DOT/FAA/AM-97/25

Office ofAviation Medicine

Washington, D.C. 20591

VJbrkshift and Antihistamine

Effects on Task Performance

Kirby Gilliland

Robert E. Schlegel

The University of Oklahoma

Norman, OK 73019

Thomas E. Nesthus

Civil Aeromedical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

December 1997

Saoved iss gnh&s jEelcea::-; f

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

©

DTXC QUALITY D^rECTED 3

U.S. Department

of Transportation

Federal Aviation

Administration 19980406 009
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents or use thereof.
Technical Report Documentation Page

1. Report No.

DOT/FAA/AM-97/25

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

Workshift and antihistamine effects on task performance

5. Report Date

December 1997

6. Performing Organization Code

7. Authorlsl

Gilliland, K., Schlegel, R.E.1, and Nesthus, T.E.2

8. Performing Organization Report No.

9. Performing Organization Name and Address

University of Oklahoma

School of Industrial Engineering

Norman, OK 73019

FAA Civil Aeromedical Institute

P. O. Box 25082

Oklahoma City, OK 73125

10. Work Unit No. ITRAIS)

11. Contract or Grant No.

DTFA-02-93D-93088

12. Sponsoring Agency name and Address

Office ofAviation Medicine

Federal Aviation Administration

800 Independence Avenue, S.W.

Washington, DC 20591

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplemental Notes

16. Abstract

Sixteen male subjects, well-trained on a battery of cognitive performance assessment tasks, participated in a study to

investigate the effects on human operator performance ofwork shift (Day Shift vs. Midshift), a specific antihistamine

drug (4 mg of ChlorTrimeton® brand chlorpheniramine maleate), and time on task accompanying three successive

drug doses spaced every four hours. Five performance tasks, two work sample tasks, and four subjective scales were

included in the study. In summary, chlorpheniramine maleate alone had a strong negative influence on a wide range of

task performance and mood measures. There was a rather complex relationship between work shift and time on the shift

such that performance and mood during the Day Shift tended to get better and during the Midshift tended to get

worse. No evidence was found that chlorpheniramine maleate and work shift combine to produce a multiplicative

effect.

17. Key Words

Shift work, Human Performance, Chlorpheniramine

Maleate

18. Distribution Statement

Document is available to the public through the

National Technical Information Service

Springfield, Virginia 22161

19. Security Classif. lot this report!

Unclassified

20. Security Classif. lof this pagel

Unclassified

21. No. of Pages

100

22. Price

Form DOT F 1700.7 (8 721 Reproduction of completed page authorized

^CQiöMiHr^ ISIETSSÜTED S
PREFACE

This is a report of a Civil Aeromedical Institute

(CAMI) project completed at the University of

Oklahoma under Task Order DTFA-02-94-T80067 ofcontract DTFA-02-93-D-93088 for the

Federal Aviation Administration. Funding for the

effort was provided by the CAMI Human Factors

Research Laboratory (CAMI/AAM-500).

The authors gratefully acknowledge several

individuals for their contributions to the project.

Luz-Eugenia Cox-Fuenzalida, Ioannis Vasmatzidis,

Rhonda Swickert, and Randa L. Shehab served to

coordinate the numerous facets of the study.

Their contributions to the recruitment and retention ofsubjects, and to the collection, reduction,

and analysis of data were invaluable. Scott Mills

provided valuable hardware and software support

and Arasendran Sellakannu and Tamy L. Fry

made important contributions to the collection,

reduction, summarization, and analysis of the

data. The graduate and undergraduate assistants

who worked on this project devoted long hours,

often at unusual times ofthe day, to the collection

of the data reported here. We appreciate their

efforts very much.

The authors are also very grateful to Richard D.

Havel, M.D., Director of Goddard Health Center at the University of Oklahoma, who served as

the Medical Monitor for this project. Dr. Havel

generously provided his time, interest, and support for this project.

Finally, the authors thank Dr. Robert E.

Blanchard and Dr. David J. Schroeder of CAMI

for their general guidance and their comments on

drafts of this report.

in
TABLE OF CONTENTS

Page

1.0 INTRODUCTION 1

2.0 BACKGROUND 2

2.1 Shiftwork 2

2.2 Antihistamines 3

3.0 OBJECTIVES 5

4.0 METHODOLOGY 5

4.1 Project Design 5

4.2 Subjects 6

4.3 Test Battery 6

4.4 Equipment 7

4.5 Test Facilities 7

4.6 Experimental Procedure 8

5.0 RESULTS 9

5.1 Data Reduction 10

5.2 ANOVA Results by Task 10

5.3 Subjective (Self-Report) Measures 14

6.0 CONCLUSIONS 16

REFERENCES 20

APPENDIX A: Subject Characteristics Al

APPENDIX B: Task Descriptions Bl

APPENDIX C: Performance Measures Cl
LIST OF FIGURES

Figure Page

1. Laboratory Model of Readiness-to-Perform Testing 25

2. Spatial Processing Mean RT 26

3. Spatial Processing Percent Correct 27

4. Critical Tracking Mean Lambda 28

5. Critical Tracking Maximum Lambda '. 29

6. Dual Tracking-Group Lambda RMS Error 30

7. Dual Tracking-Group Lambda Control Losses 31

8. Dual Memory Search-Group Lambda Mean RT 32

9. Dual Memory Search-Group Lambda Percent Correct ." 33

10. Dual Memory Search-Group Lambda Throughput 34

11. Dual Tracking-Individual Lambda RMS Error 35

12. Dual Tracking-Individual Lambda Control Losses..... 36

13. Dual Memory Search-Individual Lambda Mean RT 37

14. Dual Memory Search-Individual Lambda Percent Correct 38

15. Dual Memory Search-Individual Lambda Throughput 39

16. Switching-Manikin Task Mean RT 40

17. Switching-Manikin Task Mean RT for Transitions 41

18. Switching-Manikin Task Percent Correct 42

19. Switching-Manikin Task Percent Correct for Transitions 43

20. Switching-Manikin Task Throughput 44

21. Switching-Mathematical Processing Mean RT 45

22. Switching-Mathematical Processing Mean RT for Transitions 46

23. Switching-Mathematical Processing Percent Correct 47

24. Switching-Mathematical Processing Percent Correct for Transitions 48

25. Switching-Mathematical Processing Throughput 49

26. NovaScan -Visual Search and Vector Projection Task Mean RT 50

27. NovaScan -Visual Search and Vector Projection Percent Correct 51

28. NovaScan -Continuous Spatial Memory Task Mean RT 52

29. NovaScan -Continuous Spatial Memory Task Percent Correct 53

30. NovaScan -Attention Task Percent Correct 54

31. Air Traffic Scenarios Test Percent at Destination 55

32. Air Traffic Scenarios Test Delay 56

33. MATB-Monitoring Task Mean RT 57

34. MATB-Monitoring Task Errors 58

35. MATB-Communications Task Mean RT 59

36. MATB-Communications Task Errors 60

37. MATB-Tracking RMS Error 61

38. MATB-Resource Management Task Mean Absolute Deviation 62

39. Antihistamine Symptoms Questionnaire Mean Score 63
Figure Page

40. Activity State Questionnaire Mean PHYSICAL Score 64

41. Activity State Questionnaire Mean PREP Score 65

42. Mood Scale II Activity Scale 66

43. Mood Scale II Happiness Scale 61

44. Mood Scale II Depression Scale 68

45. Mood Scale II Anger Scale 69

46. Mood Scale II Fatigue Scale 70

47. Mood Scale II Fear Scale 71

48. Mood Scale II Response Time 72

LIST OF TABLES

Table Page

1. Summary of Task Codes 73

2. Example Protocols for Dosing and Testing during Day Shift and Midshift Sessions 74

3. Individualized Lambda Values for Dual Task 74

4. Summary of Significant ANOVA Effects 75

VI1
EXECUTIVE SUMMARY

Considerable concern has been raised in recent

years about factors in the work place that compromise

the effectiveness ofworkers. Ofspecial concern for the

aviation environment is the fact that shift work appears to have negative effects on sleep patterns, selfreported satisfaction with working conditions, and

task performance. In 1983, Schroeder and Goulden

compiled a bibliography of research related to shift

work. At that time, over 1300 studies had been published—a clear indication of the level of concern for

this issue. Such concern seems all the more relevant

today, especially for Air Traffic Control Specialists

(ATCSs) who have in the U.S. historically worked a

rapid rotating shift schedule called the "2-2-1". This

schedule involves working two afternoon shifts, followed by two morning shifts, and one "midshift" or

night shift, within a five-day period. Recent laboratory and field research suggests that such schedules do

disrupt sleep patterns among ATCSs. Further evidence based on a wide range of jobs in the aviation

environment supports this finding.

Also ofincreasing concern for the aviation environment is a recent finding regarding common over-thecounter (OTC) drug use. A comprehensive survey of

aviation accidents involving fatalities revealed that

OTC antihistamines were among the most frequently

found drugs. While antihistamines were present in

only 3.7% ofthe 1845 fatalities with positive toxicology results between 1989 and 1993, they presented a

relatively high frequency rate compared to all other

drugs. As early as 1968, Federal Aviation Administration (FAA) researchers had discovered that the administration ofone common antihistamine, chlorpheniramine

maleate, significantly reduced psychomotor performance. Equally important was the additional finding

that, while both chlorpheniramine maleate and altitude had significant negative effects on psychomotor

performance, there was a negative "synergistic effect"

such that performance decrements under the two factors

in combination were much greater than the simple sum

ofthe decrements for each factor.

Such findings raise serious concerns regarding the

effects ofshift work and OTC antihistamine use in the

aviation environment. Perhaps more troubling is the

possibility that antihistamines may well be used during shift work, thereby possibly compounding these

negative effects. The purpose of the study reported

here was to investigate the effects of work shift (Day

Shift vs. Midshift) and a specific antihistamine drug

(chlorpheniramine maleate) on human operator performance. In addition, the effect of time on task, or

successive drug doses, through the shift (Session effect) was also assessed. A battery of cognitive and

human performance tasks was used to investigate

these effects, as well as various self-report measures of

physical state, mood, and workload.

Sixteen male subjects, well-trained on the battery

oftasks, participated in the study. Subjects ranged in

age from 21 to 38 years with a mean of 25.9 and a

standard deviation of 5.6 years. All subjects were

surveyed for self-reported normal (or corrected-tonormal) vision, normal hearing, and the absence of

any central nervous system stimulant or depressant

medications. Due to the nature of the study, additional relevant information about alcohol, caffeine,

medication, and possible drug use was obtained. On

average, subjects consumed 3.8 alcoholic beverages

per week. Average coffee consumption was 2.6 cups per

day, and average cola consumption was 1.5 cans per day.

Several factors were considered in selecting tasks

for this study. One critical factor was the specific

information processing skills necessary in typicalsafetysensitive jobs, such as air traffic control or aircraft

piloting. Another relevant issue was the information

provided by a specific task with respect to the cognitive processes or information processing stages affected by one or more risk factors. These and other

factors were taken into account in reviewing a large

number of human performance task batteries. As a

result, five performance tasks (Spatial Processing,

Critical Tracking, Dual Task—Tracking / Sternberg

Memory, Attention Switching—Manikin Task /
Mathematical Processing, and the NovaScan FAA

Task), two work sample tasks (Air Traffic Scenarios

Test and Multi-Attribute Task Battery), and four

subjective scales (Antihistamine Symptoms Questionnaire, Activity State Questionnaire, Mood Scale II,

and NASA Task Load Index) were selected for inclusion in the study.

All tasks were presented on eight microcomputer

workstations. Each workstation consisted of a Gateway 486-33 MHz processor with the necessary input

devices ("Anykey" keyboard, Microsoft mouse,

Kensington Expert Mouse 4.0 trackball, CH Products Flightstick, and NovaScan interface box). All

data were recorded on these machines and on subject

diskettes and then downloaded to a central data managementsystem. The software automatically performed

all functions such as subject identification, file naming, test sequencing, and data backup.

Testing was conducted on four weekends with all

subjects tested under all four ofthe drug-shift combinations. Two of the weekends involved "Day Shift"

testing and two weekends involved "Midshift" testing. Day Shift testing began on Saturday mornings at

8:00 a.m. or 10:00 a.m. and ended 11 hours later.

Midshift testing began on Friday evenings at 8:00

p.m. or 10:00 p.m. and also lasted 11 hours. Day Shift

and Midshift testing conditions were randomly assigned to the four weekend test days. Subjects were

randomly assigned to counterbalanced orderings of

the antihistamine-placebo conditions on both Day

Shift and Midshift test days.

Subjects were advised to eat only light meals well in

advance of the initial test session and to avoid any

foods that might slow drug absorption rate (e.g., high

fat foods or dairy products). On each test day, subjects

entered the laboratory, completed some general questionnaires, and were given a beverage containing approximately 6ozoffruitjuice,loz ofcrushed ice, and

either 4 mg of ChlorTrimeton® brand chlor—

pheniramine maleate in syrup form or a placebo. The

placebo beverage had two to three drops of the

chlorpheniramine maleate syrup floated on the surface. After consuming the beverage, subjects waited

one hour for the drug to reach an effective blood level,

answered some questionnaires related to mood, and

physical symptoms, and then performed the battery of

performance tasks. Following the two-hour test session, subjects were immediately given a light meal and

were allowed to relax for the remainder (approximately 40 minutes) ofthat hour. Again, the light meal

was devoid of any foods that might slow drug absorption rate. During the relaxation period, subjects typically watched television, read, or joined in small

group conversations. At the end of the first rest

period, which was four hours after consuming their

first drug/placebo beverage, subjects were administered their second drug/placebo beverage. On any

specific test day, a subject received only one drug

condition. That is, all beverages on any specific day

either contained the drug dose or the placebo. After

one hour, subjects again completed the entire battery

ofquestionnaires and performance tasks. A light snack

was served and subjects relaxed for the remainder of

that hour, which was followed by the third drug/

placebo beverage, one-hour waiting period, and the

third battery ofquestionnaires and performance tasks.

The results ofthis study can best be summarized by

the following table, which provides an overview ofthe

main effects and two-way interactions from the various analyses of variance. (There were no three-way

interactions that were statistically significant.) What

is immediately apparent from the table is that the

greatest number ofsignificant findings existed for the

Drug main effect and the Shift x Session interaction.

The Shift and Session main effects evidenced considerably fewer significant findings, and the other two

interactions had very few significant findings. Of

course, the simple frequency or even magnitude of

significant effects may not be the best way in which to

evaluate the outcome ofan investigation. However, in

this case, the general trends in significant findings

were not only apparent but were also consistent with

prior research evidence.

Because this study attempted to explore factors that

may affect safety-sensitive job performance, a broad

range of tasks and self-report measures was used and

a somewhat conservative examination of the results

was conducted. This conservative approach possibly

resulted in an over-inclusiveness with regard to significant findings. Thus, an examination of general

trends in the findings is an important consideration in

this study. Evidence of a factor having an influence
Summary of Significant ANOVA Effects.

(In small italics: 0.05 < p < 0.10; all others: p = .05)

TASK Drug Shift Session Drug x Shift Drug x Session Shift x Session

Spatial MNCORRT MNCORRT

Processing PC PC

Critical MEANL MEANL

Tracking MAXL

Dual Task RMS RMS RMS RMS

(Group Lambda) CTLOSS

MNCORRT

PC

THRPUT

CTLOSS

MNCORRT

PC

THRPUT

Dual Task RMS RMS RMS

(Indiv. Lambda) CTLOSS

MNCORRT

THRPUT

PC

CTLOSS

MNCORRT

THRPUT

IML Manikin MANCORRT

Attention MANCORTX

Switching MANPCX MANPC MANTP

Task Math MTHCORRT

MTHCORTX

MTHTP

MTHCORRT

MTHCORTX

MTHTP

NovaScan Vec VECPC VECCRT

FAA Test Mem MEMPC MEMPC

Air Traffic DELAY DELAY

Scenarios Test PCDEST

(ATST)

Multi- Monitor MONRT MONRT

Attribute Comm. COMCRT COMCRT COMCRT COMCRT

Task COMER COMER COMER

Battery Track TRKRMS TRKRMS

(MATB) Tanks TNKMAD TNKMAD

QUESTIONNAIRE Drug Shift Session Drug x Shift Drug x Session Shift x Session

Antihistamine

Symptoms

Questionnaire

Activity State PHYSICAL PHYSICAL PHYSICAL PHYSICAL

Questionnaire PREPARED PREPARED PREPARED

Mood Scale II ACTIVITY ACTIVITY ACTIVITY ACTIVITY ACTIVITY

HAPPINESS HAPPINESS

DEPRESSION

FATIGUE

HAPPINESS

FATIGUE

HAPPINESS

DEPRESSION

ANGER

FATIGUE

Overall RT Overall RT Overall RT

NASA-TLX FRUSTRATION

(ATST)

NASA-TLX MENTAL MENTAL

(MATB) PHYSICAL

PERFORMANCE

FRUSTRATION
across the performance and self-report measures, especially ifthat influence is consistently negative, would

raise much more concern regarding that factor than

would a random pattern ofsignificant findings. Such

a case of consistent negative influence existed for two

factors in this study.

