Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health.

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.

[1]  P. Holland.  Statistics and Causal Inference , 1985 .

[2]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[3]  Board on Human-Systems Integration.  Commercial Motor Vehicle Driver Fatigue, Long-Term Health, and Highway Safety: Research Needs , 2016 .

[4]  Stephen E. Fienberg,et al.  Fitting Science Into Legal Contexts , 2014 .

[5]  H. Colten,et al.  Sleep Disorders and Sleep Deprivation , 2006 .

[6]  Ann Williamson,et al.  The link between fatigue and safety. , 2011, Accident; analysis and prevention.

[7]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction , 2016 .

[8]  David R. Holtgrave,et al.  Alternatives to the randomized controlled trial. , 2008, American journal of public health.

[9]  P. Diggle,et al.  Analysis of Longitudinal Data , 2003 .

[10]  Edward M. Hitchcock,et al.  Obesity and other risk factors: the national survey of U.S. long-haul truck driver health and injury. , 2014, American journal of industrial medicine.

[11]  W C Dement,et al.  Sleep and sleep-disordered breathing in commercial long-haul truck drivers. , 1995, Chest.

[12]  M. Ohayon,et al.  Sleep disorders, medical conditions, and road accident risk. , 2011, Accident; analysis and prevention.

[13]  D. Dinges,et al.  Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. , 2011, Sleep.

[14]  A. Pack,et al.  Impaired performance in commercial drivers: role of sleep apnea and short sleep duration. , 2006, American journal of respiratory and critical care medicine.

[15]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT , 1994 .

[16]  A. Malhotra,et al.  Employer-Mandated Sleep Apnea Screening and Diagnosis in Commercial Drivers , 2012, Journal of occupational and environmental medicine.

[17]  C. Czeisler.  Duration, timing and quality of sleep are each vital for health, performance and safety. , 2015, Sleep health.

[18]  M. Robins James,et al.  Estimation of the causal effects of time-varying exposures , 2008 .

[19]  D. Dinges,et al.  Validity and Sensitivity of a Brief Psychomotor Vigilance Test (PVT-B) to Total and Partial Sleep Deprivation. , 2011, Acta astronautica.

[20]  P. Rosenbaum.  Design of Observational Studies , 2009, Springer Series in Statistics.

[21]  Anup V. Desai,et al.  Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers. , 2004, American journal of respiratory and critical care medicine.