Cognitive, sensory and physical factors enabling driving safety in older adults

Clinical Psychology Review 25 (2005) 45 – 65

Cognitive, sensory and physical factors enabling driving safety in older adults Kaarin J. Ansteya,*, Joanne Woodb, Stephen Lordc, Janine G. Walkera a

Centre for Mental Health Research, Australian National University, Canberra, ACT 0200, Australia b School of Optometry, Queensland University of Technology, Australia c Prince of Wales Medical Research Institute, Australia Received 25 November 2003; accepted 22 July 2004

Abstract We reviewed literature on cognitive, sensory, motor and physical factors associated with safe driving and crash risk in older adults with the goal of developing a model of factors enabling safe driving behaviour. Thirteen empirical studies reporting associations between cognitive, sensory, motor and physical factors and either selfreported crashes, state crash records or on-road driving measures were identified. Measures of attention, reaction time, memory, executive function, mental status, visual function, and physical function variables were associated with driving outcome measures. Self-monitoring was also identified as a factor that may moderate observed effects by influencing driving behavior. We propose that three enabling factors (cognition, sensory function and physical function/medical conditions) predict driving ability, but that accurate self-monitoring of these enabling factors is required for safe driving behaviour. D 2004 Elsevier Ltd. All rights reserved. Keywords: Crash risk; Cognitive aging; Driving; Sensorimotor function; Physiological aging

Individuals aged 65 years and over represent the most rapidly growing segment of the driving population, and are keeping their licenses longer (Lyman, Ferguson, Braver, & Williams, 2002). It is estimated that there will be 50 million elderly persons eligible to drive in the US by the year 2020

* Corresponding author. Tel.: +61 2 6125 8410; fax: +61 2 61260733. E-mail address: [email protected] (K.J. Anstey). 0272-7358/$ - see front matter D 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.cpr.2004.07.008

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(Retchin & Anapolle, 1993). Motor vehicle crash rates adjusted for distance travelled are higher for elderly drivers, with an exponential increase above the age of 75 (Guerrier, Manivannan, & Nair, 1999; Preusser, Williams, Ferguson, Ulmer, & Weistein, 1998; Retchin & Anapolle, 1993). A similar pattern is evident for driver fatality rates (Preusser et al., 1998; Retchin & Anapolle, 1993). As well as the obvious health, injury, and potential disability consequences for the older driver involved in a motor vehicle accident, there is a significant financial burden placed on the community (Miller, Lestina, & Spicer, 1998). Importantly, not all older drivers are unsafe, and driving capability is very important for maintaining the independence of older adults, especially for those in rural or remote areas. It is also intimately linked to other activities of daily living (Gilhotra, Mitchell, Ivers, & Cumming, 2001; Marotolli et al., 2000; Marshall, Spasoff, Nair, & van Walraven, 2002; Persson, 1993). Indeed, individuals who have reduced or ceased driving may be at increased risk of isolation, depression, and associated functional impairment (Fonda, Wallace, & Herzog, 2001; Wiseman & Souder, 1996). Research conducted in the fields of cognitive aging, geriatrics and vision indicates that areas of vulnerability associated with aging may have implications for older drivers at a population level, though not necessarily at an individual level, and that deficits in specific cognitive, sensory and motor capacities are associated with increased crash risk. However, investigations of driver competence and crash risk in older drivers have often lacked a multidisciplinary framework that incorporates cognitive, sensory, motor and physical factors and which accounts for the dynamic interaction between these factors in the driving process. Hence, the major aims of this review are to: examine key factors of cognitive, sensory and physical functioning that enable driving performance in older individuals; and, propose a multifactorial model of driving competence, and crash risk and prevention in older individuals.

1. Aging changes relevant to driving Even in normal aging there is a decline in many cognitive abilities that are relevant to performing complex tasks such as driving. Of particular relevance are age-related changes in various aspects of visual attention including selective attention, divided attention, sustained attention (i.e., vigilance) and switching attention. Driving in traffic requires the ability to attend to relevant information and to ignore irrelevant information in often complex visual scenes with potential hazards occurring in any part of the visual field. Therefore, the speed at which visual information is processed may be an important factor for successfully negotiating difficult or dangerous traffic situations. Likewise, adequate reaction times are crucial for avoiding collisions. Another aspect of cognition relevant to driving that declines in normal aging is executive function (Bryan & Luszcz, 2000), and this is supported by neuroimaging studies showing age-related changes in the prefrontal cortex (Raz, Gunning-Dixon, Williamson, & Acker, 2002). Executive function is necessary for integrating information and planning a response, and so theoretically seems highly relevant to competent driving. Given that the types of crashes in which older adults are involved often occur in complex traffic situations such as intersections (McGwin & Brown, 1999), it is reasonable to hypothesize that difficulties are occurring at the level of executive function (i.e., the planning and decision making part of the driving task). Age-related changes in visual function also have implications for safe driving. Visual impairment becomes significantly more common with increasing age (Attebo, Mitchell, & Smith, 1996), through

