Older Drivers Research Articles

Exploring perceptions of Advanced Driver Assistance Systems (ADAS) in older drivers with age-related declines

Perceptions of Advanced Driver Assistance Systems (ADAS) were explored in two semi-structured face-to-face focus group studies of 42 older drivers (aged 65 years and older) with and without age-related declines. Study 1 explored perceptions regarding ADAS, focusing on visual, auditory, physical, and cognitive factors. Study 2 extended this by additionally exploring perceptions following exposure to videos and stationary vehicle demonstrations of an ADAS. Participants had a range of visual, hearing, memory, and health characteristics which impacted on their daily life. In both studies, some participants had insights regarding various ADAS technologies prior to the study, but many were unfamiliar with these systems. Nevertheless, overall, participants reported that ADAS would assist them to drive as they age and increase their mobility and independence. There were comments regarding the benefits of warning alerts, although the potential for them to be distracting was also highlighted. Participants with vision impairment preferred audio alerts and participants with hearing impairment preferred visual display alerts. Findings highlighted the potential for ADAS to assist those with age-related declines and the need to increase the flexibility of warning system alerts to suit the varying requirements of older drivers, as well as to reduce the complexity of vehicle interfaces. Collectively, these strategies would maximize the benefits of these vehicles to increase the mobility, independence, and quality of life of older drivers with and without age-related declines.

GENDER AND AGE DIFFERENCES IN ACCEPTANCE OF ADVANCED DRIVER ASSISTANCE SYSTEMS: INSIGHTS FROM OLDER AUSTRALIANS

Abstract Advanced Driver-Assistance Systems (ADAS) are in-vehicle technologies that promise to improve driver safety and may help support older drivers to drive safer for longer, however there is little research examining acceptance and use of ADAS among older adults. This study investigated age and gender differences in attitudes to ADAS and use of ADAS. We conducted an online survey of 1330 drivers aged 65 years or older (M=72, SD=6.9, 23% women) in partnership with National Seniors Australia. ADAS use was self-reported, classifying respondents in to users (96%) and non-users (4%). ADAS acceptance was measured by the Partial Automation Acceptance Scale. Linear regression estimated gender and age differences in acceptance. Logistic regression estimated differences in ADAS use by gender, age and acceptance. Older age was associated with higher levels of trust in ADAS (β = 0.032, p = 0.04) and lower perceived ease of use (β = -0.02, p = 0.02). Women were less likely to report ADAS use (β = -0.99, p = 0.008) but had higher levels of trust (β = 0.86, p = 0.001) than men. After adjusting for age and gender, positive attitudes towards ADAS (β = 0.12, p = 0.003) and lower perceived risk (β = 0.22, p = 0.046) were associated with higher levels of ADAS use. Consideration of the gender and age differences may inform future vehicle design, and older drivers may benefit from education on the risks and benefits of ADAS to aid in the acceptance and consequent use of ADAS.

DEMAND-ABILITIES FIT AND PRESTIGE INFLUENCE OLDER WORKERS’ INTEREST IN ALTERNATIVE JOBS

Abstract With the increasing displacement of workers due to technological innovations, many older workers may need or want to make career transitions. Therefore, it is critical to understand the factors that influence older workers acceptance of potential alternative careers. In this study, long-distance truck drivers (n=202), a profession at-risk of technological disruption, completed an online survey where they rated six alternative transition occupations (e.g., machine setting, operator and tender, cargo and freight agent) to understand what makes them interested in alternate jobs. Using multilevel modeling (with occupations nested in participants), we evaluated the role of perceptions of demand-abilities fit and prestige, as moderated by age, on interest in alternative careers. Demand-abilities fit and job prestige were positively associated with rated job appeal, which was positively associated with interest in the job as a transition occupation. Older drivers, relative to younger truck drivers, found job prestige to be more appealing, which appeared to make them more interested in transitioning into these jobs. Our results extend person-environment fit and socio-emotional selectivity theory by showcasing how age relates to evaluations of occupations for career transitions, an area that is rising in importance given the increases in an aging workforce and potential disruptions from automation. Results carry practical importance for preparing the workforce for career disruptions given automation by identifying qualities that may shape workers’ willingness to transition, and identifying how these qualities differ based on age.

