Factors influencing smoking behaviour of online ride-hailing drivers in China: a cross-sectional analysis

Abstract Background Online ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers' smoking behaviour to provide a basis for the...

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Main Authors: Xinlin Chen (Author), Xuefei Gu (Author), Tingting Li (Author), Qiaoyan Liu (Author), Lirong Xu (Author), Bo Peng (Author), Nina Wu (Author)
Format: Book
Published: BMC, 2021-07-01T00:00:00Z.
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001 doaj_61ad0a8f0af84fe39c831a3a7ef69cfc
042 |a dc 
100 1 0 |a Xinlin Chen  |e author 
700 1 0 |a Xuefei Gu  |e author 
700 1 0 |a Tingting Li  |e author 
700 1 0 |a Qiaoyan Liu  |e author 
700 1 0 |a Lirong Xu  |e author 
700 1 0 |a Bo Peng  |e author 
700 1 0 |a Nina Wu  |e author 
245 0 0 |a Factors influencing smoking behaviour of online ride-hailing drivers in China: a cross-sectional analysis 
260 |b BMC,   |c 2021-07-01T00:00:00Z. 
500 |a 10.1186/s12889-021-11366-8 
500 |a 1471-2458 
520 |a Abstract Background Online ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers' smoking behaviour to provide a basis for the formulation of tobacco control policies. Methods We derived our cross-sectional data from an online survey of full-time ride-hailing drivers in China. We used a survey questionnaire to collect variables, including sociodemographic and work-related characteristics, health status, health behaviour, health literacy and smoking status. Finally, we analysed the influencing factors of current smoking by conducting chi-square test and multivariate logistic regression. Results A total of 8990 ride-hailing drivers have participated in the survey, in which 5024 were current smokers, accounting to 55.9%. Nearly one-third of smokers smoked in their cars (32.2%). The logistic regression analysis results were as follows: male drivers (OR = 0.519, 95% CI [0.416, 0.647]), central regions (OR = 1.172, 95% CI [1.049, 1.309]) and eastern regions (OR = 1.330, 95% CI [1.194, 1.480]), working at both daytime and night (OR = 1.287, 95% CI [1.164, 1.424]) and non-fixed time (OR = 0.847, 95% CI [0.718, 0.999]), ages of 35-54 years (OR = 0.585, 95% CI [0.408, 0.829]), current drinker (OR = 1.663, 95% CI [1.526, 1.813]), irregular eating habits (OR = 1.370, 95% CI [1.233, 1.523]), the number of days in a week of engaging in at least 10 min of moderate or vigorous exercise ≥3 (OR = 0.752, 95% CI [0.646, 0.875]), taking the initiative to acquire health knowledge occasionally (OR = 0.882, 95% CI [0.783, 0.992]) or frequently (OR = 0.675, 95% CI [0.591, 0.770]) and underweight (OR = 1.249, 95% CI [1.001, 1.559]) and overweight (OR = 0.846, 95% CI [0.775, 0.924]) have association with the prevalence of current smoking amongst online ride-hailing drivers. Conclusion The smoking rate of ride-hailing drivers was high. Sociodemographic and work-related characteristics and health-related factors affected their smoking behaviour. Psychological and behavioural interventions can promote smoking control management and encourage drivers to quit or limit smoking. Online car-hailing companies can also establish a complaint mechanism combined with personal credit. 
546 |a EN 
690 |a Smoking 
690 |a Online ride-hailing 
690 |a Drivers 
690 |a Influencing factors 
690 |a China 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n BMC Public Health, Vol 21, Iss 1, Pp 1-11 (2021) 
787 0 |n https://doi.org/10.1186/s12889-021-11366-8 
787 0 |n https://doaj.org/toc/1471-2458 
856 4 1 |u https://doaj.org/article/61ad0a8f0af84fe39c831a3a7ef69cfc  |z Connect to this object online.