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both the normal aging process and the increased prevalence of eye disease. Normal aging is associated with increased yellowing and cloudiness of the crystalline lens, a decrease in pupil size and alterations in the integrity of the macular pigment and neural pathways. These changes lead to reductions in visual acuity and contrast sensitivity and increased glare sensitivity observed in older populations (Haegerstrom-Portnoy, Schneck, & Brabyn, 1999). Cataract, glaucoma and age-related maculopathy are the most prevalent ocular diseases in the older population (Klein et al., 1995), and are reflected in self-reports of visual problems in the elderly. Klein et al. (1992), in a questionnairebased study, reported that the visual problems of older individuals increase with age for speed of visual processing, light sensitivity, dynamic vision, near vision and visual search. It is not surprising that these age-related changes in visual function have been widely investigated as risk factors for crashes among older adults given the generally held belief that vision comprises the major sensory input for driving (see Owsley & McGwin, 1999 for a review). Burg’s (1967, 1968) early work has served as a model for much of this research. With large samples (over 17,000 drivers), Burg found statistically significant correlations between some vision test results and crash records. However, the correlations were typically about 0.1 or less, indicating that static visual acuity could account for only 1% of the variance in crashes, even when the sample was stratified for age (Hills & Burg, 1974). Other visual functions, such as the extent of the visual fields, have shown more promise as predictors of driving outcomes. Johnson and Keltner (1983) found that in a survey of 10,000 driver’s license applicants in California, those drivers with visual field loss in both eyes had twice the traffic accident conviction rates than those with visual field loss in one eye or no visual field loss. Other research has shown that constriction of the binocular field significantly impairs driving performance in on-road tests (Wood & Troutbeck, 1992). Physical frailty and medical conditions may also put older adults at risk of unsafe driving and increase the likelihood of injury when involved in accidents (McGwin, Sims, Pulley, & Roseman, 2000). A recent study estimated that compared to drivers aged 30–59, drivers aged 70–74 were twice as likely to die when involved in a crash and those drivers aged 80 and older were about five times as likely to die (Li, Braver, & Chen, 2003). In addition to the higher prevalence of physical illnesses among older adults, physical fragility may affect driving safety in some older individuals and explain the increased mortality rate for very old adults involved in crashes (Klavora & Heslegrave, 2002). Physical frailty may also compound the deleterious effect that various physical, sensory and cognitive impairments have on driving skills (Li et al., 2003). In particular, chest injuries and fractures occur much more frequently among older vehicle occupants (Lyman et al., 2002; Zhou, Rouhana, & Melvin, 1996). A number of medical conditions have been identified as potential risk factors for crash involvement and predictors of driving cessation (Gilhotra et al., 2001; Wallace & Retchin, 1992) due to their effects on physiological and cognitive function. It may be that the effects of physical illness on driving capacity are amplified in older individuals because the negative impact of a medical condition may be superimposed on the general decline of neuromuscular, cognitive and perceptual function (Wallace & Retchin, 1992). In addition, medical conditions and age-related physiological changes increase the likelihood that when older drivers are involved in crashes, they will die from their injuries (Ball et al., 1998; Lyman, McGwin, & Sims, 2001). Reductions in grip and muscle strength and endurance, flexibility and motor speed as a result of aging or age-related disease are other factors that may impair driving ability. Reduced neck rotation may impair

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Study

Design

Sample

Outcome measures

Ratings

Daigneault et al. (2002) Margolis et al. (2002)

30+30=60, aged 60+ mean age 70.2 1416 women, 65–84

self report accidents and state records state crash records

1+2+1=4 1+2+2=5

McGwin et al. (2000)

case-control accident free vs. N2 accidents Osteoporotic fractures study, Oregon site, drivers only from baseline assessment Population-based case-control

state crash records

2+2+1=5

Owsley et al. (2001)

Case control with respect to crashes

state crash records prospective

2+2+2=6

Sims et al. (2000)

Prospective cohort study - followup of sample reported by Owsley et al. (2001) Correlational study

age 65+, 244 at fault drivers involved in crashes, 182 not at fault drivers involved in crashes, 475 drivers not involved in crashes 294 adults age 55–84; 33% had no crashes, 49% had 1 to 3 crashes, 18% had N3 crashes in previous 5 years 174 participants from original 294 recruited for study attended follow-up session

state crash records, 5 years prospective

2+2+2=6

84 adults aged 65–96

on road test

1+3+1=5

healthy volunteers (n=154)

on road test

2+3+1=6

referred incident-involved (n=253)

unsafe driving incidents reported by family, police and licensing personnel

De Raedt & PonjaertKristoffersen (2000) McKnight & McKnight (1999)

Correlational and controlled study of incident involved drivers

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Table 1 Characteristics of studies reviewed

Hu et al. (1998) Marottoli et al. (1998)

Ivers et al., 1999 Stutts et al. (1998)

3673 participants at baseline

Cohort study (Blue Mountains Eye study) Cohort of drivers applying for licence renewal Prospective cohort study

2379 current drivers aged 49 and older 3238 aged 65+

Case-control study

Odenheimer et al. (1994)

Case-control study

Ball et al. (1993)

Case-control study

Owsley et al. (1991)

33% no crashes 49% 1–3 crashes 18% 4 or more crashes Correlational study

125 drivers mean age 76.8

294 aged 55–90 174 participants from original 294 recruited for study 30 participants chosen to reflect broad range of cognitive skills including 9 from dementia clinic, 17 from studies of normal aging, 4 community 294 aged 55–90, mean age 71

54 participants from Optometry clinic aged 57–83 years

state crash data from 1985 to 1993 self report of crash or moving violation, or being stopped by police over 5 years retrospectively self reported car accidents retrospective state crash statistics state crash data 3 years follow-up state crash records— retrospective 6 years on road assessment

3+2+2=7 2+1+1=4

2+1+1=4 3+2+1=6 2+2+2=6 2+2+1=5 1+3+1=5

state crash data previous 5 years

2+2+1=5

state crash data previous 5 years

1+2+1=4

The quality rating comprises three parts: sample size (3 for N1000, 2 for N100, 1 for b100) plus outcome measure (three for on road test; two for state crash records and one for self-report crash history); plus prospective (2) versus retrospective or concurrent (1).

