Prediction for return to driving after the first-ever stroke in Korea: The KOSCO study

Jee, Sungju; Sohn, Min Kyun; Lee, Jongmin; Kim, Deog Young; Lee, Sam-Gyu; Shin, Yong‐Il; Oh, Gyung-Jae; Lee, Yang-Soo; Joo, Min Cheol; Han, Eun Young; Kim, Yun-Hee

doi:10.2340/16501977-2373

Cited by 11 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among physical factors, stroke and lower extremity function were identified as predictors of DC. Stroke, as a neurologic problem, leads to decreases in visual field, attention and information processing speed and physical function, eventually causing stroke survivors to reduce or cease driving [ 41 , 42 ]. The results showed that older adults who had difficulty climbing stairs as an indicator of lower extremity function were 1.50 times more likely to stop driving.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the assessment of physical function, such as lower extremity function, is required to determine the need for DC. However, in terms of stroke survivors, one study reported that 66.1% of patients with stroke returned to driving within one year after the stroke [ 42 ]. Another study reported that a continuous exercise rehabilitation program allowed stroke patients to recover their extremity function and driving ability [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Predictors of Driving Cessation among Older Drivers in Korea

Moon

2020

IJERPH

View full text Add to dashboard Cite

Background: As the elderly population and the number of older drivers grow, public safety concerns about traffic accidents involving older drivers are increasing. Approaches to reduce traffic accidents involving older drivers without limiting their mobility are needed. This study aimed to investigate the driving cessation (DC) rate among older Korean adults and predictors of DC based on the comprehensive mobility framework. Method: In this cross-sectional study, data from 2970 to 10,062 older adults over 65 years old from the 2017 National Survey of Elderly People were analyzed in April 2020. Multivariate logistic regression analyses were conducted to identify the predictors of DC. Results: Residential area, an environmental factor, was a strong predictor of DC (Odds Ratio (OR) 2.21, 95% Confidential Interval (CI) 1.86–2.62). Older drivers living in an area with a metro system were 2.21 more likely to stop driving than those living in an area without a metro system. Other demographic, financial, psychosocial, physical, and cognitive variables also predicted DC. Conclusion: Environmental factors were strong predictors of older adults’ DC. Therefore, political and environmental support, such as the provision of accessible public transportation, is essential to increase the DC rate among older adults to increase public safety without decreasing their mobility.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Predictors of Driving Cessation among Older Drivers in Korea

Moon

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Obwohl es auch einige Studien gibt, die Vorhersagealgorithmen für die Kommunikation [4], Kognition [5], Depression [6], Rückkehr an den Arbeitsplatz [7] und Fahrtüchtigkeit [8] beleuchten, sind die Algorithmen für die Vorhersage der Erholung der Motorik nach Schlaganfall bisher am umfassendsten untersucht. Einen Überblick über diese Verfahren finden Sie in ▶ Tab.…”

Section: Prognose: Die Erholung Nach Schlaganfall Vorhersagenunclassified

Algorithmen vs. Experten in der Neuroreha

Elsner¹,

Mehrholz²

2021

neuroreha

View full text Add to dashboard Cite

show abstract

“…Nevertheless, many stroke survivors return to driving. In the Republic of Korea, 66.1% of patients with first-ever stroke return to driving at a mean of 2.15 months after stroke [4]. It is essential to decide the returning time for stroke survivors since they are all different in the severity of residual symptoms and the degree of recovery.…”

Section: Introductionmentioning

confidence: 99%

“…In the United States, 30% to 43% of the approximately 6 million stroke survivors drive again within a year, and 87% of them return to driving without formal driving evaluation [18]. Furthermore, a relatively small percentage of stroke survivors (25.9%) received driving evaluations or advice before return to driving in the Republic of Korea [19]. Also, stroke patients themselves and their families tend to overestimate the driving performance of stroke survivors [20].…”

Section: Introductionmentioning

confidence: 99%

Driving-PASS: A Driving Performance Assessment System for Stroke Drivers Using Deep Features

et al. 2021

View full text Add to dashboard Cite

Despite any doubts about driving safety, many stroke drivers drive again due to the absence of valid screening tools. The on-road test is considered a formal assessment, but there are safety issues in testing directly on stroke patients who are not fully capable of driving. A driving simulator is a promising tool since it provides meaningful information for identifying hazards to driving safety across different driver populations and driving conditions. Using the advantages of a driving simulator, we propose a Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS is designed not only to pre-screen invalid stroke drivers before the on-road test but also to provide problematic driving items for the use in driving rehabilitation. To design assessment classifiers, i.e., the core engine of Driving-PASS, we collect driving data from a total of twenty-seven participants in thirteen driving scenarios. Thereafter, we get subjective assessment results from ten driving evaluators in eleven assessment items. By using driving data and subjective assessment results, we construct eleven assessment classifiers for ten driving ability items and one driving suitability item. We addressed the technical challenges such as handcrafted features and imbalanced dataset by a feature extraction method using pre-trained CNN models and a resampling method. Through comprehensive performance evaluation, we build eleven accurate assessment classifiers in Driving-PASS by carefully selecting deep features in each assessment item. We envision that Driving-PASS can be used as a pre-screening tool for evaluating stroke drivers and will ultimately improve road safety.

show abstract

Prediction for return to driving after the first-ever stroke in Korea: The KOSCO study

Abstract: Male patients, education about return to driving, lower mRS, and higher FMA at 7 days after stroke are predictors of return to driving. This model could be used by clinicians to help counsel patients and their families.

Cited by 11 publications

References 24 publications

The Predictors of Driving Cessation among Older Drivers in Korea

The Predictors of Driving Cessation among Older Drivers in Korea

Algorithmen vs. Experten in der Neuroreha

Driving-PASS: A Driving Performance Assessment System for Stroke Drivers Using Deep Features

Contact Info

Product

Resources

About