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The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure
Applied Ergonomics 42 (2010) 52e61
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The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure Rebecca Tremaine a, *, Jill Dorrian a, Leon Lack b, Nicole Lovato b, Sally Ferguson a, Xuan Zhou a, Greg Roach a a b
Centre for Sleep Research, University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia School of Psychology, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2009 Accepted 20 April 2010
The aim of the present study was to investigate the relationship between perceived and actual sleepiness and performance during a simulated night-shift that included a 30-min night-nap as an on-duty sleepiness countermeasure. Twenty-four healthy young adults (nine males, fifteen females) participated in a repeated measures design comprising two experimental conditions: no night-nap and 30-min nightnap. Both groups were given a 2-h prophylactic afternoon sleep opportunity (1500e1700 h). Measures of subjective sleepiness (Stanford Sleepiness Scale, Karolinska Sleepiness Scale and Visual Analogue Scale), objective sleepiness (sleep latency tests), objective performance (SymboleDigit Substitution Task) and reaction time (Psychomotor Vigilance Task) were taken before the night-nap (0230 h) and at several intervals post-nap. Timeeseries correlation analyses indicated that subjective sleepiness was less correlated with objective sleepiness and objective performance when participants were given a 30-min night-nap. However subjective sleepiness and reaction time performance was strongly correlated in both conditions, and there was no significant difference between the nap and no-nap conditions. Consistent with previous research, results of the present study indicate that subjective and objective indicators of sleepiness and performance may not always correspond, and this relationship may be reduced by the inclusion of a night-nap. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Subjective sleepiness Objective sleepiness Night-nap
1. Introduction Working shifts during the hours of darkness has become a reality of the modern world, and brings with it a host of dangers to health and safety that arise from living in misalignment with the circadian clock (Akerstedt, 1995; Costa, 1996; Dinges, 1995; Monk et al., 1996). Arguably the easiest method to assess the suitability of shift-workers to start or continue work is simply to ask them, and in many real-world situations this is the only information upon which an individual can base his or her decision. If subjective estimates of sleepiness and performance were accurate, then the individual would be best placed to make these decisions that could avert sleep-related accidents. If they were not accurate, then the implementation of education and safeguards would be vitally
* Corresponding author. Tel.: þ61 8 8302 6624; fax: þ61 8 8302 6623. E-mail addresses: [email protected] (R. Tremaine), Jill. [email protected] (J. Dorrian), Leon.Lack@flinders.edu.au (L. Lack), Nicole. Lovato@flinders.edu.au (N. Lovato), [email protected] (S. Ferguson), [email protected] (X. Zhou), [email protected] (G. Roach). 0003-6870/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2010.04.005
important to ensure that workers are protected against sleeprelated accidents by work rules and conditions. 1.1. Subjective and objective measures of sleepiness and performance A number of studies have looked at these relationships and found that subjective assessments of performance often closely mirror actual or objective performance (Akerstedt et al., 2005; Gillberg et al., 1994). Baranski et al. (1994) were able to show that even increasing sleep deprivation (up to 64-h) did not impair subjective performance estimates. Dorrian et al. found moderate to high correlations between objective performance and both pre- and posttest ratings of subjective performance during a unitary period of sleep deprivation (28-h) (Dorrian et al., 2000), during a week of simulated night-shifts (Dorrian et al., 2003), and at different levels of sleepiness using a rail-road driving simulator (Dorrian et al., 2007). Other studies have investigated whether objective performance might also be related to subjective estimates of sleepiness. In studies that have included some degree of sleep deprivation (partial or total) but no sleepiness countermeasure, much of the
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research has yielded only low associations between subjective sleepiness and objective performance (e.g. Casagrande et al., 1997; Owens et al., 1998). There have been some with more favourable findings however: Gillberg et al. (1994) found moderate to high correlations (r ¼ 0.49e0.79) between subjective sleepiness and objective performance measures, as did Hoddes et al. (1973; r ¼ 0.47e0.70), however it should be noted that these two studies are limited by their relatively small number of participants (6 and 5 respectively). It would seem reasonable to expect that subjective sleepiness would correlate fairly high with objective sleepiness, but research results have not always borne this out. Studies that have yielded high correlations are generally those which measure objective sleepiness using EOG and/or EEG (e.g. Akerstedt and Gillberg, 1990; Horne and Reyner, 1996). For those using measures such as sleep latency or pupillography, the correspondence between subjective sleepiness and objective sleepiness has frequently been low or nonsignificant (Danker-Hopfe et al., 2001; LaFrance and Dumont, 2000; Johnson et al., 1991). The majority of the studies finding low correspondence have also used a daytime testing protocol, whereas those yielding high correlations have generally been studies using partial or total sleep restriction protocols (Akerstedt and Gillberg, 1990; Horne and Reyner, 1996; Torsvall and Akerstedt, 1987). Similarly varied results have been found regarding the relationship between subjective sleepiness and performance on the PVT (psychomotor vigilance task), a 10-min serial reaction time task. Kaida et al. (2006) found a moderate correlation between the two (r ¼ 0.57) under normal sleeping conditions. Under irregular sleeping conditions, Dorrian et al. (2003) found that PVT performance deteriorated at a corresponding rate to subjective sleepiness ratings during a week of simulated night-shifts. In a later study, Dorrian et al. (2007) also found that subjective sleepiness and PVT performance were significantly related under increasing sleepiness levels. However there have been other studies which have found low or inconsistent results in their investigation of the relationship between subjective sleepiness and neurobehavioural function (e.g. Rosekind et al., 1991). In summary, the relationship between subjective and objective sleepiness, and performance measures remains unclear. However, differential findings across studies may be due in part to different methodologies employed, including (but not limited to): sample size, type of assessment instruments used to measure subjective and objective performance and sleepiness, day or night-time testing protocol, and the inclusion of sleep restriction (partial or total) in the protocol.
Many of the studies examining the effects of a countermeasure on sleepiness and performance have focused on pharmacological stimulants as countermeasures, such as caffeine or modafinil. Unfortunately, research examining the perception of sleepiness and performance with the use of a countermeasure has garnered inconclusive results. For example, Biggs et al. (2007) found that although caffeine was a beneficial countermeasure to sleepiness, subjective and objective performance measures were not significantly correlated in any of the experimental conditions. Although subjective performance and sleepiness were strongly and significantly correlated in sleep restriction and placebo conditions, this relationship was no longer significant after the addition of a caffeine countermeasure. Other studies with a caffeine countermeasure have found improved subjective sleepiness, but not corresponding improvements in objective performance (Johnson et al., 1990; Lieberman et al., 1984). Such pharmacological countermeasures, while useful, may not be as healthy, economical, or effective in reducing sleepiness as sleep itself (Akerstedt and Landström, 1998). A nap countermeasure would therefore seem to offer significant benefits over other alternatives, and this renders an understanding of how it affects people’s sleepiness and performance variables potentially valuable. Interestingly, studies using a nap countermeasure have shown improved performance, but not improved subjective sleepiness (Dinges et al., 1987; Rosekind et al., 1994). The inclusion of a napping countermeasure may thus complicate the relationship between perceived and actual sleepiness and performance, and therefore warrants further investigation.
1.2. Napping as a countermeasure
1.4. Summary and aims
Due to the dangers of working when sleepy, numerous countermeasures have been proposed to help alleviate the concomitant sleepiness of working night-shifts. Diurnal napping has consistently produced demonstrable improvements in alertness and performance (see Dinges, 1989). More recently e and perhaps of greater importance to shift-workers e interest in the effects of naps has widened to include an examination of the efficacy of night-time naps. Short naps taken during the night to ameliorate the effects of sleepiness have also been shown to be beneficial (Bonnefond et al., 2001; Purnell et al., 2002). This is particularly promising for shiftworkers in industries that require extended periods of work without the opportunity to go home and sleep, for example, in the medical profession, military, or long-haul transport operations. One difficulty, however, with the implementation of napping, is the ensuing sleep inertia or brief period of impaired alertness that routinely follows awakening from a substantial sleep (e.g. more than 30-min; Dinges, 1992a; Muzet et al., 1995). It has therefore
In summary, the relationship between subjective sleepiness and objective sleepiness and/or performance has been investigated under various conditions, but it is yet to be investigated in the context of a simulated night-shift that includes a 30-min night-nap. It is suggested that this napping protocol may reduce the relationships between sleepiness and performance measures as can occur with a caffeine countermeasure during the day. There are significant empirical and practical implications of the findings in the present study. A deeper understanding of the accuracy of people’s perception of their sleepiness with and without napping has the potential to aid in the design of policies to promote safe work practices and sleepiness countermeasures that rely on an individual’s decision about their suitability to work. Research in this area may therefore provide valuable insights which can guide the formation and implementation of napping best-practice, and into how much weight should be placed on an individual’s capacity to self-monitor their sleepiness and performance.
