Associations between Maternal Sleep Quality Throughout Pregnancy and Newborn Birth Weight

ABSTRACT

Objective: Poor sleep quality is common during pregnancy. Although a few studies have investigated the associations between maternal sleep quality and fetal birth weight (BW), no evidence has been clearly demonstrated. Our aim was to investigate the effects of sleep quality during pregnancy on the newborn BW z-scores.

Participants: 1466 mother-infant pairs were included in the present study based on an ongoing prospective cohort.

Methods: Questionnaires including the Pittsburgh Sleep Quality Index (PSQI) and scales for psychosocial status were administered at each trimester. BW z-scores were calculated based on the INTERGROWTH-21st standard. A generalized estimating equation model was applied to evaluate the associations between trimester-specific sleep quality and newborn BW after adjusting for potential confounders. Multivariable logistic regression models were applied to examine the impacts of maternal sleep quality on small-for-gestational-age (SGA) or low birth weight (LBW).

Results: We found that maternal PSQI scores in the first and third trimesters were negatively associated with BW z-scores among female newborns (β = ?0.032, 95% CI: ?0.063, ?0.001, P= .043; β = ?0.031, 95% CI: ?0.060, ?0.003, P= .033, respectively). However, no relationship was observed between maternal sleep quality and BW in male neonates. Additionally, poor sleep quality in late pregnancy was a risk factor for LBW (OR = 1.501, 95% CI: 1.082–2.082).

Conclusions: The BW z-scores of female newborns decreased as maternal sleep quality in the first and third trimesters worsened. This finding suggests that sleep during pregnancy may influence fetal weight in a trimester- and gender-specific manner.     

Schlaf in einer H?ngematte verglichen mit dem Schlaf im Bett

We compared the sleep quantity and quality of healthy subjects when sleeping in a bed or in a hammock using an actigraphy and questionnaires. The study was initiated as part of biology lessons at a high school. Participants in the study were five female and five male pupils of a high school with an average age of 18.0?±?1.8 years and a body mass index of 21.3?±3?kg/m². To exclude chronic sleep disturbances all participants completed the Pittsburgh sleep quality questionnaire and all had a value below 5 (3.4?±?0.5). A significant restless legs syndrome was excluded by using a standardized questionnaire. The pupils slept in a random order in the usual bed or in a hammock which was installed in the bedroom. All measurements were done on week days. Sleep data were calculated using an actigraph (SOMNOwatch, SOMNOmedics, Randersacker, Germany) worn on the non-dominant arm. There were no significant differences in sleep data between a bed or a hammock. Total sleeping time in bed was 5:57?±?1:04 (h:min) and in a hammock 6:01?±?1:12 (h:min), the sleep efficiency in bed was 79.2%?±?10.5% in a hammock 80.7%?±?9.8%, sleep latency in bed 3:26?±?7:56 (min:s) and in a hammock 6:47?±?9:11 (min:s). There were no first night effects and the data were similar during both nights. Subjective sleep data were similar under both conditions. Sleep in a hammock was not worse than in the usual bed. Sleep efficiency was much lower than expected for this age group perhaps due to the fact that this was the first self-performed scientific investigation for the participants but as the measurements were done in a randomized order this should have no effect on the results.     

Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography

Study ObjectivesTo assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal.MethodsA combination of convolutional and recurrent neural networks was used for PPG-based sleep staging. The models were trained using two large clinical datasets from Israel (n = 2149) and Australia (n = 877) and tested separately on three-class (wake/NREM/REM), four-class (wake/N1 + N2/N3/REM), and five-class (wake/N1/N2/N3/REM) classification. The relationship between OSA severity categories and sleep fragmentation was assessed using survival analysis of mean continuous sleep. Overlapping PPG epochs were applied to artificially obtain denser hypnograms for better identification of fragmented sleep.ResultsAutomatic PPG-based sleep staging achieved an accuracy of 83.3% on three-class, 74.1% on four-class, and 68.7% on five-class models. The hazard ratios for decreased mean continuous sleep compared to the non-OSA group obtained with Cox proportional hazards models with 5-s epoch-to-epoch intervals were 1.70, 3.30, and 8.11 for mild, moderate, and severe OSA, respectively. With EEG-based hypnograms scored manually with conventional 30-s epoch-to-epoch intervals, the corresponding hazard ratios were 1.18, 1.78, and 2.90.ConclusionsPPG-based automatic sleep staging can be used to differentiate between OSA severity categories based on sleep continuity. The differences between the OSA severity categories become more apparent when a shorter epoch-to-epoch interval is used.     

Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation

ObjectiveIn this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation.MethodsFirst, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distinguishing between active and inactive intervals of IEGMs. Then, we show that the natural logarithm of features corresponding to active and inactive intervals exhibits a mixture of two Gaussian distributions in three dimensional feature space. An Expectation Maximization algorithm for Gaussian mixtures is then applied for automatic clustering of the features into two categories.ResultsThe absolute error in onset and offset estimation of active intervals is 6.1 ms and 10.7 ms, respectively, guaranteeing a high resolution. The true positive rate for the proposed method is also 98.1%, proving the high reliability.ConclusionThe proposed method can extract the active intervals of IEGMs during AF with a high accuracy and resolution close to manually annotated results.SignificanceIn contrast with some of the conventional methods, no windowing technique is required in our approach resulting in significantly higher resolution in estimating the onset and offset of active intervals. Furthermore, since the signal characteristics at inflection points are analyzed instead of signal samples, the computational time is significantly low, ensuring the real-time application of our algorithm. The proposed method is also robust to noise and baseline variations thanks to the Laplacian of Gaussian filter employed for extraction of inflection points.     

A deep learning-based algorithm for detection of cortical arousal during sleep

Study ObjectivesThe frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep.MethodsThis study included 1,547 polysomnography records that met study inclusion criteria and were selected from the Multi-Ethnic Study of Atherosclerosis database. We developed an end-to-end deep learning model consisting of convolutional neural networks and recurrent neural networks which: (1) accepted varying length physiological data; (2) directly extracted features from the raw ECG signal; (3) captured long-range dependencies in the physiological data; and (4) produced arousal probability in 1-s resolution.ResultsWe evaluated the model on a test set (n = 311). The model achieved a gross area under precision-recall curve score of 0.62 and a gross area under receiver operating characteristic curve score of 0.93.ConclusionThis study demonstrated the end-to-end deep learning approach with a single-lead ECG has the potential to be used to accurately detect arousals in home sleep tests.     

Effects of nocturnal wearing of dentures on the quality of sleep and oral-health-related quality in edentate elders with untreated sleep apnea: a randomized cross-over trial

Study ObjectivesThis study aims to assess whether the nocturnal wear of dentures has an effect on the quality of sleep and oral-health-related quality of life of the edentulous elderly with untreated sleep apnea.MethodsA single-blind randomized cross-over design with two sequences and two periods was used. Participants (n = 77) were randomly assigned either to sequence 1 (nocturnal wear followed by nocturnal nonwear of the denture for 30–30 days) or sequence 2 (nocturnal nonwear followed by nocturnal wear of denture for 30–30 days). The primary sleep outcome was the quality of sleep, assessed through sleep fragmentation measured as Apnea–Hypopnea Index (AHI) and respiratory arousal from portable polysomnography. Secondary outcomes were daytime sleepiness, sleep quality (Pittsburgh Sleep Quality Index, PSQI) and oral-health-related quality of life measured by validated questionnaires.ResultsThe mean paired difference in AHI scores for the period of wearing versus not wearing dentures at night was small 1.0 event per hour (p = 0.50; 95% confidence interval (CI) = −2.0 to 4.1). The mean respiratory arousal index was higher when wearing dentures at night than when not wearing dentures at night, with a mean paired difference of 2.3 events per hour (p = 0.05; 95% CI = 0.0 to 4.6). No difference in sleepiness and PSQI were noted. Wearing dentures at night resulted in a statistically significantly higher mean score of psychological discomfort when compared to not wearing dentures at night.ConclusionsThe results provide some support to usual practice guidelines to remove dentures at night in edentulous elders suffering from sleep apnea.Clinical trial registrationNCT01868295.     

