Citation

Stern HS, Blower D, Cohen ML, Czeisler CA, Dinges DF, Greenhouse JB, Guo F, Hanowski RJ, Hartenbaum NP, Krueger GP, Mallis MM, Pain RF, Rizzo M, Sinha E, Small DS, Stuart EA, Wegman DH. Accid. Anal. Prev. 2019; 126: 37-42.

Affiliation

Department of Work Environment, School of Health and Environment, University of Massachusetts, Lowell, MA 01854, United States. Electronic address: david_wegman@uml.edu.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2018.02.021

PMID

29530304

Abstract

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Language: en

Keywords

Causal inference; Driver performance; Longitudinal studies; Observational studies; Obstructive sleep apnea