been suggested that the optimal length for a nap is less than 30min, as most often the greater the length and depth of sleep obtained, the more severe the subsequent sleep inertia (Brooks and Lack, 2006). In most cases this disorienting state usually only persists for a short time (15e30-min; Dinges, 1989, 1992a), but it may linger longer e even up to several hours e if the period of sleep follows extended wakefulness or is during the early morning (Dinges et al., 1985; Naitoh, 1981). In addition to naps taken during the night, studies have shown that naps taken prior to sleep restriction can improve subsequent performance and reduce sleepiness in the following 24-h (Bonnet, 1991). Known as a ‘prophylactic nap’, this is a tactic used by shiftworkers to try to ‘store’ sleep before the start of a work period (Dinges et al., 1987). 1.3. Effects of a countermeasure on sleepiness and performance
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2. Method 2.1. Participants The sample comprised nine male and fifteen female participants (mean age ¼ 22.21 years, SD ¼ 2.45) who were recruited through advertisements and emails from the University of South Australia, Flinders University, and the University of Adelaide. Each received a monetary payment of AU$450 for their participation. Participants were self-reported good sleepers with no history of sleep complaints, and were not regular nappers or taking drugs known to affect sleep architecture. The study received approval from the Flinders University Social and Behavioural Research Ethics Committee and the University of South Australia Human Research Ethics Committee. All participants gave informed consent. 2.2. Design In a randomised cross-over design, each participant took part in two experimental conditions: (a) a no night-nap condition and (b) a 30-min night-nap condition. Both experimental conditions included a 2-h afternoon sleep opportunity as it reproduced a common practice of night-shift workers to nap in preparation for the first of a series of night-shifts. The order of conditions was counterbalanced to prevent order effects, with at least one week intervening between conditions to allow sufficient recovery from one night of sleep loss. Prior to the experimental sessions, all participants were required to attend a training and adaptation session. 2.2.1. Training and adaptation session For the training and adaptation session, participants arrived at the sleep laboratory at 1200 h after waking no later than 0700 h that morning. They were provided with lunch, and from 1300 h they were given training and practice on the tasks they would be using during the experimental sessions. At 1500 h participants were given a 2-h afternoon sleep opportunity, and at 1700 h participants were awoken and able to leave the sleep laboratory. 2.2.2. Prior to the laboratory session Participants were instructed to maintain regular sleep and wake times for the week prior to each laboratory session. Compliance with these instructions was monitored with sleep diaries and activity monitors. Participants were asked to refrain from consuming alcohol or caffeine for 3 days prior to and including the laboratory sessions. 2.2.3. Experimental sessions For the two experimental sessions, participants arrived at the sleep laboratory at 1200 h, consumed lunch, and at 1300 h had electrodes applied for standard polysomnographic (PSG) montage in accordance with the International 10/20 System (Carskadon and Rechtschaffen, 1994). Two EEG electrodes were placed on the scalp over each hemisphere of the brain, with references for each on the mastoid behind the opposite ear (C3/A2 and O2/A1); EOG electrodes were placed below the left and right outer canthus, referenced to the mastoids (ROC/A1 and LOC/A2); and EMG electrodes were attached to the chin, approximately 3 cm apart. From 1500 to 1700 h participants in both conditions (night-nap and no night-nap) were given a 2-h prophylactic afternoon sleep opportunity. From 1700 h until the first assessment session at 0155 h, participants were allowed to engage in quiet activities such as reading, watching videos, or listening to music. During this time they were also provided with dinner (at 1900 h). During all periods of wake, the bedroom environment was consistently illuminated by
a 75-W light globe, producing 50 lux illumination. External time cues were eliminated (e.g. clocks and watches, sunlight, mobile phones, internet). As illustrated in Table 1, each experimental session included eight Sleepiness Assessment Sessions (SAS), six Performance Assessment Sessions (PAS), and five Sleep Latency Tests (SLTs). These assessment sessions began at 0155 h and continued at 30e60 min intervals throughout the night until approximately 0735 h. Participants in the night-nap condition were given a 30min nap at 0230 h, while those in the no night-nap condition underwent a SLT at 0230 h.
2.3. Assessment instruments Assessment batteries were grouped as either SAS or PAS. The SAS battery consisted of the Stanford Sleepiness Scale (SSS; Hoddes et al., 1973), Karolinska Sleepiness Scale (KSS; Reyner and Horne, 1998), and a 100 mm Visual Analogue Scale for sleepiness (VAS; Monk, 1989). The PAS battery included the SymboleDigit Substitution Task (SDST; Army Beta format), a subtest of the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1958) in which the performance measure was the number of correct responses. Twelve parallel forms of this task were constructed to provide novel forms for each assessment session (Royer, 1971). Performance on a 10-min serial visual reaction time task was also assessed using the psychomotor vigilance task (PVT). The PVT has been adapted for a personal digital assistant (PDA) by researchers at the Walter Reed Army Institute of Research, and validated by subsequent studies (Lamond et al., 2005, 2008; Thorne et al., 2005). PVT response time was used as the dependent variable in all analyses, and in accordance with standard methodology, PVT data were transformed to a 1/RT to correct for proportionality between the mean and standard deviation (Dinges and Kribbs 1991). Sleep latency tests (SLTs) were used to assess objective sleepiness. SLTs assess physiologic sleep tendency by measuring the time it takes a person to fall asleep while lying down in a quiet, dark room. Sleep is measured using PSG, and the test is terminated after
Table 1 Summary of the experimental protocol. Testing time (clock time)
Mins pre/post-nap
1500e1700 0155 0200 0225 0230 0310 0315 0340 0345 0410 0415 0445 0510 0515 0545 0610 0615 0645 0710 0715
35 30 5 5 10 35 40 65 70 95 125 130 155 185 190 215 245 250
Condition Night-nap condition
No night-nap condition
Afternoon sleep opportunity SAS PAS SAS Night-nap SAS PAS SAS PAS SAS SLT PAS SAS SLT PAS SAS SLT PAS SAS SLT
Afternoon sleep opportunity SAS PAS SAS SLT SAS PAS SAS PAS SAS SLT PAS SAS SLT PAS SAS SLT PAS SAS SLT
SAS: Sleepiness Assessment Session (includes SSS, KSS, and VAS). PAS: Performance Assessment Session (includes SDST and PVT). SLT: Sleep Latency Test.
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sleep onset, which is classified as three consecutive epochs of any stage of sleep (one epoch ¼ 30 s), or after 20-min without sleep onset. Sleep was monitored using Compumedics 10e20 recording system (Melbourne, Australia). All SLTs taken throughout the laboratory sessions were determined according to this three-epoch criterion with participants awoken immediately after the criterion to avoid any further napping benefit (<1.5 min of sleep). This allowed a sleep latency measure but without any beneficial effect of sleep (Tietzel and Lack, 2002). An initial SLT was conducted immediately prior to the night-nap (latency to nap), and subsequent SLTs were taken at 70, 130, 190 and 250 min after the nightnap. In the night-nap condition participants were awoken when they had slept for a total of 30-min, with sleep onset also determined as three consecutive epochs (i.e. 90 s) of any sleep stage. In the no night-nap condition, participants were awoken after the initial SLT and were asked to get up and engage in quite activities until the next assessment session. 2.4. Statistical analysis A technical issue with the PDAs used to administer PVT resulted in some missing data; therefore PVT analyses were run using twenty-one of the twenty-four participants. To ensure subsequent analyses were comparable these three participants had their PVT data for both nap conditions removed. The effect of condition and time on all subjective and objective measures was analysed using two-way repeated measures analysis of variance (ANOVA), with factors ‘nap condition’ (30-min night-nap and no night-nap) and ‘time’ (assessment session time points). Planned contrasts were conducted to investigate the changes from baseline to each assessment session time point for the nap and no-nap conditions. A Bonferroni correction was applied to adjust for multiple comparisons, and degrees of freedom were corrected using GreenhouseeGeisser estimates of sphericity. Timeeseries correlation (TSC) coefficients were calculated for each participant 3 to þ3 time-lags (1 lag ¼ 1 assessment session), for comparisons between subjective and objective sleepiness and performance measures. Subjective sleepiness was measured more frequently across the night (8 times) than PVT and SDST (6 times). Therefore, in order to compare these measures, the six ratings most closely time-matched with PVT and SDST were used such that the sleepiness ratings were taken less than 10-min prior to or following each performance battery. Similarly, for sleep latency, the most closely time-matched 5 sleepiness ratings were used for comparison. For all TSCs, r-values were found to be highest at a time-lag of 0, indicating no clear lag effect. As such, r-values will be reported at time-lag 0 only. Since distributions of r-values are highly skewed, an average r across all participants for each test was obtained using Fisher’s rez transformation. 3. Results 3.1. Preliminary analysis The mean total sleep time (TST) for the three nights leading up to each laboratory session, as indicated by wrist actigraphy data and sleep diaries, were analysed using a two-way repeated measures ANOVA. Similar amounts of TST were obtained in both the night-nap condition (M ¼ 7.10 h, SD ¼ 1.04), and the no nightnap condition (M ¼ 7.13 h, SD ¼ 0.86). There was no significant main effect of night-nap condition, F (1,23) ¼ 0.134, p > 0.10. The mean TST of the afternoon sleep opportunity (1500e1700 h), and the baseline pre-nap scores of the six dependent variables (SSS, KSS, VAS, PVT, SDST, and SLT) were examined
55
for the two conditions using paired-samples t-tests. All mean values showed no significant difference between nap conditions. The night-naps were visually scored as participants slept and then rescored posthoc by sleep technicians to check for accuracy. The posthoc scoring revealed that ten participants had obtained slightly more than 30 min of sleep during the night-nap (M ¼ 32.50, SD ¼ 6.38). 3.2. Effect of time and napping on subjective and objective measures The mean scores and standard errors of each subjective measure (SSS, KSS and VAS) are illustrated in Fig. 1, and the objective measures are illustrated in Fig. 2 (PVT, SDST, SLT). In Figs. 1 and 2, decreased scores on the y-axis indicate increasing sleepiness. Therefore some axes (SSS and KSS) were reversed for consistency and to provide easier understanding of the large number of dependent variables. Thus “1” on the SSS and KSS (after reversing) refers to the highest degree of sleepiness on these scales. Results of the two-way repeated measures ANOVA are presented in Table 2. There was a main effect of time (p < 0.001) for each of the subjective measures (SSS, KSS, VAS), but none of these measures demonstrated a main effect of condition. Two of the subjective measures (SSS and VAS) showed a significant conditionetime interaction effect (p < 0.005). For PVT and SDST there was a main effect of time (p < 0.001) and a conditionetime interaction (p < 0.001), but no main effect of condition. For sleep latency, there was a significant main effect of time only (p < 0.001), and no interaction effect. A series of planned contrasts were conducted to investigate the difference from baseline to each assessment session time point. Figs. 1 and 2 show the time points at which significant differences were found (indicated by filled shapes). As illustrated in Figs. 1 and 2, overall both conditions became sleepier and their performance declined as the night progressed. However, the no night-nap condition showed a trend for relatively steady decline over the course of the night, while in the night-nap condition participants demonstrated a sharp dip in alertness and performance in the assessment session immediately following the cessation of the night-nap, suggestive of the effects of sleep inertia. For a more detailed discussion on the effects of a night-nap, including other measures collected in this study not included in this manuscript, the reader is directed to Lovato et al. (2009). 3.3. Comparing subjective and objective measures The mean r-values of TSCs are displayed in Table 3. Singlesample t-tests compared TSCs for each measure across participants from zero. As indicated in Table 3, all TSC were significantly different from zero (p < 0.05) except those comparing the subjective sleepiness measures with SDST in the nap condition. Fig. 3 provides an illustration of the differences in TSC scores across measures (subjective sleepiness represented by VAS, correlated with SLT, SDST, and PVT) and between conditions (night-nap/ no night-nap). As all subjective sleepiness measures were highly correlated (r ¼ 0.81e0.96), VAS was chosen as a representative measure of the subjective sleepiness scales in Fig. 3 (however full results are presented in Table 3). Fig. 4 was constructed to further illustrate the relationship between each subjective sleepiness measure (SSS, KSS and VAS) and SLT, SDST and PVT respectively. In order to plot data from these different measures on the same scale, raw data were z-transformed (relative to group mean and standard deviation). These figures show the mean (standard error) standardised score across participants at corresponding assessment session time points.
