scholarly journals Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8214
Author(s):  
Suwhan Baek ◽  
Hyunsoo Yu ◽  
Jongryun Roh ◽  
Jungnyun Lee ◽  
Illsoo Sohn ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Quantitative Evaluation ◽  
Sleep Stage ◽  
Qualitative Evaluation ◽  
Parameter Analysis ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Quality Of Sleep ◽  
Sleep Scoring

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

Sleep Electroencephalogram Analysis Based on Average Energy Dissipation

2013 ◽  
Vol 634-638 ◽  
pp. 1352-1356
Author(s):  
Zheng Zhong Zheng ◽  
Hao Feng ◽  
Feng Hua Yao ◽  
Xiao Ru Jing ◽  
Jun Wang
Keyword(s):  
Energy Dissipation ◽  
Sleep Stage ◽  
Average Energy ◽  
Parameter Analysis ◽  
Rapid Eye Movement Sleep ◽  
Quality Of Sleep ◽  
Important Indicator ◽  
Stage Classification ◽  
Sleep Stage Classification

The quality of sleep has a great relationship with health. The result of sleep stage classification is an important indicator to measure the quality of sleep. It was found that the average energy dissipation about wake and the first stage of non-rapid eye movement sleep reflect on the changes of sleep stage. And it was confirmed by T test and multi-samples experiments. The average energy dissipation can apply into automatic sleep stage classification. By Multi-parameter analysis it could achieve a higher accuracy of sleep stage classification.

scholarly journals The Effects of Aromatherapy, Meditation, and Blue Light on Sleep Quality

2019 ◽  
Vol 13 (6) ◽  
pp. 144
Author(s):  
Amy Potter
Keyword(s):  
Sleep Quality ◽  
Blue Light ◽  
Deep Sleep ◽  
Large Problem ◽  
Quality Of Sleep ◽  
Sleep Patterns ◽  
Waking Up ◽  
The Times

Sleep insomnia, which reduces the effectiveness of sleep, is a large problem in America that affects individuals with a range of ages. Nearly 60 million people suffer from insomnia in America every year. My goal was to see if the use of aromatherapy, reduction of blue light, and practice of meditation before bed would increase sleep quality. More specifically, I will be focused on increasing the time spent in deep sleep and reducing the times one wakes up during the night. To test my hypothesis, I used the Fitbit Versa to monitor the sleep patterns of the participants of three people with and then the same three people without the pre-bed routines that may affect their sleep. While the results showed that aromatherapy meditation did improve the sleep of the participants, allowing the participant to sleep without waking up as many times are normal and feel more refreshed upon waking, the reduction of blue light had no significant impacts on the participant’s quality of sleep.

scholarly journals Using Nutrition Based Therapy for Improving Sleep Quality

2019 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Sleep Quality ◽  
Human Life ◽  
Deep Sleep ◽  
Improve Quality ◽  
Quality Of Sleep ◽  
Hormonal Profile ◽  
Sleeping Pills ◽  
The People ◽  
Improve Sleep Quality

Sleep is an important factor in human life cycle. Many of the people are struggling from sleep disorders that may range from sleep deprivation to insomnia. Other major factor with respect to sleep is quality of sleep. It tends to happen with many of the people now a days that they get sleep for 6 to 8 hours but still they feel fatigue or sleepy throughout the day or even they are not satisfied with their sleep. Many people move towards the clinical/medical way to get good deep sleep like sleeping pills or tablets having alprazolam [1]. This may have seductive side effects throughout the day. Here in this paper we focus to improve sleep quality by maintain efficient hormonal profile of tryptophan i.e. melatonin through food based therapy to improve quality of sleep [2-4].

scholarly journals Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors (Preprint)

2019 ◽  
Author(s):  
Zilu Liang ◽  
Mario Alberto Chapa-Martell
Keyword(s):  
Sleep Quality ◽  
Medical Device ◽  
Rem Sleep ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Specific Factors ◽  
Stage Transition ◽  
Light Sleep

