Comparing Nighttime to Rush Hour Avoidance as Initial Driving Self-Regulation Behaviors

Aging is associated with an increase in avoidance of challenging driving situations (e.g., driving at night, during rush hour, on freeways, and in unfamiliar areas). Such avoidance behavior may be due to driving self-regulation (SR), an intentional response to perceived declining abilities, or it may be due to other factors such as lifestyle changes or preferences. Most previous research has not studied SR as the reason for avoidance, and has treated avoidance behaviors interchangeably. In addition, previous research has not differentiated one’s first SR behavior from those reported later in the process. This study included 1,557 participants from the AAA Longitudinal Research on Aging Drivers (LongROAD) to assess older adults’ initial self-regulatory behavior by comparing the frequency of nighttime, rush hour, freeway, and unfamiliar area avoidance among those who reported only one SR behavior. Nighttime SR was most common (58.8%), followed by rush hour (25.5%), unfamiliar areas (11.0%), and freeways (4.8%). Binary logistic regression was used to assess how demographics, function, and self-reported driving variables were related to different odds of reporting nighttime vs. rush hour avoidance (the two most common) as one’s initial SR behavior. Higher odds of reporting nighttime avoidance (compared to rush hour) as one’s initial SR behavior were related to female gender, low income, impaired visual acuity, better self-reported ability to see during the day, worse self-reported ability to see at night, less comfort driving at night, and more comfort driving during rush hour, and in unfamiliar areas.

Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study

Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.

The Influence of Hearing Impairment on Driving Avoidance Among a Large Cohort of Older Drivers

As people age, some of the commonly experienced psychomotor, visual, and cognitive declines can interfere with the ability to safely drive, often leading to situational avoidance of challenging driving situations. The effect of hearing impairment on these avoidance behaviors has not been comprehensively studied. Data from the American Automobile Association (AAA) Longitudinal Research on Aging Drivers (LongROAD) study were used to assess the effect of hearing impairment on driving avoidance, using three measures of hearing. Results indicated that hearing loss plays a complex role in driving avoidance, and that an objective hearing measure was a stronger predictor than hearing aid use and self-rated hearing. Greater hearing impairment was related to less nighttime and freeway driving, more trips farther than 15 mi from home, and lower odds of avoiding peak driving times. The moderating influence of hearing on both vision and cognition is also discussed, along with study implications and future research.

Exploring How Hearing, Vision, and Cognition Affect Older Adults’ Driving Exposure Patterns

Better information is needed about how declines in sensory and cognitive function affect older drivers. This study assessed how hearing loss affects engagement in four challenging driving patterns. Data from the AAA Longitudinal Research on Aging Drivers study was used, including objectively-measured driving; three measures of hearing: reported hearing aid use, self-rated hearing, and the Whisper Test; visual acuity (Tumbling E); and cognition (Trail Making B). Failing the Whisper Test in both ears was related to significantly lower percentage of trips (%trips) at night, on freeways, and during rush hour, but a higher %trips >15 miles. Hearing aid use and self-rated hearing were not associated with any driving differences. Worse vision was related to a lower %trips >15 miles, while worse cognition was associated with a lower %trips at night, on freeways, and during rush hour. The Whisper Test interacted with cognition for rush hour trips.

Semi-Autonomous Vehicles as a Cognitive Assistive Device for Older Adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors’ features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers.