U.S. Department of 

Transportation 

Federal Railroad 

Administration

Work Schedules and Sleep Patterns of 

Railroad Maintenance of Way Workers 

Office of Research 

and Development 

Washington, DC 20590 

DOT/FRA/ORD-06/25 Final Report 

December 2006 

This document is available to the 

U.S. public through the National 

Technical Information Service, 

Springfield, VA 22161. 
NOTICE 

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report. 

 
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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE

December 2006 

3. REPORT TYPE AND DATES COVERED

Final Report 9/2003-8/2005 

4. TITLE AND SUBTITLE

Work Schedules and Sleep Patterns of Railroad Maintenance of Way Workers 

6. AUTHOR(S)

Judith Gertler and Alex Viale 

5. FUNDING NUMBERS 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Foster-Miller, Inc. 

350 Second Avenue 

Waltham, MA 02451-1196 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

DFRA.010350.002 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

U.S. Department of Transportation 

Federal Railroad Administration 

Office of Research and Development 

Washington, DC 20590 

10. SPONSORING/MONITORING 

 AGENCY REPORT NUMBER

DOT/FRA/ORD-06/25 

11. SUPPLEMENTARY NOTES 

COTR: Dr. Thomas Raslear 

12a. DISTRIBUTION/AVAILABILITY STATEMENT

This document is available to the public through the National Technical Information Service, 

Springfield, VA 22161 and at www.fra.dot.gov. 

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

This report presents the results of a study designed to characterize the work/rest schedules and sleep patterns of U.S. railroad 

maintenance of way (MOW) employees and to examine the relationship between these schedules and levels of alertness of the 

individuals working the schedules. The study methodology was a survey of a random sample of currently working U.S. MOW 

employees who completed a background survey and kept a daily log for 2 weeks. MOW workers are a predominantly healthy 

middle-aged male population. They work either production (construction) or non-production (maintenance) jobs and focus on 

either track or bridge and building infrastructure. A majority of non-production jobs have a 5-day work week, but nearly half of 

production jobs work a 4-day week and 20 percent work 8-on 6-off. Overall, 24 percent of MOW workers traveled on their own 

time to an out-of-town worksite during the study’s 2-week period. Both groups get the same amount of sleep, but it is 

significantly less than U.S. adult norms. Several work schedule characteristics, including time without a break, total hours 

worked, weeknight emergency calls, and commute time, were related to daytime alertness, but their relationship was weak. 

15. NUMBER OF PAGES

87 

14. SUBJECT TERMS

Railroad maintenance of way worker, track maintenance, work schedule, fatigue, sleep pattern, 

sleep disorder 16. PRICE CODE

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NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

Prescribed by ANSI Std. 239-18 

298-102 
ii

METRIC/ENGLISH CONVERSION FACTORS 

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE) 

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in) 

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in) 

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft) 

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd) 

 1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE) 

1 square inch (sq in, in2) = 6.5 square centimeters (cm2) 1 square centimeter (cm2) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, yd2) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2) 

1 square mile (sq mi, mi2) = 2.6 square kilometers (km2) 10,000 square meters (m2) = 1 hectare (ha) = 2.5 acres 

1 acre = 0.4 hectare (he) = 4,000 square meters (m2) 

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE) 

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz) 

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb) 

1 short ton = 2,000 pounds 

(lb) 

= 0.9 tonne (t) 1 tonne (t) =

=

1,000 kilograms (kg) 

1.1 short tons

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE) 

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz) 

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt) 

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt) 

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal) 

1 pint (pt) = 0.47 liter (l) 

 1 quart (qt) = 0.96 liter (l) 

1 gallon (gal) = 3.8 liters (l) 

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

TEMPERATURE (EXACT) TEMPERATURE (EXACT) 

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 34 5

Inches

Centimeters 01 3 2 6 11 4 5 7 13 8 9 10 12

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSION

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

 °C -40° -30° -20° -10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100°

For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

Measures. Price $2.50 SD Catalog No. C13 10286 Updated 6/17/98
iii

Contents 

Illustrations ..................................................................................................................................... v 

Tables .................................................................................................................................... vi 

Acknowledgements....................................................................................................................... vii 

Executive Summary........................................................................................................................ 1 

1. Introduction ................................................................................................................. 7 

1.1 Nature of the MOW Worker’s Job.............................................................................. 7 

1.2 Objectives.................................................................................................................... 8 

1.3 Overall Approach ........................................................................................................ 9 

1.4 Scope ......................................................................................................................... 10 

1.5 Organization of the Report ........................................................................................ 10 

2. Survey Design ........................................................................................................... 11 

2.1 Survey Instruments.................................................................................................... 11 

2.2 Data Collection Period .............................................................................................. 11 

2.3 Sampling Plan............................................................................................................ 12

2.4 Procedure................................................................................................................... 13 

3. Analysis of Survey Data............................................................................................ 15 

3.1 Survey Response Rate............................................................................................... 15 

3.2 Non-Response Bias Study ......................................................................................... 15 

3.3 MOW Worker Demographic Characteristics ............................................................ 16 

3.4 Job Characteristics..................................................................................................... 22 

3.5 Sleep Characteristics ................................................................................................. 33 

3.6 Textual Analysis of Work and Sleep Comments ...................................................... 42 

4. Findings and Recommendations................................................................................ 49 

4.1 Key Study Findings................................................................................................... 49 

4.2 Recommendations for Improvements in Study Procedures ...................................... 50 

4.3 Recommendations for Additional Research.............................................................. 51 

5. References ................................................................................................................. 53 

Appendix A. Survey Materials .................................................................................................... 55 

Appendix B. Adjustments to Data ............................................................................................... 73 

Appendix C. Supporting Data...................................................................................................... 75 

Abbreviations and Acronyms ....................................................................................................... 77 

v

Illustrations 

Figure 1. Overall approach .......................................................................................................... 10 

Figure 2. Distribution of respondents by type of MOW job........................................................ 17 

Figure 3. Distribution of MOW workers by age group ............................................................... 18 

Figure 4. Self-assessment of overall health ................................................................................. 20

Figure 5. Workdays lost due to illness in the last 6 mo ............................................................... 21 

Figure 6. Commute time and workday by job type ..................................................................... 28 

Figure 7. Commute time and workday by job schedule .............................................................. 28 

Figure 8. Sources and levels of stress .......................................................................................... 31 

Figure 9. Duration of nighttime sleep on workdays for MOW workers versus U.S. adults........ 35 

Figure 10. Company provisions for nights away from home ...................................................... 38 

Figure 11. Nap 1 start times for workdays................................................................................... 40
vi

Tables 

Table 1. Breakdown of survey responses ...................................................................................... 2 

Table 2. Experience as an MOW employee (yr).......................................................................... 17 

Table 3. Experience with current employer (yr).......................................................................... 17 

Table 4. MOW worker age (yr) ................................................................................................... 18 

Table 5. Discrepancies between chronological and perceived age by age group (percent) ........ 19 

Table 6. Work schedule by job type (percent)............................................................................. 22 

Table 7. Length of work week for track versus B&B jobs (percent)........................................... 23 

Table 8. Workday by job type...................................................................................................... 23 

Table 9. Start time variability by job type (percent).................................................................... 24 

Table 10. Longest time working without a break by job type (h:min) ........................................ 24 

Table 11. Typical, nominal, and actual work for 2-week period (h:min).................................... 25 

Table 12. Travel time to lodging/rally point by job type and type of day (h:min) ...................... 26 

Table 13. Percentiles for travel time to lodging/rally point by job type and type of day (h:min) 27 

Table 14. Travel time to lodging/rally point by job type and work week (h:min) ...................... 27 

Table 15. Alertness at work by job type (percent)....................................................................... 29 

Table 16. Drained after work by job type (percent)..................................................................... 29 

Table 17. Alertness throughout the day by job type .................................................................... 29 

Table 18. Alertness and callbacks................................................................................................ 30 

Table 19. Stress ratings by job type............................................................................................. 32 

Table 20. Nighttime sleep duration versus U.S. adult norms by type of day (h:min) ................. 34 

Table 21. Mean nighttime sleep duration–tests for significance ................................................. 34 

Table 22. Total sleep by type of day and job type (h:min).......................................................... 35 

Table 23. Sleep ratings by job type and type of day.................................................................... 36 

Table 24. Sleep ratings by location on workdays (production jobs)............................................ 37 

Table 25. Sleep ratings by location on workdays (non-production jobs) .................................... 37 

Table 26. Sleep ratings at home versus away from home by sleeping arrangement ................... 39 

Table 27. Sleep ratings and duration by sleep disorder status (all days) ..................................... 41 

Table 28. Alertness and sleep disorders (workdays only) ........................................................... 41 

Table 29. Keywords used for each topic area search................................................................... 44 

Table 30. Frequency of comments by topic area and source....................................................... 45 
vii

Acknowledgements 

This report presents the results of a research study designed to characterize the work schedules 

and sleep patterns of U.S. railroad maintenance of way employees and to examine the 

relationship between these schedules and level of alertness for the individuals working the 

schedules. Foster-Miller, Inc. conducted the work for the Federal Railroad Administration 

(FRA) under contract DTFR53-01-D-00029 with guidance from Dr. Thomas Raslear, Office of 

Research and Development, Human Factors Program. The authors worked closely with the 

Brotherhood of Maintenance of Way Employes Division (BMWED) of the International 

Brotherhood of Teamsters. Special thanks are due to Mr. Richard Inclima and Mr. James 

Superfisky, BMWED, who provided invaluable insight on the job of a maintenance of way 

employee and facilitated the distribution of the survey materials to their members. Their 

participation was a key element in the successful conduct of the study. The authors are 

especially appreciative of the efforts of Mr. Robert Brogan, FRA Office of Safety, who 

facilitated the Office of Management and Budget approval process for this study. 

The authors also wish to thank several individuals at Foster-Miller for their significant 

contributions to this survey. Ms. Susan McDonough designed and managed the database for 

tracking and compensating the survey participants. Ms. Sarah Acton answered questions from 

survey participants and helped to review the log books before the data was coded. Ms. Gayle 

Staffiere from Foster-Miller’s Publications Department designed the layout of the log books and 

oversaw printing and mailing of the survey documents. Ms. Jeanne Seaquist designed the 

database for the survey data, and Ms. Deidra Bradley entered data from the background survey 

and log books into the database. 

1

Executive Summary 

In a continuing effort to improve rail safety and to reduce the number of injuries and fatalities to 

railroad workers, the Federal Railroad Administration (FRA) and the railroad industry, through 

the North American Rail Alertness Partnership (NARAP), have focused on the issue of fatigue 

among train and engine crew personnel. Because railroading is a round-the-clock, 7-days-aweek operation, and because a wide array of workers are needed to operate and to maintain the 

Nation’s railroads, other crafts besides train and engine crews may also be subject to fatigue. 

The non-operating crafts, which include locomotive and car repair, right-of-way production and 

maintenance, signal system production and maintenance, and telecommunications, fall into this 

category. With all of the non-operating craft groups, staff shortages, seasonal work, expanding 

territories, and response to emergency situations can result in long work hours leading to fatigue. 

In 2001, FRA suggested, and NARAP concurred, on the need to study the fatigue issues of the 

non-operating crafts. An initial study focused on signalmen. This study investigated the fatigue 

issues of maintenance of way (MOW) workers. 

This study had two primary objectives: 

• To document and characterize the work/rest schedules and sleep patterns of MOW 

workers. 

• To examine the relationship between these schedules and level of alertness/fatigue for the 

individuals who work the schedules. 

MOW jobs fall into two categories, production (construction) and non-production (maintenance). 

Within each of these categories, some jobs work on track infrastructure, and a smaller portion 

work on bridges and buildings. The goal was to characterize U.S. MOW workers as a group, not 

to characterize MOW workers on a specific railroad. 

The research described in this report had three phases: preparation, field data collection, and 

data analysis. Since no existing data source would provide answers to the study’s research 

questions, a survey of MOW workers was the only means to obtain the necessary data. The 

preparation phase included securing approval from the Office of Management and Budget 

(OMB) for the survey. Representatives from the Brotherhood of Maintenance of Way Employes 

Division of the International Brotherhood of Teamsters (BMWED) worked closely with the 

researchers throughout the study. 

Survey Design 

The study used two survey instruments, a background survey and a daily log. Survey 

participants used the background survey to provide demographic information, descriptive data 

for the MOW worker’s job type and work schedule, and a self-assessment of overall health. The 

daily log provided a place for recording sleep and work periods on both regular workdays and 

planned days off. MOW workers recorded not only the starting and ending times for each sleep 

and work period, but also a subjective assessment of alertness at different times during the day. 

Capturing the work cycle of production MOW workers required collecting 14 days (d) of work 

and sleep data. 

Researchers drew a random sample of 845 MOW workers from the BMWED database of 

actively working U.S. MOW workers. Retirees, full-time union officials, and anyone currently 
2

holding a railroad management position were specifically excluded from the sampling frame. 

Determination of the sample size assumed a 95 percent confidence interval on the estimates for 

mean sleep time, an error tolerance of 15 percent and a 40 percent response rate. OMB approved 

this collection of information under OMB control number 2130-0561 on May 14, 2004. 

Mailing of the survey materials took place on June 29, 2004. One month (mo) later, every 

survey recipient received a reminder postcard encouraging him/her to participate and to call the 

researchers if he/she needed additional materials. In October, because the response rate was not 

at the desired level, anyone who had not either returned the completed survey materials or 

indicated a lack of interest in participating in the study received a second reminder postcard. 

Survey Response Rate 

The overall response rate for the survey was 31 percent. Table 1 provides a breakdown of the 

survey responses. 

Table 1. Breakdown of survey responses 

 Number Percent

Returned both background survey and daily log 262 31 

Returned only one survey instrument 30 4 

Materials undeliverable due to invalid address or deceased 4 0 

No response 549 65 

Total number of surveys mailed 845 

Of the 262 complete responses, 8 were not usable due to failure to follow the instructions. 

The non-response bias study based on age found no difference between respondents and nonrespondents. 

MOW Worker Demographics 

The survey respondents held primarily track non-production (maintenance) jobs (52 percent) and 

track production (construction) jobs (34 percent). The remainder worked bridge and building 

(B&B) jobs, either non-production or production. Average MOW worker experience was 

approximately 23 years (yr), with those working production and non-production having nearly 

the same level of experience. The majority of MOW workers are middle-aged with nearly half 

being 50 yr and older. As with years of experience, the average age for production and nonproduction MOW workers was nearly identical. All but two participants were male so 

segregation of the survey results by sex was not meaningful. 

Nearly all MOW workers (81 percent) were married with no children under the age of 2 yr. In 

contrast, 59 percent of the U.S. male population 18 and older is married. Since many railroaders 

report that their work schedule strains marital relationships, finding such a high proportion of 

MOW workers to be married was surprising. The lack of young children is consistent with the 

average age of this population. 

Nearly 80 percent of the MOW workers reported their health as good or excellent. The vast 

majority, 89 percent, had not taken a day off due to illness in the last 6 mo. The low number of 
3

workdays lost due to illness may be due in part to the fact that MOW labor agreements do not 

provide compensation for sick days. 

Approximately 7 percent of this population of railroad workers reported having a diagnosed 

sleep disorder, and one third of those have gone without treatment. Since the survey asked about 

a diagnosed sleep disorder and not sleep apnea specifically, this result is not readily comparable 

with the estimate of the prevalence of sleep apnea in the U.S. adult male working population. 

The true rate of sleep disorders may be higher, as some may have an undiagnosed sleep disorder. 

Job Characteristics 

The work schedules of production and non-production MOW workers differ in several respects. 

While labor agreements define both types of jobs to work 80 hours (h) in a 2-week period, a 

majority of non-production jobs (74 percent) have a 5-d work week, but less than a third of the 

production jobs have this schedule. Nearly half of all production jobs work a 4-d week and 

20 percent work 8-on 6-off. B&B MOW workers work either a 4-d or 5-d week. 

During the 2-week survey period, non-production MOW workers worked 87:01 (hours:minutes) 

and the production MOW workers 89:28. For both groups of MOW workers, this was about 

what they reported typically working but more than their nominal schedules dictate. The small 

difference between the two groups was likely due to the extra hours required of production gangs 

on major construction projects. 

Nearly a quarter of non-production MOW workers experienced start time variability at least once 

during the survey period, most likely as a result of an emergency call or unscheduled work. 

(Start time variability was defined as a change in start time of more than 1 h from the previous 

day.) In contrast, 84 percent of the production MOW workers experienced no start time 

variability. 

On any given day, the probability of an unscheduled work period was .045. Once called for an 

unscheduled work period, an MOW worker had less than a 1 percent chance of being called back 

a second time. Callbacks, defined as an unscheduled work period that began on a workday after 

the individual arrived home, affected next day morning alertness; however, the effect size was 

somewhat small. 

Many MOW jobs require the worker to travel, usually on his/her own time, to an out-of-town 

lodging or rally point. Overall, 24 percent of the MOW workers reported this type of travel 

during the 2-week study period. Half of these individuals made more than one trip. Production 

MOW workers traveled more than the non-production people, 41 percent of production versus 

12 percent of non-production. This type of travel compromises personal time that would 

otherwise be available to spend with family members and to attend to personal business. It may 

also compromise the worker’s sleep time. 

The study examined the relationship between characteristics of the MOW workers’ work 

schedules and alertness. The characteristics examined included time without a break, total hours 

worked, and commute time. While the correlations between alertness and these factors were 

statistically significant, the strengths of the relationships were weak. For example, the 

correlation coefficient between number of hours worked and alertness upon arriving home was 

-.214, but r

2

 = .046 so number of hours worked explained only 4.6 percent of the variance. 

Sources of work-related stress were different for the two groups of MOW workers. Only in the 

case of travel to work and lodging at the worksite did production MOW workers report a 
4

statistically different and higher level of stress than their non-production counterparts. The 

greatest sources of stress for non-production MOW workers were inadequate staff, management 

policies, and ambiguous work rules. 

Sleep Characteristics 

MOW workers get less nighttime sleep than U.S. adults on workdays, but on planned days off 

they are getting more sleep. In terms of median nighttime sleep, however, MOW workers get 

less sleep regardless of type of day. While 39 percent of U.S. adults get less than 7 h of sleep on 

workdays, 66 percent of MOW workers have this amount of sleep. Total daily sleep, which 

includes naps as well as nighttime sleep for production and non-production MOW workers, is at 

least 1 h longer on planned days off than on workdays. MOW workers tend not to nap. For 

those who do, nearly half of all naps on workdays begin between 2 and 6 p.m., which 

corresponds with the circadian afternoon nadir, making it a convenient time for naps. This time 

period also follows the end of the workday for many MOW workers. 

Both production and non-production MOW workers gave higher ratings to their sleep on planned 

days off than on regular workdays. Only one statistically significant difference existed in sleep 

quality by sleep location (home versus away). Production workers slept approximately 

40 minutes (min) longer when away from home. Nearly two-thirds reported that their employer 

provides sleeping accommodations when the work location requires sleeping away from home. 

No statistically significant differences existed in sleep ratings by type of sleeping arrangement. 

Textual Analysis of Log Book Comments 

A systematic qualitative analysis of the textual comments in the daily logs provided greater 

insight into the concerns of MOW workers and, in many cases, added further insight to the 

quantitative survey results. Over 1000 comments were in the log books. The most frequently 

mentioned topics were weather, fatigue/alertness, and travel. 

Findings and Recommendations 

The following lists the key findings with respect to the MOW worker’s nominal work periods, 

unscheduled work periods, and sleep patterns: 

• The overall nominal length of the MOW workday, including commuting and lunch 

breaks, allows adequate time for nighttime sleep. In addition to overtime work, other 

aspects of the job, such as emergency call and travel to a lodging/rally point, place 

demands on the MOW worker’s time that may compromise his/her ability to get adequate 

rest. 

• While the average hours worked during the study period do not indicate excessive 

overtime, one quarter of the production MOW workers worked at least 8 h of overtime 

per week, and one quarter of the non-production MOW workers worked at least 6.5 h of 

overtime. This level of overtime, if done on a regular basis, may prevent the employee 

from achieving full rest and recovery. 

• Callbacks (work periods that occurred on workdays after the employee arrived home) 

were related, to a limited degree, to lower alertness the following morning. 

• Both groups of MOW workers get less nighttime sleep during the work week than the 

norm for U.S. adults. Not only is weeknight sleep significantly different than the U.S. 
5

norms, but the percentage of MOW workers getting less than 7 h of sleep is also 

significantly greater than the norm for U.S. adults. A total of 15 percent are getting less 

than 6 h of nighttime, and 1.8 percent are getting less than 5 h. Research has shown that 

this level of sleep deprivation leads to performance degradation. Railroad industry and 

labor organizations’ fatigue education programs should emphasize the performance 

consequences of inadequate sleep. 

• Seven percent of the study population reported having diagnosed sleep disorders, but 

only two-thirds reported being treated for the sleep disorder. To encourage these 

individuals to seek treatment, railroads and unions should continue their education 

programs, pointing out the possible performance consequences of untreated sleep 

disorders. 

Based on the experience of this study, several methodological changes should be a part of any 

future studies of this nature. The following lists the recommended changes: 

• Avoid data collection over holiday period. Because mailing of the survey materials for 

this study occurred at the end of June, some participants recorded data over the Fourth of 

July weekend. As a result, reporting of a full 2-week cycle was not possible with the 

holiday. 

• If the study population includes workers who must travel long distances on their own time 

to reach a rally point or lodging site, the daily log should have a better way to record 

travel to rally point/lodging. This study included a place for recording such travel; 

however, participants experienced some confusion over the difference between travel to 

lodging/rally point and commute to worksite. 

• The background survey should inquire whether or not the participant has been diagnosed 

with sleep apnea, as well as a sleep disorder, so that the results can be compared with 

U.S. adult norms for sleep apnea from the Wisconsin Cohort Study. A question on sleep 

disorders is also necessary to be certain that poor sleep due to a sleep disorder does not 

confound the survey data. 

• The instructions should state that when an employee works on a planned day off, he/she 

should record this work period in the same section of the log that is used for regular 

workdays, rather than in the unscheduled work period section. Participants had some 

confusion over where to record unscheduled work on a planned day off. 

Further analysis and use of this survey data is possible. Fatigue modelers may want to refine 

their models using the data and predict how the typical MOW work schedule may be affecting 

on-the-job alertness and related performance. Separately, further statistical analysis of the data 

could identify additional explanatory factors for the reported alertness levels and sleep quality. 

7

1. Introduction 

In a continuing effort to improve railroad safety and to reduce the number of injuries and 

fatalities to railroad workers, FRA and the railroad industry, through NARAP, have focused on 

the issue of fatigue among train and engine crew personnel. Because railroading is a round-theclock, 7-days-a-week operation, and because a wide array of workers are needed to operate and 

to maintain the Nation’s railroads, other crafts besides train and engine crews can also be subject 

to fatigue. The non-operating crafts, which include locomotive and car repair, right-of-way 

construction and maintenance, signal system construction and maintenance, and 

telecommunications, fall into this category. With all of the non-operating craft groups, staff 

shortages, seasonal work, expanding territories, and response to emergency situations can result 

in long work hours leading to fatigue. In 2001, FRA suggested and NARAP concurred on the 

need to study the fatigue issues of the non-operating crafts. 

In 2001 FRA decided to focus initially on signalmen in exploring fatigue of non-operating craft 

workers. A separate report presents the results of the signalmen study (Gertler & Viale, 2006). 

The study described in this report, which is similar in scope and methodology to the signalmen 

study, concerns MOW workers. Obtaining insight into the schedule-related fatigue issues of any 

population of workers requires data on their work and sleep patterns. FRA undertook the study 

described in this report to collect the necessary data and to develop an understanding of the 

potential work schedule-related fatigue issues for MOW workers. 

1.1 Nature of the MOW Worker’s Job 

Over the past decade, the volume of shipments of goods by rail increased significantly while the 

railroad work force declined. In 1990, the volume of goods transported by railroads amounted to 

1.1 trillion ton-miles. By the year 2000, the volume of goods carried by railroads totaled 

1.5 trillion ton-miles. Meanwhile, the number of railroad MOW employees working for U.S. 

railroads decreased from 44,282 to 37,744 or 15 percent, during the 10-yr period 1990 to 2000. 

Increasing traffic combined with the shrinking work force intensified the demands on MOW 

employees who must maintain the track. At the same time, the decision of Presidential 

Emergency Board 219 in 1991 allowed railroads to utilize track construction crews systemwide. 

Railroads had previously assigned construction crews to a specific geographic region. The 

impact of this ruling is that MOW employees who work production (construction) jobs many 

times travel distances of up to 1000 miles (mi), on their own time, to reach the worksite. 

MOW employees work outside year round in all types of weather conditions and over varying 

terrain. The work is heavy and physically demanding and often involves personnel and heavy 

equipment working in close proximity. There is no question that this work exposes railroad 

MOW employees to an extremely hazardous environment because of the nature of the work and 

the operation of trains and other on-track equipment within the MOW work environment. The 

introduction of Roadway Worker Protection regulations in 1997 has helped to reduce the risk to 

this group of railroad workers of being struck by trains and other on-track equipment. 

Nevertheless, because of the hazardous work environment of the MOW worker, maintaining 

alertness on the job is critical. 
8

MOW workers build and maintain the tracks, bridges, buildings, and other structures on the 

railroad. MOW work has two fundamental job classifications, production and non-production. 

Production jobs involve either track or B&B construction while non-production jobs are 

responsible for inspection, maintenance, and repair of the same infrastructure. The nonproduction MOW worker is responsible for inspecting and certifying the condition of the rightof-way in a specific territory and initiating repairs or other remedial action when he/she finds 

defects. Non-production workers typically work either a 4-d or 5-d week, and they often support 

the work of production crews when on their territory. Non-production workers are also subject 

to call to handle emergency problems at night and on rest days. Railroads typically assign nonproduction workers to a specific geographic area, which may encompass several hundred miles 

end-to-end. 

In contrast to the non-production jobs, an MOW worker on a production gang will frequently 

work a compressed schedule of, for example, 8 workdays followed by 6 or 7 d off, and is rarely 

called for an emergency. The industry frequently refers to this type of schedule as compressed 

halves. The railroad may assign its production workers anywhere on the railroad’s system, and, 

as such, these workers must often travel long distances on their own time to reach the lodging 

site or rally point for the construction project. 

Most construction work occurs during months of good weather, especially in the colder climate 

areas of the country, while non-production inspection and maintenance is done year round. Cold 

weather and snow lead to increased track maintenance problems during the winter months. This 

increased winter workload can result in long workdays and emergency call at night and on rest 

days. 

Unlike the operating crafts (i.e., train and engine crews) and signalmen, no statutory limits exist 

on the number of hours that MOW workers may work. A few railroads have taken voluntary 

steps to reduce fatigue by limiting work hours, but this is the exception rather than the norm. 

Labor agreements with individual railroads address company provisions for meal allowances and 

sleeping accommodations. Some agreements provide for at least a shared motel room or shared 

housing in railroad camp cars, but some offer only a fixed per diem, which the MOW worker 

may use to offset a portion of the costs associated with his/her meals and overnight 

accommodations. If this is the arrangement, the MOW worker may chose to sleep in a personal 

vehicle or tent because the daily per diem does not adequately cover the actual expense of 

purchasing meals and/or lodging. In some instances, the railroad provides a camper allowance in 

lieu of other housing and meal provisions. The purpose of the camper allowance is to offset 

some of the production employee’s costs for maintaining a personal camper, which the employee 

uses when assigned to work away from home over extended periods. 

1.2 Objectives 

This study had two primary objectives: 

• To document and characterize the work/rest schedules and sleep patterns of railroad 

MOW workers. 

• To examine the relationship between these schedules and level of alertness/fatigue for the 

MOW workers who work the schedules. 
9

The goal was to characterize U.S. railroad MOW workers as a group, not to characterize MOW 

workers on a specific railroad. 

Specific research issues that the study sought to answer include the following: 

• What is the distribution of MOW employees among different types of jobs? 

• What is the average number of hours worked per day? per week? per work cycle1? 

• How does average hours worked vary by type of job? type of schedule? 

• What is the average number of hours of sleep on workdays? on non-workdays? at 

home? away from home? 

• Does the quality of sleep differ between home and away from home? 

• What is the relationship between quality of sleep when away from home and type of 

sleeping arrangement? 

• Does alertness upon arising deteriorate with each successive workday? 

• What is the relationship between time worked before a break in work period and end of 

day fatigue? 

• What is the average number of hours that each MOW employee spends traveling to and 

from work? to and from the rally or lodging point? Is travel time related to level of 

sleepiness? 

• How frequently are MOW employees called back to respond to emergencies? 

1.3 Overall Approach 

Since no existing data sources could provide answers to the above issues, a survey of MOW 

workers was the only means to obtain the necessary work schedule and sleep data. The research 

project consisted of three phases: preparation, field data collection, and data analysis (see Figure 

1). The preparation phase involved designing the survey methodology and procedures, 

conducting a pilot survey to refine the survey instruments and data collection procedure, securing 

approval from OMB, and preparing the final survey instruments. (Because this survey involved 

more than nine participants, Federal regulations required that OMB approve the overall study 

design.) Activities during this phase included discussions with BMWED to assure that the 

survey instruments were suitably worded and would collect the data necessary to address the 

research issues. A pilot survey, conducted in parallel with the OMB review process, assured that 

the survey would capture the data needed to meet the survey objectives. 

The second phase of the research consisted of distributing the survey materials and collecting the 

survey data. Analysis of the survey data was the final phase. A non-response bias study 

validated that no difference existed between the survey participants and the non-respondents. 

The data analysis methods for the survey data included descriptive statistics, analysis of variance 

(ANOVA), and correlation analysis. 

 

1

 A work cycle refers to several workdays followed by planned days off. 
10

Figure 1. Overall approach 

1.4 Scope 

This survey involved railroad MOW workers working in the United States. It was designed to 

characterize these workers as a group. The study did not attempt to characterize MOW workers 

employed by specific railroads. Making recommendations regarding fatigue countermeasures 

was beyond the scope of the study. 

1.5 Organization of the Report 

Section 2 describes the overall survey design and procedures. Section 3 provides analysis of the 

survey results, and Section 4 contains the findings and recommendations. Appendix A contains 

copies of the survey materials, and Appendix B describes adjustments that were made to the data 

as part of the analysis process. Appendix C provides detailed data summaries that support the 

technical analysis. A list of the abbreviations and acronyms used in the report follows the 

appendices. 
11

2. Survey Design 

One of the objectives of this study was to characterize the work schedules and sleep patterns of 

U.S. railroad MOW workers. Achieving this objective required a nationwide survey. The only 

practical means of reaching these individuals was through their union, the BMWED. This 

section describes the survey instruments, sampling plan, and procedures that the researchers 

developed to survey this population. This methodology is similar to that used in an earlier study 

of railroad signalmen. 

