New predictors of sleep efficiency

Electroencephalography Microstate Class D is a Brain Marker of Subjective Sleep Quality for College Students with High Habitual Sleep Efficiency

Subjective sleep quality is an individual's subjective sleep feeling, and its effective evaluation is the premise of improving sleep quality. However, people with autism or mental disorders often experience difficulties in verbally expressing their subjective sleep quality. To solve the above problem, this study provides a non-verbal and convenient brain feature to assess subjective sleep quality. Reportedly, microstates are often used to characterize the patterns of functional brain activity in humans. The occurrence frequency of microstate class D is an important feature in the insomnia population. We therefore hypothesize that the occurrence frequency of microstate class D is a physiological indicator of subjective sleep quality. To test this hypothesis, we recruited college students from China as participants [N = 61, mean age = 20.84 years]. The Chinese version of the Pittsburgh Sleep Quality Index scale was used to measure subjective sleep quality and habitual sleep efficiency, and the state characteristics of the brain at this time were assessed using closed eyes resting-state brain microstate class D. The occurrence frequency of EEG microstate class D was positively associated with subjective sleep quality (r = 0.32, p < 0.05). Further analysis of the moderating effect showed that the occurrence frequency of microstate class D was significantly and positively correlated with subjective sleep quality in the high habitual sleep efficiency group. However, the relationship was not significant in the low sleep efficiency group (βsimple = 0.63, p < 0.001). This study shows that the occurrence frequency of microstate class D is a physiological indicator of assessing subjective sleep quality levels in the high sleep efficiency group. This study provides brain features for assessing subjective sleep quality of people with autism and mental disorders who cannot effectively describe their subjective feelings.

Poor Subjective Sleep Quality Predicts Symptoms in Irritable Bowel Syndrome Using the Experience Sampling Method

INTRODUCTION

Sleep quality may affect symptom experience in irritable bowel syndrome (IBS). Our aim was to investigate the relationship between sleep quality and gastrointestinal (GI) symptoms using actigraphy and the experience sampling method.

METHODS

Patients with IBS were recruited from a tertiary Neurogastroenterology clinic and the community. GI symptoms and mood were recorded on a smartphone application, 10 times per day, over 7 consecutive days. Subjective sleep quality was recorded every morning to reflect the night before. Objective measures of sleep quality were estimated from wrist-worn actigraphy. Cross-lagged structural equation models were built to assess the directionality of sleep-symptom relationships over time.

RESULTS

Eighty patients with IBS completed the study (mean age: 37 years [range 20-68], 89% female, 78% community). Approximately 66% had a Pittsburgh Sleep Quality Index score ≥ 8, indicating a clinically significant sleep disturbance. Approximately 82% (95% CI: 72-90) screened positive for a sleep disorder, most commonly insomnia. In cross-lagged analysis, poor subjective sleep quality predicted next-day abdominal pain (0.036 < P < 0.040) and lower GI symptoms (0.030 < P < 0.032), but not vice versa. No significant relationship with GI symptoms was found for any objective sleep measure using actigraphy.

DISCUSSION

Poor subjective sleep quality was associated with higher next-day lower GI symptom levels, but not vice versa. Objective sleep measures did not predict next-day abdominal symptoms, potentially supporting the conclusion that it is the perception of sleep quality that is most influential. This study may be used to guide future research into the effect of sleep interventions on GI symptoms.

The temporal relationships between sleep disturbance and autonomic dysregulation: A co-twin control study

INTRODUCTION

Sleep disturbance is associated with autonomic dysregulation, but the temporal directionality of this relationship remains uncertain. The objective of this study was to evaluate the temporal relationships between objectively measured sleep disturbance and daytime or nighttime autonomic dysregulation in a co-twin control study.

METHODS

A total of 68 members (34 pairs) of the Vietnam Era Twin Registry were studied. Twins underwent 7-day in-home actigraphy to derive objective measures of sleep disturbance. Autonomic function indexed by heart rate variability (HRV) was obtained using 7-day ECG monitoring with a wearable patch. Multivariable vector autoregressive models with Granger causality tests were used to examine the temporal directionality of the association between daytime and nighttime HRV and sleep metrics, within twin pairs, using 7-day collected ECG data.

RESULTS

Twins were all male, mostly white (96%), with mean (SD) age of 69 (2) years. Higher daytime HRV across multiple domains was bidirectionally associated with longer total sleep time and lower wake after sleep onset; these temporal dynamics were extended to a window of 48 h. In contrast, there was no association between nighttime HRV and sleep measures in subsequent nights, or between sleep measures from previous nights and subsequent nighttime HRV.

