Fit2Drive: Screening Older Drivers with Cognitive Concerns.

OBJECTIVES: The purpose of this study was to identify the most parsimonious combination of cognitive tests that accurately predicts the likelihood of passing an on-road driving evaluation in order to develop a screening measure that can be administered as an in-office test. DESIGN: This was a psychometric study of the new test's diagnostic accuracy. SETTINGS AND PARTICIPANTS: The study was conducted at the Florida Atlantic University's Memory Center and Clinical Research Unit, both easily accessible to older drivers. Participants were older drivers who received a driving evaluation at the Memory Center and agreed to have their results included in the Driving Repository and community-based older drivers who volunteered to participate. METHODS: Mini-Mental State Exam (MMSE), Trail Making Tests A and B, Clock Test, Hopkins Verbal Learning Test, and Driving Health Inventory results were compared with an on-road driving evaluation to identify those tests that best predict the ability to pass the on-road evaluation. RESULTS: Altogether, 412 older drivers, 179 men and 233 women, were included in the analysis. Fifty-four percent of Driving Repository participants failed the on-road evaluation compared with 8% of the community sample. The highest correlation to the on-road evaluation was Trails B time in seconds r = -0.713 (P < .001). Variables with high multicollinearity and/or low correlation with the on-road evaluation were eliminated and sets of receiver operating characteristics curves were generated to assess the predictive accuracy of the remaining tests. A linear combination of Trails B in seconds and MMSE using the highest of the Serial 7s or WORLD spelled backward scores accounted for the highest area under the curve of 0.915. Finally, an algorithm was created to rapidly generate the prediction for an individual patient. CONCLUSIONS AND IMPLICATIONS: The Fit2Drive algorithm demonstrated a strong 91.5% predictive accuracy. Usefulness in office-based patient consultations is promising but remains to be rigorously tested.

Cross-cultural Diagnostic Validity of the Multilingual Naming Test (MINT) in a Sample of Older Adults.

OBJECTIVE: We aimed to evaluate the psychometric properties and diagnostic accuracy of the 32-item version of the Multilingual Naming Test (MINT) in participants from 2 ethnic groups (European Americans [EA; n = 106] and Hispanic Americans [HA; n = 175]) with 3 diagnostic groups (cognitively normal [CN], n = 94, mild cognitive impairment [MCI], n = 148, and dementia, n = 39). METHOD: An Item Response Theory model was used to evaluate items across ethnicity and language groups (Spanish and English), resulting in a 24-item version. We analyzed the MINT discriminant and predictive validity across diagnostic groups. RESULTS: A total of 8 items were differentially difficult between languages in the 32-item version of the MINT. EA scored significantly higher than HA, but the difference was not significant when removing those 8 items (controlling for Education). The Receiver Operating Characteristics showed that the MINT had poor accuracy when identifying CN participants and was acceptable in identifying dementia participants but unacceptable in classifying MCI participants. Finally, we tested the association between MINT scores and magnetic resonance imaging volumetric measures of language-related areas in the temporal and frontal lobes. The 32-item MINT in English and Spanish and the 24-item MINT in Spanish were significantly correlated with the bilateral middle temporal gyrus. The left fusiform gyrus correlated with MINT scores regardless of language and MINT version. We also found differential correlations depending on the language of administration. CONCLUSIONS: Our results highlight the importance of analyzing cross-cultural samples when implementing clinical neuropsychological tests such as the MINT.

Study protocol for "In-vehicle sensors to detect changes in cognition of older drivers".

BACKGROUND: Driving 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. METHOD: A 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. RESULTS: Data 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. CONCLUSION: It 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.

Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis.

OBJECTIVE: The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans). METHODS: Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time. RESULTS: There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

Abnormal Driving Detection using GPS Data.

Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. Results showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions.

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.

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.

The association of depression and apathy with Alzheimer's disease biomarkers in a cross-cultural sample.