An Improved Methodology for Individualized Performance Prediction of Sleep-Deprived Individuals with the Two-Process Model

We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual''s performance measurements with a priori performance information using a Bayesian framework, while retaining the strategy of transforming the nonlinear optimization problem of finding the optimal estimates of the two-process model parameters into a series of linear optimization problems. Second, by taking advantage of the linear representation of the two-process model, this new method enables the analytical computation of statistically based measures of reliability for the model predictions in the form of prediction intervals.Two distinct data sets were used to evaluate the proposed method.Results using simulated data with superimposed white Gaussian noise showed that the new method yielded 50% to 90% improvement in parameter-estimate accuracy over the previous method. Moreover, the accuracy of the analytically computed prediction intervals was validated through Monte Carlo simulations. Results for subjects representing three sleep-loss phenotypes who participated in a laboratory study (82 h of total sleep loss) indicated that the proposed method yielded individualized predictions that were up to 43% more accurate than group-average prediction models and, on average, 10% more accurate than individualized predictions based on our previous method.

Citation:

Rajaraman S; Gribok AV; Wesensten NJ; Balkin TJ; Reifman J. An improved methodology for individualized performance prediction of sleep-deprived individuals with the two-process model.     

An analysis of the standard curve parameters of cytomegalovirus,BK virus and hepatitis B virus quantitative polymerase chain reaction from a clinical virology laboratory in eastern India

PurposeQuantitative real-time polymerase chain reactions (qPCRs) are important for accurate detection of nucleic acid target including that for viral load determination. Assessment of the quality of a PCR run is essential for quality control, diagnostics and research. In order to reduce subjectivity qPCR standard curves are accompanied with parametric values for slope, Y- intercept, correlation coefficient (R²) and PCR efficiency. In this study the performance of three qPCR assays-cytomegalovirus, hepatitis B virus and BK virus-with respect to standard curve parameters-slope, Y intercept, R² and efficiency were examined.MethodsUsing ideal values (Slope (minus 3.32); Y intercept ?= ?the number of PCR cycles; R2 ?= ?1 and efficiency ?= ?100%) we estimated the intra-assay variability (range) and deviation from ideal parameters (Δ). We also calculated the standard deviation (SD) and coefficient of variation (CV) for each of these parameters. We have evaluated the quality of each of the three viral load assays (CMV, HBV, BKV) using these statistical approach.ResultsWe found lab developed tests (CMV) to have least deviation from ideal Y intercept (limit of detection); however, commercial kit based assays had better linearity (scatter plot correlation between amplification factor and PCR efficiency). Using a scatter plot for the three assays we found the correlation with calculated amplification factor and PCR efficiency was most linear in case of BKV (0.9974), closely followed by the HBV assay (R² ?= ?0.9968). Although the CMV quantitative standards were least linear (0.868), the CV (coefficient of variation) was also the least in case of the CMV assay.ConclusionThe study highlights an objective way of assessing qPCR assay quality and demonstrates a method to compare assays, validate tests and perform quality control.     

