Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

Study Objectives Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics. Methods 41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall’s tau-b correlations compared the group categorization’s concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores. Results There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold. Conclusion Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

Pengembangan Sistem Deteksi Kantuk Menggunakan Pengklasifikasi Random Forest pada Sinyal Elektrokardiogram

Kantuk merupakan salah satu penyebab utama kecelakaan dalam lalu lintas, industri manufaktur, maupun pada bidang lain. Untuk itu, sistem yang dapat mendeteksi kantuk secara dini merupakan hal yang sangat penting dalam rangka mengurangi angka kecelakaan akibat kantuk. Kantuk dapat dianalisis melalui Heart Rate Variability (HRV) dari sinyal EKG yang menunjukkan perubahan aktivitas saraf otonom. Pengklasifikasi Random Forest diketahui berkinerja sangat baik serta kuat terhadap overfitting. Oleh karena itu, pada makalah ini dikembangkan sistem deteksi kantuk menggunakan sinyal elektrokardiogram (EKG) dan Random Forest. Sistem deteksi kantuk dilatih menggunakan rekaman EKG dari database DROZY yang dilengkapi Karolinska Sleepiness Scale (KSS). Fitur masukan sistem diekstraksi berdasarkan metode ranah waktu dan ranah frekuensi. Tingkat kantuk diklasifikasikan berdasarkan KSS yang disederhanakan menjadi dua kelas, yaitu kantuk dan terjaga. Random Forest divalidasi dengan metode Out-of-Bag (OOB). Efek dari variasi jumlah estimator dan max feature terhadap kinerja sistem dievaluasi. Fitur diurutkan berdasarkan kepentingannya dan dikombinasikan sebagai masukan sistem dengan berbagai panjang segmentasi EKG. Kinerja terbaik sistem deteksi kantuk yaitu rata-rata akurasi 94,61%, sensitivitas 96,67%, dan specificity 91,67%, yang diperoleh dengan segmentasi 40 detik.

Trait Interindividual Differences in the Magnitude of Subjective Sleepiness from Sleep Inertia

In shift work settings and on-call operations, workers may be at risk of sleep inertia when called to action immediately after awakening from sleep. However, individuals may differ substantially in their susceptibility to sleep inertia. We investigated this using data from a laboratory study in which 20 healthy young adults were each exposed to 36 h of total sleep deprivation, preceded by a baseline sleep period and followed by a recovery sleep period, on three separate occasions. In the week prior to each laboratory session and on the corresponding baseline night in the laboratory, participants either extended their sleep period to 12 h/day or restricted it to 6 h/day. During periods of wakefulness in the laboratory, starting right after scheduled awakening, participants completed neurobehavioral tests every 2 h. Testing included the Karolinska Sleepiness Scale to measure subjective sleepiness, for which the data were analyzed with nonlinear mixed-effects regression to quantify sleep inertia. This revealed considerable interindividual differences in the magnitude of sleep inertia, which were highly stable within individuals after both baseline and recovery sleep periods, regardless of study condition. Our results demonstrate that interindividual differences in subjective sleepiness due to sleep inertia are substantial and constitute a trait.

Einfluss von abendlicher Computerspielnutzung auf Parameter der Alertness und des Schlafs bei Jugendlichen

Zusammenfassung Hintergrund Computerspielnutzung vor dem Schlafengehen ist vor allem bei Heranwachsenden ein übliches Verhalten. Die exzessive Nutzung kann mit somatischen Beschwerden, Aufmerksamkeitsdefiziten und familiären Interaktionsproblemen verbunden sein. Es gibt aber nur wenige Erkenntnisse über die Auswirkungen auf den nachfolgenden Schlaf. Fragestellung Ziel dieser Pilotstudie war es, die Auswirkungen eines Computerspiels am Abend auf die Alertness und den Schlaf von Jugendlichen zu untersuchen. Material und Methoden Fünfundzwanzig gesunde Jugendliche (mittleres Alter 15 Jahre, w = 20, m = 5) aus einer Schulklasse wurden in diese Pilotstudie eingeschlossen. In einem randomisierten Crossover-Design an zwei aufeinanderfolgenden Tagen wurde jeweils am Abend für 2 h entweder ein Jump-and-Run-Computerspiel durchgeführt oder eine Jugendzeitschrift gelesen. Vor und nach der jeweiligen Intervention wurde mittels Fragebögen die Stimmung (Aktuelle Stimmungsskala, ASTS) und die Schläfrigkeit (Karolinska Sleepiness Scale, KSS) erhoben sowie Parameter der tonischen Alertness mittels Psychomotor Vigilance Task (PVT) gemessen. Jeweils im Anschluss wurde der Schlaf mittels Polysomnografie (PSG) untersucht. Ergebnisse Zweistündiges Computerspielen am Abend führte unmittelbar danach zu einer verringerten Alertness – der PVT-Parameter Reaktionszeit (RT) erhöhte sich von 272,0 ± 30,5 ms auf 305,2 ± 41,3 ms (p < 0,01) während die RT im gleichen Zeitfenster am anderen Abend unter der Lesebedingung unverändert blieb. Abendliche Computerspielnutzung führte in der darauffolgenden Nacht zu einem erhöhten Leichtschlafanteil (N1 + N2: 48,9 ± 9,1 %-TST vs. 44,6 ± 9,8 %-TST, p < 0,05) und einem reduzierten Tiefschlafanteil (N3: 36,0 ± 10,0 %-TST vs. 39,5 ± 9,0 %-TST, p < 0,05) gegenüber dem Schlaf nach zweistündigem Lesen. Die Einschlaflatenz und die Schlafeffizienz unterschieden sich zwischen beiden Bedingungen nicht. Diskussion Bei den in dieser Pilotstudie untersuchten gesunden Jugendlichen wirkten sich abendliche Computerspiele negativ auf die Alertness und die Schlafarchitektur der darauffolgenden Nacht aus. Zusammen mit häufig nicht ausreichenden Schlafzeiten an Schultagen bei Jugendlichen könnten kumulativ diese Befunde entwicklungsphysiologische Relevanz haben. Untersuchungen an Jugendlichen in weiteren Kollektiven mit entsprechender Gruppengröße sind jedoch notwendig, um die Ergebnisse weiter zu verifizieren und ggf. schlafhygienische Verhaltensempfehlungen für diese Altersgruppen entsprechend anzupassen.

