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People living with dementia (PLWD) often exhibit marked sleep disturbances. These cause substantial care challenges and may be causally related to dementia progression. Collecting ecologically valid data on sleep disturbance in naturalistic settings has been difficult. As a result, sleep assessments in PLWD are generally limited to short studies in sleep laboratories or data collection from wearables, where compliance is problematic. Here, we demonstrate how passive internet of things (IoT) sensors can be used to monitor the effects of dementia on nocturnal behaviour and physiology.Using the Withings under-mattress pressure sensor, we validated bed occupancy and physiological measures in 35 older adults tested both at home and in the laboratory. We then examined data collected between 2019 and 2021 from the general population (N=13,663) and from a cohort of PLWD taking part in the UK DRI study of home monitoring for PLWD (N=46). More than 4 million unique bed mat observations were analysed.Arise time across all subjects was negatively correlated with time to bed (Fig.1a, rTaken together our work demonstrates how digital biomarkers collected passively using under mattress technology can be used to automatically monitor the nocturnal effects of dementia across large populations. The approach is also potentially applicable for preclinical screening and the evaluation of treatments effects.

Contactless sleep technologies hold promise for longitudinal, unobtrusive sleep monitoring in health and disease at scale, but few have been evaluated in older people. The performance of three contactless sleep technologies (a bedside radar [Somnofy] and two under-mattress devices [Withings sleep analyser and Emfit-QS]) were compared to polysomnography and actigraphy [Actiwatch Spectrum] in a sleep laboratory study during a 10-hour time in bed period which induced mild sleep disturbance. The data were collected in a group of older men and women (n=35; 70.8±4.9 years; 14 women) several of whom had health problems and/or sleep apnoea. We conducted a comprehensive and rigorous evaluation of the devices involving estimation of robust performance metrics (both sleep summary and epoch by epoch concordance) for classification of sleep vs wake and NREM and REM sleep stages. All devices overestimated total sleep time and the sleep stage estimation was unsatisfactory in this group of older people in whom sleep efficiency was relatively low. Our findings indicate that even though these devices provide some useful information on sleep behaviour, the performance of contactless sleep technologies requires improvement before they can serve as an alternative to polysomnography and actigraphy for estimating most sleep variables in older individuals.

Disturbances of sleep and night-time behaviours are amongst the most disabling symptoms of dementia. They often increase carers' burden and the risk of institutionalization. The causes are complex and are difficult to investigate because of a lack of acceptable methods for monitoring behaviours in the home. Here we show that a passive under-mattress can be used to track changes in night-time behaviour and physiology, and that a range of digital biomarkers produced are informative in understanding the effects of medication changes, disease progression and intercurrent illness in patients living with dementia (PLWD).We used contactless Withings Sleep Mattress (WSM) to monitor bed-occupancy in 4 PLWD (age 74-93, 3males) enrolled into the CRT MINDER cohort study. Each participant was tracked over 1000 nights between 2019 and 2021. Minute-to-minute timeseries were extracted from WSM to calculate bed occupancy metrics and nocturnal physiology measures (heart and breathing rates (HR/BR)). Raw measures were standardized within subjects by comparing each time point to the mean of the time points that preceded it. We then investigated the relationship between these metrics and clinical events such as infections and medication.The 4 case studies illustrate the potential of this technology for passive health monitoring in PLWD. High levels of intraindividual variability in behavioural and physiological metrics were observed. Progressive changes in bed occupancy were observed in two patients with frontotemporal dementia and Alzheimer's disease (Cases 12). Intercurrent illness and medications changes influenced the measures. For example, Patient 1 showed progressive night-time wandering with increasing time spent out of bed, which improved following the initiation of risperidone. Case 2 showed recurrent episodes of heart failure accompanied by increased nocturnal HR. Cases 3 and 4 showed urinary tract infections, which were accompanied by transient changes in bed occupancy measures.Passive monitoring of night-time behaviour and physiology is a simple and cost-effective way to monitor the effects of medication change and medical events in PLWD. Digital biomarkers derived from these simple measures hold great promise as measures of dementia progression, to enhance dementia care by identifying at risk individuals and as novel clinical outcome measures in clinical trials.