2.1 Survey Instruments 

The study used two survey instruments, a background survey and a daily log. (Copies of both 

instruments appear in Appendix A.) The background survey gatherered demographic 

information, descriptive data for the MOW worker’s job type and work schedule, and a selfassessment of overall health. The purpose for collecting this data was twofold. First, it provided 

data for characterizing the U.S. MOW worker population. Second, it provided identifying data 

that the researchers used in conjunction with the daily logs to characterize the work/sleep 

patterns of the two major categories of MOW jobs, production, or construction, and nonproduction, or maintenance. If the respondent’s job required that he/she be away from home 

overnight, he/she provided information on the type of sleeping accommodation provided by the 

employer. This instrument also asked participants to rate, using a Likert scale of 1 to 4, potential 

sources of stress at work. The background survey also included a list of life stress events. In the 

event that a participant’s daily log indicated frequent nighttime awakenings or excessive fatigue, 

survey researchers could use the individual’s response to this section of the background to assure 

that no non-work circumstances were confounding the survey data. Completion of the survey 

required less than 15 min. 

A daily log provided a place for recording sleep and work periods on regular workdays and 

planned days off. The log also included a place to record travel time to a lodging or rally point.2

 

MOW workers recorded not only the starting and ending times for each sleep and work period, 

but also a self-assessment of alertness at different times during the day. These subjective 

assessments used a five-point Likert scale. The daily log included space to record “Comments 

on today’s sleep experience” and “Comments on today’s work experience.” The instructions for 

the log encouraged participants to use this space to explain anything unusual about the day’s 

sleep or work. These comments proved useful in understanding an irregular work or sleep 

pattern. The work log portion of the daily log included space to record unscheduled work 

periods. The purpose of this section was to capture response to emergency calls beyond the 

normal workday. Completion of the daily log required less than a total of 10 min daily. 

2.2 Data Collection Period 

Examination of the relationship between work schedules and fatigue requires data that 

encompasses a full work cycle. Fatigue is cumulative, and its effects on the individual are not 

 

2

 Production jobs frequently require the employee to assemble at a location referred to as the rally point. From this 

location, the workers travel to the worksite in the employer’s vehicle. 
12

readily identified from 1 or 2 d of data. In addition, adequate data must be available to compare 

sleep periods from both work and rest days. The length of the typical MOW worker’s work 

cycle was also a consideration in determining the length of the data collection period. Nonproduction workers tend to work a regular work week of 4 or 5 d followed by 2 or 3 d off. In 

contrast, those working production jobs tend to have a 14-d or 15-d compressed work cycle. To 

capture the work cycle of the production jobs, it was necessary to collect 14 d of work and sleep 

data. Since it was not possible a priori to identify those individuals who work a production job, 

all participants provided 2 weeks of data. 

2.3 Sampling Plan 

BMWED maintains a database with the names, mailing addresses, and dates of birth for all of its 

members. Because MOW workers frequently change from working one type of job to another, 

this information is not in the BMWED database. Only actively working BMWED members 

living in the United States could be in the sampling frame. Retirees, full-time union officials, 

and anyone currently holding a railroad management position were specifically excluded. The 

effective sampling frame was 30,800 after the exclusions. The researchers drew a random 

sample from these individuals. 

One of the most important issues in conducting this study was determining how large a sample 

was necessary for the estimates obtained in the sample survey to be reliable enough to meet the 

study’s objectives. In general, the larger the sample the greater the reliability of the resulting 

estimates, but this must be traded off against the expense of a larger sample. The first step in this 

process is to specify the level of reliability needed for the resulting estimates. 

Since the study design includes examining characteristics of two subgroups of MOW workers 

(production versus non-production), the study design must assure that the subgroups have 

adequate numbers within the overall sample to support reliable estimates of their characteristics. 

One statistic of interest is the mean number of hours of sleep per day for each subgroup. Using 

the BMWED estimate of the workforce breakdown, approximately 16,940 (55 percent) nonproduction and 13,860 (45 percent) production MOW workers are in the sampling frame. 

According to Levy and Lemeshow (1999), the appropriate sample size, n, for estimating the 

mean daily sleep time can be computed from the following: 

2 2 2

2 2

( 1)

( )

+ − ε

≥

z V N

z NV

n

x

x

where z = reliability coefficient (1.96 for 95 percent confidence level) 

N = population size (non-production = 16,940, production = 13,860) 

Vx = unknown population variance (1) 

ε = error tolerance (.15) 

Webb (1992) estimates the standard deviation for daily sleep for the general population is 1 h 

(Webb, p. 72). Applying this estimate of standard deviation (and hence Vx, variance) to the two 

MOW worker subpopulations and using an error tolerance of ± 7.5 percent (ε = .15), 169 
13

production workers must be in the sample, as well as 169 non-production MOW workers, for a 

total of 338 participants. These methods for estimating sample size also apply to other mean 

values, such as work and commute time that the study seeks to estimate. 

Since not every BMWED member who is selected to participate in this study would choose to do 

so, over sampling was necessary. The extent of over sampling was a function of the anticipated 

response rate. A recent member survey by another railroad labor organization had a 40 percent 

response rate (personal communication with labor representative). BMWED representatives felt 

their membership was similar and would likely have a similar response rate. The study design 

reflected the conservative assumption that the planned study could likely achieve at least this 

response rate. Based on experience with other FRA research efforts that sought participation 

from railroad workers, FRA researchers have found that many are suspicious of any efforts to 

collect data, even if the effort has the endorsement of their labor union and the researchers assure 

the information’s confidentiality. Moreover, this survey differed significantly from most mail 

surveys in that it required responses every day for a 14-d period. For these reasons, a goal of 

40 percent response rate appeared reasonable and realistic. If 40 percent of the selected 

individuals in fact participate in the study, then the random sample must be 845 (338/.4) to yield 

338 participants. 

2.4 Procedure 

In accordance with government regulation, FRA sought approval for the proposed survey from 

OMB. OMB approved this collection of information under OMB control number 2130-0561 on 

May 14, 2004. 

Concurrent with submittal of the OMB application, the researchers conducted a 1-week pilot 

survey with six participants. The purpose of the pilot study was to refine the data collection 

procedures and instruments. (Eight individuals volunteered to participate, but one returned only 

the log, and the eighth individual never returned the survey materials.) In addition to completing 

the Railroad Maintenance of Way Worker Background Survey and Maintenance of Way 

Worker’s Daily Log, pilot participants also completed a brief Post-Survey Form to provide 

feedback on the survey instruments and procedures. Similar to the full survey, pilot participants 

received a $75 gift certificate to a national retail establishment. Based on the experience with the 

pilot survey, the researchers added several additional sources of stress to the Background Survey,

and they modified the instructions for reporting travel to/from the rally point and home. 

Following the pilot survey, during the spring of 2004, BMWED publicized the survey through an 

article in its publication, BMWED Journal, and on its Web site. 

The researchers drew a simple random sample of 845 MOW workers, without replacement, from 

the sampling frame derived from the BMWED membership list. The package mailed to each 

participant on June 29, 2004, consisted of the following items: 

• Railroad Maintenance of Way Worker Background Survey in booklet form. Each page 

was 5.5 x 8.5 inches (in), printed on white paper with no questions on the cover page. 

• Railroad Maintenance of Way Worker’s Daily Log in spiral notebook form. Each page 

was 5.0 x 3.25 in. The log included 14 sections, one for each day of the data collection 

period. One of the introductory pages contained brief instructions on completing the log. 
14

• Cover letter signed by the President, BMWED. This letter explained the purpose of the 

study and encouraged BMWED members to participate. 

• Instructions explaining the survey procedures and how to complete the daily log. 

• Return envelope, postage paid. 

• $5 bill.

Copies of the cover letter and instructions appear in Appendix A along with the survey 

instruments. 

All materials were printed on high quality paper, and each letter was individually addressed to 

the recipient. The mailing envelope had the BMWED return address, rather than Foster-Miller, 

because it would be familiar to recipients. The purpose of the $5 was to encourage participation. 

Those who returned both the background survey and daily log also received a $75 gift certificate 

to a national retail establishment. 

The instructions emphasized that (a) a total of 14 consecutive d of data should be provided, 

(b) data collection should begin on the first day of the next work cycle, and (c) data should not be 

reported during vacation periods. Both the instructions and the log included contact information 

for two Foster-Miller researchers who were available to answer questions regarding the survey 

instruments and procedures. Because capturing the time for production workers to travel to the 

assigned lodging or rally point was of particular interest and proved problematic in the pilot 

survey, a portion of the instructions addressed how to record this information. 

One mo after the materials were mailed, every survey recipient received a reminder postcard 

encouraging him/her to participate and to call Foster-Miller if he/she needed additional materials. 

In October, because the response rate was not at the desired level, anyone who had either not 

returned the completed survey materials or indicated a lack of interest in participating in the 

study received a second reminder postcard. 
15

3. Analysis of Survey Data 

This chapter presents the survey findings based on data provided in respondent background 

surveys and daily logs. The quantitative results are organized into five subtopic headings: 

• Survey response rate 

• Non-response bias study 

• MOW worker demographic characteristics 

• Job characteristics 

• Sleep patterns 

A separate subsection contains the results of a textual analysis of the log books. This study used 

a confidence interval of 95 percent. The researchers used SPSS 13.0 to analyze the data. 

3.1 Survey Response Rate 

The survey materials were mailed to 845 MOW employees. A total of 262 people returned both 

the background survey and the daily log. Thirty individuals returned only one of the survey 

items, four mailings failed to reach the addressees due to bad addresses, one individual on the 

mailing list was deceased, and three individuals could not participate due to injuries. If a log 

contained at least one work cycle of data for both workdays and planned days off, then the 

researchers included the data in the analysis. Eight responses were disqualified because either 

they did not provide 14 consecutive days of data or their data was erroneous. The overall 

response rate was 31.0 percent. The final analysis used data from 254 of those individuals who 

returned both data collection instruments. 

3.2 Non-Response Bias Study 

OMB requires that a non-response bias study be conducted if the survey response rate is below 

75 percent. The purpose of the non-response bias study is to assure that no difference exists in 

the characteristics of the survey respondents versus the non-respondents. 

Information about non-respondents was limited to information available from the BMWED 

membership database. In addition to each member’s address, this database includes birth date. 

Birth date (or age) is an appropriate variable to use for determining non-response bias. For a 

number of reasons, age is an important characteristic for assessing potential bias in this study. 

First, human sleep patterns change with age (Van Cauter, Leproult, & Plat, 2000). In addition, 

age is highly correlated with years of work experience and seniority. Seniority allows an MOW 

employee more opportunity to select work schedules that meet his/her personal needs. 

All 262 individuals who returned both the background survey and the daily log were 

respondents, and the remaining 583 were non-respondents. Analysis of mean age for each of the 

groups found no significant difference between the respondents and the non-respondents, t(843) 

= -.041, p = .968. 
16

3.3 MOW Worker Demographic Characteristics 

This section provides demographics, as well as basic job-, family-, and health-related 

information based on responses in the background survey. Where appropriate, there are 

comparisons of the study results with national norms. 

Characterization of MOW employees considered a number of factors. These factors are job type, 

work experience, sex and age, marital and family status, overall health, incidence of sleep 

disorders, and consumption of caffeinated beverages. Each of these elements is discussed below, 

followed by a brief summary of this information. 

3.3.1 Job Type 

Respondents reported the type of MOW job they worked. The background survey offered the 

following five choices: 

• Construction/Production Crew 

• Track Maintenance (non-production) 

• B&B (production) 

• B&B (non-production) 

• Other 

Fifty-nine individuals selected Other as their job type and provided a description of their jobs. 

Based on information provided by the participant, as well as assistance from BMWED 

representatives, re-categorization of all but two of these individuals into one of the other four job 

type categories was possible.3

Figure 2 displays the distribution of MOW job types from the survey. Approximately half of 

MOW employees (52 percent) worked Track Maintenance (non-production) jobs, while about a 

third (34 percent) worked Construction/Production jobs. The remaining 14 percent were split 

among B&B (non-production)–8 percent, B&B (production)–5 percent, and Other–1 percent. 

Because those working construction/production jobs tend to have different work schedules and 

are rarely subject to emergency call, all further analyses by job type compares 

construction/production jobs with all non-construction/non-production jobs. The nonproduction group includes track maintenance, B&B (non-production), and other job types, while 

the production group consists of construction/production jobs, as well as B&B (production) jobs. 

 

3

 One reported an Electrical Technician Lineman position, and the other was a Safety Assistant Trainer and 

Coordinator. 
17

Track 

Maintenance 

(nonproduction)

52%

B&B 

(production)

5%

B&B (nonproduction)

8%

Other

1% Construction/ 

Production

34%

Figure 2. Distribution of respondents by type of MOW job 

3.3.2 Experience 

The average MOW employee had 22.8 yr experience. No statistically significant difference 

existed in the level of experience between production and non-production jobs, t(251) = .220, 

p = .826. The median level of experience was 27.1 yr. The higher median value indicates that 

individuals with years of experience in excess of the mean dominate the group. 

Both production and non-production jobs had nearly all of their experience with their current 

employer. Table 2 and Table 3 provide further details on experience. 

Table 2. Experience as an MOW employee (yr) 

 Mean Median 

Standard 

Deviation 

All MOW Jobs 22.8 27.1 10.7

Production 23.0 27.6 10.6 

Non-Production 22.7 26.1 10.7 

Table 3. Experience with current employer (yr) 

 Mean Median 

Standard 

Deviation 

All MOW Jobs 19.7 24.9 11.2 

Production 19.8 25.1 11.3 

Non-Production 19.7 23.8 11.2 
18

3.3.3 Sex and Age 

Railroad MOW employees are a predominantly male population. Of the 254 total usable 

responses, 252 (99.2 percent) were from male participants and only 2 (0.8 percent) were from 

females. Because of the limited number of females, segregation of results by sex was not 

meaningful. 

The overall age for this group was 47.3. Just as experience levels between production and nonproduction job types were similar, age comparisons between production and non-production job 

types were similar as well–production (46.9) and non-production (47.5). Table 4 contains the 

age statistics. 

Table 4. MOW worker age (yr) 

 Mean Median 

Standard 

Deviation 

All MOW Jobs 47.3 49.0 8.9 

Production 46.9 49.0 9.5 

Non-Production 47.5 50.0 8.5 

Figure 3 displays the age distribution for MOW workers, based on the survey results. As is 

typical for other railroad crafts, this is an aging work force. Half of all MOW workers are 50 yr 

and older, and 83 percent are 40 yr and older. 

6.3

11

33.1

47.2

2.4

0

5

10

15

20

25

30

35

40

45

50

20-29 30-39 40-49 50-59 60+

Age Group

Percent

Figure 3. Distribution of MOW workers by age group 
19

Research has found that a higher perceived age, relative to chronological age, can be an indicator 

of chronic stress and poor psychological well-being (Barnes-Farrell & Petrowski, 1989, 1991). 

Overall MOW workers reported a lower perceived age (44.8 yr) in comparison with their 

average chronological age (47.3 yr). As shown in Table 5, the MOW population tends to feel 

younger as they age. This is the same pattern that Barnes-Farrell and Petrowski found with 

permanent day shift workers in a manufacturing plant. Barnes-Farrell and Petrowski point out 

that younger people tend to report feeling older to reflect perceived maturity. 

