Longitudinal Predictors of Driving Cessation Among Older Adults From the ACTIVE Clinical Trial

Edwards, Jerri D.; Ross, Lesley A.; Ackerman, Michelle L.; Small, Brent J.; Ball, Karlene; Bradley, Stacy; Dodson, Joan E.

doi:10.1093/geronb/63.1.p6

Cited by 130 publications

(155 citation statements)

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“…Reduced speed of visual processing/attention (measured with UFOV score) was an independent risk factor in line with studies of driving cessation in aging. 29,30 The UPDRS-ADL score (risk factor for "ex-driver" status in a crosssectional study 31 ) and the daily total levodopa equivalent amount were independent risk factors of future driving cessation in the multivariate model suggesting that these measures better predict future functional impairment compared to the motor UPDRS score in the medicated ("on") phase.…”

Section: Discussionmentioning

confidence: 99%

Real-life driving outcomes in Parkinson disease

Uç¹,

Rizzo

Johnson³

et al. 2011

Neurology

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Objective: To determine the incidence of and risk factors for driving outcomes in drivers with Parkinson disease (PD). Methods:In a prospective cohort study, we ascertained the time until driving cessation, a crash, or a traffic citation using self-report and state Department of Transportation records in 106 licensed, active drivers with PD and 130 controls.Results: Drivers with PD stopped driving earlier than controls, hazard ratio (95% confidence interval) ϭ 7.09 (3.66-13.75), p Ͻ 0.001. Cumulative incidence of driving cessation at 2 years after baseline was 17.6% (11.5%-26.5%) for PD and 3.1% (1.2%-8.1%) for controls. No significant differences between groups on times to first crash or citation were detected. However, the number of observed crashes was low. Cox proportional hazards models showed that significant baseline risk factors for driving cessation in PD were older age, preference to be driven by somebody else, positive crash history, use of compensatory strategies, low driving exposure, impairments in visual perception (especially visual processing speed and attention) and cognitive abilities, parkinsonism (especially activities of daily living score and total daily dose of antiparkinsonian medications), and higher error counts on a road test. Within PD, crashes were associated with poorer postural stability and history of driving citations, and citations were associated with younger age and road errors at baseline. Conclusions:Drivers with PD are at a higher risk of driving cessation than elderly control drivers. A battery evaluating motor and nonmotor aspects of PD, driving record, and performance can be useful in assessing future driving outcomes in PD. Neurology Cognitive, visual, and motor impairments in Parkinson disease (PD) can affect driving performance on standardized road tests 1-11 and driving simulator experiments. 12-16 Cross-sectional or retrospective surveys show higher rates of driving cessation in PD, 17-21 which may lead to greater inactivity, social isolation, depression, and caregiver burden. 22,23 Driving simulation studies have shown increased crash rates in PD 13,16 and retrospective surveys have suggested increased crashes in drivers with PD. 17,19 However, real-life driving outcomes in PD have not been determined in prospective, controlled PD cohort studies. 21This study examines the real-life outcomes in a PD driver cohort, whose baseline features, experimental road test, and driving simulator performance were reported previously. 1,5,6,8,16 The main outcome measures in this study were time to driving cessation, time to first crash, and time to first citation. We hypothesized that the incidence of unfavorable real-life driving outcomes in drivers with PD would be higher than in neurologically normal control drivers,

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Section: Discussionmentioning

confidence: 99%

Real-life driving outcomes in Parkinson disease

Uç¹,

Rizzo

Johnson³

et al. 2011

Neurology

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Section: Discussionmentioning

confidence: 99%

“…It is known that slower processing speed predicts a greater likelihood of driving cessation within 5 years (Anstey, Windsor, Luszcz, & Andrews, 2006;Edwards et al, 2008). Processing speed measures such as the Digit Symbol Substitution test (Wechsler, 1981) predicted driving cessation, both independently and after adjusting for effects of age and self-rated health/physical function (Anstey, Windsor, et al, 2006;Edwards et al, 2008). Additionally, decline of processing speed (as well as general cognitive, verbal, and memory abilities) has been linked to increases in rates of falling and fall risk in elderly participants (Anstey, von Sanden, & Luszcz, 2006;Welmerink, Longstreth, Lyles, & Fitzpatrick, 2010).…”

