Abstract
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Abstract
Year 2021
September 2021

SHBC1253

Abstract Title
Predictability of touchscreen ‘DriveSafe DriveAware’ on the actual driving performance of Singapore drivers’ with medical conditions
Authors

H.H.LIM1, Y.H LIM1, H. LEE1

Institutions

Tan Tock Seng Hospital1

Background & Hypothesis

Touchscreen DriveSafe and DriveAware (tDSDA) is a touchscreen assessment tool to predict for safe and unsafe driving among older and/or cognitively impaired drivers (Kay, Bundy & Clemson, 2009; Hines & Bundy, 2014). However, tDSDA’s predictive validity has not been established locally. The aim of this study is to examine the validity of tDSDA in predicting the on-road driving performance of drivers with medical conditions in Singapore.

Methods

A prospective observational study was conducted. One hundred participants were recruited using purposive sampling. All participants completed the tDSDA before their on-road assessment. Both assessors and participants were double-blinded to the tDSDA results during on-road assessment. Descriptive statistics were used to summarise the data. Diagnostic accuracy analyses were performed to compare touchscreen DSDA results against on-road driving assessment outcomes.

Results

Two of the 100 participants were excluded due to incompletion of on-road assessment. Among the remaining 98 participants, 88.8% were male with a mean age 56.6years. The mean duration of driving experience was 28.3 years. 93.8% of them had a neurological condition (e.g. stroke).

The sensitivity, specificity, positive predictive value and negative predictive value comparing tDSDA outcome and the drivers’ on-road assessment outcome was 25.0%, 97.6%, 33.3% and 96.5% respectively.

Discussion & Conclusion

The tDSDA specificity was consistent when compared with previous validity studies. The tDSDA has high specificity to predict drivers who will pass on-road assessment but not those who will fail on-road assessment. This is similar to other studies which concluded that there is no one clinical standardised assessment that can be predict one’s on-road assessment.

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