Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

The Influence of Personality Traits on Driving Behaviors in Preclinical Alzheimer Disease

INTRODUCTION

Alzheimer disease (AD) has a long preclinical phase in which AD pathology is accumulating without detectable clinical symptoms. It is critical to identify participants in this preclinical phase as early as possible since treatment plans may be more effective in this stage. Monitoring for changes in driving behavior, as measured with GPS sensors, has been explored as a low-burden, easy-to-administer method for detecting AD risk. However, driving is a complex, multifaceted process that is likely influenced by other factors, including personality traits, that may change in preclinical AD.

METHODS

We examine the moderating influence of neuroticism and conscientiousness on longitudinal changes in driving behavior in a sample of 203 clinically normal older adults who are at varying risk of developing AD.

RESULTS

Neuroticism moderated rates of change in the frequency of speeding as well as the number of trips taken at night. Conscientiousness moderated rates of change in typical driving space.

CONCLUSIONS

Personality traits change in early AD and also influence driving behaviors. Studies that seek to utilize naturalistic driving behavior to establish AD risk need to accommodate interpersonal differences, of which personality traits are one of many possible factors. Future studies should explicitly establish how much benefit is provided by including personality traits in predictive models of AD progression.

Impact of cognitive impairment on driving behaviour and route choices of older drivers: a real-world driving study

Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving abilities and overall safety on the roads. This study explores how cognitive impairment influences driving patterns and driving choices among older adults. We analyzed real-world driving patterns of 246 older adults using GPS dataloggers. Our sample included 230 cognitively normal older adults (CN; Clinical Dementia RatingR [CDR] = 0) and 16 older adults with incident cognitive impairment (ICI; CDR = 0.5). The CN group had an average age of 68.2 years, with 46% females and an average of 16.5 years of education, while the ICI group's average age was 69.2 years, with 36% females and an average of 16.0 years of education. We employed spatial clustering and hashing algorithms to evaluate driving behaviours. Significant differences emerged: The ICI group used fewer distinct routes to their most common destination. These differences can be leveraged to develop driving as a digital biomarker for the early detection and continuous monitoring of cognitive impairment.

Driving among individuals with chronic conditions: A systematic review of applied research using kinematic driving sensors

BACKGROUND

Kinematic driving data studies are a novel methodology relevant to health care, but prior studies have considerable variance in their methods, populations, and findings suggesting a need for critical analysis and appraisal for feasibility and methodological guidelines.

METHODS

We assessed kinematic driving studies of adults with chronic conditions for study feasibility, characteristics, and key findings, to generate recommendations for future study designs, and to identify promising directions for applications of kinematic driving data. PRISMA was used to guide the review and searches included PubMed, CINAHL, and Compendex. Of 379 abstract/titles screened, 49 full-text articles were reviewed, and 29 articles met inclusion criteria of analyzing trip-level kinematic driving data from adult drivers with chronic conditions.

RESULTS

The predominant chronic conditions studied were Alzheimer's disease and related Dementias, obstructive sleep apnea, and diabetes mellitus. Study objectives included feasibility testing of kinematic driving data collection in the context of chronic conditions, comparisons of simulation with real-world kinematic driving behavior, assessments of driving behavior effects associated with chronic conditions, and prognostication or disease classification drawn from kinematic driving data. Across the studies, there was no consensus on devices, measures, or sampling parameters; however, studies showed evidence that driving behavior could reliably differentiate between adults with chronic conditions and healthy controls.

CONCLUSIONS

Vehicle sensors can provide driver-specific measures relevant to clinical assessment and interventions. Using kinematic driving data to assess and address driving measures of individuals with multiple chronic conditions is positioned to amplify a functional outcome measure that matters to patients.

Cortical atrophy and leukoaraiosis, imaging markers of cerebrovascular small vessel disease, are associated with driving behavior changes among cognitively normal older adults

BACKGROUND

Cerebral small vessel disease (CSVD) as measured by cortical atrophy and white matter hyperintensities [leukoaraiosis], captured via magnetic resonance imaging (MRI) are increasing in prevalence due to the growth of the aging population and an increase in cardiovascular risk factors in the population. CSVD impacts cognitive function and mobility, but it is unclear if it affects complex, functional activities like driving.