The administration of chlorpheniramine maleate

appeared to have fairly profound negative effects

across a wide range of performance measures. Statistically significant negative effects resulted for some

component of every simple, dual, and complex task

that subjects performed. This trend in the results

suggested that, in general, the effects of chlorpheniramine maleate were not selective across various

types oftasks. Such broad-ranging effects might also

support the view that the actions ofthis drug disrupt

performance resources at a more fundamental and

broad level, such as basic central nervous system structures as opposed to a higher order or more selective level.

Despite the broad ranging effects of this drug, it

was possible to identify more specific aspects of performance that were disrupted by its administration.

For example, chlorpheniramine maleate exposure led

to degraded tracking performance on every task that

had a tracking component (Critical Tracking, both

variations of the Dual Task, and the MATB).

Chlorpheniramine maleate exposure also led to degraded response speed in a wide range oftasks. While

some ofthe simple tasks did not show RT differences

(e.g., Spatial Processing), significantly slower response

speed under chlorpheniramine maleate administration was found for five of the seven tasks that had

response speed components. This result would suggest that chlorpheniramine maleate did have significant negative effects on the speed with which subjects

responded to critical elements of these tasks. The

negative effects on response speed of this drug were

also recognized at another cognitive level. Subjects

were not only significantly slowed in their processing

ofthe performance tasks, they were also significantly

slowed in their response to self-reported mood ratings. This would suggestthat chlorpheniramine maleate not only caused slowing of the sensory motor

components oftask performance, but also caused slowing of the central processing of verbal tasks as well.

While it is clear that chlorpheniramine maleate had

a negative effect on a broad range of tasks and that it

negatively affected the speed with which subjects

responded to many tasks, it is less clear what effect

chlorpheniramine maleate had on accuracy. Of the

several tasks that provided accuracy measures, only

two yielded significant Drug main effects for percent

correct measures. Of interest though is the fact that

the two tasks yielding significant effects for accuracy

were both spatial tasks in nature. It is unclear whether

chlorpheniramine maleate may exert negative effects

on the accuracy of tasks related to spatial ability

because of its pharmacological actions on neurological centers that bear on spatial perception, or whether

the effects on accuracy are more broad-ranging, but

simply not pronounced enough in this study given the

dosages and tasks used. Thus, the accuracy results are

unclear, but the findings regarding spatial processing

seem very much worth additional study, especially because this is such a critical skill for both ATCs and pilots.

The other area where many significant effects were

found was the Shift x Session interaction. This refers

to the complex changes that occur across the three

testing sessions for the Day Shift vs. the Midshift

testing conditions. Once again, some component of

every task showed a Shift x Session interaction. In

general terms, the form ofthese interactions suggested

that performance for the Midshift was better than for

the Day Shift during the initial session of the work

shift (Session 1), but was worse than the Day Shift for

the final session (Session 3). In many cases, this was a

fairly complete cross-over interaction with the shift

groups appearing to differ at Session 1 and then

reversing the direction oftheir differences by Session

3. In other cases, the performance was different between the shift groups only at the beginning or at the

end ofthe shift. In these cases, the differences were in

the same direction as the differences in the cross-over

interactions. For example, in nearly all cases where

performance was comparable at the beginning ofthe

shift, performance for the Day Shift was better at the

end of the shift, and in nearly all cases where performance was comparable at the end ofthe shift, performance during the first session was better for the

Midshift. Viewed from a more general perspective,
these results revealed that across the sessions, performance during the Day Shift generally got better and

performance during the Midshift generally got worse.

From the results of the Activity State Questionnaire, it was clear that subjects reported significantly

more physical symptoms during the Midshift, as compared to the Day Shift, and they reported increasing

levels of physical symptoms across the testing sessions. It should be noted that, in general, these are

considered negative physical symptoms, and

chlorpheniramine maleate administration did not play

an important role in this effect. It appears that either

the Session factor has a quite different effect on the

sense of physical well being of the subjects, given the

shift they are working, or there might be a circadian

cycle ofchanges in physical symptoms in the opposite

direction across the two shifts. The level of negative

physical symptoms declined slowly across the Day

Shift. Across the Midshift, the level ofnegative physical symptoms increased markedly. What is interesting

is that the level of physical symptoms reported by

subjects at the end ofthe Day Shift is quite similar to

the level reported by subjects when they begin the

Midshift. This is typically the lowest level ofthe day.

In fact, these two points in time represent the same

time of day.

Otherself-report measures provided additional findings of interest. The Mood Scale II results revealed

that chlorpheniramine maleate administration resulted

in feelings of being less active and less happy. The

Shift factor seemed to have an even greater effect in

that not only did subjects feel less active and less

happy during the Midshift as compared to the Day

Shift, but also they felt more depressed and fatigued.

As noted previously, the size ofsome of these effects

was not great in absolute terms. The Shift x Session

interaction discussed above was also present for these

self-reported mood measures and these mood results

paralleled the findings noted for the Activity State

Questionnaire—that is, Day Shift ratings suggested

mood improvement across the shift, while Midshift

ratings suggested mood decline across the shift.

Self-report measures ofworkload were not as effective in revealing significant differences as were other

self-report measures, but the pattern ofself-reported

workload differences was interesting. The NASATLX measure yielded essentially no differences due to

chlorpheniramine maleate administration. This would

suggest that the NASA-TLX was relatively insensitive

to the antihistamine effect. On the other hand, there

were several self-reported differences in workload for

the Shift X Session interaction. Again, the ratings of

various aspects ofworkload were much like the ratings

for mood and physical condition. Subjects generally

felt that workload decreased across the Day Shift, but

seemed to increase across the Midshift. In this situation, the combined influence of these factors may

have been enough to cause subjects to subjectively

view the workload as having changed.

The interpretation of the remaining significant

findings for the Shift and Session main effects are

difficult. These significant findings are clearly far

fewer in number and are further clouded by the fact

that the Shift X Session interaction was so influential.

Prudence would suggest that these results be cautiously generalized. In all cases, however, these effects

seemed to parallel the general findings already seen for

the Drug and Shift x Session factors.

Finally, there are a few additional observations that

are noteworthy. First, there did not seem to be strong

evidence for a pronounced negative effect as the result

of combining antihistamine and shift. This was surprising because logic would suggest that a sedating

drug in combination with a fatiguing work shift

might lead to an even more dramatic negative effect

on performance than either factor alone or than their

simple additive effect. This did not appear to be the

case in this study. Prudence, and perhaps simple

skepticism, would suggest that this finding be further

explored.

Second, the value of assessing change within a shift

was self-evident in this study. The dynamics ofperformance and mood change across shifts were quite

different, and the presence of cross-over interactions

would suggest that conducting studies without repeated measures across shifts (i.e., using global measures summed across the entire shift) would obscure

these very dramatic differences.

Third, asking subjects about antihistamine effects

based on known side-effects does not seem like a very

productive way to assess their influence, at least in the

manner used in this study. Perhaps a more sensitive
response scale might gain better results. The measure

used in this study was clearly not effective for the dose

and level ofside-effects experienced.

Fourth, there was some limited evidence of differential sensitivity within tasks for various risk factors.

For example, the Attention Switching Task provided

some interesting results in addition to those already

noted. This task directed the subject to perform either

the Manikin Task or Mathematical Processing Task

component. The Mathematical Processing Task appeared to be reasonably sensitive to the effects of

chlorpheniramine maleate, while the Manikin Task

appeared to be sensitive to the combined effects of

Shift and Session. The Critical Tracking Task provided another possible example of differential sensitivity, and perhaps even the Spatial Processing Task.

This apparent differential sensitivity, while intriguing, must be interpreted cautiously. It may be that

some ofthese tasks do offer dependent measures that

respond differentially to various risk factors. It may

also be the case that other factors may have simply

reduced the possibility of finding significant differences for that measure in relationship to other factors

in this study.

In summary, the findings ofthis study suggest that

chlorpheniramine maleate alone has a fairly strong

negative influence on a wide range of task performance and mood measures. There also seems to be a

rather complex relationship between work shift and

time on the shift (Session) such that performance and

mood during the Day Shift tend to get better and

during the Midshift tend to get worse. No evidence

was found that chlorpheniramine maleate and work

shift combine in producing a multiplicative effect on

performance or mood.
WORKSHIFT AND ANTIHISTAMINE EFFECTS ON TASK PERFORMANCE

1.0 INTRODUCTION

Considerable concern has been raised in recent

years about factors in the work place that compromise

the effectiveness ofworkers. Ofspecial concern for the

aviation environment is the fact that shift work appears

to have negative effects on sleep patterns (Akerstedt,

1990; Turek, 1986), self-reported satisfaction with

working conditions (Melton, McKenzie, Smith, Polis,

Higgins, Hoffman, Funkhouser, & Saldivar, 1973),

and task performance (Alluisi & Morgan, 1982). In

1983, Schroeder and Goulden compiled a bibliography ofresearch related to shift work. At that time, over

1300 studies had been published—a clear indication

of the level of concern for this issue.

Such concern seems all the more relevant today,

especially for Air Traffic Control Specialists (ATCSs)

who have in the U.S. historically worked a rapid

rotating shift schedule called the "2-2-1" (Melton &

Bartanowicz, 1986; Price & Holley, 1990). This

schedule involves working two afternoon shifts, followed by two morning shifts, and one "midshift" or

night shift, within a five-day period. Recent laboratory

and field research suggests that such schedules do

disrupt sleep patterns among ATCSs in particular

(Cruz & Delia Rocco, 1995; Delia Rocco & Cruz,

1995), and appear to disrupt work performance in

numerous job categories (Alluisi & Morgan, 1982).

Further evidence based on a wide range of jobs in the

aviation environment supports this finding (Rosekind,

Gander, Miller, Gregory, Smith, Weldon, Co,

McNally, & Lebacqz, 1994).

Also of increasing concern for the aviation environment is a recent finding regarding common over-thecounter (OTC) drug use. A comprehensive survey of

aviation accidents involving fatalities revealed that

OTC antihistamines were among the most frequently

found drugs (Canfield, Flemig, & Hordinsky, 1995).

These authors noted that "Chlorpheniramine and

diphenhydramine, two antihistamines found in 68 of

the pilots analyzed [i.e., fatalities], are sedative and

may cause impairment of a pilot's ability to react to an

emergency." While antihistamines were present in

only 3.7% of the 1845 fatalities with positive toxicology results between 1989 and 1993, they presented a

relatively high frequency rate compared to all other

drugs. The antihistamines were also about the only

drugs that had a sedating effect and were clearly

disproportionately represented among all drug categories (with only common analgesics rating noticeably higher).

As early as 1968, Federal Aviation Administration

(FAA) researchers had discovered that the administration of one common antihistamine, chlorpheniramine

maleate, significantly reduced psychomotor performance (Higgins, Davis, Fiorica, Iampietro, Vaughan,

& Funkhouser, 1968). Equally important was the

additional finding that, while both chlorpheniramine

maleate and altitude had significant negative effects on

psychomotor performance, there was a negative "synergistic effect" such that performance decrements

under the two factors in combination were much

greater than the simple sum ofthe decrements for each

factor. This would suggest that common OTC antihistamines may pose a significant risk to the aviation

community. Also of interest is the fact that the frequency of OTC drugs found in aviation fatalities has

increased over 140% between 1989 and 1993

(Canfield et al., 1995). The authors of this report

judiciously note that this increased frequency may be

the result of improved biochemical analysis techniques

or may be due in part to the broader availability and

use of OTC drugs during this period.

Such findings raise serious concerns regarding the

effects ofshift work and OTC antihistamine use in the

aviation environment. Perhaps more troubling is the

possibility that antihistamines may well be used during shift work, thereby possibly compounding these

negative effects. The purpose of this study was to

investigate these potential effects. The implementation and completion of this study were significantly

aided by the fact that the FAA was simultaneously

supporting an assessment of both simple and complex

performance tasks that appear to be sensitive to the

effects of drugs and other risk factors, such as fatigue,

alcohol, and sleep loss (see Gilliland & Schlegel,

1997). By recruiting highly trained subjects from this

prior study, considerable reductions in training costs,

laboratory setup costs, implementation costs, and

startup time were realized.
2.0 BACKGROUND

2.1 Shift Work

The role of biological rhythms in regulating physiological and behavioral functioning has been extensively investigated. It has been well established that

human biological functions (Aschoff, 1965, 1981;

Wever, 1979, 1985) and a wide range of performance

capabilities (Colquhoun, 1982; Folkard & Monk,

1985; Kleitman, 1963) vary within a reasonably welldefined circadian cycle. As might be expected, sleep/

wake and temperature cycles also assume rhythmic

patterns and interact in a complex fashion with biological and performance rhythms, all seemingly regulated by time cues (or "Zeitgebers," see Rutenfranz,

Aschoff, & Mann, 1972), not all of which are fully

understood (Wever, 1981). What has been well agreed

upon, however, is that there appears to be a complex

interaction between sleep cycles and the overall circadian pattern of human biological and behavioral functioning (Aschoff, 1981; Wever, 1981), and that either

shifts in the sleep cycle (see Monk, 1990; Taub &

Berger, 1973, 1976) or sleep loss (Collins, 1976;

Naitoh, 1976; Wilkinson, 1965; Williams, Lubin, &

Goodnow, 1959) can have quite negative effects on

performance capability.

Of course, one factor that typically alters the sleep

cycle is a change in work shift. Changing work shifts

has been the subject of intense study (see Folkard,

1981; Folkard & Monk, 1985). In fact, Schroeder and

Goulden (1983) compiled over 1300 references on the

topic. Change in sleep appears to be such a critical

factor in degraded performance (Akerstedt, 1990) that

it even exceeds sleep loss (Taub & Berger, 1973) in its

negative influence. However, despite several decades of

research, it is not uncommon to find a hint ofexasperation in researchers trying to unravel the myriad factors

influencing the relationship between shift work and

performance. Folkard (1981) reflects this in his summary statement, "At this stage all that can be concluded with certainty is that the problem of impaired

night shift performance is far more complex than has

been recognized in the past" (p. 301).

From a more circumscribed perspective, the influence of change in work shift on air traffic controllers

has received careful investigation. As noted previously,

ATC facilities typically use the 2-2-1 shift schedule,

and it is generally recommended over other shift

schedules (Melton & Bartanowicz, 1986). This schedule usually results in a reduction in sleep per day across

the week in comparison to a regular five-day, "straight"

shift schedule (Schroeder, Rosa, Witt, & Banks, 1995;

Saldivar, Hoffman, & Melton, 1977).

In one ofthe earliest studies among ATCs of the 2-

2-1 work shift schedule compared to the straight shift

schedule, Melton et al. (1973) found that heart rate

was elevated during the night shift of the 2-2-1 shift,

as compared to the straight shift. Other differences

were only slight or inconsistent. The authors concluded that any differences between the shifts was too

slight to be of serious concern. However, it was

noteworthy that in the case of both shift schedules, the

ratings for the midshift condition were considerably

more negative than those for the day or evening shift.

Melton and his colleagues also studied two different

air traffic control centers and, in the course of this

investigation, again confirmed that there was little

evidence for differences in stress or performance due to

the 2-2-1 schedule, as compared to the straight shift

schedule (Melton, Smith, McKenzie, Saldivar,

Hoffman, & Fowler, 1975).

More recently, Delia Rocco and Cruz (1995) investigated the effects of the 2-2-1 schedule on a variety of

measures and found that the 2-2-1 schedule disrupted

sleep patterns more than the straight-shift pattern,

and sleep quality ratings also declined under this

schedule. While their report focused on the dynamics

of the sleep cycle, the authors did note that performance was degraded, but primarily on the night shift

component. Cruz and Delia Rocco (1995) have also

reported a field study of sleep patterns among ATCs

who were working various shift schedules (including

straight, 2-2-1, and 2-1-2 shifts). The results of this

study revealed that there was little, if any, difference

between the shift schedules in terms of sleep function

or quality.

