Research Directions for Studying the Impact of Fatigue on Commercial Motor Vehicle Drivers’ Health and Wellness
Truck and bus drivers are susceptible to many chronic health issues, including obstructive sleep apnea (OSA), hypertension, cardiovascular disease, adult-onset diabetes, and various other conditions commonly associated with obesity (see Chapter 2). The statement of task for this study includes the following: “The panel will also assess the relationship of these factors [hours of driving, hours on duty, and periods of rest] to drivers’ health over the longer term.” The need to study the relationship between fatigue and various health problems is supported in Chapter 8, where Czeisler (2015) is quoted: “Persons experiencing sleep insufficiency are more likely to have chronic diseases such as cardiovascular disease, diabetes, depression, or obesity.” Chapter 8 summarizes what is currently known about the relationship between commercial motor vehicle (CMV) driving and various long-term health issues, while Chapter 9 summarizes approaches to fatigue management and health and wellness management.
This chapter describes research that the Federal Motor Carrier Safety Administration (FMCSA) and other agencies could support to address long-term health problems in the population of CMV drivers. It offers the panel’s recommendations concerning fatigue management and health and wellness management, with the goal of improving the long-term health of CMV drivers. Discussed in turn are a framework for assessing factors related to CMV drivers’ health and wellness; the need for an ongoing survey of CMV drivers to inform understanding of the causal role of these factors; obstructive sleep apnea (OSA) a particular fatigue-related
health problem affecting drivers’ health, as well as crash risk; the utility of commercial driver medical examination (CDME) data as a longitudinal health data set on CMV drivers; the need for research on drug use and driving performance; and research directions for evaluation of health and wellness programs.
Chapter 10 stresses the importance of collecting information on a wide variety of factors with a potentially important, causal role in crashes involving commercial motor vehicles, as well as on the various outcomes of interest, such as crash rate. Such information gathering is relevant here as well, to help understand the extent to which various fatigue-related risks and other causal factors impact driver health and wellness. Without such efforts, analyses may be biased by confounding influences. What is needed is a comprehensive view of what causes long-term sleep insufficiency and the long-term health conditions experienced by CMV drivers. Table 11-1 is somewhat analogous to Table 10-1 in Chapter 10, but it omits some of the rows of that table since the specific driving environment—other than what would be related to years of experience—and various factors concerning the performance of one’s truck or bus would not be likely to affect a driver’s long-term health profile (although this association is at least somewhat unclear since, for example, stresses due to winter or high-density driving could accumulate). The column on granularity in Table 10-1 also is omitted in Table 11-1 since most of the relevant data will be at the individual level. In addition, the outcomes of interest are much different from those in Table 10-1, focusing on a driver’s long-term health in addition to long-term sleep insufficiency. Given the somewhat broader scope of Table 11-1, completing it may be more difficult than is the case for Table 10-1.
One goal is to understand the nature and extent of the various long-term health conditions experienced by CMV drivers, and so understand whether these conditions are a by-product of their occupation or would have developed regardless of the work they do. To understand what aspects of their occupation or other behaviors are causal for these conditions, it may be necessary to conduct a longitudinal survey (or a series of surveys) of CMV truck and bus drivers over time. The goal would be to collect information as their health changed over decades spent in the
TABLE 11-1 Key Domains of Factors That Influence Driver Health
|Predictor Domain||Predictors/Variable Set||Database/Data Source||Private or Public||Outcomes|
profession. Observing what behaviors, and over what duration, are associated with changes in health status and result in these conditions may be crucial to understanding how regulatory and policy modifications to hours-of-service (HOS) regulations, requirements for medical standards and examinations, and educational programs can help reduce the risk of developing these conditions.
Specifically, to develop an in-depth understanding of the health problems of CMV drivers, one would ideally follow individual drivers for a number of years, collecting information on the nature of their job; their average number of working hours; their sleep, diet, and exercise habits; their medical condition (weight, blood pressure, use of medicines and drugs, even caffeine use); their incidence and degree of acute and chronic driver fatigue; and their crash experience—all at regular intervals. The above list may be large, but tracking many or most of these variables longitudinally would be the best way to answer key questions about CMV drivers’ long-term health. To this end, previous efforts to collect fairly intrusive information from CMV drivers could be repeated and improved upon. Examples include the Commercial Driver Individual Difference Study by FMCSA, a cohort study of 20,000 drivers followed for 2-3 years, and the National Institute for Occupational Safety and Health (NIOSH) survey (Sieber et al., 2014) described in Chapter 5. The lessons learned from such studies could help in the design of the longitudinal data collection envisioned by the panel.
