Karolinska Sleepiness Scale Ratings Research Articles

0170 Slow Oscillation Power and Heart Rate Variability During Sleep Predicts Next-day Subjective Sleepiness in Healthy, Young Adults

Abstract Introduction High amplitude, slow oscillations in the electroencephalogram (EEG) often characterize the central nervous system’s homeostatic drive for sleep. Slow oscillations dominate the first part of the night and often dissipate as sleep need is satiated. These physiological changes are also reflected in next day subjective measures of sleepiness. Fluctuations in the autonomic nervous system, particularly parasympathetic activity, also coincide with slow wave sleep and are understood to represent bodily homeostasis. However, it is unclear if autonomic indicators effectively predict next-day sleepiness. Here, we investigated whether slow oscillatory (SO) power and cardiac autonomic activity during a night of sleep can predict next day subjective sleepiness. Methods 88 young (aged 18-35), healthy participants spent the night in a University sleep lab. Before and after sleep, participants completed the Karolinska Sleepiness Scale (KSS) to measure subjective sleepiness. Each participant slept with polysomnography, including electroencephalography and electrocardiography. From these measures, we assessed slow oscillation power (0.5-1Hz) and high frequency heart rate variability (HRV; 0.15-0.45 Hz) --an indicator of parasympathetic, vagally-mediated cardiac tone-- during the first quartile of slow wave sleep. Paired T-tests compared the differences in KSS scores pre- and post-sleep. Pearson’s correlations assessed bivariate associations between slow oscillatory power, high frequency HRV, and KSS scores. Mixed linear models assessed the ability of SO power and high frequency HRV to predict next day subjective sleepiness. Results No significant differences were found in KSS ratings pre- (M±SD = 4.23±1.92) and post-sleep (4.38±1.80). No bivariate correlations were present between pre-sleep KSS, SO, or high frequency HRV. A significant correlation between KSS-post sleep and SO power emerged (r=0.286, p=0.001). In Model 1, we found that SO power was a predictor of subjective sleepiness (p<0.001; AIC: 777.469). In Model 2, we included high frequency HRV and reduced the AIC to 515.588. Both slow oscillatory power (p<0.001) and high frequency HRV (p<0.001) were significant predictors of KSS after sleep in Model 2. Conclusion We found evidence that both central and autonomic indicators of sleep predict psychological measures of sleepiness. Using autonomic indicators to characterize physiological sleepiness, compared to in-lab polysomnography, may be a more generalizable and cost-effective approach. Support (If Any)

Characteristics of driver fatigue and fatigue-relieving effect of special light belt in extra-long highway tunnel: A real-road driving study

To study the characteristics of driver fatigue and effect of a special light belt to relieve driver fatigue in an extra-long highway tunnel, real-road driving tests were conducted in the Yanglin extra-long tunnel. The tests were conducted separately under two conditions: a condition with a conventional tunnel lighting environment and a condition with a special light belt zone in the tunnel. In the tests, the electrocardiography method and Karolinska Sleepiness Scale (KSS)-based self-rating method were selected as the fatigue assessment methods. The study established mathematical models between the heart rate variability (HRV) indicator and driving time in the tunnel under the aforementioned conditions by employing principal component analysis and obtained the KSS rating values before and after driving through the tunnel. The results demonstrated that driver fatigue occurred after driving through the extra-long tunnel with variations in the HRV indicators and an increase in the KSS self-rating. The driver fatigue accumulated slowly in the first 5 min in the tunnel, followed by a rapid accumulation after the sixth minute, and it slowed down again at the end of the tunnel owing to the outside light. When the special light belt was turned on, the variations in HRV indicators and increase in the KSS rating diminished. The paired samples t-test showed significant differences in the HRV indicators and KSS ratings between the two driving conditions after passing the light belt zone. This indicates that the special light belt was effective in relieving driver fatigue in the extra-long tunnel.