The waking brain’s ubiquitous alpha oscillations are thought to play an important role in managing the brain’s resources, inhibiting neural activity as a function of their phase and amplitude. In accordance with this physiological excitability, perceptual and cognitive processes fluctuate with alpha oscillations. Here we demonstrate that the alpha rhythm can be manipulated with sound in a phase-dependent manner, showing that repeated phase-locked sounds alter the frequency of these oscillations in a spatially localised manner. We draw on oscillator theory to explore the origin of this frequency change, using phase-locked auditory evoked potentials to show a putative phase-reset mechanism, which is dependent on the amplitude of the endogenous alpha oscillations. Finally, we demonstrate the functional relevance of this approach by showing that we can modulate the transition to sleep, using sound, in an alpha phase-dependent manner. Overall, we conclude that the phase of alpha oscillations can be exploited in real-time, and highlight alpha phase-locked auditory stimulation as a powerful method by which to both selectively augment and investigate the brain’s pertinent oscillations.

Wearable heart rate monitors offer a cost-effective way of non-invasive, long-term monitoring of cardiac health. Validation of wearable technologies in an older populations is essential for evaluating their effectiveness during deployment in healthcare settings. To this end, we evaluated the validity of heart rate measures from a wearable device, Empatica E4, and compared them to the electrocardiography (ECG). We collected E4 data simultaneously with ECG in thirty-five older men and women during an overnight sleep recording in the laboratory. We propose a robust approach to resolve the missing inter-beat interval (IBI) data and improve the quality of E4 derived measures. We also evaluated the concordance of heart rate (HR) and heart rate variability (HRV) measures with ECG. The results demonstrate that the automatic E4 heart rate measures capture long-term HRV whilst the short-term metrics are affected by missing IBIs. Our approach provides an effective way to resolve the missing IBI issue of E4 and extracts reliable heart rate measures that are concordant with ECG. Clinical Relevance- This work discusses data quality challenges in heart rate data acquired by wearables and provides an efficient and reliable approach for extracting heart rate measures from the E4 wearable device and validates the metrics in older adults.

Sleep disturbances have been reported as one of the most common symptoms among people living with dementia (PLWD). Few technologies to longitudinally monitor sleep across the 24-h day are available. Here, we develop machine learning models that use multimodal data to accurately monitor sleep across the day and night.

Introduction\ud Sleep disturbances are commonly reported in people living with Alzheimer's disease (AD), but it is currently unknown whether night-to-night variation in sleep predicts day-to-day variation in vigilance, cognition, mood, and behavior (daytime measures).\ud \ud Methods\ud Subjective and objective sleep and daytime measures were collected daily for 2 weeks in 15 participants with mild AD, eight participants with mild cognitive impairment (MCI), and 22 participants with no cognitive impairment (NCI). Associations between daytime measures and four principal components of sleep (duration, quality, continuity, and latency) were quantified using mixed-model regression.\ud \ud Results\ud Sleepiness, alertness, contentedness, everyday memory errors, serial subtraction, and behavioral problems were predicted by at least one of the components of sleep, and in particular sleep duration and continuity. Associations between variations in sleep and daytime measures were linear or quadratic and often different between participants with AD and those with NCI.\ud \ud Discussion\ud These findings imply that daytime functioning in people with AD may be improved by interventions that target sleep continuity.

Sleep problems and circadian misalignment affect health and well-being and are highly prevalent in those with co-morbid neuropsychiatric disorders. Interventions altering light exposure patterns of affected individuals are a promising non-pharmacological treatment option, shown by previous meta-analyses to improve sleep, and often described as minimally invasive. To best translate laboratory-based mechanistic research into effective treatments, acceptability and barriers to adherence should be understood, but these have not yet been systematically evaluated. Here, we examined evidence regarding adherence and acceptability in studies of light or dark interventions using various delivery devices and protocols to improve sleep in intrinsic circadian rhythm sleep–wake disorders and neuropsychiatric illness. Attrition during intervention was low, and reported experiences were largely positive, but measurement and reporting of self-reported experiences, expectations, and adverse effects were poor. Approaches to management and measurement of adherence were varied, and available light monitoring technology appeared under-exploited, as did mobile technology to prompt or track adherence. Based on these findings we suggest recommended reporting items on acceptability and adherence for future investigations. Few studies assessed baseline light exposure patterns, and few personalised interventions. Overall, many applied studies exhibited an approach to light schedule interventions still reminiscent of laboratory protocols; this is unlikely to maximise acceptability and clinical effectiveness. For the next phase of translational research, user acceptability and adherence should receive increased attention during intervention design and study design. We suggest framing light therapies as complex interventions, and emphasise the occupationally embedded (daily activity routine embedded) context in which they occur.

