• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4467-4474
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• 2011

The purpose of this study was to examine the factor of the quality of elderly women's sleep. The subjects in this study were 198 elderly women who were at the age of 60 and up. One instrument used to assess their quality of sleep was Buysse, et. al.(1989)'s Pittsburgh Sleep Quality Index(PSQI), and the other used to check their urinary incontinence was Jackson, et. al.(1996)'s Urination Symptom Inventory. The collected data were analyzed by the statistical package SPSS 18.0. The elderly women got $7.09{\pm}4.29$ in the quality of sleep of the PSQI, and 67.7 percent suffered from sleep disorders. Among the subfactors of the PSQI, their subjective quality of sleep was low, and the quality of sleep during, sleep latency was low as well. And their sleep disturbance was great. In contrast, the effectiveness of their habitual sleep was relatively good, and they didn't take medicine such as a sleep pill a lot, and their functional sleep disorder in the daytime wasn't great. There were differences in the quality of sleep according to their education, financial state, self-rated health status and family relations, and whether they worked out on a regular basis or not made a difference to that as well. No differences were found according to age, religion, occupation and having a coffee or not. There was a positive correlation between their quality of sleep in the PSQI and urinary incontinence(r=.51, p=.000). Out of the subfactors of the PSQI, the sleep disturbance factor was most closely linked to urinary incontinence(r=.465, p=.001). Finally, urinary incontinence and subjective health status were identified as the variables that exerted a statistically significant influence on the quality of sleep. Those variables made a 31% prediction of it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8A
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• pp.790-797
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• 2006

IEEE 802.16e specifies two different power saving modes(PSM). One is sleep mode and the other is idle mode. These modes are different in that whether a mobile node maintains its state information with the serving base station or not. This difference results in different efficiency in consuming battery power of a mobile terminal. Therefore, it becomes important to analyze the performance of each power saving mode considering the parameters affecting the power consumption. In this paper, we propose a performance modeling framework of sleep mode and idle mode in terms of power saving efficiency. The analytical results are verified by computer simulations that idle mode is superior to sleep mode in power consumption of mobile node.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.243-246
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• 2005

Recent ubiquitous computing environments increasingly impact on our lives using the current technologies of sensor network and ubiquitous services. In this paper, we propose ubiquitous architectural framework for ubiquitous sleep aid service(UbiSAS) in the subset of ubiquitous computing for refreshing of human's sleep. And we examine technical feasibility. Human can recover his health through refreshing sleep from fatigue. Ubiquitous architectural framework for UbiSAS in digital home offers agreeable sleeping environment and improves recovery from fatigue. So we present new concept of ubiquitous architectural framework dissolving stress. Specially, we apply context to context-aware framework module. This context is transferred to context adaptive inference engine which has service invocation function in intelligent agent module. Ubiquitous architectural framework for UbiSAS using context adaptive rule inference engine without user intervention is technical issue. That is to say, we should take sleep comfortably during our sleeping. And sensed information during sleeping is changed to context-aware information. This presents significant information in context adaptive rule inference engine for UbiSAS. This information includes all sleeping state during sleeping in context-aware computing technique. So we propose more effective and most suitable ubiquitous architectural framework using context adaptive rule inference engine for refreshing sleep in this paper.

• Journal of the Korean Institute of Oriental Medical Informatics
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• v.19 no.2
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• pp.29-37
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• 2013

Objectives: This case report presents a 29-years-old male patient. His chief complaint was insomnia, anxiety and chest discomfort. We diagnosed him with sleep disorder. During the therapeutic period, he experienced temporary improvement. Methods: We treated him with emotional freedom techniques(EFT), giungoren-therapy. Also, acupuncture and chuna therapy were accessorily practiced. The effects of treatment were measured by Beck depression inventory(BDI), state-trait anxiety inventory(STAI), insomnia severity index(ISI). Results: After treatment, the chief complaint have been subsided and improved. But, BDI, STAI, ISI score were not remarkably decreased. Conclusions: This result suggests that EFT and giungoren-therapy might be effective for sleep disorder patient with anxiety.

• Sleep Medicine and Psychophysiology
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• v.18 no.2
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• pp.76-81
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• 2011

Objectives: This study aims to analyze the association between the severity of sleep apnea, sleep and mood related scales, and activity during sleep in obstructive sleep apnea syndrome (OSAS) patients. Methods: 176 drug-free male patients confirmed as OSAS (average age=$43{\pm}11$ years) were selected through nocturnal polysomnography (NPSG). OSAS was diagnosed with apnea-hypopnea index (AHI) >5, mean AHI was $39.6{\pm}26.0$. Sleep related scales were Stanford Sleepiness Scale (SSS), Epworth Sleepiness Scale (ESS), Pittsburg Sleep Quality Index (PSQI) and Morningness-Eveningness Scale (MES). Mood related scales were Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), State-Trait Anxiety Inventory (STAI) I, II and Profile of Mood States (POMS). NPSG was performed overnight with both wrist actigraphy (WATG). Parameters produced from WATG were total activity score, mean activity score and fragmentation index. We analyzed the correlation between each scale, AHI scored from NPSG and activity score analyzed from WATG. Results: ESS showed significant positive correlation with PSQI, BDI, BAI and STAI I, II, respectively (p<0.01). SSS showed significant positive correlation with PSQI and BAI (p<0.05, p<0.01). BAI showed significant positive correlation with total activity score, mean activity score and fragmentation index (p<0.05, p<0.01, p<0.05).Total activity score showed significant positive correlation with ESS and BAI, respectively (p<0.05). Fragmentation index showed significant positive correlation with ESS, PSQI and BAI (p<0.05, p<0.01, p<0.05). AHI, indicator of sleep apnea is showed no significant correlation with each sleep and mood related scale. Conclusion: The degree of daytime sleepiness tends to be associated with night sleep satisfaction, depression and anxiety, and the activity during sleep rather than the severity of sleep apnea.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.47.1-47.14
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• 2023