Table 5. Discrepancies between chronological and perceived age by age group (percent) 

MOW Worker Age (yr) Age 

Perception 18-29 30-39 40-49 50+ 

Younger 28.6 30.8 50.6 55.4 

Same age 50.0 38.5 33.8 28.9 

Older 21.4 30.8 15.6 15.7 

The researchers also investigated the difference between actual age and perceived age by job 

type. Both groups reported feeling approximately 3 yr younger than their chronological age. 

3.3.4 Marital and Family Status 

Most recent statistical data from the U.S. Census indicates that 56.6 percent of the U.S. 

population age 18 and older and 58.9 percent of the U.S. male population 18 and older are 

married (U.S. Census Bureau, 2003). At the time of the study, 80.6 percent of participants were 

married, 10.3 were single, 7.5 divorced, 0.8 widowed, and 0.8 fell into the other category (these 

people were likely separated or living together). Since many railroaders report that their work 

schedule strains marital relationships, finding such a high proportion of MOW workers who are 

married was surprising. This data, however, does not indicate whether or not the married 

individuals were in an initial marriage or one subsequent to a divorce. 

The survey asked participants whether or not their family included young children, a factor that 

can lead to disrupted sleep. While a large percentage of MOW workers are married, very few 

have children under the age of two (3.5 percent). No participants reported more than one child 

under the age of 2 yr. This finding is not surprising given the average age of an MOW worker. 

3.3.5 Health 

Participants rated their health as excellent, good, fair, or poor. Nearly 80 percent of MOW 

workers rated themselves in good (63.6 percent) or excellent (15.8 percent) health (see Figure 4). 

These ratings are reflected in the relatively small number of workdays missed due to sickness in 

the last 6 mo. Almost 90 percent of MOW workers had not taken a day off due to illness in the 

last 6 mo. Only 6.7 percent took 1 d off, 2.8 percent took 2 d, and 2 percent took 3 d or more in 

the previous 6 mo (see Figure 5). The low number of workdays lost due to illness may be due in 

part to the fact that MOW workers must use vacation or personal days for these absences. 

(MOW labor agreements do not provide for sick days.) 
20

Perceived health was not linearly correlated with self-assessment of how often the individual 

reported feeling well rested and alert over the course of his/her work period. Although perceived 

health did have a statistically significant positive linear correlation with how often the individual 

reported feeling drained after work, r = .172, r2 = .026, p = .005, the strength of this correlation is 

very weak, with perceived health explaining only 2.6 percent of the variability in ratings of 

feeling drained after work. 

0

20.6

63.6

15.8

0

10

20

30

40

50

60

70

Poor Fair Good Excellent

Rating

Percent

Figure 4. Self-assessment of overall health 

3.3.6 Incidence of Sleep Disorders 

The Wisconsin Sleep Cohort Study, a longitudinal study of cardiopulmonary sleep disorders 

among middle-aged working adults, estimated that 2 percent of women and 4 percent of men 

have sleep apnea (Young, et al., 1993). (The definition of sleep apnea for this study was an 

apnea-hypopnea score of 5 or higher and daytime hypersomnolence.) The National Sleep 

Foundation (NSF) and the National Institutes of Health report the numbers from the Wisconsin 

study as an estimate of the prevalence of sleep apnea among U.S. adults. Some sleep researchers 

hypothesize that the prevalence of sleep apnea may in fact be higher because many remain to be 

diagnosed. According to the Wisconsin study, 9 percent of women and 24 percent of men have 

undiagnosed sleep-disordered breathing, a condition that in some people results in excessive 

daytime sleepiness. 
21

88.6

6.7

2.8 2

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3+

Days

Percent

Figure 5. Workdays lost due to illness in the last 6 mo 

Of the 254 participants in the MOW study, 17, or 6.7 percent, reported having a diagnosed sleep 

disorder. Eleven of those people (65 percent) reported being treated for the sleep disorder, with 

six reporting no treatment (35 percent). The six individuals with a diagnosed but untreated sleep 

disorder account for 2.4 percent of the total group of MOW employees. The background survey 

that solicited this information inquired about diagnosed sleep disorders, not sleep apnea 

specifically. It is possible that some of the people reporting a diagnosed sleep disorder have 

sleep-disordered breathing and not sleep apnea. (Sleep-disordered breathing does not necessarily 

lead to excessive daytime sleepiness and, as such, is a less problematic sleep disorder than sleep 

apnea.) For this reason it is not possible to conclude with certainty that railroad MOW workers 

have a higher rate of sleep apnea than the U.S. adult male population. The fact that MOW 

workers do report a higher rate of sleep apnea and/or sleep disorders may be due to increased 

awareness of the condition among this group of railroad employees. In recent years the media 

have publicized the symptoms of sleep disorders and their associated risks. Some railroads have 

also conducted educational campaigns on the subject and encouraged employees with symptoms 

of a sleep disorder to seek evaluation and treatment. 

3.3.7 Consumption of Caffeinated Beverages 

NSF reports that 250 mg of caffeine a day, the equivalent of a soda and a couple of coffees, 

generally poses no harm. Almost all participants reported consuming caffeinated beverages on a 

daily basis (93.3 percent), and those who did averaged 3.2 beverages a day. Based on this level 

of caffeine consumption, MOW workers are within normal healthy limits, and their sleep, in 

general, is not likely disrupted due to caffeine unless caffeine consumption occurs close to 

bedtime (NSF, 2002). 
22

3.3.8 Summary of MOW Worker Demographic Characteristics 

At the time of the study, half of the MOW workers held track maintenance–non-production jobs 

and one-third worked construction/production jobs. The remainder of the group was split among 

B&B–non-production (8 percent), B&B–production (5 percent), and other (1 percent). Average 

experience for production and non-production workers was 22.8 yr. All but two participants 

were male. The majority of MOW workers are middle-aged but reported feeling 2.5 yr younger 

than their chronological age. Approximately 80 percent of participants were married, and few 

had young children. Eighty percent of participants reported being in good or excellent health, 

which is supported by 89 percent of them reporting no workdays lost due to illness in the last 

6 mo. Seven percent of respondents reported having a diagnosed sleep disorder, with one-third 

of those not undergoing treatment. The true number of sleep disorders may be higher, as some 

may have an undiagnosed sleep disorder. Participants averaged 3.2 caffeinated beverages daily, 

a level which poses no harm or health risks. 

3.4 Job Characteristics 

This section explores several aspects of the MOW worker’s job, including work schedule, 

number of hours worked, unscheduled work periods, travel to the lodging/rally point, commute 

time, and sources of stress. A separate subsection addresses the relationship between alertness 

and work schedule. Selected participant comments illustrate MOW workers’ concerns regarding 

their jobs. 

3.4.1 Work Schedule 

Weekly work schedules fell into three basic categories: 4-d week, 5-d week, and 8-on 6-off 

(8 straight days of work followed by 6 straight days off). Almost half of those holding 

production jobs worked a 4-d week, one-third worked a 5-d week, and 20 percent worked 8-on 

6-off. Nearly 75 percent of the non-production people worked a 5-d week, nearly a quarter 

worked a 4-d week, and a small number worked 8-on 6-off (see Table 6). 

Table 6. Work schedule by job type (percent) 

Job Type 

Work Schedule Production Non-Production 

4-d week 46.5 23.4 

5-d week 32.5 74.0 

8-on 6-off 20.0 2.6 

Other 1.0 0.0 

Comparison of the work schedules for B&B jobs with those of the track jobs revealed some 

differences between the two groups (see Table 7). B&B people do not work compressed work 

schedules (8-on 6-off), and nearly all of the production B&B jobs require a 4-d week while only 

39 percent of track jobs work a 4-d week. With regard to non-production, more than half of 

B&B jobs work a 4-d week in contrast with 18 percent of track jobs. 
23

Table 7. Length of work week for track versus B&B jobs (percent) 

Track B&B 

Work Schedule 

Production 

(n = 86) 

Non-Production 

(n = 134) 

Production 

(n = 14) 

Non-Production 

(n = 20) 

4-d week 39.0 17.9 92.9 60.0 

5-d week 36.6 79.1 7.1 40.0 

8-on 6-off 23.3 3.0 0 0 

Other 1.2 0 0 0 

Note: “n” refers to the number of survey respondents holding this type of job. 

MOW workers provided information about their nominal workday (as defined by their labor 

agreement), in terms of start and end times of the workday, in the background survey. They 

reported their actual start and end times in the daily logs. The computation for actual work 

includes only those individuals who reported a full 2 weeks of data, although these individuals 

may not have worked a full 2 weeks. That is, their logs contained complete records for each 

workday. 

Table 8 presents both the mean and median values for nominal and actual workday start and end 

times by job type. Median values provide the most meaningful comparison for this information. 

The median start time for both production and non-production jobs is 7 a.m. Production jobs 

tend to work a 10-h day so the end time for these jobs is later than for the non-production jobs. 

All MOW workers have a 30-min lunch break. Nearly all MOW people work during the day. 

Of the 254 study participants, three non-production MOW workers worked third shift, and two 

production workers worked second shift. 

Appendix C contains the workday information for track versus B&B jobs. Two primary 

differences exist between the two groups. The track production jobs have a median start time of 

6:45 a.m., while the B&B production jobs start at 7 a.m. In addition, because more of the B&B 

non-production jobs work a 4-d week, their workday ends later than their track counterparts. 

Due to the small number of B&B people in the survey and the similarity of their work schedules 

to those of the larger track group, all subsequent analysis does not differentiate between B&B 

and track MOW workers. 

Table 8. Workday by job type 

Production Non-Production 

Mean Median Mean Median 

Start time (nominal) 6:50 a.m. 7 a.m. 7:14 a.m. 7 a.m. 

Start time (actual) 6:55 a.m. 6:35 a.m. 7:11 a.m. 6:50 a.m. 

End time (nominal) 4:42 p.m. 5 p.m. 3:48 p.m. 3:30 p.m. 

End time (actual) 4:38 p.m. 5 p.m. 4:10 p.m. 4 p.m. 

Length of meal break 

(nominal) 27 min 30 min 28 min 30 min 
24

Start time variability can lead to fatigue if it disrupts the worker’s normal sleep pattern. 

Backward rotation of the start time (i.e., when one starts work earlier than the prior day) can be 

especially problematic. Investigation of start time variation provided a means to estimate work 

schedule variability. This study defined a variation in start time as a change in start time of more 

than 1 h from the previous day. During the 2-week timeframe of the study, 16 percent of the 

production MOW workers and 22 percent of the non-production MOW workers experienced 

start time variation at least once (see Table 9). This difference between job types is not 

statistically significant, X2 (4, n = 254) = 5.21, p = 0.267.

Table 9. Start time variability by job type (percent) 

Number of Start Time Job Type 

Variations (in 2-Week 

Period) Production Non-Production 

0 84.0 77.9 

1 10.0 15.6 

2 3.0 5.8 

3 2.0 0.6 

4+ 1.0 0 

In the daily log, production and non-production workers reported that the longest amount of time 

that they worked without a break is approximately 4 h (see Table 10). This result is reasonably 

consistent with the contractual provision for a meal break after 4 h on the job. 

Table 10. Longest time working without a break by job type (h:min) 

Job Type Mean Median 

Production 4:17 4:00 

Non-Production 3:56 3:40 

3.4.2 Number of Hours Worked 

The study collected data on a typical work week, nominal work week, and actual hours worked. 

On average, production workers reported (in the background survey) a typical work week to be 

44:39, and non-production workers reported 43:58. For a 2-week period, this is equivalent to 

89:18 and 87:56, respectively (see Table 11). The employee’s job characteristics, as reported in 

the background survey, determined the employee’s nominal work. The researchers computed 

actual work for 2 weeks using data from the daily logs. Based on the actual work data, half of 

each group worked about 4 h or more of overtime during the 2-week period. One quarter of the 

production group worked 95:58 or more (see 75th percentile column in Table 11.) This is 

equivalent to one additional 8-h day per week. Similarly, a quarter of the non-production group 

worked 93:12 or more during the same period. These values are nearly the same as the 75th

percentile values for typical work and indicate that the data collection period was probably a 
25

truly typical work week, in terms of overtime hours, for a quarter of non-production MOW 

workers. 

For production jobs, both the median nominal work and actual work were less than typical for 

the survey period. For non-production jobs, the median actual work exceeded both the typical 

and nominal work. Actual work exceeds nominal work due to any overtime extension of the 

regular workday or, in some circumstances, work on a planned day off or response to an 

emergency call. 

The production group worked on average 21⁄2 h more in the 2-week period than their nonproduction counterparts. This data collection occurred during the summer months when most 

production work occurs so it is not surprising that this group worked a slightly longer work 

week. 

Table 11. Typical, nominal, and actual work for 2-week period (h:min) 

Production Non-Production 

Mean Median 

Std. 

Dev. 25th % 75th % Mean Median 

Std. 

Dev. 25th % 75th % 

Typical 

work 89:18 88:00 10:36 80:00 95:30 87:56 80:00 11:22 80:00 94:00 

Nominal 

work 82:58 80:00 6:46 80:00 85:20 80:24 80:00 4:51 80:00 80:00 

Actual 

work 89:28 84:30 18:50 76:35 95:58 87:01 83:39 16:35 76:10 93:12 

3.4.3 Unscheduled Work Periods 

This study defined unscheduled work periods as any work period that was not in the employee’s 

nominal work schedule and that occurred after the employee began the trip home at the end of 

the workday or on a planned day off. Overtime that was an extension of the nominal work 

schedule was not an unscheduled work period. Callbacks, a subset of unscheduled work periods, 

were unscheduled work periods that occurred on a regular workday. 

One-third of participants had an unscheduled work period at least once in the 2-week period. An 

MOW worker was 10 times more likely to be called for an unscheduled work period on a 

planned day off than on a regular workday (.10 and .01, respectively). Overall, the probability of 

an MOW worker being called for an unscheduled work period was .045, production employees 

.03, and non-production .05. Based on the study period, MOW workers averaged .62 

unscheduled work periods per worker per 2-week period. If an MOW employee worked an 

unscheduled work period, less than a 1 percent chance existed of being called back a second time 

on that day. 

The time between the end of shift (on a workday) and the time called back to work averaged 

3:24. At this point, the MOW worker was home, on average, 2:52. Callbacks lasted an average 

of 4:24. Unscheduled work periods on planned days off lasted 9:07, an indication that these 

work periods were probably a planned additional workday rather than response to an emergency. 
26

If data collection had taken place during the winter months, more callbacks to handle weatherrelated problems would likely have occurred.

3.4.4 Travel to Lodging/Rally Point 

Many MOW jobs, both production and non-production, require the worker to travel, usually on 

his/her own time, to a meeting point. This meeting point may be the lodging site for out-of-town 

work or merely a location from which the employees are transported to the worksite (referred to 

as a rally point). The requirement for this type of travel is more common with production jobs. 

Regardless of the type of job, however, the travel is usually done on a planned day off because it 

requires substantial time that cannot be accommodated on a workday. 

Overall, 23.6 percent of the MOW workers reported travel to a lodging/rally point at some point 

during the 2-week study. Half of these individuals made more than one trip. Production MOW 

workers traveled more than the non-production MOW jobs, with 41 percent of the production 

MOW workers making at least 1 trip to a lodging/rally point over the 2 weeks of the study, and 

only 12 percent of the non-production MOW jobs having to travel to a lodging/rally point. As 

shown in Table 12, approximately one-third of the production workers traveled on a planned day 

off, and 8 percent made the trip on a regular workday. Non-production workers traveled less 

frequently. Seven percent made trips on planned days off and on workdays. Travel that occurs 

on a workday may be either travel from home at the beginning of the work cycle or travel 

midweek to move to a new worksite. Only two instances of relocation travel midweek occurred. 

