Effects of night work, sleep loss and time on task on simulated threat detection performance.
OBJECTIVE
To investigate the effects of night work and sleep loss on a simulated luggage screening task (SLST) that mimicked the x-ray system used by airport luggage screeners.


METHODS
We developed more than 5,800 unique simulated x-ray images of luggage organized into 31 stimulus sets of 200 bags each. 25% of each set contained either a gun or a knife with low or high target difficulty. The 200-bag stimuli sets were then run on software that simulates an x-ray screening system (SLST). Signal detection analysis was used to obtain measures of hit rate (HR), false alarm rate (FAR), threat detection accuracy (A'), and response bias (B"(D)).


METHODS
Experimental laboratory study


METHODS
24 healthy nonprofessional volunteers (13 women, mean age +/- SD = 29.9 +/- 6.5 years).


METHODS
Subjects performed the SLST every 2 h during a 5-day period that included a 35 h period of wakefulness that extended to night work and then another day work period after the night without sleep.


RESULTS
Threat detection accuracy A' decreased significantly (P < 0.001) while FAR increased significantly (P < 0.001) during night work, while both A' (P = 0.001) and HR decreased (P = 0.008) during day work following sleep loss. There were prominent time-on-task effects on response bias B"(D) (P= 0.002) and response latency (P = 0.004), but accuracy A' was unaffected. Both HR and FAR increased significantly with increasing study duration (both P < 0.001), while response latency decreased significantly (P <0.001).


CONCLUSIONS
This study provides the first systematic evidence that night work and sleep loss adversely affect the accuracy of detecting complex real world objects among high levels of background clutter. If the results can be replicated in professional screeners and real work environments, fatigue in luggage screening personnel may pose a threat for air traffic safety unless countermeasures for fatigue are deployed.
Basner
Mathias
M
Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA. basner@mail.med.upenn.edu
Rubinstein
Joshua
J
Fomberstein
Kenneth M
KM
Coble
Matthew C
MC
Ecker
Adrian
A
Avinash
Deepa
D
Dinges
David F
DF
D000328:Adult; D000401:Aircraft; D001288:Attention; D003198:Computer Simulation; D005221:Fatigue / Q000523:psychology; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening; D009784:Occupational Diseases / Q000175:diagnosis / Q000523:psychology*; D010364:Pattern Recognition, Visual; D012637:Security Measures; D020178:Sleep Disorders, Circadian Rhythm / Q000175:diagnosis / Q000523:psychology*; D023481:Terrorism / Q000517:prevention & control*; D014195:Travel; D014940:Work Schedule Tolerance
Sleep Med Rev. 2006 Oct;10(5):347-56
16920370
Neurobiol Learn Mem. 2006 May;85(3):263-71
16423541
Sleep. 2001 Jun 15;24(4):401-6
11403524
J Exp Psychol Gen. 1992 Sep;121(3):275-7
1402701
Psychol Sci. 2004 May;15(5):302-6
15102138
Sleep. 2003 Mar 15;26(2):117-26
12683469
Accid Anal Prev. 1995 Dec;27(6):769-75
8749280
Neurobiol Learn Mem. 2007 May;87(4):679-87
17336554
Hum Factors. 2006 Winter;48(4):682-92
17240717
Int J Chronobiol. 1976;4(2):97-110
1027738
Behav Res Methods Instrum Comput. 1999 Feb;31(1):137-49
10495845
Q J Exp Psychol. 1971 Nov;23(4):449-52
5148928
Hum Factors. 1974 Aug;16(4):339-53
4435787
Sleep. 2005 Nov;28(11):1392-402
16335329
Nature. 2005 May 26;435(7041):439-40
15917795
Sleep. 1983;6(4):347-58
6665397
Occup Med. 2002 Apr-Jun;17(2):247-59, iv
11872439
Semin Neurol. 2005 Mar;25(1):117-29
15798944
Arch Ital Biol. 2001 Apr;139(3):253-67
11330205