Sleep-related Data Research Articles

Modeling Risk Factors for Sleep- and Adiposity-Related Cardiometabolic Disease: Protocol for the Short Sleep Undermines Cardiometabolic Health (SLUMBRx) Observational Study.

BackgroundObesity and short sleep duration are significant public health issues. Current evidence suggests that these conditions are associated with cardiovascular disease, metabolic syndrome, inflammation, and premature mortality. Increased interest in the potential link between obesity and short sleep duration, and its health consequences, has been driven by the apparent parallel increase in the prevalence of both conditions in recent decades, their overlapping association with cardiometabolic outcomes, and the potential causal connection between the two health issues. The SLUMBRx (Short Sleep Undermines Cardiometabolic Health) study seeks to contribute to the development of a comprehensive adiposity-sleep model while laying the groundwork for a future research program that will be designed to prevent and treat adiposity- and sleep-related cardiometabolic disease risk factors.ObjectiveThis SLUMBRx study aims to address 4 topics pertinent to the adiposity-sleep hypothesis: the relationship between adiposity and sleep duration; sex-based differences in the relationship between adiposity and sleep duration; the influence of adiposity indices and sleep duration on cardiometabolic outcomes; and the role of socioecological factors as effect modifiers in the relationship between adiposity indices, sleep, and cardiometabolic outcomes.MethodsSLUMBRx will employ a large-scale survey (n=1000), recruiting 159 participants (53 normal weight, 53 overweight, and 53 obese) to be assessed in 2 phases.ResultsSLUMBRx was funded by the National Institutes of Health, Heart, Lung, and Blood Institute through a K01 grant award mechanism (1K01HL145128-01A1) on July 23, 2019. Institutional Review Board (IRB) approval for the research project was sought and obtained on July 10, 2019. Phase 1 of SLUMBRx, the laboratory-based component of the study, will gather objective adiposity indices (air displacement plethysmography and anthropometrics) and cardiometabolic data (blood pressure, pulse wave velocity and pulse wave analysis, and a blood-based biomarker). Phase 2 of SLUMBRx, a 1-week, home-based component of the study, will gather sleep-related data (home sleep testing or sleep apnea, actigraphy, and sleep diaries). During phase 2, detailed demographic and socioecological data will be collected to contextualize hypothesized adiposity and sleep-associated cardiometabolic disease risk factors. Collection and analyses of these data will yield information necessary to customize future observational and intervention research.ConclusionsPrecise implementation of the SLUMBRx protocol promises to provide objective and empirical data on the interaction between body composition and sleep duration. The hypotheses that will be tested by SLUMBRx are important for understanding the pathogenesis of cardiometabolic disease and for developing future public health interventions to prevent its conception and treat its consequences.International Registered Report Identifier (IRRID)PRR1-10.2196/27139

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research.

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that canhandle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation.

BackgroundThe proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns.ObjectiveWe aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms.MethodsIn this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified.ResultsWhen applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants.ConclusionsOur unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.

Cognitive behavioral therapy for insomnia in restless legs syndrome patients

ObjectivesThe purpose of this study was to investigate the effects of cognitive behavioral therapy for insomnia (CBTI) in patients with Restless Legs Syndrome (RLS). MethodsThis is a randomized controlled study. The patients were sequentially selected and randomly assigned to either a CBTI group or a non-CBTI group. A total of 25 RLS patients with comorbid insomnia were recruited from a tertiary university hospital sleep center. Twelve were assigned to the CBTI group, and 13 were assigned to the non-CBTI group. The CBTI group received 4 sessions of behavioral therapy, while the non-CBTI group received one informative session on sleep hygiene. All patients completed sleep and psychiatric-related questionnaires. In addition, each individual completed a one-week sleep log for collecting subjective sleep data and actigraphy for objective sleep data. ResultsAfter conducting the CBTI, there were significant improvements in severity of insomnia symptoms, subjective sleep efficiency, total sleep time, latency to sleep onset, wake after sleep onset, objective latency to sleep onset, and anxiety in the CBTI group as compared to the non-CBTI group. The effect of CBTI on sleep-related data was maintained for up to three months. ConclusionsCBTI was effective in RLS patients by improving sleep quality and anxiety symptoms. CBTI may be considered in clinical practice for RLS patients with comorbid insomnia.

