LAUSR

Introduction: In developing countries, traditional motorcycle taxis are operated as a type of public transport mode. Recently, the development of mobile technology has enabled this transport mode to become an app-based motorcycle taxi service, leading to a rapid increase in travel demand. However, the emergence of app-based motorcycle taxi riders is considered to contribute directly and indirectly to the high proportion of road traffic crashes through their risky riding behaviours. The associations between unhealthy habits such as smoking, drink riding and other risky behaviours among motorcyclists have received less attention. Therefore, the aim of this study is to explore the effect of unhealthy lifestyle behaviours on risky riding behaviours among app-based motorcycle taxi riders. This study also explores the prevalence of risky riding behaviours and their associations. Methods: An online survey and field surveys were carried out to obtain information about app-based motorcycle taxi riders’ demographics, work patterns and risky riding behaviours. More than 600 riders (mean age = 25.74 years, SD = 7.3), who were working in the three largest cities in Vietnam, completed a structured questionnaire. Results: Binary logistic regression modelling showed that riders who regularly smoked and drunk alcohol were more likely to engage in the risky behaviours of smoking while riding and drink riding (Adj. OR = 18.61, 95%CI, p < 0.001 and Adj. OR = 9.02, 95%CI, p < 0.01 respectively). The results also indicated that those who sometimes smoked cigarettes were more likely not to wear helmets when riding (Adj. OR = 2.55, p < 0.01). While smoking habits were associated with smoking while riding and not wearing a helmet, most surveyed risky riding behaviours were associated with drinking habits. Significant associations between smoke/drink riding and all other risky riding behaviours were also found in this study. Conclusion: A better understanding of these relationships will help authorities and ride-hailing firms to improve safe riding behaviours among app-based motorcycle taxi riders.