Associations of loneliness and social isolation with actigraph and self-reported sleep quality in a national sample of older adults

Study ObjectivesTo examine associations of social isolation and loneliness with sleep in older adults and whether associations differ for survey and actigraph sleep measures.MethodsThis study used data from the National Social Life, Health, and Aging Project (NSHAP), a nationally representative study of community-dwelling older adults born 1920–1947. A random one-third of participants in 2010–2011 were invited to participate in a sleep study (N = 759) that included survey questions, 72 hours of wrist actigraphy, and a sleep log. Perceived loneliness was measured using three questions from the UCLA Loneliness Scale. An index of social isolation was constructed from nine items that queried social network characteristics and social interactions. We used ordinary least squares and ordinal logistic regression to examine whether sleep measures were associated with loneliness and social isolation adjusted for potential sociodemographic confounders.ResultsSocial isolation and loneliness had a low correlation (Spearman’s correlation = 0.20). Both loneliness and social isolation were associated with actigraphy measures of more disrupted sleep: wake after sleep onset and percent sleep. Neither was associated with actigraph total sleep time. Increased loneliness was strongly associated with more insomnia symptoms and with shorter sleep duration assessed by a single question, but social isolation was not. More isolated individuals spent a longer time in bed.ConclusionsWe found that both loneliness and social isolation were associated with worse actigraph sleep quality, but their associations with self-reported sleep differed. Only loneliness was associated with worse and shorter self-reported sleep.     

Sleep health,diseases, and pain syndromes: findings from an electronic health record biobank

Study ObjectivesImplementation of electronic health record biobanks has facilitated linkage between clinical and questionnaire data and enabled assessments of relationships between sleep health and diseases in phenome-wide association studies (PheWAS). In the Mass General Brigham Biobank, a large health system-based study, we aimed to systematically catalog associations between time in bed, sleep timing, and weekly variability with clinical phenotypes derived from ICD-9/10 codes.MethodsSelf-reported habitual bed and wake times were used to derive variables: short (<7 hours) and long (≥9 hours) time in bed, sleep midpoint, social jetlag, and sleep debt. Logistic regression and Cox proportional hazards models were used to test cross-sectional and prospective associations, respectively, adjusted for age, gender, race/ethnicity, and employment status and further adjusted for body mass index.ResultsIn cross-sectional analysis (n = 34,651), sleep variable associations were most notable for circulatory system, mental disorders, and endocrine/metabolic phenotypes. We observed the strongest associations for short time in bed with obesity, for long time in bed and sleep midpoint with major depressive disorder, for social jetlag with hypercholesterolemia, and for sleep debt with acne. In prospective analysis (n = 24,065), we observed short time in bed associations with higher incidence of acute pain and later sleep midpoint and higher sleep debt and social jetlag associations with higher incidence of major depressive disorder.ConclusionsOur analysis reinforced that sleep health is a multidimensional construct, corroborated robust known findings from traditional cohort studies, and supported the application of PheWAS as a promising tool for advancing sleep research. Considering the exploratory nature of PheWAS, careful interrogation of novel findings is imperative.     

Association between sleep duration and high blood pressure in adolescents: a systematic review and meta-analysis

Context: Sleep has been assessed as a risk factor for health consequences. Among adults, excessively longer and shorter sleep durations are associated with high blood pressure (BP), but knowledge of the association between sleep duration and high BP among adolescents is limited.

Objectives: To estimate the associations between sleep duration and high BP in adolescents.

Methods: PubMed, Web of Science, and Cochrane databases were searched for eligible publications up until 20 November 2017. This study reviewed the reference lists from retrieved articles to search for relevant studies. Pooled odds ratios (ORs) were calculated using a random-effects meta-analysis. Sub-group and sensitivity analyses were conducted to identify heterogeneity. Publication bias was evaluated using Egger’s test.

Results: Seven studies involving 21,150 participants were included, with ages ranging from 10–18 years. For primary analysis, compared with the reference sleep duration, the pooled OR for high BP was 1.51 (95% confidence interval [CI]?=?1.04–2.19) for the short sleep duration overall. For long sleep duration, the pooled OR was 1.04 (95% CI?=?0.78–1.38). Further sub-group analysis showed that short sleep duration had a higher risk of incident high BP in males (OR?=?1.55, 95% CI?=?1.24–1.93) than in females (OR?=?1.23, 95% CI?=?0.47–3.22).

