Transportation, Warehousing, and Utilities

Participating core and specialty programs: Center for Occupational Robotics Research, National Center for Productive Aging and Work, Safe●Skilled●Ready Workforce, Surveillance and Translation Research.

Other federal agencies, trade associations, labor organizations, employers, owner/operators, and researchers use NIOSH information to reduce obesity and chronic disease among transportation, warehousing and utilities workers.

  Health Outcome Research Focus Worker Population Research Type
A Metabolic disorders, sleep disorders Risk factors (obesity, sedentary work, lack of healthy eating options, stress, boredom) Long-haul truck drivers, short-haul truck drivers, bus and transit drivers, rail workers Intervention
B Metabolic disorders, Sleep disorders Understanding link between obesity and fatigue Long-haul truck drivers, short-haul truck drivers, bus and transit drivers, rail workers Intervention
C Cardiovascular disease (CVD), Metabolic disorders, Sleep disorders Depression, Anxiety Explore existing data and ways to efficiently monitor contribution of fatigue and stress Long-haul truck drivers, short-haul truck drivers, bus and transit drivers, rail workers, aviation, utility workers, maritime, couriers Surveillance Research
D CVD, Metabolic disorders, Sleep disorders, Depression, Anxiety Address socioeconomic risk factors (access to healthcare, non-standard work arrangements) Long-haul truck drivers, short-haul truck drivers, couriers, rail transit and bus, warehousing workers, utilities workers Intervention

Activity Goal 7.6.1 (Intervention Research): Conduct studies to assess effectiveness of interventions to address work organization and socioeconomic factors that contribute to obesity and chronic disease among transportation, warehousing and utility workers.

Activity Goal 7.6.2 (Surveillance Research): Conduct surveillance research on risk factors for chronic disease among transportation workers.


Obesity is a national problem and is prevalent in the transportation, warehousing and utilities (TWU) sector. Health conditions associated with obesity include metabolic disorders such as hypertension and diabetes, cardiovascular disease (CVD), and stroke [Thompson et al. 1999]. An estimated 34.2% of all TWU workers are obese [NIOSH 2013] and 6.1% have had a diagnosis of heart disease [Helmkamp et al. 2013]. 6.1 percent of TWU workers have been told they have diabetes and 21.1% have been told they have hypertension [NIOSH 2013]. The work demands and other psychological stressors of TWU workers create special challenges: tasks may be sedentary in nature, limited options may be available for where and when to eat while working or resting away from home, and sleep periods may often be less than the recommended 7-9 hours daily [Hirschkowitz et al. 2015]. Sixty-seven percent of TWU workers did not meet CDC guidelines for physical activity [Helmkamp et al. 2013], while 38.0% of TWU workers indicate less than 7 hours of sleep in a 24-hour period [CDC 2012]. Twenty-eight percent of TWU workers work more than 48 hours per week compared to 18.7% of the U.S. workforce [NIOSH 2010]. Thirty-six percent work non-standard shifts, compared to 26.6% of the U.S. workforce [NIOSH 2015]. Job insecurity may increase the odds of reporting poor health by 50%; high job demands raise the odds of having a physician-diagnosed illness by 35%, and long work hours increase mortality by almost 20% [Goh et al. 2015].

Obesity and related disorders manifest themselves in premature death and disability, increases in health care costs, lost productivity, social stigmatization, and increases risk of involvement in transportation incidents. [NIH 1998; Thompson et al. 1999; Martin et al. 2009; Anderson et al. 2012]. Obesity’s related medical factors may limit a commercial motor vehicle driver’s driving certification [Thiese et al. 2015]. Job stress is associated, in the short term, with affective reactions (e.g., irritability, anger), and, in the long term, with anxiety and depressive symptoms [Griffin et al. 2007]. Job stress also has cognitive and behavioral effects. High levels of stress can cause narrowing of attention and reduce working memory capacity, which can reduce performance accuracy. Work stressors are related to unsafe behaviors, accidents and injuries [Nahrgang et al. 2011]


Research is needed to better understand the link between metabolic disorders, CVD, obesity, sleep disorders, depression, anxiety, fatigue, and stress and create interventions to mitigate negative effects. These conditions not only affect quality of life, but may also interfere with the ability to operate a vehicle safely. Previous NIOSH obesity TWU surveillance research has focused on long and short haul truck drivers, and more efficient methods to monitor obesity among other TWU workers are needed. The importance of obesity and heart disease to TWU is highlighted by the fact that individual trucking companies and insurance companies have initiated health and fitness programs based on NIOSH research findings [Baleka 2017]. High body mass index (obesity) is also a key investigatory variable called for in a recent report from the National Academy of Sciences on research needs for commercial motor driver fatigue, long-term health, and highway safety [National Academy of Sciences 2016]. Because non-standard work arrangements are understudied but increasingly prevalent, and their determinants and health and safety consequences are poorly understood, surveillance is needed. Particularly needed are models on the determinants and effects of work arrangements and efforts to improve the taxonomy of work arrangements and their characteristics. Shift work and long work hours represent complex workplace hazards. This complex hazard further requires research on many types of interventions to reduce risks. Similarly, job stress is a widespread problem in the working population and is one of the costliest risk factors to industry due to its effects on such a broad range of health, safety, productivity, social, and non-work factors.