302 Sleep, Sleepiness, and Performance Across Three In-Flight Bunk Rest Opportunities

Abstract Introduction Airline pilots are required to take a rest break in a bunk during long-haul flights in an effort to reduce sleepiness during critical phases of flight. It is unclear, however, whether each rest break affords equal opportunity for sleep. We aimed to characterize sleep, sleepiness, and performance outcomes across three in-flight rest breaks during long-haul flights. Methods Thirty-seven pilots wore actiwatches and completed sleep diaries for approximately two weeks while flying a variety of long-haul routes (n=126 flights). Self-reported in-flight bunk rest (BR) periods were used to set rest intervals and sleep was estimated within these intervals using actigraphy software (wake threshold set to medium). Pilots provided Karolinska Sleepiness Scale ratings (KSS) and performed a 5-minute psychomotor vigilance task (PVT) before landing. A linear mixed-effects model with participant included as a random effect and allowed to vary by intercept was used to assess differences between BR opportunities. Results The majority (97%, n=122) of BR periods contained sleep (as estimated by actigraphy). The mean (+/- standard deviation) sleep duration for the first, middle, and third BR opportunity was 152.8 (69.7), 149.2 (44.1), 125.2 (44.9) minutes, respectively. There was a significant effect of BR opportunity for sleep duration (F(2,54) = 3.747, p=.03) and KSS (F(2,44) = 7.869, p=.001). Bonferroni adjusted planned pairwise contrasts revealed that pilots using the third BR obtained significantly less sleep than in the first BR (p=.029). KSS ratings prior to landing were higher for the third BR compared to both the first (p=.001) and middle BR (p=.017). There were no significant differences for PVT speed or lapses (all p>05). Conclusion These results suggest that the last rest break is associated with shorter sleep, lower alertness, and no differences in performance relative to the other rest breaks. Further analysis is required to determine whether the higher KSS ratings following the third rest break are associated with sleep inertia, or whether other factors may be involved. Support (if any) NASA Airspace Operations and Safety Program, System-Wide Safety Project

Flight Crew Alertness and Sleep Relative to Timing of In-Flight Rest Periods in Long-Haul Flights.

BACKGROUND: In-flight breaks are used during augmented long-haul flight operations, allowing pilots a sleep opportunity. The U.S. Federal Aviation Administration duty and rest regulations restrict the pilot flying the landing to using the third rest break. It is unclear how effective these restrictions are on pilots ability to obtain sleep. We hypothesized there would be no difference in self-reported sleep, alertness, and fatigue between pilots taking the second vs. third rest breaks.METHODS: Pilots flying augmented operations in two U.S.-based commercial airlines were eligible for the study. Volunteers completed a survey at top-of-descent (TOD), including self-reported in-flight sleep duration, and Samn-Perelli fatigue and Karolinska Sleepiness Scale ratings. We compared the second to third rest break using noninferiority analysis. The influence of time of day (home-base time; HBT) was evaluated in 4-h blocks using repeated measures ANOVA.RESULTS: From 787 flights 500 pilots provided complete data. The second rest break was noninferior to the third break for self-reported sleep duration (1.5 0.7 h vs. 1.4 0.7 h), fatigue (2.0 1.0 vs. 2.9 1.3), and sleepiness (2.6 1.4 vs. 3.8 1.8) at TOD for landing pilots. Measures of sleep duration, fatigue, and sleepiness were influenced by HBT circadian time of day.DISCUSSION: We conclude that self-reported in-flight sleep, fatigue, and sleepiness from landing pilots taking the second in-flight rest break are equivalent to or better than pilots taking the third break. Our findings support providing pilots with choice in taking the second or third in-flight rest break during augmented operations.Gregory KB, Soriano-Smith RN, Lamp ACM, Hilditch CJ, Rempe MJ, Flynn-Evans EE, Belenky GL. Flight crew alertness and sleep relative to timing of in-flight rest periods in long-haul flights. Aerosp Med Hum Perform. 2021; 92(2):8391.

A Large-Scale European Union Study of Aircrew Fatigue During Long Night and Disruptive Duties.