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Owsley, Ball et al. (1998) Sims et al. (1998)

Panel from Iowa 65+ Rural Health Study Cohort study of incident involved drivers

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the ability of the driver to turn the head to see relevant stimuli in the periphery necessary for safe driving in complex traffic situations and when changing lanes.

2. Empirical literature on factors associated with driving outcomes in older adults A literature review of empirical studies of predictors of crash risk and on-road driving performance was conducted as part of the process for developing the proposed multidimensional driving model and to elucidate areas that require further research and validation. Peer reviewed articles on cognitive, sensory, and physical function factors as predictors of crashes and driving performance were identified by searching Psycinfo and Medline databases from 1991 to 2002. To be included, studies had to report either associations (correlation, relative risk, odds ratio) or a pvalue for an association between predictors and crash rates, or between predictors and an index of driver safety such as on-road driving performance, in adults aged 60 or older. Studies including only dementia patients were beyond the scope of the review because our aim was to develop a model that could be applied more generally to older adults, and not specifically to those with dementia. The results are shown in four tables: Table 1 lists the studies, their sampling characteristics, design and provides a quality rating of each study. The rating is a score based on the sample size (N1000=3, 200–1000=2, b200=1), outcome measure (on road test=3, state crash records=2, self-report crashes=1) and design (prospective=2, retrospective or concurrent=1). Table 2 summarizes data on cognitive abilities and driving performance; Table 3 summarizes research in vision, as an example of sensory function; and Table 4 summarizes information on medical and physical functioning in relation to driving performance and crash risk. Results will be discussed according to the order of findings reported in the tables. 2.1. Types of studies identified for review A total of 16 articles were identified (Table 1). Results were reported for four large epidemiological studies (Hu, Trumble, Foley, Eberhard, & Wallace, 1998; Ivers, Mitchell, & Cumming, 1999; Margolis et al., 2002; Stutts, Stewart, & Martell, 1998) and the remainder was made up of smaller cohort, casecontrol or correlational studies with sample sizes ranging from 30 to 901. The study reported by Ball, Owsley, Sloane, Roenker, and Bruni (1993) was followed up with prospective results reported in Owsley, Ball et al. (1998); Owsley, McGwin, and Ball (1998). Sims, McGwin, Allman, Ball, and Owsley (2000) reports a prospective follow-up of the sample reported in Sims, Owsley, Allman, Ball, and Smoot (1998), which appears to be based on the same sample as that reported in Ball et al. (1993) and Owsley et al. There appear to be 13 independent studies reported in the literature between 1991 and 2002. In general, larger effect sizes were reported when on-road driving tests were used as outcome measures than when crash data were used, reflecting the far greater sensitivity of the on-road driving tests as a measure. This is shown clearly in the results of the study by De Raedt and PonjaertKristofferson (2000) where the associations of cognitive measures with scores from the on-road test were consistently higher than the associations between cognitive measures and self-reported crashes over 12 months. Larger effect sizes also appeared to occur in case-control studies when compared with cohort studies. In terms of study design quality, the larger epidemiological studies were rated higher, but these tended to evaluate fewer risk factors of interest.

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Table 2 Associations between cognitive performance and crash risk or on-road driving performance Cognitive ability Attention Selective attention

Divided attention

Visual Attention

Range of attention

Test

Study

Association with crashes or on-road test

Number cancellation Shifting attention from one aspect of an image to another Dot counting test

Maratolli et al. (1998) McKnight & McKnight (1999)

RR=1.97, p=0.006 r=0.29

De Raedt & PonjaertKristoffersen (2000) McKnight & McKnight (1999)

Crashes r=0.03, On-road test r= 0.44 Time r=0.15, Errors r=0.33 Crashes r=0.14, on-road, r= 0.39 r=0.36 r=0.52 RR=2.08 CI(1.15–3.44) p=0.01 Crashes r=0.32, On-road r= 0.66 RR=1.87, p=0.05 SRT r=0.20, CRT, Time r=0.33, Errors r=0.23

Sharing attention between images presented together Tracking task UFOV UFOV UFOV UFOV UFOV UFOV Response to parafoveal image - simple

Perceptual and visuo-spatial ability Visuo-perceptual Movement perception test Mental flexibility Incompatibility test

De Raedt & PonjaertKristoffersen (2000) Owsley et al. (1991) Ball et al. (1993) Owsley, Ball et al. (1998) Sims et al. (1998) De Raedt & PonjaertKristoffersen (2000) Sims et al. (2000) McKnight & McKnight (1999)

De Raedt & PonjaertKristoffersen (2000) De Raedt & PonjaertKristoffersen (2000) McKnight & McKnight (1999)

Digit matching

Figure matching

Perceptual speed

Identifying target within image field Hooper organization test AARP dRTT Missing pattern

McKnight & McKnight (1999)

Respond to simple image Paperfolding test

McKnight & McKnight (1999)

Visuospatial ability

Speed and Reaction Time Psychomotor speed Number tracking Abstract RT Respond to square Meaningful RT Respond to brake lights

Maratolli et al. (1998) Stutts et al. (1998) McKnight & McKnight (1999)