METHODS

In a cohort of 163 cognitively normal, community-dwelling older adults (age ≥ 65), we compared naturalistic driving behavior with mild/moderate leukoaraiosis, cortical atrophy, or their combined rating in a clinical composite termed, aging-related changes to those without any, over a two-and-a-half-year period.

RESULTS

Older drivers with mild or moderate cortical atrophy and aging-related changes (composite) experienced a greater decrease in the number of monthly trips which was due to a decrease in the number of trips made within a one-to-five-mile diameter from their residence. Older drivers with CSVD experience a larger reduction in daily driving behaviors than drivers without CSVD, which may serve as an early neurobehavioral marker for functional decline.

CONCLUSIONS

As CSVD markers, leukoaraiosis and cortical atrophy are standard MRI metrics that are widely available and can be used for screening individuals at higher risk for driving safety risk and decline in community mobility.

Everyday Driving and Plasma Biomarkers in Alzheimer's Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit

BACKGROUND

Driving behavior as a digital marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer's disease (AD).

OBJECTIVE

This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD.

METHODS

We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aβ42/Aβ40, where Aβ42/Aβ40 < 0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables.

RESULTS

All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aβ42/Aβ40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aβ42/Aβ40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [>0.051].

CONCLUSION

Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.

Cognitive and brain reserve predict decline in adverse driving behaviors among cognitively normal older adults

Daily driving is a multi-faceted, real-world, behavioral measure of cognitive functioning requiring multiple cognitive domains working synergistically to complete this instrumental activity of daily living. As the global population of older adult continues to grow, motor vehicle crashes become more frequent among this demographic. Cognitive reserve (CR) is the brain's adaptability or functional robustness despite damage, while brain reserve (BR) refers the structural, neuroanatomical resources. This study examined whether CR and BR predicted changes in adverse driving behaviors in cognitively normal older adults. Cognitively normal older adults (Clinical Dementia Rating 0) were enrolled from longitudinal studies at the Knight Alzheimer's Disease Research Center at Washington University. Participants (n = 186) were ≥65 years of age, required to have Magnetic Resonance Imaging (MRI) data, neuropsychological testing data, and at least one full year of naturalistic driving data prior to the beginning of COVID-19 lockdown in the United States (March 2020) as measured by Driving Real World In-vehicle Evaluation System (DRIVES). Findings suggest numerous changes in driving behaviors over time were predicted by increased hippocampal and whole brain atrophy, as well as lower CR scores as proxied by the Wide Range Achievement Test 4. These changes indicate that those with lower BR and CR are more likely to reduce their driving exposure and limit trips as they age and may be more likely to avoid highways where speeding and aggressive maneuvers frequently occur.

Adverse driving behaviors are associated with sleep apnea severity and age in cognitively normal older adults at risk for Alzheimer's disease

Alzheimer's disease (AD) pathology accumulates for decades before the onset of cognitive decline. Cognitively normal individuals with biomarker evidence of AD brain pathology (i.e. biomarker + or preclinical AD) can be differentiated from individuals without AD brain pathology based on naturalistic driving data, such as hard acceleration or braking and speeding, measured using in-vehicle dataloggers. Older adults are at increased risk of injury and death from motor vehicle crashes and driving cessation is also linked to negative health outcomes. Identifying potentially modifiable risk factors that increase driving risk may prolong safe driving in old age. Sleep apnea is associated with adverse driving behaviors across the age span. In this study, we hypothesized that high-risk driving behaviors would be associated with increased sleep apnea severity and AD pathology. We found that higher sleep apnea severity measured by a home sleep apnea test was associated with a higher incidence of adverse driving behaviors even after controlling for multiple confounders (β = 0.24 ± 0.09, p < 0.01). This association was independent of AD biomarker positivity (i.e. increased t-tau/Aβ 42 ratio). Increasing age was associated with a higher likelihood of high-risk driving behaviors in individuals with AD brain pathology (β = 0.12 ± 0.04, p < 0.01), but a lower likelihood in individuals without AD brain pathology (β = -0.06 ± 0.03, p < 0.05). These findings suggest that adverse driving behaviors linked to a higher rate of traffic crashes in older adults are associated with sleep apnea severity and AD pathology even in cognitively unimpaired individuals. Further studies are needed to determine if treatment of sleep apnea decreases high-risk driving behaviors and therefore motor vehicle crashes.

