Distraction of cyclists: how does it influence their risky behaviors and traffic crashes?

Background: Undisputedly, traffic crashes constitute a public health concern whose impact and importance have been increasing during the past few decades. Specifically, road safety data have systematically shown how cyclists are highly vulnerable to suffering traffic crashes and severe injuries derived from them. Furthermore, although the empirical evidence is still very limited in this regard, in addition to other human factors involved in cycling crashes, distractions while cycling appear to be a major contributor to the road risk of cyclists.

Objectives: The main objectives of this study were, first, to explore the prevalence and trends of cycling distractions within an international sample of bike users, and second, to determine the influence of such distractions on road crashes suffered by cyclists, simultaneously considering the explanatory role of risky behaviors (errors and traffic violations) as potentially mediating variables between cycling distractions and traffic crashes.

Methods: For this cross-sectional study, we analyzed the data obtained from 1,064 cyclists-61.2% male and 38.8% female-from 20 different countries, who answered an on-line questionnaire on cycling-related features, habits, behaviors and accidents.

Results: The prevalence of different cycling distractions oscillated between 34.7% and 83.6%. The most common distractions were those related to the behavior of other users, physical elements of the road, weather conditions and phone calls. Age trends and differences were also found, thus establishing a positive association between age and distractibility during cycling. Furthermore, the effect of distractions on traffic crashes of cyclists was significant when tested together with age, risk perception and risky behaviors on the road.

Conclusion: The results of this study support the hypotheses that distractions have a major prevalence among bike users, and that they play a significant role in the prediction of the traffic crash rates of cyclists, through the mediation of risky behaviors.