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R. Tremaine et al. / Applied Ergonomics 42 (2010) 52e61
SSS
“Awake but relaxed” 5
Nap No Nap
“Fogginess”
Mean score
“A little foggy” 4
3
N A P
“Fighting sleep” 2
“Sleep onset soon” 1 0155 0225
KSS
0310 0340 0410
0510
0610
0710
“Neither alert nor sleepy” 5
“No effort to stay awake”
Mean score
“Some signs of sleepiness” 4
3
N A P
“Effort to stay awake” 2
“Fighting sleep” 1
0155 0225
VAS
0310 0340 0410
0510
0610
0710
50
M ean score
40
30
N A P
20
10 0155 0225
0310 0340 0410
0510
0610
0710
Time Fig. 1. Mean score and standard error bars for the nap condition (triangles & solid lines) and no-nap condition (circles & dotted lines) for each subjective sleepiness variable. Decreased scores on the y-axis indicate increasing sleepiness. Nap time is indicated by the grey bar. Significant differences from baseline at each assessment session time point are indicated by filled triangles (nap condition) and filled circles (no-nap condition).
Paired-samples t-tests were used to compare TSCs between conditions (results are presented in Table 3). Comparing the magnitude of correlations between conditions, measures of subjective sleepiness were moderately correlated with sleep latency in the nap condition (r ¼ 0.41e0.47), and very strongly correlated in the no-nap condition (r ¼ 0.78e0.85), with all correlations significantly weaker in the nap condition (p < 0.05). Subjective sleepiness and objective performance (SDST) correlates ranged from weak (r ¼ 0.11e0.26) to moderate (r ¼ 0.40e0.46) for the nap and no-nap conditions respectively. For SDST, t-tests indicated significant differences in TSC between conditions for KSS
(p < 0.05). TSCs between PVT-measured reaction time and subjective sleepiness demonstrated strong to very strong correlations for the nap (r ¼ 0.72e0.76) and no-nap (r ¼ 0.65e0.77) conditions respectively. The mean correlations of PVT with subjective sleepiness measures were not significantly different for the nap condition than the no-nap condition (p < 0.05). 4. Discussion Results suggest that during a simulated night-shift, alertness and performance decline considerably across time; however this
R. Tremaine et al. / Applied Ergonomics 42 (2010) 52e61
PVT
4.5
Nap No Nap
Mean RRT
4.0
3.5
N A P
3.0
2.5
0 20 0
SDST
0 31 5 0 34 5
0445
0 31 5 0 34 5
0445
05 45
06 45
70
Mean Score
65
60
55
N A P
50
45
0 20 0
SLT
05 45
06 45
14 13
T i m e ( m i n u te s )
12 11 10 9 8 7
N A P
6 5 4 3 2
02 30
04 15
0 51 5
0 61 5
0715
Time Fig. 2. Mean score and standard error bars for the nap condition (triangles & solid lines) and no-nap condition (circles & dotted lines) for the objective indicators. Decreased scores on the y-axis indicate decreasing performance (PVT and SDST) and increasing sleepiness (SLT). Nap time is indicated by the grey bar. Significant differences from baseline at each assessment session time point are indicated by filled triangles (nap condition) and filled circles (no-nap condition).
decline is ameliorated when the shift includes a nap. The trend for sleepiness and performance to worsen over the course of the night is in accordance with the combined effects of increasing time awake (known as the homeostatic drive for sleep), and the increased pressure of the body’s natural circadian rhythm, which promotes sleep during the night (Borbély and Achermann, 1999). For these reasons, it is particularly difficult for shift-workers to function optimally during the hours of darkness, as humans are predisposed to be sleeping during this time. Participants in the night-nap condition also demonstrated a sharper decline or dip in alertness and performance immediately after the nap, consistent with the effects of sleep inertia. Sleep inertia, the feeling of
57
drowsiness that can occur after waking as the body transitions from sleep to wake, is frequently accompanied by sleepiness and performance impairments (Folkard and Akerstedt, 1992; Jewett et al., 1999). After participants in the night-nap condition recovered from their post-nap drowsiness, their subsequent decline in alertness and performance levels over the remainder of the night was less pronounced than for the no night-nap condition (see Lovato et al., 2009 for further detail). As illustrated in Fig. 4, there was a differential correspondence between the three subjective sleepiness measures and each of the objective indicators (SLT, SDST, and PVT). Standardised SLT scores reflected greater sleepiness from 0415 h (in the no-nap condition) and 0515 h (in the nap condition) onwards than the subjective measures. Conversely, standardised SDST performance scores were suggestive of less sleepiness than the subjective measures from 0445 h onwards for both conditions, although the magnitude of the difference between subjective and objective measures was greater in the nap condition. For PVT, however, standardised scores corresponded very closely with subjective sleepiness estimates in both conditions and at most assessment time points; there was only a slight difference in the no-nap condition at the first assessment session (0200 h), and at the 0345 h and 0445 h sessions where subjective sleepiness scores indicated that participants felt more alert than PVT scores demonstrated. Timeeseries correlation analyses showed that in the no nightnap condition, objective sleepiness (SLT) was highly correlated with each of the subjective sleepiness measures. However the inclusion of a nap countermeasure significantly reduced this relationship, although there was still a moderate correlation. If sleep latency is an accurate indicator of sleepiness levels, then this suggests that the ability to self-report sleepiness is less robust during a nightshift that includes a short nap. The finding that participants given no night-nap (or having a night of sleep deprivation) had stronger correlations between their subjective and objective sleepiness scores is consistent with the previous research reviewed. Studies using partial or total sleep restriction protocols were also those that found high correlations between subjective and objective sleepiness (Akerstedt and Gillberg, 1990; Torsvall and Akerstedt, 1987). This may mean that people are best able to estimate their level of sleepiness when they are sleep-deprived, but that this relationship deteriorates with the inclusion of a sleepiness countermeasure. This would be consistent with the research using daytime testing protocols and without sleep deprivation, which have only yielded low correlations between subjective and objective sleepiness (Danker-Hopfe et al., 2001; LaFrance and Dumont, 2000). The nap countermeasure also corresponded with lower correlations between subjective sleepiness and objective performance (as measured by SDST). There were moderate correlations in the no-nap condition, but with a night-nap the associations were weaker. As the subjective sleepiness assessment sessions were conducted immediately prior to the performance assessment sessions, this may contribute some explanation as to why these measures did not yield higher correlations in either condition. Research has shown that subjective sleepiness ratings more accurately reflect actual performance when the rating is given after completing the performance task, possibly as a result of the task itself affecting sleepiness levels (Van Dongen and Dinges, 1994). In the no-nap condition both subjective sleepiness and SDST performance declined fairly steady over the course of the night. This contributed to higher correlations between these measures in the no-nap than the nap condition, in which participants’ performance levels gradually improved, while their sleepiness levels continued to worsen. It appears then, that although participants who napped felt consistently sleepier as the night progressed, their performance did not correspondingly decline. One obvious explanation for the
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Table 2 Summary of two-way ANOVA for condition (nap/no-nap), time point (1e8), and interaction for all dependent measures. SSS
KSS
VAS
SLT
SDST
PVT
Condition
F df
0.368 (1,23)
1.293 (1,23)
0.020 (1,23)
2.870 (1,23)
0.665 (1,23)
0.118 (1,20)
Time
F df
17.108** (3.46,79.54)
21.04** (3.80,87.34)
23.955** (3.10,71.33)
65.336** (2.96,68.16)
13.775** (3.51,80.77)
25.955** (2.46,49.20)
Conditionetime
F df
4.312* (4.20,96.57)
3.745 (3.96,90.99)
4.809* (3.50,80.45)
2.474 (2.38,54.81)
9.050** (3.58,82.24)
6.680** (3.57,71.43)
GreenhouseeGeisser adjustment reported for df as Mauchly’s test of sphericity was not met. *p < 0.005, **p < 0.001.