BACKGROUND It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. OBJECTIVE This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. METHODS A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. RESULTS Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. CONCLUSIONS Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Sleep Electroencephalogram Analysis Based on Symbolic Transfer Entropy

2013 ◽  
Vol 765-767 ◽  
pp. 2678-2681 ◽  
Author(s):  
Rui Jun Chang ◽  
Yang Liu ◽  
Qiu Ping Chen ◽  
Jun Wang
Keyword(s):  
Sleep Stage ◽  
Transfer Entropy ◽  
Parameter Analysis ◽  
Rapid Eye Movement Sleep ◽  
Quality Of Sleep ◽  
Important Indicator ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Symbolic Transfer Entropy

The quality of sleep has a great relationship with health. The result of sleep stage classification is an important indicator to measure the quality of sleep. It was found that the symbolic transfer entropy about wake and the first stage of non-rapid eye movement sleep reflect on the changes of sleep stage. And it was confirmed by T test and multi-samples experiments. The symbolic transfer entropy can apply into automatic sleep stage classification. By Multi-parameter analysis it could achieve a higher accuracy of sleep stage classification.

scholarly journals Enhancement of Sleep Quality and Stability Using Acoustic Stimulation During Slow Wave Sleep

2018 ◽  
Vol 5 (4) ◽  
pp. 126-134
Author(s):  
Aminollah Golrou ◽  
Ali Sheikhani ◽  
Ali Motie Nasrabadi ◽  
Mohammad Reza Saebipour
Keyword(s):  
Sleep Quality ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Acoustic Stimulation ◽  
Deep Sleep ◽  
Quality Of Sleep ◽  
Slow Oscillations ◽  
Random Frequency ◽  
Improve Sleep Quality

Background: One of the challenges today is that the quality of sleep has weakened by many external factors, which we are not even aware of and which directly affect sleep. Sleep quality has an essential role in maintaining the cognitive function and memory consolidation of individuals. So far, various studies have been done to improve the quality of sleep by using external electrical stimulation, vestibular and olfactory system stimulation. Methods: In this study, the increase in sleep quality was considered by simultaneous acoustic stimulation in a deep sleep to increase the density of slow oscillations. Slow oscillations are the important events recorded in electroencephalography (EEG) and hallmark deep sleep. Acoustic stimulation of pink noise with random frequency ranging from 0.8 to 1.1 Hz was used to improve sleep quality. Results: Eight healthy adults (mean age: 28.4±7.8 years) studied in 3 nights under 3 conditions: accommodation night, stimulation night (STIM) and no stimulation night (SHAM), in counterbalanced order. Significant characteristics of the objective and subjective quality of sleep have been extracted from the acquired EEG and compared in the last 2 nights. Also, the arousal and cyclic alternating pattern characteristics have been measured to assess sleep stability over 2 nights of STIM and SHAM. Conclusion: Our findings confirm this goal of the study that applying designed acoustic stimulation simultaneously in the slow wave sleep (SWS) stage increases the duration of deep sleep and ultimately improves overall sleep stability and quality. Keywords: Sleep quality enhancement; Acoustic stimulation; Slow wave sleep; CAP & arousals; Sleep stability; EEG

scholarly journals Phase discontinuities underlie increased drowsiness and diminished sleep quality in older humans

2019 ◽  
Author(s):  
Teresa Hinkle Sanders
Keyword(s):  
Sleep Quality ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Cortical Inhibition ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Older Individuals ◽  
Phase Discontinuity ◽  
Eeg Power ◽  
Smooth Transitions