UTILIZING ADVANCED DRIVER ASSISTANCE SYSTEMS IN LATER LIFE: INSIGHTS FROM A LONGITUDINAL STUDY ON AGING DRIVERS

Abstract Level 1 and 2 Advanced Driver Assistance System (ADAS) technologies (e.g., adaptive cruise control, blind spot warning, lane keep assist) are predicted to be available in approximately three-fourths of all vehicles worldwide by 2025. To realize the potential of these systems in extending safe mobility for aging adults, it is critical that the driver understands the functionality of the technology and uses the system appropriately. The current study examined demographic and vehicle technology questionnaire data from the Longitudinal Research on Aging Drivers (LongROAD) cohort study which concluded in December 2022. A total of 1,417 participants changed their vehicle throughout the study. There were statistically significant increases in the prevalence of all 15 ADAS technologies examined. Despite increases in prevalence, frequency of using the technologies remained unchanged across 5 years. Frequency of use also varied by functionality of the technology whereby participants reported higher frequency of using technologies that provide alerts, such as blind spot warning, than technologies that take action to assist drivers with vehicle operations, such as adaptive cruise control. Results showed differences in prevalence and use of technologies by income and education, suggesting disparities in access to vehicles with technologies that could help to create a safer driving experience. In consideration of the rapid proliferation of ADAS into the vehicle fleet, increased research into how older drivers learn about and use ADAS technologies will assist in efforts to develop tailored and accessible programs for training older adults to properly utilize ADAS available in their own vehicles.

LOCATION MODERATES THE EFFECT OF COGNITIVE TRAINING ON CRASH RISK OVER 20 YEARS: AN ANALYSIS OF THE ACTIVE STUDY

Abstract Driving is a critical everyday function that helps older adults remain independent. The Advanced Cognitive Training in Vital Elderly (ACTIVE) study has shown cognitive training improves driving safety outcomes for older adults. Few studies have examined the longitudinal effects of cognitive training on crash risk beyond 5 years. To address this empirical gap, we aimed to determine the effects of cognitive training on crash risk over 20 years, and how urbanicity moderates these relationships. Using participants from the no-contact control and intervention (speed of processing, memory, and reasoning training) groups in the ACTIVE study (n=2170) and state-recorded crash reports, cox proportional hazards models were used to examine the effect of three cognitive training interventions as predictors of time to first at-fault crash over 20 years. Participants (mean age 73, 73% White, 23% Male) reported driving 90.6 miles/week on average and 12.6% experienced an at-fault crash. Overall, speed of processing training was the only intervention related to lower crash risk, but this varied by urbanicity. Namely, speed of processing training was related to lower crash risk for individuals living in rural environments (HR=.16, 95% CI [.04-.73], p=.018) but not for individuals in urban environments (HR=.89, 95% CI [.54-1.50], p=.679). Findings highlight the safety benefits of speed of processing training for older drivers living in rural environments where more fatal crashes occur. However, interventions are still needed to improve safety in urban environments where more crashes occur generally. Consideration of location in future interventions may help improve safe driving for older adults.

DISPARITIES IN MOCA AND MMSE SCORES AMONG DIVERSE OLDER ADULTS: A CASE ANALYSIS OF CREOLE-SPEAKING PARTICIPANTS

Abstract Almost 11% of older adults in the US have Alzheimer’s or related dementia. Most studies suggest that those who are Black or Hispanic are at higher risk. A culturally diverse sample, including Haitian older adults, was recruited from South Florida communities to participate in a longitudinal study, “In-Vehicle Sensors to Detect Change in Cognition of Older Drivers.” An extensive cognitive battery was administered for comparison with driving behaviors. No validated translations in Creole were available, so three Haitian-born examiners translated and back-translated test items into Creole. Two additional bilingual examiners verified the results. An analysis of variance (ANCOVA) comparing Haitian participants (N=12) and European Americans (N=135) on the MoCA controlling for age and education indicated the European American group scored higher (M = 25.78, SD = 2.57) than the Haitian Group (M =24.17, SD = 3.01), a significant difference in test performance, F (1, 124) = 6.11, P= .015. Results on four subscales of the MoCA evidenced significantly higher scores for the European American group: Visual EX Total, F (1, 124) = 22.52, p < .001; Serial-7, F (1, 124) = 12.09, p <.001; Attention Total, F (1, 124) = 5.10, p <.001; Letter Score F (1, 124) = 13.09, p <.001. No differences in language, abstract, delayed recall, or orientation were found. These preliminary results suggest cultural influences may account for differences in test performance. Further investigation with a larger sample of Haitian older adults is needed to explain differences in performance on this widely used test.

