• Sleep Medicine and Psychophysiology
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• v.7 no.2
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• pp.88-95
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• 2000

Objectives: The object of this experiment was to evaluate the effect of sleep deprivation on mood states of normal adults using a subjective scale and an objective scale, minimizing the effect of other factors other than that of sleep deprivation. Methods: Seventy volunteers were first participated in this sleep deprivation schedule, and 36 of them completed this experiment. The subjects and the control group members were all in their early 20's (mean $age=20.8{\pm}1.35$ vs $20.6{\pm}1.10$) and in good health. A log was checked by these subjects from a week before the laboratory study started. Drugs, alcohol and beverages containing any caffeine had been prohibited for a week before and during sleep deprivation periods. The study was performed only in summer to control other factors like sunlight, temperature and moisture. Before this experiment, the subjects had slept adequately for a week at least. On day 1 of the experiment the subjects got up at 6 a.m. and stayed in a sleep laboratory without sunlight or external noises. They could only go about their daily routines. They were forbidden to have a nap and be drowsy. GVA (Global Vigor and Affect) and MADRS (Montgomery-Asberg Depression Rating Scale) were checked 11 times. The data was analysed focusing on the changing mood states. Results: The mood during sleep deprivation became worse as the sleep deprivation time progressed. Especially 20 hours ($GA=59.25{\pm}8.06$, $MADRS=3.43{\pm}1.25$) and 40 hours ($GA=38.83{\pm}9.22$, $MADRS=6.08{\pm}1.46$) after sleep deprivation, there were significant changes compared to the control group ($MADRS=6.08{\pm}1.46$ vs $1.07{\pm}1.18$, p<0.001). Conclusions: While controlling factors other than sleep deprivation might have had some influence on mood changes, significant mood changes during sleep deprivation were observed. The mood states became worse as the sleep deprivation progressed.

• Sleep Medicine and Psychophysiology
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• v.9 no.2
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• pp.122-126
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• 2002

Objectives: The purpose of this study is to investigate the effects of the 37-hour sleep deprivation on visual discrimination using the Cybernetic Flicker Fusion Analyzer (FLIMK) and to see the correlation between this finding and various subjective scale scores. Methods: Twenty six subjects (20 men 6 women, $24.50{\pm}1.45$ years of age) participated in this study. Subjects remained awake for 37 hours under continuous surveillance. In the morning and evening of two study days, the FLIMK and the self-reporting scale of sleepiness, fatigue, anxiety and mood states were instituted. Results: In FLIMK, the flicker fusion threshold was significantly decreased after sleep deprivation (F=7.66, p=0.01). The number of trials (responses) before reaching fusion frequency threshold was significantly increased after sleep deprivation (F=13.16, p=0.001). The reduction of fusion frequency was correlated with the increase of sleepiness and fatigue (p<0.05), and the number of the trials was correlated with the scores of negative mood, anxiety, and fatigue (p<0.05). Conclusions: These results suggest that 37 hour total sleep deprivation cause the decrement in visual discrimination. The decrease of the fusion frequency after sleep deprivation was correlated with sleepiness and fatigue. Further study is needed to enlighten the biological mechanism of the decrement in visual function after sleep deprivation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Safety and Health at Work
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• v.9 no.2
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• pp.236-238
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• 2018

Sleep deprivation may cause accidents, and it has deteriorating effects on health. A measurement of postural steadiness by a portable and affordable Nintendo Wii Fit balance board can be used to quantify a person's alertness. At work, people are under the influence of their environment-often other peopl-dthat may affect their alertness. This work investigates whether sleep deprivation among people is "contagious," as quantified by sway measures. We measured 21 volunteers' postural steadiness while alert and sleep deprived. During the measurements, a screen placed in front of the participants showed a footage of either alert or sleep-deprived faces. We found a significant difference between the day time and night time steadiness, but found no effect resulting from watching footage of sleep-deprived people. This finding shows that a posturographic sleepiness tester quantifies physiological sleep deprivation, and is insensitive to the influence of social factors.

• Sleep Medicine and Psychophysiology
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• v.10 no.2
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• pp.108-112
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• 2003

Objectives: The purpose of this study is to investigate the psychophysiological effects of total sleep deprivation by using auditory event-related potentials. Methods: Twenty four healthy college students (18 men, 6 women) participated in this study. The subjects remained awake for 37 hours under continuous surveillance. In the morning and the evening of two consecutive study days, auditory event-related potentials were checked. Results: The latencies of P300 and N200 were significantly prolonged (p<0.001) and their amplitudes decreased (p<0.05) as a consequence of sleep deprivation. The amplitude of P200 was significantly increased during sleep deprivation (p<0.05). However, there was no significant change in the latency and amplitude of N100. Conclusion: Among the components of auditory event-related potentials, P300 and N200 are good indicators of sleep-deprived status. Further investigation is necessary to elucidate the neurophysiological mechanisms of these findings.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.47-55
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• 2022

This study was conducted to identify the correlation between dental hygiene students' overdependence on smartphones, sleep deprivation, and self-control, and factors influencing self-control. 267 college students in the department of dental hygiene located in the Chungcheong area were surveyed and analyzed. Smartphone overdependence was positively correlated with sleep deprivation and smartphone overdependence was negative correlated with self-control. The sleep deprivation was negative correlated with self-control. Factors affected to self-control were problematic results and sleep deprivation. Therefore, it is necessary to develop education and programs that can increase the self-control of dental hygiene students and to prepare measures to prevent sleep problems and overdependence on smartphone.