Background: This study aimed to examine the impact of chronotype on depressive symptoms and explore the mediating effects of sleep quality, pre-sleep cognitive arousal, and social jetlag in a sample of wage earners. Methods: A total of 3,917 waged workers were surveyed online in July 2022. Logistic regression and mediation analysis were used to assess the relationship between chronotype (morningness, intermediate, and eveningness) and depressive symptoms (Patient Health Questionnaire ≥ 5), and the mediating effects of Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Pre-Sleep Arousal Scale (PSAS). All analyses were adjusted for age, education level, income level, marital status, coffee consumption, alcohol consumption, physical activity, occupation, employment status, and working hours to calculate odds ratios (ORs). Results: The chronotypes of all the participants were divided into morningness (4.7%), intermediate (93.5%), and eveningness (1.8%). Multiple logistic regression analysis showed an increased risk of depression in the eveningness chronotype (OR: 2.96; 95% confidence interval [CI]: 1.51, 5.86). Regarding the mediation analysis, ISI mediated 28.44% (95% CI: 16.39-40.5), PSQI for 31.25% (95% CI: 19.36, 43.15), and PSAS-Cognitive Score (PSAS-C) for 23.58% (95% CI: 10.66, 36.50) of the association between chronotype and depressive symptoms. However, social jetlag did not significantly mediate this relationship. (percentage mediated = 0.75%, 95% CI: -3.88, 5.39) Conclusions: Evening chronotypes exhibit an increased risk of depressive symptoms, which ISI, PSQI, and PSAS-C partially mediated. This suggests that interventions to improve sleep quality and maintain adequate sleep habits may effectively prevent and treat depression in employees with an eveningness chronotype.

• Korean Journal of Biological Psychiatry
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• v.21 no.2
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• pp.74-80
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• 2014

Objectives More than half of the elders suffer from chronic sleep disturbances. Moreover, sleep disturbances are more prevalent in patients with depressive disorder than in community dwelling elderly. In this study, we aim to estimate the risk factors of poor sleep quality and its effect on quality of life in patients with late life depression. Methods This study included 159 depressive patients aged 65 years or older who completed Pittsburgh Sleep Quality Index (PSQI). A global PSQI score of 5 or greater indicates a poor sleeper. Structural diagnostic interviews were performed using the Korean version of Mini International Neuropsychiatric Interview (MINI). Depression was evaluated by the Korean form of Geriatric Depression Scale (KGDS). Global cognition was assessed by Mini-Mental State Examination in the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease assessment packet. Quality of life was evaluated by the Korean version of Short-Form 36-Item Health Survey (SF-36). Results The frequencies of poor sleepers were 90.5% in major depressive disorder, 71.8% in minor depressive disorder, 47.1% in subsyndromal depressive disorder, and 73.0% in all types of depressive disorders. Multivariate logistic regression analysis indicated that female [odds ratio (OR) = 2.83, 95% confidence interval (CI) = 1.20-6.67] and higher KGDS score (OR = 1.13, 95% CI = 1.05-1.21) were risk factors of poor sleep quality in patients with late life depression. In the analysis of ANCOVA adjusted for age, gender, education and KGDS score, the mean scores of vitality mental health and mental component summary of SF-36 were lower in poor sleepers than in good sleepers. Conclusions Poor sleepers among patients with late life depression are very common and are associated with female and higher KGDS scores. Poor sleep quality causes a significant negative effect on mental health quality. So researchers and clinicians should be more vigilant in the evaluation and treatment of sleep disturbances in patients with late life depression.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1283-1288
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• 2011

In this paper we collect data concerning sleep environments in a bedroom and analyze the relationship between the collected condition data and sleep. In addition, this paper detects scene changes from the subjects in a sleeping state and presents the physical conditions, reactions during sleep, and physical sensations and stimuli. To detect scene changes in image sequences, we used color histogram for the difference between the preceding frame and the current frame. In addition, to extract the tossing and turning for different situations, the subjects were instructed to enter the level of fatigue, the level of drinking, and the level of stomach emptiness. For the sleep experiment system, we used the H-MOTE2420 Sensor composed of temperature, humidity, and light sensors. This paper is intended to provide the best sleep environment that enhances sleep quality, thus inducing people today to get regular and comfortable sleep.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.971-976
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• 2014

In this paper, we proposed a low power algorithm using state transition ready method. The proposed algorithm defined a sleep state, a idle state and a run state for the task. A state transition occurring at the time due to the delay time created in order to reduce the power consumption state in the middle of each inserted into the ready state. The ready state considering a power consumption and a delay time in state transition. A scheduling step of performing the steps in excess of the increasing problems have the delay time is long. The power consumption increased for the operation step increase. A state transition from a sleep state with the longest delay time in operating state occurs when the state is switched by the time delay caused by the increase in operating time reduces the overall power consumption reduced. Experiments [6] were compared with the results of the power consumption. The experimental results [6] is reduced power consumption than the efficiency of the algorithm has been demonstrated.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.