Clearly, the collection of information on some of these variables would have to rely on driver self-reporting, raising the possibility of misresponse. Assurance of confidentiality of any data collected would perhaps alleviate many of the concerns of the respondents. Nonetheless, questions about sleep habits and conditions related to OSA in particular are likely to elicit misresponse. If this problem became widespread, it might be necessary to conduct a medical examination for a subset of the respondents as a means of calibrating the responses.
One important objective would be to identify career drivers who do and do not develop various health conditions to determine what factors may have contributed to either a negative change in a driver’s health status or maintenance of the status quo. For instance, survey data collected to date indicate that CMV drivers have a high rate of obesity, which is associated with an increased risk of OSA, diabetes, hypertension, and cardiovascular problems. To design an effective behavior modification program for reducing the frequency of obesity, one would need to know what factors help discriminate between those drivers that do and do not become obese. Another advantage of a longitudinal data collection is that it would make it possible to keep track of entry and exit to and from
the profession, and as a result help in understanding the impacts of such movements on driver health and driver safety.
Given the data gaps that will be clear upon completion of Table 11-1, the panel is convinced that a longitudinal survey, or a survey administered frequently over time, of the health and wellness of CMV drivers will be necessary to collect the needed information. The establishment of a longitudinal survey has greatly improved understanding of the dynamics of changes in health characteristics in many areas of public health. Examples of such surveys include the Health and Retirement Survey and the Framingham Study. Further, the databases built with the results of these surveys have proven useful for addressing some unanticipated questions about the subject populations. Longitudinal surveys also can often support natural experiments and other types of analyses that provide clues to understanding the causal factors for various outcomes.
The costs of such an undertaking would depend on the survey’s sample design, especially the sample size. The sample size would depend largely on whether subnational estimates are needed; the expected attrition rate for survey participants; the costs of following up, which are likely to be substantial given the difficulty of contacting and tracking this population; and whether the inclusion of medical measurements or tests is desired, either for the entire survey population or for a subset.1
While a longitudinal study design is preferable for collecting the needed information, a repeated cross-sectional design may be more feasible. A repeated cross-sectional design can provide baseline estimates and capture trends over time for variables of interest, and is appealing for studying subpopulations when sample sizes are small in individual cross-sectional data sets. Thiese and colleagues (2015b) relied on a repeated cross-sectional data set to quantify the prevalence and trends over time of multiple medical conditions in CMV drivers. The data set was drawn from the Road Ready database of CMV driver medical examinations for 2005 to 2012. There also are methods for using repeated cross-sectional studies in a way that allows inferences almost as if the studies were longitudinal. These methods include (1) designing questionnaires that ask similar questions when the survey is fielded at different periods, and (2) constructing pseudo-cohorts. One can define a cohort by restricting the time period of birth (age) or some other common characteristic (carrier size, employer, type of load). A simple example would be comparing CMV drivers aged 25 to 45 across different cross-sectional data sets. Another example would be comparing the prevalence of certain medical conditions among drivers employed by large fleets and among a group of independent owner-operators across different cross-sectional data sets.
However, cross-sectional studies are ultimately limited in terms of tracking changes in an individual or a cohort over time.
RECOMMENDATION 10: The U.S. Department of Health and Human Services and/or the U.S. Department of Transportation should fund, design, and conduct an ongoing survey that will allow longitudinal comparisons of commercial motor vehicle drivers to enable tracking of changes in their health status, and the factors likely to be associated with those changes, over time. In addition, it would be highly desirable for the survey data thus collected to include sufficient information to enable linking of the data to relevant electronic health records, with a particular focus on conditions that may threaten drivers’ health and safety.
As noted in earlier chapters of this report, OSA is a particular problem directly associated with driver fatigue, highway safety, and driver health. As described in Chapter 8, there is very strong evidence in the case of drivers of passenger vehicles that OSA is a risk factor for negative safety outcomes (Tregear et al., 2009b), as well as for other health problems, such as hypertension. It is widely believed that a high incidence of OSA in CMV drivers also presents a significant risk of driver fatigue and therefore a safety risk on the nation’s roadways.