Accidents r= 0.26, Road test r=0.73 Road test r= 0.36, Crashes r=0.14 Time r=0.21, Errors r=0.30 Time r=0.28, Errors r=0.22 NS

De Raedt & PonjaertKristoffersen (2000)

pb0.0001 Time r=NS, Errors r=0.38 Time r=0.28, Errors r=0.18 Crashes r=0.33, Road test r=0.42

Maratolli et al. (1998) McKnight & McKnight (1999) McKnight & McKnight (1999)

NS Time r=0.24 Time r=0.30 (continued on next page)

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Table 2 (continued ) Cognitive ability

Test

Study

Association with crashes or on-road test

Odenheimer et al. (1994)

r= 0.25

Odenheimer et al. (1994)

r= 0.70

McKnight & McKnight (1999)

Time r=0.33–0.37

McKnight & McKnight (1999)

Errors r=0.31

McKnight & McKnight (1999)

Memory

Identifying previous figure series Word recall

Digit memory Figure memory

Digit matching Figure matching

McKnight & McKnight (1999) McKnight & McKnight (1999)

Visual Memory Verbal memory Traffic sign recognition

Wechsler memory scale Wechsler memory scale

Odenheimer et al. (1994) Odenheimer et al. (1994) Odenheimer et al. (1994) Stutts et al. (1998)

Time r=0.28, Errors r=0.32 Men who performed worse, on word recall task had 50% increased risk of crash Time 0.31, Errors 0.28 Time r=0.28, Errors r=0.32 r=0.54 r=0.51 r=0.65 pb0.001

Trails Trails Trails Trails Trails Trails

Odenheimer et al. (1994) Maratolli et al. (1998) Stutts et al. (1998) Stutts et al. (1998) Daigneault et al. (2002) Daigneault et al. (2002)

Speed and Reaction Time Simple RT Computer-generated neurobehavioral evaluation system Complex RT Computer-generated neurobehavioral evaluation system Choice RT Respond to nature of stimulus Visual tracking Tracking a laterally moving image Memory Delayed memory

Hu et al. (1998)

Trailmaking Test A B A B A B

Trails B

Margolis et al. (2002)

Stroop Colour Word

Daigneault et al. (2002)

Tower of London Wisconson Card Sorting

Daigneault et al. (2002) Daigneault et al. (2002)

Identify missing card from deck of 52

Maratolli et al. (1998)

r=0.52 NS pb0.0001, OR 1.05 pb0.001, OR 1.06 NS NS for time taken; Errors, pb0.001 NS

Executive function pb0.01 for all indices of Stroop pb0.05 for all indices pb0.01 for preservation error but NS for categories and attention errors NS

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Table 2 (continued ) Cognitive ability

Test

Study

Association with crashes or on-road test

Blessed MOMSSE MOMSSE MMSE

Stutts et al. (1998) Owsley et al. (1991) Ball et al. (1993) Odenheimer et al. (1994) Maratolli et al. (1998) Owsley, Ball et al. (1998) Sims et al. (1998) Margolis et al. (2002)

pb0.01 r=0.34 r=0.26 r=0.72

Mental Status

MMSE MOMSSE MOMSSE MMSE

NS NS p=0.024 NS

RR=relative risk; OR=odds ratio; r=correlation; UFOV=Useful field of View Test; RT=reaction time; CI=confidence interval; SRT=simple reaction time; CRT=choice reaction time; PR=prevalence ratio. The RRs in Sims et al. (2000) were calculated controlling for age, race, gender, and days driven per week. The p-values in Sims et al. (1998) relate to the significant difference between crashers and non-crashers.

2.2. Key methodological considerations In evaluating the results of each study, key considerations included (a) the effect size in relation to the sample size; (b) the effect size in relation to the study design and sample, i.e. larger effects are expected in case-control studies of drivers with crash histories; (c) the outcome measures used (more weight is given to state crash records than self reports, and to on road driving performance than to crash history); (d) statistical controls used such as age, driving exposure, and gender and (e) the type of predictor variables assessed, i.e. whether it was a well established measure or purpose built for a study.

3. Cognitive factors in relation to driving outcomes For the purposes of this review and discussion cognitive tests are grouped according to the general cognitive ability they are intended to measure. 3.1. Attention Of the studies reviewed in Table 1, low scores on various measures of attention were associated with crash risk and on-road driving performance. These included selective attention, divided attention, visual attention and range of attention. Interestingly, a simple letter cancellation task also had a very strong association with crash risk in drivers in the study by Maratolli et al. (1998). In general the results from Table 1 show consistent low to moderate associations between measures of attention and driving outcome measures, with moderate sized associations being reported for the Useful Field of View (UFOV). Poor performance on the UFOV (a measure of visual attention, involving identifying peripheral targets presented either in the presence or absence of distractors while completing a central discrimination task) was associated with increased crash risk of 87 to 107% in two prospective reports

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Table 3 Outcomes of studies reporting associations between sensory performance and crash risk or on-road driving performance Sensory ability

Test

Study

Association with crashes or on-road test

Examination Examination

Ball et al. (1993) Owsley, Ball et al. (1998)

r=0.23 r=0.10

Bailey Lovie Bailey Lovie Bailey Lovie Bailey Lovie EDRS Graham Field Chart Rosenbaum (Near) Bailey Lovie (Near) Static acuity differentiating stimuli in high contrast images Dynamic acuity differentiating stimuli in moving images

