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

Brain amyloid in preclinical Alzheimer\u27s disease is associated with increased driving risk

INTRODUCTION: Postmortem studies suggest that fibrillar brain amyloid places people at higher risk for hazardous driving in the preclinical stage of Alzheimer's disease (AD). METHODS: We administered driving questionnaires to 104 older drivers (19 AD, 24 mild cognitive impairment, and 61 cognitive normal) who had a recent (18)F-florbetapir positron emission tomography scan. We examined associations of amyloid standardized uptake value ratios with driving behaviors: traffic violations or accidents in the past 3 years. RESULTS: The frequency of violations or accidents was curvilinear with respect to standardized uptake value ratios, peaking around a value of 1.1 (model r(2) = 0.10, P = .002); moreover, this relationship was evident for the cognitively normal participants. DISCUSSION: We found that driving risk is strongly related to accumulating amyloid on positron emission tomography, and that this trend is evident in the preclinical stage of AD. Brain amyloid burden may in part explain the increased crash risk reported in older adults

The effects of white matter hyperintensities and amyloid deposition on Alzheimer dementia

Background and purpose: Elevated levels of amyloid deposition as well as white matter damage are thought to be risk factors for Alzheimer Disease (AD). Here we examined whether qualitative ratings of white matter damage predicted cognitive impairment beyond measures of amyloid. Materials and methods: The study examined 397 cognitively normal, 51 very mildly demented, and 11 mildly demented individuals aged 42–90 (mean 68.5). Participants obtained a T2-weighted scan as well as a positron emission tomography scan using 11[C] Pittsburgh Compound B. Periventricular white matter hyperintensities (PVWMHs) and deep white matter hyperintensities (DWMHs) were measured on each T2 scan using the Fazekas rating scale. The effects of amyloid deposition and white matter damage were assessed using logistic regressions. Results: Levels of amyloid deposition (ps < 0.01), as well as ratings of PVWMH (p < 0.01) and DWMH (p < 0.05) discriminated between cognitively normal and demented individuals. Conclusions: The amount of amyloid deposition and white matter damage independently predicts cognitive impairment. This suggests a diagnostic utility of qualitative white matter scales in addition to measuring amyloid levels

GPS driving: A digital biomarker for preclinical Alzheimer disease

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. METHODS: We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. RESULTS: The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. CONCLUSION: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults

Evaluation of naturalistic driving behavior using in-vehicle monitoring technology in preclinical and early Alzheimer\u27s disease

Cognitive impairment is a significant risk factor for hazardous driving among older drivers with Alzheimer\u27s dementia, but little is known about how the driving behavior of mildly symptomatic compares with those in the preclinical, asymptomatic phase of Alzheimer\u27s 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

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

Neutron-Capture Elements in the Early Galaxy: Insights from a Large Sample of Metal-Poor Giants

New abundances for neutron-capture (n-capture) elements in a large sample of metal-poor giants from the Bond survey are presented. The spectra were acquired with the KPNO 4-m echelle and coude feed spectrographs, and have been analyzed using LTE fine-analysis techniques with both line analysis and spectral synthesis. Abundances of eight n-capture elements (Sr, Y, Zr, Ba, La, Nd, Eu, Dy) in 43 stars have been derived from blue (lambda = 4070--4710, R~20,000, S/N ratio~100-200) echelle spectra and red (lambda = 6100--6180, R~22,000, S/N ratio~100-200) coude spectra, and the abundance of Ba only has been derived from the red spectra for an additional 27 stars. Overall, the abundances show clear evidence for a large star-to-star dispersion in the heavy element-to-iron ratios. The new data also confirm that at metallicities [Fe/H] <~ --2.4, the abundance pattern of the heavy (Z >= 56) n-capture elements in most giants is well-matched to a scaled Solar System r-process nucleosynthesis pattern. The onset of the main r-process can be seen at [Fe/H] ~ --2.9. Contributions from the s-process can first be seen in some stars with metallicities as low as [Fe/H] ~ --2.75, and are present in most stars with metallicities [Fe/H] > --2.3. The lighter n-capture elements (Sr-Y-Zr) are enhanced relative to the heavier r-process element abundances. Their production cannot be attributed solely to any combination of the Solar System r- and main s-processes, but requires a mixture of material from the r-process and from an additional n-capture process which can operate at early Galactic time.Comment: Text + 5 Tables and 11 Figures: Submitted to the Astrophysical Journa

