Ophthalmol. 2010 Mar 4;55(2):146–161. doi: 10.1016/j.survophthal.2009.06.006. [DOI] [PubMed] [Google Scholar]
  • 89.Lorenzana L, Lankaranian D, Dugar J, et al. A new method of assessing ability to perform activities of daily living: design, methods and baseline data. Ophthalmic Epidemiol. 2009 Mar-Apr;16(2):107–114. doi: 10.1080/09286580902738142. [DOI] [PubMed] [Google Scholar]
  • 90.Owsley C, McGwin G, Jr, Sloane ME, Stalvey BT, Wells J. Timed instrumental activities of daily living tasks: relationship to visual function in older adults. Optom Vis Sci. 2001 May;78(5):350–359. doi: 10.1097/00006324-200105000-00019. [DOI] [PubMed] [Google Scholar]
  • 91.Szlyk JP, Taglia DP, Paliga J, Edward DP, Wilensky JT. Driving performance in patients with mild to moderate glaucomatous clinical vision changes. J Rehabil Res Dev. 2002 Jul-Aug;39(4):467–482. [PubMed] [Google Scholar]
  • 92.Coeckelbergh TR, Brouwer WH, Cornelissen FW, Van Wolffelaar P, Kooijman AC. The effect of visual field defects on driving performance: a driving simulator study. Arch Ophthalmol. 2002 Nov;120(11):1509–1516. doi: 10.1001/archopht.120.11.1509. [DOI] [PubMed] [Google Scholar]
  • 93.McKendrick AM, Sampson GP, Walland MJ, Badcock DR. Contrast sensitivity changes due to glaucoma and normal aging: low-spatial-frequency losses in both magnocellular and parvocellular pathways. Invest Ophthalmol Vis Sci. 2007 May;48(5):2115–2122. doi: 10.1167/iovs.06-1208. [DOI] [PubMed] [Google Scholar]
  • 94.Sample PA, Weinreb RN. Color perimetry for assessment of primary open-angle glaucoma. Invest Ophthalmol Vis Sci. 1990 Sep;31(9):1869–1875. [PubMed] [Google Scholar]
  • 95.Drance SM, Lakowski R, Schulzer M, Douglas GR. Acquired color vision changes in glaucoma. Use of 100-hue test and Pickford anomaloscope as predictors of glaucomatous field change. Arch Ophthalmol. 1981 May;99(5):829–831. doi: 10.1001/archopht.1981.03930010829007. [DOI] [PubMed] [Google Scholar]
  • 96.Loughman J, Davison P, Flitcroft I. Open angle glaucoma effects on preattentive visual search efficiency for flicker, motion displacement and orientation pop-out tasks. Br J Ophthalmol. 2007 Nov;91(11):1493–1498. doi: 10.1136/bjo.2006.108084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.McKendrick AM, Badcock DR, Morgan WH. The detection of both global motion and global form is disrupted in glaucoma. Invest Ophthalmol Vis Sci. 2005 Oct;46(10):3693–3701. doi: 10.1167/iovs.04-1406. [DOI] [PubMed] [Google Scholar]
  • 98.Silverman SE, Trick GL, Hart WM., Jr Motion perception is abnormal in primary open-angle glaucoma and ocular hypertension. Invest Ophthalmol Vis Sci. 1990 Apr;31(4):722–729. [PubMed] [Google Scholar]
  • 99.Thibos LN. Acuity perimetry and the sampling theory of visual resolution. Optom Vis Sci. 1998 Jun;75(6):399–406. doi: 10.1097/00006324-199806000-00024. [DOI] [PubMed] [Google Scholar]
  • 100.Brooks JO, Goodenough RR, Crisler MC, et al. Simulator sickness during driving simulation studies. Accident Anal Prev. 2010 May;42(3):788–796. doi: 10.1016/j.aap.2009.04.013. [DOI] [PubMed] [Google Scholar]
  • 101.Lee GCH, Yoo Y, Jones S. Investigation of driving performance, vection, postural sway, and simulator sickness in a fixed-based driving simulator. Comput Ind Eng. 1997 Dec;33(3-4):533–536. [Google Scholar]
  • 102.Freund B, Green TR. Simulator sickness amongst older drivers with and without dementia. Advances in Transportation Studies. 2006:71–74. [Google Scholar]

RESOURCES