Naturalistic driving measures of route selection associate with resting state networks in older adults

Our objective was to identify functional brain changes that associate with driving behaviors in older adults. Within a cohort of 64 cognitively normal adults (age 60+), we compared naturalistic driving behavior with resting state functional connectivity using machine learning. Functional networks associated with the ability to interpret and respond to external sensory stimuli and the ability to multi-task were associated with measures of route selection. Maintenance of these networks may be important for continued preservation of driving abilities.

Louis EK. Cognition and driving ability in isolated and symptomatic REM sleep behavior disorder

STUDY OBJECTIVES

To analyze cognitive deficits leading to unsafe driving in patients with REM Sleep Behavior Disorder (RBD), strongly associated with cognitive impairment and synucleinopathy-related neurodegeneration.

METHODS

Twenty isolated RBD (iRBD), 10 symptomatic RBD (sRBD), and 20 age- and education-matched controls participated in a prospective case-control driving simulation study. Group mean differences were compared with correlations between cognitive and driving safety measures.

RESULTS

iRBD and sRBD patients were more cognitively impaired than controls in global neurocognitive functioning, processing speeds, visuospatial attention, and distractibility (p < .05). sRBD patients drove slower with more collisions than iRBD patients and controls (p < .05), required more warnings, and had greater difficulty following and matching speed of a lead car during simulated car-following tasks (p < .05). Driving safety measures were similar between iRBD patients and controls. Slower psychomotor speed correlated with more off-road accidents (r = 0.65) while processing speed (-0.88), executive function (-0.90), and visuospatial impairment (0.74) correlated with safety warnings in sRBD patients. Slower stimulus recognition was associated with more signal-light (0.64) and stop-sign (0.56) infractions in iRBD patients.

CONCLUSIONS

iRBD and sRBD patients have greater selective cognitive impairments than controls, particularly visuospatial abilities and processing speed. sRBD patients exhibited unsafe driving behaviors, associated with processing speed, visuospatial awareness, and attentional impairments. Our results suggest that iRBD patients have similar driving-simulator performance as healthy controls but that driving capabilities regress as RBD progresses to symptomatic RBD with overt signs of cognitive, autonomic, and motor impairment. Longitudinal studies with serial driving simulator evaluations and objective on-road driving performance are needed.

Driving exposure, patterns and safety critical events for older drivers with and without mild cognitive impairment: Findings from a naturalistic driving study

OBJECTIVE

The aim of the study is to compare driving exposure, patterns and factors associated with safety critical events between drivers with MCI and a comparison group without cognitive impairment.

DESIGN, SETTING, PARTICIPANTS

Naturalistic driving data using an in-vehicle monitoring device were collected from 36 older drivers with MCI and 35 older drivers without cognitive impairment over a two-week period in Western Australia.

MEASUREMENTS

Naturalistic driving exposure, patterns (eg. night-time trips, peak-hour trips) and safety critical events (harsh acceleration, harsh braking and harsh cornering).

RESULTS

Drivers with MCI had a lower number of safety critical events (mean = 7.20, SD = 11.44) compared to drivers without cognitive impairment (mean = 10.89, SD = 23.30) however, this was not statistically significantly. There were also no statistically significant differences between drivers with and without MCI for measures of driving exposure or any of the driving patterns including weekday trips, night-time trips and trips on highways/freeways. The results of the multivariable modelling found only binocular contrast sensitivity was associated with the rate of safety critical events. For every increase of 0.1 log units in contrast sensitivity (better contrast sensitivity) the rate of safety critical events significantly decreased by 30 % (IRR = 0.70, 95 % CI = 0.50-0.98, p = 0.04).