CONCLUSIONS

Daytime, but not nighttime, autonomic function indexed by HRV has bidirectional associations with several sleep dimensions. Dysfunctions in autonomic regulation during wakefulness can lead to subsequent shorter sleep duration and worse sleep continuity, and vice versa, and their influence on each other may extend beyond 24 h.

Actigraphy-Based Characteristics of Sleep in Paediatric Cancer Patients in Remission and a Comparison with Their Healthy Peers in the Recovery Stay

BACKGROUND

Previous research has demonstrated that paediatric cancer survivors (PCS) have lower sleep quality than their healthy peers. However, the research to date has focused mainly on self-reported data. Therefore, the aim of this cross-sectional study was to characterise selected sleep parameters in PCS using objective monitoring techniques and to compare them with a control group (CG) of their healthy peers during a structured recovery stay. A specific objective was to characterise sleep with respect to gender, age, and cancer type.

METHODS

26 PCS and 38 CG aged 7-15 years participated in the study. Selected sleep indicators (time in bed, total sleep time, sleep efficiency) were objectively assessed with an Actigraph wGT3X-BT accelerometer for 12 days during the recovery stay.

RESULTS

No significant differences were found between the PCS and CG groups in terms of the selected sleep parameters. The total time in bed was 543.1 min/day in the PCS and 537.2 min/day in the CG (p=0.91). The total sleep time was 455.3 min/day in the PCS and 457.5 min/day in the CG (p=0.57). Sleep efficiency was 85.3% in the PCS and 86.3% in the CG (p=0.36). Sleep efficiency >85% was achieved by 62% of the PCS (n=16) and 68% of the CG (n=26). There were no significant differences in sleep parameters in terms of variables such as gender, age, or cancer type.

CONCLUSION

The results of our study suggest that - under the same conditions - the PCS did not differ from their healthy peers in terms of the indicators of time in bed, total sleep time, and sleep efficiency. No significant differences according to age, gender, or cancer type were found.

Sleep Efficiency May Predict Depression in a Large Population-Based Study

OBJECTIVES

The purpose of our study was to investigate the effect of objective sleep characteristics on the incidence of depression.

METHODS

The participants of our study (1,595 men and 1,780 women with 63.1 ± 10.7 years) were selected from the Sleep Heart Health Study (SHHS) datasets. Depression was defined as the first occurrence between SHHS visit 1 and visit 2. Objective sleep characteristics, including sleep efficiency (SE), wake after sleep onset (WASO), sleep fragmentation index (SFI) and arousal index (ArI), were monitored by polysomnography. Multivariable logistic regression was used to explore the relationship between sleep characteristics and depression.

RESULTS

A total of 248 patients with depression (7.3%) were observed between SHHS visits 1 and 2. After adjusting for covariates, SE (odds ratio [OR], 0.891; 95% confidence interval [CI] 0.811-0.978; P = 0.016) and WASO (OR, 1.021; 95% CI 1.002-1.039; P = 0.026) were associated with the incidence of depression. Moreover, the relationship between SE and depression was more pronounced in men (OR, 0.820; 95% CI 0.711-0.946; P = 0.007) than in women (OR, 0.950; 95% CI 0.838-1.078; P = 0.429) in subgroup analysis (P _interaction < 0.05).

CONCLUSIONS

SE and WASO may be markers for the incidence of depression. The association between SE and depression was intensified in men.

Co-ordination of brain and heart oscillations during non-rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r² = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r² = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health.

Rotating between day and night shifts: Factors influencing sleep patterns of hospital nurses

AIMS AND OBJECTIVES

This study aimed to investigate sleep patterns of hospital nurses using a wearable electronic device and determine the influence of rotating day and night shifts and lifestyle factors on their sleep efficiency.

BACKGROUND

Nurses working in shifts are vulnerable to sleep disturbances. However, little is known about the influence of rotating day and night shift schedules and healthy lifestyle on nurses' daily sleep patterns.

DESIGN

Descriptive correlational design.

METHODS

Thirty-two hospital nurses working in shifts and 32 hospital nurses not working in shifts participated in data collection. Their sleep patterns were measured for six consecutive days using Fitbit Charge 3^™ , and information on alcohol consumption, exercise, and eating habits were assessed using a questionnaire. Data were analysed using repeated measures analysis of variance with post hoc Scheffe's test and hierarchical multiple regression analysis. The study was conducted in accordance with the STROBE guideline.

RESULTS

Overall, nurses working on rotating day and night shifts had significantly shorter total sleep time, longer sleep onset latency, and lower sleep efficiency than those not working in shifts. In particular, nurses working for 3 or 4 consecutive night shifts had significantly shorter total sleep time, lower sleep efficiency and longer sleep onset latency than those working for 0-2 consecutive night shifts. Rotating day and night shifts and alcohol consumption significantly influenced sleep efficiency.