Conclusions: Among adolescents, and particularly male adolescents, short sleep duration may be a risk factor for high BP. More attention should be given to this lifestyle factor.     

Agreement in Regard to Total Sleep Time During a Nap Obtained Via a Sleep Polygraph and Accelerometer: A Comparison of Different Sensitivity Thresholds of the Accelerometer

Background

Appropriate sensitivity threshold of accelerometer to measure total sleep time during nap is not established.

Purpose

Actigraphy-derived total sleep times during naps were calculated using three different sensitivity threshold values and compared with polysomnography.

Method

The mean age of the 60 subjects (53 men and 7 women) was 22.8, ranging from 22 to 27?years. Determination of the sleep stage by the polygraph and the sleep/wake judgment by the accelerometer obeyed the sleep/wake judgment, and the accelerometer was monitored under different sensitivity threshold settings. The study was carried out during one afternoon with a 3-h nap opportunity. Kappa statistics, correlations, and several indices of accuracy were compared using statistical methods.

Results

The mean total sleep times during a nap set for 180?min were 160.4, 151.8, and 140.5?min, respectively, as judged under the low-sensitive, middle-sensitive, high-sensitive settings of an accelerometer worn on the non-dominant wrist. The corresponding mean total sleep time as calculated using a sleep polygraph was 133.0?min. Sleep/wake judgment by three levels of threshold values for the accelerometer showed that high-sensitive threshold showed relatively high specificity (0.452) compared with specificities by the low-sensitive threshold (0.249) or by the middle-sensitive threshold (0.358). The concordance correlation coefficients and 95% confidence intervals (in parenthesis) between the total sleep time judged by polygraph and low-sensitive, middle-sensitive, or high-sensitive accelerometer were 0.40 (0.26?C0.51), 0.53 (0.38?C0.65), and 0.64 (0.49?C0.75), respectively. The Bland?CAltman plot of the measurements showed higher agreement between the total sleep time by polygraph and by the accelerometer using the high-sensitive threshold.

Conclusions

From the result obtained in this study, the high-sensitive accelerometer showed the strongest agreement of total sleep time and sleep/wake judgment with the calculated value using the sleep polygraph.     

The Association of Optimism with Sleep Duration and Quality: Findings from the Coronary Artery Risk and Development in Young Adults (CARDIA) Study

Abstract

Optimism is associated with better health outcomes with hypothesized effects due in part to optimism’s association with restorative health processes. Limited work has examined whether optimism is associated with better quality sleep, a major restorative process. We test the hypothesis that greater optimism is associated with more favorable sleep quality and duration. Main analyses included adults aged 32–51 who participated in the Coronary Artery Risk Development in Young Adults (CARDIA) study (n?=?3,548) during the fifth (Year 15: 2000–2001) and sixth (Year 20: 2005–2006) follow-up visits. Optimism was assessed using the revised Life-Orientation Test. Self-report measures of sleep quality and duration were obtained twice 5?years apart. A subset of CARDIA participants (2003–2005) additionally provided actigraphic data and completed the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Multivariate regression analyses were used to examine associations of optimism and sleep indicators. In cross-sectional analyses of 3548 participants, each standard deviation (SD) higher optimism score resulted in 78% higher odds of self-reporting very good sleep quality. Prospectively, a 1-SD higher optimism score was related to higher odds of reporting persistently good sleep quality across 5-years relative to those with persistently poor sleep [OR = 1.31; 95%CI:1.10,1.56]. In participant with supplementary data, each SD higher optimism score was marginally associated with 22% greater odds of favorable sleep quality [OR = 1.22; 95%CI:1.00,1.49] as measured by the PSQI, with possible mediation by depressive symptoms. Optimism was unrelated to objective actigraphic sleep data. Findings support a positive cross-sectional and prospective association between optimism and self-reported sleep behavior.     

