Aging Brains Degrade Driving Safety Performances of the Healthy Elderly.

The relationship between aging brains and driving safety performances (DSPs) of elderly drivers was studied. A total of 90 dementia-free participants (63 men and 27 women, mean age 75.31 ± 4.795 years) were recruited and their DSPs were analyzed on actual vehicles running through a closed-circuit course. DSPs were comprehensively evaluated on the basis of driving instructors' scores (DIS). Signaling and visual research behaviors, part of DSPs, were measured to supplement the DIS evaluation by driving recorders (DR) and wearable wireless sensors (WS), respectively. Aging brains were evaluated via magnetic resonance imaging (MRI) findings and experimentally assigned to two grades (high vs. low) of brain atrophy (BA) and leukoaraiosis (LA). Regression analyses on DIS and DR data, and logistic analysis on WS scores showed significant correlations of aging brains with degradation of DSPs. The participant group with more advanced BAs and LAs showed lower DIS, DR data, and WS scores representing degraded DSP regardless of age. These results suggest that MRI examinations from both volumetric and pathological perspectives of brains have the potential to help identify elderly drivers with dangerous driving behaviors. Brain healthcare, lifestyle improvements and medical treatments to suppress BA and LA, may contribute to preventing DSP degradation of elderly drivers with aging brains.

Leukoaraiosis, a common brain magnetic resonance imaging finding, as a predictor of traffic crashes.

BACKGROUND: There are no reported studies on the relationship between traffic crashes and brain tissue changes in healthy drivers. The relationship between traffic crashes and leukoaraiosis, a common magnetic resonance imaging finding, was investigated in this study. METHODS: A total of 3,930 automobile drivers (2,037 men and 1,893 women; age, 21-87 years) who underwent brain magnetic resonance imaging as part of total health check-ups and answered a road traffic questionnaire were examined to determine whether asymptomatic leukoaraiosis was associated with various types of traffic crashes. Multiple logistic regression analysis was performed to elucidate the relationship between leukoaraiosis and various types of traffic crashes. RESULTS: Subcortical leukoaraiosis was diagnosed in 28.52% of all subjects, whereas periventricular leukoaraiosis was diagnosed in 9.57% of all subjects. Adjusted odds ratios for involvement in all types of traffic crashes were not significant for subjects with periventricular leukoaraiosis; however, they were significant for subjects with multiple and large multiple subcortical leukoaraiosis. Adjusted odds ratios for involvement in crashes at crossroads were 1.09 (95% confidence interval [CI], 0.60-2.00) for subjects with single subcortical leukoaraiosis, 3.35 (95% CI, 2.36-4.77) for subjects with multiple subcortical leukoaraiosis, and 2.45 (95% CI, 2.36-4.98) for subjects with large multiple subcortical leukoaraiosis. Periventricular leukoaraiosis was not significantly associated with crossroad crashes. Involvement in crashes of any type, parking lot crashes, and rear-end collisions showed no significant association with either subcortical or periventricular leukoaraiosis. CONCLUSIONS: Multiple subcortical leukoaraiosis, but not periventricular leukoaraiosis, is significantly associated with traffic crashes, in particular, crossroad crashes. This association is independent of sex, age, and driving exposure. To our knowledge, this is the first evidence describing the relationship between brain tissue changes and traffic crashes.

Elderly drivers' everyday behavior as a predictor of crash involvement--Questionnaire responses by drivers' family members.

When or whether elderly drivers stop driving is concerning not only to the drivers themselves but also to their family members. Therefore, it is important for family members to take the initiative if they wish to obtain information on the likelihood of the drivers' involvement in crashes. On the basis of the older drivers' Everyday Behavior Questionnaire (EBQ) developed in this paper, we attempt to predict drivers' involvement in crashes using the responses given by their family members. The results revealed that this 14-item questionnaire has a sufficient level of internal consistency as well as a significant correlation (r=0.29) with the experience of involvement in crashes in the last three years (p<0.01). Although the EBQ is a proxy-reported questionnaire and does not include items directly related to driving behaviors, the correlation between the EBQ and crash involvement is stronger than that of the self-reported Driver Behavior Questionnaire reported in deWinter and Dodou (2010), who conducted a meta-analysis and estimated the overall correlation among samples of earlier studies. In addition, logistic regression analysis showed that the EBQ score and the exposure to driving risks, measured by the frequency of driving, are significant predictors of involvement in crashes.