The Relationship between Subjective Sleepiness and Changes in Breath and Beat Rates among the Clinical Night Workers

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P144 Sleep-Related Limb Movements and Subjective Sleepiness in a Professional Rugby League Team

Introduction This study aims to assess the relationship between LMs, PLMD and limb related arousals during sleep and subjective sleepiness in a group of professional athletes (National Rugby League (NRL) players). Methods 25 type II HSAT polysomnographic (PSG) studies were performed on 23 male individuals (2 repeated studies). 2 x Piezo limb EMG sensors were applied to each HSAT. PSG data analysed as per AASM Manual for the Scoring of Sleep and Associated events. Epworth Sleepiness Scale (ESS) collected for each participant obtaining subjective daytime sleepiness. PSG data was checked for normality using the Shapiro-Wilk normality test. Parametric data was then subsequently analysed using Pearson correlation coefficients, whereas non-parametric data was analysed with the Spearman correlation coefficient. A line of best fit was implemented using Deming’s linear regression model to report r2. Results The most significant relationships were noted between daytime sleepiness and the frequency of limb-related arousals (Pearson’s r 0.273) and the relationship between PLM arousals and ESS (p-0.082). No significant relationships were noted between LMs, PLMs, limb related arousals and daytime sleepiness were found. No results were found to be statistically significant. Conclusions This PSG data demonstrates a mildly positive correlation with all limb movement parameters measured against the athletes’ self-reported sleepiness. This may therefore be of significance with their performance and recovery. Further research is recommended to verify these relationships.

P017 Am I sleepy? – Subjective Sleepiness and Drowsy Driving: A systematic review and meta-analysis

Introduction Driving impairment due to sleep loss is a major contributor to motor vehicle crashes resulting in severe injury or fatalities. Ideally, drivers should be aware of their sleepiness and cease driving to reduce risk of a crash. However, there is little consensus on how accurately drivers can identify sleepiness, and how this relates to subsequent driving impairment. To examine whether drivers are aware of their sleepiness, we systematically reviewed the literature. Methods The research question for this review was “are drivers aware of sleepiness while driving, and to what extent does subjective sleepiness accurately reflect driving impairment?”. Our search strategy led to thirty-four simulated/naturalistic driving studies for review. We then extracted the relevant data. Correlational data were examined using meta-analysis, while predictive data were assessed via narrative review. Results Results showed that drivers were aware of sleepiness, and this was associated with both driving impairment and physiological drowsiness. Overall, subjective sleepiness was more strongly correlated (a) with ocular and EEG-based outcomes (rweighted = .70 and .73, respectively, p<.001), rather than lane position and speed outcomes (rweighted = .46 and .49, respectively, p<.001); (b) under simulated driving conditions compared to naturalistic drives; and (c) when the Karolinska Sleepiness Scale was used to measure subjective sleepiness. Lastly, high levels of sleepiness significantly predicted crash events and lane deviations. Discussion This review presents evidence that drivers are aware of sleepiness when driving, and suggests that interventions such as stopping driving when feeling ‘sleepy’ may significantly reduce crash risk.

The Effect of Experimental Recuperative and Appetitive Post-lunch Nap Opportunities, With or Without Caffeine, on Mood and Reaction Time in Highly Trained Athletes

Purpose: To investigate the effects of placebo (PLA), 20 min nap opportunity (N20), 5mg·kg−1 of caffeine (CAF), and their combination (CAF+N20) on sleepiness, mood and reaction-time after partial sleep deprivation (PSD; 04h30 of time in bed; study 1) or after normal sleep night (NSN; 08h30 of time in bed; study 2).Methods: Twenty-three highly trained athletes (study 1; 9 and study 2; 14) performed four test sessions (PLA, CAF, N20 and CAF+N20) in double-blind, counterbalanced and randomized order. Simple (SRT) and two-choice (2CRT) reaction time, subjective sleepiness (ESS) and mood state (POMS) were assessed twice, pre- and post-intervention.Results: SRT was lower (i.e., better performance) during CAF condition after PSD (pre: 336 ± 15 ms vs. post: 312 ± 9 ms; p < 0.001; d = 2.07; Δ% = 7.26) and NSN (pre: 350 ± 39 ms vs. post: 323 ± 32 ms; p < 0.001; d = 0.72; Δ% = 7.71) compared to pre-intervention. N20 decreased 2CRT after PSD (pre: 411 ± 13 ms vs. post: 366 ± 20 ms; p < 0.001; d = 2.89; Δ% = 10.81) and NSN (pre: 418 ± 29 ms vs. post: 375 ± 40 ms; p < 0.001; d = 1.23; Δ% = 10.23). Similarly, 2CRT was shorter during CAF+N20 sessions after PSD (pre: 406 ± 26 ms vs. post: 357 ± 17 ms; p < 0.001; d = 2.17; Δ% = 12.02) and after NSN (pre: 386 ± 33 ms vs. post: 352 ± 30 ms; p < 0.001; d = 1.09; Δ% = 8.68). After PSD, POMS score decreased after CAF (p < 0.001; d = 2.38; Δ% = 66.97) and CAF+N20 (p < 0.001; d = 1.68; Δ% = 46.68). However, after NSN, only N20 reduced POMS (p < 0.001; d = 1.05; Δ% = 78.65) and ESS (p < 0.01; d = 0.71; Δ% = 19.11).Conclusion: After PSD, all interventions reduced sleepiness and only CAF enhanced mood with or without napping. However, only N20 enhanced mood and reduced sleepiness after NSN. Caffeine ingestion enhanced SRT performance regardless of sleep deprivation. N20, with or without caffeine ingestion, enhanced 2CRT independently of sleep deprivation. This suggests a different mode of action of napping and caffeine on sleepiness, mood and reaction time.

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.

Thermal Comfort and Sleep Quality of Indonesian Students Living in Japan during Summer and Winter

Thermal comfort is crucial in satisfaction and maintaining quality sleep for occupants. In this study, we investigated the comfort temperature in the bedroom at night and sleep quality for Indonesian students during summer and winter. Eighteen male Indonesian students aged 29 ± 4 years participated in this study. The participants had stayed in Japan for about six months. We evaluated the sleep parameters using actigraphy performed during summer and winter. All participants completed the survey regarding thermal sensation, physical conditions, and subjective sleepiness before sleep. The temperature and relative humidity of participants’ bedrooms were also measured. We found that the duration on the bed during winter was significantly longer than that during summer. However, sleeping efficiency during winter was significantly worse than that during summer. The bedroom temperature of the participants was in the range of comfort temperature in Indonesia. With the average bedroom air temperature of 22.2 °C, most of the participants still preferred “warm” and felt “slightly comfortable” during winter. The average comfort temperature each season calculated using the Griffiths method was 28.1 °C during summer and 23.5 °C during winter. In conclusion, differences in adaptive action affect bedroom thermal conditions. Furthermore, habits encourage the sleep performance of Indonesian students.

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.