Predicting safe street-crossing for older adults using a multifactorial model: the role of visual perceptual, cognitive and physical factors

Older adult pedestrians (60+ years) are overrepresented in road-related crash statistics. Apart from age, other individual factors such as visual perceptual, physical, and cognitive decline, may explain street-crossing behaviours which are likely contributing to these statistics. Although studies in the driving domain have considered these factors in combination, there is a scarcity of studies that have investigated whether these can predict safe street-crossing skills such as detecting road hazards and identifying a safe gap to cross in an older adult population. This study explored the key predictors of safe street-crossing from a sample of 100 older adults. Participants completed a suite of visual perceptual (e.g., contrast sensitivity), cognitive (e.g., executive function), and physical (e.g., balance) tasks. Hazard perception and gap acceptance were measured using an established virtual reality pedestrian street-crossing task, where a series of 360-degree video clips captured from real-world pedestrian situations were presented. We showed that: (1) Contrast sensitivity (perceptual), and attention/executive function, working memory and reaction time (cognitive), significantly predicted hazard perception response time (large effect size); (2) Working memory (cognitive) was a significant predictor of hazard perception accuracy (medium effect size); (3) Working memory and response inhibition (cognitive) significantly predicted safe hazard perception (medium effect size); (4) Working memory (cognitive) was the most important predictor for gap acceptance response time; and (5) Working memory (cognitive) and comfortable walking speed (physical) were the most important predictors for safe gap selection. The outcomes of this study have informed the development of an evidence-based street-crossing training program that will aim to reduce the number of older adult pedestrian fatalities.