Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences. J Sleep Res 15: 47-53

National Institute for Psychosocial Medicine, Stockholm, Sweden.
Journal of Sleep Research (Impact Factor: 3.35). 03/2006; 15(1):47-53. DOI: 10.1111/j.1365-2869.2006.00504.x
Source: PubMed

ABSTRACT The present study aimed to provide subject-specific estimates of the relation between subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS) and blink duration (BLINKD) and lane drifting calculated as the standard deviation of the lateral position (SDLAT) in a high-fidelity moving base driving simulator. Five male and five female shift workers were recruited to participate in a 2-h drive (08:00-10:00 hours) after a normal night sleep and after working a night shift. Subjective sleepiness was rated on the KSS in 5-min intervals during the drive, electro-occulogram (EOG) was measured continuously to calculate BLINKD, and SDLAT was collected from the simulator. A mixed model anova showed a significant (P < 0.001) effect of the KSS for both dependent variables. A test for a quadratic trend suggests a curvilinear effect with a steeper increase at high KSS levels for both SDLAT (P < 0.001) and BLINKD (P = 0.003). Large individual differences were observed for the intercept (P < 0.001), suggesting that subjects differed in their overall driving performance and blink duration independent of sleepiness levels. The results have implications for any application that needs prediction at the subject level (e.g. driver fatigue warning systems) as well as for research design and the interpretation of group average data.

1 Follower
63 Reads
    • "Baynard, Maislin, & Dinges, 2004; Franzen, Siegle, & Buysse, 2008). This variability has also been demonstrated on ocular measures of drowsiness, including blink parameters (Ingre et al., 2006). It is therefore important to assess individual variations in automated ocular measures "
    [Show abstract] [Hide abstract]
    ABSTRACT: Slow eyelid closure is recognized as an indicator of sleepiness in sleep deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure is evident following acute sleep deprivation as assessed by an automated device, and how ocular parameters relate to performance after sleep deprivation. Twelve healthy professional drivers (45.58±10.93 years) completed two randomized sessions; after a normal night of sleep and after 24-hours of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task. Following sleep deprivation, drivers displayed significantly more eyelid closure (p<0.05), greater variation in lane position (p<0.01) and more attentional lapses (p<0.05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r=0.68, p<0.05) and variation in lane position on the driving task (r=0.61, p<0.05). Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.
    Traffic Injury Prevention 05/2015; DOI:10.1080/15389588.2015.1055327 · 1.41 Impact Factor
  • Source
    • "It was concluded that there was always awareness of sleepiness before line crossings. In the previously mentioned simulator study (Ingre et al., 2006), the risk of line crossings with four wheels was increased 28 times at KSS = 8 and 185 times at KSS = 9, compared to the risk at KSS = 5. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The main consequence of insufficient sleep is sleepiness. While measures of sleep latency, continuous encephalographical/electro-oculographical (EEG/EOG) recording and performance tests are useful indicators of sleepiness in the laboratory and clinic, they are not easily implemented in large, real-life field studies. Subjective ratings of sleepiness, which are easily applied and unobtrusive, are an alternative, but whether they measure sleepiness sensitively, reliably and validly remains uncertain. This review brings together research relevant to these issues. It is focused on the Karolinska Sleepiness Scale (KSS), which is a nine-point Likert-type scale. The diurnal pattern of sleepiness is U-shaped, with high KSS values in the morning and late evening, and with great stability across years. KSS values increase sensitively during acute total and repeated partial sleep deprivation and night work, including night driving. The effect sizes range between 1.5 and 3. The relation to driving performance or EEG/EOG indicators of sleepiness is highly significant, strongly curvilinear and consistent across individuals. High (>6) KSS values are associated particularly with impaired driving performance and sleep intrusions in the EEG. KSS values are also increased in many clinical conditions such as sleep apnea, depression and burnout. The context has a strong influence on KSS ratings. Thus, physical activity, social interaction and light exposure will reduce KSS values by 1–2 units. In contrast, time-on-task in a monotonous context will increase KSS values by 1–2 units. In summary, subjective ratings of sleepiness as described here is as sensitive and valid an indicator of sleepiness as objective measures, and particularly suitable for field studies.
    Journal of Sleep Research 04/2014; 23(3). DOI:10.1111/jsr.12158 · 3.35 Impact Factor
  • Source
    • "Both blink and saccade parameters, derived from EOG such as blink rate [14,16], blink duration [19,20,23], blink amplitude and eye closing time [11], saccade rate and eye activity [11,13,17], and saccade velocity parameters [11,17,24,25] have shown to be sensitive for fatigue (caused by sleepiness). Our previous study suggested that the peak velocity of horizontal saccades could be the most potential eye parameter for monitoring sleepiness [15]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.
    BioMedical Engineering OnLine 10/2013; 12(1):110. DOI:10.1186/1475-925X-12-110 · 1.43 Impact Factor
Show more