Table 3 Timeeseries correlations between subjective and objective measures in each nap condition. R-values are reported at time-lag 0. No-Nap
SLT
SDST
PVT
Nap
t
df
Mean
SD
Mean
SD
SSS KSS a VAS
0.779*** 0.852*** 0.852***
0.610 0.625 0.689
0.465*** 0.468*** 0.412***
0.627 0.688 0.713
2.249* 3.884** 3.229**
21b 21b 23
SSS KSS a VAS
0.395*** 0.405*** 0.460***
0.608 0.532 0.667
0.255 0.108 0.239
0.596 0.627 0.624
0.807 2.251* 1.455
22b 22b 23
SSS KSS a VAS
0.652*** 0.720*** 0.773***
0.462 0.438 0.502
0.721*** 0.742*** 0.756***
0.511 0.551 0.612
1.002 0.257 0.233
19b 19b 20b
*Significant at p < 0.05 prior to Bonferroni correction. **Paired-samples t-test for condition, p < 0.005. ***Significant difference from zero, p < 0.05. a These relationships are illustrated in Fig. 3. b Indicates missing data (PVT) or no change at relevant time points and hence no r-value could be calculated.
button as quickly as possible. The PVT is thus a relatively repetitive and monotonous task, and tasks of this nature have been shown to reflect greater sleepiness than shorter, more interesting tasks (Dinges et al., 1988; Dorrian et al., 2003). To complete the SDST, each session uses a novel form of symboledigit combinations, and the participant must balance speed and accuracy as they try to substitute as many digits for symbols as they can while being timed on a stop-watch for 90 s. As the SDST is arguable more interesting than the PVT, participants may have found the task more engaging and less laborious to complete, and therefore performed with higher motivation on the SDST than they did while completing the PVT. It has been shown that motivation or compensatory effort can reduce or eliminate performance impairments on various tasks, and that tasks requiring sustained attention and lacking in complexity (such as the PVT) are more susceptible to the effects of sleep loss, compared to tasks that are visually engaging and require motor activity (Dinges, 1992b; Dinges et al., 1988; Gillberg and Akerstedt, 1998). In light of the present results, the implications are that shiftworker’s subjective sleepiness estimates may better correspond with performance on tasks that are more sensitive to the effects of sleep loss than those that are novel and interesting. For a number of shift-workers, their roles may consist of varying periods of monotonous or repetitive activity (e.g. transport, healthcare, factory work), and in these cases their subjective sleepiness estimates may be seen as a relatively representative indicator of their performance levels. In a practical sense, when shift-workers are asked to self-monitor their sleepiness and/or fitness to continue work, the resultant estimates (assuming they based on subjective sleepiness) may be relatively accurate if the work they are completing is not stimulating or engaging to them, and in such
1.0
Mean TSC values (+ standrd error)
reduced correlations between subjective and objective measures in the nap condition is the truncation of range of sleepiness and performance measures that occurred after participants were given a night-nap. In each instance the decline across the night in alertness and performance was ameliorated to some extent by the nap, therefore reducing the range of results in that condition, as compared to the no-nap condition. Or perhaps, as past research has shown, the association between subjective sleepiness and objective performance is at best only moderate, and significantly weakened by the inclusion of a sleepiness countermeasure such as a nap (Dinges et al., 1987; Johnson et al., 1990; Lieberman et al., 1984). The relationship that was least affected by the nap was that of subjective sleepiness and PVT reaction time. Very high correlations were found between these measures in the nap condition, and high correlations were also evident in the no-nap condition. A robust relationship between subjective sleepiness and PVT scores is consistent with the majority of past studies (e.g. Dorrian et al., 2003, 2007; Kaida et al., 2006), although it does differ to Rosekind et al. (1991) who found a low correspondence between the two. However, in contrast to the present study, Rosekind et al. (1991) found that subjective sleepiness was not affected by the inclusion of a countermeasure, and this may account for some disparity between findings. The seemingly incongruent finding of low correlations between each subjective sleepiness measure and SDST performance, but high correlations between subjective sleepiness and PVT performance may be partially attributable to the effects of motivation and compensatory effort elicited by these different measures of performance. When completing the PVT, participants are required to monitor the device continuously for 10-min, waiting for a stimulus to appear (a red light), and when it does, to press a response
Nap
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Fig. 3. Mean (þ standard error) time-series correlation values (y-axis) for subjective sleepiness ratings (as measured by VAS) compared to SLT, SDST and PVT scores (x-axis) in the nap (black bars) and no-nap (grey bars) conditions. VAS was chosen as a representative measure due to all subjective sleepiness measures being highly correlated. Full timeeseries correlation results are presented in Table 3.
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59
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Fig. 4. Relationship between objective measures (SLT, SDST and PVT) and subjective sleepiness measures (SSS, KSS and VAS). Time points indicate the objective assessment session and the closest matched subjective assessment session. Decreased scores on the y-axis indicate increasing sleepiness/decreasing performance.
instances, the inclusion of a night-nap does not appear to significantly change this ability. Although there are some limitations in this study, the results show promise for understanding how the relationship between sleepiness and performance measures is changed by a night-nap. Despite attempting to simulate a night-shift, there are obvious
limitations to the generalisability of research undertaken in a laboratory setting. Thus it would be important to replicate these findings in field studies to discover what differences, if any, may result. For example, this study was conducted with young, healthy participants who did not suffer from sleep-related problems. Further studies are needed to determine if these results can be generalised to older,
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sleep-disordered, or shift-working populations. Replicating this study with real shift-workers as participants would thus be highly beneficial, as their perception of sleepiness and performance during a night-shift and after a night-nap may be different to individuals who are not used to being awake during the night. In conclusion, the results of the current study suggest that subjective and objective indicators of sleepiness and performance do not always correspond. Moreover, the inclusion of a nap during a simulated night-shift appears to increase the likelihood of a disassociation between perceived and actual sleepiness and performance. Findings indicate that the nap had a differential effect on measures of sleepiness and performance. While there was a strong relationship between subjective measures and PVT that was consistent across conditions, the relationship between subjective measures, SLT and SDST was reduced in the nap condition. Therefore asking someone how sleepy they are after a nightnap may not necessarily be a reliable indicator of their likelihood of falling asleep (as indicated by sleep latency), or their performance on all types of tasks. In instances where the nature of the work is repetitive and monotonous (e.g. PVT), a nap does not appear to differentially affect the correspondence between task performance and subjective sleepiness estimates. However, tasks that are more engaging and novel may be able to elicit a degree of motivation or compensatory effort that can help shift-workers to perform better than might be expected based on their sleepiness levels (i.e. SDST). Practically, this suggests that for jobs which involve tedious or repetitive work (e.g. long-haul transport operations or factory work), subjective sleepiness estimates are more likely to reflect subsequent performance, while more interesting or engaging work (e.g. military combat or surgery) may encourage shift-workers to apply additional effort and focus to overcome their sleepiness. The results of the current study are of operational importance as nightshift-workers’ decisions regarding their fitness to start or continue work will be influenced by their subjective degree of sleepiness. Acknowledgements The authors would like to thank Sara Dawson, Raymond Matthews and Alison Teare for their assistance in conducting this study. This research was supported by Australian Research Council (ID: DP0558960). Appendix. Table of acronyms
EEG EMG EOG KSS PAS PDA PSG PVT SAS SDST SLT SSS TSC TST VAS
Electroencephalogram Electromyogram Electrooculogram Karolinska Sleepiness Scale Performance Assessment Session Personal Digital Assistant Polysomnography Psychomotor Vigilance Task Sleepiness Assessment Session SymboleDigit Substitution Task Sleep Latency Test Stanford Sleepiness Scale Time-Series Correlation Total Sleep Time Visual Analogue Scale
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Contents lists available at ScienceDirect
Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure Rebecca Tremaine a, *, Jill Dorrian a, Leon Lack b, Nicole Lovato b, Sally Ferguson a, Xuan Zhou a, Greg Roach a a b
Centre for Sleep Research, University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia School of Psychology, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2009 Accepted 20 April 2010