CONCLUSIONS

A work schedule of ≥3 consecutive night shifts and the habit of alcohol consumption before bed time influence nurses' sleep efficiency.

RELEVANCE TO CLINICAL PRACTICE

Given the poor sleep pattern among nurses working in shifts, particularly those working on ≥2 consecutive night shifts, it is necessary to develop an optimal shift schedule and a program to promote healthy lifestyle among hospital nurses.

Objective Sleep Efficiency Predicts Cardiovascular Disease in a Community Population: The Sleep Heart Health Study

Background There was little evidence about the role of objective sleep efficiency (SE) in the incidence of major cardiovascular disease (CVD) events. The purpose of this study was to investigate the correlation between objective SE and CVD based on polysomnography. Methods and Results A total of 3810 participants from the SHHS (Sleep Heart Health Study) were selected in the current study. CVD was assessed during an almost 11-year follow-up period. The primary composite cardiovascular outcome was major adverse cardiovascular events, defined as CVD mortality, congestive heart failure, myocardial infarction, and stroke. The secondary composite cardiovascular outcome was major adverse cardiovascular event plus revascularization. Objective measured SE, including SE and wake after sleep onset, was based on in-home polysomnography records. Cox regression analysis was used to explore the association between SE and CVD. After multivariate Cox regression analysis, poor SE (<80%) was significantly associated with primary (hazard ratio [HR], 1.338; 95% CI, 1.025-1.745; P=0.032) and secondary composite cardiovascular outcomes (HR, 1.250; 95% CI, 1.027-1.521; P=0.026); it was also found to be a predictor of CVD mortality (HR, 1.887; 95% CI, 1.224-2.909; P=0.004). Moreover, wake after sleep onset of fourth quartile (>78.0 minutes) was closely correlated with primary (HR, 1.436; 95% CI, 1.066-1.934; P=0.017), secondary composite cardiovascular outcomes (HR, 1.374; 95% CI, 1.103-1.712; P=0.005), and CVD mortality (HR, 2.240; 95% CI, 1.377-3.642; P=0.001). Conclusions Poor SE and long wake after sleep onset, objectively measured by polysomnography, were associated with the increased risk of incident CVD.

Anxiety and executive functions in mid-to-late life: the moderating role of sleep

Objectives: The goal of the study was to examine the influence of sleep efficiency on the relationship between anxiety and executive functions.Method: Secondary data analyses of 82 community-dwelling middle-aged and older adults were performed (M age = 63.00, SD = 8.64). Anxiety was measured using the trait anxiety subscale of the State-Trait Anxiety Inventory. Sleep efficiency was measured using one-week of sleep diary data. Two executive functions, cognitive flexibility and inductive reasoning, were measured using the Trail-Making Test and Letter Series task, respectively. SPSS PROCESS macro software version 2 was used to assess the moderating role of sleep efficiency in the relationship between anxiety and executive functions.Results: Sleep significantly moderated the relationship between anxiety and inductive reasoning. Among middle-aged and older adults with high anxiety, those with good sleep efficiency displayed significantly better inductive reasoning than those with poor sleep efficiency after controlling for age, gender, and education (ΔR2 = .05, p = .017). Sleep efficiency did not significantly moderate the relationship between anxiety and cognitive flexibility.Conclusion: Sleep efficiency weakened the association between anxiety and inductive reasoning in middle-aged and older adults. Evidence from the study suggests better sleep may limit the negative effects of anxiety on executive functions in mid-to-late life. Further research is needed to elucidate the impact of anxiety and sleep on executive functions in clinical populations with anxiety.

Heart rate variability at bedtime predicts subsequent sleep features

Getting enough sleep during the night is important for preventing adverse short- and long-term health outcomes. The sympathetic-parasympathetic autonomic balance, characteristics of the pre-bed time resting period, correlates with sleep efficiency. We investigated in healthy subjects whether Low/High Frequencies (LF/HF) and other Heart Rate Variability (HRV) metrics, extracted in the period immediately before sleep onset, are able to predict quality/architecture sleep parameters in the sample group and in the Evening-Intermediate- chronotype subgroups. Linear correlations were found between HRV metrics and the investigated quality/architecture sleep parameters. The possibility to predict sleep parameters from the HRV metrics while falling asleep might pave the way to behavioral interventions during the bedtime period to increase the quality of sleep.

The association between sleep efficiency and diabetes mellitus in community-dwelling individuals with or without sleep-disordered breathing

BACKGROUND

Sleeping habits have been reported to be associated with diabetes mellitus. This study aimed to explore the relationship of sleep efficiency with diabetes mellitus in individuals with or without sleep-disordered breathing based on polysomnography records.