CONCLUSION

Drivers with MCI were found to have similar driving exposure and patterns compared to older drivers without cognitive impairment, however drivers with better contrast sensitivity experienced fewer safety critical events. Future research should consider a longitudinal study design with an extended driving monitoring period and a larger sample with a clinical diagnosis of MCI to assess changes in cognition and its impact on driving.

Identifying Preclinical Alzheimer's Disease Using Everyday Driving Behavior: Proof of Concept

We examined whether driving behavior can predict preclinical Alzheimer's disease (AD). Data from 131 cognitively normal older adults with cerebrospinal fluid (CSF) and/or positron emission tomography (PET) biomarkers were examined with naturalistic driving behavior. Receiver operating characteristic curves were used to predict the highest 10%, 25%, and 50% of values for CSF tau/Aβ42, ptau181/Aβ42, or amyloid PET. Six in vivo driving variables alone yielded area under the curves (AUC) from 0.64-0.82. Addition of age, Apolipoprotein ɛ4, and neuropsychological measures to the models improved the AUC (0.81 to 0.90). Driving can be used as novel neurobehavioral marker to identify presence of preclinical AD.

Evaluation of Naturalistic Driving Behavior Using In-Vehicle Monitoring Technology in Preclinical and Early Alzheimer's Disease

Cognitive impairment is a significant risk factor for hazardous driving among older drivers with Alzheimer's dementia, but little is known about how the driving behavior of mildly symptomatic compares with those in the preclinical, asymptomatic phase of Alzheimer's disease (AD). This study utilized two in-car technologies to characterize driving behavior in symptomatic and preclinical AD. The goals of this pilot study were to (1) describe unsafe driving behaviors in individuals with symptomatic early AD using G-force triggered video capture and (2) compare the driving habits of these symptomatic AD drivers to two groups of cognitively normal drivers, those with and those without evidence of cerebral amyloidosis (CN/A+ and CN/A-) using a global positioning system (GPS) datalogger. Thirty-three drivers (aged 60+ years) were studied over 3 months. G-force triggered video events captured instances of near-misses/collisions, traffic violations, risky driver conduct, and driving fundamentals. GPS data were sampled every 30 s and all instances of speeding, hard braking, and sudden acceleration were recorded. For the early AD participants, video capture identified driving unbelted, late response, driving too fast for conditions, traffic violations, poor judgment, and not scanning intersections as the most frequently occurring safety errors. When evaluating driving using the GPS datalogger, hard breaking events occurred most frequently on a per trip basis across all three groups. The CN/A+ group had the lowest event rate across all three event types with lower instances of speeding. Slower psychomotor speed (Trail Making Part A) was associated with fewer speeding events, more hard acceleration events, and more overall events. GPS tracked instances of speeding were correlated with total number of video-captured near-collisions/collisions and driving fundamentals. Results demonstrate the utility of electronic monitoring to identify potentially unsafe driving events in symptomatic and preclinical AD. Results suggest that drivers with preclinical AD may compensate for early, subtle cognitive changes by driving more slowly and cautiously than healthy older drivers or those with cognitive impairment. Self-regulatory changes in driving behavior appear to occur in the preclinical phase of AD, but safety concerns may not arise until symptoms of cognitive impairment emerge and the ability to self-monitor declines.

Reaction to a Pandemic: Social Distancing and Driving Among Older Adults During COVID-19

Coronavirus disease 2019 (COVID-19) has produced considerable morbidity and mortality worldwide, and older adults are at especially high risk for developing severe COVID-19. A cohort study of driving behavior from January 1, 2019, to April 25, 2019, and January 1, 2020, to April 25, 2020, was conducted. We hypothesized that older adults would reduce the number of days driving and number of trips/day they make after COVID-19 case acceleration. Data from 214 adults aged 66.5 to 92.8 years were used. Women comprised 47.6% of the sample and 15.4% were African American. Participants reduced the proportion of days driven during the pandemic (.673 vs. .382 [p < .001]) compared with same period the year before (.695 vs. .749). Trips/day showed a similar decline (p < .001). Participants also took shorter trips (p = .02), drove slower (p < .001), had fewer speeding incidents (p < .001), and had different trip destinations (p < .001). These results indicate that older adults reduce their driving behavior when faced with a pandemic.