Because this is paid travel while on the job, further analyses involving travel to a lodging/rally 

point excluded these two cases. 

Travel on planned days off compromises personal time that would otherwise be available to 

spend with family members and to attend to personal business. It may also compromise the 

worker’s sleep time. The average trip for production workers who traveled on planned days off 

was 5:36; non-production averaged 6:36. In contrast, travel to the lodging/rally point that 

occurred on workdays averaged 2:25 for production jobs and 2:09 for non-production. Table 12 

summarizes the travel time to the lodging/rally point by job type and type of day. Table 13 

displays the percentiles for travel time to a lodging/rally point by job type and type of day. Both 

groups had significantly longer travel on planned days off. 

Table 12. Travel time to lodging/rally point by job type and type of day (h:min) 

Production Non-Production All 

Type of Day 

Percent 

of 

Workers Mean Median 

Percent 

of 

Workers Mean Median 

Percent of 

Workers Mean Median 

Regular 

Workday 

8.1% 2:25 2:13 6.5% 2:09 2:00 7.1% 2:16 2:05 

Planned Day 

Off 

35.7% 5:36 4:45 6.7% 6:36 7:15 18.1% 5:48 5:00 
27

Table 13. Percentiles for travel time to lodging/rally point by job type and type of day 

(h:min) 

 Production Non-Production All 

Type of Day 

25th

Percentile 

75th

Percentile 

25th

Percentile 

75th

Percentile 

25th

Percentile 

75th

Percentile 

Regular Workday 1:56 3:01 1:20 2:33 1:30 2:38 

Planned Day Off 3:45 6:30 3:30 8:30 3:41 7:34 

Overall for both groups of MOW workers who traveled, the median travel to the lodging/rally 

point was 2:05 on regular workdays and 5:00 on planned days off. A quarter of the group 

traveling on a planned day off traveled 7:34 or longer. A total of 24 trips occurred on a regular 

workday, and 70 happened on a planned day off. 

Table 14 presents this same travel data by work week and job type. This data indicates that those 

working 8-on 6-off have significantly longer trips to the lodging/rally point. The reason for the 

8-on 6-off schedule is usually because the work location is far from the workers’ homes, and 

staying onsite for the extended period avoids an extra round trip to the site. This situation 

supports this finding. 

Table 14. Travel time to lodging/rally point by job type and work week (h:min) 

Production Non-Production 

Work Week Mean Median Mean Median 

4-d week 4:50 4:10 4:05 3:30 

5-d week 4:57 4:45 3:59 2:40 

8-on 6-off 6:21 7:15 8:00* 10:30* 

All schedules 5:06 4:20 4:27 3:00 

* Only three trips existed for this category. 

The survey materials did not collect data for the return trip home from the lodging/rally point 

separately. Study participants reported it in the commute home on the last day of the work week. 

It is reasonable to assume that, on average, the return trip is of the same duration. 

3.4.5 Commute Time 

For the purposes of this study, commute time refers to local travel from home to the daily 

reporting point. This type of travel is most common with non-production jobs. If the individual 

slept away from home, in lodging or otherwise, commute time reflects the trip from the lodging 

to the worksite. This type of travel applies primarily to production workers. It may include 

travel from the lodging to a rally point, as well as travel from the rally point to the day’s 

worksite. Because the survey included travel back home from a lodging or rally point in the 

commute home reported at the end of the work week, commute home is somewhat of an 

overestimate for both groups. Figure 6 depicts the average workday, including commute and 

lunch break, for production and non-production workers. Figure 7 shows the average workday 

by job schedule. 
28

38

37

569

622

53

66

0 100 200 300 400 500 600 700 800 900

Non-Production

Production

Time (min)

Commute to Work

Work

Commute Home

Figure 6. Commute time and workday by job type 

29

39

38

686

556

638

69

54

63

0 100 200 300 400 500 600 700 800 900

8 on 6 off

5-day week

4-day week

Time (min)

Commute to Work 

Work

Commute Home

Figure 7. Commute time and workday by job schedule 

3.4.6 Work Schedules and Alertness 

Through questions on the background survey, MOW workers rated their overall alertness at work 

and after work. Approximately 60 percent of each group reported that they always or frequently 

feel alert at work (see Table 15). With regard to the end of the workday, 65 percent of the 

production workers and 56 percent of the non-production group reported that they occasionally 

or never felt drained after work (see Table 16). No statistically significant difference existed 

between the two groups on either of these self-assessments, X2 (3, n = 254) = 1.13, p = 0.77 and 

X2 (3, n = 254) = 2.49, p = 0.48. 
29

Table 15. Alertness at work by job type (percent) 

Alert at Work? Production Non-Production 

Always 11.0 8.4 

Frequently 49.0 55.2 

Occasionally 39.0 35.7 

Never 1.0 0.6 

Data from participants’ daily logs provided real-time assessments of their alertness. These data 

did not reveal a statistically significant difference between the two groups in their daily alertness 

assessments (see Table 17). Peak alertness for both groups occurred after the commute to work 

about 7 a.m., after which alertness levels declined throughout the day. 

The study explored the relationship between several aspects of the MOW workers’ schedules and 

alertness. One issue was the relationship between consecutive workday and morning alertness. 

No significant correlation existed between consecutive workday and mean morning alertness 

rating, r = -.024, r

2

 = .0006, p = .263. 

Table 16. Drained after work by job type (percent) 

Drained After Work? Production Non-Production 

Always 4.0 4.5 

Frequently 31.0 39.0 

Occasionally 62.0 55.2 

Never 3.0 1.3 

Table 17. Alertness throughout the day by job type 

 Job Type 

Time of Rating Production Non-Production Significance Test 

Upon awakening 3.39 3.47 t(2226) = -1.90, p = .058

After commute to work 3.55 3.61 t(2170) = -1.25, p = .211

After lunch 3.52 3.55 t(2045) = -0.70, p = .487

After arriving home 3.06 3.11 t(1824) = -1.17, p = .241

At bedtime 2.48 2.47 t(1531) = 0.14, p = .892 

After callback 2.50 2.32 t(28) = 0.22, p = .828
30

Callbacks appear to affect morning alertness ratings, although to a limited degree (see Table 18). 

Alertness levels the morning following a callback were significantly lower than on mornings not 

following a callback, t(2140) = -3.38, p < .05. The effect size for this relationship is 0.34. (The 

t-test is an indication of the strength of the relationship while effect size is a measure of the 

degree to which a relationship exists between callback and morning alertness.) According to 

Cohen (1988), this is a small to medium effect size. Cohen (1988, p. 24) also suggests that effect 

size (ES) can be converted to r and r

2

 using the following relationship: 

ES (1/pq)

ES

r

2 +

=

where p = proportion with callback on the prior night and q = 1-p. Using this formula, r = .074 

and r2 = .0055, which means that callbacks only explain .55 percent of the variance in morning 

alertness. A X2 test investigating callbacks and morning alertness supports the hypothesis that 

alertness ratings are not independent of callbacks (i.e., a relationship exists between callbacks 

and morning alertness ratings, X2 (4, n = 2142) = 31.69, p < .05). 

Table 18. Alertness and callbacks 

 Morning Alertness Rating 

Mean Median 

Following callback 2.85 3.0 

No callback 3.50 4.0 

This effort also explored alertness and its relationship to commute time, number of hours 

worked, and time without a break. Commute time did not affect alertness levels. Although 

statistically significant relationships existed between commute times (to and from work) and 

alertness, the correlations were very weak. For commute to work and alertness level upon 

arriving at work, r = -.129, r

2

 = .017, p < .05, and for commute home and alertness when arriving 

home, r = -.204, r

2 = .042, p < .05. A statistically significant correlation existed between time 

without a break and alertness upon arriving home; however, this correlation was very weak, 

r = -.052, r

2 = .003, p < .05. Length of workday had the strongest relationship with alertness 

when arriving home, although even this correlation is considered weak, r = -.214, r

2

 = .046, 

p < .05. 

3.4.7 Sources of Stress 

In the background survey, participants rated job-related sources of stress. They rated stress using 

a Likert scale with values from 1 to 4, with 1–no stress, 2–a little stress, 3–stressful, and 4–very 

stressful. The sources and levels of stress differed by job type. Overall, participants gave higher 

stress ratings to sources arising from organizational and management issues than those related to 

the work schedule and associated travel. The three greatest sources of stress for production 

MOW workers were management policies, job pressure, and inadequate staff. For nonproduction workers, the three greatest sources of stress were inadequate staff, management 

policies and ambiguous rules (see Figure 8). As shown in Table 19, for 9 of the 17 sources of 
31

stress, there were statistically different ratings from the two groups of MOW workers. Only in 

the case of travel to work and lodging at the worksite did production MOW workers report a 

statistically different and higher level of stress than their non-production counterparts. 

0

0.5

1

1.5

2

2.5

3

3.5

On call schedule

Emergencies

Lack of control

Sleep loss

Coordination with other departments

Job pressure

Ambiguous rules

Management policies

Travel to work

Job security

Work rules

Inadequate staff

Responsibility for others’ safety

Lodging at worksite

Equipment quality

Equipment availability

Next job uncertainty

Source of stress

Rating

Production

Non-Production

Figure 8. Sources and levels of stress 

3.4.8 Job Characteristics Summary 

The work schedules of production and non-production employees differ in several respects. 

Nearly half of those holding production jobs work 4-d weeks, one-third work 5-d weeks, and 

20 percent work 8-on 6-off. In contrast, nearly 75 percent of those holding non-production jobs 

work 5-d weeks, approximately one-quarter work 4-d weeks, and only a small number work 8-on 

6-off. In addition, because production jobs work primarily a 4-d week, they tend to work a 10-h 

day, while non-production crews generally work 81⁄2 h a day. 

Similarities between production and non-production workdays include a similar amount of starttime variability, a 40-min commute to work, 30-min lunch break, generally no more than 4 h of 

work without a break, and approximately a 1-h commute home. Again, the commute home may 

be artificially high due to some participants recording their entire travel back home at the end of 

a work week. 

Both production and non-production workers traveled to a lodging or rally point during the study 

period. Forty-one percent of production workers but only 12 percent of non-production workers 
32

had this type of travel. Average travel time on planned days off was more than double that on 

workdays. This type of travel on planned days off compromises personal time and may affect a 

worker’s sleep. 

Table 19. Stress ratings by job type 

Sources of Stress Production 

NonProduction Significance Test 

On call schedule 1.37 1.76 t(245) = -3.95, p < .05* 

Emergencies 1.69 2.13 t(247) = -3.92, p < .05*

Lack of control 2.06 2.07 t(249) = -0.09, p = .925 

Sleep loss 2.14 2.17 t(248) = -0.22, p = .828 

Coordination with 

other departments 

1.91 1.95 t(248) = -0.38, p = .704 

Job pressure 2.38 2.40 t(249) = -0.14, p = .887 

Ambiguous rules 2.30 2.54 t(248) = -2.02, p < .05*

Management policies 2.61 2.65 t(249) = -0.36, p = .717 

Travel to work 2.10 1.77 t(250) = 2.97, p < .05*

Job security 2.34 2.31 t(249) = 0.24, p = .808 

Work rules 2.13 2.44 t(249) = -2.53, p < .05*

Inadequate staff 2.37 2.89 t(249) = -3.88, p < .05*

Responsibility for 

others’ safety 

2.11 2.38 t(249) = -2.21, p < .05*

Lodging at worksite 1.86 1.62 t(245) = 2.03, p < .05*

Equipment quality 2.06 2.18 t(249) = -0.98, p = .329 

Equipment availability 1.93 2.28 t(249) = -2.88, p < .05*

Next job uncertainty 2.23 2.05 t(246) = 1.36, p = .177 

Note: 1 = no stress, 2 = a little stress, 3 = stressful, 4 = very stressful 

*

statistically significant at α = .05

Both production and non-production MOW workers tend to work more than their nominal 

schedules require, most likely due to overtime and unscheduled work periods. Although results 

did not indicate excessive overtime, approximately one-quarter of both MOW groups worked 

nearly 2 d or more of overtime in the 2-week period. 

One-third of participants had at least one unscheduled work period in the 2-week period, with a 

.045 probability of being called for an unscheduled work period on any given day. An MOW 

worker was 10 times more likely to have an unscheduled work period on a planned day off than 

on a workday, with callbacks lasting approximately 41⁄2 h on workdays and 9 h on planned days 
33

off. There likely would have been more frequent unscheduled work periods had this data 

collection occurred during the winter months, when weather can cause track problems. 

Callbacks affected morning alertness ratings; alertness levels the morning following a callback 

were significantly lower than on mornings not following a callback. This relationship, however, 

was considered weak. 

For all MOW workers, level of alertness generally increased upon arriving at work and 

deteriorated throughout the day. The study examined several factors contributing to selfassessed alertness. Of the job-related factors, length of workday had the largest impact, although 

this relationship was weak. 

3.5 Sleep Characteristics 

This study examined nighttime sleep, as well as supplementary naps. The analysis considered 

the duration and quality of sleep for both workdays and planned days off. 

Analysis of the sleep and nap data required a way to distinguish between naps and split nighttime 

sleep. For workday entries, if the nap began after the person went to sleep, but before he/she 

began the commute to work, then it was considered split nighttime sleep and added to nighttime 

sleep duration. No adjustment was necessary for those who worked night shift (defined as a start 

time between 6 p.m. and 1 a.m.) since these individuals could potentially have a legitimate nap 

after bedtime but before the commute to work. For nap entries on planned days off, if the nap 

began between 12 a.m. and 7 a.m., then the researchers added nap duration to nighttime sleep 

duration. The nap analysis did not include naps that were part of split nighttime sleep and, as a 

result, were combined with nighttime sleep duration. 

3.5.1 Nighttime Sleep 

Table 20 presents nighttime sleep duration for the two groups of MOW workers and U.S. adults. 

The NSF 2002 “Sleep in America” Poll is the source of the data for U.S. adult norms. MOW 

workers are averaging less nighttime sleep on regular workdays than U.S. adults, but on planned 

days off they are averaging more. In terms of median nighttime sleep, however, MOW workers 

get less sleep regardless of type of day. As shown in Table 21, a statistically significant 

difference existed between MOW workers’ sleep and that of U.S. adults on workdays. On 

planned days off, however, only non-production MOW workers differed from U.S. adults. Both 

production and non-production groups get significantly more sleep on planned days off than on 

workdays, production: t(95) = -8.87, p < .05, and non-production: t(145) = -11.92, p < .05. 

Figure 9 presents a frequency distribution of nighttime sleep on workdays for all MOW workers 

and U.S. adults. Two-thirds of MOW workers get less than 7 h sleep on work nights in contrast 

with 39 percent of U.S. adults. The proportion getting less than 6 h is the same as that for U.S. 

adults. Only 3.2 percent of MOW workers averaged less than 51⁄2 h sleep on workdays, and only 

1 MOW worker (less than 1 percent) averaged less than 5 h of sleep. 
34

Table 20. Nighttime sleep duration versus U.S. adult norms by type of day (h:min) 

Day Group Mean Median Std. Dev. 25% 75% 

Production 6:41 6:45 0:39 6:16 7:08 

Non-Production 6:43 6:42 0:45 6:13 7:08 Regular 

Workday 

U.S. Adults 6:54 7:00 -- -- -- 

Production 7:41 7:38 1:00 6:52 8:24 

Non-Production 7:52 7:43 1:13 7:04 8:36 Planned 

Day Off 

U.S. Adults 7:30 8:00 -- -- -- 

Table 21. Mean nighttime sleep duration–tests for significance 

Type of Day Comparison Significance Test 

Production vs. U.S.* t(99) = -3.33, p < .05 

Non-Production vs. U.S.* t(151) = -3.01, p < .05 Regular Workday 

Production vs. NonProduction t(250) = -0.22, p > .05

Production vs. U.S. t(96) = 1.81, p > .05 

Non-Production vs. U.S.* t(144) = 3.63, p < .05 Planned Day Off 

Production vs. NonProduction t(240) = -1.25, p > .05

* Statistically significant at α = .05

Research has shown that performance declines even with mild sleep restriction. Belenky et al. 

