Effects of night work, sleep loss and time on task on simulated threat detection performance

Abstract

Study objectives: 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.

Design: 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)).

Setting: Experimental laboratory study

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

Interventions: 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.

Figure 1

Examples of simulated x-ray images of threat bags with typical hit rates. A: gun with low target difficulty in the center (HR was 75%), B: knife with low target difficulty in upper right corner (HR was 56.5%), C: gun with high target difficulty in lower right corner (HR was 50%), D: knife with high target difficulty in lower left corner (HR was 32.5%)

Figure 2

Effects of night work, sleep loss and time in study on HR, FAR, A′, B″D, and bout duration (Dur) depending on time of day. Mixed model estimates of least square means are shown together with standard errors. P-values indicate differences between experimental and control conditions, i.e., between night work and day work, between deprived and rested subjects, and between last and first bouts in the study.

Figure 3

Effects of time-on-task on HR, FAR, A′, B″D, and response latency are shown. Each 200-bag SLST bout was divided in 10 consecutive sets of 20 bags for all 24 work bouts of each of the 24 subjects. Each point represents the average of 24 individual values within a given bag set.

Figure 4

Effects of category (threat bag, safe bag), type of threat (gun or knife), and target difficulty (high difficulty HD or low difficulty LD) on average response latency plus standard errors. Figure 4A shows average response latencies irrespective of how subjects classified the bag. In Figure 4B response latencies are differentiated for bags classified as threats and safe bags.