In more general terms, past research suggests that

the disruption of circadian cycles, especially sleep, can

have a detrimental effect on performance. While changes

in work shift can certainly cause circadian rhythm

disruption, the degree to which various shift changes

differ from one another in their effects on performance

is unclear. It would appear that the myriad factors that

mediate the relationship between sleep disruption and

performance may cause performance decrements just

as large as the differences between various shift schedules. What does seem clear, is that the midshift (or
night shift) is the most unpopular and the most

potentially degrading shift with respect to performance (Melton & Bartanowicz, 1986).

2.2 Antihistamines

Antihistamine drugs act primarily by blocking the

receptor sites of histamine, a depressor amine that is

produced by the enzyme histidine decarboxylase acting on the amino acid histidine. Histamine is stored

mainly in mast cells. When the body is irritated, the

mast cells release varying amounts of histamine, which

causes vasodilation and increased permeability of the

blood vessel wall, the physiological precursors of common allergic reactions. The biochemical action of

histamine is due to its direct binding at two known

receptor sites, which are classified as either H, or H2 sites.

The origins of antihistamine drugs can be traced to

the early work of Bovet and Staub (1937), although

widespread therapeutic use of antihistamines did not

occur until the introduction of pyrilamine maleate

(Bovet, Horclois, and Walthert, 1944), which is still

among the most effective antihistamines. These early

antihistamines, as well as scores of others synthesized

since the introduction of pyrilamine, are effective in

opposing or blocking the action of histamine at the Hj

receptor sites. These antihistamines are particularly

effective in opposing the symptoms commonly associated with allergic reactions, but they pose a problem

because one oftheir major side-effects is sedation. Almost

all Hj-blocking antihistamines cause some level ofsleepiness, so much so that they form the active ingredients in

common, over-the-counter sleep medications.

Recently, new generations of Hj-blocking antihistamines have been introduced, as well as H2-blocking

antihistamines. These are often referred to as nonsedating antihistamines, because they have far less, or

nearly no, sedation side-effect. Several studies have

shown that these newer, non-sedating antihistamines

have few, if any, negative effects in comparison to the

earlier Hj-blocker antihistamines. For example, it has

been shown that newer, non-sedating antihistamines

have no apparent negative effects on various cognitive

and psychomotor abilities (Cingi, Cingi, & Cingi,

1990; Clarke & Nicholson, 1978; Fink & Irwin,

1979; Kulshrestha, Gupta, Turner, & Wadsworth,

1978; Nesthus, Schiflett, Eddy, & Whitmore, 1991;

Nicholson & Stone, 1982; Nicholson & Stone, 1986;

Philpot, Biegalski, & Brooker, 1993; Reeves, Blackwell,

Molina, & Hixson, 1989; Rice & Snyder, 1993;

Snyder & Berg, 1990; Tharion, McMenemy, & Rauch,

1994; Witek, Canestrari, Miller, Yang, & Riker,

1995). They also do not appear to disrupt EEG

activity (Loring & Meador, 1989; Stephens, Caldwell,

Comperatore, Pearson, & Delrie, 1992; Tharion et al.,

1994) or more basic sensory-psychophysical functions

(Fink & Irwin, 1979; Kulshrestha et al., 1978;

Nicholson, Smith, & Spencer, 1982; Nicholson &

Stone, 1982; Nicholson & Stone, 1986), and they do

not typically influence self-reported levels of subjective state (Philpot et al., 1993; Reeves et al., 1989;

Tharion et al., 1994). In addition, investigations of

complex job-related performance have revealed that

non-sedating antihistamines do not appear to affect

driving performance (Betts, Markman, Debenham,

Mortiboy, & McKevitt, 1984), marksmanship

(Johnson & McMenemy, 1989), flight simulator

performance (Philpot et al., 1993; Stephens et al.,

1992), or complex command, control, and communications (C3

) team performance (Eddy, Dalrymple, &

Schiflett, 1992).

Despite the lowered risk ofside-effects from the new

generation of non-sedating antihistamines, many

people use older Hj-blocking antihistamines, no doubt

because they can be purchased without prescription

and are prevalent in many OTC preparations. Many of

these antihistamines have been shown to produce

significant decrements in cognitive skills (Burns,

Shanaman, & Shellenberger, 1994; Cingi et al., 1990;

Hindmarch, Kerr, & Sherwood, 1991; Nesthus et al.,

1991; Nicholson & Stone, 1982; Nicholson & Stone,

1986; Reeves et al., 1989; Rice & Snyder, 1993;

Snyder & Berg, 1990; Witek et al., 1995) and basic

psychomotor abilities (Cingi et al., 1990; Clarke &

Nicholson, 1978; Fink & Irwin, 1979; Hindmarch et

al., 1991; Kulshrestha et al., 1978; Nesthus et al.,

1991; Nicholson, 1979; Nicholson & Stone, 1982;

Nicholson & Stone, 1986; Reeves et al., 1989; Rice &

Snyder, 1993; Snyder & Berg, 1990; Witek et al.,

1995). Further, negative effects due to the older Ht-

blocking antihistamines have been shown in studies of

job performance and studies of operational environments (Betts et al., 1984; Eddy et al., 1992; Johnson

& McMenemy, 1989; Stephens et al., 1992). So

obvious are the risks involved in using these drugs in

operational environments, a number of recent reviews

have warned of their negative consequences (Meltzer,

1990, 1991; Nicholson, 1985).
As noted previously, chlorpheniramine maleate is

among the more common drugs found in fatal aircraft

accidents. It is also one of the more common antihistamines used by allergy sufferers and is one ofthe most

commonly found antihistamines in a wide variety of

OTC cold, flu, and allergy medications. Chlorpheniramine maleate is a traditional H receptor site

blocker and has moderately high sedating effects (see

Goodman & Gilman, 1990; Manning & Gengo, 1993).

Chlorpheniramine maleate has also been shown to

have fairly wide-ranging negative effects on a variety of

performance abilities. Early studies by FAA researchers revealed that administration of a compound containing chlorpheniramine maleate (4 mg) caused a

significant reduction in psychomotor ability (Higgins

et al., 1968), as did altitude. These researchers also

studied the combined effects of altitude and the

chlorpheniramine maleate compound and concluded

that, "There was...a synergistic effect, so that performance under both drug B [the chlorpheniramine

maleate compound] and 14,000 foot altitude conditions was poorer than the sum of the two separate

influences" (p. 3). These findings are particularly

sobering given the recent findings of Canfield et al.

(1995) regarding the incidence of chlorpheniramine

maleate in fatal aircraft accidents.

In later research, Clarke and Nicholson (1978)

investigated the effect of chlorpheniramine maleate (4

mg) on visuo-motor ability, as assessed by a pursuittype tracking task, and found that chlorpheniramine

maleate caused a significant degradation in performance with respect to the placebo condition. The

most negative effects on performance were found

approximately 1.5 hours after ingestion. This was also

one of the first studies to include self-report measures

ofthe subjects' performance effectiveness and sense of

well-being. Subjects reported no perceived decline in

performance ability as a result of chlorpheniramine

maleate, and reported only that they were significantly

less energetic than under placebo conditions. This

finding was important because it suggests that those

under the influence of chlorpheniramine maleate do

not always recognize the degree to which their performance is compromised, even when that performance is

significantly degraded. This finding must also be

tempered by the fact that other researchers have

reported no wide ranging negative effects of

chlorpheniramine maleate (4 mg) on performance

(such as critical flicker fusion, pursuit rotor, and

simple reaction time), but did find increases in subject

feelings offatigue among their subjects (Kulshrestha et

al., 1978). This disparity between the effects of

chlorpheniramine maleate on measures of performance

and self-reported mood has been noted by several

authors (Manning & Gengo, 1993; Meltzer, 1990,

1991; Nicholson, 1985). It serves as a point of considerable concern, especially in the case when an individual has taken chlorpheniramine maleate and

cognitively perceives little effect, yet demonstrates

significantly degraded performance.

Chapman and Rawlins (1982) conducted a study

in which subjects performed a letter cancellation task

and also provided self-reported assessments ofsedation

level (with a simple scale ranging from "wide awake" to

"almost asleep") following administration of

chlorpheniramine maleate (16 mg). The authors reported that subjects showed a significant reduction in

letter cancellation ability both 2 hours and 4 hours

after dosing and reported significantly more sedation

as compared to a control condition. Similar results

were found in a study of digit symbol substitution.

Nicholson, Pascoe, Turner, Ganellin, Greengrass, Casy,

and Mercer (1991) found that, in comparison to a

placebo condition, subjects administered 10 mg of

chlorpheniramine maleate showed significant declines

in digit symbol substitution performance, had significant reductions in sleep latency, and reported that

they felt significantly more sleepy.

Lee, Lader, and Kitler (1988) studied the effects of

chlorpheniramine maleate (12 mg) on tapping and

reaction time performance, as well as EEG activity and

self-reported measures of mood and physical symptoms. The results of this study revealed that

chlorpheniramine maleate administration led to initial increases in tapping rate and then fairly profound

decreases. Reaction time was significantly slowed overall by the introduction of chlorpheniramine maleate,

and EEG patterns were disrupted, causing shifts

toward increasing alpha wave activity (8.0 - 13.0 Hz),

a pattern commonly associated with lower activation

states. Subjects in this study reported improvement in

general alertness and contentedness after the administration of chlorpheniramine maleate, but also reported

less ability to concentrate. It is unclear why subjects in

this study would report subjective feelings that differ

so from those reported in other studies, that is, either

no difference as a result of chlorpheniramine maleate

administration or various negative effects. The only

factor that might explain this finding is the form of

chlorpheniramine maleate administered in this study.
These authors administered a sustained-release preparation noting that, "...performance impairment is less

than that with standard formulations" (p.111). The

authors concluded that, "...sustained-release preparations seem a useful advance on standard formulations

with respect to obviating central sedative effects" (p. 117).

In another study of chlorpheniramine maleate effects on EEG activity, Loring and Meador (1989)

found that an 8 mg dose caused significant slowing of

the P300 cortical evoked response. The P300 response

is an EEG measure that has been linked to both speed

of cortical processing and sustained attention ability.

These results, as well as those of Lee et al. (1988),

suggest that the degrading effects that chlorpheniramine maleate seems to exert on performance

may well be mediated indirectly by the suppression of

midbrain H, histaminergic receptors that play a central role in regulating general arousallevel (see Goodman

& Gilman, 1990; Manning & Gengo, 1993; Prell &

Green, 1986).

More recently, the effects of chlorpheniramine

maleate (16 mg) have been studied with respect to

several cognitive and psychomotor performance tasks

including digit symbol substitution, critical flicker

fusion, and digit cancellation tasks (Khosla, Saha,

Koul, Chakrabarti, Sankaranarayanan, & Sharma,

1993). The data from this study are difficult to

interpret due to contradictory statements by the

authors, but there is at least clear evidence of impairment on several of the cognitive and psychomotor

performance tasks studied, and the subjects also reported significant increases in self-reported sleepiness,

tiredness and difficulty in concentrating. Witek et al.

(1995) also performed a study of chlorpheniramine

maleate effects on a wide range of computerized tasks

including reaction time, divided attention, and hand

steadiness. Chlorpheniramine maleate (4 mg) was

shown to degrade reaction time and resulted in higher

levels of self-reported sleepiness.

Finally, one study investigated chlorpheniramine

maleate effects on pilot performance. Philpot et al.

(1993) examined the performance of military pilots

under the influence of chlorpheniramine maleate (4

mg) and found no negative influence on flight simulator performance, and a wide range of psychological

and neuropsychological tests. The subjects did report

a wide range of negative symptoms associated with

exposure to chlorpheniramine maleate. The authors of

this study note an important point with regard to

chlorpheniramine maleate effects on performance:

While many studies do report degraded performance

following administration of chlorpheniramine maleate, it is possible for highly motivated subjects to

overcome these negative effects and perform to competent or even exceptional levels.

In general, there is considerable evidence that

chlorpheniramine maleate has the capacity to exert a

negative influence on a wide range of performance

capabilities and to negatively influence self-reported

cognitive states. Admittedly, there is some evidence to

the contrary, but the preponderance of research results

suggests fairly clear evidence of detrimental effects.

Equally critical is exploring the possibility of interactive effects of chlorpheniramine maleate and other risk

factors.

3.0 OBJECTIVES

The primary objective ofthis project was to provide

the FAA with a highly-controlled laboratory investigation addressing the effects ofshift work and antihistamine administration on both simple and complex task

performance. Three major issues were addressed:

(1) the effect ofshift work on simple and complex task

performance,

(2) the effect of multiple recommended therapeutic

doses of OTC antihistamine on simple and complex task performance, and

(3) the interactive effect of shift work and an OTC

antihistamine on simple and complex task performance.

4.0 METHODOLOGY

4.1 Project Design

As noted above, this research was significantly aided

by the recruitment of subjects who had been thoroughly trained on simple and complex performance

tasks that were believed to be sensitive to drug and

other risk factor effects. The prior study is presented in

its simplest form in Figure 1. The overall study

consisted of four stages extending over a six-week

period. The first stage lasted one week and included

subject screening, pre-testing, and selection. During

the second stage, which also lasted one week, subjects

underwent orientation and training on several tasks

that are described below.
The third stage provided an extended series of test

sessions spanning four additional weeks. The fourth

stage of the study provided Specialized Investigation

Periods that were typically conducted on weekends so

as to minimize any influence on daily testing sessions

during the week. Testing periods varied according to

the requirements of the specific research protocol. The

subjects in this prior study were extensively trained

and practiced on a number of simple and complex

performance tasks assessing a wide range of cognitive

and psychomotor functions. An investigation of the

learning characteristics of this group, as well as information on the reliability and stability of the performance measures across the five weeks of task

performance, are available in a report by Gilliland and

Schlegel (1997). The present study was conducted

immediately following the conclusion of the prior

study.

4.2 Subjects

Subjects were recruited from among those who

participated in the previously mentioned study (see

Gilliland & Schlegel, 1997). From among those subjects who volunteered, 16 subjects were randomly

selected to participate in the present study. Subjects

were originally recruited from University of Oklahoma

psychology and engineering classes, the general student body, and the Norman, Oklahoma, regional

community. All subjects were male. Subjects in the

present study ranged in age from 21 to 38 years with

a mean of 25.9 and a standard deviation of 5.6 years.

All subjects signed an Informed Consent Form approved by the University of Oklahoma Institutional

Review Board—Norman Campus. A bonus payment

system was used to increase motivation and study

completion rate. Subjects were paid a base rate for the

number of hours they participated and upon completion of the study, they were given an additional bonus

for every hour of participation.

All subjects were surveyed for self-reported normal

(or corrected-to-normal) vision, normal hearing, and

the absence of any central nervous system stimulant or

depressant medications. Due to the nature of the

study, additional relevant information about alcohol,

caffeine, medication, and possible drug use was obtained. On average, subjects consumed 3.8 alcoholic

beverages per week, although individual consumption

ranged from 0 to 18 beverages per week. Subjects

reported that, on average, they drank two or three

times a month with beer as the primary alcoholic

beverage. Caffeine consumption was relatively low.

Average coffee consumption was 2.6 cups per day. The

average coffee consumption was skewed by one subject

who reportedly drank two to three 12-cup pots of

coffee daily. Average cola consumption was 1.5 cans

per day. A detailed breakdown of subject characteristics is provided in Appendix A.

4.3 Test Battery

Several factors were considered in selecting tasks for

this study. One critical factor was the specific information processing skills necessary in typical safety-sensitive jobs, such as air traffic control or aircraft piloting.

Another relevant issue was the information provided

by a specific task with respect to the cognitive processes

or information processing stages affected by one or

more risk factors. These and other factors were taken

into account in reviewing a large number of human

performance task batteries. As a result, seven tasks, and

four subjective scales from various sources were selected for inclusion in the study.

Brief descriptions of the RTP tests, work samples,

and subjective rating scales used in the study follow.

Detailed descriptions of the tasks and a list of relevant

publications are provided in Appendix B.

Performance Tasks

Spatial Processing (SPA) — involves indicating

whether a rotated pattern of histograms is the same as

one previously presented. The test lasts 3 minutes.

