Stability of physical assessment of older drivers over 1 year

Smith, Andrew; Porter, Michelle M.; Ha, Linda; Bédard, Michel; Gélinas, Isabelle; Man-Son-Hing, Malcolm; Mazer, Barbara; Rapoport, Mark J.; Tuokko, Holly; Vrkljan, Brenda

doi:10.1016/j.aap.2013.02.007

Cited by 17 publications

(10 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RPW is included in the Assessment of Driving-Related Skills (ADReS), which is a test battery that consists of vision, cognition, and motor/somatosensory function measures that assess skills necessary for safe driving (Carr et al 2010). A study on the stability of physical assessment measures found that the RPW had a moderate relative reliability and low coefficients of variability (CV) values (Smith et al 2013). …”

Section: Methodsmentioning

confidence: 99%

Select physical performance measures and driving outcomes in older adults

et al. 2017

View full text Add to dashboard Cite

BackgroundImproving physical functioning may be a future intervention to keep older adults driving safely longer as it can help maintain both physical and cognitive health longer. This systematic review assesses the evidence on the association between three physical functioning measures: the Short Physical Performance Battery, the Timed Up-and-Go test, and the Rapid Pace Walk with driving outcomes in older adults.MethodsOlder adult studies published between 1994 and 2015 that included the Short Physical Performance Battery, the Timed Up-and-Go test, or the Rapid Pace Walk as a measure of physical functioning and included a driving-related outcome were identified through a comprehensive search and reviewed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.ResultsThirteen studies involving 5,313 older adults met the inclusion criteria. Lower Short Physical Performance Battery scores were associated with reduced driving exposure and increased cessation in all three Short Physical Performance Battery studies. The Timed Up-and-Go test was not associated with the driving outcomes (cessation, ability, crashes, and citations) in either of the two Timed Up-and-Go studies. Poorer Rapid Pace Walk scores were associated with decreased driving ability in two studies and with reduced driving exposure in one study, but not associated with driving ability, crashes, citations, or cessation in the remaining five Rapid Pace Walk studies.ConclusionsThe Timed Up-and-Go test measure appears not to be a useful measure of physical functioning for the driving outcomes included here. The Rapid Pace Walk may be useful in studies of driving ability and exposure. More driving studies should consider using the Short Physical Performance Battery to determine if it may be useful as a risk factor assessment for identifying individuals at risk of certain driving outcomes.

show abstract

Section: Methodsmentioning

confidence: 99%

Select physical performance measures and driving outcomes in older adults

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Fourteen drivers that demonstrated stable general, physical and cognitive health over their first year in the Candrive study [135], from the Ottawa region and that did not share their vehicle were chosen for detailed analysis. Stable general, cognitive, and physical health allows for analysis while controlling for any influence on their driving behaviours caused by changing health.…”

Section: Driver Behaviour Attributes -Month To Month Trendsmentioning

confidence: 99%

An Integrated Approach for Episodic Cognition Assessment

Wallace¹

View full text Add to dashboard Cite

Dementia and Mild Cognitive Impairment (MCI) are significant health issues that are a rising cost to society. They are monitored with cognitive tests during clinical appointments that are limited by the healthcare system capacity and patient's ability/willingness to attend. Current cognitive tests use behavioural measures and not direct measures of underlying cellular change. This causes delay in This work could never have been possible without the inspiration, support and acceptance of the long hours of work from my family including

show abstract

“…Previous analysis has identified a number of measures of specific driver behaviours including choice of road, trip length and trip duration [18]. A focused study of acceleration [19] for drivers with stable general, physical and cognitive health [20] that are sole drivers of their vehicles showed drivers have differing acceleration and deceleration behaviors [1,21,22]. This paper explores the trip level calculated features individually and in combination for their ability to allow two different drivers to be distinguished.…”

Section: Problem Statementmentioning

confidence: 99%

Features that Distinguish Drivers: Big Data Analytics of Naturalistic Driving Data

Wallace¹,

Knoefel²,

Goubran³

et al. 2017

STBD

Self Cite

View full text Add to dashboard Cite

The unique behaviours of drivers have many emerging applications. These include the personalization of automated/selfdriving vehicles so the owners are more comfortable with them, and the identification of changing driving behaviours that may be associated with aging or disease. This paper explores measures of driving behaviours that might allow for the differentiation of drivers based on their individual driving characteristics. An emerging challenge within longitudinal studies of drivers is to distinguish between different drivers of a shared vehicle. It also has application in the insurance industry where insurance risk and associated owner premium depends on the diversity or lack thereof of drivers for a vehicle such as a vehicle driven/never driven by secondary drivers that have higher risk driving behaviours. In this paper, a big data set of driving data for 14 older drivers is analyzed -a single year of data includes over 250,000 km and almost 5000 hours of driving for the 14 drivers. A set of 162 trip level calculated features are analyzed to determine their ability to be used to distinguish between two drivers of a vehicle. The results show that features based on road choice and driver chosen velocity provide the best performance individually and in feature pairs with 2 features providing error rates less than 5% for some driver pairs. The set of features that provided the best performance differed for each driver pair and was found to include features from measures of a driver's road choice, velocity and velocity ratio in addition to the features measuring trip similarity to two phase acceleration and deceleration relationships for the driver. The best error rate obtained was 1.5% for a driver pair. On the other hand, the results suggest that a number of features and feature groups do not allow for older driver differentiation. For instance, overnight driving and high rates of acceleration are not sufficiently exhibited by these drivers to be useful.

show abstract

Stability of physical assessment of older drivers over 1 year

Cited by 17 publications

References 50 publications

Select physical performance measures and driving outcomes in older adults

Select physical performance measures and driving outcomes in older adults

An Integrated Approach for Episodic Cognition Assessment

Features that Distinguish Drivers: Big Data Analytics of Naturalistic Driving Data

Contact Info

Product

Resources

About