AbstractHealthy humans switch seamlessly between activity states, wake up and fall asleep with regularity, and cycle through sleep stages necessary for restored homeostasis and memory consolidation each night. This study tested the hypothesis that such smooth behavioral transitions are accompanied by smooth transitions between stable neural states within the brain. A method for detecting phase discontinuities across a broad range of frequencies was created to quantify phase disruptions in the Fp-Cz EEG channel from 20 annotated sleep files. Phase discontinuities decreased with increasingly deep sleep, and increased phase discontinuity was associated with increased drowsiness, reduced deep sleep, and shorter REM sleep. A 10s phase discontinuity summary measure (the phase jump indicator) closely tracked the annotated sleep stages and enabled discrimination between short (< 10 min) and longer REM periods. Overall phase discontinuity correlated inversely with broadband EEG power, suggesting that reduced spurious signaling may facilitate increased synchronization. However, the correlation between phase discontinuity and power varied with sleep stage and age. Older individuals spent significantly more time in the Awake and Drowsy stages and less time in the deepest sleep stage and REM sleep. Interestingly, although EEG power was reduced in older individuals across all sleep stages, increased phase discontinuity only occurred in stages that showed impairment. In older patients the power vs. phase discontinuity correlation shifted to positive during drowsiness, suggesting potential deficits in cortical inhibition. These results provide evidence that phase discontinuity measures extend current capabilities for assessing sleep and may yield new insights into pathological brain states.Significance statementEvidence continues to accumulate regarding the positive relationship between healthy sleep and brain function. Recent studies also show that more healthful sleep can be induced with timely application of non-invasive therapies. Accordingly, the ability to accurately assess sleep quality in real-time has become increasingly important. Here, a newly defined measure, referred to as phase discontinuity, enabled rapid identification of unhealthful neural patterns associated with increased drowsiness, reduced deep sleep, and early termination of REM sleep. Moreover, the measure was linked to underlying neuronal and circuit properties known to impact sleep quality. Thus, the phase discontinuity measure defined in this study provides new insight into sleep pathology and has potential implications for closed-loop therapeutic intervention.

scholarly journals Characterisation of ICU sleep by a commercially available activity tracker and its agreement with patient-perceived sleep quality

2020 ◽  
Vol 7 (1) ◽  
pp. e000572 ◽  
Author(s):  
Patricia Louzon ◽  
Jessica Andrews ◽  
Xavier Torres ◽  
Eric Pyles ◽  
Mahmood Ali ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Low Cost ◽  
Sleep Time ◽  
Sleep Apnoea ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Activity Tracker ◽  
Perceived Sleep Quality

BackgroundA low-cost, quantitative method to evaluate sleep in the intensive care unit (ICU) that is both feasible for routine clinical practice and reliable does not yet exist. We characterised nocturnal ICU sleep using a commercially available activity tracker and evaluated agreement between tracker-derived sleep data and patient-perceived sleep quality.Patients and methodsA prospective cohort study was performed in a 40-bed ICU at a community teaching hospital. An activity tracker (Fitbit Charge 2) was applied for up to 7 ICU days in English-speaking adults with an anticipated ICU stay ≥2 days and without mechanical ventilation, sleep apnoea, delirium, continuous sedation, contact isolation or recent anaesthesia. The Richards-Campbell Sleep Questionnaire (RCSQ) was administered each morning by a trained investigator.ResultsAvailable activity tracker-derived data for each ICU study night (20:00–09:00) (total sleep time (TST), number of awakenings (#AW), and time spent light sleep, deep sleep and rapid eye movement (REM) sleep) were downloaded and analysed. Across the 232 evaluated nights (76 patients), TST and RCSQ data were available for 232 (100%), #AW data for 180 (78%) and sleep stage data for 73 (31%). Agreement between TST (349±168 min) and RCSQ Score was moderate and significant (r=0.34; 95% CI 0.18 to 0.48). Agreement between #AW (median (IQR), 4 (2–9)) and RCSQ Score was negative and non-significant (r=−0.01; 95% CI −0.19 to 0.14). Agreement between time (min) spent in light (259 (182 to 328)), deep (43±29), and REM (47 (28–72)) sleep and RCSQ Score was moderate but non-significant (light (r=0.44, 95% CI −0.05 to 0.36); deep sleep (r=0.44, 95% CI −0.11 to 0.15) and REM sleep (r=0.44; 95% CI −0.21 to 0.21)).ConclusionsA Fitbit Charge 2 when applied to non-intubated adults in an ICU consistently collects TST data but not #AW or sleep stage data at night. The TST moderately correlates with patient-perceived sleep quality; a correlation between either #AW or sleep stages and sleep quality was not found.