Critical Tracking (TRK) — requires tracking an

unstable object on the display using a trackball for 2

minutes.

Dual Task (DUL) — involves performing the

Sternberg Memory Search while Tracking. The

Sternberg Memory Search involves indicating whether

a letter is the same as one of those in a previously

memorized set. The test lasts 3 minutes.

IML Switching Task (NTI) — involves responding

to 1 of 2 tasks presented simultaneously on each screen

display. In the Manikin task, the subject presses a key

to indicate which hand of a manikin holds a matching

symbol. In the Mathematical Processing task, the

subject presses a key to indicate whether a sum ofthree

numbers is greater or less than 5. The test lasts 4

minutes.

NovaScan FAA Task (NSF) — consists of

integrated responses to three tasks. For two of the

tasks, stimulus screens are presented in directed attention fashion with a series of stimuli from one task
alternating with a series ofstimuli from the other task.

In addition, a vigilance/attention task is performed for

every stimulus screen. In the Visual Search and Vector

Projection task, the subject searches for two labeled

vectors, makes mental rotations ofthe vectors based on

verbal on-screen instructions, and responds as to

whether the rotated vectors would intersect either on

or off the screen. In the Spatial Memory task, the

subject memorizes the position and shape of a missing

symbol for later comparison with the next spatial

memory stimulus screen. For the Attention task,

subjects look for the presence of small symbols in the

corners of each screen. The test is based on a fixed

number of stimuli and test time is thus a function of

subject proficiency.

Work Sample Tasks

Air Traffic Scenarios Test (ATST) — an approximation of the air traffic control environment which

involves the directing of planes to their destinations

using altitude, speed, and heading changes. The work

version of the task lasts 25 minutes.

Multi-Attribute Task Battery (MATB) — an approximation of the air crew operations environment

which includes a monitoring task (a set of lights and a

set of dials), an auditory communications task, a

compensatory tracking task, and a resource management task involving the monitoring and control offuel

tank levels. The work version of this task lasts 40

minutes.

Subjective (Self-Report) Measures

Antihistamine Symptoms Questionnaire (AHSQ)

— consists of a checklist of common side-effects

associated with the use of antihistamines. This scale

was rationally constructed based on side-effects listed

in the medical and pharmaceutical literature. The test

takes approximately 1 to 2 minutes.

Activity State Questionnaire (ACTSQ) — consists

of 25 items scored on a seven-point scale, which was an

expanded form of the Pennebaker Physical Symptoms

Checklist to assess the current state of physical health.

Subjects also responded to two questions regarding

their level of preparedness for task performance. The

test takes approximately 2 minutes.

Mood Scale II (MOOD) — involves pressing a

numbered key on the computer keyboard to indicate

the level of agreement with each of 36 descriptive

adjectives to assess the current mood in the categories

of activity, happiness, depression, anger, fatigue and

fear. The test takes approximately 2 minutes.

NASA Task Load Index (TLX) — consists ofratings

of task workload using the categories of mental demand, physical demand, temporal demand, performance, effort, and frustration. This collection ofratings

was obtained following each work sample task. The

ratings take approximately 1 minute. (Note: While

the NASA TLX ratings were among the subjective

ratings administered, they were completed by the

subjects after each of the simulated work tasks and,

thus, were linked logically to the workload generated

by these tasks. The use ofthese TLX ratings can not be

used as a reflection of overall workload experienced by

the subject during the entire testing session.)

Table 1 presents a summary of the task codes used

throughout the remainder ofthe report when referring

to the various tasks and self-report measures.

4.4 Equipment

All tasks were presented on eight microcomputer

workstations. Each workstation consisted of a Gateway

486-33 MHz processor with the necessary input

devices ("Anykey" keyboard, Microsoft mouse,

Kensington Expert Mouse 4.0 trackball, CH Products

Flightstick, and NovaScan interface box). All data

were recorded on these machines and on subject

diskettes and then downloaded to a central data

management system (Gateway 486-66 MHz) for data

reduction and analysis using Microsoft Excel and SAS.

In cases of emergency, this machine also served as a

backup workstation. Testing was automated to allow

a subject to perform the tests independently and in the

minimal amount of time. Of course, multiple experimenters were present at all times to monitor the

subject's safety and performance, and provide assistance, if needed. The software automatically performed all functions such as subject identification, file

naming, test sequencing, and data backup.

4.5 Test Facilities

All testing was conducted in laboratory space located in the basement of Dale Hall at the University of

Oklahoma. The testing workstations were approximately 3 ft wide and 3 ft deep and were located in one

room (approximately 13 ft by 20 ft). The stations were

divided by 3-inch thick acoustic panels. The comput-
ers and response devices were placed on tables at the

testing stations positioned at a height of approximately 28 inches.

Another room of approximately the same size served

as the data reduction and project management office.

The third room served as an auxiliary room for interviewing, orientation, and miscellaneous activities. All

of these rooms represent modern laboratory space with

centrally controlled heating and air conditioning.

Temperature in the testing room was maintained at

approximately 68° F throughout the test sessions.

4.6 Experimental Procedure

Data were collected on weekend test days that were

conducted over a five-week period. The term "test day"

will be used to refer to the actual time period on the

weekend during which testing occurred. This was

either during the day or during the Midshift period (as

described below). The term "session" will refer to a

series of tasks performed at a specific time on the test

day. "Trial" will usually refer to a discrete response by

a subject to the presentation of a single stimulus for

any specific task. For example, subjects performed a

series oftrials for each of the tasks (which is referred to

as a "session") three times on each test "day."

Subjects returned to the laboratory for two, twohour re-training sessions during the week before the

first weekend test day. These re-training sessions were

strategically introduced prior to the study and between the weekend test days to insure that subjects

maintained reliable performance on the various tasks.

On the first re-training day, subjects completed consent forms and questionnaires and were provided

additional information regarding the antihistamine to

be administered and detailed information about the

testing protocol. They were also required to sign

agreements to comply with restrictions on their transportation and activity after testing sessions during

which they might be administered antihistamines. All

subjects were also scheduled for one additional retraining day in the week prior to the first weekend test

day, and were assigned to testing groups for the

coming weekends.

Testing days were conducted on four weekends. A

holiday fell in the middle of the sequence ofweekends,

and thus two successive weekends with testing days

were followed by a holiday weekend, which was in turn

followed by two more successive weekends with test

days. Two of the weekends involved "Day Shift"

testing days and two weekends involved "Midshift"

testing days. Testing days conducted under the Day

Shift condition began on Saturday mornings at 8:00

a.m. (or 10:00 a.m., see group sequence below) and

ended 11 hours later. Testing days conducted under

the Midshift condition began on Friday evenings at

8:00 p.m. (or 10:00 p.m., see group sequence below)

and also lasted 11 hours. Day Shift and Midshift

testing conditions were randomly assigned to the four

weekend test days. Subjects were randomly assigned to

counterbalanced orderings of the antihistamine-placebo

conditions on both Day Shift and Midshift test days.

Because the laboratory contained eight microcomputer workstations, the 16 subjects were randomly

assigned to one of two groups. During each testing

day, one group of subjects began the experimental

protocol two hours after the other group to avoid

conflicts in using the workstations. In this manner, the

experimental protocol was carefully structured to provide complete and identical test session sequences for

both groups of eight subjects (see Table 2). The

experimental protocol for each test day required eleven

hours and included three, standard OTC doses of

chlorpheniramine maleate (or placebo) at recommended 4-hour intervals followed by testing sessions

on the various performance tasks.

The protocol included the following series of activities. Subjects were advised to eat only light meals well

in advance of the initial test session and to avoid any

foods that might slow drug absorption rate (e.g., high

fat foods or dairy products). On each test day, subjects

entered the laboratory, completed some general questionnaires, and were given a beverage containing approximately 6 oz offruit juice, 1 oz of crushed ice, and

either 4 mg of ChlorTrimeton® brand chlorpheniramine maleate in syrup form or a placebo. The

placebo beverage had two to three drops of the

chlorpheniramine maleate syrup floated on the surface. The fruit juice beverage seemed to mask very

effectively the presence of the drug, and any slight

difference in aroma was equalized in the placebo

beverage by the few drops of the drug syrup that were

floated on top.

After consuming the beverage, subjects waited one

hour for the drug to reach an effective blood level,

answered some questionnaires related to mood, and

physical symptoms, and then performed the battery of

performance tasks. Following the two-hour test session, subjects were immediately given a light meal and

were allowed to relax for the remainder (approximately

40 minutes) of that hour. Again, the light meal was
devoid of any foods that might slow drug absorption

rate. During the relaxation period, subjects typically

watched television, read, or joined in small group

conversations. At the end ofthe first rest period, which

was four hours after consuming their first drug/placebo beverage, subjects were administered their second drug/placebo beverage. On any specific test day,

a subject received only one drug condition. That is, all

beverages on any specific day either contained the drug

dose or the placebo. After one hour, subjects again

completed the entire battery of questionnaires and

performance tasks. A light snack was served and subjects relaxed for the remainder of that hour, which was

followed by the third drug/placebo beverage, onehour waiting period, and the third battery of questionnaires and performance tasks.

When subjects completed their third testing session for the day, they were escorted to their homes.

Due to the potential sedating nature of antihistamines, no subject was allowed to drive from the

laboratory or leave for activities that would involve

physical risk or the use of machinery. At the end ofthe

fourth and final test day, subjects were paid for their

involvement in the study and, once again, were escorted to their homes.

During the course of the study, there were a total of

18 testing sessions (6 re-training sessions and 12 drug

sessions). For all testing sessions, subjects first performed the simple single and dual-type performance

tasks and then performed longer trials of both the

ATST (25 min) and MATB (40 min) complex tasks.

Nine different orders of the simple tasks were developed to minimize interference between consecutive

tasks (e.g., hand fatigue from consecutive TRK and

DUL trials). For each subject, these orders were

randomly assigned in two blocks (i.e., each order was

used once in the first nine sessions and once in the

second nine sessions). For a given subject, the same

order was never presented on consecutive sessions.

The order of the ATST and MATB tasks was

alternated, with either ATST performed before MATB

(order 1) or vice versa (order 2). For the first 16

sessions, the orders were blocked in sets of 4 sessions.

The order for the first session was randomly selected

and this specified the order of the remaining three

sessions in the block. For example, if the first session

was order 1, then the order of the four sessions in the

block was 1-2-2-1. On the other hand, if the first

session was order 2, then the order of the four sessions

in the block was 2-1-1-2. The last two sessions used

randomly assigned orders balanced across subjects and

across sessions within subjects.

For the ATST task, six different 25-minute scenarios involving 45 planes were used by all subjects.

For a given subject, the same scenario was used for the

first re-training session and for the third session on

each drug test day (i.e., for four specific sessions) to

enable a baseline comparison across all treatment

conditions. The remaining scenarios were randomly

assigned to the other sessions with a restriction prohibiting the assignment of the same scenario to two

consecutive sessions for the same subject.

There were five unique scripts for the MATB task.

As with ATST, the same script was used for the first retraining session and for the third session on each drug

test day. The remaining scripts were randomly assigned to the other sessions with the restriction prohibiting the assignment of the same script to two

consecutive sessions for the same subject.

During each session, two versions of the Dual Task

were performed by each subject in order to make the

task more sensitive to variations in subject ability. One

version (DULI) used an individualized lambda value

set to 70% of the average of the subject's maximum

lambda values for training Sessions 7 through 10 ofthe

TRK task. The other version (DULG) used a group

lambda value which was established as the average of

all subjects' individualized lambda's. The group lambda

value was set at 3.7. Table 3 presents the individualized lambda values for each subject.

5.0 RESULTS

This investigation can be described as having a

multifactorial design. The main effects were Drug

(antihistamine or placebo), Shift (Day Shift or

Midshift), Session (testing sessions across the Shift

period, that is, first, second, or third testing session

within each test day), and Group (first or second group

within each test day). An initial analysis of the data

revealed no significant findings for the Group main

effect suggesting that there were no differences due to

the two-hour offset in group starting time. Therefore,

the data were collapsed across this variable, and all

analyses reported below are based on a totally withinsubjects, Drug X Shift X Session ANOVA approach.

Results ofthese analyses for each task are presented below.
When exploring phenomena that may have implications for human safety, additional concern should be

exercised in statistical testing. Setting the alpha level

for statistical significance constitutes a tradeoff between the probabilities of Type I and Type II errors.

Traditional alpha levels of p = .05 or .01 may be

favored by some readers, but these are merely conventional alpha levels that must be considered within the

context ofthe investigation and the meaning of experimental results for broader issues. It could be argued

that in situations involving human safety, protecting

against Type II errors should take priority. That is, it

is possible that by chance alone a truly significant

effect will not be detected, which in this case would

mean that an important potentially hazardous drug

effect might go undetected due to a fairly stringent

decision rule for statistical significance. It was decided

to make it somewhat more difficult for this to occur in

this study by establishing alpha at p = .10. The

authors recognize that this has the simultaneous effect

of increasing the probability of a Type I error or "false

alarm" — detecting an apparent significant finding

that is not truly significant. However, the authors

believe that where human safety may be involved, the

slightly higher probability of a false alarm has less

potential risk than railing to detect real threats to safety.

5.1 Data Reduction

The procedure for data reduction involved several

phases. Raw and summary data files from the individual subject PC diskettes and workstation hard

drives were transferred to the Gateway 486/66 MHz

data management computer. Statistical Analysis System (SAS) DATA step input programs were used to

extract the data from the summary files and to create

individual SAS databases for each task. The SAS

UNIVARIATE procedure was used to provide extensive descriptive statistics for each dependent variable.

These analyses were reviewed for questionable data

points that could be the result of procedural errors or

data outliers. Very few outlier data points were removed prior to the summaries and analyses. The few

deleted observations were due to identifiable hardware, software, or subject errors. Data points in question were corrected where possible and removed when

necessary. Appendix D presents means and standard

deviations for all main effects (Drug, Shift, Session)

and two-way and three-way interactions for the major

performance task measures and subjective questionnaire measures.

5.2 ANOVA Results by Task

Spatial Processing Task

The primary significant effect for the mean response

time for Spatial Processing correct responses

(MNCORRT) was a Shift x Session interaction, F(2,

30) = 4.64, p = .02. This interaction was due to the

fact that while MNCORRT was somewhat faster

during the Midshift, as compared to the Day Shift, for

Sessions 1 and 2, this trend was reversed for Session 3

(see Figure 2). The MNCORRT measure also had a

significant Drug X Shift interaction, F(l, 15) = 3.52,

p = .08. Response time was faster for subjects under

antihistamine exposure during the Midshift (as compared to the Day Shift), but faster for subjects under

the placebo condition during the Day Shift (as compared to the Midshift).

A main effect for Drug was found for percent correct

(PC) for the Spatial Processing task, F(l, 15) = 4.77,

p = .04. As illustrated in Figure 3, PC for the Antihistamine condition (M = 92.56%, SD = 6.78%) was

significantly lower than PC for the Placebo condition

(M = 94.39%, SD = 4.89%). The Shift x Session

interaction was also significant for the PC measure,

F{2, 30) = 5.05, p = .01. This interaction effect was

apparently due primarily to the fact that the Midshift

condition had a lower PC than the Day Shift condition

during the first session as compared to the second and

third sessions. No other main effects or interactions

were significant for PC.

Critical Tracking Task

Antihistamine administration had a significant effect on the mean of the lambda's at control loss

(MEANL), F{\, 15) = 8.33, p = .01, but not on the

maximum lambda during the trial (MAXL). Figure 4

reveals that across all conditions, antihistamine had

the effect of reducing MEANL (overall effect: antihistamine M= 5.11, SD = .89; placebo M= 5.43, SD =

.79). A lower lambda is associated with poorer tracking

performance.

The Shift X Session interaction was significant for

both MEANL, F(2, 30) = 6.85, p = .004 (see Figure

4), and MAXL, F(2, 30) =11.87,/> = .0002 (see Figure

5). In both cases, the Midshift condition resulted in

better tracking performance than the Day Shift during

Session 1, but by Session 3 this relationship reversed.