Recruitment of African American and Non-Hispanic White Older Adults for Alzheimer Disease Research Via Traditional and Social Media: a Case Study

The increasing prevalence of Alzheimer disease (AD), higher risk among certain ethnoracial groups, and lack of effective therapies highlights the need to recruit and enroll diverse populations in prospective, observational studies and clinical trials. However, there is little known about the effectiveness of traditional media vs. social media outreach on recruitment in aging study studies. This study retrospectively examined the effectiveness and differences in using both traditional and social media materials for the recruitment of African American (AA) versus non-Hispanic white (NHW) participants for a prospective, longitudinal study examining preclinical AD and driving outcomes. Participants needed to be at least 65 years old, drive at least an average of once weekly, own a vehicle that was manufactured in 1996 or later, and agree to cognitive testing, psychometric testing, brain magnetic resonance imaging (MRI), brain amyloid positron emission tomography (PET), and cerebrospinal fluid collection via lumbar puncture. A total of 546 individuals contacted the study coordinator by phone or email. Of those individuals, 97 enrolled and 192 were not contacted secondary to filling enrollment capacity. Sixteen participants (16.5%) were AA and the remainder were NHW. Of the 354 individuals whom the coordinator contacted back, approximately 73% declined or did not return calls. Social media was more effective with recruiting NHW participants, while traditional advertisement (newspaper) was more successful in recruiting AA participants in this urban setting. Prospective studies should balance participant burden and enrollment with a targeted, multi-tiered recruitment plan and sufficient budget to reach the population of interest.

The Road to Recovery: A Pilot Study of Driving Behaviors Following Antibody-Mediated Encephalitis

Introduction: Safe driving requires integration of higher-order cognitive and motor functions, which are commonly compromised in patients with antibody-mediated encephalitis (AME) associated with N-methyl-D-aspartate receptors or leucine-rich glioma-inactivated 1 autoantibodies. How these deficits influence the return to safe driving is largely unknown. Recognizing this, we piloted non-invasive remote monitoring technology to longitudinally assess driving behaviors in recovering AME patients. Methods: Five recovering AME patients [median age, 52 years (range 29-67); two females] were recruited from tertiary care clinics at Washington University (St. Louis, MO). Trip data and aggressive actions (e.g., hard braking, sudden acceleration, speeding) were continuously recorded using a commercial Global Positioning System data logger when the patient's vehicle was driven by the designated driver. Longitudinal driving data were compared between AME patients and cognitively normal older adults (2:1 sex-matched) enrolled within parallel studies. Results: Driving behaviors were continuously monitored for a median of 29 months (range, 21-32). AME patients took fewer daily trips during the last vs. the first 6 months of observation, with a greater proportion of trips exceeding 10 miles. Compared to cognitively normal individuals, AME patients were more likely to experience hard braking events as recovery progressed. Despite this, no accidents were self-reported or captured by the data logger. Conclusion: Driving behaviors can be continuously monitored in AME patients using non-invasive means for protracted periods. Longitudinal changes in driving behavior may parallel functional recovery, warranting further study in expanded cohorts of recovering AME patients.

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest.

Differences in Driving Outcomes Among Cognitively Normal African American and Caucasian Older Adults

OBJECTIVE

To examine the effect of race in driving performance and behavior prospectively among cognitively normal older adults.

METHODS

Cognitively normal participants (Clinical Dementia Rating 0), ≥ 65 years of age (n = 177) were selected from prospective, longitudinal studies at the Knight Alzheimer Disease Research Center at Washington University. Self-reported driving behavior (Driving Habits Questionnaire) and driving performance (road test) were annually assessed. Daily driving behavior data were collected using the Driving Real World In-Vehicle Evaluation System (DRIVES). Baseline differences between African Americans and Caucasians were tested using t tests and general linear models. Amyloid imaging and cerebrospinal fluid Alzheimer disease (AD) biomarkers were compared across groups. Linear mixed models examined change in daily driving behavior over time. Survival analyses tested time to a marginal or fail rating on the road test.