Other federal agencies, trade associations, labor organizations, employers, owner/operators, and researchers use NIOSH information to reduce injuries and fatalities related to fatigue and stress among transportation and utility workers.

  Health Outcome Research Focus Worker Population Research Type
A Fatal and non-fatal injuries Develop fatigue and stress interventions Truck drivers, bus and transit drivers, aviation, marine, rail, and utility Intervention
B Fatal and non-fatal injuries Develop medication and substance use (stress or fatigue-induced) interventions Truck drivers, bus and transit drivers, aviation, marine, rail, and utility Intervention

Activity Goal 7.7.1 (Intervention Research): Conduct intervention studies to develop and assess the effectiveness of interventions to reduce fatigue and stress (and related medication and substance use) to prevent injuries and fatalities among transportation and utility workers.


In 2015, workers in the transportation and warehousing industry had a fatal work injuries rate of 13.8 per 100,000 workers, the second highest rate for all workers [BLS 2017]. The organization of work in the transportation, warehousing and utilities (TWU) sector exacerbates risk of work-related injuries and fatalities. The long hours of work and irregular work schedules typical of the sector often lead to chronic sleep deprivation, disruption of circadian rhythms, and poor sleep quality. Insufficient sleep is associated with a broad range of health and safety risks including, vehicle crashes and disturbances to cognition [AAA Foundation for Traffic Safety 2016; DOT 2015; FMCSA 2007]. For TWU sector workers, delivery deadlines, time pressures, long periods away from home, and pay-by-the-mile compensation can contribute to work stress and incentivize non-compliance with the U.S. Department of Transportation safety regulations that limit driving and duty hours.

Previous research suggests that stimulant use is an important problem for U.S. truck drivers. Couper et al. [2002] reported that 9.5% of truck drivers in Oregon and Washington State tested positive for central nervous system stimulants such as amphetamine, cocaine, and pseudoephedrine. Use during driving has been shown to multiply the risk of a fatal crash by 3 to 4.5 [Elvik 2013]. Results for a cross-sectional intercept study showed the prevalence of at-risk drinking (five or more drinks in one day) was significantly higher for male long-haul drivers, during break periods from work [Birdsey et al. 2015]. It has been reported that engaging in even one or two days of at-risk drinking per year increases the prevalence of alcohol abuse and alcohol dependence [Dawson et. al. 2005] causing problems such as failure to fulfill expectations at work or home, increased physical hazards, legal problems, social/interpersonal problems, or an inability to control drinking behavior [Maisto et. al. 2003].


The Institute of Medicine [IOM 2006] calls poor sleep health, shift work, and long work hours a critical unmet public health problem, because of the societal requirements of a 24/7 clock. Scientific evidence on the topic of sleep health, shift work, and long work hours has mounted in recent decades, but information has not been adequately disseminated or implemented in the TWU sector. Despite the quite extensive body of research showing the links between stress and health and safety outcomes, there have been few studies to identify workplace psychosocial and work organization risk factors by sector and fewer studies of interventions to address these risk factors. There is a critical need to develop effective tools that organizations can use to assess sources of job stress and develop interventions to address these risk factors. Research is needed to develop effective administrative controls for managers and workers to improve sleep and reduce workplace stress. In addition, research is needed to determine effective interventions that reduce workplace injuries and fatalities correlated to fatigue, stress, and stimulants used by workers to personally mitigate these effects. This complex hazard requires research on many types of interventions to reduce risks: testing various work scheduling patterns; manipulating light exposure, pharmacology agents, and diet regimes; work organization strategies and efforts to change workplace cultures; workplace interventions including policies, fatigue risk management systems, and education programs; mathematical models to predict risks; and studies of the impact of broader public policy measures (for example, impact of hours of service rules).

Other federal agencies, trade associations, labor organizations, employers, owner/operators, and researchers use NIOSH information to reduce injuries associated with human-machine interaction among TWU workers.