(2003) have shown that performance declines initially with the mild to moderate sleep restriction 

of 7 and 5 h, and after a few days stabilizes at a less than fully rested level. Van Dongen, 

Maislin, Mullington, and Dinges (2003, p. 117) concluded that: 

Since chronic restriction of sleep to 6 h or less per night produced cognitive performance 

deficits equivalent to up to two nights of total sleep deprivation, it appears that even 

relatively moderate sleep restriction can seriously impair waking neurobehavioral 

functions in healthy adults. Alertness ratings suggest that subjects were largely unaware 

of these increasing cognitive deficits, which may explain why the impact of chronic sleep 

restriction on waking cognitive functions is often assumed to be benign. 

Based on the survey results, the 15 percent of MOW workers who are getting less than 6 h of 

nighttime sleep on workdays may be performing significantly below that of a well rested MOW 

worker. More disconcerting, based on the Van Dongen study, is that these individuals are 

probably unaware of the extent of their performance degradation. 
35

15

24

29 30

15

51

31

3

0

10

20

30

40

50

60

< 6 6 to 6.9 7 to 7.9 8 or more

Hours

Percent

U.S. Adults

MOW

Figure 9. Duration of nighttime sleep on workdays for MOW workers versus U.S. adults 

ANOVA showed no significant effect of job schedule on nighttime sleep duration (on 

workdays), F(2, 247) = 2.78, p = .064. 

As might be expected, a positive correlation existed between nighttime sleep duration and 

morning alertness ratings, r = .290, r

2

 = .084, p < .05. Those getting more sleep at night tended 

to feel more alert in the morning, and those getting less sleep tended to feel less alert. The r

2

value indicates that nighttime sleep accounts for 8.4 percent of the variance in morning alertness. 

Total daily sleep is the combined sleep from nighttime sleep and naps. Because MOW workers 

in general tend not to nap (see Section 3.5.3 for further information on naps), total sleep was not 

a great deal more than nighttime sleep (see Table 22). 

Table 22. Total sleep by type of day and job type (h:min) 

Day Job Type Mean Median 

Production 6:44 6:48 

Regular Workday Non-Production 6:46 6:43 

Production 7:50 7:49 

Planned Day Off Non-Production 8:00 7:52 
36

3.5.2 Sleep Ratings 

Participants recorded subjective ratings for sleep on both workdays and planned days off. 

Participants also rated their ease of falling asleep, ease of arising, length of sleep, quality of 

sleep, and alertness upon arising. The ratings shown in Table 23 used a Likert scale ranging 

from 1 to 5, with 1 being the lowest or worst rating, while 5 indicated the highest or best. While 

no significant differences existed between the two groups on either workdays or planned days 

off, both groups rated their sleep significantly higher on planned days off than on workdays. 

Table 23. Sleep ratings by job type and type of day 

 Production Non-Production 

Regular 

Workday 

Planned 

Day Off Significance Test 

Regular 

Workday 

Planned 

Day Off Significance Test 

Ease of 

Falling 

Asleep 

3.71 4.00 t(95) = -4.69, p < .05 3.78 4.03 t(147) = -5.75, p < .05 

Ease of 

Arising 3.30 3.66 t(95) = -5.82, p < .05 3.25 3.51 t(147) = -4.52, p < .05 

Length of 

Sleep 3.19 3.68 t(95) = -7.49, p < .05 3.32 3.72 t(147) = -8.58, p < .05 

Quality of 

Sleep 3.45 3.88 t(95) = -6.31, p < .05 3.49 3.82 t(147) = -6.79, p < .05 

Alertness 

Upon 

Arising 

3.39 3.77 t(95) = -6.34, p < .05 3.42 3.75 t(147) = -6.21, p < .05 

The researchers investigated sleep location as a possible influential factor on sleep ratings. This 

analysis examined nighttime sleep on workdays only. As previously mentioned, MOW 

employees may work significant distances from their primary residence and therefore sleep away 

from home during their work cycle in a hotel or other arrangement closer to the worksite. 

Production crews, because they travel considerable distances to various locations for work, sleep 

away from home more often than non-production crews. Production crews spent less than half 

(48.1 percent) of their work nights at home, while non-production crews slept at home more than 

80 percent (83.6 percent) of the time. 

Comparisons of sleep ratings for sleeping at home versus away from home revealed few 

differences. Dependent measures t-tests indicated no significant differences between home and 

away ratings, except that production crews got 40 min less sleep at home than away from home 

and found it easier to fall asleep at home (see Table 24). Two possible reasons exist for the 

difference in sleep duration. An absence of distractions when away from home may allow the 

MOW employee more opportunity for sleep. Another possible explanation is that the trip from 

the lodging to the worksite is shorter than from home to a worksite, so the reduced commute time 

allows for more nighttime sleep. Interestingly, although their actual sleep was statistically longer 

when away from home, no statistical difference existed in their ratings of length of sleep. 

Although non-production crews gave higher ratings to their at-home sleep, no statistically 

significant differences existed by sleep location (see Table 25). 
37

When sleeping away from home, both production and non-production crews may have various 

sleeping arrangements, often dependent on company provisions, and may range from sleeping in 

an individual room (not shared), to sharing a hotel room or camp car with other workers, to 

sleeping in a vehicle, camper, or tent. For two-thirds of MOW workers, the company generally 

provides accommodations when away from home. One-third receives a per diem and spends it 

as desired. Only 1 percent reported receiving no provision (see Figure 10). 

Table 24. Sleep ratings by location on workdays (production jobs) 

 Home Away Significance Test 

Ease of Falling Asleep 3.86 3.43 t(19) = 2.08, p = .05* 

Ease of Arising 3.23 3.03 t(19) = 1.15, p = .26 

Length of Sleep 2.93 3.05 t(19) = -0.54, p = .59 

Quality of Sleep 3.67 3.34 t(18) = 1.81, p = .08 

Alertness Upon Arising 3.14 3.31 t(19) = -1.15, p = .27 

Nighttime Sleep Duration 5:51 6:30 t(19) = -2.14, p < .05* 

* Statistically significant at α = .05 

Table 25. Sleep ratings by location on workdays (non-production jobs) 

 Home Away Significance Test 

Ease of Falling Asleep 3.65 3.46 t(26) = 1.15, p = .26 

Ease of Arising 3.36 3.42 t(26) = -0.42, p = .68 

Length of Sleep 3.31 3.14 t(26) = 1.21, p = .24 

Quality of Sleep 3.54 3.25 t(26) = 1.90, p = .07 

Alertness Upon Arising 3.28 3.23 t(27) = 0.29, p = .77 

Nighttime Sleep Duration 6:22 6:47 t(25) = -1.78, p = .09 

Since sleeping arrangements may affect away from home quality of sleep, a separate analysis 

compared sleep at home to sleep away from home based on sleeping arrangements. Dependent 

measures t-tests indicated only one statistically significant difference between sleeping home and 

sleeping away from home by sleeping arrangement. Those sharing a room with others when 

away from home actually slept longer than when they sleep at home. While those sleeping in an 

individual room and those sleeping in a vehicle, camper, or tent also had longer nighttime sleep 

duration when away from home, those differences were not statistically significant. Table 26 

contains the data for these comparisons. Table C-2 in Appendix C contains the results of the 

significance tests related to the comparisons in Table 26. A total of 48 survey participants 

reported sleeping both at home and away from home during the survey, and 43 of these people 

reported their typical sleeping arrangement on the background survey. The information was 

missing for the remaining five people. 
38

Provides 

accommodations 

65%

Provides per diem

34%

No provisions

1%

Figure 10. Company provisions for nights away from home 

The effect of age on sleep quality was also of interest. Research by Ohayon et al. (2004) 

suggests that the durations of Stage 1 and Stage 2 sleep increase with age, while rapid eye 

movement sleep and slow wave sleep decreases. No significant relationship existed between age 

and sleep quality ratings for this MOW population, r = .020, r 2 = .0004. 

3.5.3 Naps 

Data from participants’ daily logs indicate that MOW employees, in general, do not nap a great 

deal. Participants averaged 1.3 naps in 2 weeks. Approximately 60 percent of the MOW 

employees took no naps during the 2 weeks of the study, and 11 percent took only one nap over 

the 2-week period. The average nap duration was 00:56, and the median was 00:45. 

The production group napped slightly more often than the non-production group, 1.4 naps in a 

2-week period, compared with 1.2 naps, respectively. This is not a statistically significant 

difference, t(252) = 0.38, p = .702. 

MOW workers nap more frequently and longer on planned days off. They averaged .14 naps/d 

on planned days off and .06 naps/d on workdays. The average nap length was 1:02 on planned 

days off and 0:50 on workdays. MOW workers may be making up for a weeknight sleep deficit 

with longer naps on their days off. 

Nap frequency did not vary greatly by work schedule. Those working 4-d weeks averaged 

1.5 naps in 2 weeks, those working 5-d weeks averaged 1.1 naps in 2 weeks, and those working 

8-on 6-off averaged 1.6 naps over the 2-week period. A one-way ANOVA showed no 

statistically significant difference between groups, F(2,249) = 0.66, p = .518. 
39

Table 26. Sleep ratings at home versus away from home by sleeping arrangement 

Sleeping Arrangement 

Share with Others 

(n = 20) 

Individual Room 

(n = 20) 

Vehicle, Camper, or 

Tent (n = 3; 

Inadequate for 

Statistical 

Comparisons) 

Home Away Home Away Home Away 

Nighttime Sleep 

Duration 5:51* 6:36* 6:15 6:44 6:28 6:52 

Ease of Falling 

Asleep 3.76 3.36 3.57 3.44 4.83 4.71 

Ease of Arising 3.50 3.32 2.97 2.99 4.10 3.72 

Length of Sleep 3.28 3.06 2.95 3.08 3.55 3.75 

Quality of Sleep 3.53 3.18 3.55 3.29 4.33 4.05 

Alertness Upon 

Awakening 3.24 3.23 3.06 3.24 3.90 4.33 

* Statistically significant at α = .05 

Naps occur at various times throughout the workday. As illustrated in Figure 11, most first naps 

occurred after work, between 4 p.m. and 6 p.m. (35.7 percent). The second most popular 

naptime was between 6 p.m. and 8 p.m. (16.8 percent), followed by 10 a.m. to 12 p.m., and 

12 p.m. to 2 p.m. (14.7 percent each). Individuals napping on the way to work while someone 

else drove and those napping during an early lunch account for the early morning nap times and 

pre-noon naptimes. 

Sixteen MOW employees reported more than one nap on a given day. This group reported 28 

occurrences of a second nap (of the day) during the 2 weeks of the survey. Of those taking 

2 naps in a day, over 80 percent were older than the average age of 47.3 yr, and over 50 percent 

were 58 yr or older. In addition, over 60 percent of this group rated their health as fair, while in 

the overall sample only 20 percent rated themselves as being in fair health. It is possible that 

overall health and/or age may be related to the frequency of naps. 
40

0 0

2.1 1.4 2.1

14.7 14.7

9.8

35.7

16.8

2.1

0.7

0

5

10

15

20

25

30

35

40

12:01-2 a.m.

2:01-4 a.m.

4:01-6 a.m.

6:01-8 a.m.

8:01-10 a.m.

10:01 a.m.-12 p.m.

12:01-2 p.m.

2:01-4 p.m.

4:01-6 p.m.

6:01-8 p.m.

8:01-10 p.m.

10:01 p.m.-12 a.m.

Start time

Percent

Figure 11. Nap 1 start times for workdays 

3.5.4 Sleep Disorders–Alertness and Sleep Ratings 

Seventeen MOW employees (6.7 percent) of 254 total survey respondents reported having a 

diagnosed sleep disorder. Eleven of those individuals (65 percent) reported receiving treatment 

for their disorder. Six of those with a diagnosed sleep disorder (35 percent) reported that their 

problem was untreated. 

A separate analysis compared sleep ratings and alertness levels across three groups: (1) the 

untreated sleep disorder group (n = 6), (2) the treated sleep disorder group (n = 11), and (3) those 

with no diagnosed sleep disorder, or the normal group (n = 237). 

For most sleep rating categories, those with untreated sleep disorders reported poorer sleep 

ratings than the other two groups (see Table 27). The only statistically significant difference 

between those with untreated sleep disorders and those with treated sleep disorders, however, 

was the difference in quality of sleep. Interestingly, in the majority of categories, those with 

treated sleep disorders reported equal or better mean value sleep ratings than the normal group, 

although again these differences were not statistically significant. Table C-3 in Appendix C 

provides the significance tests for these comparisons. 
41

Table 27. Sleep ratings and duration by sleep disorder status (all days) 

Untreated Sleep 

Disorder (n = 6) 

Treated Sleep 

Disorder (n = 11) 

Normal 

(n = 237) 

Ease of Falling Asleep 3.75 3.92 3.86 

Ease of Arising 3.38 3.34 3.40 

Length of Sleep 3.24 3.57 3.44 

Quality of Sleep 3.10 3.72 3.63 

“How Feel” in Morning 

(Alertness) 

3.34 3.76 3.56 

Nighttime Sleep Duration 7:09 7:00 7:06 

Similar to their sleep ratings, those with untreated sleep disorders also reported lower mean 

alertness levels throughout the workday (see Table 28). At every point throughout the day that 

alertness was rated, those with untreated sleep disorders had lower alertness scores than the other 

two groups. These differences, however, were not statistically significant. Also similar to the 

sleep ratings, those with treated sleep disorders generally had equal or higher alertness ratings 

than the normal group, although again these differences were not statistically significant (see 

Table C-4 in Appendix C). 

Table 28. Alertness and sleep disorders (workdays only) 

Untreated Sleep 

Disorder (n = 6) 

Treated Sleep 

Disorder (n = 11) 

Normal 

(n = 237) 

Upon Awakening 3.40 3.62 3.42 

After Commute to Work 3.45 3.62 3.58 

After Lunch 2.99 3.68 3.53 

After Arriving Home 2.84 2.90 3.06 

At Bedtime 2.37 2.68 2.49 

3.5.5 Sleep Characteristics Summary 

During the study period MOW workers averaged less nighttime sleep than U.S. adults on 

workdays. A much larger percentage of MOW workers got less than 7 h of sleep on weeknights 

(66 percent) compared to U.S. adults (39 percent). Fifteen percent of MOW workers are getting 

less than 6 h of sleep on workdays and may be performing below that of a well-rested MOW 

worker. 

Conversely, on planned days off, MOW workers averaged more nighttime sleep than U.S. adults. 

Both groups of MOW workers got approximately 1 h more sleep and reported better sleep 

ratings on planned days off than on workdays. 
42

Production and non-production workers differed in the percentage of time spent sleeping home 

versus away from home on workdays. Those working production jobs slept away from home on 

half of their workdays, while non-production crews slept away from home on only 20 percent of 

their workdays. Very few differences existed in sleep ratings comparing sleep at home and sleep 

away from home, or by sleeping arrangement. MOW workers did, however, sleep longer when 

away from home, although only for those sharing a room with others was this a statistically 

significant difference. 