A prediction model of sleep disturbances among female nurses by using the BP-ANN.

To describe sleep disturbances and fatigue among female registered nurses in Beijing and to develop a prediction model for sleep disturbances. Chinese nurses are required to work rotating shifts on a weekly basis, which could negatively impact their sleep and well-being. A total of 647 registered nurses participated in this study. Self-reported sleep-related data and selected physiological data were collected. Back propagation artificial neural networks was used to develop the prediction model by using the risk management and population health framework. Majority of them reported clinically significant poor sleep (69.4%) and fatigue (75.4%). A total of eight predictors were identified for sleep disturbances, and the top four normalized importance predictors are morning fatigue (100%), body mass index (30.5%), gastrointestinal symptoms (17.6%) and drinking caffeinated beverages at work (17.3%). The cross-entropy error was 206.58, and the model accounted for 77.6% of the variance in sleep disturbances. Female registered nurses in China experience clinically significant sleep disturbances. Morning fatigue severity along with seven significant influencing factors may be used to identify shift nurses who face a higher risk of sleep disturbances. The back propagation artificial neural networks model could be used as the foundation for health promotion interventions for registered nurses.

The role of fatigue and sleepiness in drivers with obstructive sleep apnea

BackgroundThe present investigation examines the role of daytime sleepiness and fatigue and how these relate to driving behaviors and risk assessment in people newly diagnosed with obstructive sleep apnea (OSA). MethodsWe recruited 47 individuals, (24 female, 23 male), between the ages of 25 and 71 (mean age = 51, SD = 11.28). Of those, 24 individuals were newly diagnosed with OSA and 23 individuals were in a comparison sample with similar proportions of biological sex and ages, who tested negative for OSA. All participants completed questionnaire measures related to sleep, psychological adjustment, driving behavior, sleepiness and fatigue, immediately after their follow-up appointment. We collected data on driving violations from registered driving records for the 5 years preceding their enrolment in the study, as well as sleep-related data for all participants. ResultsResults show that individuals with OSA (M = 1.08, SD = 1.38) do not commit more driving violations than control participants (M = 0.64, SD = 1.26). Although drivers with OSA indicate significantly worse scores for fatigue (M = 7.73, SD = 3.71) compared with controls (M = 4.26, SD = 3.66), there was no significant difference for sleepiness between drivers with OSA (M = 10, SD = 3.57) and Controls (M = 8, SD = 3.69). An association between driving violations and sleepiness was found for drivers with OSA – r (24) = −0.45, p < .05 – but not for Controls – r (23) = −0.22, p > .05. ConclusionsFatigue, and sleepiness should be assessed as distinct constructs, and each should be taken into account separately in studies of driving risk.

Disturbed sleep and excessive daytime sleepiness in a Saudi population-based sample

Background: National data on the prevalence of daytime sleepiness and other sleep-related issues are limited for the general Saudi population.Objectives: The aim of this study was to screen a sample of the Saudi population for the prevalence of symptoms of disturbed sleep and excessive daytime sleepiness (EDS), in addition to the sleep duration of the sample.Patients and Methods: A cross-sectional, descriptive population-based sample of n = 1419 participants was randomly recruited from two major public places in the Eastern Province of the Kingdom of Saudi Arabia. The sociodemographic information was collected, and the Epworth Sleepiness Scale (ESS) was used to measure EDS. These and other sleep-related data were assessed after verbal agreements were received from the participants.Results: The total number of participants was n = 1419, with a mean age of 34.1 ± 9.8 years. The response rate was 76.2%, and 917 (65%) were males. The EDS based on the ESS was 53.8% for the sample, with an increasing trend in the higher age groups (11.4 ± 5.8), particularly in the 30–39 years' old age category. Snoring was highly prevalent in the males at 44% versus the females at 21%, and 55% of the participants were smokers. The mean sleep duration was 6.72 ± 1.97 h.Conclusion: EDS was prevalent in particular higher age groups in the general studied population. Snoring was among the highest prevalent sleep-related symptoms with reported shorter sleep duration in the general population. Public awareness of sleep hygiene or psychoeducation and large epidemiological studies are needed to rule out cultural factors and improve the general well-being of the population of the Kingdom of Saudi Arabia.