Assessment Of Dry Eye Symptoms And Quality Of Sleep In Engineering Students During The Covid-19 Pandemic

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1202-1207
Author(s):  
Pavithra S ◽  
Dheepak Sundar M
Keyword(s):  
Sleep Quality ◽  
Dry Eye ◽  
Screen Time ◽  
Engineering Students ◽  
Statistical Tests ◽  
Quality Of Sleep ◽  
Cross Sectional ◽  
Eye Symptoms ◽  
Dry Eye Symptoms

To assess dry eye symptoms (DES) and quality of sleep in engineering students during the Covid19 pandemic lockdown and also to assess the association between DES and sleep quality. A cross-sectional questionnaire-based study was carried out among 396 engineering students studying in Saveetha engineering college. The study tool used was a semi-structured google form questionnaire designed for assessing digital device usage, symptoms of dry eye disease and sleep pattern. Responses were analyzed using appropriate statistical tests. Overall 64.1% attained a score of more than 10, indicating the presence of DES. 70.2% of the study population used digital screens for more than 13 hours. A statistically significant association was found between increased screen time and presence of DES(p<0.05). 64.9% had a score of >18 indicating reduced sleep quality. About 77.1% of the students with DES had reduced sleep quality, and a significant association (p<0.01) was observed between the two. During the Covid19 pandemic lockdown, there appears to be rising prevalence of DES in student population, one of the reasons being increased screen time. The sleep quality was also found to be reduced, and a significant association was found between DES and sleep quality.

scholarly journals 696 Quality of sleep is the only predictor of suicide during Covid-19 lockdown in University students?

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A272-A272
Author(s):  
Alessandra Castelnuovo ◽  
Samantha Mombelli ◽  
Daniela Bottoni ◽  
Antonella Somma ◽  
Andrea Fossati ◽  
...  
Keyword(s):  
Medical Students ◽  
Suicidal Ideation ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Social Stress ◽  
Depression Symptoms ◽  
Daily Routine ◽  
Quality Of Sleep ◽  
Suicidal Thoughts

Abstract Introduction COVID-19 epidemic led to great psychological and social stress, related to anxiety, depression, sleep disorders, suicidal risk and behavior, and changes in daily routine. The aim of this study was to assess the psychological impact of COVID-19 lockdown in Italian students. We focused on perceived sleep quality, anxiety and depression symptoms, and mostly on risk of suicide. Methods A total of 307 students (mean age 22.84±2.68) completed Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Beck Anxiety Inventory (BAI), and Beck Depression Inventory-II (BDI-II). In our study, we focused on item 9 of BDI-II, that is related to suicide. We divided our sample on presence or absence of suicidal ideation based on this item. Results We found that 30.1% showed depressive, while 38.2% anxious symptoms. Concerning item 9 of BDI-II (suicidal thoughts or wishes), 84.7% answered that they do not have any thoughts of killing themselves, while 15.3% answered that they have some suicidal ideation. Concerning sleep variables, we found that 58% of our sample showed a PSQI total score higher than 5 (poor quality of sleep), and a global worsening in sleep quality and increase of insomnia both in student with and without suicidal ideation. Conclusion Our results on risk of suicide are in line with literature. Recent reviews focused on suicidal ideation in medical students found that depressive symptoms and suicidal ideation are common among medical students, finding a prevalence of suicidal ideation of 11%. Several studies suggest that sleep disorders are a risk factor for suicidal thoughts and behaviours. Our findings show that sleep cannot considered a predictive factor of risk of suicide during health emergency, because the risk is polyfactorial. Support (if any) None

Sign in / Sign up

Export Citation Format

Share Document