  Health Outcome Issue Worker population Research needed
A Fatal and non-fatal injuries Repetitive tasks, mental exhaustion Warehousing workers; couriers and messengers; marine, rail, and aviation workers; truck drivers; transit workers Intervention
B Fatal and non-fatal injuries Displacement by autonomous vehicles Truck drivers; aviation, marine and rail workers Intervention
C Fatal and non-fatal injuries Robotics and exoskeletons and interplay with fatigue and stress (displacement, psychosocial) Warehousing workers, couriers, messengers, utilities workers, baggage handlers Surveillance Research

Activity Goal 7.8.1 (Intervention Research): Conduct studies to develop and assess the effectiveness of interventions to reduce injuries associated with monotonous tasks and autonomous vehicles among transportation and warehousing workers.

Activity Goal 7.8.2 (Surveillance Research): Conduct surveillance research to better understand relationship between injuries, stress and robotics and exoskeletons and interplay with fatigue and the psychosocial stress of displacement among TWU workers.


Interactions between vehicles and machines have been beneficial to the employer and worker by reducing work load, repetitive tasks, and increasing production capabilities. The International Federation of Robotics reports sharp increases in sales and is projecting that a new type of robot, collaborative robots that work alongside and in conjunction with human workers, will have a market breakthrough in the next several years [IFR 2016]. Wearable robotics, such as exoskeletons to reduce physical loads on workers, are being marketed [Lowe et al. 2016]. Vehicles increasingly have automated safety features, and fully autonomous vehicles, including commercial trucks and transit vehicles, are currently being piloted on U.S. roadways. Projections on when autonomous vehicles will be commonplace vary, but some project this could be within the next decade [Kessler 2017]). As robotics and automation integrate into the transportation, warehousing and utilities (TWU) sector, workers are being tasked with working with these complex systems. Examples of these systems in TWU include: air traffic management, unmanned aviation systems, positive train control systems, motor vehicle dashboards, autonomous vehicles, ship control systems, automated warehousing, wearable robotic exoskeletons, and use of drones in warehousing and utilities [Volpe 2012; Banker 2016; Schneider and Demi 2017]. Introduction of these highly automated systems has the potential to improve safety in many areas including reducing vehicle crashes, but there are increased risks with highly automated systems. These systems are highly complicated and more emphasis needs to be placed on operator training and maintenance [Moniz and Krings 2016] Changes in the roles and responsibilities of the operator introduce increased risk of operator errors especially in the context of unforeseen or atypical events. In the past introductions of new technologies occurred at a slow pace; the current faster pace of technology introduction increases the potential for unforeseen hazards being introduced in the workplace.


Current Injury statistics illustrate the need for continued research on the human/machine interface for machines used in today’s workplace, and this research will need to be expanded to address future machines and vehicles. Researchers can begin to mitigate these hazards, and reduce injuries and fatalities, through hazard identification strategies and hazard mitigation methods, human factors analysis, educational programs on human factors engineering elements for system design for engineers, and integration of human factors engineering principles in technical engineering and design standards [Leva et al. 2016; Murashov et al. 2016]. Surveillance research is needed to better understand the relationship between injuries and stress, robotics and exoskeletons among TWU workers and the interplay with fatigue and psychosocial stress of displacement robotics, exoskeletons, and autonomous vehicles. Intervention studies are needed to assess effectiveness of autonomous vehicle interventions to reduce injuries associated with monotonous and repetitive tasks.

AAA Foundation for Truck Safety [2016]. Acute sleep deprivation and risk of motor vehicle crash involvement. AAA Foundation for Traffic Safety, iconexternal icon.

Anderson, JE, Govada M, Steffen TK, Thorne CP, Varvarigou V, Kales SN, Burks SV [2012]. Obesity is associated with the future risk of heavy truck crashes among newly recruited commercial drivers. Accid Anal Prev 49:378-384. icon

Baleka S [2017]. Fitness Trucking, icon

Banker S [2016]. Robots In The Warehouse: It’s Not Just Amazon. Forbes: Logistics & Transportation.

Birdsey J, Sieber WK, Chen GX, Hitchcock EM, Lincoln JE, Nakata A, Robinson CF, Sweeney, MH [2015]. National survey of us long-haul truck driver health and injury health behaviors. JOEM 57:2, 210-216.

BLS [2017]. Databases, Tables & Calculators by Subject: Workplace Injuries. Washington, DC: Bureau of Labor Statistics, icon

CDC [2012]. Short sleep duration among workers – United States, 2010. MMWR 61(16):281-285.

Couper FJ, Pemberton M, Jarvis A, Hughes M, Logan BK [2002]. Prevalence of drug use in commercial tractor-trailer drivers. J Forensic Sci. 47:562–567.

Dawson DA, Grant BF, Li TK [2005]. Quantifying the risks associated with exceeding recommended drinking limits. Alcohol Clin Exp Res. 29:902– 908.