The aim of the present study was to investigate the relationship between perceived and actual sleepiness and performance during a simulated night-shift that included a 30-min night-nap as an on-duty sleepiness countermeasure. Twenty-four healthy young adults (nine males, fifteen females) participated in a repeated measures design comprising two experimental conditions: no night-nap and 30-min nightnap. Both groups were given a 2-h prophylactic afternoon sleep opportunity (1500e1700 h). Measures of subjective sleepiness (Stanford Sleepiness Scale, Karolinska Sleepiness Scale and Visual Analogue Scale), objective sleepiness (sleep latency tests), objective performance (SymboleDigit Substitution Task) and reaction time (Psychomotor Vigilance Task) were taken before the night-nap (0230 h) and at several intervals post-nap. Timeeseries correlation analyses indicated that subjective sleepiness was less correlated with objective sleepiness and objective performance when participants were given a 30-min night-nap. However subjective sleepiness and reaction time performance was strongly correlated in both conditions, and there was no significant difference between the nap and no-nap conditions. Consistent with previous research, results of the present study indicate that subjective and objective indicators of sleepiness and performance may not always correspond, and this relationship may be reduced by the inclusion of a night-nap. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Subjective sleepiness Objective sleepiness Night-nap
1. Introduction Working shifts during the hours of darkness has become a reality of the modern world, and brings with it a host of dangers to health and safety that arise from living in misalignment with the circadian clock (Akerstedt, 1995; Costa, 1996; Dinges, 1995; Monk et al., 1996). Arguably the easiest method to assess the suitability of shift-workers to start or continue work is simply to ask them, and in many real-world situations this is the only information upon which an individual can base his or her decision. If subjective estimates of sleepiness and performance were accurate, then the individual would be best placed to make these decisions that could avert sleep-related accidents. If they were not accurate, then the implementation of education and safeguards would be vitally
* Corresponding author. Tel.: þ61 8 8302 6624; fax: þ61 8 8302 6623. E-mail addresses: [email protected] (R. Tremaine), Jill. [email protected] (J. Dorrian), Leon.Lack@flinders.edu.au (L. Lack), Nicole. Lovato@flinders.edu.au (N. Lovato), [email protected] (S. Ferguson), [email protected] (X. Zhou), [email protected] (G. Roach). 0003-6870/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2010.04.005
important to ensure that workers are protected against sleeprelated accidents by work rules and conditions. 1.1. Subjective and objective measures of sleepiness and performance A number of studies have looked at these relationships and found that subjective assessments of performance often closely mirror actual or objective performance (Akerstedt et al., 2005; Gillberg et al., 1994). Baranski et al. (1994) were able to show that even increasing sleep deprivation (up to 64-h) did not impair subjective performance estimates. Dorrian et al. found moderate to high correlations between objective performance and both pre- and posttest ratings of subjective performance during a unitary period of sleep deprivation (28-h) (Dorrian et al., 2000), during a week of simulated night-shifts (Dorrian et al., 2003), and at different levels of sleepiness using a rail-road driving simulator (Dorrian et al., 2007). Other studies have investigated whether objective performance might also be related to subjective estimates of sleepiness. In studies that have included some degree of sleep deprivation (partial or total) but no sleepiness countermeasure, much of the
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research has yielded only low associations between subjective sleepiness and objective performance (e.g. Casagrande et al., 1997; Owens et al., 1998). There have been some with more favourable findings however: Gillberg et al. (1994) found moderate to high correlations (r ¼ 0.49e0.79) between subjective sleepiness and objective performance measures, as did Hoddes et al. (1973; r ¼ 0.47e0.70), however it should be noted that these two studies are limited by their relatively small number of participants (6 and 5 respectively). It would seem reasonable to expect that subjective sleepiness would correlate fairly high with objective sleepiness, but research results have not always borne this out. Studies that have yielded high correlations are generally those which measure objective sleepiness using EOG and/or EEG (e.g. Akerstedt and Gillberg, 1990; Horne and Reyner, 1996). For those using measures such as sleep latency or pupillography, the correspondence between subjective sleepiness and objective sleepiness has frequently been low or nonsignificant (Danker-Hopfe et al., 2001; LaFrance and Dumont, 2000; Johnson et al., 1991). The majority of the studies finding low correspondence have also used a daytime testing protocol, whereas those yielding high correlations have generally been studies using partial or total sleep restriction protocols (Akerstedt and Gillberg, 1990; Horne and Reyner, 1996; Torsvall and Akerstedt, 1987). Similarly varied results have been found regarding the relationship between subjective sleepiness and performance on the PVT (psychomotor vigilance task), a 10-min serial reaction time task. Kaida et al. (2006) found a moderate correlation between the two (r ¼ 0.57) under normal sleeping conditions. Under irregular sleeping conditions, Dorrian et al. (2003) found that PVT performance deteriorated at a corresponding rate to subjective sleepiness ratings during a week of simulated night-shifts. In a later study, Dorrian et al. (2007) also found that subjective sleepiness and PVT performance were significantly related under increasing sleepiness levels. However there have been other studies which have found low or inconsistent results in their investigation of the relationship between subjective sleepiness and neurobehavioural function (e.g. Rosekind et al., 1991). In summary, the relationship between subjective and objective sleepiness, and performance measures remains unclear. However, differential findings across studies may be due in part to different methodologies employed, including (but not limited to): sample size, type of assessment instruments used to measure subjective and objective performance and sleepiness, day or night-time testing protocol, and the inclusion of sleep restriction (partial or total) in the protocol.
Many of the studies examining the effects of a countermeasure on sleepiness and performance have focused on pharmacological stimulants as countermeasures, such as caffeine or modafinil. Unfortunately, research examining the perception of sleepiness and performance with the use of a countermeasure has garnered inconclusive results. For example, Biggs et al. (2007) found that although caffeine was a beneficial countermeasure to sleepiness, subjective and objective performance measures were not significantly correlated in any of the experimental conditions. Although subjective performance and sleepiness were strongly and significantly correlated in sleep restriction and placebo conditions, this relationship was no longer significant after the addition of a caffeine countermeasure. Other studies with a caffeine countermeasure have found improved subjective sleepiness, but not corresponding improvements in objective performance (Johnson et al., 1990; Lieberman et al., 1984). Such pharmacological countermeasures, while useful, may not be as healthy, economical, or effective in reducing sleepiness as sleep itself (Akerstedt and Landström, 1998). A nap countermeasure would therefore seem to offer significant benefits over other alternatives, and this renders an understanding of how it affects people’s sleepiness and performance variables potentially valuable. Interestingly, studies using a nap countermeasure have shown improved performance, but not improved subjective sleepiness (Dinges et al., 1987; Rosekind et al., 1994). The inclusion of a napping countermeasure may thus complicate the relationship between perceived and actual sleepiness and performance, and therefore warrants further investigation.
1.2. Napping as a countermeasure
1.4. Summary and aims
Due to the dangers of working when sleepy, numerous countermeasures have been proposed to help alleviate the concomitant sleepiness of working night-shifts. Diurnal napping has consistently produced demonstrable improvements in alertness and performance (see Dinges, 1989). More recently e and perhaps of greater importance to shift-workers e interest in the effects of naps has widened to include an examination of the efficacy of night-time naps. Short naps taken during the night to ameliorate the effects of sleepiness have also been shown to be beneficial (Bonnefond et al., 2001; Purnell et al., 2002). This is particularly promising for shiftworkers in industries that require extended periods of work without the opportunity to go home and sleep, for example, in the medical profession, military, or long-haul transport operations. One difficulty, however, with the implementation of napping, is the ensuing sleep inertia or brief period of impaired alertness that routinely follows awakening from a substantial sleep (e.g. more than 30-min; Dinges, 1992a; Muzet et al., 1995). It has therefore
In summary, the relationship between subjective sleepiness and objective sleepiness and/or performance has been investigated under various conditions, but it is yet to be investigated in the context of a simulated night-shift that includes a 30-min night-nap. It is suggested that this napping protocol may reduce the relationships between sleepiness and performance measures as can occur with a caffeine countermeasure during the day. There are significant empirical and practical implications of the findings in the present study. A deeper understanding of the accuracy of people’s perception of their sleepiness with and without napping has the potential to aid in the design of policies to promote safe work practices and sleepiness countermeasures that rely on an individual’s decision about their suitability to work. Research in this area may therefore provide valuable insights which can guide the formation and implementation of napping best-practice, and into how much weight should be placed on an individual’s capacity to self-monitor their sleepiness and performance.