RESULTS

There were no differences between African Americans (n = 34) and Caucasians (n = 143) in age, sex, education, or vascular risk factors. Baseline self-reported driving behavior and road test performance were largely similar for both races. Longitudinal analyses using the DRIVES data aggregated monthly showed that African Americans had a greater reduction in number of trips made per month, miles driven per month, and trips with aggressive behavior compared to Caucasians. These effects remained after controlling for AD biomarkers, age, education, and sex.

CONCLUSIONS

In this sample of cognitively normal older adults, African Americans had a greater reduction of daily driving behavior compared to Caucasians. Observed racial differences may reflect differences in environmental/social factors, changes in cognition, and/or physical functioning.

Alzheimer Disease Biomarkers and Driving in Clinically Normal Older Adults: Role of Spatial Navigation Abilities

PURPOSE

Older adults experience impaired driving performance, and modify their driving habits, including limiting amount and spatial extent of travel. Alzheimer disease (AD)-related pathology, as well as spatial navigation difficulties, may influence driving performance and driving behaviors in clinically normal older adults. We examined whether AD biomarkers [cerebrospinal fluid (CSF) concentrations of Aβ42, tau, and ptau181] were associated with lower self-reported spatial navigation abilities, and whether navigation abilities mediated the relationship of AD biomarkers with driving performance and extent.

METHODS

Clinically normal older adults (n=112; aged 65+) completed an on-road driving test, the Santa Barbara Sense of Direction scale (self-report measure of spatial navigation ability), and the Driving Habits Questionnaire for an estimate of driving extent (composite of driving exposure and driving space). All participants had a lumbar puncture to obtain CSF.

RESULTS

CSF Aβ42, but not tau or ptau181, was associated with self-reported navigation ability. Lower self-reported navigation was associated with reduced driving extent, but not driving errors. Self-reported navigation mediated the relationship between CSF Aβ42 and driving extent.

CONCLUSIONS

Findings suggest that cerebral amyloid deposition is associated with lower perceived ability to navigate the environment, which may lead older adults with AD pathology to limit their driving extent.

Digital biomarkers for Alzheimer's disease: the mobile/ wearable devices opportunity

Alzheimer's Disease (AD) represents a major and rapidly growing burden to the healthcare ecosystem. A growing body of evidence indicates that cognitive, behavioral, sensory, and motor changes may precede clinical manifestations of AD by several years. Existing tests designed to diagnose neurodegenerative diseases, while well-validated, are often less effective in detecting deviations from normal cognitive decline trajectory in the earliest stages of the disease. In the quest for gold standards for AD assessment, there is a growing interest in the identification of readily accessible digital biomarkers, which harness advances in consumer grade mobile and wearable technologies. Topics examined include a review of existing early clinical manifestations of AD and a path to the respective sensor and mobile/wearable device usage to acquire domain-centric data towards objective, high frequency and passive digital phenotyping.

A Naturalistic Study of Driving Behavior in Older Adults and Preclinical Alzheimer Disease: A Pilot Study

A clinical consequence of symptomatic Alzheimer's disease (AD) is impaired driving performance. However, decline in driving performance may begin in the preclinical stage of AD. We used a naturalistic driving methodology to examine differences in driving behavior over one year in a small sample of cognitively normal older adults with ( n = 10) and without ( n = 10) preclinical AD. As expected with a small sample size, there were no statistically significant differences between the two groups, but older adults with preclinical AD drove less often, were less likely to drive at night, and had fewer aggressive behaviors such as hard braking, speeding, and sudden acceleration. The sample size required to power a larger study to determine differences was calculated.

A 2.5-Year Longitudinal Assessment of Naturalistic Driving in Preclinical Alzheimer's Disease

BACKGROUND

Emerging evidence shows that cognitively normal older adults with preclinical Alzheimer's disease (AD) make more errors and are more likely to receive a marginal/fail rating on a standardized road test compared to older adults without preclinical AD, but the extent to which preclinical AD impacts everyday driving behavior is unknown.

OBJECTIVE

To examine self-reported and naturalistic longitudinal driving behavior among persons with and without preclinical AD.

