Background and Objectives: The increasing number of senior drivers may introduce new road risks due to age-related declines in physical and cognitive abilities. Advanced driver assistance systems (ADAS) have been proposed as solutions to minimize age-related declines, thereby increasing both senior safety and mobility. This study examined factors that influence seniors’ attitudes toward adopting ADAS after significant exposure to the technology in naturalistic settings. Research Design and Methods: This study recruited 18 senior drivers aged 70–79 to drive vehicles equipped with ADAS for 6 weeks in their own environments. Afterward, each participant was enrolled in 1 of the 3 focus group sessions to discuss their changes in attitude toward ADAS based on their driving experiences. We applied structural topic modeling (STM) on the focus group transcripts to reveal key topics deemed important to seniors. Results: STM revealed 5 topics of importance for seniors. In order of prevalence, these were (i) safety, (ii) confidence concerning ADAS, (iii) ADAS functionality, (iv) user interface/usability, and (v) non-ADAS–related features. Based on topics and associated keywords, seniors perceived safety improvement with ADAS but expressed concerns about its limitations in coping with adverse driving conditions. Experience and training were suggested for improving seniors’ confidence in ADAS. Blind spot alert and adaptive cruise control received the most discussion regarding perceived safety and comfort. Discussion and Implications: This study indicated that promoting road safety for senior drivers through ADAS is feasible. Acceptance and appropriate use of ADAS may be supported through intuitive and senior-friendly user interfaces, in-depth training programs, and owner’s manuals specifically designed and tested for senior drivers.

This study investigated the association between driving safety and seven sleep disorders amongst 3,541 participants of the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study. SHRP 2 collected naturalistic driving data from participants between 16 to 98 years old by instrumenting participants’ vehicles. The analyses used logistic regression to determine the likelihood of crash/near-crash involvement, Poisson log-linear regression to assess crash/near-crash rate, and ordinal logistic regression to assess driver maneuver appropriateness and crash/near-crash severity. These analyses did not account for any medical treatments for the sleep disorders. Females with restless legs syndrome/Willis–Ekbom disease (RLS/WED), drivers with insomnia or nacrolepsy are associated with significantly higher risk of crash/near-crash. Drivers with shift work sleep disorder (SWSD) are assoicated with significantly increased crash/near-crash rate. Females with RLS/WED or sleep apnea and drivers with SWSD are associated with less safe driver maneuver and drivers with periodic limb movement disorder are assoicated with more severe events. The four analyses provide no evidence of safety decrements associated with migraine. This study is the first examination on the association between seven sleep disorders and different measures of driving risk using large-scale naturalistic driving study data. The results corroborate much of the existing simulator and epidemiological research related to sleep disorder patients and their driving safety, but add ecological validity to those findings. These results contribute to the empirical basis for medical professionals, policy makers and employers in making decisions to aid individuals with sleep disorders in balancing safety and personal mobility.

Patient flow management is a system-wide process but many healthcare providers do not integrate multiple departments into the process to minimize time between treatments or medical services for maximum patient throughput. This paper presents a case study of applying Distributed Situation Awareness (DSA) to characterize system-wide patient flow management and identify opportunities for improvements in a healthcare system. This case study employed a three-part method of data elicitation, extraction, and representation to investigate DSA. Social, task, and knowledge networks were developed and then combined to characterize patient flow management and identify deficiencies of the command and control centre of a healthcare facility. Social network analysis provided centrality metrics to further characterize patient flow management. The DSA model helped identify design principles and deficiencies in managing patient flow. These findings indicate that DSA is promising for analysing patient flow management from a system-wide perspective. Practitioner Summary: This article examines Distribution Situation Awareness (DSA) as an analysis framework to study system-wide patient flow management. The DSA yields social, task, and knowledge networks that can be combined to characterise patient flow and identify deficiencies in the system. DSA appears promising for analysing communication and coordination of complex systems.