Lack of association between acute stroke, post-stroke dementia, race, and β-amyloid status

INTRODUCTION: Stroke and Alzheimer disease share risk factors and often co-occur, and both have been reported to have a higher prevalence in African Americans as compared to non-Hispanic whites. However, their interaction has not been established. The objective of this study was to determine if preclinical Alzheimer disease is a risk factor for stroke and post-stroke dementia and whether racial differences moderate this relationship. METHODS: This case-control study was analyzed in 2019 using retrospective data from 2007 to 2013. Participants were adults age 65 and older with and without acute ischemic stroke. Recruitment included word of mouth and referrals in Saint Louis, MO, with stroke participants recruited from acutely hospitalized patients and non-stroke participants from community living older adults who were research volunteers. Our assessment included radiologic reads of infarcts, microbleeds, and white matter hyperintensitites (WMH); a Pittsburgh Compound B PET measure of cortical β-amyloid binding; quantitative measures of hippocampal and WMH volume; longitudinal Mini Mental State Examination (MMSE) scores; and Clinical Dementia Rating (CDR) 1 year post-stroke. RESULTS: A total of 243 participants were enrolled, 81 of which had a recent ischemic stroke. Participants had a mean age of 75, 57% were women, and 52% were African American. Cortical amyloid did not differ significantly by race, stroke status, or CDR post-stroke. There were racial differences in MMSE scores at baseline (mean 26.8 for African Americans, 27.9 for non-Hispanic whites, p = 0.03), but not longitudinally. African Americans were more likely to have microbleeds (32.8% vs 22.6%, p = 0.04), and within the acute stroke group, African Americans were more likely to have small infarcts (75.6% vs 56.8%, p = 0.049). CONCLUSION: Preclinical Alzheimer disease did not show evidence of being a risk factor for stroke nor predictive of post-stroke dementia. We did not observe racial differences in β-amyloid levels. However, even after controlling for several vascular risk factors, African Americans with clinical stroke presentations had greater levels of vascular pathology on MRI

OVIS 3.2 user's guide.

This document describes how to obtain, install, use, and enjoy a better life with OVIS version 3.2. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect meaningful conditions and anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 3.2 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It provides an interactive 3-D physical view in which the cluster elements can be colored by raw or derived element values (e.g., temperatures, memory errors). The visual display allows the user to easily determine abnormal or outlier behaviors. Additionally, it provides search capabilities for certain scheduler logs. The OVIS capabilities were designed to be highly interactive - for example, the job search may drive an analysis which in turn may drive the user generation of a derived value which would then be examined on the physical display. The OVIS project envisions the capabilities of its tools applied to compute cluster monitoring. In the future, integration with the scheduler or resource manager will be included in a release to enable intelligent resource utilization. For example, nodes that are deemed less healthy (i.e., nodes that exhibit outlier behavior with respect to some set of variables shown to be correlated with future failure) can be discovered and assigned to shorter duration or less important jobs. Further, HPC applications with fault-tolerant capabilities would respond to changes in resource health and other OVIS notifications as needed, rather than undertaking preventative measures (e.g. checkpointing) at regular intervals unnecessarily

Preclinical Alzheimer's disease and longitudinal driving decline

Introduction: Links between preclinical Alzheimer's disease (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: One hundred four 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 cerebrospinal fluid (CSF) tau/Aβ42 and phosphorylated tau (ptau181)/Aβ42 ratios, but not uptake on Pittsburgh compound B 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