been suggested that the optimal length for a nap is less than 30min, as most often the greater the length and depth of sleep obtained, the more severe the subsequent sleep inertia (Brooks and Lack, 2006). In most cases this disorienting state usually only persists for a short time (15e30-min; Dinges, 1989, 1992a), but it may linger longer e even up to several hours e if the period of sleep follows extended wakefulness or is during the early morning (Dinges et al., 1985; Naitoh, 1981). In addition to naps taken during the night, studies have shown that naps taken prior to sleep restriction can improve subsequent performance and reduce sleepiness in the following 24-h (Bonnet, 1991). Known as a ‘prophylactic nap’, this is a tactic used by shiftworkers to try to ‘store’ sleep before the start of a work period (Dinges et al., 1987). 1.3. Effects of a countermeasure on sleepiness and performance
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2. Method 2.1. Participants The sample comprised nine male and fifteen female participants (mean age ¼ 22.21 years, SD ¼ 2.45) who were recruited through advertisements and emails from the University of South Australia, Flinders University, and the University of Adelaide. Each received a monetary payment of AU$450 for their participation. Participants were self-reported good sleepers with no history of sleep complaints, and were not regular nappers or taking drugs known to affect sleep architecture. The study received approval from the Flinders University Social and Behavioural Research Ethics Committee and the University of South Australia Human Research Ethics Committee. All participants gave informed consent. 2.2. Design In a randomised cross-over design, each participant took part in two experimental conditions: (a) a no night-nap condition and (b) a 30-min night-nap condition. Both experimental conditions included a 2-h afternoon sleep opportunity as it reproduced a common practice of night-shift workers to nap in preparation for the first of a series of night-shifts. The order of conditions was counterbalanced to prevent order effects, with at least one week intervening between conditions to allow sufficient recovery from one night of sleep loss. Prior to the experimental sessions, all participants were required to attend a training and adaptation session. 2.2.1. Training and adaptation session For the training and adaptation session, participants arrived at the sleep laboratory at 1200 h after waking no later than 0700 h that morning. They were provided with lunch, and from 1300 h they were given training and practice on the tasks they would be using during the experimental sessions. At 1500 h participants were given a 2-h afternoon sleep opportunity, and at 1700 h participants were awoken and able to leave the sleep laboratory. 2.2.2. Prior to the laboratory session Participants were instructed to maintain regular sleep and wake times for the week prior to each laboratory session. Compliance with these instructions was monitored with sleep diaries and activity monitors. Participants were asked to refrain from consuming alcohol or caffeine for 3 days prior to and including the laboratory sessions. 2.2.3. Experimental sessions For the two experimental sessions, participants arrived at the sleep laboratory at 1200 h, consumed lunch, and at 1300 h had electrodes applied for standard polysomnographic (PSG) montage in accordance with the International 10/20 System (Carskadon and Rechtschaffen, 1994). Two EEG electrodes were placed on the scalp over each hemisphere of the brain, with references for each on the mastoid behind the opposite ear (C3/A2 and O2/A1); EOG electrodes were placed below the left and right outer canthus, referenced to the mastoids (ROC/A1 and LOC/A2); and EMG electrodes were attached to the chin, approximately 3 cm apart. From 1500 to 1700 h participants in both conditions (night-nap and no night-nap) were given a 2-h prophylactic afternoon sleep opportunity. From 1700 h until the first assessment session at 0155 h, participants were allowed to engage in quiet activities such as reading, watching videos, or listening to music. During this time they were also provided with dinner (at 1900 h). During all periods of wake, the bedroom environment was consistently illuminated by
a 75-W light globe, producing 50 lux illumination. External time cues were eliminated (e.g. clocks and watches, sunlight, mobile phones, internet). As illustrated in Table 1, each experimental session included eight Sleepiness Assessment Sessions (SAS), six Performance Assessment Sessions (PAS), and five Sleep Latency Tests (SLTs). These assessment sessions began at 0155 h and continued at 30e60 min intervals throughout the night until approximately 0735 h. Participants in the night-nap condition were given a 30min nap at 0230 h, while those in the no night-nap condition underwent a SLT at 0230 h.
2.3. Assessment instruments Assessment batteries were grouped as either SAS or PAS. The SAS battery consisted of the Stanford Sleepiness Scale (SSS; Hoddes et al., 1973), Karolinska Sleepiness Scale (KSS; Reyner and Horne, 1998), and a 100 mm Visual Analogue Scale for sleepiness (VAS; Monk, 1989). The PAS battery included the SymboleDigit Substitution Task (SDST; Army Beta format), a subtest of the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1958) in which the performance measure was the number of correct responses. Twelve parallel forms of this task were constructed to provide novel forms for each assessment session (Royer, 1971). Performance on a 10-min serial visual reaction time task was also assessed using the psychomotor vigilance task (PVT). The PVT has been adapted for a personal digital assistant (PDA) by researchers at the Walter Reed Army Institute of Research, and validated by subsequent studies (Lamond et al., 2005, 2008; Thorne et al., 2005). PVT response time was used as the dependent variable in all analyses, and in accordance with standard methodology, PVT data were transformed to a 1/RT to correct for proportionality between the mean and standard deviation (Dinges and Kribbs 1991). Sleep latency tests (SLTs) were used to assess objective sleepiness. SLTs assess physiologic sleep tendency by measuring the time it takes a person to fall asleep while lying down in a quiet, dark room. Sleep is measured using PSG, and the test is terminated after
Table 1 Summary of the experimental protocol. Testing time (clock time)
Mins pre/post-nap
1500e1700 0155 0200 0225 0230 0310 0315 0340 0345 0410 0415 0445 0510 0515 0545 0610 0615 0645 0710 0715
35 30 5 5 10 35 40 65 70 95 125 130 155 185 190 215 245 250
Condition Night-nap condition
No night-nap condition
Afternoon sleep opportunity SAS PAS SAS Night-nap SAS PAS SAS PAS SAS SLT PAS SAS SLT PAS SAS SLT PAS SAS SLT
Afternoon sleep opportunity SAS PAS SAS SLT SAS PAS SAS PAS SAS SLT PAS SAS SLT PAS SAS SLT PAS SAS SLT
SAS: Sleepiness Assessment Session (includes SSS, KSS, and VAS). PAS: Performance Assessment Session (includes SDST and PVT). SLT: Sleep Latency Test.
R. Tremaine et al. / Applied Ergonomics 42 (2010) 52e61
sleep onset, which is classified as three consecutive epochs of any stage of sleep (one epoch ¼ 30 s), or after 20-min without sleep onset. Sleep was monitored using Compumedics 10e20 recording system (Melbourne, Australia). All SLTs taken throughout the laboratory sessions were determined according to this three-epoch criterion with participants awoken immediately after the criterion to avoid any further napping benefit (<1.5 min of sleep). This allowed a sleep latency measure but without any beneficial effect of sleep (Tietzel and Lack, 2002). An initial SLT was conducted immediately prior to the night-nap (latency to nap), and subsequent SLTs were taken at 70, 130, 190 and 250 min after the nightnap. In the night-nap condition participants were awoken when they had slept for a total of 30-min, with sleep onset also determined as three consecutive epochs (i.e. 90 s) of any sleep stage. In the no night-nap condition, participants were awoken after the initial SLT and were asked to get up and engage in quite activities until the next assessment session. 2.4. Statistical analysis A technical issue with the PDAs used to administer PVT resulted in some missing data; therefore PVT analyses were run using twenty-one of the twenty-four participants. To ensure subsequent analyses were comparable these three participants had their PVT data for both nap conditions removed. The effect of condition and time on all subjective and objective measures was analysed using two-way repeated measures analysis of variance (ANOVA), with factors ‘nap condition’ (30-min night-nap and no night-nap) and ‘time’ (assessment session time points). Planned contrasts were conducted to investigate the changes from baseline to each assessment session time point for the nap and no-nap conditions. A Bonferroni correction was applied to adjust for multiple comparisons, and degrees of freedom were corrected using GreenhouseeGeisser estimates of sphericity. Timeeseries correlation (TSC) coefficients were calculated for each participant 3 to þ3 time-lags (1 lag ¼ 1 assessment session), for comparisons between subjective and objective sleepiness and performance measures. Subjective sleepiness was measured more frequently across the night (8 times) than PVT and SDST (6 times). Therefore, in order to compare these measures, the six ratings most closely time-matched with PVT and SDST were used such that the sleepiness ratings were taken less than 10-min prior to or following each performance battery. Similarly, for sleep latency, the most closely time-matched 5 sleepiness ratings were used for comparison. For all TSCs, r-values were found to be highest at a time-lag of 0, indicating no clear lag effect. As such, r-values will be reported at time-lag 0 only. Since distributions of r-values are highly skewed, an average r across all participants for each test was obtained using Fisher’s rez transformation. 3. Results 3.1. Preliminary analysis The mean total sleep time (TST) for the three nights leading up to each laboratory session, as indicated by wrist actigraphy data and sleep diaries, were analysed using a two-way repeated measures ANOVA. Similar amounts of TST were obtained in both the night-nap condition (M ¼ 7.10 h, SD ¼ 1.04), and the no nightnap condition (M ¼ 7.13 h, SD ¼ 0.86). There was no significant main effect of night-nap condition, F (1,23) ¼ 0.134, p > 0.10. The mean TST of the afternoon sleep opportunity (1500e1700 h), and the baseline pre-nap scores of the six dependent variables (SSS, KSS, VAS, PVT, SDST, and SLT) were examined
55
for the two conditions using paired-samples t-tests. All mean values showed no significant difference between nap conditions. The night-naps were visually scored as participants slept and then rescored posthoc by sleep technicians to check for accuracy. The posthoc scoring revealed that ten participants had obtained slightly more than 30 min of sleep during the night-nap (M ¼ 32.50, SD ¼ 6.38). 3.2. Effect of time and napping on subjective and objective measures The mean scores and standard errors of each subjective measure (SSS, KSS and VAS) are illustrated in Fig. 1, and the objective measures are illustrated in Fig. 2 (PVT, SDST, SLT). In Figs. 1 and 2, decreased scores on the y-axis indicate increasing sleepiness. Therefore some axes (SSS and KSS) were reversed for consistency and to provide easier understanding of the large number of dependent variables. Thus “1” on the SSS and KSS (after reversing) refers to the highest degree of sleepiness on these scales. Results of the two-way repeated measures ANOVA are presented in Table 2. There was a main effect of time (p < 0.001) for each of the subjective measures (SSS, KSS, VAS), but none of these measures demonstrated a main effect of condition. Two of the subjective measures (SSS and VAS) showed a significant conditionetime interaction effect (p < 0.005). For PVT and SDST there was a main effect of time (p < 0.001) and a conditionetime interaction (p < 0.001), but no main effect of condition. For sleep latency, there was a significant main effect of time only (p < 0.001), and no interaction effect. A series of planned contrasts were conducted to investigate the difference from baseline to each assessment session time point. Figs. 1 and 2 show the time points at which significant differences were found (indicated by filled shapes). As illustrated in Figs. 1 and 2, overall both conditions became sleepier and their performance declined as the night progressed. However, the no night-nap condition showed a trend for relatively steady decline over the course of the night, while in the night-nap condition participants demonstrated a sharp dip in alertness and performance in the assessment session immediately following the cessation of the night-nap, suggestive of the effects of sleep inertia. For a more detailed discussion on the effects of a night-nap, including other measures collected in this study not included in this manuscript, the reader is directed to Lovato et al. (2009). 3.3. Comparing subjective and objective measures The mean r-values of TSCs are displayed in Table 3. Singlesample t-tests compared TSCs for each measure across participants from zero. As indicated in Table 3, all TSC were significantly different from zero (p < 0.05) except those comparing the subjective sleepiness measures with SDST in the nap condition. Fig. 3 provides an illustration of the differences in TSC scores across measures (subjective sleepiness represented by VAS, correlated with SLT, SDST, and PVT) and between conditions (night-nap/ no night-nap). As all subjective sleepiness measures were highly correlated (r ¼ 0.81e0.96), VAS was chosen as a representative measure of the subjective sleepiness scales in Fig. 3 (however full results are presented in Table 3). Fig. 4 was constructed to further illustrate the relationship between each subjective sleepiness measure (SSS, KSS and VAS) and SLT, SDST and PVT respectively. In order to plot data from these different measures on the same scale, raw data were z-transformed (relative to group mean and standard deviation). These figures show the mean (standard error) standardised score across participants at corresponding assessment session time points.