Owsley et al. (1991) Sims et al. (1998) Ivers et al. (1999) Sims et al. (2000) Owsley et al. (2001) Maratolli et al. (1998) Maratolli et al. (1998) Margolis et al. (2002) McKnight & McKnight (1999)

r=0.00 p=0.001 Right eyeb20/60 PR=2.2 NS NS NS Risk ratio=1.89, pb0.05 NS Time r=0.28, Errors r=0.18

Visual fields Kinetic peripheral fields Central fields Automated static Visual field

Custom built device Amsler grid Humphrey Humphrey

Maratolli et al. (1998) Maratolli et al. (1998) Ivers et al. (1999) Ball (1997), Owsley, Ball et al. (1998)

NS NS NS r=0.26/0.21

Colour vision Color vision

D15

Owsley et al. (1991)

r=0.15

Margolis et al. (2002)

NS

Eye health Eye health

Visual acuity Standard letter charts

Non-standard visual acuity measures

Depth perception Distance depth perception Randot stereotest contoured circles Field dependence Field dependence

Contrast sensitivity Contrast sensitivity

Low contrast acuity

McKnight & McKnight (1999) Time 0.19, Errors 0.19

Discerning a figure within a cluttered background

McKnight & McKnight (1999) Time 0.12, Errors 0.23

Pelli-Robson Pelli-Robson Pelli-Robson Pelli-Robson Pelli-Robson

Owsley et al. (1991) Maratolli et al. (1998) Sims et al. (1998) Sims et al. (2000) Owsley et al. (2001)

Pelli-Robson Differentiating low contrast images

r= 0.10 NS p=0.032 NS Impairment associated with crashes for subjects with cataracts Ball et al. (1993) r= 0.24/0.15 McKnight & McKnight (1999) Time=0.21, Errors=0.17

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Table 3 (continued ) Sensory ability

Test

Study

Association with crashes or on-road test

Glare Disability glare

BAT

Owsley et al. (2001)

NS

Inability to hear 40 dB both ears Self report

Sims et al. (1998)

NS

Ivers et al. (1999)

Audioscope

Sims et al. (2000)

Hearing loss in right ear PR 1.9; CI 1.1–3.4 NS

Hearing Hearing loss

Hearing

HR=hazard ratio; RR=relative risk; OR=odds ratio; r=correlation; UFOV=Useful Field of Vision Test; RT=reaction time; CI=confidence interval; SRT=simple reaction time; CRT=choice reaction time; PR=prevalence ratio. The RRs in Sims et al. (2000) were calculated controlling for age, race, gender, and days driven per week. The Owsley et al. (2001) study was conducted on a sample with cataract. The p-values in Sims et al. (1998) relate to the significant difference between crashers and non-crashers.

Table 4 Associations between medical conditions and driving outcome measures and physical functioning and driving outcome measures Physical functioning

Test

Study

Association with crashes or on-road test

Arthritis Heart disease Fall

Self-report Self-report

McGwin et al. (2000) McGwin et al. (2000) Margolis et al. (2002)

OR=1.8 (1.1, 2.9) females OR=1.5 (1.0, 2.2) HR=1.50 (1.23, 1.83), p=0.0001 RR=1.79, p=0.015 NS

Finger flexion Functional impairment Grip strength

Neck rotation Orthostatic systolic blood pressure drop Shoulder abduction Trunk rotation Tweezer test

Self-report activities of daily living Bilateral Bilateral Both directions

Maratolli et al. (1998) Margolis et al. (2002) Sims et al. (1998) Sims et al. (2000) Margolis et al. (2002) Maratolli et al. (1998) Margolis et al. (2002) Maratolli et al. (1998) Maratolli et al. (1998) Maratolli et al. (1998)

NS NS NS RR=2.19, p=0.001 HR=1.10 (1.00, 1.21), p=0.06 NS NS RR=1.62, p=0.038

HR=Hazard ratio; RR=relative risk; OR=Odds ratio; r=correlation; UFOV=Useful field of vision test; RT=reaction time; CI=confidence interval; SRT=Simple Reaction Time; CRT=choice reaction time; PR=prevalence ratio. The RRs in Sims et al. (2000) were calculated controlling for age, race, gender, and days driven per week. The Owsley et al. (2001) study was conducted on a sample with cataract. The p-values in Sims et al. (1998) relate to the significant difference between crashers and non-crashers. Non-significant results found for hypertension, stroke, cancer, cataracts, glaucoma, diabetes, kidney disease in McGwin et al. (2000).