>ADVOCATING FOR ENGINEERING SOLUTIONS TO IMPROVE OLDER ADULT DRIVING LONGEVITY

Abstract To age-in-place, older adults need to access their community. As most older adults live in suburban/rural areas, driving is the primary and preferred means of mobility. While recent research has shown that drivers in the seventies have fewer crashes than middle-aged drivers, it is likely due to less exposure. However, it demonstrates that driving decisions need to be based on function, not age. Moreover, with the aging population and increase numbers of older adults with cognitive impairment and/or slowed processing speed, other strategies are needed to preserve and lengthen the driving lifetime. While efforts for developing alternative transportation options are working in some places, rural areas lack the infrastructure and potential for public transportation or volunteer programs beyond medical necessity. This presentation will present some roadway engineering proven countermeasures and community strategies that can protect and benefit aging drivers so they can drive longer while reducing crash risk. Stepping outside the typical gerontological community, this presenter became a contributing member of their state’s highway safety five-year plan. As an older driver advocate, I was successful in achieving older adults as an “emphasis” area in the present plan. For the next five-year plan, researching engineering solutions was deemed essential to advocate for specific strategies that protect both aging drivers and pedestrians. Since every state is required have a five-year plan for highway safety to receive federal funding, the presenter will outline successful strategies to participate in innovative ways for improving safety such as influencing engineering and department of motor vehicles partners.

Study protocol for “In-vehicle sensors to detect changes in cognition of older drivers”

BackgroundDriving is a complex behavior that may be affected by early changes in the cognition of older individuals. Early changes in driving behavior may include driving more slowly, making fewer and shorter trips, and errors related to inadequate anticipation of situations. Sensor systems installed in older drivers’ vehicles may detect these changes and may generate early warnings of possible changes in cognition.MethodA naturalistic longitudinal design is employed to obtain continuous information on driving behavior that will be compared with the results of extensive cognitive testing conducted every 3 months for 3 years. A driver facing camera, forward facing camera, and telematics unit are installed in the vehicle and data downloaded every 3 months when the cognitive tests are administered.ResultsData processing and analysis will proceed through a series of steps including data normalization, adding information on external factors (weather, traffic conditions), and identifying critical features (variables). Traditional prediction modeling results will be compared with Recurring Neural Network (RNN) approach to produce Driver Behavior Indices (DBIs), and algorithms to classify drivers within age, gender, ethnic group membership, and other potential group characteristics.ConclusionIt is well established that individuals with progressive dementias are eventually unable to drive safely, yet many remain unaware of their cognitive decrements. Current screening and evaluation services can test only a small number of individuals with cognitive concerns, missing many who need to know if they require treatment. Given the increasing number of sensors being installed in passenger vehicles and pick-up trucks and their increasing acceptability, reconfigured in-vehicle sensing systems could provide widespread, low-cost early warnings of cognitive decline to the large number of older drivers on the road in the U.S. The proposed testing and evaluation of a readily and rapidly available, unobtrusive in-vehicle sensing system could provide the first step toward future widespread, low-cost early warnings of cognitive change for this large number of older drivers in the U.S. and elsewhere.

Mandatory Vision Test for Driver License Renewal in Association With Traffic Fatality and Injury Among Older Road Users in Utah.

In 2011, Utah began requiring that drivers aged 65years and older pass a vision test at each license renewal. This study aims to investigate if the mandatory vision test associated with motor vehicle fatality and injury rates in older road users. We fit controlled interrupted time series analysis models to compare fatality and injury rates for older adults (65+) affected by the law to younger adults (45-64) unaffected by the law. The models yielded estimates of differential level and slope changes in fatality and injury rates, which we used to estimate policy associations. We did not find evidence that implementing an accelerated vision test for older adults in Utah was associated with a reduction in injury or fatality rates among older (65+) drivers and non-drivers relative to those aged 45-64. Other strategies might be considered to prevent fatal motor vehicle crashes (MVCs) in older adults.