INTRODUCTION: We examined aircrew fatigue during the following flight duty periods (FDPs) mentioned in the European Union (EU) Flight Time Limitations (FTLs): night FDPs longer than 10 h and FDPs typical of disruptive schedules (early starts, late finishes, and nights). An alternative way of classifying night FDPs was also examined to reveal possible subcategories that warrant special attention.METHODS: A total of 392 aircrew members (96 women) representing 24 airlines participated in the study. Their FDPs were measured by a diary, sleep by the diary and wrist-actigraphy, and fatigue by the Karolinska Sleepiness Scale (KSS) over 14 consecutive days. The KSS ratings given at top of descent (TOD) served as the main outcome.RESULTS: The probability of high fatigue (KSS ≥ 7) at TOD was 0.41 and 0.32 during long (>10 h) and short night (≤10 h) FDPs, respectively. The corresponding value was 0.19 for early starts, 0.31 for late finishes, 0.34 for night FDPs, and 0.15 for day FDPs (reference). The main predictors of high fatigue were FDP's encroachment on the window of circadian low (WOCL, 02:00 h-05:59 h) and prior sleep. Within the night category, FDPs fully covering the WOCL showed the highest probability of high fatigue at TOD (0.42).DISCUSSION: Late finish and night FDPs warrant special attention in fatigue management. Within the night category, the same holds for FDPs that fully cover the WOCL. To manage fatigue, adjustments of the FTLs seem to be a limited strategy and therefore other measures, including maximizing preflight sleep, are needed.Sallinen M, van Dijk H, Aeschbach D, Maij A, Åkerstedt T. A large-scale European Union study of aircrew fatigue during long night and disruptive duties. Aerosp Med Hum Perform. 2020; 91(8):628-635.

0244 Examining Pilot Safety Performance Indicators at Critical Phases of Flight Across Multiple Flight Legs During Commercial Airline Trips

Abstract Introduction Prior simulation and operational studies have started to address whether the number of consecutive flight segments negatively affects cognitive performance, fatigue, and sleepiness, without reaching a clear consensus. This study expands this literature by determining whether there are significant changes in cognitive performance, fatigue, and sleepiness at critical phases of flight across multiple flight segments, while accounting for the number of segments, flight direction, trip day, and time-of-day. Methods Fifty commercial airline pilots were studied. Each pilot flew two separate short-haul trips, each ranging from 1–4 days and 1–10 flight segments. Cognitive performance, fatigue, and sleepiness were assessed at top-of-climb (TOC) and top-of-descent (TOD) of each flight segment and each trip day. Cognitive performance, fatigue, and sleepiness were assessed using Psychomotor Vigilance Task (PVT) speed, Samn-Perelli (SP) ratings, and Karolinska Sleepiness Scale (KSS) ratings, respectively. Data were analyzed using Wilcoxon t-tests and verified using ANOVAs. Results Mean PVT speed (Cohen’s d =0.57), SP ratings (Cohen’s d = 0.73), and KSS ratings (Cohen’s d = 0.63) were significantly worse at TOD than TOC (p < 0.001); and, significantly varied across flight segments (p<0.001). Cognitive performance, fatigue, and sleepiness were consistently and significantly degraded around the fifth flight segment, improved around the sixth to eighth flights segments, and were subsequently degraded around the eighth to tenth flight segments. Conclusion The results indicate that cognitive performance, fatigue, and sleepiness vary across flight segments, trip day, and phase of flight. Results suggest that these safety performance indices degrade after five segments, and further degrade after eight flight segments. The results presented could be used to inform future airline scheduling and regulation. Support This work has been supported by United Airlines.

Comparing two versions of the Karolinska Sleepiness Scale (KSS)

The Karolinska Sleepiness Scale (KSS) is frequently used to study sleepiness in various contexts. However, it exists in two versions, one with labels on every other step (version A), and one with labels on every step (version B) on the 9-point scale. To date, there are no studies examining whether these versions can be used interchangeably. The two versions were here compared in a 24 hr wakefulness study of 12 adults. KSS ratings were obtained every hour, alternating version A and B. Results indicated that the two versions are highly correlated, do not have different response distributions on labeled and unlabeled steps, and that the distributions across all steps have a high level of correspondence (Kappa = 0.73). It was concluded that the two versions are quite similar.

Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

Examine the use of spectral heart rate variability (HRV) metrics in measuring sleepiness under chronic partial sleep restriction, and identify underlying relationships between HRV, Karolinska Sleepiness Scale ratings (KSS), and performance on the Psychomotor Vigilance Task (PVT). Controlled laboratory study. Experimental laboratory of the Brain Work Research Centre of the Finnish Institute of Occupational Health, Helsinki, Finland. Twenty-three healthy young males (mean age ± SD = 23.77 ± 2.29). A sleep restriction group (N = 15) was subjected to chronic partial sleep restriction with 4 h sleep for 5 nights. A control group (N = 8) had 8 h sleep on all nights. Based on a search over all HRV frequency bands in the range [0.00, 0.40] Hz, the band [0.01, 0.08] Hz showed the highest correlation for HRV-PVT (0.60, 95% confidence interval [0.49, 0.69]) and HRV-KSS (0.33, 95% confidence interval [0.16, 0.46]) for the sleep restriction group; no correlation was found for the control group. We studied the fraction of variance in PVT explained by HRV and a 3-component alertness model, containing circadian and homeostatic processes coupled with sleep inertia, respectively. HRV alone explained 33% of PVT variance. The findings suggest that HRV spectral power reflects vigilant attention in subjects exposed to partial chronic sleep restriction. Henelius A, Sallinen M, Huotilainen M, Müller K, Virkkala J, Puolamäki K. Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

Subjective sleepiness is a sensitive indicator of insufficient sleep and impaired waking function.

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.

Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech

Comparing different novel feature sets and classifiers for speech processing based fatigue detection is the primary aim of this study. Thus, we conducted a within-subject partial sleep deprivation design (20.00–04.00h, N=77 participants) and recorded 372 speech samples of sustained vowel phonation. The self-report on the Karolinska Sleepiness Scale (KSS) and an observer report on the KSS, the KSS Observer Scale were applied to determine sleepiness reference values. Feature extraction methods of non-linear dynamics (NLD) provide additional information regarding the dynamics and structure of sleepiness speech. In all, 395 NLD features and the 170 phonetic features, which have been computed partially, represent so far unknown auditive-perceptual concepts. Several NLD and phonetic features show significant correlations to KSS ratings, e.g., from the NLD features for male speakers the skewness of vector length within reconstructed phase space (r=.56), and for female speaker the mean of Cao's minimum embedding dimensions (r=−.39). After a correlation-filter feature subset selection different classification models and ensemble classifiers (by AdaBoost, Bagging) were trained. Bagging procedures turned out to achieve best performance for male and female speakers on the phonetic and the NLD feature set. The best models for the phonetic feature set achieved 78.3% (NaïveBayes) for male and 68.5% (Bagging Bayes Net) for female speaker classification accuracy in detecting sleepiness. The best model for the NLD feature set achieved 77.2% (Bagging Bayes Net) for male and 76.8% (Bagging Bayes Net) for female speakers. Nevertheless, employing the combined phonetic and NLD feature sets provided additional information and thus resulted in an improved highest UA of 79.6% for male (Bayes Net) and 77.1% for female (AdaBoost Nearest Neighbor) speakers.

Chronobiological characterization of women with primary vasospastic syndrome: body heat loss capacity in relation to sleep initiation and phase of entrainment

Women with primary vasospastic syndrome (VS), but otherwise healthy, exhibit a functional disorder of vascular regulation (main symptom: cold extremities) and often suffer from difficulties initiating sleep (DIS). Diverse studies have shown a close association between distal vasodilatation before lights off and a rapid onset of sleep. Therefore, we hypothesized that DIS in women with VS could be due to a reduced heat loss capacity in the evening, i.e., subjects are physiologically not ready for sleep. The aim of the study was to elucidate whether women having both VS and DIS (WVD) or not (controls) show different circadian characteristics (e.g., phase delay of the circadian thermoregulatory system with respect to the sleep-wake cycle). Healthy young women (n = 9 WVD and n = 9 control) completed a 40-h constant routine protocol (adjusted to habitual bedtime) before and after an 8-h sleep episode. Skin temperatures [off-line calculated as distal-proximal skin temperature gradient (DPG)] and core body temperature (CBT; rectal) were continuously recorded. Half-hourly saliva samples were collected for melatonin assay and subjective sleepiness was assessed on the Karolinska Sleepiness Scale (KSS). Compared with control, WVD showed no differences in habitual bed times, but a 1-h circadian phase delay of dim light-melatonin onset (hours after lights on: WVD 14.6 +/- 0.3 h; control 13.5 +/- 0.2 h; P = 0.01). Similar phase shifts were observed in CBT, DPG, and KSS ratings. In conclusion, WVD exhibit a phase delay of the endogenous circadian system with respect to their habitual sleep-wake cycle, which could be a cause of DIS.