Continuous positive airway pressure (CPAP) is the primary treatment for OSA, with an estimated 60-70 percent adherence to the therapy. Bilevel positive airway pressure or adaptive servo-ventilation is used for patients who are intolerant to CPAP. Dental devices, surgery, and weight loss are also current treatments (Jordan et al., 2014). Use of CPAP devices helps reduce the safety risk. Because of the problem of limited adherence, CMV drivers’ compliance with OSA treatment protocols is likely to be a confounding factor in research addressing this issue. As a result, three groups of drivers with OSA probably need to be assessed for their long-term health and crash rates: (1) those who are compliant with their OSA treatment protocol, (2) those who are being treated but are not compliant with their protocol, and (3) those who have OSA but as yet are not being treated for it.
Obstructive Sleep Apnea Screening for CMV Drivers
FMCSA requires that CMV drivers maintain a current medical examiner’s certificate to drive. CMV drivers must be examined at least every 2 years to ensure that they are fit to operate their vehicle without risk of sudden or gradual impairment or incapacitation. As described in Chap-
ter 8, CMV drivers are to be examined by certified medical examiners who, when performing the medical exam, are to check drivers against 13 federal medical qualification standards. Of these standards, 4 are absolute and leave no discretion to the examiner other than determining whether the driver may be eligible for an exemption. For the other 9 standards, the examiner is responsible for the certification determination based on guidance issued by FMCSA.
One of the 13 medical standards states: “A person is physically qualified to drive a motor vehicle if that person has no established medical history or clinical diagnosis of a respiratory dysfunction likely to interfere with his ability to control and drive a motor vehicle safely.” Even though this standard does not specifically mention OSA, OSA is cited in the advisory criteria as a respiratory condition that can interfere with oxygen exchange and pose a potential safety risk. The Medical Examiner Handbook contains some, although minimal, guidance on evaluation of drivers with OSA. As discussed in Chapter 8, the FMCSA Medical Review Board and a Medical Expert Panel on Obstructive Sleep Apnea and Commercial Motor Vehicle Driver Safety presented recommendations to the agency concerning screening, diagnosis, treatment, and monitoring of CMV drivers for OSA in 2008 and 2012. However, FMCSA did not adopt these recommendations. Since there is no specific guidance on criteria for evaluating drivers at risk of OSA or on treatment and follow-up, medical examiners are inconsistent in their evaluation of drivers who may be at risk of OSA.
Until September 2014, examiners could be any health care provider licensed by their state to perform physical examinations, with neither training nor certification required. The National Registry of Certified Medical Examiners (NRCME) was fully implemented only as recently as 2014. FMCSA’s purpose was to have all CMV drivers examined by trained and certified medical examiners who understood the CMV driving profession and the pertinent medical standards in the agency’s regulations and guidelines, including those applicable to OSA. Medical examiners may include medical doctors; doctors of osteopathy; nurse practitioners; physician assistants; and in some states chiropractors, dentists, or even physical therapists.
The absence of specific guidance to certified medical examiners on assessing CMV drivers for OSA presents challenges for employers who rely on the medical examiner to make determinations but who find that inconsistent criteria are used. FMCSA issued a bulletin to medical examiners and training associations on January 20, 2015,2 stating that examiners
2 FMCSA Bulletin to Medical Examiners and Training Organizations Regarding Obstructive Sleep Apnea. See https://nationalregistry.fmcsa.dot.gov/NRPublicUI/documents/OSA%20Bulletin%20to%20MEs%20and%20Training%20Organizations-01122015.pdf [March 2016].
should use current best practice in determining which drivers should have objective testing and offering some considerations for addressing OSA, but noting that FMCSA has no specific standards.
RECOMMENDATION 11: The Federal Motor Carrier Safety Administration should continue to encourage all individuals included in the National Registry of Certified Medical Examiners to utilize current best practices in identifying drivers who should be referred for additional sleep malady testing and in making determinations about commercial driver’s license renewal extensions. It would be highly preferable, as soon as possible, to supply the examiners with clear criteria or guidance on when it is appropriate to refer presenting drivers for sleep malady testing.
Need for Additional Research on Obstructive Sleep Apnea Among CMV Drivers
Understanding the linkage between OSA and crash frequency among CMV drivers is important to the charge to this panel, to FMCSA, and to the truck and bus industries. This understanding will remain incomplete if three related questions are not addressed: (1) whether OSA severity (mild, moderate, severe) influences crash risk; (2) whether OSA influences crash severity for commercial motor vehicles (fatal, injury, property damage only); and (3) whether OSA severity influences crash severity.