P.2.d.015 - Is cognitive dysfunction in bipolar disorder related to sleep abnormalities?

Sleep disturbance is common in chronic post-traumatic stress disorder (PTSD). However, prior work has demonstrated that there are inconsistencies between subjective and objective assessments of sleep disturbance in PTSD. Therefore, we investigated whether subjective or objective sleep assessment has greater clinical utility to differentiate PTSD patients from healthy subjects. Further, we evaluated whether the combination of subjective and objective methods improves the accuracy of classification into patient versus healthy groups, which has important diagnostic implications. We recruited 32 chronic war-induced PTSD patients and 32 age- and gender-matched healthy subjects to participate in this study. Subjective (i.e. from three self-reported sleep questionnaires) and objective sleep-related data (i.e. from actigraphy scores) were collected from each participant. Subjective, objective, and combined (subjective and objective) sleep data were then analyzed using support vector machine classification. The classification accuracy, sensitivity, and specificity for subjective variables were 89.2%, 89.3%, and 89%, respectively. The classification accuracy, sensitivity, and specificity for objective variables were 65%, 62.3%, and 67.8%, respectively. The classification accuracy, sensitivity, and specificity for the aggregate variables (combination of subjective and objective variables) were 91.6%, 93.0%, and 90.3%, respectively. Our findings indicate that classification accuracy using subjective measurements is superior to objective measurements and the combination of both assessments appears to improve the classification accuracy for differentiating PTSD patients from healthy individuals.

Sleep Quality Among Elderly People Within the Outpatient Department of Songklanagarind Hospital, Thailand: A Cross-Sectional Study

Objective: To determine the prevalence of sleep quality in the elderly within our outpatient department.Material and Method: A cross-sectional study was conducted by; calculating the sample size for a survey with ‘proportion to size’ at the outpatient department of Songklanagarind Hospital from; October to November, 2015. A total of 408 participants aged between 65 and 99 were interviewed. Demographic and sleep-related data were collected. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) – Thai version. The results were analyzed in terms of descriptive statistics and conducted using R-Epicalc and R-Survey software.Results: Our subjects were predominantly married females. The proportion of those in the 65-74 age range was 63.0%.Most of the participants (70.8%) were poor sleepers, with mean and median PSQI scores of 7.7 and 7.0, respectively.Regarding poor sleepers, their problems were; short sleep duration (99.7%), poor sleep efficiency (61.6%) and daytime dysfunction (51.6%).Conclusion: Similarly to previous studies, our findings showed a high prevalence of poor sleep quality in the elderlypopulation. A sleep duration of less than 6 hours was the major problem in the poor sleeper group. Gaining a betterinsight into sleep quality correlating with comorbidities is recommended.

Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters

Sleep disturbance is common in chronic post-traumatic stress disorder (PTSD). However, prior work has demonstrated that there are inconsistencies between subjective and objective assessments of sleep disturbance in PTSD. Therefore, we investigated whether subjective or objective sleep assessment has greater clinical utility to differentiate PTSD patients from healthy subjects. Further, we evaluated whether the combination of subjective and objective methods improves the accuracy of classification into patient versus healthy groups, which has important diagnostic implications. We recruited 32 chronic war-induced PTSD patients and 32 age- and gender-matched healthy subjects to participate in this study. Subjective (i.e. from three self-reported sleep questionnaires) and objective sleep-related data (i.e. from actigraphy scores) were collected from each participant. Subjective, objective, and combined (subjective and objective) sleep data were then analyzed using support vector machine classification. The classification accuracy, sensitivity, and specificity for subjective variables were 89.2%, 89.3%, and 89%, respectively. The classification accuracy, sensitivity, and specificity for objective variables were 65%, 62.3%, and 67.8%, respectively. The classification accuracy, sensitivity, and specificity for the aggregate variables (combination of subjective and objective variables) were 91.6%, 93.0%, and 90.3%, respectively. Our findings indicate that classification accuracy using subjective measurements is superior to objective measurements and the combination of both assessments appears to improve the classification accuracy for differentiating PTSD patients from healthy individuals.