Neither group of MOW workers napped a great deal. Those who did nap more frequently tended 

to be older, and a much greater percentage of them assessed their overall health as fair. 

Almost 7 percent of MOW workers in the sample reported having a diagnosed sleep disorder. 

Almost two-thirds of those individuals reported having received treatment for their condition, 

while 35 percent reported no treatment. Data generally show lower sleep quality ratings and 

alertness throughout the day for those with untreated sleep disorders compared to those with 

treated sleep disorders and those without a sleep disorder, but only the quality of sleep rating had 

a statistically significant difference. 

3.6 Textual Analysis of Work and Sleep Comments 

The MOW worker’s daily log included two separate spaces for participants to record any 

comments regarding their sleep and work periods each day. This section presents an overview of 

participants’ comments on their sleep and work experiences throughout the 2 weeks of the study. 

Commenting on sleep or work experiences in the daily log book was not a requirement of 

participation. Rather, individuals had an opportunity to qualify part of their day. As such, some 

participants chose not to comment, while others commented frequently. For this reason, a 

statistical analysis of these comments was not possible. Researchers, however, scanned a 

number of participant log books to determine common themes presented in the comments, and 

performed a simple tabulation of the frequency of topics mentioned. The following themes 

emerged from this review: 

1. Fatigue (physical fatigue, being sleepy, worn down, sore, etc.) 

2. Sleep location (related to sleeping home or away from home) 

3. Alertness (mental alertness, vigilance) 

4. Unscheduled work (asked to work early, stay late, work off days, overtime, etc.) 

5. Stress 

6. Travel (related to commuting to/from work or lodging) 

7. Personal issues (family, etc.) 

8. Management 

9. Job security 

10. Responsibility 

11. Territory 

12. Weather 
43

13. Dispatcher, track gang, track crew, track department 

14. Safety 

ATLAS.ti® software V5.0 was used to autocode comments based on keyword searches and tally 

the number of comments made under each topic area. Table 29 lists the keywords that were the 

basis for each topic area search. 

The most frequently mentioned topics in the sleep comments were fatigue, sleep location, and 

travel. Comments on weather, fatigue, and travel dominated the work experience section. The 

comments complement the quantitative survey results by providing personal examples of the 

effect of work or sleep patterns present in the survey data. Perhaps more important is that the 

survey did not explicitly address the most frequently mentioned topic, weather. In this way, the 

comments provide a more complete picture of MOW workers’ fatigue-related concerns. 

Table 30 presents more detailed results of keyword searches. Topics of fatigue and alertness 

were combined in the table due to relatively few comments on alertness. 

The selected comments that appear following the tables illustrate the consequences of the work 

and sleep patterns in the survey data. For example: 

• The comments with regard to Weather illustrate that the survey participants frequently 

felt that weather affected their perceived level of fatigue and the overall quality of the 

workday experience. 

• The Travel comments illustrate how travel to a distant worksite compromises personal 

and sleep time. 

• Most of the Fatigue comments relate to fatigue resulting from travel and long workdays. 

• Comments with respect to Sleep Location address difficulties encountered when sleeping 

away from home. 

• Some survey participants described Personal Situations that affected their sleep. 

• While the survey participants did not report much Unscheduled or Emergency Work, 

some MOW workers who did have unplanned work reported sleep disruption as a result. 

The limited comments relating to “dispatcher, track gang, track crew, track department” and 

“responsibility” were not meaningful, and therefore they are not presented below. No comments 

were made on the topic of job security. 
44

Table 29. Keywords used for each topic area search 

Topic Keywords 

Alertness (mental) Alert*, aware*, awake, attentive*, 

watchful, vigilant, prepared 

Dispatcher, track gang, track crew, track 

department 

Dispatcher, track gang, track crew, track 

department 

Fatigue (physical) Fatigue*, tired, sleepy, exhaust*, spent, 

weary, energy, weak* 

Job security Job security, secure* 

Management Manage*, boss, supervis*, company, 

policy, organization, administration 

Personal issues (family, etc.) Personal, private, family, domestic, son, 

daughter, wife, kid*, baby, father, mother, 

grand*, relative*, child* 

Responsibility Responsib*, duty, blame, reliab*, 

accountab* 

Safety Safe*, accident, incident, injury, casualty, 

error, protection 

Sleep location Bed, hotel, motel, away, camp*, camp car, 

noise*, room*, lodging, accommodation*, 

quarters 

Stress Stress*, workload, work load, pressure, too 

much, strain, anxious, anxiety, worry, tense 

Territory Territory, coverage, area, region 

Travel Travel, commute, driv*, drove, worksite, 

trip, car, truck, camper 

Unscheduled work Schedule, overtime, call*, night call, 

weekend call, emergency, unscheduled, 

shift work, respond, crisis, trouble, 

Weather Weather, heat, hot, degrees, temperature 

cold, freezing, wet, rain, snow, sleet, light*, 

dark, sun, ice, climate, condition* 

Note: ATLAS.ti search logic uses the symbol “*” as a wildcard. For example, searching for “stress*” would result in 

all words starting with s-t-r-e-s-s and would include any ending (such as stressful, stressor, etc.). 
45

Table 30. Frequency of comments by topic area and source 

Source of Comments 

Topic Sleep Log Work Log Total 

Weather 29 252 281 

Fatigue/Alertness 122 134 275 

Travel 40 141 181 

Sleep Location 58 9 67 

Personal Issues 20 38 58 

Unscheduled Work 9 45 54 

Stress 15 35 50 

Safety 3 26 29 

Management 1 6 7 

Territory 0 3 3 

Dispatcher, Track 

Gang, Track Crew, 

Track Department 

0 2 2 

Responsibility 0 1 1 

Job Security 0 0 0 

Total 302 706 1008 

Selected comments by topic follow. 

Weather 

• “In the winter we have snow removal detail. It is nothing to get a call at 3:00 a.m. to 

come in and start removing snow...the snow removal in the winter is an everyday thing if 

it snows. There are no days off.” 

• “The weather is a big factor here. Sometimes it’s very cold or very hot. That alone 

wears you out by the end of the day and week!” 

• “Heat index around 110 degrees for today. Men on gangs having difficulty coping with 

heat and humidity. Almost lost one to the heat today.” 

• “Hot weather. Crews starting to hurry up to get things done. Some rules not being 

followed completely.” 

• “Long day. Had a lot of work to do. The heat continues to be a big factor; we’re not 

used to it. Need to keep drinking a lot of water.” 

• “Very hot and humid weather combined with very physical work made me more tired 

than usual during and after work.” 
46

Fatigue/Alertness 

• “I’m so tired that I could not work in a safe manner if I had to because just not enough 

sleep and too many hours changed. Worked so many hours that I cannot remember what 

day it is.” 

• “Sleep was only 5 hours but very sound. Felt kind of tired most of the day but that’s the 

way Monday’s are when you work 5 days a week on the road far from home and try to 

have some kind of home life on a 2 day weekend.” 

• “It was a typical Monday after traveling. It was 9 hours to the motel and between that 

and getting up between 2-3 a.m. I am very tired. On this job we are working early 

Monday hours because that is the only time that we can get the track.” 

• “The jobs at work are getting more numerous and we have not got enough help. I am 

tired most of the time because we work like dogs trying to keep up.” 

• “The reason I and the 2 people that I work with are so tired most of the time is that we are 

getting into our fifties. Lack of help is probably a big factor. There is only three of us to 

cover the whole ... terminal. That is not enough people to keep up with the workload 

that is put on us.” 

• “30 minute nap helped me to be more alert.” 

• “I am mentally exhausted after today’s work day. I worked a 12 hour day on my 

scheduled off day. Very tired!” 

Travel 

• “Normal [sleep] for Saturday night before leaving Sunday afternoon for work location. 

Have to use 8 and a half to 9 hours of planned off day to travel to lodging/rally point. 

Leave at 1530, arrive 0030-0100 on work day.” 

• “It seems Monday’s I am usually more tired than any other day of the week. It takes me 

8 hours to drive from home to my lodging motel.” 

• “I left home at 0400. How do you expect me to keep my family together? My mother is 

also in the hospital. Drove 900 miles just to get to work.” 

• “Another work schedule change. Move motel 30 miles. I’m taking the next 2 days off to 

take care of many things. Drove 287 miles home. The home front is falling apart 

because I’m never home. Situation critical.” 

• “I always try to drive to work the night before work. I need some sleep before I go in on 

a Monday or I am totally off-center all day. If I drive at night and start work with some 

sleep, I am OK. If I drive all night without sleep Monday’s are too long.” 

• “My drive home was 1,000 miles which is a 14 hour drive.” 

Sleep Location 

• “Always staying in a motel on these production gangs is difficult. Newness of motel and 

cleanliness conditions of mattresses and bedding, AC and heat, all these things account 

for the type of rest at night.” 
47

• “Did not sleep good at camp. There were passing trains that woke me up 2 times. And a 

co-worker woke up and opened the door and it woke me up.” 

• “Employees opening and closing the camp door as they come in. Employee snoring very 

loudly after being out late.” 

• “I have difficulty sleeping at times due to noise in the motel.” 

• “I have to settle down and get used to surroundings after getting to hotel.” 

Personal Issues 

• “Due to infant son that was sick and home from daycare, the day started early. Unable to 

catch a nap. Scheduled to be on the job by 1630.” 

• “Slept on couch as wife too upset with me for having to leave so early.” 

• “Had grandchildren stay overnight. Sleep was poor.” 

• “After working in that damn heat all day, now I have to look at a 3 hour drive ahead of 

me. After being away from home all week now I have to mow the lawn and other jobs 

that await me. I only have one day and a half to get everything done as well as be a 

husband and father plus a grandfather. Plus a 3 hour drive from home to lodging.” 

Unscheduled Work 

• “Did not sleep too well. Expecting to get called out again.” 

• “Difficulty falling asleep. Called out to report to work at 0530. Difficulty going back to 

sleep knowing I had to get up early.” 

• “Very tired. 28 hours overtime in last 11 days. Have to take tomorrow off, too tired and 

personal business.” 

• “... And as far as being recalled to work outside our assigned work hours, during the 

summer it is very rare. In the winter we have snow removal detail. It is nothing to get a 

call at 3:00 am to come in and start removing snow. I myself live 60 miles [away] and it 

is a long day when you start at 3:00 am and don’t get home until 4:00 pm. The snow 

removal in the winter is an everyday thing if it snows. There are no days off. My 

foreman and I are the only two that showed up to do the removal on the weekends last 

winter. Nobody else would come in....” 

• “Logged over 26 hours of overtime this week. But I really need my overtime to support 

my family, so when I get a chance to work, I take it. I only wish I didn’t have to worry 

about making a mistake and getting suspended without pay or worse. Nobody seems to 

worry about getting hurt or killed. All of us young guys worry about getting in trouble 

for making mistakes. Young/new guys will make mistakes, its human nature and part of 

the learning process.” 

• “Called on vacation to repair broken rail at 0400...” 

• “Get called about 1400 to 1430 every day to add work. They want you to work till dark 

every day.” 
48

• “Today is Saturday, a rest day. Got called at 2100, left home at 2130. Had to inspect 

track. Done at 0000. Back home at 0030.” 

Stress 

• “Busy day at work. A little stressed out mentally and physically. Too much to do and 

not enough people to do the work. ... Have another busy day tomorrow. Hopefully I 

will get a good night’s rest.” 

• “...I was with the rail detector which is pretty stressful. I had a crew behind me to 

change out rail and I think I had about 20 phone calls today relating to work. Drove me 

nuts. We have a lot of new hires working now and they just don’t get it.... By the end of 

the day I am tired from stress. Don’t like my job....” 

• “Deleting a bunch of rules would relieve a lot of stress. A lot of rules the FRA 

implemented with the pressure from the union made our job a lot harder.” 

• “Most stressful part of working is trying to follow all the rules. It is almost impossible to 

do and accomplish any work.” 

Safety 

• “I’m so tired that I could not work in a safe manner if I had to because just not enough 

sleep and too many hours changed. Worked so many hours that I cannot remember what 

day it is. Damn!” 

• “My opinion on sleep and safety. The day I am most concerned about for my men’s 

safety and mine is Monday. Some of my men, about half of surfacing gang, travel at 

night to early morning to make 0700 job briefing. During the day they are sleepy, shorttempered and not as alert as normal. Especially late in the day. This is a very poor day to 

have major projects, switch cutovers or railroad projects that require overtime. All my 

men are always very tired and ready to quit at 1530 on Mondays. The rest of the 

workdays are always much better. Just my observation over the years ....” 

• “When the union pushed for all the new rules on track safety it did no good on safety but 

made it a lot harder to do the job and a lot more stressful.” 

Management 

• “The company claims that we (employees) can take power naps no longer than 

20 minutes yet we get a lot of flack whenever we do! What’s up with this?!?” 

Territory 

• “Had to drive 70 miles to and from job site. Have too much territory!” 
49

4. Findings and Recommendations 

Analysis of the data from this study provides some insights into the demographics of the MOW 

worker population, as well as how their work schedules and sleep patterns affect their alertness 

on the job. Because a random sample of the U.S. MOW population provided the data, the results 

are representative of the Nation’s MOW worker population at the time of the survey. 

Conducting the survey during the summer months when major track construction occurs assured 

that an adequate number of production workers would participate. Had the survey taken place 

during the winter, the results likely would have been different, particularly in terms of 

emergency calls due to weather-related track problems. 

This section presents the key findings of the study, as well as some recommendations for 

methodological changes for future field studies of this nature. This section concludes with some 

suggestions for additional uses of this data. 

4.1 Key Study Findings 

The following subsections highlight the key findings with respect to the MOW worker’s nominal 

work periods, unscheduled work periods, and sleep patterns. 

4.1.1 Work Periods 

The nominal schedules for both production and non-production MOW workers have 80 h of 

work in a 2-week period. The typical workday is 7 a.m. to 5 p.m. for production jobs and 7 a.m. 

to 3:30 p.m. for non-production jobs. 

Nearly a quarter of MOW workers reported travel to a lodging/rally point during the study. 

Travel occurred on both workdays and planned days off but was more frequent and of 

significantly longer duration on planned days off. Travel on planned days off compromises 

personal time that would otherwise be available to spend with family and to attend to personal 

business. It may also compromise the worker’s sleep time. 

The weekly work schedules for production and non-production jobs differ. Almost half of those 

with production jobs worked 4 d per week, one-third worked a 5-d week, and 20 percent worked 

8-on 6-off. Nearly 75 percent of the non-production people worked a 5-d week, nearly a quarter 

worked a 4-d week, and only a small number worked 8-on 6-off. 

The overall length of the workday, including commuting and lunch breaks, was 11 h for nonproduction jobs and 12 h for production jobs. Both workdays allow for adequate time for 

nighttime sleep; however, both production and non-production MOW workers tend to work more 

than their nominal schedules require. This is likely due to overtime and unscheduled work 

periods. The mean number of hours worked by production crews in the 2-week period was 

89:28, while non-production crews worked 87:01. These results do not indicate excessive 

overtime. However, 25 percent of those with production jobs worked more than 95:58, and 

25 percent of the non-production group reported working more than 93:12. This means that a 

quarter of both production and non-production MOW crews worked nearly 2 d or more of 

overtime in the 2-week period. This level of overtime, if done on a regular basis, may prevent 

the employee from achieving full rest and recovery. 
50

4.1.2 Unscheduled Work Periods 

Overall, the probability of an MOW worker being called for an unscheduled work period was 

.045, production employees .03, and non-production .05. One-third of participants had an 

unscheduled work period at least once in the 2-week period. An MOW worker was 10 times 

more likely to be called for an unscheduled work period on a planned day off than on a regular 

workday (.10 and .01, respectively). 