As noted above, evidence of an association between OSA and safety risk is strong for drivers of passenger vehicles. Mulgrew and colleagues (2008) used crash data for patients suspected to be suffering from OSA to investigate the association between OSA severity and crash severity. The study found that patients with OSA were at an increased risk of crashes and that the crash risk did vary by OSA severity (2.6 times higher for mild OSA, 1.9 for moderate, and 2.0 for severe), but did not increase monotonically as suspected. The crash risk was disproportionately higher in the case of crashes that involved a personal injury (4.8 times higher for mild OSA, 3.0 for moderate, 4.3 for severe) (Mulgrew et al., 2008).
There also remain a number of key questions concerning OSA and CMV drivers. These include (1) what percentage of CMV drivers are affected; (2) what the increased crash risk is for CMV drivers for varying degrees of apnea-hypopnea (i.e., the severity of OSA); (3) how best those at risk for OSA can be identified as a result of the observations and tests conducted by nationally registered medical examiners; (4) to what extent CPAP and related treatment technologies reduce the risk associated with OSA; and (5) what length of treatment and what degree of treatment
compliance (i.e., the number of hours a night and number of nights a year CPAP is used) will generate such reductions.
Related to the question of identifying drivers who should be referred for diagnostic testing for OSA, expert panels and advisory boards have issued a number of recommendations as to characteristics that could identify which drivers should be evaluated for OSA. Those recommendations include utilizing body mass index (BMI) above a specified value, enlarged neck circumference, or obstructed posterior throat, or certain medical conditions such as hypertension. As noted above, however, while there are various potential screening criteria for OSA, FMCSA has not indicated which of these should be used in certifying medical exams, and as a result, medical examiners do not apply consistent criteria with respect to OSA.
What is needed is clear-cut guidance from FMCSA on the criteria medical examiners could/should use to determine which drivers need to be referred for diagnostic sleep disorder testing, on acceptable diagnostic criteria, on the level of apnea-hypopnea index (index of severity of OSA) that would necessitate treatment, and on the acceptable duration and frequency of treatment. Also needed is guidance on what criteria should be used for removing a driver from service by restricting his/her commercial driver’s license (CDL) because of the presence of untreated OSA and for how long if indicated.
The problem of finding a statistical rule for screening a population for testing by identifying groups of people much more and much less likely to have a characteristic is a classic problem in discriminant analysis. Many statistical techniques can be used to find excellent rules given a “training set” of input data (data for a set of drivers indicating whether they have OSA and their health characteristics that would be available to a medical examiner) on drivers with varying degrees of characteristics that may play a role in determining the screening rule and with varying degrees of OSA severity. The characteristics of interest include weight, height, neck circumference, degree of obstructed posterior throat, and degree of hypertension. The techniques that can be utilized include normal theory-based discriminant analysis, logistic regression, classification trees, neural nets, and support vector machines. Given a good training set, finding a good discriminant rule can be straightforward.
Screening rules need to have low errors of two types: the rules need to rarely indicate that drivers should have a test for OSA when they do not have an extreme case of OSA and to rarely indicate that drivers do not need to be tested when they do have an extreme case. It would be difficult to know the possible levels of these two error rates prior to testing. It is relatively clear that the error of not testing when it is called for is more important than the error of testing those who turn out not to have severe cases of OSA. Thus it might be sensible to set the error of not testing when
needed to some acceptable low value and then choose the procedure that minimizes the error of testing when unnecessary.
RECOMMENDATION 12: The Federal Motor Carrier Safety Administration should support peer-reviewed research on obstructive sleep apnea (OSA) and commercial motor vehicle drivers throughout all the research stages, from the drafting of requests for proposals through analysis of data. The supported research should be focused on a better understanding of the incidence of OSA in commercial motor vehicle drivers; its impact on driver fatigue, safety, and health; and the benefits of treatments. Specific research topics might include
- determining the number of commercial motor vehicle drivers whose quantity/quality of sleep and driving performance are likely affected at various levels of apnea-hypopnea (index of OSA severity);
- determining what rules for sleep-screening referrals are effective in discriminating between those commercial motor vehicle drivers with and without OSA;
- delineating the causal chain from diagnosis of OSA (preferably as a function of severity) to increased likelihood of crash frequency among commercial motor vehicle drivers;
- determining the impact of treatment with positive airway pressure (PAP) and similar devices on long-term health and crash rates among commercial motor vehicle drivers with varying degrees of apnea severity; and
- identifying the required/recommended duration of initial PAP treatment (e.g., suggested number of hours of treatment per day/week) before a driver can be certified to return to driving.