10
Dual Task - Group Lambda

Tracking. There was both a significant Drug main

effect, F(l, 15) = 9.69, p = .007, and Session main

effect, F(2, 30) = 3.82, p = .03, for the root mean

square error measure (RMS) for the tracking component ofthis Dual Task. Figure 6 presents results for the

RMS tracking performance measure. Antihistamine

administration led to poorer tracking performance (M

= 49.0, SD - 16.5), as compared to the placebo

condition (M = 43.8, SD = 16.8). The significant

Session main effect suggested that there was also a

slight, but statistically significant, decrease in performance based on higher RMS error across the three test

sessions. There were also significant Drug x Session,

F(2, 30) = 3.56, p = .04, and Shift x Session, F(2, 30)

= 13.11, p = .0001, interactions. Again, Figure 6

reveals that, when exposed to antihistamine, subjects

worsened in performance from Session 1 to Session 2,

and then showed a small amount of improvement

during Session 3—a very minor inverted-U trend.

During placebo conditions, subjects showed a very

slight decrease in performance across the three test

sessions. The Shift X Session effect revealed a cross-over

type of interaction. Subjects performed better during

Session 1 in the Midshift condition, about equally well

in both the Day Shift and Midshift during Session 2,

and then performed worse in the Midshift condition

during Session 3.

The Drug main effect for the control losses measure

(CTLOSS) revealed the same general trend as that seen

for RMS error, F(l, 15) = 4.02,p = .06. At first glance,

Figure 7 might suggest an even greater effect than that

seen for RMS error, but the low amount ofvariation in

RMS error and the greater degree of variation in

CTLOSS accounts for the lack of a higher significance

level in the CTLOSS measure. Nonetheless, antihistamine did appear to have a fairly pronounced negative

effect on CTLOSS rate across conditions. Under antihistamine, control losses were approximately doubled

during the Day Shift and increased 30-40% during

Sessions 1 and 2 for the Midshift. There was, in

addition, a significant Shift X Session interaction for

CTLOSS, F(2, 30) = 4.12, p = .03. This effect

suggested that subject performance during the Day

Shift was poorer than during the Midshift for Sessions

1 and 2, and then became considerably better than

Midshift performance for Session 3. A closer inspection ofthe data raises some serious questions, however.

The obvious deleterious effect seen as a result of

antihistamine administration may have artificially

inflated the Day Shift CTLOSS rate. Comparison of

the placebo condition data for those same sessions

would suggest that performance under the two shifts

was perhaps the same during Sessions 1 and 2, but that

the Midshift during Session 3 showed some degradation in performance.

Memory Search. Analysis of mean correct response time (MNCORRT), shown in Figure 8, yielded

a significant Drug main effect, F(l, 15) = 6.40, p =

.02, and a significant Shift X Session interaction, F(2,

30) = 3.07, p = .06. Antihistamine administration

significantly slowed response speed (antihistamine, M

= 685 msec, SD = 243 msec; placebo, M = 637 msec,

SD = 202 msec). Figure 8 also reveals the apparent

cross-over interaction of Shift x Session. Subjects

performed worse during the Day Shiftthan the Midshift

at Session 1, and better during the Day Shift than the

Midshift at Session 3.

Similar trends for the Drug main effect, F(l, 15) =

4.11, p = .06, and the Shift X Session interaction, F(2,

30) = 2.84, p = .07, were seen for the percent correct

(PC) measure. As seen in Figure 9, antihistamine

administration appeared to reduce percent correct.

The PC measure also tended to be slightly poorer

during the Day Shift for Sessions 1 and 2, but then

somewhat better during the Midshift for Session 3.

The composite throughput measure (THRPUT) followed the same pattern as the MNCORRT and PC

measures. In this case, both the Drug main effect, F(l,

15) = 7.13, p = .02, and the Shift X Session interaction,

F(2, 30) = 11.14, p = .0002, were significant (cf.

Figure 10).

Dual Task - Individual Lambda

Tracking. Results for the Dual Task - Individual

Lambda tracking measures were nearly identical to the

Group Lambda version of this task reviewed above.

Figures 11 and 12 present the results for the RMS

error and CTLOSS measures, respectively. As with the

Group Lambda version of this task, there was a significant Drug main effect, F(\, 15) = 6.95, p = .02, for

RMS error. Again, antihistamine administration led

to poorer tracking performance (M = 47.1, SD = 13.9),

as compared to the placebo condition {M = 42.5, SD

= 13.4). A significant Session main effect, F(2, 30) =

5.24, p = .01, revealed a slight increase in RMS error

across the three test sessions. There was also a significant Shift x Session, F(2, 30) = 15.32, p = .0001,

interaction. From Figure 11, the cross-over type of

interaction was evident once more. Subjects peril
formed better during Session 1 in the Midshift condition, about equally well in both the Day Shift and

Midshift during Session 2, and then performed worse

in the Midshift condition during Session 3.

Also replicated was the significant Drug main effect

for the control losses measure (CTLOSS), F(\, 15) =

3.50, p = .08, which again revealed the same general

trend as that seen for RMS error. Like the significant

Drug main effect for CTLOSS in the Group Lambda

version of this task, antihistamine appeared to have the

effect of about doubling control losses during the Day

Shift. There was also a significant Shift X Session

interaction for CTLOSS, F(2, 30) = 5.70, p = .008.

Once again, subject performance during the Day Shift

was poorer than during the Midshift for Sessions 1 and

2, and then became considerably better than Midshift

performance for Session 3. Performance during the

Midshift for Session 3 showed clear degradation compared to the first two sessions.

Memory Search. Figures 13, 14, and 15 present

data from the Memory Search measures: mean correct

response time (MNCORRT), percent correct (PC),

and throughput (THRPUT), respectively. A significant Drug main effect, F(\, 15) = 6.76, p = .02, and

a significant Shift X Session interaction, F{2, 30) =

8.62, p = .001, were found for the MNCORRT

measure. Antihistamine administration significantly

slowed response speed (antihistamine, M = 662 msec,

SD = 223 msec; placebo, M = 621 msec, SD =183

msec), just as it did in the Group Lambda version of

the task. Figure 13 also reveals the cross-over interaction of Shift X Session. Subjects performed worse

during the Day Shift than during the Midshift at

Sessions 1 and 2, and better during the Day Shift than

the Midshift at Session 3.

A significant effect for the Drug X Shift interaction,

F{\, 15) = 3.72, p = .07, was seen for the percent

correct (PC) measure. While difficult to envision from

Figure 14, antihistamine administration reduced percent correct more during the Midshift than during the

Day Shift. Not surprisingly, the throughput measure

(THRPUT) presented in Figure 15 followed much

the same pattern as the MNCORRT measure. Both

the Drug main effect, F{\, 15) = 6.47, p = .02, and

the Shift X Session interaction, F{2, 30) = 15.29,

p = .0001, were significant. There was overall better

performance under the placebo condition, and better

performance for the Midshift condition during Sessions 1 and 2, with poorer performance for the Midshift

condition during Session 3.

Switching Task

Manikin Task. Figure 16 presents the response

time variable (MANCORRT) for the Manikin Task.

The only significant finding for this measure was a

Shift X Session cross-over interaction, F(2, 30) = 8.65,

p = .001. Response speed was faster for the Midshift

during Sessions 1 and 2, as compared to the Day Shift,

and appreciably slower for the Midshift during Session

3. The mean correct response time for transition trials

(those trials immediately preceded by a trial from the

alternate task) was the MANCORTX measure. The

analysis of this measure yielded a significant cross-over

Shift X Session interaction, F{2, 30) = 4.95, p = .01.

Figure 17 reveals that the MANCORTX score for

Sessions 1 and 2 was slightly faster for the Midshift

conditions. The MANCORTX score improved in

Session 3 during the Day Shift, and worsened in

Session 3 during the Midshift.

The percent correct measure (MANPC) had a

significant main effect for Session, F{2, 30) = 2.87, p

= .07. The PC measure showed a very slight decrease

across the three sessions (see Figure 18). The percent

correct transition measure (MANPCX) analysis yielded

a significant Shift main effect, F{\, 15) = 3.82,^> = .07.

The MANPCX scores were just slightly better during

the Midshift (Day Shift, M = 98.12%, SD = 3.16%;

Midshift, M= 98.79% msec, SD = 2.53%; see Figure

19). The analysis ofthe throughput measure (MANTP)

yielded a significant cross-over Shift X Session interaction, F{2, 30) = 7.98, p = .002. The results presented

in Figure 20 illustrate that the subjects' throughput

scores improved across sessions during the Day Shift,

but generally declined across sessions during the

Midshift.

Mathematical Processing Task. Analysis of the

response time variable for Mathematical Processing

(MTHCORRT) yielded a significant main effect for

Drug, ^(1, 15) = 10.68, p = .005, and a significant

main effect for Shift, F(l, 15) = 5.73, p = .03. Figure

21 revealed that antihistamine exposure led to an

overall reduction in response speed (antihistamine, M

= 2096 msec, SD = 679 msec; placebo, M = 1995

msec, SD = 663 msec). Also, there were slightly faster

12
response speeds during the Midshift, as compared to

the Day Shift (Day Shift, M = 2076 msec, SD = 675

msec; Midshift, M = 2015 msec, SD = 670 msec).

Analysis of response time for correct Mathematical

Processing transition trials (MTHCORTX) yielded a

significant main effect for Drug, F{\, 15) = 11.52,/)

= .004, and a significant main effect for Shift, F{\, 15)

= 3.99, p = .06 (see Figure 22). Subjects had faster

transition response times during the placebo condition, as compared to the antihistamine condition

(antihistamine, M = 2268 msec, SD = 738 msec;

placebo, M = 2165 msec, SD = 742 msec). Note also

the relatively slower times for transition trials (M =

2216 msec) in comparison with the overall response

time (M = 2046 msec).

No significant differences were found for the Mathematical Processing PC measure (MTHPC; see Figure

23) or for the Mathematical Processing PC transition

measure (MTHPCX; see Figure 24). For Mathematical Processing throughput (MTHTP), there was a

significant Drug main effect, F(l, 15) = 11.46, p =

.004, and a significant main effect for Shift, F{\, 15)

= 3.78,p = .07. These data are presented in Figure 25.

Subjects had greater throughput scores in the placebo

condition than in the antihistamine condition (antihistamine, M = 30.9, SD = 10.0; placebo, M = 32.6,

SD = 10.4).

The analysis of Switching Task data yielded three

measures with both Drug and Shift main effects

(MTHCORRT, MTHCORTX, and MTHTP) and

three measures with significant Shift X Session interaction effects (MANCORRT, MANCORTX, and

MANTP).

NovaScan

Vector Projection Task. Vector projection correct

response times (VECCRT) are presented in Figure 26.

The ANOVA identified only a significant Shift x

Session interaction, F(2, 30) = 5.99, p = .006. In

comparison to their Midshift performance, subjects

responded slower during the Day Shift at Session 1,

about the same at Session 2, and considerably faster at

Session 3.

Analysis of the percent correct measure (VECPC)

yielded a significant Drug main effect, F(l, 15) =

13.60, p = .002 (see Figure 27). In general, the

introduction of antihistamine resulted in a reduced

PC measure across all sessions and shifts (antihistamine, M = 91.43%, SD = 10.84%; placebo, M =

93.26%, SD = 11.05%).

Continuous Spatial Memory Task. No significant

differences were found for the measure of correct

response time for this task (MEMCRT; see Figure 28).

The analysis of percent correct (MEMPC) yielded a

significant main effect for Session, F(2, 30) = 3.71, p

= .04. From Figure 29, it is evident that MEMPC

declined somewhat from Session 1 to Session 2, and

then recovered slightly at Session 3. There was also a

significant main effect for Shift, F(l, 15) = 3.42, p =

.08, for PC. The PC measure was slightly higher

during the Day Shift compared to the Midshift.

Attention Task. The analysis of percent correct on

the Attention Task yielded no significant differences.

This was probably due to the obvious ceiling effect

observed for this measure (see Figure 30).

In summary, one NovaScan task measure yielded

a significant main effect for Drug (VECPC) and

another measure demonstrated significant Session and

Shift effects (MEMPC). One other measure had a

significant Shift X Session interaction (VECCRT).

Air Traffic Scenarios Test

Two measures of the ATST task yielded significant

differences. The analysis of the percentage of planes

arriving at the correct destination (PCDEST) yielded

a significant main effect for Session, F(2, 30) = 8.39,

p = .001. Data presented in Figure 31 revealed that

PCDEST improved across the sessions.

The analysis of total delay time to destination

(DELAY) revealed a significant Shift X Session interaction, F(2, 30) = 7.56, p = .002, and a significant main

effect for Drug, F(l, 15) = 3.36, p = .09. From Figure

32 it can be seen that delay decreased from Session 1

to Sessions 2 and 3 for subjects during the Day Shift,

and increased from Session 1 to Sessions 2 and 3 for the

Midshift. Except for Session 1 on the Midshift, the

introduction of antihistamine increased the delay

measure.

Multi-Attribute Task Battery

Monitoring Task. Figure 33 presents the data for

the average of the two monitoring response time

variables (MONRT). The analysis ofthese data resulted

in a significant Drug main effect, F(l, 15) = 4.77,

p = .04, and a significant Shift X Session interaction,

F{2, 30) = 8.28, ^> = .001. Antihistamine exposure

13
resulted in longer response times across all conditions

(antihistamine, M = 3.18 sec, SD = 1.12 sec; placebo,

M = 2.91 sec, SD = .94 sec). In addition, a cross-over

Shift X Session interaction was demonstrated because,

in comparison to Midshift performance, subjects responded more slowly in the Day Shift at Session 1,

about the same at Session 2, and more rapidly at

Session 3.

No significant differences were found in the number of response time-outs and false alarm errors for

lights and dials combined (MONER). Results for this

measure are presented in Figure 34.

Communications Task. Figure 35 presents data for

mean response time for correct responses (COMCRT)

to the Communications Task. The analysis of this

variable yielded a significant Shift main effect, F{\, 13)

= 5.52, p = .04, and a significant Shift X Session

interaction, F(2, 26) = 5.23, p = .01. Subjects responded faster during the Day Shift compared to the

Midshift, and while they responded, about equally fast

during Session 1, they responded more quickly during

the Day Shift for Sessions 2 and 3. Significant effects

were also found for the Drug main effect, F(\, 13) =

3A2,p = .09, and the Drug X Session interaction, F(2,

26) = 2.73, p = .08. Antihistamine appeared to slow

response speed overall (antihistamine, M = 3.82 sec,

SD = 1.74 sec; placebo, M= 3.60 sec, SD =1.79 sec),

especially during Sessions 2 and 3.

The total number of errors in the Communications

Task (COMER) is plotted in Figure 36. The analysis

of this variable yielded a significant main effect for

Drug, F(l, 15) = 6.22,p = .02, and a significant main

effect for Session, F(2, 30) = 3.85, p = .03. Subjects

generally committed more errors when exposed to

antihistamine (antihistamine, M = 5.38, SD = 6.23;

placebo, M = 4.53, SD = 6.03), and committed fewer

errors during Session 1 than during Sessions 2 or 3.

Tracking Task. Root mean square error (TRKRMS)

scores for the Tracking Task data are presented in

Figure 37. The analysis of these data identified two

significant effects. There was a significant main effect

for Drug, F(\, 15) = 5.13, p = .04, and a significant

Shift x Session interaction, F(2, 30) = 5.49, p = .01.

Tracking performance while exposed to antihistamine

was generally worse (antihistamine, M = 54.50, SD =

20.97; placebo, M = 50.48, SD = 20.32). While

tracking performance remained fairly stable across

sessions for the Day Shift, it worsened considerably

across sessions for the Midshift.

Resource Management Task. No large differences

were found for the mean absolute deviation from 2500

units for Tanks A and B combined (TNKMAD).

However, two effects were found to be statistically

significant: the Drug main effect, F(l, 15) = 3.27, p

= .09, and the Shift X Session interaction, F(2, 30) =

2.88, p = .07. Figure 38 presents these data. Antihistamine typically increased the magnitude of the average

absolute error (antihistamine, M = 238.35, SD =

222.77; placebo, M = 214.56, SD = 166.01), and

while subjects were equally effective in controlling the

tank levels during the Day Shift and Midshift for

Sessions 1 and 2, they were much more effective in

controlling the tank levels during the Day Shift for

Session 3.

Overall, the analysis of the MATB data provided

numerous significant effects for a variety of measures.

Of the six major dependent measures, five yielded a

significant main effect for the Drug factor. In addition,

five variables had significant Shift X Session interaction

effects, and two other variables had significant main

effects for either Shift or Session. The MATB provided

a generous number of variables that appeared to be

responsive to manipulations of the independent variables within this study.

