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Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach

BACKGROUND: The increasing rate of traffic crashes involving motorcyclists have turned into a public health and road safety concern. Furthermore, riding behaviors and their precedent factors have been identified as potential determinants for assessing, intervening, and prevent-ing traffic injuries o...

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Autores principales: Naderpour, Sara, Heydari, Seyed Taghi, Bagheri Lankarani, Kamran, Motevalian, Seyed Abbas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kermanshah University of Medical Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369328/
https://www.ncbi.nlm.nih.gov/pubmed/36588299
http://dx.doi.org/10.5249/jivr.v15i1.1784
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author Naderpour, Sara
Heydari, Seyed Taghi
Bagheri Lankarani, Kamran
Motevalian, Seyed Abbas
author_facet Naderpour, Sara
Heydari, Seyed Taghi
Bagheri Lankarani, Kamran
Motevalian, Seyed Abbas
author_sort Naderpour, Sara
collection PubMed
description BACKGROUND: The increasing rate of traffic crashes involving motorcyclists have turned into a public health and road safety concern. Furthermore, riding behaviors and their precedent factors have been identified as potential determinants for assessing, intervening, and prevent-ing traffic injuries of motorists. This study aimed to identify the effects of a set of demographic and motorcycle-related variables as potential predictors on collision through riding behavior components. METHODS: The study sample was 1,611 motorcyclists who were selected through time-location sampling method from three cities in Iran. They responded a Motorcycle Rider Behavior Questionnaire (MRBQ) and a general questionnaire including sociodemographic and riding-related items. The chosen method to analyze the data was Structural Equation Modeling (SEM) through Lavaan package version 0.6-8 of R software version 4.1.0. RESULTS: All participants were male (100%) with a mean age of 28.1(SD=8.5) years. About 24.4% of riders experienced at least one crash during the last year and the majority of riders did not hold a motorcycle license (80.1%). The SEM model showed that riding license (0.06) and fre-quency of riding (0.09) had a direct effect on crash involvement. Some latent variables including speed violation (0.13), stunts (0.11) and traffic violation (0.07) had positive effects and safety violation (-0.07) had a negative effect on crash history. There were indirect effects between age and history of crash mediated by speed violation (-0.04), stunts (-0.04), traffic violation (-0.02) and safety violation (0.01). Also, the indirect effects of riding frequency on crash involvement were mediated by speed violation (0.01), traffic violation (0.006) and safe-ty violation (-0.01). CONCLUSIONS: This study’s main finding is that age and riding frequency are the main variables indirectly af-fecting crash involvement. Therefore, periodic training courses for younger riders is essential in order to decreasing crash involvements.
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spelling pubmed-103693282023-07-27 Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach Naderpour, Sara Heydari, Seyed Taghi Bagheri Lankarani, Kamran Motevalian, Seyed Abbas J Inj Violence Res Injury & Violence BACKGROUND: The increasing rate of traffic crashes involving motorcyclists have turned into a public health and road safety concern. Furthermore, riding behaviors and their precedent factors have been identified as potential determinants for assessing, intervening, and prevent-ing traffic injuries of motorists. This study aimed to identify the effects of a set of demographic and motorcycle-related variables as potential predictors on collision through riding behavior components. METHODS: The study sample was 1,611 motorcyclists who were selected through time-location sampling method from three cities in Iran. They responded a Motorcycle Rider Behavior Questionnaire (MRBQ) and a general questionnaire including sociodemographic and riding-related items. The chosen method to analyze the data was Structural Equation Modeling (SEM) through Lavaan package version 0.6-8 of R software version 4.1.0. RESULTS: All participants were male (100%) with a mean age of 28.1(SD=8.5) years. About 24.4% of riders experienced at least one crash during the last year and the majority of riders did not hold a motorcycle license (80.1%). The SEM model showed that riding license (0.06) and fre-quency of riding (0.09) had a direct effect on crash involvement. Some latent variables including speed violation (0.13), stunts (0.11) and traffic violation (0.07) had positive effects and safety violation (-0.07) had a negative effect on crash history. There were indirect effects between age and history of crash mediated by speed violation (-0.04), stunts (-0.04), traffic violation (-0.02) and safety violation (0.01). Also, the indirect effects of riding frequency on crash involvement were mediated by speed violation (0.01), traffic violation (0.006) and safe-ty violation (-0.01). CONCLUSIONS: This study’s main finding is that age and riding frequency are the main variables indirectly af-fecting crash involvement. Therefore, periodic training courses for younger riders is essential in order to decreasing crash involvements. Kermanshah University of Medical Sciences 2023-01 /pmc/articles/PMC10369328/ /pubmed/36588299 http://dx.doi.org/10.5249/jivr.v15i1.1784 Text en https://creativecommons.org/licenses/by/3.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Injury & Violence
Naderpour, Sara
Heydari, Seyed Taghi
Bagheri Lankarani, Kamran
Motevalian, Seyed Abbas
Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title_full Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title_fullStr Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title_full_unstemmed Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title_short Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
title_sort sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach
topic Injury & Violence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369328/
https://www.ncbi.nlm.nih.gov/pubmed/36588299
http://dx.doi.org/10.5249/jivr.v15i1.1784
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