During the medical exam required every 2 years for a CMV driver to maintain his or her CDL, the medical examiner notes the presence and absence of numerous health conditions and provides certification decisions. There are web-based platforms wherein the medical examiner can store results of the examination and certification decisions. For example, Thiese and colleagues (2015b) obtained CDME data for 88,246 CMV drivers from such a web-based platform—Road Ready, Inc. They studied medical data for the years 2005 to 2012 and analyzed them for associations among BMI, medical disorders, and driver certification (Thiese et al., 2015a).
CDME data are a valuable source of information on driver demographics, medical history, height, weight, blood pressure, heart rate, urinalysis, and medical examinations. As all CMV drivers undergo the medical examination to maintain their CDL, the CDME data capture both drivers employed by companies and independent owner-operators. Given that the medical examination is conducted at least once every 2 years, the CDME data can become a longitudinal health data set on CMV drivers, as Thiese and colleagues (2015b) demonstrated. One can use these data not only to calculate baseline estimates and trace the prevalence of various health conditions in CMV drivers (Thiese et al., 2015b) but also to estimate the impact of new and revised guidance on disqualifying medical conditions for driver certification.
One of the findings of the Large Truck Crash Causation Study was that 17 percent of truck drivers in the study sample were using over-the-counter drugs, and 2 percent were using illegal drugs. More than 27 years ago, Lund and colleagues (1988) conducted a health survey at a truck weighing station in Tennessee involving 317 randomly selected tractor-trailer drivers. Participating drivers were asked to provide urine or blood samples, which were screened for alcohol and 80 other substances. Twenty-nine percent of the drivers in the study sample had alcohol, marijuana, cocaine, or prescription or nonprescription stimulants in their blood and urine. Couper and colleagues (2002) found similar results when they investigated the prevalence of drug use among 1,067 drivers in the state of Oregon. Twenty-one percent of the urine specimens tested positive for illicit, prescription, and/or over-the-counter drugs, and 7 percent tested positive for more than one drug. In addition, there have been numerous international studies of drug use among CMV drivers, as well as attempts to assess the performance effects. For details, see Krueger et al. (2011), which includes an extensive list of salient references.
The issue of particular concern to this panel is how various drugs influence the driving performance of CMV drivers. The U.S. Food and Drug Administration (FDA) and other federal agencies have advised the public that some prescription and over-the-counter medicines can make it unsafe to drive because their use may cause drowsiness (see Krueger et al., 2011). Research is scant on the link between drug use and impairment among CMV drivers as both ethical and practical difficulties are entailed in learning about drug impairment from actual motor vehicle accidents.3
3 Presentation by Ronald Farkas, FDA, at National Transportation Safety Board (NTSB) Drowsy Driving Forum, NTSB Conference Center, Washington, D.C., October 24, 2014.
Nevertheless, some work has been done to investigate this linkage. The Large Truck Crash Causation Study found that prescription drug use did not increase the risk of being involved in a crash, but the relative risk of over-the-counter drug usage and illegal drug usage was 1.3 and 1.8 times higher, respectively. The National Transportation Safety Board, in collaboration with the National Institute on Drug Abuse, conducted drug screens on blood specimens of 168 fatally injured truck drivers to investigate the influence of alcohol and other drug usage on fatal-to-the-driver crashes. The study found concentrations of marijuana and alcohol that could lead to driver impairment (Crouch et al., 1993).
To address the issue of drugs and driving impairment would require information on the prevalence of drug use (including details on the types of substances) among CMV drivers, which could be one of the data items in the longitudinal survey recommended above. Also needed is comprehensive research investigating the association of alcohol and other substances with driving performance. For a summary of what is known, see Krueger (2010a).
From 1996 to 2006, FMCSA and the American Transportation Research Institute (ATRI), the research arm of the American Trucking Associations (ATA), conducted an educational program on fatigue for trainers of motor carriers, their trucking officials, and drivers. This program—a train-the-trainer program entitled Mastering Alertness and Managing Driver Fatigue—covered such topics as the importance of obtaining adequate rest and sleep, body and sleep physiology, circadian rhythm effects, shift-lag influences from rotating work schedules, sleep disorders, the influences of chemical substances, a list of drowsy driver warning signals, and a set of fatigue countermeasures (Krueger et al., 2007). This program was accompanied by a driver wellness train-the-trainer program known as “Gettin’-In-Gear,” offered by FMCSA and ATRI from 2001 to 2006. The latter program focused on the health, fitness, and wellness of CMV drivers and covered such topics as various health conditions affecting these drivers, sleep disorders, drug and alcohol use, individual diet and exercise plans to improve health and wellness, and relaxation techniques (Krueger and Brewster, 2002).