5.3 Subjective (Self-Report) Measures

Subjects provided self-report measures of current

antihistamine symptoms (AHSQJ, general physical

symptoms (ACTSQ), and predominant emotional

state (MOOD) during each test session. Also during

the test session, subjects rated the subjective workload

using the NASA Task Load Index (TLX) associated

with each of the two work simulation tasks (ATST and

MATB). Below is a summary of the results for each of

these measures.

Antihistamine Symptoms Questionnaire

Analysis of the AHSQ responses did not yield any

significant main effects or interactions. Evidently, the

level of symptomology experienced by the subjects as

a result of antihistamine exposure was not sufficient to

cause significant changes acknowledged at the level of

a self-report measure (see Figure 39). However, the

mean scores for the Drug main effect were in the

expected direction, demonstrating higher levels of

symptoms for the antihistamine condition compared

to the placebo condition (antihistamine, M = 3.08, SD

= 2.56; placebo, M = 2.84, SD = 2.82).

14
Activity State Questionnaire

Figure 40 presents the data from the Activity State

Questionnaire (ACTSQ). This questionnaire was included as a general measure of physical symptoms

experienced by the subjects. The questionnaire includes an expansion of the Pennebaker Symptom/

Emotion questionnaire (Pennebaker, 1982). The variable PHYSICAL represents this general scale of physical symptoms. The analysis of this scale yielded several

significant findings. There was a significant Shift main

effect, F(\, 15) = 6.92, p = .02, and a significant

Session main effect, F(2, 30) = 7.05, p = .003. Subjects

reported a significantly higher level of physical symptoms during the Midshift (M = 34.44, SD = 13.20) as

compared to the Day Shift (M = 32.81, SD = 13.47),

and subjects reported a significant increase in physical

symptoms across the sessions.

In addition, the Drug X Shift, F{1, 15) = 8.04, p =

.01, and Shift X Session, F(2, 30) = 13.20,/> = .0001,

interactions were also significant. On the average,

subjects reported a smaller increase in physical symptoms from the Day Shift to the Midshift in the

antihistamine conditions, but reported a marked increase in physical symptoms from the Day Shift to the

Midshift in the placebo conditions. The Shift X Session interaction was represented by the fact that

subjects reported approximately the same level of

physical symptoms across sessions during the Day

Shift, but reported a marked increase in physical

symptoms across sessions during the Midshift.

The ACTSQ also provided subjects the opportunity to rate their general level of perceived preparedness for performing the tasks in that session. The PREP

scale score represents the subjects' responses (see

Figure 41) for this measure. There were two significant

main effects for the PREP measure. The Shift main

effect was significant, F(l, 15) = 5.24,p = .04, as was

the Session main effect, F(2, 30) = 5.94, p = .007.

Subjects reported a significantly higher level of preparedness for task performance during the Day Shift

(M = 8.77, SD = 2.20), as compared to the Midshift

(M = 8.08, SD = 1.93), and subjects reported a

significant decrease in the level of perceived preparedness across the sessions, especially during the Midshift.

Mood Scale II

Subjects also reported their moods by responding

to adjectives on the Mood Scale II using a 3-point scale

before completing the tasks during each session. A

response of "1" indicated that the subject did not feel

that the adjective described their current mood, while

a response of "3" indicated that the adjective adequately described the subject's mood. The adjectives

are divided into six categories (Activity, Happiness,

Depression, Anger, Fatigue, and Fear). In addition,

the time taken to respond to each item is also recorded

(RTALL).

Activity. Analysis of the Activity scale (see Figure

42) resulted in a number of significant findings. The

Drug main effect, F(l, 15) = 9.79, p = .007, was

significant. Ratings of activity level during the antihistamine condition were significantly lower (M = 1.71,

SD = 0.41) than ratings during the placebo condition

{M = 1.83, SD = 0.46). The Shift main effect was also

significant, F(\, 15) = 4.63, p = .05. Ratings of activity

level during the Day Shift (A4 = 1.85, SD = 0.46) were

significantly higher than activity ratings during the

Midshift (M = 1.70, SD = 0.42). The analysis also

yielded a significant Session main effect, F(2, 30) =

10.80, p = .0003. Activity level declined across the

three sessions. In addition to the main effects, there

were also significant Drug X Shift, .F(l, 15) = 5.78, p

= .03, and Shift X Session, F(2, 30) = 8.98, p = .0009,

interactions. Subjects rated their activity level during

the Day Shift under the placebo condition as more

active than any of the other conditions (i.e., Midshiftplacebo, or either antihistamine condition). Subjects

also rated their activity level fairly constant across Day

Shift sessions, but rated their activity level progressively lower across sessions during the Midshift.

Happiness. The results for the Happiness scale (see

Figure 43) were very similar to those for the Activity

scale. Significant main effects were found for Drug,

F(l, 15) = 7.71, p = .01, Shift, F{\, 15) = 9.05, ;> =

.009, and Session, F(2, 30) = 10.27, p = .0004. A

significant Shift X Session interaction was also found,

F(2, 30) = 12.47,/ = .0001. Subjects rated themselves

happier during the placebo condition (antihistamine,

M = 1.99, SD = 0.52; placebo, M= 2.17, SD = 0.55)

and during the Day Shift (Day Shift, M= 2.17, SD =

0.48; Midshift, M = 1.99, SD = 0.59). Happiness

declined across the sessions. The significant Shift X

Session interaction revealed that subjects rated their

happiness level fairly constant across sessions during

the Day Shift, but rated their happiness progressively

lower across sessions during the Midshift.

Depression. Feelings of depression seemed to be less

affected by the manipulations in the study, although

there was a main effect for Shift, F(l, 15) = 7.65, p =

.01, and a Shift X Session interaction, F(2, 30) = 3.56,

15
p = .04 (see Figure 44). Subjects rated themselves as

slightly more depressed during the Midshift, as compared to the Day Shift, and showed a slight elevation

in depression across sessions during the Midshift,

while showing no change during the Day Shift. However, these changes were generally in the range of 0.03

of a scale point, which are probably not meaningful

differences.

Anger. The Anger scale analysis yielded only a

significant Shift X Session interaction, F(2, 30) = 5.89,

p = .007 (see Figure 45). In general, subjects reported

an increase in anger feelings across the Midshift sessions, but not for the Day Shift sessions. Again, the

absolute shift in anger was not particularly large

(about 0.09 of a scale point).

Fatigue. The Fatigue scale yielded both Shift, F(l,

15) = 8.57,p = .01, and Session main effects, F(2, 30)

= 10.15, p = .0004, and a Shift X Session interaction,

F{2, 30) = 23.17,/- = .0001 (see Figure 46). Subjects

reported greater fatigue during the Midshift (Day

Shift, M = 1.47, SD = 0.50; Midshift, M = 1.64, SD

= 0.53) and, overall, increasing fatigue across the three

sessions. Ratings of fatigue during the Day Shift

actually decreased very slightly across sessions, while

ratings of fatigue during the Midshift increased across

sessions.

Fear. There were no significant differences found for

any main effect or interaction for self-reported level of

fear (see Figure 47).

Response Time. Analysis of the response time for

registering responses to the mood scale items yielded

two significant interactions, Drug X Shift, F(\, 15) =

5.16, p = .04, and Shift x Session, F{2, 30) = 4.50, p

= .02 (see Figure 48). Subjects responded fastest under

the placebo condition during the Day Shift and

slowest under the antihistamine condition during the

Day Shift. The response speed of subjects during the

Midshift under either antihistamine or placebo was

very similar and about midway between the fastest and

slowest response speed. Ignoring drug condition, subjects appeared to get slower in responding to the mood

scale across the sessions in the Midshift condition but

not so during the Day Shift.

Self-Reported Workload Level - NASA TLX

Ratings

ATST NASA-TLX Ratings. Analysis of the NASA

TLX workload assessment scale resulted in very little

in the way of significant results. No significant differences were found for the Mental Demand, Physical

Demand, Temporal Demand, Performance, and Effort scales. The analysis of the Frustration scale yielded

one significant finding, the Drug X Session interaction, F(2, 30) = 3.45, p = .04, which was largely due

to an elevated frustration rating for the antihistamine

group during the second session only.

MATB NASA-TLX Ratings. Workload ratings for

the MATB task did yield more significant findings

than those for the ATST task. A significant Drug main

effect was found for the degree of Mental Demand,

F(l, 15) = 5.37, p = .04. Subjects rated the MATB

more mentally challenging during the antihistamine

condition (antihistamine, M = 61.0, SD = 17.6;

placebo, M = 58.6, SD = 19.3). The most consistent

finding was a Shift X Session interaction for Mental

Demand, F(2, 30) = 7.22, p = .003, Physical Demand, F(2, 30) = 8.55,/ = .001, Performance, F(2,

30) = 4.91,/= .01, and Frustration, F(2, 30) = 5.55,

/ = .009. In most of these cases, subjects rated the

MATB as generally decreasing in demand across sessions during the Day Shift, and generally increasing in

demand across sessions during the Midshift.

6.0 CONCLUSIONS

The purpose of this study was to investigate the

effects of work shift (Day Shift vs. Midshift) and a

specific antihistamine drug (chlorpheniramine maleate) on human operator performance. In addition, the

effect oftime on task, or successive drug doses, through

the shift (Session effect) was also assessed. A battery of

cognitive and human performance tasks was used to

investigate these effects, as well as various self-report

measures of physical state, mood, and workload.

The results of this study can best be summarized by

Table 4, which provides an overview ofthe main effects

and two-way interactions from the various analyses of

variance. (There were no three-way interactions that

were statistically significant.) What is immediately

apparent from Table 4 is that the greatest number of

significant findings existed for the Drug main effect

and the Shift X Session interaction. The Shift and

Session main effects evidenced considerably fewer

significant findings, and the other two interactions

had very few significant findings. Of course, the simple

frequency or even magnitude ofsignificant effects may

not be the best way in which to evaluate the outcome

of an investigation. However, in this case, the general

trends in significant findings were not only apparent

but were also consistent with prior research evidence.

16
Because this study attempted to explore factors that

may affect safety-sensitive job performance, a broad

range of tasks and self-report measures was used and a

somewhat conservative examination of the results was

conducted. This conservative approach possibly resulted in an over-inclusiveness with regard to significant findings. Thus, an examination of general trends

in the findings is an important consideration in this

study. Evidence of a factor having an influence across

the performance and self-report measures, especially if

that influence is consistently negative, would raise

much more concern regarding that factor than would

a random pattern ofsignificant findings. Such a case of

consistent negative influence existed for two factors in

this study.

The administration of chlorpheniramine maleate

appeared to have fairly profound negative effects across

a wide range of performance measures. Statistically

significant negative effects resulted for some component of every simple, dual, and complex task that

subjects performed. This trend in the results suggested that, in general, the effects of chlorpheniramine

maleate were not selective across various types of tasks.

This finding was in agreement with several past studies

that found a variety of negative cognitive and psychomotor effects due to chlorpheniramine maleate

(Chapman & Rawlins, 1982; Clarke & Nicholson,

1978; Higgins et al., 1968; Khosla et al., 1993; Lee et

al., 1988; Nicholson et al., 1991; Witek et al., 1995).

Such broad-ranging effects might also support the

view that the actions of this drug disrupt performance

resources at a more fundamental and broad level, such

as basic central nervous system structures (see Lee et

al., 1988, and Loring & Meador, 1989), as opposed to

a higher order or more selective level.

Despite the broad ranging effects ofthis drug, it was

possible to identify more specific aspects of performance that were disrupted by its administration. For

example, chlorpheniramine maleate exposure led to

degraded tracking performance on every task that had

a tracking component (Critical Tracking, both variations of the Dual Task, and the MATB). This result

was consistent with the early work of Higgins et al.

(1968) and Clarke and Nicholson (1978). In these

cases, the most critical measure of tracking performance (RMS error, control losses, or mean lambda)

showed poorer performance after chlorpheniramine

maleate administration. Chlorpheniramine maleate

exposure also led to degraded response speed in a wide

range of tasks, a finding similar to those of Lee et al.

(1988) and Witek et al. (1995). While some of the

simple tasks did not show RT differences (i.e., Spatial

Processing), significantly slower response speed under

chlorpheniramine maleate administration was found

for five of the seven tasks that had response speed

components. This result would suggest that

chlorpheniramine maleate did have significant negative effects on the speed with which subjects responded to critical elements ofthese tasks. The negative

effects on response speed of this drug were also recognized at another cognitive level. Subjects were not only

significantly slowed in their processing of the performance tasks, they were also significantly slowed in

their response to self-reported mood ratings. This

would suggest that chlorpheniramine maleate not

only caused slowing of the sensory motor components

of task performance, but also caused slowing of the

central processing of verbal tasks as well. This general

slowing in performance is not surprising given the

pharmacological action of chlorpheniramine maleate.

As a CNS Hj blocker, chlorpheniramine maleate acts

to reduce CNS activity and typically to lower general

arousal. It should be noted that other researchers have

failed to find such response time effects (Kulshrestha

et al., 1978; Philpot et al., 1993), although methodological or sampling differences may explain this

disparity.

While it is clear that chlorpheniramine maleate had

a negative effect on a broad range of tasks and that it

negatively affected the speed with which subjects

responded to many tasks, it is less clear what effect

chlorpheniramine maleate had on accuracy. Of the

several tasks that provided accuracy measures, only

two yielded significant Drug main effects for percent

correct measures. Of interest though is the fact that

the two tasks yielding significant effects for accuracy

were both spatial tasks in nature. It is unclear whether

chlorpheniramine maleate may exert negative effects

on the accuracy of tasks related to spatial ability

because of its pharmacological actions on neurological

centers that bear on spatial perception, or whether the

effects on accuracy are more broad-ranging, but simply not pronounced enough in this study given the

dosages and tasks used. The latter might have been the

case, because there was a significant difference for the

accuracy measure for the Memory Task component of

the Dual Task (Group Lambda). Another task that is

often viewed as a spatial task (the Manikin Task

component of the Attention Switching Task) did not

evidence a significant difference in accuracy. The lack

17
of a significant effect on accuracy for this task may be

due to the fact that the Manikin Task has only eight

possible orientations, and repeated performance of the

task may lead to its becoming more of a pattern

recognition task and less a task ofspatial ability. Thus,

the accuracy results are unclear, but the findings

regarding spatial processing seem very much worth

additional study, especially because this is such a

critical skill for both ATCs and pilots.

The other area where many significant effects were

found was the Shift X Session interaction. This refers to

the complex changes that occur across the three testing

sessions for the Day Shift vs. the Midshift testing

conditions. Once again, some component of every task

showed a Shift X Session interaction. In general terms,

the form of these interactions suggested that performance for the Midshift was better than for the Day

Shift during the initial session of the work shift

(Session 1), but was worse than the Day Shift for the

final session (Session 3). In many cases, this was a fairly

complete cross-over interaction with the shift groups

appearing to differ at Session 1 and then reversing the

direction of their differences by Session 3. In other

cases, the performance was different between the shift

groups only at the beginning or at the end of the shift.

In these cases, the differences were in the same direction as the differences in the cross-over interactions.

For example, in nearly all cases where performance was

comparable at the beginning of the shift, performance

for the Day Shift was better at the end ofthe shift, and

in nearly all cases where performance was comparable

at the end of the shift, performance during the first

session was better for the Midshift.

Viewed from a more general perspective, these

results revealed that across the sessions, performance

during the Day Shift generally got better and performance during the Midshift generally got worse. From

this perspective, these results support past research

that found sleep disruption or sleep loss degrade

performance (Akerstedt, 1990; Collins, 1977; Monk,

1990; Naitoh, 1976; Taub & Berger, 1973, 1976;

Wilkinson, 1965).

At first, these results from the various Shift X Session

interactions appear confusing. There is commonalty

among these findings, however. In comparative terms,

Midshift performance seemed to be better or at least

equal to Day Shift performance at the beginning ofthe

shift, and then seemed to deteriorate as the shift

continued. Performance during the Day Shift seemed

equal to or worse than Midshift performance at the

beginning of the shift, but then improved over the

shift to end up being much better or at least equal to

Midshift performance at the end of the shift. The

nature of this relationship is complex, but consistent.

Fortunately, other measures in this study provided

additional insight into this complex finding.