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of predictors of crash risk in a case control study (Owsley, Ball et al., 1998; Owsley, McGwin et al., 1998; Sims et al., 2000; Table 1). 3.2. Perceptual and visuo-spatial abilities Visuo-perceptual tests showed moderate to high associations with driving outcome measures, with movement perception having a correlation of 0.73 with performance as measured in an on road assessment (De Raedt & Ponjaert-Kristoffersen, 2000); however, the generalizability of this result is somewhat limited by the study’s small sample size. Of the visuo-spatial tasks reviewed in Table 2, the paper-folding test showed the strongest associations with driving performance, albeit in a study with small sample size. Interestingly, paper-folding is probably the most cognitively complex of the visuo-spatial tasks reviewed. Many of the tests of perceptual ability involved speed of processing (e.g. the movement perception test, identifying target within a field test) and are strongly dependent on vision suggesting that that they may be explained by more general visual and speed of processing functions. 3.3. Processing speed and reaction time Studies relating measures of reaction time (RT) and psychomotor speed to on-road driving performance and crashes are also shown in Table 2. Associations of simple (RT) with self-reported crash history are generally small (Maratolli, 1998). Moderate correlations are observed between reaction time and on-road driving performance, with larger associations being found for complex RT than simple RT (McKnight & McKnight, 1999). The McKnight and McKnight study had the largest sample that had undergone an onroad assessment of driving performance. They included many measures of response times, which generally had associations with on-road driving performance of around r=0.3. This seems to reflect the more general nature of the association between RT measures and driving outcome measures. 3.4. Memory Memory measures (recall and recognition) were included in four studies. Significant associations were found between every measure of memory and driving outcome measure. Higher associations were reported in the Odenheimer et al. (1994) study, possibly because it included dementia patients. The small sample size of this study (n=30) and sampling strategy may also have inflated some associations. Other studies found low to moderate correlations (0.28–0.32) between memory measures and driving outcome measures. 3.5. Trailmaking test and other measures of executive function Results for the Trails Test were interesting because of the lack of significant associations found with Trails B, despite this being widely used in clinical assessments of driving competence. Stutts et al. (1998) reported small effects in a sample of over 3000 subjects, and no effect was found in a sample of 125 subjects (Maratolli, 1998). One study that assessed executive function more thoroughly compared 30 participants with no crash history to 30 participants with a history of crashes and found that the latter group performed more poorly on several measures of executive function (Daigneult, Pierre, & Frigon,

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2002). Participants reporting accidents made more errors and performed more slowly on the Trails test; made more errors on the Stroop Color Word Test; and, performed worse on the Tower of London and the Wisconsin Card Sorting Test compared to those with no crash history. Although the authors controlled for education differences between the two groups, it was not clear whether there were significant agedifferences between the two groups, as age was not used as a covariate. However, both the Daigneult et al., study and the Stutts et al. study only report p-values and not effect sizes, so it is not possible to gain an indication of the overall effect sizes between measures of executive function and driving outcome measures. Nonetheless, the overall results from this review suggest that impairment in executive function may reduce driving safety. 3.6. Mental status Mental status measures are instruments designed to screen individuals for risk of dementia or cognitive impairment and typically involve a few items measuring a range of cognitive abilities. The empirical studies reported in Table 2 are inconsistent with respect to whether mental status test results predict on-road driving or crash risk. Performance on the Mini-Mental State Examination (MMSE) was not found to be associated with either self-reported (Maratolli et al., 1998) or state recorded (Margolis et al., 2002) crashes but was associated with on-road driving performance (Odenheimer et al., 1994). Odenheimer et al. included participants referred from a dementia clinic in their relatively small sample so their significant effect may reflect sample bias. In normal samples of older adults the MMSE has strong ceiling effects. Ball et al. (1993) and Owsley, Ball, Sloane, Roenker, & Bruni (1991) both report moderate associations between the Mattis Organic Mental Syndrome Examination (MOMMSE) and crash risk possibly due to the fact that the MOMSSE is a more comprehensive measure of cognition than the MMSE. 3.7. Visual function in relation to driving Table 3 shows the associations between various measures of visual function and driving outcome measures. The findings are inconsistent. One large epidemiological study found drivers with lower visual acuity to have an increased risk of self-reported crashes (Ivers et al., 1999), but the other large epidemiological study did not find such an association (Margolis, 2002). Some smaller studies reported an increased crash risk associated with poorer visual acuity (e.g., Maratolli et al., 1998) whereas other prospective studies have not (e.g., Owsley, Stalvey, Wells, Sloane, & McGwin, 2001; Sims et al., 2000). Weak associations were found between non-standard visual acuity measures and driving measures, and contrast sensitivity and driving outcome measures. No association was found between glare sensitivity and crash records in the only study reporting data on glare (Owsley et al., 2001). Overall, these results suggest that visual tests used in isolation are not strong predictors of crash involvement (Table 2) because they do not tap into the visual and cognitive complexity of the driving task (Owsley, Ball et al., 1998; Owsley, McGwin et al., 1998; Wood, 1999). 3.8. Hearing loss Findings on the effect of hearing loss on crash risk were inconsistent, with one retrospective and one prospective study (Sims et al., 2000, 1998) not finding an effect, and another large study finding an