DOT [2015]. NHTSA drowsy driving research and program plan. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration. iconexternal icon

Elvik R [2013]. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid Anal Prev. 60:254–267.

FMCSA [2007]. The large truck crash causation study – analysis brief. Publication No. FMCSA-RRA-07-017. icon

Goh J, Pfeffer J, Zenios SA [2015]. Workpace stressors and health outcomes: health policy for the workplace. Behav Sci Policy 1(1):43-52. icon.

Griffin MA, Neal A, Parker SK [2007]. A new model of work role performance: positive behavior in uncertain and interdependent contexts. Acad Manage J 50(2):327-347. icon

Helmkamp JC, Lincoln JE, Sestito J, Wood E, Birdsey J, Kiefer M [2013]. Risk actors, health behaviors, and injury among adults employed in the transportation, warehousing, and utilities super sector. Am J Ind Med 56(5S):556-568.

Hirschkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N, Herman J, Katz ES, Kheirandish-Gozal L, Neubauer DN, O’Donnell AE, Ohayon M, Peever J, Rawding R, Sachdeva RC, Setters B, Vitielljo MV, Ware J, Hillard PJA [2015]. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health 1(1):40-43. icon

IFR [2016]. Executive summary world robotics 2016 industrial robots icon

IOM [2006]. Sleep disorders and sleep deprivation: An unmet public health problem. Washington, DC: National Academies of Sciences, Engineering, Medicine; Health and Medicine Division. icon.

Kessler S [2017]. When will self-driving cars be on the road? Quartz, March 29, 2017 icon

Leva MC, Naghdali F, Alunn C [2016]. Human factors engineering in system design: a roadmap for improvement. The Fourth International Conference on Through-life Engineering Services

Lowe BD, Dick RB, Hudock S, Bobick T [2016]. Wearable exoskeletons to reduce physical load at work. NIOSH Science Blog, March, 4,

Maisto SA, McKay JR, Tiffany ST. Diagnosis. In: Allen JP, Wilson VB, eds. [2003]. Assessing alcohol problems: a guide for clinicians and researchers. 2nd ed. US Department of Health and Human Services, Public Health Service, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism; 55–73. Retrieved from: icon .

Martin BC, Church TS, Bonnell R, Ben-Joseph R, Borgstadt T [2009]. The impact of overweight and obesity on the direct medical costs of truck drivers. J Occup Environ Med 51(2):180-184. icon

Moniz AB, Krings BJ [ 2016]. Robots working with humans or humans working with robots? searching for social dimensions in new human-robot interaction in industry. Societies 6:23, p 1-21.

Murashov, V., F. Hearl., J. Howard [2016]. Working safely with robot workers: recommendations for the new workplace. J Occup Envron Hyg 13(3):D61-D71.

Nahrgang JD, Morgeson FP, Hofmann DA [2011]. Safety at work: a meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes. J Appl Psychol 96(1):71-94. iconexternal icon

National Academies of Sciences, Engineering, and Medicine [2016]. Commercial motor vehicle driver fatigue, long-term health, and highway safety: research needs. Washington, DC: The National Academies Press.

NIH [1998]. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Washington, DC: National Institutes of Health, NIH Publication No. 98-4083. iconexternal icon

NIOSH [2010]. National Health Interview Survey 2010 Occupational Health Supplement: Transportation, Warehousing, and Utilities Industry Profile – Figure 5. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.

NIOSH [2013]. Health Behavior Charts: National Health Interview Survey (NHIS), 2004 – 2013. Unadjusted Prevalence of Obesity among Workers by Industry. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.

NIOSH [2015]. NHIS Occupational Health Supplement (NHIS-OHS). Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.

Schneider M., Deml B. [2017]. Analysis of a multimodal human-robot-interface in terms of mental workload. In: Schlick C. et al. (eds) Advances in ergonomic design of systems, products and processes. Springer, Berlin, Heidelberg.

Thiese MS, Moffitt G, Hanowski RJ, Kales SN, Porter RJ, Hegmann KT [2015]. Commercial driver medical examinations: prevalence of obesity, comorbidities, and certification outcomes. J Occup Environ Med 57(6):659-665. external icon

Thompson D, Edelsberg J, Colditz GA, Bird AP, Oster G [1999]. Lifetime health and economic consequences of obesity. Arch Intern Med 159(18):2177-2183. icon.

Volpe National Transportation Systems Center [2012]. Automation and the Human: Intended and Unintended Consequences. Transportation Challenges and Opportunities: A Colloquia Series. Cambridge, MA; U.S. Department of Transportation, VOPLE National Transportation Systems Center.

Page last reviewed: April 24, 2018