Time in bed,sleep quality and associations with cardiometabolic markers in children: the Prevention and Incidence of Asthma and Mite Allergy birth cohort study

We investigated associations of time in bed and multiple sleep quality characteristics with cardiometabolic markers in children. Data from the prevention and incidence of asthma and mite allergy study, a population‐based prospective birth‐cohort study started in 1996–1997 in the Netherlands, were analysed. In total 1481 children aged 11–12 years completed a questionnaire (including questions on sleep) and underwent a medical examination. We measured body mass index, waist circumference, total‐ and high‐density lipoprotein cholesterol, blood pressure and glycated haemoglobin. Results showed that in girls, some sleep characteristics were related to anthropometrics (body mass index, waist circumference) and cholesterol. Girls who had a long time in bed (11–12.5 h) had 0.16 lower body mass index z‐score (95% confidence interval ?0.31; ?0.01) and 0.99 cm smaller waist circumference (95% confidence interval ?2.01; ?0.13) compared with girls who spent 10–10.5 h in bed. Girls who went to bed late and rose early had 0.16 mm higher total cholesterol (95% confidence interval 0.01; 0.31) and 0.08 mm higher high‐density lipoprotein cholesterol (95% confidence interval 0.01; 0.14) than ‘early to bed/early rise’ girls. Girls with night‐time awakenings had 0.14 mm higher total cholesterol (95% confidence interval 0.03; 0.25) than girls without night‐time awakenings. Girls who felt sleepy/tired ≥1 day per week had 0.10 mm lower high‐density lipoprotein cholesterol (95% confidence interval ?0.16; ?0.04) and 0.17 mm higher total cholesterol/high‐density lipoprotein cholesterol ratio (95% confidence interval 0.02; 0.32) than girls who did not feel sleepy. No associations were found for boys. Sleep characteristics were not related to blood pressure and glycated haemoglobin, and effect sizes of the associations in girls were small. Therefore, we consider it premature to propose that improved sleep could reduce cardiovascular risk during childhood.     

Insomnia in the context of short sleep increases suicide risk

Study ObjectivesThe relationship between insomnia and suicide risk is not completely understood. We aimed to investigate the influence of insomnia on suicide risk, taking both sleep duration and depression into consideration.MethodsThe present study is based on a Swedish prospective cohort study of 38,786 participants with a mean follow-up time of 19.2 years. Cox proportional hazards models with attained age as time-scale were used to estimate hazard ratios (HRs) of death by suicide with 95% confidence intervals (CI) for participants categorized by frequency of insomnia symptoms. Causal mediation analysis was performed to assess to what extent the relationship between insomnia and suicide risk is mediated by depression.ResultsInsomnia was only associated with suicide risk among short sleepers, whereas no significant association was observed among those who slept 7 h/night or more. The total effect of insomnia in the context of short sleep on suicide risk, expressed on the HR scale, was 2.85 (95% CI 1.42–5.74). The direct effect was 2.25 (95% CI 1.12–4.54) and the indirect effect, mediated by depression, was 1.27 (95% CI 1.05–1.53). Of the total effect, 32% was mediated by depression. The association between insomnia and suicide risk became more pronounced with decreasing depressive symptoms (p value for trend <0.05).ConclusionsInsomnia in the context of short sleep increases suicide risk, both directly and indirectly by affecting the risk of depression. Abnormalities of sleep duration and insomnia symptoms should be evaluated when assessing suicide risk.     

Sleep perception and the multiple sleep latency test in patients with primary insomnia