Cross-cultural differences in the association between neuropsychiatric symptoms and Alzheimer's disease (AD) biomarkers are not well understood. This study aimed to (1) compare depressive symptoms and frequency of reported apathy across diagnostic groups of participants with normal cognition (CN), mild cognitive impairment (MCI), and dementia, as well as ethnic groups of Hispanic Americans (HA) and European Americans (EA); (2) evaluate the relationship between depression and apathy with Aß deposition and brain atrophy. Statistical analyses included ANCOVAs, chi-squared, nonparametric tests, correlations, and logistic regressions. Higher scores on the Geriatric Depression Scale (GDS-15) were reported in the MCI and dementia cohorts, while older age corresponded with lower GDS-15 scores. The frequency of apathy differed across diagnoses within each ethnicity, but not when comparing ethnic groups. Reduced volume in the rostral anterior cingulate cortex (ACC) significantly correlated with and predicted apathy for the total sample after applying false discovery rate corrections (FDR), controlling for covariates. The EA group separately demonstrated a significant negative relationship between apathy and superior frontal volume, while for HA, there was a relationship between rostral ACC volume and apathy. Apathy corresponded with higher Aß levels for the total sample and for the CN and HA groups.

Methods and Tools for Monitoring Driver's Behavior.

In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementia.

The Association Between Functional Assessment and Structural Brain Biomarkers in an Ethnically Diverse Sample With Normal Cognition, Mild Cognitive Impairment, or Dementia.

OBJECTIVE: To investigate the association between the functional activities questionnaire (FAQ) and brain biomarkers (bilateral hippocampal volume [HV], bilateral entorhinal volume [ERV], and entorhinal cortical thickness [ERT]) in cognitively normal (CN) individuals, mild cognitive impairment (MCI), or dementia. METHOD: In total, 226 participants (137 females; mean age = 71.76, SD = 7.93; Hispanic Americans = 137; European Americans = 89) were assessed with a comprehensive clinical examination, a neuropsychological battery, a structural magnetic resonance imaging, and were classified as CN or diagnosed with MCI or dementia. Linear regression analyses examined the association between functional activities as measured by the FAQ on brain biomarkers, including HV, ERV, and ERT, controlling for age, education, global cognition, gender, and ethnicity. RESULTS: The FAQ significantly predicted HV, ERV, and ERT for the entire sample. However, this association was not significant for ERV and ERT when excluding the dementia group. The FAQ score remained a significant predictor of HV for the non-dementia group. Age, education, gender, ethnicity, Montreal Cognitive Assessment score, and FAQ were also significant predictors of HV for the overall sample, suggesting that younger Hispanic females with fewer years of education, higher global mental status, and better functioning, were more likely to have larger HV. CONCLUSION: FAQ scores were related to HV in older adults across clinical groups (CN, MCI, and dementia), but its association with the entorhinal cortex was driven by individuals with dementia. Demographic variables, including ethnicity, additionally influenced these associations.

Identifying Inhibitory Subcomponents Associated with Changes in Binge Drinking Behavior: A 6-Month Longitudinal Design.

BACKGROUND: Links between response inhibition and young adult problematic drinking (e.g., binge drinking) have been established, but only to an extent. Considering the presence of some inconsistent findings associated with these 2 variables, this study proposes the need to investigate the extent in which different inhibitory subcomponents are associated with binge drinking behaviors of the same sample. METHODS: Through the use of a 6-month longitudinal design, changes in Alcohol Use Questionnaire (AUQ) binge score of 163 college students (50.3% female) with a mean age of 21.06 years (SD = 1.83) were correlated with performance on 3 different inhibitory control tasks. Each task was selected to assess separate inhibitory subcomponents: Stop Signal Task (e.g., cancellation of a response), Go/No-Go Task (e.g., withholding of a response), and Simon Task (e.g., inhibiting response interference). Response inhibition was also compared between 2 groups, those who had a substantial increase in AUQ binge score during participation (inAUQ) and those who had a substantial decrease in AUQ binge score (deAUQ). RESULTS: A significant correlation was found with a change in AUQ binge score and stop signal reaction time among females only, where an increase in binge drinking score positively correlated with a reduced ability to cancel an already-initiated inhibitory response. Differences in inhibitory performance, where inAUQ performed worse than deAUQ, approached significance. CONCLUSIONS: The results suggest that the cancellation of a prepotent response, as opposed to the withholding of response or interference inhibition, is a more sensitive inhibitory measure associated with increases in binge drinking behavior among female young adult college students. Further exploration of inhibitory subcomponents relative to substance use is greatly needed (e.g., more extensive longitudinal designs and neuroimaging techniques).