P017 Am I sleepy? – Subjective Sleepiness and Drowsy Driving: A systematic review and meta-analysis

Introduction Driving impairment due to sleep loss is a major contributor to motor vehicle crashes resulting in severe injury or fatalities. Ideally, drivers should be aware of their sleepiness and cease driving to reduce risk of a crash. However, there is little consensus on how accurately drivers can identify sleepiness, and how this relates to subsequent driving impairment. To examine whether drivers are aware of their sleepiness, we systematically reviewed the literature. Methods The research question for this review was “are drivers aware of sleepiness while driving, and to what extent does subjective sleepiness accurately reflect driving impairment?”. Our search strategy led to thirty-four simulated/naturalistic driving studies for review. We then extracted the relevant data. Correlational data were examined using meta-analysis, while predictive data were assessed via narrative review. Results Results showed that drivers were aware of sleepiness, and this was associated with both driving impairment and physiological drowsiness. Overall, subjective sleepiness was more strongly correlated (a) with ocular and EEG-based outcomes (rweighted = .70 and .73, respectively, p<.001), rather than lane position and speed outcomes (rweighted = .46 and .49, respectively, p<.001); (b) under simulated driving conditions compared to naturalistic drives; and (c) when the Karolinska Sleepiness Scale was used to measure subjective sleepiness. Lastly, high levels of sleepiness significantly predicted crash events and lane deviations. Discussion This review presents evidence that drivers are aware of sleepiness when driving, and suggests that interventions such as stopping driving when feeling ‘sleepy’ may significantly reduce crash risk.

Driving Impairment Detection Due to Sun Exposure and Contrasting Shadow of Surface Objects: An Urban Case Study

A change in drivers’ vision quality might result in the deterioration of their perception and their effectiveness. The lack of an integrated algorithm to distinguish the location of drivers’ vision impairment motivates this study. The proposed model benefits from a combination of several sub-algorithms, such as sun positioning, glare detection, and contrasting shadow illustrator derived from raw geospatial data. The methodology is implemented through a case study involving a large-size metropolitan area road network, a Digital Elevation Model, the associated hillshade geographic data, and weather data from Montreal, Canada. The methodology and corresponding data analysis are implemented in Python. The result revealed a geospatial model to estimate the boundaries of transition points between the glare and contrasting shadows created by changes in roadway surroundings. The results provided by the model can be used as a tool to aid decision-makers in the new road construction and urban planning by creating safety countermeasure strategies and design review of road geometry.

Quantitation of Δ8-THC, Δ9-THC, Cannabidiol and 10 Other Cannabinoids and Metabolites in Oral Fluid by HPLC–MS-MS

Quantitative analysis of Δ9-tetrahydrocannabinol (Δ9-THC) in oral fluid has gained increasing interest in clinical and forensic toxicology laboratories. New medicinal and/or recreational cannabinoid products require laboratories to distinguish different patterns of cannabinoid use. This study validated a high-performance liquid chromatography-tandem mass spectrometry method for 13 different cannabinoids, including (-)-trans-Δ8-tetrahydrocannabinol (Δ8-THC), (-)-trans-Δ9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD), Δ9-tetrahydrocannabinolic acid-A (Δ9-THCA-A), cannabidiolic acid (CBDA), 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-Δ9-THC), 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (Δ9-THCCOOH), tetrahydrocannabivarin (THCV), cannabidivarin (CBDV), cannabidiorcol (CBD-C1), cannabichromene (CBC), cannabinol (CBN) and cannabigerol (CBG), in oral fluid. Baseline separation was achieved in the entire quantitation range between Δ9-THC and its isomer Δ8-THC. The quantitation range of Δ9-THC, Δ8-THC and CBD was from 0.1 to 800 ng/mL. Two hundred human subject oral fluid samples were analyzed with this method after solid phase extraction. Among the 200 human subject oral fluid samples, all 13 cannabinoid analytes were confirmed in at least one sample. Δ8-THC was confirmed in 11 samples, with or without the presence of Δ9-THC. A high concentration of 11-OH-Δ9-THC or Δ9-THCCOOH (>400 ng/mL) was confirmed in three samples. CBD, Δ9-THCA-A, THCV, CBN and CBG were confirmed in 74, 39, 44, 107 and 112 of the 179 confirmed Δ9-THC-positive samples, respectively. The quantitation of multiple cannabinoids and metabolites in oral fluid simultaneously provides valuable information for revealing cannabinoid consumption and interpreting cannabinoid-induced driving impairment.

Using a Performance Based Assessment Tool of Everyday IADL to Determine Driving Risk in Older Adults

For older adults living in rural/suburban communities, driving is often their only means of transportation. Although considered safe drivers, drivers older than 70 years have higher crash rates with fatality rates amplified due to the increased frailty/fragility. However, research evidence clearly indicates that cognitive factors contribute to driving impairment in older adults. Occupational therapists, as experts in observation of functional performance, use the Assessment of Motor and Process Skills (AMPS), for measurement of performance in everyday activities using two scales (motor and process). Previously demonstrated as a sensitive tool for cognitive changes, this presentation will summarize the research outcomes between older adults with cognitive impairment (N=57+) and without (N=53) who completed a comprehensive driving evaluation. Analysis of the two samples using receiving operating curves suggests the AMPS has potentially excellent specificity and sensitivity, specifically AUC = 0.826(0.73-0.92) for motor, AUC = 0.909(0.84-0.98) for process, and AUC = 0.936(0.88-0.99) together. Part of a symposium sponsored by Transportation and Aging Interest Group.