The length of an unscheduled work period on a workday was significantly shorter than one on a 

planned day off. Callbacks lasted on average 4:24. Unscheduled work periods on planned days 

off lasted 9:07, an indication that these work periods were probably a planned additional 

workday rather than response to an emergency. If data collection had occurred during the winter 

months, there probably would have been more callbacks to handle emergency weather-related 

problems. 

Morning alertness following a callback was significantly lower in comparison to mornings when 

no callback had occurred the prior night. While the difference was statistically significant, the 

effect size was small. This result implies that other factors contribute to morning alertness and 

that eliminating callbacks would not substantially improve morning alertness ratings. 

4.1.3 Sleep Patterns and Alertness 

On planned days off, both production and non-production groups get the same amount of sleep, 

with both groups averaging more than the U.S. adult norm. On workdays, again, both groups of 

MOW workers get the same amount of nighttime sleep, but this amount is significantly less than 

the norm for U.S. adults. Not only is weeknight sleep significantly less than U.S. adult norms, 

but the percentage of MOW workers getting less than 7 h of sleep is also significantly greater. 

This is a concern since research has shown that performance decrements occur with less than 7 h 

of sleep, particularly if it is consistently at this level. Even more disconcerting is that 15 percent 

of MOW workers are getting less than 6 h of nighttime sleep on workdays and that these 

individuals, who perform safety critical jobs, are probably unaware of the extent of their 

performance degradation. 

While no significant differences existed in sleep ratings between the two groups on either 

workdays or planned days off, both production and non-production workers had significantly 

higher sleep ratings on planned days off than on workdays. 

The incidence of reported sleep disorders among MOW workers exceeds the U.S. adult norm for 

sleep apnea. Because of the wording of the question on the background survey, it is not possible 

to determine if all of these MOW workers have sleep apnea. For this reason, it is not certain that 

the incidence of sleep apnea in this population exceeds U.S. norms. Railroads and union 

sponsored fatigue education programs should point out the possible performance consequences 

of untreated sleep disorders. These education programs should encourage those with untreated 

sleep disorders to seek treatment. 

4.2 Recommendations for Improvements in Study Procedures 

Based on the experience of this study, several methodological improvements should be a part of 

any future studies to collect work schedule and sleep pattern data. The recommended changes 

are the following: 
51

• The data collection period should avoid a holiday period. Because mailing of the survey 

materials for this study occurred at the end of June, some participants recorded data over 

the Fourth of July weekend. As a result, some participants did not work their normal 

work schedule during the data collection period so data for a full 2-week work cycle was 

not reported. 

• If the study population includes workers who must travel long distances on their own time 

to reach a rally point or lodging site, the daily log should have a better way to record 

travel to the rally point/lodging site. The daily log for this study included a place for 

recording such travel, and the instructions described how to record this travel. 

Nevertheless, some participants did not understand the difference between travel to 

lodging/rally point and commute to worksite, and they entered the information in the 

incorrect place. 

• The background survey should inquire whether or not the participant has been diagnosed 

with sleep apnea, as well as a sleep disorder, so that the results can be compared with 

U.S. norms for sleep apnea from the Wisconsin Cohort Study. A question on sleep 

disorders in general is necessary to be certain that poor sleep due to any sleep disorder 

does not confound the survey data. 

• The instructions should state that when an employee works on a planned day off, he/she 

should record this work period in the same section of the log that is used for regular 

workdays, rather than in the unscheduled work period section. Participants were not sure 

whether to record this information in the regular workday section or in the unscheduled 

work period section. 

4.3 Recommendations for Additional Research 

A number of mathematical models exist for predicting human fatigue and alertness. 

Development of the majority of these models used laboratory data on the human sleep cycle. A 

need exists for data on both work schedules and sleep patterns for further refinement of these 

models. In particular, the only data for railroad workers available to date is from locomotive 

engineers. The availability of the MOW employee data will allow the modelers to refine their 

models and predict how the typical MOW worker work schedule may be affecting on-the-job 

alertness. 

Finally, the analysis presented in this report characterizes the work schedules and sleep patterns 

of MOW employees. Further analysis of the data could identify explanatory factors for the 

reported alertness levels. For example, the data indicates that a difference exists in morning 

alertness following an unscheduled work period, but statistically, the occurrence of the 

unscheduled work period only accounts for a small portion of variance in alertness. Other 

factors, such as length of the prior days’ sleep periods and the time of awakening, also merit 

investigation. 

53

5. References 

Barnes-Farrell, J., & Piotrowski, M. (1989). Workers’ perceptions of discrepancies between 

chronological age and personal age: you’re only as old as you feel. Psychology and Aging, 

4, 376-377. 

Barnes-Farrell, J., & Piotrowski, M. (1991). Discrepancies between chronological age and 

personal age as a reflection of unrelieved worker stress. Work & Stress, 5, 177-187. 

Belenky, G., Wesensten, N.J., Thorne, D.R., Thomas, M.L., Sing, H.C., Redmond, D.P., Russo, 

M.B., & Balkin, T.J. (2003). Patterns of performance degradation and restoration during 

sleep restriction and subsequent recovery: a sleep dose-response study. Journal of Sleep 

Research, 12, 1-12. 

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: 

Lawrence Erlbaum Associates, Inc. 

Gertler, J., & Viale, A. (2006). Work Schedules and Sleep Patterns of Railroad Signalmen. 

(Report No. DOT/FRA/ORD-06/19). Washington, DC: Federal Railroad Administration. 

Levy, P., & Lemeshow, S. (1999). Sampling of Populations: Methods and Applications. New 

York: John Wiley & Sons, Inc. 

National Sleep Foundation. (2002). 2002 “Sleep in America” Poll. Washington, DC: author. 

National Sleep Foundation. (2002). Sleep Problems? Watch The Caffeine, Says The National 

Sleep Foundation. Retrieved December 30, 2004, from 

http://www.sleepfoundation.org/PressArchives/caffcalc.cfm.

Ohayon, M.M., Carskadon, M., Guilleminault, C., & Vitiello, M.V. (2004). Meta-analysis of 

quantitative sleep parameters from childhood to old age in healthy individuals: developing 

normative sleep values across the human lifespan. SLEEP, 27, 1255-73. 

U.S. Census Bureau. (2003). Marital Status of People 15 Years and Over, by Age, Sex, 

Personal Earnings, Race, and Hispanic Origin. Retrieved December 30, 2004, from

http://www.census.gov/population/socdemo/hh-fam/cps2003/tabA1-all.pdf.

Van Cauter, E., Leproult, R., & Plat, L. (2000). Age-related changes in slow wave sleep and 

REM sleep and relationship with growth hormone and cortisol levels in healthy men. JAMA, 

284(7), 861-867. 

Van Dongen, H., Maislin, G., Mullington, J., & Dinges, D. (2003). The cumulative costs of 

additional wakefulness: dose-response effects on neurobehavioral functions and sleep 

physiology from chronic sleep restriction and total sleep deprivation. SLEEP, 26, 117-126. 

Webb, W.B. (1992). Sleep, the Gentle Tyrant. Bolton, MA: Anker Publishing. 

Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., & Bare, S. (1993). The occurrence of 

sleep-disordered breathing among middle aged adults. New England Journal of Medicine, 

328, 1230-5. 

55 

Appendix A. 

Survey Materials 

This appendix includes copies of the following survey materials: 

• Cover letter to BMWE members from BMWE International President 

• Instructions to participants on making entries in the Daily Log 

• Railroad Maintenance of Way Worker Background Survey 

• Railroad Maintenance of Way Worker Daily Log (1 day) 
56 
57 
58 
59 
60 
61 
62 
63 
64 
65 
66 
67 
68 
69 
70 
71 

73 

Appendix B. 

Adjustments to Data 

Travel to the lodging/rally point 

Travel to the lodging/rally point was intended to capture the long distance trip from home to the 

lodging/rally point, not daily commutes. Survey participants did not always correctly report 

Travel to the lodging/rally point in this way. Often, participants incorrectly recorded short daily 

trips from a motel or home to the worksite in the Travel to lodging/rally point field. When this 

occurred, researchers moved the entry from the Travel to lodging/rally point field to the 

Commute to worksite field. 

In other instances, participants incorrectly coded daily travel from a motel or home to a rally 

point (a meeting place for further travel by company vehicle) as Travel to lodging/rally point. 

Researchers again corrected these cases by moving the entry from the Travel to lodging/rally 

point field to the Commute to worksite field. 

In some cases, individuals drove a long distance from home directly to work on the first day of a 

work week, and incorrectly entered this in the Commute to worksite field, rather than the Travel 

to lodging/rally point field. As mentioned earlier, long distance traveling to a work location was 

intended for the Travel to lodging/rally point field, whereas the shorter daily commutes were 

intended for the Commute to work field. Researchers made the proper adjustments in these 

cases. 

Finally, if the first day of the log was a workday and did not capture travel to a lodging/rally 

point the prior night, an extra field was added to the database to properly record this travel. 

Total nighttime sleep versus naps 

The survey instructions asked participants to record their nighttime sleep in the Upon Awakening 

section of the Sleep and Nap Log. If their nighttime sleep was disrupted due to emergency call 

or other circumstances, they were to use the Nap 1 section to record any subsequent sleep. 

Some of the entries in the Nap section of the daily log that were in fact split nighttime sleep 

rather than naps required an adjustment to nighttime sleep. For workday entries, any nap that 

began after Time Feel Asleep but before Time You Began Commute To Work was added to 

nighttime sleep duration. The researchers did not make this adjustment for those who worked 

night shift (defined as a start time between 6 p.m. and 1 a.m.) since these individuals could 

potentially have a legitimate nap after bedtime but before the commute to work. For nap entries 

on planned days off, if the nap began between 12 a.m. and 7 a.m., the nap duration was added to 

nighttime sleep duration. 

Naps that were combined with nighttime sleep duration were not a part of the nap analysis. 

Unscheduled work periods 

If an individual worked on a planned day off, the researchers treated the work period as an 

unscheduled work period. Hence, unscheduled work periods were counted not only for people 

who were called back to work after returning from their regular work period, but also for those 

who worked on a planned day off. 
74 

The researchers did not treat work hours that were an extension of the regular work period as an 

unscheduled work period because the individual had not yet gone home. 

Population means versus mean of individual means 

For some analyses of the daily log data, a mean was calculated for each survey participant and 

then the analysis was done with the individual means. The following measures were analyzed in 

this manner: actual hours worked (for 2 weeks); nighttime sleep by job type, type of day and job 

schedule; total sleep by job type; nap duration for everyone and by job type; sleep latency for all, 

by job type, and by type of day; and all data used in sleep disorder comparisons. For all other 

analysis of the data from the daily logs, data from all participants was used without computing a 

mean for each individual. For example, the analysis of commute time was based on the mean of 

the data for all workdays in the survey data. This latter approach was used where it was 

desirable to characterize a typical day rather than the individual MOW worker’s experience. 

Data from vacation periods 

Some survey participants collected sleep data during a vacation period. These data were not a 

part of the analysis of MOW worker’s sleep. 
75 

Appendix C. 

Supporting Data 

This appendix contains detailed data summaries that support the technical analysis in the main 

sections of this report. 

Table C-1. Comparison of Track Maintenance and B&B daily work schedules 

 Track B&B 

Production (n = 85) 

Non-Production 

(n = 134) Production (n = 14) 

Non-Production 

(n = 20) 

Mean Median Mean Median Mean Median Mean Median 

Start Time 

(Nominal) 

6:51 a.m. 6:45 a.m. 7:11 a.m. 7 a.m. 6:41 a.m. 7 a.m. 7:30 a.m. 7 a.m. 

End Time 

(Nominal) 

4:40 p.m. 5 p.m. 3:46 p.m. 3:30 p.m. 4:58 p.m. 5 p.m. 4 p.m. 5 p.m. 

Length of Meal 

Break (Nominal) 

27 min 30 min 28 min 30 min 29 min 30 min 25 min 30 min 

Length of 

Workday 

(Nominal) h:min 

9:40 10:00 8:29 8:00 9:49 10:00 9:08 9:35 

2 Weeks of Work 

(Nominal) h:min 

83:32 80:00 80:33 80:00 79:28 80:00 79:25 80:00 

Table C-2. Significance tests for sleep ratings at home versus away from home by sleeping 

arrangement 

Sleeping Arrangement 

Share with Others Individual Room 

Nighttime Sleep Duration t(19) = -2.39, p < .05* t(18) = -1.82, p = .086 

Ease of Falling Asleep t(19) = 2.00, p = .060 t(19) = 0.65, p = .521 

Ease of Arising t(19) = 1.17, p = .257 t(19) = -0.13, p = .901 

Length of Sleep t(19) = 1.37, p = .186 t(19) = -0.64, p = .529 

Quality of Sleep t(18) = 1.71, p = .105 t(19) = 1.77, p = .093 

Alertness Upon 

Awakening t(19) = 0.10, p = .919 t(19) = -1.26, p = .222 

* Statistically significant at α = .05
76 

Table C-3. Significance tests for sleep ratings by sleep disorder status (all days) 

Untreated versus 

Treated Untreated versus Normal 

Treated versus 

Normal 

Ease of 

Falling 

Asleep 

t(15) = -0.44, p = .667 t(241) = -0.40, p = .688 t(246) = 0.30, p = .764 

Ease of 

Arising t(15) = 0.09, p = .933 t(241) = -0.07, p = .941 t(246) = -0.25, p = .803 

Length of 

Sleep t(15) = -1.02, p = .324 t(241) = -0.82, p = .416 t(246) = 0.72, p = .471 

Quality of 

Sleep t(15) = -2.17, p < .05* t(241) = -1.88, p = .062 t(246) = 0.43, p = .665 

“How Feel” 

in Morning 

(Alertness) 

t(15) = -1.27, p = .225 t(241) = -0.77, p = .441 t(246) = 0.98, p = .330 

Nighttime 

Sleep 

Duration 

t(15) = 0.47, p = .647 t(239) = 0.19, p = .441 t(244) = -0.46, p = .643 

*Statistically significant at α = .05 

Table C-4. Statistical tests for alertness and sleep disorders (workdays only) 

Untreated versus 

Treated 

Untreated versus 

Normal Treated versus Normal 

Upon Awakening t(15) = -0.63, p = .536 t(241) = -0.07, p = .943 t(246) = 0.89, p = .373 

After Commute to Work t(15) = -0.44, p = .670 t(241) = -0.44, p = .663 t(246) = 0.19, p = .852 

After Lunch t(15) = -1.98, p = .066 t(239) = -1.82, p = .070 t(244) = 0.67, p = .501 

After Arriving Home t(15) = -0.17, p = .867 t(240) = -0.69, p = .488 t(245) = -0.68 p = .500 

At Bedtime t(14) = -0.95, p = .357 t(224) = -0.39, p = .700 t(228) = 0.81, p = .420 
77 

Abbreviations and Acronyms 

ANOVA analysis of variance 

BMWED Brotherhood of Maintenance of Way Employes Division, International 

Union of Teamsters 

B&B bridge and building 

d day 

FRA Federal Railroad Administration 

h hour 

in inches 

mi miles 

min minutes 

mo month 

MOW maintenance of way 

NARAP North American Rail Alertness Partnership 

NSF National Sleep Foundation 

OMB Office of Management and Budget 

yr year