The proportions of severe and less severe bicycle crashes and how to avoid them

Abstract

Background

In collaboration with a bicycle airbag helmet company, data were collected to help explain events where head protections are deployed. The head protection records activations continuously, and when a head protection is deployed, this information is sent to the company. The company invited affected cyclists to (i) participate in a web survey, and (ii) share their data with researchers. The first aim of the study was to investigate the proportions between different severities of crashes, i.e., how many crashes with serious injuries occur for every crash with minor injuries, while the second aim was to predict when bicycle crashes will occur. Method: A total of 196 cyclists completed the web survey. Participants were 20–76 years old (mean age 46 years) and consisted of 125 women and 55 men. The cyclists were highly educated, and 73 percent had completed a university or college education. In addition, head protection data were collected from 355 other cyclists, of which 264 had their helmet deployed. Results: One of the 182 (included events) cyclists ended up in hospital care. The data collected indicated the proportions of cyclists who needed hospital care (1 = severe injuries), cyclists with injuries (15), slight injures (85) and cyclists who could continue as before (81 = no injuries). The head protection data confirmed the web survey findings, but also demonstrated that the head protection, on journeys that ended with head protection deployment, had a higher degree of activations before the event) compared to journeys where it was not deployed. Furthermore, on trips made after deployment, the head protection had lower levels of activations, which can be understood as the cyclists adapting their behavior by, for example, riding more carefully (but not slower). Conclusion: This study highlights the proportions of events leading to minor injuries versus hospitalization. Activation measures (head protection conditions) can predict when events will occur, and cyclists will adjust their behavior accordingly following events.