Determining the incidence of distraction among trauma patients in all modes of transportation.

BACKGROUND: The use of distracting technology is an increasing source of risk for injury among trauma patients. Both drivers and pedestrians show increased unsafe behavior. The data for prevalence and risk for distraction in trauma has varied widely. Our hypothesis is that distraction is more highly prevalent and widely distributed among all mechanisms of injury and variety of trauma patients. METHODS: A 10-question survey of adult trauma victims at a Level I trauma center regarding distraction at time of event was performed, examining age, sex, ethnicity, education level, mode of injury and role in the accident (driver, passenger, pedestrian, bicyclist, motorcyclist). Multiple-variable logistic regression was performed to identify risk factors for distraction. RESULTS: From June 2016 to October 2018, 1,316 patients were surveyed, and 1,011 (76.8%) patients reported their role in the traffic accident. The prevalence of distraction was 21.73% among drivers, 9.01% among passengers, 16.50% among pedestrians, 20.00% among bicyclists, and 8.09% among motorcyclists. Males (odds ratio [OR], 1.84; 95% confidence interval [CI], 1.26-2.67) as well as all Others (OR, 2.09; 95% CI, 1.10-3.98) showed statistically significant increased risk for distraction. Motorcyclist (OR, 0.25; 95% CI, 0.13-0.50) and passenger (OR, 0.37; 95% CI, 0.18-0.77) roles during collision were a lowered risk of distraction. Furthermore, Asian/Pacific Islanders (OR, 1.62; 95% CI, 0.94-2.79) trended toward being at greater risk for distraction. CONCLUSION: Distraction is prevalent among a wide range of traffic accident victims, not just drivers. Males as well as all Others are more likely to be distracted. In contrast, motorcyclists and passengers are less likely to be distracted. Further studies to assist in determining effective interventions and public safety efforts aimed at specific at-risk groups beyond motor vehicle drivers are warranted. LEVEL OF EVIDENCE: Epidemiological, level V.

Video-based assessment of pedestrian behavior: development and testing of methods / Estudio de comportamiento peatonal basado en video: desarrollo y prueba de los métodos

ABSTRACT Objective The aim of this paper is to develop a computer algorithm that analyzes pedestrian behavior at an urban site in Bogota, Colombia, considering that the assessment of pedestrian behavior is a road safety priority. Methods Pedestrians were video-taped as they crossed a selected road. An algorithm was developed in order to record, from these videos, pedestrian and vehicle positions and speeds. This information made possible the identification of hazardous behaviors, which were compared through visual assessments. Results 429 pedestrians crossed the selected road at an average distance of 4.5 meters from vehicles that moved at an average speed of 21 km/h. With a maximum difference of 19 % with respect to visual assessments, the algorithm estimated that 58.5 % pedestrians crossed through non-designated locations; 62.2 % crossed near moving vehicles, and that 41.2 % ran while they were crossing the road. Conclusions Video-based analysis can be used to assess pedestrians' behavior. Future research work should focus on improving both the accuracy and the number of safety parameters of the algorithm.(AU)

RESUMEN Objetivo La medición del comportamiento de peatones es una prioridad de la seguridad vial. Por lo anterior, se desarrolló un algoritmo para analizar el comportamiento de los peatones en una zona urbana de Bogotá, Colombia. Métodos Los peatones fueron filmados mientras cruzaban la calle. Mediante el algoritmo se midieron las posiciones y velocidades de peatones y vehículos en los videos. Se identificaron los comportamientos riesgosos y se compararon visualmente. Resultados 429 peatones cruzaron la vía a una distancia promedio de 4.5 metros de los vehículos (velocidad promedio 21 km/h). El algoritmo estimó, con una diferencia máxima de 19 % con respecto a lo observado, que 58.5 % de los peatones cruzaron por zonas incorrectas, 62.2 % cruzaron cerca de vehículos en movimiento y 41.2 % corrieron al cruzar. Conclusiones El análisis basado en video puede utilizarse para medir el comportamiento de los peatones. Los trabajos sobre el tema que se realicen en el futuro deben enfocarse en mejorar la precisión y los parámetros de seguridad del algoritmo.(AU)