From the results of the Activity State Questionnaire, it was clear that subjects reported significantly

more physical symptoms during the Midshift, as

compared to the Day Shift, and they reported increasing levels of physical symptoms across the testing

sessions. It should be noted that, in general, these are

considered negative physical symptoms, and

chlorpheniramine maleate administration did not play

an important role in this effect. It appears that either

the Session factor has a quite different effect on the

sense of physical well being of the subjects, given the

shift they are working, or there might be a circadian

cycle of changes in physical symptoms in the opposite

direction across the two shifts. The level of negative

physical symptoms declined slowly across the Day

Shift. Across the Midshift, the level of negative physical symptoms increased markedly. What is interesting

is that the level of physical symptoms reported by

subjects at the end of the Day Shift is quite similar to

the level reported by subjects when they begin the

Midshift. This is typically the lowest level of the day.

In fact, these two points in time represent the same

time of day.

Thus, while time on shift (or the Session effect)

seems to improve the level of negative physical symptoms during the Day Shift, time on shift during the

Midshift may simply reflect a growing decline in

resources and adjustive capacity following a day of

activity. In this regard, it is important to remember

that this was not a study of sleep loss effects. Subjects

in this study knew, much like they would know if they

were actually working a job during the midshift time

period, that they were going to be up through the

night. Not unlike those who work midshift jobs, these

subjects probably involved themselves in considerable

daytime activity before reporting to the lab for testing.

Thus, it is probably accurate to suspect that the

Midshift is a shift worked after a period of daily

activity, while the Day Shift is often followed by a

period of daily activity. This suspicion is also supported by past research on sleep cycles that confirmed

that sleep typically preceded day work shifts and

followed afternoon and night work shifts (Tepas,

1982). The cumulative effect of prior daily activity,

18
combined with accumulated job fatigue across the

shift, probably resulted in increased physical symptoms and degraded performance. This suspicion was

further supported by the ratings of subjects regarding

how prepared they were to work. Ratings across the

three sessions during the Day Shift for the "Prepared"

item on the questionnaire were essentially equal (with

a slight peak during Session 2). Ratings across the

Midshift revealed that subjects felt less and less prepared to perform their work as the shift continued.

Overall, these results of self-report measures revealed

substantial consistency with other studies reporting

negative worker reaction to the Midshift (Melton &

Bartanowicz, 1986; Melton et al., 1973).

Other self-report measures provided additional findings ofinterest. The Mood Scale II results revealed that

chlorpheniramine maleate administration resulted in

feelings of being less active and less happy. These

results are consistent with other studies that have

found similar effects on self-report measures due to

chlorpheniramine administration (Chapman &

Rawlins, 1982; Clarke & Nicholson, 1978; Kulshrestha

et al., 1978; Nicholson et al., 1991), but once again

are not in agreement with other researchers that have

failed to find such self-report effects (Kulshrestha et

al., 1978; Philpot et al., 1993). The Shift factor

seemed to have an even greater effect in that not only

did subjects feel less active and less happy during the

Midshift as compared to the Day Shift, but also they

felt more depressed and fatigued. As noted previously,

the size of some of these effects was not great in

absolute terms. The Shift X Session interaction discussed above was also present for these self-reported

mood measures and these mood results paralleled the

findings noted for the Activity State Questionnaire—

that is, Day Shift ratings suggested mood improvement across the shift, while Midshift ratings suggested

mood decline across the shift. This pattern of mood

change is quite consistent with the patterns of mood

change across shift found by Schroeder, Rosa, Witt,

and Banks (1995).

Self-report measures of workload were not as effective in revealing significant differences as were other

self-report measures, but the pattern of self-reported

workload differences was interesting. The NASA-TLX

measure yielded essentially no differences due to

chlorpheniramine maleate administration. This would

suggest that the NASA-TLX was relatively insensitive

to the antihistamine effect. This finding was not

necessarily surprising, because the NASA-TLX was

not necessarily designed as a measure ofStressor effects.

It is possible that subjective task difficulty can vary as a

result of changing objective task parameters or by

changing the state of the person performing the task

(by introducing drugs that would increase or decrease

performance capability, for example). However, only

when state factors, such as drug effects or fatigue, reach

levels that require considerable compensatory effort

(and thereby obscure the difference between increased

task difficulty and degraded personal capability) would

subjects rate workload as higher. Chlorpheniramine

maleate exposure alone was only powerful enough to

make a modest change in NASA TLX ratings, as

evidenced by the increased Mental Effort for the

MATB under the drug condition. Assuming that this

finding was not just Type I error, it is in agreement

with other findings that suggest chlorpheniramine

maleate leads to greater difficulty in concentration

(Khosla et al., 1993; Lee et al., 1988). On the other

hand, there were several self-reported differences in

workload for the Shift X Session interaction. Again, the

ratings of various aspects of workload were much like

the ratings for mood and physical condition. Subjects

generally felt that workload decreased across the Day

Shift, but seemed to increase across the Midshift. In

this situation, the combined influence of these factors

may have been enough to cause subjects to subjectively

view the workload as having changed.

The fact that there were significant differences in

subjective workload ratings for the MATB and not for

the ATST is also not necessarily surprising. The ATST

is a more intrinsically motivating task than the MATB,

it does not have the same degree of pressure to perform

at increasingly higher levels as the MATB, and it was

not performed for as long as the MATB. For these

reasons, the MATB was a more demanding task

overall. This higher level of demand may have increased the possibility that workload differences were

more recognizable for the MATB, as compared to the

ATST.

The interpretation of the remaining significant

findings for the Shift and Session main effects are

difficult. These significant findings are clearly far

fewer in number and are further clouded by the fact

that the Shift X Session interaction was so influential.

Prudence would suggest that these results be cautiously generalized. In all cases, however, these effects

seemed to parallel the general findings already seen for

the Drug and Shift X Session factors.

19
Finally, there are a few additional observations that

are noteworthy. First, there did not seem to be strong

evidence for a pronounced negative effect as the result

of combining antihistamine and shift, as Higgins et al.

(1968) found with chlorpheniramine maleate and

altitude. This was surprising because logic would

suggest that a sedating drug in combination with a

fatiguing work shift might lead to an even more

dramatic negative effect on performance than either

factor alone or than their simple additive effect. This

did not appear to be the case in this study. Prudence,

and perhaps simple skepticism, would suggest that

this finding be further explored.

Second, the value of assessing change within a shift

was self-evident in this study. The dynamics of performance and mood change across shifts were quite

different, and the presence of cross-over interactions

would suggest that conducting studies without repeated measures across shifts (i.e., using global measures summed across the entire shift) would obscure

these very dramatic differences.

Third, asking subjects about antihistamine effects

based on known side-effects does not seem like a very

productive way to assess their influence, at least in the

manner used in this study. Perhaps a more sensitive

response scale might gain better results. The measure

used in this study was clearly not effective for the dose

and level of side-effects experienced.

And fourth, there was some limited evidence of

differential sensitivity within tasks for various risk

factors. For example, the Attention Switching Task

provided some interesting results in addition to those

already noted. This task directed the subject to perform either the Manikin Task or Mathematical Processing Task component. The Mathematical Processing

Task appeared to be reasonably sensitive to the effects

of chlorpheniramine maleate, while the Manikin Task

appeared to be sensitive to the combined effects of

Shift and Session. The Critical Tracking Task provided another possible example of differential sensitivity, and perhaps even the Spatial Processing Task. This

apparent differential sensitivity, while intriguing, must

be interpreted cautiously. It may be that some ofthese

tasks do offer dependent measures that respond differentially to various risk factors. It may also be the case

that other factors may have simply reduced the possibility of finding significant differences for that measure in relationship to other factors in this study.

In summary, the findings of this study suggest that

chlorpheniramine maleate alone has a fairly strong

negative influence on a wide range oftask performance

and mood measures. There also seems to be a rather

complex relationship between work shift and time on

the shift (Session) such that performance and mood

during the Day Shift tend to get better and during the

Midshift tend to get worse. No evidence was found

that chlorpheniramine maleate and work shift combine in producing a multiplicative effect on performance or mood.

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24
FIGURES

r~ STAGE 1 STAGE 2 STAGE 3

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i

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Figure 1. Laboratory Model of Readiness-to-Perform Testing.

25
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26
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29
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59
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64
Activity State Questionnaire

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Figure 45. Mood Scale II Anger Scale.

69
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Figure 46. Mood Scale II Fatigue Scale.

70
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c

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Drug Effect and Shift by Session Interaction

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71
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Figure 48. Mood Scale II Response Time.

72
TABLES

Table 1. Summary of Task Codes.

Task Code

Spatial Processing SPA

Critical Tracking TRK

Dual Task (Group Lambda) DULG

Dual Task (Individual Lambda) DULI

IML Attention Switching NTI

NovaScan FAA NSF

Air Traffic Scenarios Test ATST

Multi-Attribute Task Battery MATB

Antihistamine Symptoms Questionnaire AHSQ

Activity State Questionnaire ACTSQ

Mood Scale II MOOD

NASA Task Load Index TLX

73
Table 2. Example Protocols for Dosing and Testing during Day Shift and Midshift Sessions.

Day Shift

Time Group 1 Group 2

800 Dose 1

900 Session 1

1000 Dose 1

1100 Meal Session 1

1200 Dose 2

1300 Session 2 Meal

1400 Dose 2

1500 Snack Session 2

1600 Dose 3

1700 Session 3 Snack

1800 Dose 3

1900 End Session 3

2000

2100 End

Midshift

Time Group 1 Group 2

2000 Dose 1

2100 Session 1

2200 Dose 1

2300 Meal Session 1

2400 Dose 2

100 Session 2 Meal

200 Dose 2

300 Snack Session 2

400 Dose 3

500 Session 3 Snack

600 Dose 3

700 End Session 3

800

900 End

Table 3. Individualized Lambda Values

for Dual Task.

ID Lambda ID Lambda

201 4.0 224 3.3

204 3.6 225 3.7

206 3.9 226 4.2

211 3.9 227 3.4

216 4.7 229 3.2

217 3.9 230 3.6

218 4.0 232 3.3

223 3.4 233 3.7

74
Table 4. Summary of Significant ANOVA Effects.

(In small italics: 0.05 < p < 0.10; all others: p = .05)

TASK iDrug | Shift | Session Drug x Shift Drug x Session | Shift x Session

Spatial MNCORRT MNCORRT

Processing PC PC

Critical MEANL MEANL

Tracking MAXL

Dual Task RMS RMS RMS RMS

(Group Lambda) CTLOSS

MNCORRT

PC

THRPUT

CTLOSS

MNCORRT

PC

THRPUT

Dual Task RMS RMS RMS

(Indiv. Lambda) CTLOSS

MNCORRT

THRPUT

PC

CTLOSS

MNCORRT

THRPUT

IML Manikin MANCORRT

Attention MANCORTX

Switching MANPCX MANPC MANTP

Task Math MTHCORRT

MTHCORTX

MTHTP

MTHCORRT

MTHCORTX

MTHTP

NovaScan Vec VECPC VECCRT

FAA Test Mem MEMPC MEMPC

Air Traffic DELAY DELAY

Scenarios Test PCDEST

(ATST)

Multi- Monitor MONRT MONRT

Attribute Comm. COMCRT COMCRT COMCRT COMCRT

Task COMER COMER COMER

Battery Track TRKRMS TRKRMS

(MATB) Tanks TNKMAD TNKMAD

QUESTIONNAIRE Drug Shift Session Drug x Shift Drug x Session Shift x Session

Antihistamine

Symptoms

Questionnaire

Activity State PHYSICAL PHYSICAL PHYSICAL PHYSICAL

Questionnaire PREPARED PREPARED PREPARED

Mood Scale II ACTIVITY ACTIVITY ACTIVITY ACTIVITY ACTIVITY

HAPPINESS HAPPINESS

DEPRESSION

FATIGUE

HAPPINESS

FATIGUE

HAPPINESS

DEPRESSION

ANGER

FATIGUE

Overall RT Overall RT Overall RT

NASA-TLX FRUSTRATION

(ATST)

NASA-TLX MENTAL MENTAL

(MATB) PHYSICAL

PERFORMANCE

1 FRUSTRATION

75
APPENDIX A

SUBJECT CHARACTERISTICS

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APPENDIX B

TASK DESCRIPTIONS

Bl
BRIEF DESCRIPTION OF TASKS

Spatial Processing. This test, developed by

Shingledecker (1984), uses four-bar histogram patterns. Each histogram bar can be one of six heights at

random. At the beginning of each trial, the subject

views a pattern. After a fixed delay, the pattern is

immediately erased and a new one is presented. The

subject must decide as quickly as possible if the new

pattern is identical to the preceding pattern (except for

a 90° rotation). The subject then presses one key for

"same" or another key for "different." As soon as the

response is made, a new comparison pattern appears.

Both mean reaction time for correct responses and

percentage correct are used as dependent measures.

Tracking. This task, developed by McRuer and Jex

(1967), requires that the subject maintain an unstable

target in the center of a horizontal line by manipulating a control device to nullify the input disturbance.

An instability parameter (lambda) is used to control

the difficulty of the task. This parameter varies as a

function of subject performance and actually serves as

the primary performance measure.

Sternberg Memory Search. The general Sternberg

paradigm (Sternberg, 1969) requires that subjects

respond as rapidly and accurately as possible to visually presented letters. At the beginning ofthe test, a set

ofletters drawn randomly from a restricted alphabet is

presented to the subject for memorization. The set of

letters (positive set) stays on the screen for a maximum

of five seconds, then the screen is cleared and a series

ofsingle test letters is presented. Ifthe presented letter

matches one of the letters in the previously memorized

positive set, the subject responds "same" (key press). If

a different letter appears (negative set), then the

subject responds "different" (key press), indicating a

non-matching letter was presented. The Sternberg

task included in the Dual Task for this study uses a set

size of four letters that are changed for each block of

stimuli (session). Thus, a letter can be a target in one

session and a distracter in another.

Dual Task - Tracking and Sternberg Memory

Search. One of the most critical and potentially

sensitive higher cognitive functions that might be

susceptible to risk factor exposure is the ability of the

subject to allocate attentional resources among several

tasks. To investigate this, the present study used the

time-sharing paradigm that has been well studied in

cognitive psychology (Damos and Wickens, 1980;

O'Donnell and Eggemeier, 1986; Damos, 1991).

The specific form of this paradigm was the Dual Task

included in the UTC-PAB (Perez, Masline, Ramsey,

and Urban, 1987). This consists of the Sternberg task

and the Tracking task being presented simultaneously.

In this implementation ofthe Dual Task, the Tracking

task is presented in the middle of the screen and the

letters of the Sternberg task appear in a fixed location

directly above the center null point. The target of the

compensatory tracking task moves laterally. A different four-letter memory set was used for each session.

That is, only one positive memory set was presented

with several probe letters for each daily session. For a

recent study discussing the implementation of the

Dual Task when investigating the effects of antihistamines on military weapon system controllers, see

Nesthus, Schiflett, Eddy, and Whitmore (1991).

Attention Switching Task - Manikin and Mathematical Processing. Time-sharing, as explained above

in the Dual Task, is different from attention switching, another required attentional process that could be

sensitive to risk factors. Workers must often make

rapid shifts in attentional focus, as well as in the skills

required to respond to a change in task demands. This

externally-directed behavior defies automaticity in

any true sense, since it must be flexible enough to

respond to unusual demands. Thus, a test is needed to

probe the subject's ability to shift attention and

resource allocation in response to rapidly changing

and unpredictable external demands. Such a procedure has been created using two tasks currently in the

UTC-PAB.

In this procedure, the subject has two distinct and

discrete tasks to perform. One is a spatially-based task,

and the other is a mathematically-based task. Each of

these appears, side-by-side, simultaneously on every

stimulus screen. However, an indicator appears at the

same time directing the subject to the task that is

"active" (i.e., requires a response). The subject must

make an exclusive response to the active task, where

reaction time and percent correct data are obtained

only for that task. The switching from task to task for

each stimulus is random (within constraints). Therefore, the subject must remember to watch the indicator, allocate the appropriate resources to respond to

that stimulus, and then make the appropriate response. This paradigm provides a test ofthe switching

skills described above. Findings and results using this

test can be found in O'Donnell (1991) and Schlegel,

Shehab, and Gilliland (1994).

The two tests selected to exercise this paradigm are

the Manikin test and the Mathematical Processing

test. The Manikin test has a long history of use

(Benson and Gedye, 1963; Reader, Benel, and Rahe,

1981; Schlegel and Storm, 1983) and is presented in

a wide variety of formats by military psychologists

(Miller, Takamoto, Bartel, and Brown, 1985). As

implemented in this experiment, a manikin "stick

figure" is presented facing either forward or backward.