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SSS
“Awake but relaxed” 5
Nap No Nap
“Fogginess”
Mean score
“A little foggy” 4
3
N A P
“Fighting sleep” 2
“Sleep onset soon” 1 0155 0225
KSS
0310 0340 0410
0510
0610
0710
“Neither alert nor sleepy” 5
“No effort to stay awake”
Mean score
“Some signs of sleepiness” 4
3
N A P
“Effort to stay awake” 2
“Fighting sleep” 1
0155 0225
VAS
0310 0340 0410
0510
0610
0710
50
M ean score
40
30
N A P
20
10 0155 0225
0310 0340 0410
0510
0610
0710
Time Fig. 1. Mean score and standard error bars for the nap condition (triangles & solid lines) and no-nap condition (circles & dotted lines) for each subjective sleepiness variable. Decreased scores on the y-axis indicate increasing sleepiness. Nap time is indicated by the grey bar. Significant differences from baseline at each assessment session time point are indicated by filled triangles (nap condition) and filled circles (no-nap condition).
Paired-samples t-tests were used to compare TSCs between conditions (results are presented in Table 3). Comparing the magnitude of correlations between conditions, measures of subjective sleepiness were moderately correlated with sleep latency in the nap condition (r ¼ 0.41e0.47), and very strongly correlated in the no-nap condition (r ¼ 0.78e0.85), with all correlations significantly weaker in the nap condition (p < 0.05). Subjective sleepiness and objective performance (SDST) correlates ranged from weak (r ¼ 0.11e0.26) to moderate (r ¼ 0.40e0.46) for the nap and no-nap conditions respectively. For SDST, t-tests indicated significant differences in TSC between conditions for KSS
(p < 0.05). TSCs between PVT-measured reaction time and subjective sleepiness demonstrated strong to very strong correlations for the nap (r ¼ 0.72e0.76) and no-nap (r ¼ 0.65e0.77) conditions respectively. The mean correlations of PVT with subjective sleepiness measures were not significantly different for the nap condition than the no-nap condition (p < 0.05). 4. Discussion Results suggest that during a simulated night-shift, alertness and performance decline considerably across time; however this
R. Tremaine et al. / Applied Ergonomics 42 (2010) 52e61
PVT
4.5
Nap No Nap
Mean RRT
4.0
3.5
N A P
3.0
2.5
0 20 0
SDST
0 31 5 0 34 5
0445
0 31 5 0 34 5
0445
05 45
06 45
70
Mean Score
65
60
55
N A P
50
45
0 20 0
SLT
05 45
06 45
14 13
T i m e ( m i n u te s )
12 11 10 9 8 7
N A P
6 5 4 3 2
02 30
04 15
0 51 5
0 61 5
0715
Time Fig. 2. Mean score and standard error bars for the nap condition (triangles & solid lines) and no-nap condition (circles & dotted lines) for the objective indicators. Decreased scores on the y-axis indicate decreasing performance (PVT and SDST) and increasing sleepiness (SLT). Nap time is indicated by the grey bar. Significant differences from baseline at each assessment session time point are indicated by filled triangles (nap condition) and filled circles (no-nap condition).
decline is ameliorated when the shift includes a nap. The trend for sleepiness and performance to worsen over the course of the night is in accordance with the combined effects of increasing time awake (known as the homeostatic drive for sleep), and the increased pressure of the body’s natural circadian rhythm, which promotes sleep during the night (Borbély and Achermann, 1999). For these reasons, it is particularly difficult for shift-workers to function optimally during the hours of darkness, as humans are predisposed to be sleeping during this time. Participants in the night-nap condition also demonstrated a sharper decline or dip in alertness and performance immediately after the nap, consistent with the effects of sleep inertia. Sleep inertia, the feeling of
57
drowsiness that can occur after waking as the body transitions from sleep to wake, is frequently accompanied by sleepiness and performance impairments (Folkard and Akerstedt, 1992; Jewett et al., 1999). After participants in the night-nap condition recovered from their post-nap drowsiness, their subsequent decline in alertness and performance levels over the remainder of the night was less pronounced than for the no night-nap condition (see Lovato et al., 2009 for further detail). As illustrated in Fig. 4, there was a differential correspondence between the three subjective sleepiness measures and each of the objective indicators (SLT, SDST, and PVT). Standardised SLT scores reflected greater sleepiness from 0415 h (in the no-nap condition) and 0515 h (in the nap condition) onwards than the subjective measures. Conversely, standardised SDST performance scores were suggestive of less sleepiness than the subjective measures from 0445 h onwards for both conditions, although the magnitude of the difference between subjective and objective measures was greater in the nap condition. For PVT, however, standardised scores corresponded very closely with subjective sleepiness estimates in both conditions and at most assessment time points; there was only a slight difference in the no-nap condition at the first assessment session (0200 h), and at the 0345 h and 0445 h sessions where subjective sleepiness scores indicated that participants felt more alert than PVT scores demonstrated. Timeeseries correlation analyses showed that in the no nightnap condition, objective sleepiness (SLT) was highly correlated with each of the subjective sleepiness measures. However the inclusion of a nap countermeasure significantly reduced this relationship, although there was still a moderate correlation. If sleep latency is an accurate indicator of sleepiness levels, then this suggests that the ability to self-report sleepiness is less robust during a nightshift that includes a short nap. The finding that participants given no night-nap (or having a night of sleep deprivation) had stronger correlations between their subjective and objective sleepiness scores is consistent with the previous research reviewed. Studies using partial or total sleep restriction protocols were also those that found high correlations between subjective and objective sleepiness (Akerstedt and Gillberg, 1990; Torsvall and Akerstedt, 1987). This may mean that people are best able to estimate their level of sleepiness when they are sleep-deprived, but that this relationship deteriorates with the inclusion of a sleepiness countermeasure. This would be consistent with the research using daytime testing protocols and without sleep deprivation, which have only yielded low correlations between subjective and objective sleepiness (Danker-Hopfe et al., 2001; LaFrance and Dumont, 2000). The nap countermeasure also corresponded with lower correlations between subjective sleepiness and objective performance (as measured by SDST). There were moderate correlations in the no-nap condition, but with a night-nap the associations were weaker. As the subjective sleepiness assessment sessions were conducted immediately prior to the performance assessment sessions, this may contribute some explanation as to why these measures did not yield higher correlations in either condition. Research has shown that subjective sleepiness ratings more accurately reflect actual performance when the rating is given after completing the performance task, possibly as a result of the task itself affecting sleepiness levels (Van Dongen and Dinges, 1994). In the no-nap condition both subjective sleepiness and SDST performance declined fairly steady over the course of the night. This contributed to higher correlations between these measures in the no-nap than the nap condition, in which participants’ performance levels gradually improved, while their sleepiness levels continued to worsen. It appears then, that although participants who napped felt consistently sleepier as the night progressed, their performance did not correspondingly decline. One obvious explanation for the
58
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Table 2 Summary of two-way ANOVA for condition (nap/no-nap), time point (1e8), and interaction for all dependent measures. SSS
KSS
VAS
SLT
SDST
PVT
Condition
F df
0.368 (1,23)
1.293 (1,23)
0.020 (1,23)
2.870 (1,23)
0.665 (1,23)
0.118 (1,20)
Time
F df
17.108** (3.46,79.54)
21.04** (3.80,87.34)
23.955** (3.10,71.33)
65.336** (2.96,68.16)
13.775** (3.51,80.77)
25.955** (2.46,49.20)
Conditionetime
F df
4.312* (4.20,96.57)
3.745 (3.96,90.99)
4.809* (3.50,80.45)
2.474 (2.38,54.81)
9.050** (3.58,82.24)
6.680** (3.57,71.43)
GreenhouseeGeisser adjustment reported for df as Mauchly’s test of sphericity was not met. *p < 0.005, **p < 0.001.