• Korean Journal of Culture and Social Issue
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• v.12 no.3
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• pp.69-83
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• 2006

The purpose of the present driving simulation study was to investigate the effects of fatigue induced both by sleep deprivation and time-on-task on driver's driving performance. The results are as followings. First, although the drivers in almost every experimental conditions showed faster driving speed than that was required, the drivers in the sleep-deprivation and over 1-hour time-on-task condition drove slower than the target speed. Second, the drivers in the sleep-deprivation and over 1-hour time-on-task condition crossed the lane less frequently than those in the other conditions, they weaved more in the lane. Third, as the drivers became more fatigued both by sleep deprivation and continuous driving, they reported higher fatigue scores in the subjective fatigue ratings. The results suggested that although the drivers might adapt a compensative driving strategies when they were fatigued, their driving performances were indeed impaired, in general.

• Safety and Health at Work
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• v.9 no.2
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• pp.159-163
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• 2018

Background: Fatigue and sleepiness are inter-related and common among road transport drivers. In this study, sleep deprivation and fatigue among chemical transportation drivers were examined. Methods: A cross-sectional study surveying 107 drivers from three hazardous types of chemical production and transportation industries (nonflammable gases, flammable gases, and flammable liquids) was conducted. Data on sleep deprivation were collected using questionnaires of the Stanford Sleeping Scale and the Groningen Sleep Quality Scale. Fatigue was assessed using an interview questionnaire and a flicker fusion instrument. Results: Chemical drivers had a mean sleeping scale (Stanford Sleeping Scale) of 1.98 (standard deviation 1.00) and had a mean score of 1.89 (standard deviation 2.06) on the Groningen Sleep Quality Scale. High-risk drivers had higher scores in both the Stanford Sleeping Scale and the Groningen Sleep Quality Scale with a mean score of 2.59 and 4.62, respectively, and those differences reached statistical significance (p < 0.05). The prevalence of fatigue, as assessed through a critical flicker fusion analyzer, subjective fatigue question, and either of the instruments, was 32.32%, 16.16%, and 43.43%, respectively. Drivers who slept <7 hours and had poor sleep quality were found to have more fatigue than those who slept enough and well. Drivers who had a more sleepiness score resulted in significantly more objective fatigue than those who had a less sleepiness score. Conclusion: Sleep quality and sleeping hour can affect a driver's fatigue. Optimization of work-rest model should be considered to improve productivity, driver retention, and road safety.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.311-320
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• 2019

This study is a descriptive research designed to determine the effects of smartphone addiction on sleeping time and sleep deprivation among college students. For this purpose, the study made a questionnaire survey of 280 health science college students attending at C college located U Metropolitan city. Data collected from the survey were analyzed using SPSS 22.0 program. The findings of this study can be summarized as follows. there were correlations between smartphone addiction and students' sleep deprivation but weren't correlations between their smartphone addiction and sleeping time. furthermore college students' sleep deprivation was influenced by their smartphone addiction. Therefore, in order to induce sufficient sleep for physical and mental health, it is needed to reduce the smartphone addiction rate of college students. To do that, it is necessary to develop and provide interventive programs to eradicate unnecessary smartphone use.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.1-6
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• 2021

[Purpose] We determined the effect of partial sleep deprivation (PSD) after an exercise session on exercise performance on the following morning. [Methods] Eleven male athletes performed either a normal sleep trial (CON) or a PSD trial. On the first day (day 1), all subjects performed an exercise session consisting of 90 min of running (at 75% ${\dot{V}}O_{2max}$) followed by 100 drop jumps. Maximal strength (MVC) was evaluated before and after exercise. In the CON trial, the sleep duration was 23:00-7:00, while in the PSD trial, the sleep duration was shortened to 40% of the regular sleep duration. On the following morning (day 2), MVC, the metabolic responses during 20 min of running (at 75% ${\dot{V}}O_{2max}$), and time to exhaustion (TTE) at 85% ${\dot{V}}O_{2max}$ were evaluated. [Results] On day 2, neither the MVC nor ${\dot{V}}O_2$ during 20 min of running differed significantly between the two trials. However, the respiratory exchange ratio was significantly lower in the PSD trial than in the CON trial (p = 0.01). Moreover, the TTE was significantly shorter in the PSD trial than in the CON trial (p = 0.01). [Conclusion] A single night of PSD after an exercise session significantly decreased endurance performance without significantly changing muscle strength or cardiopulmonary response.