B2
In addition, the figure can be either upright or upsidedown. The figure is standing on a box and inside the

box is either a rectangle or a circle. In the figure's two

hands are a rectangle and a circle. The subject's task is

to note which symbol is inside the box, and then to

determine which of the manikin's hands is holding the

designated symbol. The subject then presses the left or

right of two keys corresponding to the manikin's left

or right hand.

The Mathematical Processing test is based on similar tasks described by Perez et al. (1987). It presents

three single-digit numbers that must be added or

subtracted. If the answer is greater than 5, one response is given. If the answer is less than 5, another

response is required. This task has been reported by

Shingledecker (1984) to be a relatively pure index of

mathematical functioning.

NovaScan FAA Task This NovaScan paradigm (NTI: O'Donnell, 1995) requires integrated

responses to three tasks. For two of the tasks, stimulus

screens are presented in directed attention fashion

with a series of stimuli from one task alternating with

a series of stimuli from the other task. In addition, a

vigilance/attention task is performed for every stimulus screen. Thus, it is similar in form to the Attention

Switching task, but only one task display is on the

screen at a time. In the Visual Search and Vector

Projection task, the subject searches for two labeled

vectors, makes mental rotations ofthe vectors based on

verbal on-screen instructions, and responds as to

whether the rotated vectors would intersect either on

or off the screen. In the Spatial Memory task, the

subject memorizes the position and shape of a missing

symbol for later comparison with the next spatial

memory stimulus screen. For the Attention task,

subjects look for the presence of small symbols in the

corners of each screen. The test is based on a fixed

number of stimuli and test time is thus a function of

subject proficiency.

Air Traffic Scenarios Test. The ATST (Aerospace

Sciences, 1991; Broach and Brecht-Clark, 1994;

Gilliland and Schlegel, 1992; Weltin, Broach,

Goldbach, and O'Donnell, 1992) provides an approximation of the air traffic control environment and

involves the directing of planes to their destinations

using altitude, speed, and heading changes. The work

version of the task lasts 25 minutes and involves 40 to

45 airplanes.

Multi-Attribute Task Battery. The MATB

(Comstock and Arnegard, 1992) is an approximation

of the air crew operations environment and requires

the simultaneous performance of a monitoring task (a

set of lights and a set of dials), an auditory communications task, a compensatory tracking task, and a

resource management task involving the monitoring

and control offuel tank levels. The work version ofthis

task lasts 40 minutes.

Activity State Questionnaire. The Activity State

Questionnaire is a version of the Pennebaker Physical

Symptoms Checklist (Pennebaker, 1982) and consists

of 25 items scored with a seven-point scale. It is used

to assess the current state of physical health. Two

additional items assessing level of preparedness for task

performance were added.

Mood Scale II. The Mood Scale II is a variation of

the Profile of Mood States (POMS; McNair, Lorr, and

Droppleman, 1971). The Mood Scale II has 36

adjectives which address Activity, Happiness, Depression, Anger, Fatigue, and Fear.

NASA Task Load Index (TLX). The NASA TLX

(Hart and Staveland, 1988) allows subjects to provide

ratings oftask workload using the categories ofmental,

physical, temporal, performance, effort, and frustration. This collection of ratings was obtained following

each work sample task. The TLX is an integral part of

the Multi-Attribute Task Battery but was programmed

as a stand alone task to follow the Air Traffic Scenarios

Test.

REFERENCES

Aerospace Sciences, Inc. (1991, November). Air Traffic Control Specialist pre-training screen preliminary validation: Final report. (DTFA-01-90-Y01034). Washington, DC: Federal Aviation Administration.

Benson, A.J., and Gedye, J.L. (1963). Logical processes

in the resolution of orienting conflict. (RAF Rpt.

259). Farnborough, UK: Royal Air Force Institute of Aviation Medicine.

Broach, D., and Brecht-Clark, J. (1994). Validation of

the Federal Aviation Administration Air Traffic

Control Specialistpre-training screen. (DOT/FAA/

AM-94/4). Washington, DC: Federal Aviation

Administration, Office of Aviation Medicine.

Comstock, J.R., and Arnegard, RJ. (1992). The

Multi-Attribute Task Battery for human operator workload and strategic behavior research.

(NASA-TM-104174).

Damos, D.L. (1991). Multiple task performance. London, UK: Taylor & Francis, LTD.

Damos, D.L., and Wickens, CD. (1980). The identification and transfer of timesharing skills. Acta

Psychologica, 46, 15-39.

B3
Gilliland, K., and Schlegel, R.E. (1992). Evaluation of

extendedpractice effects on the Air Traffic Scenarios

Test. (DTFA-02-92-P-13359). Oklahoma City,

OK: Federal Aviation Administration, Civil Aeromedical Institute.

Hart, S.G., and Staveland, L.E. (1988). Development

of NASA-TLX (Task Load Index): Results of

empirical and theoretical research. In P.A.

Hancock and N. Meshkati (eds.) Human mental

workload. New York: Elsevier Scientific Publishers.

McNair, D.M., Lorr, M., and Droppleman, L.F.

(1971). Profile of mood states. San Diego, CA:

Educational and Industrial Testing Service.

McRuer, D.T., and Jex, H.R. (1967). A review of

quasi-linear pilot models. IEEE Transactions on

Human Factors in Electronics, 8, 231-49.

Miller, J.C., Takamoto, G.M., Barrel, G.M., and

Brown, M.D. (1985). Psychophysiological correlates of long-term attention to complex tasks.

Behavior Research Methods, Instruments, and Computers, 17, 186-90.

Nesthus, T.E., Schiflett, S.G., Eddy, D.R., and

Whitmore, J.N. (1991). Comparative effects of

antihistamines on aircrew performance of simple

and complex tasks under sustained operations. (ALTR-91-104). Brooks AFB, TX: USAF Armstrong

Laboratory, Crew Technology Division. (NTIS

# AD A248 752/8).

O'Donnell, R.D. (1991). Scientific validation of the

Novascan (tm) tests: Theoretical basis and initial

validation studies. NTI Report to Nova Technology, Inc., 19460 Shenango Drive, Tarzana, CA:

NTI, Incorporated.

O'Donnell, R.D. (NTI: 1995). The effect of alcohol

and fatigue on an FAA readiness-to-perform test.

(DOT/FAA/AM-95/24). Washington, DC: Federal Aviation Administration, Office of Aviation

Medicine.

O'Donnell, R.D., and Eggemeier, F.T. (1986).

Workload assessment methodology. In K.R. Boff

et al. (eds.), Handbook ofperception and human

performance, Vol II, New York: Wiley, 42-1 to

42-49.

Pennebaker, J.W. (1982). The psychology ofphysical

symptoms. New York, NY: Springer-Verlag.

Perez, W.A., Masline, P.J., Ramsey, F.R., and Urban,

K.E. (1987). Unified Tri-Services Cognitive Performance Assessment Battery: Review and methodology. (AAMRL-TR-87-007). Wright-Patterson

AFB, OH: Armstrong Aerospace Medical Research Laboratory.

Reader, D.C., Benel, R.A., and Rahe, A.J. (1981).

Evaluation of a manikin psychomotor task.

(USAFSAM-TR-81-10). Brooks AFB, TX: USAF

School of Aerospace Medicine.

Schlegel, R.E., and Storm, W.F. (1983). Speed-accuracy tradeoffs in spatial orientation information

processing. Proceedings ofthe 27th Annual Meeting ofthe Human Factors Society, Santa Monica,

CA: Human Factors Society.

Schlegel, R.E., Shehab, R.L., and Gilliland, K (1994).

Microgravity effects on cognitive performance measures: Practice schedules to acquire and maintain

performance stability. (AL-CF-TR-1994-0040).

Brooks AFB, TX: USAF Armstrong Laboratory,

Crew Technology Division.

Shingledecker, CA. (1984). A task battery for applied

humanperformance assessmentresearch. (AFAMRLTR-84-071). Wright-Patterson AFB, OH: Air

Force Aerospace Medical Research Laboratories.

Sternberg, S. (1969). The discovery of processing

stages: Extensions of Donders' method. Acta

Psychologica, 30, 276-315.

Weltin, M., Broach, D., Goldbach, K., and O'Donnell,

R. (1992). Concurrent criterion-related validation

of the Air Traffic Control Specialist pre-training

screen. (DTFA-01-89-Y-01019). Washington,

DC: Federal Aviation Administration.

B4
APPENDIX C

PERFORMANCE MEASURES

ci
REFERENCE GUIDE FOR TASK VARIABLES AND CODES

General Information

The identification scheme for the antihistamine trials is the following four-character code:

c c c c

1 21 4

h-antihistamine; p-placebo; r-refresher

(1,2,3,4,5,6)-refresher session number; d-daytime testing;

n-nighttime testing

a-first group tested; b-second group tested; x-not used

(l,2,3)-test trial; x-not used

Examples: r5xx - fifth refresher session

hdb3 - antihistamine, daytime, second test group, third test trial (dose)

General Variables Used in Many or All Tasks

ID

SESSION

DATE

TIME

TASK

INST

LENGTH

Subject identification number; subjects for antihistamine study were:

201, 204, 206, 211, 216, 217, 218, 223, 224, 225, 226, 227, 229, 230, 232, 233

Session number; antihistamine study consisted of Sessions 51 through 68

Date of session

Time of session

Task name

Whether or not instructions were included

(indicated by -Nl for instructions)

Program option to specify task length

Antihistamine State Scale (ASH)

TOTAL Total score for antihistamine symptom impact

Mood Scale (MOOD)

xxxN Total number of adjective responses in category xxx

xxxSUM Sum ofscores for adjectives in category xxx

xxxMN Mean of scores for adjectives in category xxx

xxxPCT Percent score for category xxx; (xxxPCT = [xxxMN - l]/2)

xxxRT Average response time for responses to adjectives in category xxx

RTALL Overall response time for all responses

xxx CATEGORY

ACT Activity °

HAP Happiness

DEP Depression

ANG Anger

FAT Fatigue

FER Fear

C2
Activity State Questionnaire (ACTSQ)

PHYSICAL Total (weighted) score for physical state

PREP Total (weighted) score for preparedness

Spatial Processing (SPA)

MNCORRT Mean correct response time

SDCORRT Standard deviation of correct response times

N Number of stimuli

PC Percent correct stimuli

PINC Percent incorrect stimuli

PLAPSE Percent lapsed (i.e., timed-out) stimuli

NC Number of correct stimuli

NINC Number of incorrect stimuli

NLAPSE Number of lapsed (i.e., timed-out) stimuli

MNCRTPOS Mean correct response time for positive stimuli

SDCRTPOS Standard deviation of correct response times for positive stimuli

NPOS Number of positive stimuli

PCPOS Percentage correct for positive stimuli

PINCPOS Percentage incorrect for positive stimuli

Critical Tracking (TRK)

MAXL Maximum lambda during trial

CTLOSS Number of control losses

RMS Average root mean square error

MEANL Mean of lambda's at control losses

Dual Task - Individual Lambda and Group Lambda (DULI/DULG)

SET Positive memory set

NULLSET Negative memory set

VIEWRT Memory set viewing time

PCRESP Percent of responses that were correct (excluding time-outs)

MNALLRT Mean overall response time

MNCORRT Mean correct response time

MNINCRT Mean incorrect response time

MAXL Maximum lambda during trial

CTLOSS Number of control losses

RMS Average root mean square error

MEANL Mean of lambdas at control losses

PC Percent correct of all stimuli

SPEED Responses per minute (60,000/MNALLRT)

THRPUT Throughput (SPEED * PC)

C3
Switching Task (NTI)

xxxCORRT Mean correct response time for xxx task

Percent correct of all stimuli for xxx task

Throughput for xxx task; (60,000/xxxcorrt) * xxxpc

Mean correct response time for xxx transition trials (xxx trials preceded by trial from other

task)

xxxPCX Percent correct of all stimuli for xxx transition trials

xxxPC

xxxTP

xxxCORTX

xxx TASK

MAN Manikin Task

MTH Mathematical Processing Task

NovaScan FAA Task (NSF)

xxxCOR Number of correct responses for xxx task

xxxPC Percent correct for xxx task

xxxCRT Mean correct response time for xxx task

xxxCSD Standard deviation of correct response times for xxx task

xxxINC Number of incorrect responses for xxx task

xxxPI Percent incorrect for xxx task

xxxTO Number of time-outs for xxx task

xxxPTO Percent time-outs for xxx task

xATNREQ Number of attention requests during xxx task

xATNACK Number of attention acknowledgments during xxx task

xATNFA Number of false alarms during xxx task

(x)xx TASK

(V)EC Visual Search and Vector Projection

(M)EM Continuous Spatial Memory

Air Traffic Scenarios Test (ATST)

SCEN Scenario

CRSHAC Number of crashes with other aircraft

CRSHBD Number of crashes into air space boundary

CRSHAP Number of crashes into the airport

SEPAC Number ofseparation errors with other aircraft

SEPBD Number ofseparation errors with air space boundary

ERRAPSPD Number ofspeed errors at airport

ERRAPALT Number of altitude errors at airport

ERRGTSPD Number ofspeed errors at boundary gates

ERRGTALT Number of altitude errors at boundary gates

ERRDEST Number of destination errors

NDEST Number of planes at destination

PCDEST Percentage of planes at destination

C4
DELAY Delay score in routing planes for planes arriving at destination

NDIR Number of direction changes

NALT Number of altitude changes

NSPD Number of speed changes

TLX for ATST

MENTAL

PHYSICAL

TEMPORAL

PERFORM

EFFORT

FRUST

Rating of mental workload

Rating of physical workload

Rating of time-related workload

Rating of performance

Rating of required effort

Rating of frustration level

Multi-Attribute Task Battery (MATB)

SCRIPT Specific MATB run script

Systems Monitoring

LTSRT Mean response time for lights

DLSRT Mean response time for dials

MONRT Mean response time for lights and dials

LTSSD Standard deviation for lights

DLSSD Standard deviation for dials

MONSD Standard deviation for lights and dials

LTSTO Time Out errors for lights

DLSTO Time Out errors for dials

MONTO Time Out errors for lights and dials

LTSFA False Alarm errors for lights

DLSFA False Alarm errors for dials

MONFA False Alarm errors for lights and dials

LTSER Time Out and False Alarm errors for lights

DLSER Time Out and False Alarm errors for dials

MONER Time Out and False Alarm errors for lights and dials

MONKR Key Repeats (See explanation for COMRPT under Communications dependent variables

below.)

Communications

COMCRT Mean response time for correct responses

COMCSD Standard deviation for correct responses

COMORT Mean overall response time

COMOSD Standard deviation for overall responses

COMER Total number of errors

(This includes othership false alarms, othership accuracy errors, unexplained errors,

ownship accuracy errors, and ownship time-outs. It does not include repeated ENTERs,

described below.)

C5
COMYFA Othership false alarms

(correct radio and frequency, but message was for other ship)

COMYAC Othership accuracy errors

(Message was for other ship; either radio or frequency were incorrect.)

COMYIG Othership messages correctly ignored.

COMAC Accuracy errors

(response to ownship message, but either radio or frequency incorrect)

COMTO Time out errors

COMUNER Unexplained errors

(some response without identifiable cause, possibly false alarm)

COMRPT Repeated ENTERs (Number of times ENTER was pressed within 5 seconds of a previous ENTER press. Some subjects hold the ENTER key down for several seconds during

this task. Matproc does not count these repeats as errors, but reports them with this

dependent variable.)

Tracking

TRKRMS Root Mean Square (calculated for the each entire epoch)

Resource Management

TNKMAD Mean absolute deviation of tanks A and B from 2500

TNKAMN Mean of Tank A

TNKBMN Mean of Tank B

TNKACT Tank activity (number of pump changes ON or OFF)

Workload Rating Scale

TLXOMN Overall mean of subscales

TLXMEN Mean for Mental Demand subscale

TLXPHS Mean for Physical Demand subscale

TLXTMP Mean for Temporal Demand subscale

TLXPER Mean for Performance subscale

TLXEFT Mean for Effort subscale

TLXFRU Mean for Frustration subscale

TLXDUR Mean for duration of rating screen presentation

C6 &u.s. GOVERNMENT PRINTING OFFICE: 1997 .WO-088/60050