METHODS

We enrolled participants from the Sleep Heart Health Study. Objective indicators of sleep characteristics including sleep efficiency, sleep latency, slow-wave sleep, wake after sleep onset, and total arousal index were monitored via in-home polysomnography. Sleep efficiency was divided into grade 1 (≥85%), grade 2 (80%-84.9%), and grade 3 (<80%). Multivariate logistic regression models were utilized to investigate the association between sleep quality and diabetes mellitus.

RESULTS

The present study comprised 4737 participants with a mean age of 63.6 ± 11.0 years. The prevalence of diabetes mellitus was higher in those with grade 3 sleep efficiency than that in those with grade 1 and grade 2 sleep efficiency in participants with (10.9% vs 8.5% vs 8.3%, respectively; P =.134) or without (9.5% vs 5.6% vs 3.5%, respectively; P <.001) sleep-disordered breathing. After adjusting for potential confounding factors, sleep efficiency <80% was associated with the prevalence of diabetes mellitus only in participants without sleep-disordered breathing (odds ratio, 1.894; 95% confidence interval, 1.187-3.022, P =.007).

CONCLUSION

Poor sleep efficiency is associated with diabetes mellitus in those without sleep-disordered breathing. Therefore, the relationship between sleep efficiency and diabetes mellitus is worth further investigation.

Brain Structural and Functional Alterations Specific to Low Sleep Efficiency in Major Depressive Disorder

Background

Sleep disturbance is common in patients with major depressive disorder (MDD), but the exploration of its neural underpinnings is limited by subjective sleep measurement and single-modality neuroimaging analyses.

Methods

Ninety six patients with MDD underwent polysomnography examinations and multi-modal magnetic resonance imaging (MRI) scans. According to sleep efficiency, patients were subdivided into well-matched normal sleep efficiency (NSE, N = 42; 14 men; aged 43 ± 10 years) and low sleep efficiency (LSE, N = 54; 23 men; aged 45 ± 12 years) groups. Inter-group differences in brain structure and function were examined by applying voxel-based morphometry (VBM), regional homogeneity (ReHo) and functional connectivity strength (FCS), and tract-based spatial statistics (TBSS) approaches to structural, functional, and diffusion MRI data, respectively.

Results

There was no significant difference in gray matter volume (GMV) between the NSE and LSE groups. Compared with the NSE group, the LSE group showed increased axial diffusivity in the left superior and posterior corona radiata, and left posterior limb and retrolenticular part of internal capsule. In addition, the LSE group exhibited decreased ReHo in the bilateral lingual gyri and right postcentral gyrus yet increased FCS in the left angular gyrus relative to the NSE group. Moreover, validation analyses revealed that these results remained after adjusting for the medication effect.

Conclusion

Our data indicate that preserved gray matter morphology, impaired white matter integrity, and decreased local synchronization degree yet increased FCS are specific to low SE in MDD patients. These findings of disassociation between structural and functional alterations might provide insights into the neural mechanisms of sleep disturbance in depression.

Common pathways and communication between the brain and heart: connecting post-traumatic stress disorder and heart failure

Psychiatric illnesses and cardiovascular disease (CVD) contribute to significant overall morbidity, mortality, and health care costs, and are predicted to reach epidemic proportions with the aging population. Within the Veterans Administration (VA) health care system, psychiatric illnesses such as post-traumatic stress disorder (PTSD) and CVD such as heart failure (HF), are leading causes of hospital admissions, prolonged hospital stays, and resource utilization. Numerous studies have demonstrated associations between PTSD symptoms and CVD endpoints, particularly in the Veteran population. Not only does PTSD increase the risk of HF, but this relationship is bi-directional. Accordingly, a VA-sponsored conference entitled "Cardiovascular Comorbidities in PTSD: The Brain-Heart Consortium" was convened to explore potential relationships and common biological pathways between PTSD and HF. The conference was framed around the hypothesis that specific common systems are dysregulated in both PTSD and HF, resulting in a synergistic acceleration and amplification of both disease processes. The conference was not intended to identify all independent pathways that give rise to PTSD and HF, but rather identify shared systems, pathways, and biological mediators that would be modifiable in both disease processes. The results from this conference identified specific endocrine, autonomic, immune, structural, genetic, and physiological changes that may contribute to shared PTSD-CVD pathophysiology and could represent unique opportunities to develop therapies for both PTSD and HF. Some recommendations from the group for future research opportunities are provided.

Smart technologies toward sleep monitoring at home

With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea-hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.