“Did you fall asleep?” – Younger and older drivers’ recollection of prior sleepiness while driving

Determining causality and driver culpability in fall asleep crashes requires extensive forensic examination, with post-crash interviews forming a critical part of the process. To date, there is no evidence regarding whether drivers’ recollection of sleepiness and associated symptoms accurately reflects driving impairment during an earlier drive. To address this, our study examined whether established questionnaires assessing sleepiness (Karolinska Sleepiness Scale) and sleepiness symptoms (Sleepiness Symptoms Questionnaire) taken post-drive accurately reflected earlier driving impairment as characterised by near-crash events and lane deviations during a closed loop track drive. Sixteen younger (21–33 years) and seventeen older (50–65 years) drivers completed two 2 h afternoon track drives under supervision of a licensed driving instructor under two conditions: well rested (8 h sleep opportunity) and sleep deprived (0 h sleep). For younger drivers, all sleepiness symptoms (except mind wandering) significantly predicted prior severe (near-crashes AUC 0.78–1.00) and moderate (lane deviations AUC 0.78–0.94) driving impairment, with good to excellent accuracy. For older drivers, only severe impairment was accurately reflected in post-drive ratings (near crashes AUC 0.86–0.94), and for fewer symptoms (8/9 vs. 6/9). Optimal thresholds for the frequency of these symptoms, in addition to thresholds for minimum acceptable false positives are presented. Our data suggest that post-crash interviews should utilise questions around subjective sleepiness and associated symptoms, providing evidence regarding the presence and awareness of drowsiness while driving, taking into account the reliability of self-report evidence. This may aid in the investigation of fall-asleep crashes where drowsiness is suspected.

Anomalous Behavior Detection in Trajectory Data of Older Drivers.

Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.

Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Relationship between Walking and Driving, and Cognitive Functioning Common to Both Modes of Mobility, in Healthy Older Adults

Introduction: Driving and walking, the two main modes of mobility, require numerous common skills in the motor, sensory, and cognitive domains that deteriorate with age. The objective of this study was to investigate the relationship between walking and driving in healthy older drivers and to determine whether certain cognitive processes are involved in both modes of mobility. Method: Seventy-six older drivers from the Safe Move cohort were assessed in the following three domains: (1) cognition, using parts A and B of the Trail Making Test (TMT), the digit symbol substitution test (DSST), the Stroop test, and the Digit span; (2) gait, using a dual-task (DT) paradigm with a counting task; and (3) driving, assessed via a 40–50 min on-road test. Analyses were also performed on 2 subgroups: young-old (70–74 years old; n = 43) and old-old (≥75 years; n = 33). Results: Four significant correlations were found across the whole sample between gait performance under DT conditions and driving scores. One correlation was also found in old-old adults. None were found in young-old adults. Furthermore, several cognitive measures were significantly correlated to both modes of mobility: TMT-A and B completion time in the whole sample, and DSST performance in the whole sample and old-old adults. Discussion/Conclusion: Walking in complex conditions and on-road driving performance are closely related in healthy older drivers. Visuospatial attention, processing speed, and executive function are crucial and common cognitive processes to both modes of mobility in this population. Impairment in these cognitive functions should thus alert health professionals as it can quickly lead to mobility disorders, loss of autonomy and social isolation. Developing specific preventive programs and mobility support systems for healthy older adults is also crucial.

Risk of motor vehicle collisions and culpability among older drivers using cannabis: a meta‐analysis

AbstractBackgroundWhile the effects of cannabis use on driving have been established in younger populations, limited studies have investigated this association among older adults, who represent the fastest growing segment of drivers globally.MethodWe conducted a systematic review and meta‐analysis to evaluate the effect of THC exposure on (1) risk of motor vehicle collisions (MVC) and (2) risk of culpability (being responsible for the collision), among adults 50 years and older. Three reviewers screened 7,022 studies identified through MEDLINE, EMBASE, CENTRAL, and PsycINFO and published between inception and 2021. Odds Ratios (OR) were calculated with a random‐effects model using the Mantel‐Haenszel method in Review Manager 5.4.1. Heterogeneity was assessed using I2. An adapted version of the National Heart, Lung, and Blood Institute quality assessment tool was used to assess the quality of each study.Result7 cross‐sectional studies were included. Three studies were rated ‘Good’ quality, one rated ‘Fair’, and three rated ‘Poor’. Three of the included studies evaluated culpability (NTHC+ = 239 NTHC‐ = 4609) while four evaluated MVC risk (NTHC+ = 158, NTHC‐ = 6152). We found that the pooled risk of MVC was not significantly different between older drivers exposed to THC and those who were not (OR, 95% CI 1.15 [0.40, 3.31]; I2 = 72%). In the studies assessing driver culpability, THC exposure was not significantly associated with an increased risk of being culpable for MVC among adults over the age of 50 (OR, 95% CI 1.24 [0.95, 1.61]; I2 = 0%). A visual inspection of the funnel plots did not indicate any publication bias.ConclusionOur review found that THC exposure was not associated with MVC involvement nor with culpability. However, it would be an oversimplification to conclude that cannabis use does not pose a safety risk to older drivers. Rather, our results may reflect several methodological limitations of the studies included in this review. Due to the increasing prevalence of cannabis use worldwide, we are optimistic that future research will be able to address this research question using more objective methodologies.