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effect for self-reported hearing loss in the right ear only (Ivers et al., 1999). However self-reported hearing loss may significantly underestimate the incidence of hearing loss in the population (Eekhof, De Bock, Schaapveld, & Springer, 2000). Compared with visual deficits, hearing deficits do not appear to be as important a risk factor for poor driving performance or crash risk. 3.9. Physical function and medical conditions Table 4 reports data on medical conditions and physical function measures associated with driving performance. McGwin et al. (2000) conducted a population-based case-control study of chronic medical conditions and motor vehicle crashes among 901 older drivers. They report an increased crash risk associated with heart disease, stroke and in women only, arthritis. Margolis and colleagues report a 10year prospective study of 1416 women aged 65–84 years who were assessed on measures of cognitive, visual, and physical functioning. They found that about one third of the participants had a motor vehicle crash during a mean follow-up time of 5.7 years. After adjustment for age and weekly driving mileage, the risk factors significantly associated with motor vehicle crashes were a fall in the previous year (hazard ratio (HR) 1.53, 95% CI: 1.26, 1.86), a greater orthostatic systolic blood pressure drop (HR 1.11 per 12.5 mm Hg. 95% CI: 1.01, 1.22), and increased foot reaction time (HR 1.10 per 0.06 second, 95% CI: 1.00, 1.22). Physical function measures are also reviewed in Table 4. Poor neck rotation was associated with twice the risk of crashing in one study (Maratolli et al., 1998). However, in the studies reviewed there were no significant effects of disability status, trunk rotation, shoulder abduction or grip strength on crash risk. It appears that while there are logical reasons for expecting physical function to be associated with driving performance and crash risk, there is little data at this stage to support this view. This may be due to a lack of studies examining physical functioning in relation to crash risk, poor measurement of disease severity, and possible failure to take into account physical function, strength and flexibility as factors contributing to driving cessation (Bickenbach, Chatterji, Badley, & Ustun, 1999). Few studies consider that individuals may alter their driving behaviors or motor vehicle to compensate for physical impairments and limited mobility. Alternatively, the inconsistent findings of associations between physical functioning and driving behaviors may reflect the possibility that older individuals with physical limitations are more likely to perceive their impairment than older adults with attentional, visual or memory difficulties, and voluntarily stop driving. 3.10. Methodological limitations of current research There has been moderate agreement between studies in determining cognitive and visual risk factors for crashes and safe driving performance, but the other areas reviewed have shown inconsistencies across studies. Differences between studies may be partly attributable to differences in study methodology including differences in study design, measurement of risk factors, measurement of outcome variables and statistical analyses (McGwin et al., 2000). 3.11. Study design Many investigations have been limited by cross-sectional and retrospective study designs, small sample sizes, and limited information on potential risk factors. Few of the studies reviewed reported

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prospective follow-up data (Table 1). We note that the correlations between predictors and crashes were lower in the prospective study reported by Owsley, Ball et al. (1999); Owsley, McGwin et al. (1998) than the retrospective study reported by Ball (1993). This raises the issue of the extent to which tests associated with crashes in a retrospective study will also be associated with crashes in a prospective study. It is possible that the interval between assessment of risk factors and follow-up will influence the strength of associations observed. 3.12. Measurement of risk factors The main limitation in the measurement of risk factors is the diversity of measures used, making it difficult to compare results between studies. Another consideration is that some tests are actually composites of a number of tests (e.g. Useful Field of Vision, UFOV; Blessed; MOMSSE), thus increasing the potential for shared variance and higher correlations with the outcome measure. 3.13. Measurement of outcome variables Crashes are an infrequent occurrence and difficult to model statistically making it difficult to observe statistically significant associations in anything but very large studies. Measures of driving performance that provide continuous outcome measures are more sensitive to changes in sensorimotor, and physical function, but on-road driving performance measures are also likely to vary in their reliability and validity. A limitation of studies based on state reports of crashes is that near misses, and less serious crashes were not ascertained, and some studies did not report if the motor vehicle crashes resulted in injuries (e.g., Margolis et al., 2002). 3.14. Data analytic issues Not all studies report enough information to enable a proper evaluation of the utility of the measures used. Information is required on the sensitivity and specificity of each test to detect crashes, as well as the overall measure of association between the risk factor and driving outcome measure. Results from some of the articles reviewed were not adjusted for any other covariates and represent a correlation between crashes and the measure, or an odds ratio or relative risk. Few of the studies examining older individuals’ involvement in motor vehicle crashes consider driving exposure. This is important given that older drivers voluntarily limit their driving as they develop progressive sensory, cognitive, and physical impairment (Guibert et al., 1998). Ignoring exposure may lead researchers to underestimate the impact that these impairments have on motor vehicle crash risk (Margolis et al., 2002). It is also possible that some measures are only accurate predictors of crashes in very old age groups. Inclusion of such wide age ranges in some of the studies reviewed may have reduced the sensitivity of the studies to identify important predictors. 3.15. The importance of insight into age-related changes Older adults appear to alter their driving behavior according to their beliefs of how a range of factors including physical illness, medication, and cognitive and sensory decline, might impact on their ability to drive safely (Persson, 1993; Rabbitt, Carmichael, Shilling, & Sutcliffe, 2002). Self-monitoring beliefs

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influence the decision to drive in challenging driving situations such as peak travel times and nighttime driving, or adverse weather conditions (Carr, 2000; Marottoli et al., 1998; Persson, 1993; Retchin & Anapolle, 1993). Self-monitoring beliefs may also influence the decision to cease driving. In a 9-year study of 404 individuals over 84, Brayne et al. (2000) found that the most common reasons given for driving cessation were health problems (28.6%), and loss of confidence (17.9%), while one third reported giving up driving on advice from family, friends or a medical specialist (see also Gilhotra et al., 2001). Lack of insight into possible cognitive, sensory or physical limitations may constitute a risk factor for poor driving performance and the rate of crashes. Indeed, Kruger and Dunning (1999) found that those individuals who performed poorly relative to their peers tended to think that they did well. This paradox has obviously serious implications for driver licensing and ongoing review of driver’s competency as they age. 3.16. A multifactorial model for enabling driving safety Having reviewed the diverse range of factors associated with safe driving in older adults, it is apparent that the field would benefit from drawing together the diverse factors into a single framework or model. We propose a multifactorial model based on both our review of empirical studies of predictors of safe driving and crash risk, as well as an understanding of the age-related changes in cognition, sensory function and motor abilities. The model (Fig. 1) explicitly separates bcapacity to drive safelyQ from bdriving behaviorQ reflecting our view that cognitive, sensory and physical variables determine an individual’s capacity, but that it is self-monitoring beliefs (i.e. insight into one’s driving capacity) that determine the choices an individual makes about driving behavior and hence driving safety.