The purpose of this study was to examine the relationship between overnight sleep perception and the daytime multiple sleep latency test (MSLT) among individuals who were primary insomnia patients (PIPs) or good sleeper controls (GSCs). We collected overnight sleep data via polysomnography (PSG), subjective sleep data via a morning questionnaire (self‐evaluated) and MSLT data via four 20‐min naps over 8 h. Subjects included 122 PIPs and 48 GSCs. Sleep perception was calculated as subjective sleep time/objective sleep time × 100%. PIPs showed a significant difference (P < 0.001) between sleep time, as determined by PSG (387.8 ± 100 min) and self‐report (226.3 ± 160 min), but no difference was obtained for GSCs (440.6 ± 53 versus 435.4 ± 65 min). The means for sleep perception were 56.4 ± 38.8% for the PIPs and 99.3 ± 13.6% for the GSCs (P < 0.001). In the PIPs group, weak but statistically significant negative correlations (r: ?0.20 to ?0.25) were found for MSLT versus sleep perception and versus self‐ and PSG‐evaluated sleep time. Compared to PIPs with low scores on the MSLT, those with high scores had less sleep perception (%), less self‐ and PSG‐evaluated sleep time and greater sleep misperception time. GSCs did not show significant correlations between MSLT and sleep measures or differences in comparisons between individuals with high and low scores on the MSLT. These results add novel data to the literature by suggesting that 24‐h hyperarousal potentially plays a key role in the pathophysiological issues of insomnia.     

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

BackgroundThis paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.MethodsA convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.ResultsF1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.ConclusionThe results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.     

Auswirkungen passiver Änderungen des Bettklimas auf den Schlaf

Background

During sleep, a number of thermoregulatory processes that are important for the quality of sleep take place. In the literature, the endogenous reduction of the core body temperature during sleep has been described as a precondition for restful sleep. Most of the investigations on thermoregulatory aspects involve changes in the external temperature.

Methods

This investigation examined the effects of passive changes in the climate of the bed (temperature/humidity) on the quality of sleep in healthy subjects. With constant external conditions, two different blankets with different heat transfer and moisture dissipation characteristics were used to create different microclimates in the space between the mattress and the blanket. The effects of this passive change in climate on the quality of sleep were investigated on a polysomnographic basis in this pilot study under standardized, randomized, and double-blind conditions with 12 healthy subjects who each slept in the sleep laboratory for three consecutive nights. Measurements of the temperature and humidity of the room and the space between the mattress and the blanket were performed in six of the subjects. The subjects slept under one of the two bed covers in each case.

Results

The study blanket was better at dissipating temperature and humidity to the surroundings. The climate under the study blanket was drier and cooler. Total sleep time, sleep efficiency, and arousal tended to improve under the study blanket. None of the differences reached significance.

Conclusion

This pilot study indicated a tendency towards improvement (p<0.10) of sleep quality associated with a drier climate in the area of the bed between the mattress and the blanket. However, further investigations involving a larger number of test subjects are required to confirm these preliminary findings.     

Sleep quality and risk of cancer: findings from the English longitudinal study of aging

Study ObjectiveTo prospectively examine the association between sleep quality and incident cancer risk in the elderly.MethodsA total of 10,036 participants aged ≥50 years free of cancer at baseline from the English Longitudinal Study of Ageing at wave 4 (2008) were included, and followed up until 2016. The primary endpoint was new onset physician-diagnosed cancer. Sleep quality was assessed by four questions regarding the frequency of sleep problems and overall subjective feeling of sleep quality in the last month, with higher score denoting poorer sleep quality. The multivariable Cox regression model was used to calculate hazard ratio (HR) with 95% confidence interval (CI) for incident cancer risk according to sleep quality.ResultsAt 8-year follow-up, a total of 745 (7.4%) participants developed cancer. Compared with good sleep quality at baseline, HR (95% CI) for incident cancer risk was 1.328 (1.061, 1.662) for intermediate quality, 1.586 (1.149, 2.189) for poor quality. Similarly, compared with maintaining good sleep quality in the first 4 years, HR (95% CI) for incident cancer risk was 1.615 (1.208, 2.160) for maintaining intermediate quality and 1.608 (1.043, 2.480) for maintaining poor quality. The exclusion of participants with family history of cancer or abnormal sleep duration yielded consistent results.ConclusionsPoor sleep quality is positively associated with the long-term risk of developing cancer in an elderly cohort. Both medical staffs and the general public should pay more attention to improving sleep hygiene.