Roadway Weather Challenges Illuminate Real‐World Driving Biomarkers of Dementia Risk

AbstractBackgroundCognitive decline is a leading early predictor of dementia risk and may be evident in real‐world (RW) digital data even before patients ever present to clinic. This study examined the feasibility of using video and digital sensor profiles of real world (RW) driving in adverse weather as a marker of dementia risk.Method70 older drivers (mean age = 75.6 years) without dementia participated in a RW driving study for 2, 3‐month periods, one year apart (Wang, et al. 2021) . Self‐reported demographics and neuropsychological tests relevant to dementia and driving were collected at study start. Drivers were classed as cognitively neurotypical (N = 33) or impaired (N = 44) based on normed neuropsychological scores. Sensors installed in each driver’s vehicle collected video, speed, and GPS data across 405,104 miles driven. GIS data categorized roadway type (residential, commercial, interstate). A computer vision model (99% accuracy) we developed classed roads as clear or poor (rain, snow). Bayesian mixed‐effect logistic regressions assessed driver speed limit compliance (vehicle speed‐posted speed limit) across driver class, roadway, and weather.ResultDrivers were 8.01 times more likely to speed on interstates (95% CI: 7.61‐8.61) than residential roads (15‐25 mph). Impaired drivers were 15% more likely to drive slower than neurotypical drivers on clear roadways (95% C: 1.0413‐1.2829). This pattern reversed in poor weather with impaired drivers driving faster than neurotypical (b = 0.74, 95% CI: 0.57‐0.94)ConclusionDigital biomarkers from RW data show promise to screen for early dementia risk, advancing research and innovative clinical trials to prevent dementia. Driving challenges, like poor weather, discriminate cognitive decline before dementia. Impaired drivers showed self‐restriction by driving slower on clear roads and reduced self‐restriction by driving faster in poor weather despite risk. Big data strategies, like computer vision, complement driver performance data to identify RW environmental risk, paving the road for the car as a medical diagnostic device.

A Study on the effects of seasonal variations in cognitive function on driving behaviour.

AbstractBackgroundThe population in Japan is ageing, and approximately 30% of the elderly are said to have Alzheimer’s disease. On the other hand, however, there are areas with insufficient transport services due to the declining birth rate, and the elderly have to drive cars. In order to reduce accidents caused by drivers with dementia, it is necessary to know the relationship between cognitive function and driving, but cognitive function is said to fluctuate daily. The aim of this study is to obtain knowledge that contributes to the prediction of accidents by observing the seasonal variation in cognitive function and driving using the same subjects.MethodA cognitive function test, a driving skill test at a driving school and an observation 1‐month survey of driving behaviour on public open roads were conducted on 40 drivers aged 70 years or older who drive their cars daily. The survey was conducted twice on the same subjects, once between April and June and once between October and December. From the data obtained, seasonal differences were verified.ResultIt is said that elderly drivers often drive short distances in their neighbourhood, but in reality they also make long trips. Comparing spring/summer and autumn/winter, autumn/winter drivers are more likely to be in a state of cognitive decline and drive more unevenly.ConclusionCognitive function and driving behaviour of older drivers differ depending on the season. As the passage of time and more short‐term fluctuations may have an impact, it is important to systematise the fluctuating changes in various cycles and link them to safe driving support.