METHOD

We prospectively followed cognitively normal drivers (aged 65 + years) with (n = 10) and without preclinical AD (n = 10) for 2.5 years. Preclinical AD was assessed using amyloid positron emission tomography (PET) with Pittsburgh Compound B. The Driving Habits Questionnaire assessed self-reported driving outcomes. Naturalistic driving was captured using a commercial GPS data logger plugged into the on-board diagnostics II port of each participant's vehicle. Data were sampled every 30 seconds and all instances of speeding, hard braking, and sudden acceleration were recorded.

RESULTS

Preclinical AD participants went to fewer places/unique destinations, traveled fewer days, and took fewer trips than participants without preclinical AD. The preclinical AD group reported a smaller driving space, greater dependence on other drivers, and more difficulty driving due to vision difficulties. Persons with preclinical AD had fewer trips with any aggression and showed a greater decline across the 2.5-year follow-up period in the number of days driving per month and the number of trips between 1-5 miles.

CONCLUSION

Changes in driving occur even during the preclinical stage of AD.

Existentialism in Occupational Therapy: Implications for Practice, Research, and Education

As the profession of occupational therapy enters a second century, its growth in an increasingly complex and globalized world requires an adaptive and diverse philosophical foundation. The existentialist school of thought offers a complementary focus, which enhances existing philosophical foundations of the profession and supports two major tenets: (1) humans as self-making beings always in the process of becoming and (2) emotions and feelings as foundations for being-in-the-world. This article explores these two themes both in the context of existentialism and occupational therapy, and then provides an examination of existentialist utility in occupational therapy practice, research, and education.

Passive Assessment of Routine Driving with Unobtrusive Sensors: A New Approach for Identifying and Monitoring Functional Level in Normal Aging and Mild Cognitive Impairment

BACKGROUND

Driving is a key functional activity for many older adults, and changes in routine driving may be associated with emerging cognitive decline due to early neurodegenerative disease. Current methods for assessing driving such as self-report are inadequate for identifying and monitoring subtle changes in driving patterns that may be the earliest signals of functional change in developing mild cognitive impairment (MCI).

OBJECTIVE

This proof of concept study aimed to establish the feasibility of continuous driving monitoring in a sample of cognitively normal and MCI older adults for an average of 206 days using an unobtrusive driving sensor and demonstrate that derived sensor-based driving metrics could effectively discriminate between MCI and cognitively intact groups.

METHODS

Novel objective driving measures derived from 6 months of routine driving monitoring were examined in older adults with intact cognition (n = 21) and MCI (n = 7) who were enrolled in the Oregon Center for Aging and Technology (ORCATECH) longitudinal assessment program.

RESULTS

Unobtrusive continuous monitoring of older adults' routine driving using a driving sensor was feasible and well accepted. MCI participants drove fewer miles and spent less time on the highway per day than cognitively intact participants. MCI drivers showed less day-to-day fluctuations in their driving habits than cognitively intact drivers.

CONCLUSION

Sensor-based driving measures are objective, unobtrusive, and can be assessed every time a person drives his or her vehicle to identify clinically meaningful changes in daily driving. This novel methodology has the potential to be useful for the early detection and monitoring of changes in daily functioning within individuals.

Driving Outcomes among Older Adults: A Systematic Review on Racial and Ethnic Differences over 20 Years

The population of older adults (aged 65 years and older) in the United States will become more racially and ethnically diverse in the next three decades. Additionally, the growth of the aging population will come with an expansion in the number of older drivers and an increased prevalence of chronic neurological conditions. A major gap in the aging literature is an almost exclusive focus on homogenous, non-Hispanic white samples of older adults. It is unclear if this extends to the driving literature. A systematic review of SCOPUS, PubMed, CINAHL Plus, and Web of Science examined articles on driving and racial/ethnic differences among older adults. Eighteen studies met inclusion criteria and their results indicate that racial and ethnic minorities face a greater risk for driving reduction, mobility restriction, and driving cessation. The majority of studies compared African Americans to non-Hispanic whites but only examined race as a covariate. Only four studies explicitly examined racial/ethnic differences. Future research in aging and driving research needs to be more inclusive and actively involve different racial/ethnic groups in study design and analysis.

