Use of potentially driver-impairing drugs among older drivers

Background Road safety is a major issue among seniors. Potentially Driver-Impairing (PDI) drugs are known to increase the risk of car accident. The aim of this cross-sectional study was to describe PDI-drug consumption among older drivers and determine associated factors. Methods The S.AGES cohort is a French non-interventional real-life prospective study of 3700 community-dwelling participants aged ≥65 years old, suffering from type 2 diabetes (T2DM), chronic pain or atrial fibrillation (AF). Baseline data of drivers with known treatment (n = 1783) were used for the analyses. PDI drugs were defined according to the French classification. Results One thousand seven hundred eighty-three drivers were included (66% males; mean age 76 (Standard deviation = 5.78) years old). 21% (n = 373) took PDI drugs, 64% of which took only one (n = 239). The most frequent PDI drugs were: Zolpidem (11%; n = 60); Zopiclone (8%; n = 45); Bromazepam (8%; n = 44); Tramadol (7%; n = 39); Pregabalin (6%; n = 31). Drivers taking PDI drugs had more often chronic pain (OR [95% CI] = 2.30 [1.54–3.46]), history of depressive disorder (4.28 [3.00–6.14]) and polypharmacy (taking at least 5 different medications; 4.32 [2.97–6.41]), and less often T2DM (0.54 [0.37–0.79]), and AF (0.48 [0.32–0.71]). Conversely, they had a lower Activities of Daily Living score (0.34 [0.17–0.68]). Conclusions The rate of aged drivers in the S.AGES cohort taking PDI drugs is concerning and highlights the need to carefully assess and reassess PDI-drug prescriptions in this population, particularly hypnotics, anxiolytics and opioids. Trial registration ClinicalTrials.gov NCT01065909 (First posted: February 9th, 2010).

Older Driver Safety: A Renewed Perspective in a Survey Study in Illinois, U.S

Older adults (aged 65 or older) are at higher risk of involvement in motor vehicle crashes. Many studies have been conducted on older road users’ safety, but how older people’s driving behavior and demographic characteristics, and warnings of side effects of prescription medication, are associated with their crash risk has not been fully investigated. Aimed to address this knowledge gap, a mail survey of older drivers in Illinois, U.S. was conducted. Information on respondents’ driving behaviors, demographic characteristics, physical conditions, medication use, crash experience, etc. was gathered. Response distributions, odds ratios, and logistic regression models were employed to analyze the survey data. The results showed that most respondents kept a high level of mobility despite driving difficulty and medication use. Older drivers’ crash risk is mainly affected by external factors (driving exposure, alcohol consumption, and medication use) rather than their demographic characteristics and driving difficulty. Warnings from physicians on the side effects of prescription drugs had no significant effects on older drivers’ crash risk. Given the importance of mobility to older adults, the focus needs to be placed on providing a safe roadway system and safe driving advice for older drivers, particularly those who are on medication.

Speed Management Across Road Environments of Varying Complexities and Self-Regulation Behaviors in Drivers with Cataract

Evidence suggests that drivers with cataract self-regulate their driving, but there is a lack of objective information. This study compared speed behavior in older drivers with and without cataract and how the parameter is influenced by road traffic complexity and driver characteristics. The study included 15 drivers with cataract and a control group of 20 drivers. Visual status was assessed using visual acuity, contrast sensitivity, and intraocular straylight. Speed management was studied using a driving simulator. Driving difficulty and self-regulation patterns were evaluated by means of the Driver Habits Questionnaire (DHQ). The cataract group showed a significant decrease in visual function in all the parameters evaluated (p<0.05). These drivers tended to drive at lower speeds than the control group. Road characteristics, gender, and intraocular straylight in the better eye were identified as significant predictors of speed management. Drivers with cataract experience greater driving difficulty, particularly when driving at night (p<0.05). Drivers with cataract reduce their driving speed more than older drivers without visual impairment. The straylight parameter may be a good indicator of each driver’s subjective perception of their own visual ability to drive. This work helps shed light on the mechanisms through which age-related visual impairment influences driving behavior.

Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition

Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (&gt;65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept and adopt this technology. Current potential barriers to their acceptance of AS include a lack of trust in the systems and hesitation to adopt emerging technology. Technology readiness, perceived ease of use, perceived barriers, and intention to use the technology, are particularly important constructs to consider in older adults' acceptance and adoption practices of AS. Likewise, person factors, i.e., age, life space mobility, driving habits, and cognition predict driving safety among older drivers. However, we are not sure if and how these factors may also predict older adults' intention to use the AS. In the current study, we examined responses from 104 older drivers (Mage = 74.3, SDage = 5.9) who completed the Automated Vehicle User Perception Survey (AVUPS) before and after riding in an on-road automated shuttle (EasyMile EZ10). The study participants also provided information through the Technology Readiness Index, Technology Acceptance Measure, Life Space Questionnaire, Driving Habits Questionnaire, Trail-making Test Part A and Part B (TMT A and TMT B). Older drivers' age, cognitive scores (i.e., TMT B), driving habits (i.e., crashes and/or citations, exposure, and difficulty of driving) and life space (i.e., how far older adults venture from their primary dwelling) were entered into four models to predict their acceptance of AVs—operationalized according to the subscales (i.e., intention to use, perceived barriers, and well-being) and the total acceptance score of the AVUPS. Next, a partial least squares structural equation model (PLS-SEM) elucidated the relationships between, technology readiness, perceived ease of use, barriers to AV acceptance, life space, crashes and/or citations, driving exposure, driving difficulty, cognition, and intention to use AS. The regression models indicated that neither age nor cognition (TMT B) significantly predicted older drivers' perceptions of AVs; but their self-reported driving difficulty (p = 0.019) predicted their intention to use AVs: R2 = 6.18%, F (2,101) = 4.554, p = 0.040. Therefore, intention to use was the dependent variable in the subsequent PLS-SEM. Findings from the PLS-SEM (R2 = 0.467) indicated the only statistically significant predictors of intention to use were technology readiness (β = 0.247, CI = 0.087-0.411) and barriers to AV acceptance (β = −0.504, CI = 0.285-0.692). These novel findings provide evidence suggesting that technology readiness and barriers must be better understood if older drivers are to accept and adopt AS.

On-road driving impairment following sleep deprivation differs according to age

Impaired driving performance due to sleep loss is a major contributor to motor-vehicle crashes, fatalities, and serious injuries. As on-road, fully-instrumented studies of drowsy driving have largely focused on young drivers, we examined the impact of sleep loss on driving performance and physiological drowsiness in both younger and older drivers of working age. Sixteen ‘younger’ adults (M = 24.3 ± 3.1 years [21–33 years], 9 males) and seventeen ‘older’ adults (M = 57.3 ± 5.2, [50–65 years], 9 males) undertook two 2 h drives on a closed-loop track in an instrumented vehicle with a qualified instructor following (i) 8 h sleep opportunity the night prior (well-rested), and (ii) after 29-h of total sleep deprivation (TSD). Following TSD, both age groups displayed increased subjective sleepiness and lane departures (p < 0.05), with younger drivers exhibiting 7.37 × more lane departures, and 11 × greater risk of near crash events following sleep loss. While older drivers exhibited a 3.5 × more lane departures following sleep loss (p = 0.008), they did not have a significant increase in near-crash events (3/34 drives). Compared to older adults, younger adults had 3.1 × more lane departures (p = < 0.001), and more near crash events (79% versus 21%, p = 0.007). Ocular measures of drowsiness, including blink duration, number of long eye closures and PERCLOS increased following sleep loss for younger adults only (p < 0.05). These results suggest that for older working-aged adults, driving impairments observed following sleep loss may not be due to falling asleep. Future work should examine whether this is attributed to other consequences of sleep loss, such as inattention or distraction from the road.

Survey of Occupational Therapy Driver Assessors’ Rehabilitation Interventions With Older Drivers

The rehabilitation strategies used by occupational therapy driver assessors with older drivers with age-related decline or health conditions are not well understood. The objective of the study was to describe driver rehabilitation interventions used by Australian driver assessors, identify factors that guide rehabilitation choices, and identify barriers and facilitators encountered. An online survey was emailed to 300 driver assessors. Descriptive statistics were used to summarize and to rank order participant responses. A total of 148 respondents selected from a combined total of 655 interventions. The four most common rehabilitation methods were (a) graded driving (18%, n = 118), (b) practicing specific maneuvers (17.7%, n = 116), (c) using a modified vehicle (16.9%, n = 111), and (d) graded driving in local areas only (15.1%, n = 99). The most common barrier limiting driver rehabilitation was cost ( M = 2.92, SD = 1.24). The most frequently used driver rehabilitation method was on-road training. Practice can be enhanced by collating and evaluating resources, and ensuring effective interventions are more accessible.