Qualitative Experience of Sleep in Individuals With Spinal Cord Injury.

Poor sleep contributes to adverse health outcomes making it important to understand sleep in medically vulnerable populations, including those with spinal cord injury (SCI). However, little attention has been paid to circumstances specific to SCI that may negatively affect sleep, or to consequences of poor sleep in this population. The objective of this study was to examine the experience of sleep among individuals with SCI. Secondary analysis using thematic coding of qualitative data from an ethnographic study of community-dwelling adults with SCI was conducted. Sleep-related data were found in transcripts for 90% of the sample. Participants described diminished sleep duration and irregular sleep patterns. Several factors contributing to poor sleep were identified, including SCI-related circumstances and sleep environment. Participants also discussed how poor sleep affected occupational engagement. This study highlights the extent of sleep disturbance experienced after SCI and the subsequent impact on occupational performance, and provides direction for clinical practice.

INSIGHT Responsive Parenting Intervention and Infant Sleep.

Inadequate sleep during infancy is associated with adverse outcomes for infants and families. We sought to improve sleep behaviors and duration through a responsive parenting (RP) intervention designed for obesity prevention. The Intervention Nurses Start Infants Growing on Healthy Trajectories (INSIGHT) study is a randomized trial comparing a RP intervention with a safety control. Primiparous mother-newborn dyads were randomized after childbirth, and research nurses delivered intervention content at home visits at infant ages 3, 16, 28, and 40 weeks and at a research center visit at 1 year. The RP sleep component included developmentally appropriate messages about bedtime routines, sleep location and behaviors, and responses to wakings. Portions of the Brief Infant Sleep Questionnaire were administered 2, 8, and 52 weeks after birth with expanded sleep-related data collection at 16 and 40 weeks. Two hundred and seventy-nine dyads completed the first home visit; 90.7% completed the 1-year visit. Compared with controls, RP group infants were less likely to have prolonged bedtime routines >45 minutes and more likely to have earlier bedtimes at 16 and 40 weeks. They were less likely to be fed immediately before bed and more likely to self-soothe to sleep. At 8, 16, and 40 weeks, RP group nocturnal sleep duration was longer by 35, 25, and 22 minutes, respectively (P < .05 for all). Sleep duration at 1 year was similar between groups. The INSIGHT RP intervention positively influenced developmentally appropriate bedtime routines, sleep-related behaviors, and sleep duration for infants.

Gender, nocturnal hypoxia, and arousal influence brainstem auditory evoked potentials in patients with obstructive sleep apnea.

Brainstem auditory evoked potential (BAEP) outcomes in obstructive sleep apnea syndrome (OSAS) patients are inconsistent in some studies because the affecting factors are not well controlled. This study aimed to evaluate BAEP of male and female patients with moderate and severe OSAS and further assess the possible factors influencing BAEP abnormalities. Polysomnography were applied to 118 subjects, composed of 84 OSAS patients and 34 control subjects (22 male, 12 female), during the night. OSAS groups included 40 moderate (32 male, 8 female) and 44 sever (36 male, 8 female) OSAS patients. Binaural BAEPs were performed for every subject before polysomnography. The comparison of BAEP among control, moderate, and severe OSAS groups was performed according to different gender. Independent sample t test was used to examine the difference between male and female groups. Pearson's correlation was applied between BAEP parameters and age, body mass index, and sleep-related data in OSAS group. Prolonged latencies of waves I, III, and V were observed in OSAS patients. They were significantly correlated with apnea hypopnea index, arousal index, and hypoxia-related parameters. Compared to female patients, male patients had longer latencies of waves III and V. For severe male OSAS patients, BAEP of right ear showed additional prolongations of IPLs of I-III and I-V than that of left ear, and the IPL of I-III was correlated with the severity of hypoxia. Moderate and severe OSAS patients showed abnormal BAEP which could be influenced by gender, nocturnal hypoxia, and arousal.