Section: Discussionmentioning

confidence: 99%

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

Yesavage

Adamson

et al. 2011

The Journals of Gerontology Series B: Psychological Sciences an

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P ROCESSING-SPEED theory proposes that much of the age-related decline observed in measures of fluid cognition can be accounted for by age-related slowing in the speed of elementary cognitive operations (Salthouse, 1996). A large body of research shows that both behavioral and brain measures of processing speed can account for a substantial proportion of age-related variance in fluid cognitive abilities (e.g., Birren & Fisher, 1995;Bucur et al., 2008;Kwong See & Ryan, 1995;Lindenberger, Mayr, & Kliegl, 1993;Salthouse, 1991;Taylor, O'Hara, Mumenthaler, Rosen, & Yesavage, 2005;Walhovd et al., 2005). Recently, a large study of twins confirmed that genetic variance for processing speed was a leading indicator of variation in a number of age-related cognitive changes (Finkel, Reynolds, McArdle, Hamagami, & Pedersen, 2009).Nonetheless, a processing speed model alone does not necessarily explain certain aspects of normal age-related decline in fluid abilities (Bugg, Zook, DeLosh, Davalos, & Davis, 2006;Schaie, 1989;Schretlen et al., 2000). In particular, decline in executive function has been proposed as a second mechanism underlying age-related decline in fluid cognition (Buckner, 2004;Dempster, 1992; Hasher & Zacks, 1988;Hasher, Zacks, & May, 1999;West, 1996).Although the best way to measure executive function remains a challenge, it has been suggested that executive function be characterized as a collection of related cognitive control processes, in particular, "updating" working memory representations, "shifting" mental set, "inhibiting" automatic or prepotent response to irrelevant information, and "coordinating" complex, sequential behaviors to reach a goal (Friedman et al., 2008). A number of behavioral and neuroimaging studies have demonstrated links among executive control processes and age-related differences in performance of working memory and episodic memory tasks that stress interference, temporal processing, recollection, or contextual binding (Gray, Chabris, & Braver, 2003;Gunning-Dixon & Raz, 2003;Head, Kennedy, Rodrigue, & Raz, 2009;Hedden & Park, 2001;Kwong See & Ryan, 1995;Taylor et al., 2005). Together the results of these studies imply that when examining the explanatory power of processing speed, it may be advantageous to also examine other cognitive abilities, such as executive function.Relatively few studies have examined the processingspeed theory from a longitudinal perspective (Hertzog, Dixon, Hultsch, & MacDonald, 2003;Schaie, 1989;Zimprich & Martin, 2002 Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age.Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40-77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1-13) data points per participant. ...

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“…A series of investigations demonstrated that reduced UFOV is a predictor of poor driving performance [21] and crash risk [22], and that the UFOV can be improved with training, leading to improved driving-related outcomes [23] and faster performance of timed IADLs [24]. Analyses of the speed-of-processing training arm in ACTIVE confirmed that individuals with reduced UFOV are at risk for imminent driving cessation [25] and that the intervention delayed the onset of driving cessation [26]. Additional studies demonstrated sustained improvement in cognitive self-efficacy [27], health-related quality of life [28], depressive symptoms [29], and health care expenditures [30].…”

Section: Recent Findings In Technology-enabled Cognitive Trainingmentioning

confidence: 99%

Minding the Aging Brain: Technology-Enabled Cognitive Training for Healthy Elders

Steinerman

2010

Curr Neurol Neurosci Rep

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Cognitive training refers to theory-driven behavioral intervention, ideally supported by a strong conceptual framework and specified neurocognitive mechanisms. Within this field, neurotechnology promises many advantages, and a growing literature establishes technology-enabled cognitive training as a promising modality to promote positive cognition in consumer, research, clinical, and public health settings. Methodologic challenges remain, and specific cognitive training recommendations for healthy elders must be tentative in the context of an emerging evidence base.

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Longitudinal Predictors of Driving Cessation Among Older Adults From the ACTIVE Clinical Trial

Cited by 130 publications

References 41 publications

Real-life driving outcomes in Parkinson disease

Real-life driving outcomes in Parkinson disease

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

Minding the Aging Brain: Technology-Enabled Cognitive Training for Healthy Elders

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