Literature DB >> 28411723

Fatigue of Chinese railway employees and its influential factors: Structural equation modelling.

Liuxing Tsao1, Jing Chang2, Liang Ma3.   

Abstract

Fatigue is an identifiable and preventable cause of accidents in transport operations. Regarding the railway sector, incident logs and simulation studies show that employee fatigue leads to lack of alertness, impaired performance, and occurrence of incidents. China has one of the largest rail systems in the world, and Chinese railway employees work under high fatigue risks; therefore, it is important to assess their fatigue level and find the major factors leading to fatigue. We designed a questionnaire that uses Multidimensional Fatigue Instrument (MFI-20), NASA-TLX and subjective rating of work overtime feelings to assess employee fatigue. The contribution of each influential factor of fatigue was analysed using structural equation modelling. In total, 297 employees from the rail maintenance department and 227 employees from the locomotive department returned valid responses. The average scores and standard deviations for the five subscales of MFI-20, namely General Fatigue, Physical Fatigue, Reduced Activity, Reduced Motivation, and Mental Fatigue, were 2.9 (0.8), 2.8 (0.8), 2.5 (0.8), 2.5 (0.7), and 2.4 (0.8) among the rail maintenance employees and 3.5 (0.8), 3.5 (0.7), 3.3 (0.7), 3.0 (0.6), and 3.1 (0.7), respectively, among the locomotive employees. The fatigue of the locomotive employees was influenced by feelings related to working overtime (standardized r = 0.22) and workload (standardized r = 0.27). The work overtime control and physical working environment significantly influenced subjective feelings (standardized r = -0.25 and 0.47, respectively), while improper work/rest rhythms and an adverse physical working environment significantly increased the workload (standardized r = 0.48 and 0.33, respectively).
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fatigue; Railway safety; Structural equation modelling

Mesh:

Year:  2017        PMID: 28411723     DOI: 10.1016/j.apergo.2017.02.021

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  2 in total

1.  A Field Study of Work Type Influence on Air Traffic Controllers' Fatigue Based on Data-Driven PERCLOS Detection.

Authors:  Jianping Zhang; Zhenling Chen; Weidong Liu; Pengxin Ding; Qinggang Wu
Journal:  Int J Environ Res Public Health       Date:  2021-11-13       Impact factor: 3.390

2.  A Preliminary Review of Fatigue Among Rail Staff.

Authors:  Jialin Fan; Andrew P Smith
Journal:  Front Psychol       Date:  2018-05-07
  2 in total

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