Sleepiness at the wheel across Europe: a survey of 19 countries.

The European Sleep Research Society aimed to estimate the prevalence, determinants and consequences of falling asleep at the wheel. In total, 12 434 questionnaires were obtained from 19 countries using an anonymous online questionnaire that collected demographic and sleep-related data, driving behaviour, history of drowsy driving and accidents. Associations were quantified using multivariate logistic regression. The average prevalence of falling asleep at the wheel in the previous 2 years was 17%. Among respondents who fell asleep, the median prevalence of sleep-related accidents was 7.0% (13.2% involved hospital care and 3.6% caused fatalities). The most frequently perceived reasons for falling asleep at the wheel were poor sleep in the previous night (42.5%) and poor sleeping habits in general (34.1%). Falling asleep was more frequent in the Netherlands [odds ratio = 3.55 (95% confidence interval: 1.97; 6.39)] and Austria [2.34 (1.75; 3.13)], followed by Belgium [1.52 (1.28; 1.81)], Portugal [1.34 (1.13, 1.58)], Poland [1.22 (1.06; 1.40)] and France [1.20 (1.05; 1.38)]. Lower odds were found in Croatia [0.36 (0.21; 0.61)], Slovenia [0.62 (0.43; 0.89)] and Italy [0.65 (0.53; 0.79)]. Individual determinants of falling asleep were younger age; male gender [1.79 (1.61; 2.00)]; driving ≥20 000 km year [2.02 (1.74; 2.35)]; higher daytime sleepiness [7.49 (6.26; 8.95)] and high risk of obstructive sleep apnea syndrome [3.48 (2.78; 4.36) in men]. This Pan European survey demonstrates that drowsy driving is a major safety hazard throughout Europe. It emphasizes the importance of joint research and policy efforts to reduce the burden of sleepiness at the wheel for European drivers.

Sleep apps and the quantified self: blessing or curse?

Sleep apps and the quantified self: blessing or curse?

Association of sleep apnea with clinically silent microvascular brain tissue changes in acute cerebral ischemia

The aim of this study was to determine the importance of sleep apnea in relation to clinically silent microvascular brain tissue changes in patients with acute cerebral ischemia. Patients with acute cerebral ischemia prospectively underwent nocturnal respiratory polygraphy within 5days from symptom-onset. Sleep apnea was defined as apnea-hypopnea-index (AHI) ≥5/h. Experienced readers blinded to clinical and sleep-related data reviewed brain computed tomography and magnetic resonance imaging scans for leukoaraiosis and chronic lacunar infarctions. Ischemic lesions were considered clinically silent when patients did not recall associated stroke-like symptoms. Functional outcome was assessed with modified Rankin Scale at discharge, 6 and 12months. Fifty-one of 56 (91%) patients had sleep apnea of any degree. Patients with moderate-to-severe leukoaraiosis (Wahlund score ≥5) were found to have higher mean AHI than those with none or mild leukoaraiosis (34.4 vs. 12.8/h, p<0.001). Moderate-to-severe sleep apnea (AHI ≥15/h) was found to be an independent predictor of moderate-to-severe leukoaraiosis (adjusted OR 6.03, 95% CI 1.76-20.6, p=0.0042) and of moderate-to-severe leukoaraiosis associated with clinically silent chronic lacunar infarctions (adjusted OR 10.5, 95% CI 2.19-50.6, p=0.003). The higher the Wahlund score and the AHI, the more likely unfavorable functional outcome resulted over time (p=0.0373). In acute cerebral ischemia, sleep apnea is associated with clinically silent microvascular brain tissue changes and may negatively influence functional outcome. Routine sleep apnea screening and further investigation of possible long-term effects of non-invasive ventilatory treatment of sleep apnea appear warranted in this at-risk population.

Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS.

Mining Statistically Significant Associations for Exploratory Analysis of Human Sleep Data

We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.