Depression and Alzheimer's Disease Biomarkers Predict Driving Decline

BACKGROUND

Symptomatic Alzheimer's disease (AD) and depression independently increase crash risk. Additionally, depression is both a risk factor for and a consequence of AD.

OBJECTIVE

To examine whether a depression diagnosis, antidepressant use, and preclinical AD are associated with driving decline among cognitively normal older adults.

METHODS

Cognitively normal participants, age ≥65, were enrolled. Cox proportional hazards models evaluated whether a depression diagnosis, depressive symptoms (Geriatric Depression Scale), antidepressant use, cerebrospinal fluid (amyloid-β42 [Aβ42], tau, phosphorylated tau181 [ptau181]), and amyloid imaging biomarkers (Pittsburgh Compound B and Florbetapir) were associated with time to receiving a rating of marginal/fail on a road test. Age was adjusted for in all models.

RESULTS

Data were available from 131 participants with age ranging from 65.4 to 88.2 years and mean follow up of 2.4 years (SD = 1.0). A depression diagnosis was associated with a faster time to receiving a marginal/fail rating on a road test and antidepressant use (p = 0.024, HR = 2.62). Depression diagnosis and CSF and amyloid PET imaging biomarkers were associated with driving performance on the road test (p≤0.05, HR = 2.51-3.15). In the CSF ptau181 model, depression diagnosis (p = 0.031, HR = 2.51) and antidepressant use (p = 0.037, HR = 2.50) were statistically significant predictors. There were no interaction effects between depression diagnosis, antidepressant use, and biomarker groups. Depressive symptomology was not a statistically significant predictor of driving performance.

CONCLUSIONS

While, as previously shown, preclinical AD alone predicts a faster time to receiving a marginal/fail rating, these results suggest that also having a diagnosis of depression accelerates the onset of driving problems in cognitively normal older adults.

Tau and Amyloid Positron Emission Tomography Imaging Predict Driving Performance Among Older Adults with and without Preclinical Alzheimer's Disease

Abnormal levels of Alzheimer's disease (AD) biomarkers, measured by positron emission tomography imaging using amyloid-based radiotracers and cerebrospinal fluid, are associated with impaired driving performance in older adults. We examined whether preclinical AD staging, defined using amyloid imaging and tau imaging using the radiotracer T807 (AKA flortaucipir or AV-1451), was associated with receiving a marginal/fail rating on a standardized road test (n = 42). Participants at Stage 2 (positive amyloid and tau scans) of preclinical AD were more likely to receive a marginal/fail rating compared to participants at Stage 0 or 1. Stage 2 preclinical AD may manifest in worse driving performance.

Preclinical Alzheimer's disease and longitudinal driving decline

INTRODUCTION

Links between preclinical AD and driving difficulty onset would support the use of driving performance as an outcome in primary and secondary prevention trials among older adults (OAs). We examined whether AD biomarkers predicted the onset of driving difficulties among OAs.

METHODS

104 OAs (65+ years) with normal cognition took part in biomarker measurements, a road test, clinical and psychometric batteries and self-reported their driving habits.

RESULTS

Higher values of CSF tau/Aβ42 and ptau181/Aβ42 ratios, but not uptake on PIB amyloid imaging (p=.12), predicted time to a rating of Marginal or Fail on the driving test using Cox proportional hazards models. Hazards ratios (95% confidence interval) were 5.75 (1.70-19.53), p=.005 for CSF tau/Aβ42; 6.19 (1.75-21.88) and p=.005 for CSF ptau181/Aβ42.

DISCUSSION

Preclinical AD predicted time to receiving a Marginal or Fail rating on an on-road driving test. Driving performance shows promise as a functional outcome in AD prevention trials.

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device ^TM was installed in each participant's vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.  Setting: The Knight Alzheimer's Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance.  Participants were aged 65+ years and had normal cognition. Measurements:  Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations.  Temporal components included number of trips taken during different times of the day.  Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods:  Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.  Results: Primary locations were confirmed to be 100% accurate when compared to known addresses.  Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns.  Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night.  Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.