Table 3 Timeeseries correlations between subjective and objective measures in each nap condition. R-values are reported at time-lag 0. No-Nap
SLT
SDST
PVT
Nap
t
df
Mean
SD
Mean
SD
SSS KSS a VAS
0.779*** 0.852*** 0.852***
0.610 0.625 0.689
0.465*** 0.468*** 0.412***
0.627 0.688 0.713
2.249* 3.884** 3.229**
21b 21b 23
SSS KSS a VAS
0.395*** 0.405*** 0.460***
0.608 0.532 0.667
0.255 0.108 0.239
0.596 0.627 0.624
0.807 2.251* 1.455
22b 22b 23
SSS KSS a VAS
0.652*** 0.720*** 0.773***
0.462 0.438 0.502
0.721*** 0.742*** 0.756***
0.511 0.551 0.612
1.002 0.257 0.233
19b 19b 20b
*Significant at p < 0.05 prior to Bonferroni correction. **Paired-samples t-test for condition, p < 0.005. ***Significant difference from zero, p < 0.05. a These relationships are illustrated in Fig. 3. b Indicates missing data (PVT) or no change at relevant time points and hence no r-value could be calculated.
button as quickly as possible. The PVT is thus a relatively repetitive and monotonous task, and tasks of this nature have been shown to reflect greater sleepiness than shorter, more interesting tasks (Dinges et al., 1988; Dorrian et al., 2003). To complete the SDST, each session uses a novel form of symboledigit combinations, and the participant must balance speed and accuracy as they try to substitute as many digits for symbols as they can while being timed on a stop-watch for 90 s. As the SDST is arguable more interesting than the PVT, participants may have found the task more engaging and less laborious to complete, and therefore performed with higher motivation on the SDST than they did while completing the PVT. It has been shown that motivation or compensatory effort can reduce or eliminate performance impairments on various tasks, and that tasks requiring sustained attention and lacking in complexity (such as the PVT) are more susceptible to the effects of sleep loss, compared to tasks that are visually engaging and require motor activity (Dinges, 1992b; Dinges et al., 1988; Gillberg and Akerstedt, 1998). In light of the present results, the implications are that shiftworker’s subjective sleepiness estimates may better correspond with performance on tasks that are more sensitive to the effects of sleep loss than those that are novel and interesting. For a number of shift-workers, their roles may consist of varying periods of monotonous or repetitive activity (e.g. transport, healthcare, factory work), and in these cases their subjective sleepiness estimates may be seen as a relatively representative indicator of their performance levels. In a practical sense, when shift-workers are asked to self-monitor their sleepiness and/or fitness to continue work, the resultant estimates (assuming they based on subjective sleepiness) may be relatively accurate if the work they are completing is not stimulating or engaging to them, and in such
1.0
Mean TSC values (+ standrd error)
reduced correlations between subjective and objective measures in the nap condition is the truncation of range of sleepiness and performance measures that occurred after participants were given a night-nap. In each instance the decline across the night in alertness and performance was ameliorated to some extent by the nap, therefore reducing the range of results in that condition, as compared to the no-nap condition. Or perhaps, as past research has shown, the association between subjective sleepiness and objective performance is at best only moderate, and significantly weakened by the inclusion of a sleepiness countermeasure such as a nap (Dinges et al., 1987; Johnson et al., 1990; Lieberman et al., 1984). The relationship that was least affected by the nap was that of subjective sleepiness and PVT reaction time. Very high correlations were found between these measures in the nap condition, and high correlations were also evident in the no-nap condition. A robust relationship between subjective sleepiness and PVT scores is consistent with the majority of past studies (e.g. Dorrian et al., 2003, 2007; Kaida et al., 2006), although it does differ to Rosekind et al. (1991) who found a low correspondence between the two. However, in contrast to the present study, Rosekind et al. (1991) found that subjective sleepiness was not affected by the inclusion of a countermeasure, and this may account for some disparity between findings. The seemingly incongruent finding of low correlations between each subjective sleepiness measure and SDST performance, but high correlations between subjective sleepiness and PVT performance may be partially attributable to the effects of motivation and compensatory effort elicited by these different measures of performance. When completing the PVT, participants are required to monitor the device continuously for 10-min, waiting for a stimulus to appear (a red light), and when it does, to press a response
Nap
0.9
No Nap
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
SLT
SDS T
PV T
Fig. 3. Mean (þ standard error) time-series correlation values (y-axis) for subjective sleepiness ratings (as measured by VAS) compared to SLT, SDST and PVT scores (x-axis) in the nap (black bars) and no-nap (grey bars) conditions. VAS was chosen as a representative measure due to all subjective sleepiness measures being highly correlated. Full timeeseries correlation results are presented in Table 3.
R. Tremaine et al. / Applied Ergonomics 42 (2010) 52e61
59
No Nap
Nap
SLT 2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
Objective measure SSS KSS
Standardised score
VAS
-1
-1 0215
0415
0515
0615
0715
0215
0415
0515
0615
0715
0445
0545
0645
0445
0545
Standardised score
SDST 2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1 0200
0315
0345
0445
0545
0645
Standardised score
PVT 2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
0200
0315
0345
0200
0315
0345
-1
-1 0200
0315
0345
0445
0545
0645
Time
0645
Time
Fig. 4. Relationship between objective measures (SLT, SDST and PVT) and subjective sleepiness measures (SSS, KSS and VAS). Time points indicate the objective assessment session and the closest matched subjective assessment session. Decreased scores on the y-axis indicate increasing sleepiness/decreasing performance.
instances, the inclusion of a night-nap does not appear to significantly change this ability. Although there are some limitations in this study, the results show promise for understanding how the relationship between sleepiness and performance measures is changed by a night-nap. Despite attempting to simulate a night-shift, there are obvious
limitations to the generalisability of research undertaken in a laboratory setting. Thus it would be important to replicate these findings in field studies to discover what differences, if any, may result. For example, this study was conducted with young, healthy participants who did not suffer from sleep-related problems. Further studies are needed to determine if these results can be generalised to older,
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sleep-disordered, or shift-working populations. Replicating this study with real shift-workers as participants would thus be highly beneficial, as their perception of sleepiness and performance during a night-shift and after a night-nap may be different to individuals who are not used to being awake during the night. In conclusion, the results of the current study suggest that subjective and objective indicators of sleepiness and performance do not always correspond. Moreover, the inclusion of a nap during a simulated night-shift appears to increase the likelihood of a disassociation between perceived and actual sleepiness and performance. Findings indicate that the nap had a differential effect on measures of sleepiness and performance. While there was a strong relationship between subjective measures and PVT that was consistent across conditions, the relationship between subjective measures, SLT and SDST was reduced in the nap condition. Therefore asking someone how sleepy they are after a nightnap may not necessarily be a reliable indicator of their likelihood of falling asleep (as indicated by sleep latency), or their performance on all types of tasks. In instances where the nature of the work is repetitive and monotonous (e.g. PVT), a nap does not appear to differentially affect the correspondence between task performance and subjective sleepiness estimates. However, tasks that are more engaging and novel may be able to elicit a degree of motivation or compensatory effort that can help shift-workers to perform better than might be expected based on their sleepiness levels (i.e. SDST). Practically, this suggests that for jobs which involve tedious or repetitive work (e.g. long-haul transport operations or factory work), subjective sleepiness estimates are more likely to reflect subsequent performance, while more interesting or engaging work (e.g. military combat or surgery) may encourage shift-workers to apply additional effort and focus to overcome their sleepiness. The results of the current study are of operational importance as nightshift-workers’ decisions regarding their fitness to start or continue work will be influenced by their subjective degree of sleepiness. Acknowledgements The authors would like to thank Sara Dawson, Raymond Matthews and Alison Teare for their assistance in conducting this study. This research was supported by Australian Research Council (ID: DP0558960). Appendix. Table of acronyms
EEG EMG EOG KSS PAS PDA PSG PVT SAS SDST SLT SSS TSC TST VAS
Electroencephalogram Electromyogram Electrooculogram Karolinska Sleepiness Scale Performance Assessment Session Personal Digital Assistant Polysomnography Psychomotor Vigilance Task Sleepiness Assessment Session SymboleDigit Substitution Task Sleep Latency Test Stanford Sleepiness Scale Time-Series Correlation Total Sleep Time Visual Analogue Scale
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