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.

Evaluation of Subjective and Objective Levels of Drowsiness, it’s Rating and its Relation with Facial Dynamics

Objective: To determine the subjective and observed levels of lethargy and changes in facial dynamics. Materials and Methods: This study was conducted at Department of community medicine, Services Institute of Medical Sciences Lahore for one-year duration from January 2017 to December 2018. The changes in facial dynamics, such as changes in eyes, lips and eyebrows, were tested in KKS (Karolinska Sleepiness Scale) in twenty-five drivers. And ORD (drowsiness observer rating). Repeated ANOVA measurements and repeated MANOVA measurements were used to analyze the data. In addition, a neural network and Viola-Jones were used to detect facial features. PERCLOS (percentage of eye closure), blink frequency and blink time were examined to see eye parameters. The size of the open mouth during sleep was examined for oral parameters. When examining the eyebrows, the number 50 indicates that the eyebrow is in the normal position. For eyebrows above the normal position, a range of 50 to 55 was specified; In addition, 45-50 was found to be a defined range for normal eyebrows. Results: Descriptive statistics of dynamic changes in the mouth and eyes showed increased drowsiness while driving, as well as changes in the eyes and mouth. However, statistical findings made while driving showed that dynamic eyebrow changes had a clear expression with a continuous trend. Similar studies on data obtained from CSR and ORD showed that both parameters increased at the same time and lethargy level. There was also a significant relationship between facial expressions and lethargy. Conclusion: This study will be an effective and efficient tool for alerting and detecting sleep in a timely and accurate manner.

Deteksi Kantuk Menggunakan Jaringan Saraf Tiruan Radial Basis Function dan Particle Swarm Optimization dengan RR Interval Elektrokardiogram

<p>Sistem deteksi kantuk dirancang menggunakan Elektrokardiogram (EKG) dengan Jaringan Saraf Tiruan <em>Radial Basis Function </em>dan <em>Particle Swarm Optimization </em>(JST RBF-PSO). <em>Karolinska Sleepiness Scale </em>(KSS) menjadi acuan tingkat kantuk yang dikelompokkan menjadi kelas terjaga dan kelas mengantuk. Sistem ini menggunakan algoritma Pan-Tomkins untuk menentukan interval RR dari EKG. Fitur yang digunakan adalah 15 parameter fitur statistik. Pelatihan dan pengujian data menggunakan JST RBF-PSO dengan metode validasi silang. PSO digunakan untuk mengoptimasi parameter utama JST RBF yaitu bobot, pusat dan lebar. Sistem deteksi kantuk ini diuji menggunakan DROZY <em>Database. </em>Hasil penelitian menunjukkan akurasi sistem ini pada segmentasi 40 detik, jumlah neuron 150 dan 15 fitur statistik sebesar 88,36%.</p>

Effect of prenap coffee on daytime sleepiness in university students

Background: Daytime sleepiness impairs academic performance in college students. Napping is a counter to daytime sleepiness, but often causes sleep inertia on waking up. Caffeine absorption from beverages peaks 30 minutes after their ingestion presenting a window of opportunity to have a short nap such that the time of waking up is in synchrony with onset of action of caffeine; thereby abolishing post-nap inertia and achieving synergistic mitigation of fatigue.Objective of this study to assess effect of nap, coffee, ‘coffee and nap’ and ‘wakeful break without coffee’ on daytime sleepiness using Psychomotor Vigilance Tests (PVTs) and Karolinska Sleepiness Scale (KSS) score.Methods: After Institutional Review Board clearance, 10 subjects (aged 19-21 years) were selected using their Epworth Sleepiness Scale score (ESS >5) and called to the study site 8 times on different days to be exposed to these four conditions twice - only coffee (standardized), only nap (30min), coffee immediately followed by 30min nap, wakeful break (30min) without coffee or nap. Pre and post scores were recorded for electronic PVT (Reaction Time and Motor Responsiveness) and KSS for each attempt.Results: Test outcome was associated with intervention used (p=0.00001). ‘Nap only’ group was associated with deterioration in outcomes (p=0.00001), accounting for highest percentage (41%) of all deteriorated test outcomes. ‘Coffee only’ group was associated with improvement in test scores (p=0.00001), responsible for highest share (38.8%) of all improved test outcomes. ‘Nap only’ and ‘Coffee-nap’ group showed improvement in 11.67% and 21.67% of outcomes respectively. Conclusions: Pre-nap coffee is a proactive counter-measure to post nap sleep inertia.