Navigational Decision‐Making Among Older Drivers: An Analysis of Real‐World Driving Patterns

AbstractBackgroundDriving is an important aspect of independence for older adults. However, cognitive decline may impair driving abilities and safety. A deeper understanding of the impact of cognitive impairment on driving behaviours in older adults can inform public health strategies to support maintaining their ability to drive safely. This study investigates how older adults with and without cognitive impairment make navigational decisions (e.g., route choice) while driving in real‐world settings.MethodParticipants were enrolled in longitudinal driving studies at Washington University School of Medicine. GPS dataloggers tracked the daily driving patterns of 246 older drivers (aged 65+), including 230 cognitively normal older adults (CTL; Clinical Dementia Rating [CDR] = 0) and 16 older adults with cognitive impairment (DEM; CDR>0). Spatial clustering and hashing algorithms were employed to analyze each participant’s destinations and route choices. Trips with hard‐braking, sudden‐acceleration, and over‐speeding were considered “adverse behavior.” The Mann‐Whitney U‐test was performed to identify significant differences between the two groups.ResultThe CTL group had an average age of 68.2 ± 5.7 years and was composed of 46% females, while the DEM group had an average age of 69.2 ± 6.8 years and had 36% females. Additionally, the CTL group had a mean of 16.5 ± 2.3 years of education, while the DEM group had a mean of 16.0 ± 2.3 years of education. The results indicated that drivers with cognitive impairment had fewer full‐cycle trips (i.e., trips with the same start point and destination; p = 0.0165), visited fewer new destinations (p = 0.0210), used fewer distinct routes to reach their most common destination (p = 0.0174), and took fewer distinct routes to reach their most common destination when normalized by the average monthly frequency of this trip (p = 0.0062). No significant differences were observed in the other driving measures.ConclusionThis study reveals significant differences in driving patterns between cognitively normal older adults and those with cognitive impairment. Older adults with cognitive impairment exhibited more conservative driving behaviors, such as taking fewer full‐cycle trips and having less variety in their daily driving routes. These findings have important implications for designing interventions to improve mobility for older adults.

Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups

Rapid population aging has significantly impacted labor supply and posed substantial challenges for the taxi industry, which have not yet been fully comprehended. Here, for the first time, we employ a large-scale dataset of taxi driver operations from China, establishing a comprehensive indicator system of taxi service performance, encompassing economic, environmental, and safety aspects. Through the application of multivariate regression models and other statistical analysis techniques, we have thoroughly investigated the mechanisms through which aging influences taxi service performance. Our research reveals that older drivers, despite exhibiting higher operational efficiency and greater inclination towards stability, underperform in time efficiency metrics, such as income per hour worked, owing to a more conservative working style. Furthermore, aging manifests negative effects on safety and environmental performance. Adjusting the driving strategies of older taxi drivers, such as regulating daily working hours and refining passenger-searching area preferences, can help mitigate these adverse impacts.

Cognitive tests for renewal of driving licences for older drivers should be abolished

Cognitive tests for renewal of driving licences for older drivers should be abolished

Older Drivers Reduced Engagement in Distracting Behaviors Over a Six-Year Period: Findings From the Candrive Longitudinal Study.

Baltes and Baltes' "selective optimization with compensation" model is pertinent to driving but evidence about the use of compensation using longitudinal designs is scarce. Therefore, we sought to determine if older drivers reduced their engagement in distracting behaviors while driving, over a 6-year period. We used data captured over several annual assessments from a cohort of 583 drivers aged 70 and older to determine if their engagement in 12 distracting behaviors (e.g., listening to the radio, talking with passengers) declined over time. We adjusted our multivariable model for several potential confounders of the association between our outcome variable and time. Overall, and after adjustment for potential confounders, the participants reduced their engagement in distracting behaviors over the study period (odds ratio [OR] = 0.96, 95% confidence interval [CI] = 0.95-0.97). Baseline age was negatively associated with engagement in distracting behaviors (OR = 0.95, 95% CI = 0.94-0.96). Men engaged in more distracting behaviors than women (OR = 1.15, 95% CI = 1.03-1.27), as did participants living in the largest urban centers compared to participants living in the smallest areas (OR = 1.21, 95% CI = 1.04-1.41). The number of kilometers driven per year (for every 10,000 km) was positively associated with the proportion of distracting behaviors drivers engaged in (OR = 1.13, 95% CI = 1.08-1.19). Drivers in our cohort reduced their engagement in distracting behaviors over the study period. This suggests that older drivers adjust their driving over time, which aligns with age-related theories and models about compensation.