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.

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.

Road tests and driving simulators are most Background/Objectives commonly used in research studies and clinical evaluations of older drivers. We adapted an existing, commercial, off-the-shelf, in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers.: The Azuga G2 Tracking Device was installed in each participant's Design vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.: The Knight Alzheimer's Disease Research Center at Washington Setting University School of Medicine.: Five individuals enrolled in a larger, longitudinal study assessing Participants preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition.: Spatial components included Primary Location(s), Driving Measurements 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.: Individual 30-second observations, each comprising one Methods breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.: Primary locations were confirmed to be 100% accurate when Results 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.: This pilot study indicated that Driving Profiles for older adults Conclusions can be created and compared month-to-month or year-to-year, allowing

Background: Early-onset Alzheimer's disease (EOAD) (<65 years of age) is often associated with atypical symptoms including language and visuospatial dysfunction. In late-onset Alzheimer's disease (LOAD), by contrast, there is a higher prevalence of memory impairment [1]. Neuropsychiatric symptoms such as apathy and depression are common in Alzheimer's disease (AD) [2]. However, a comparison of the progression of neuropsychiatric symptoms in EOAD and LOAD has not been done. Therefore, we examined possible variances in neuropsychiatric features between EOAD and LOAD. Additionally, we carried out a machine-learning approach to classify EOAD and LOAD characteristics. Methods: Retrospective analysis of the National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative 2 datasets was carried out in two groups: EOAD (N¼128, 60.07+8.6 years) and LOAD (N¼509, 79.77+2.9 years). A longitudinal analysis (over a period of 4 years) of the Neuropsychiatric Inventory (NPI) was carried out. Additionally, a multivariate regression and simple machine learning (with a support vector machine and 10fold cross-validation) was used to infer EOAD and LOAD given two markers of language (Boston Naming Test (BNT) and category fluency) and the Geriatric depression scale (GDS). Results: There was no significant difference due to sex between the two groups. Preliminary analysis suggests individuals with EOAD were associated with higher incidences of depression (p¼.001) and anxiety (p<.001), though only during their initial visit. GDS scores did not differ significantly between groups (EOAD¼2.62, LOAD¼2.29, p¼.32). Additionally, there was a significant increase in the incidence of delusions (X 2 (3)¼8.21, p < .05), hallucinations (X 2 (3)¼15.45, p<.05), agitation (X 2 (3)¼13.85, p<.05), euphoria (X 2 (3)¼9.95, p<.05), and apathy (X 2 (3)¼18.02, p<.05) over time in LOAD. The EOAD group, by contrast, did not demonstrate any significant changes in these variables over time. Multivariate linear regression gives significant (p<0.05) effects of BNT and fluency on predicting EOAD vs. LOAD, but only R 2 ¼0.02 in total. However, a support vector machine correctly characterizes the two groups, given only three indicators (BNT, fluency and GDS), with 78.5% accuracy, on average (s¼0.029). Conclusions: This study revealed several significant differences in neuropsychiatric features between EOAD and LOAD. Moreover, it demonstrates that EOAD and LOAD have different patterns of deficits in language and neuropsychiatric features.Background: Evaluation of older drivers has been largely dominated by controlled conditions such as on-the-road tests and driving simulators. Global positioning systems (GPS) and geographic information systems (GIS) provide a method for understanding driving behavior by analyzing continuous, objective data to determine driving patterns and the influence of personal, temporal, and environmental factors. The purpose of this investigation was to adapt existing, commercial, off-the-shelf, in-vehicle devices for res...

participated). Harlie was programmed to randomly choose one topic at a time out of a repertoire of six total topics, including the specialised topic and five generic topis, such as 'music' or 'good memories'. Participants were seated individually and shown how to chat with Harlie on a smartphone. Headphones were used and each participant spoke to Harlie for approximately 5 minutes. Audio and text recordings were securely saved to an online server for analysis by the research team and participants were encouraged to share feedback on the experience during a group discussion. Results:The feedback on the experience of using Harlie was positive in both groups. While the knitting module was successful in prolonging conversational engagement, the woodworking module was not eliciting longer engagement for this specialised topic. Conclusions: All community members were able to successfully use the Harlie application, feedback was positive and differential chat preferences could be identified. This is a promising finding with potential future applications for using this chatbot with isolated older adults including those with dementia. By following the approach described here, individual chat preferences can be explored for each potential user, which could further enhance the experience of chatting with Harlie. Background: Changes in driving behavior among older drivers has become an increasing area of public and research interest as older adults continue to drive longer. Controlled conditions like on-theroad tests and driving simulators evaluate driving ability in clinical populations and older adults, but are limited in understanding daily driving behavior. Existing research that measure naturalistic driving behavior are often expensive, obtrusive, require extensive modifications to a vehicle and involve cumbersome download procedures. To address this gap in research technology, we developed a new methodology including, data acquisition, curation and archiving procedures, and wrote custom computer codes (using R and ArcGIS) to modify a commercial off the shelf global positioning system (GPS) solution for our own use. We then conducted a pilot study to assess the degree to which our naturalistic driving system was associated with self-reported driving behavior among older adults with and without preclinical Alzheimer's disease (AD). Methods: Participants (N¼20) with normal cognition (Clinical Dementia Rating, 0), aged 65 years and older were recruited from the Knight Alzheimer Disease Research Center. Participants completed a clinical assessment and psychometric battery, brain amyloid imaging, and cerebrospinal fluid (CSF) collection. Driving behavior was evaluated using the self-report, Driving Habits Questionnaire (DHQ) and by our system, which incorporated, a GPS device plugged into participant's vehicle. We examined correlations between five main outcomes collected by both methods: speeding, driving space, total miles driven, total number of trips, and number of days-per-week driven. Group differences were also exami...