Starting in July 2013, FMCSA and its international partners in Canada began offering the North American Fatigue Management Program (NAFMP) (see Chapter 8). However, FMCSA does not know the extent to which these PowerPoint slides are being read by CMV drivers or the extent to which reading them is helping to change the drivers’ behaviors
to reduce their susceptibility to fatigue. (The primary program evaluation activities can be found in Moscovitch et al.  and Smiley et al. .) Answering either of these questions will not be easy. Any type of request for personal information from visitors to the website is likely to have a very high percentages of nonresponse, and attempts to collect such information may reduce the number of visitors. Absent any type of evaluation, however, FMCSA does not know the extent to which the current program is working. The agency has held at least one set of preliminary user focus group interviews, but has not yet expressed a clear idea as to which of the 10 training modules might be in need of modification. More generally, FMCSA needs to include in any education or training program a summative evaluation phase to test its efficacy
There are two possible approaches FMCSA could use to evaluate the NAFMP. First, a surprising amount of information can be acquired passively through web analytics. This information includes (1) the number of visits to the website; (2) the number of unique visitors; (3) the number of page views per visit; (4) the average visit duration; (5) the percentage of people who visit the site and immediately move on without looking at any other pages; and (6) the number of first-time visitors, which can be used to determine the percentage of new versus returning users. One can also learn about the different devices used to access the site, which tells something about the visitors. Further, one can learn whether a visitor was searching for a particular type of content, was referred to the site by another site, or typed in the website’s address directly. One also can identify the variety of keywords that brought a user to the website from a search engine. These various statistics can be cross-classified to obtain greater detail, and these statistics are available for 30 days to enable examining trends over time. To FMCSA’s credit, much of this type of analysis can be found in the monthly reports prepared by ATRI for the NAFMP Steering Committee. The panel supports the continuation of these analyses. Additionally, FMCSA needs to find a way of identifying when NAFMP training modules are downloaded from the website for corporate use, and determining whether these training materials were subsequently used in group training, say, at a carrier’s own training classroom equipped to reach larger numbers of drivers.
Analyses of individual interactions with the NAFMP website are limited because one cannot determine whether a visitor has changed his or her behavior after completing various course modules and whether those who have made such recommended lifestyle changes have actually improved their health. For FMCSA to learn about the effectiveness of the NAFMP, it will be necessary to recruit a sample of truck and bus drivers to learn what their interaction with the program has been, whether this degree of interaction has affected their behavior, and whether there has
been an associated change in their degree of fatigue or their health status. Clearly, if such a questionnaire were directed at web visitors, one would have to rely on self-reports to determine changes in health status, which, as discussed above, could be subject to considerable misresponse. Instead, direct measures of, for example, the amount of sleep obtained, current blood pressure, and weight would be desirable and perhaps even necessary. As a stand-alone survey, this inquiry would likely be costly. However, the collection of such information could be incorporated into the longitudinal data collection mentioned in Recommendation 10, further justifying that recommendation.
In addition to the NAFMP, some success in modifying behavior has recently been achieved through the use of incentive-based programs, such as the Safety and Health Involvement for Truckers Program, funded by the National Institutes of Health’s National Heart, Lung, and Blood Institute and targeted at truck drivers aiming to manage or lose weight.4 Such a program could have important advantages over the NAFMP in modifying behavior. Therefore, research is needed to examine the advantages and disadvantages of such an approach.
RECOMMENDATION 13: The Federal Motor Carrier Safety Administration (FMCSA) should carry out a research program on driver fatigue management and training. This research program should include
- evaluating the effectiveness of the North American Fatigue Management Program (NAFMP) for educating truck and bus drivers in how to modify their behavior to remedy various potential sources of fatigue;
- determining how effective the NAFMP training modules are in meeting the needs of drivers’ employers, including fleet managers, safety and risk managers, dispatchers, driver trainers and other corporate officials (e.g., those conducting carrier-sponsored employee health and wellness programs);
- evaluating any new education programs regarding sleep apnea that FMCSA has or plans to develop; and
- examining possibilities for the development and evaluation of incentive-based programs for improving health and fitness, including regular coaching, assessment, and support.