Fig. 1. Schematic model of factors enabling safe driving behaviour. Driving capacity is purely determined by cognition, sensory and physical function, but self-monitoring driving beliefs about driving capacity, plus driving capacity itself, contribute to driving behaviour, e.g. avoiding night driving due to poor vision.

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It is proposed that self-monitoring and beliefs about driving capacity involve the capacity to evaluate or to have insight into one’s physical and cognitive abilities and deficits, to adapt driving habits accordingly, and interact with three Enabling Factors to produce safe driving: Cognition, Vision and Physical Function. Cognitive abilities shown to have consistent associations with driving safety include reaction-time and speed of processing, measures of visual attention and short term memory and executive function (Table 2). Visual abilities include visual acuity and contrast sensitivity (Table 3). Physical measures include neck rotation and potentially factors associated with falls risk, arthritis, and heart disease (Table 4). Reaction time identifies speed of response and movement speed as essentials for being able to respond adequately to on-road situations (Table 2); attention reflects the need to accurately direct attention to relevant traffic information and the capacity to ignore irrelevant stimuli; executive function is necessary to plan and coordinate sensorimotor and cognitive responses to complex driving situations, and requires adequate working memory resources so that relevant information may be held in mind during the decision making process (Table 2). Short-term memory may be required for integrating information and deficits in this area and may also be indicative of neurological disorders. Adequate visual abilities (Table 3) provide basic sensory information regarding the driving scene, including information regarding the roadway ahead, other road vehicles, pedestrians and other potential hazards. The physical requirements necessary for controlling a car such as functional mobility, and medical conditions that may impact on driving behavior (Table 4) are also included under the Physical Function factor in the model. Interactions between self-monitoring and beliefs about driving with enabling Factors may ultimately influence safe driving behavior, and related motor vehicle crash risk. Impairment in any one of these factors or a combination of factors may be associated with crash risk. To give an example, an older individual may have impaired reaction time, and also be aware of their slow speed of response to traffic and on-road situations. In turn, this may lead them to avoid peak time traffic, thus influencing safe driving behavior. On the other hand, an older driver may have poor visual abilities that impair their capacity to see traffic signs and signals, or reduced night vision; however, they may have limited insight and continue to drive at night. Subsequently, the individual is engaging in behaviors that reduce their driving safety, and are putting themselves at risk of a motor vehicle crash. These examples illustrate that the interactions between self-monitoring and enabling factors are not considered to be a static phenomenon, but involve complex and dynamic interactions over time that may impact positively or negatively on safe driving behavior. The enabling factors also have ongoing interrelationships throughout the driving process. For instance accurate encoding of traffic information requires adequate sensory ability to perceive the situation correctly, speed of processing as decisions have to be made quickly enough to allow time for response execution, and adequate mobility (such as neck rotation) necessary for checking blind spots. As well, competent driving behavior requires adequate decision-making abilities. This is where executive function is important, as the available information must be integrated and evaluated prior to deciding on a response. Information processing speed also influences decision-making because there are often brief time intervals during which drivers must make appropriate responses. Safe driving also involves adequate sensory, motor and cognitive functions to ensure the correct responses are made within adequate time. The proposed model also assumes that increasing interdependence among sensorimotor functions occurs in aging due to progressive resource limitations. Due to the weak associations between physical function and driving outcomes, and medical conditions and driving outcomes, further research is necessary to establish whether or not physical

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function is important for driving. If not, it may be because individuals self-monitor and adapt their behavior due to physical deficits. Self-report studies (e.g., Anstey & Smith, 2003; Rabbitt et al., 2002) suggest this is the case. The preliminary model shown in Fig. 1 is a schematic representation of the components necessary for identifying crash risk. The model may be useful in guiding research, and it is expected that it will develop as more research data become available. 3.17. Practical and research implications We envisage three applications of this model. First, it can be used as a research tool, providing a framework for testing hypotheses and explaining findings relating the variety of measures reviewed to driving safety outcomes. As more data become available, we expect the model to become more specific and finely tuned. Second, the model provides a framework for clinical assessment of driving competence. For example, a clinical psychologist or occupational therapist using this conceptual model would be guided to assess metacognitive processes (insight into cognitive, sensory and physical function that may affect driving safety), in addition to obtaining objective cognitive measures. Third, this approach provides the framework for designing and assessing interventions to improve driver safety. Interventions are required that not only address specific abilities (e.g., improve vision via cataract surgery), but also address awareness of physical, sensory and cognitive limitations leading to successful adaptation. Further work needs to be done to identify the processes by which older adults make decisions about their driving habits, in addition to identifying the sensorimotor and cognitive factors that predict crash risk. Normative data sets on representative populations of older persons will yield reliable sensitivities and specificities for determined cutoffs on tests that may be applied in clinical assessments. Ideally, empirically based evaluation of self-monitoring belief processes involved in decisions to drive will alleviate the need for functional assessments in some individuals. Hence there remains a need for evidence-based validation of all the components involved in crash risk that can be used to develop screening instruments and interventions to improve driving performance. The Multifactorial Model for Enabling Driving Safety proposed here may be useful in this process.

Acknowledgements Kaarin Anstey, Centre for Mental Health Research, Australian National University, Joanne Wood, Queensland University of Technology, Stephen Lord, Prince of Wales Medical Research Institute and University of New South Wales, Janine Walker, Centre for Mental Health Research, Australian National University. This research was funded by grants from the National Health and Medical Research Council (179839). We thank Dr Tim Windsor for his assistance.

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