Using quantitative EEG (qEEG) analysis, we investigated sleep EEG microstructure as correlates of neurobehavioural performance after 24 h of extended wakefulness in untreated OSA. Eight male OSA patients underwent overnight polysomnography (PSG) at baseline followed by 40 h awake with repeated performance testing (psychomotor vigilance task [PVT] and AusEd driving simulator). EEG slowing during REM and spindle density during NREM sleep were calculated using power spectral analysis and a spindle detection algorithm at frontal and central electrode sites. Correlations between sleep EEG microstructure measures and performance after 24-h awake were assessed. Greater EEG slowing during REM sleep was associated with slower PVT reaction times (rho = − 0.79, p = 0.02), more PVT lapses (rho = 0.87, p = 0.005) and more AusEd crashes (rho = 0.73, p = 0.04). Decreased spindle density in NREM sleep was also associated with slower PVT reaction times (rho = 0.89, p = 0.007). Traditional PSG measures of disease severity were not consistent correlates of neurobehavioural performance in OSA. Sleep EEG microstructure measures recorded during routine PSG are associated with impaired vigilance in OSA patients after sleep deprivation. Quantitative brain oscillatory (or EEG)–based measures of sleep may better reflect the deleterious effects of untreated OSA than traditional PSG metrics in at-risk individuals. Trial Registration ACTRN12606000066583

Biological rhythms permeate all living organisms at a variety of timescales. These rhythms are fundamental to physiological homeostasis, and their disruption is thought to play a key role in the initiation, progression, and expression of disease. In the last two decades, neuromodulation has been established as an effective adjunct therapy for medically refractory neurological disorders. To date, however, due to the limited sensing and algorithm capabilities of neuromodulation devices, exploring the influence of biological rhythms on therapy efficacy has not been feasible. However, with the development of new bioelectronic devices capable of long-term data recording and adaptive stimulation parameter adjustments, clinical neuroscience researchers are now gaining unprecedented insight into patient physiology across a variety of neurological diseases, including longitudinal rhythmic behavior. In this perspective, we propose that future bioelectronic devices should integrate chronobiological considerations in their physiological control structure to maximize the benefits of therapy. We specifically highlight this need for deep brain stimulation (DBS) chronotherapy, where the DBS therapeutic dosage would be titrated based on the time-of-day and synchronized to each patient’s individual chronotype/sleep-wake cycle. This is motivated by preliminary longitudinal data recorded from both patients with Parkinson’s disease (PD) and epilepsy, which show periodic symptom biomarkers synchronized to sub-daily (ultradian), daily (circadian), and longer time scale (infradian) rhythms. In addition, considering side effects, tonic stimulation can undermine diurnal patterns and cause fragmentation of sleep-wake rhythms. Based on these observations, we suggest a control structure for future bioelectronic devices which incorporates anticipatory, time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical feedforward and feedback control methods. Initial results from three case studies using chronotherapy-enabled prototypes will illustrate the concept. The proposed control architecture for a future bioelectronic implant mimics more closely the classical integration of adaptive, feedforward, and feedback control methods found in physiology, and could be useful as a general method for personalized therapy refinement.

Sleep deficiencies and associated performance decrements are common among astronauts during spaceflight missions. Previously, sleep in space was analyzed with a focus on global measures while the intricate structure of sleep oscillations remains largely unexplored. This study extends previous findings by analyzing how spaceflight affects characteristics of sleep spindles and slow waves, two sleep oscillations associated with sleep quality and quantity, in four astronauts before, during and after two Space Shuttle missions. Analysis of these oscillations revealed significantly increased fast spindle density, elevated slow spindle frequency, and decreased slow wave amplitude in space compared to on Earth. These results reflect sleep characteristics during spaceflight on a finer electrophysiological scale and provide an opportunity for further research on sleep in space.

Several cellular pathways contribute to neurodegenerative tauopathy-related disorders. Microglial activation, a major component of neuroinflammation, is an early pathologic hallmark that correlates with cognitive decline, while the unfolded protein response (UPR) contributes to synaptic pathology. Sleep disturbances are prevalent in tauopathies and may also contribute to disease progression. Few studies have investigated whether manipulations of sleep influence cellular pathologic and behavioral features of tauopathy. We investigated whether trazodone, a licensed antidepressant with hypnotic efficacy in dementia, can reduce disease-related cellular pathways and improve memory and sleep in male rTg4510 mice with a tauopathy-like phenotype. In a 9 week dosing regimen, trazodone decreased microglial NLRP3 inflammasome expression and phosphorylated p38 mitogen-activated protein kinase levels, which correlated with the NLRP3 inflammasome, the UPR effector ATF4, and total tau levels. Trazodone reduced theta oscillations during rapid eye movement (REM) sleep and enhanced REM sleep duration. Olfactory memory transiently improved, and memory performance correlated with REM sleep duration and theta oscillations. These findings on the effects of trazodone on the NLRP3 inflammasome, the unfolded protein response and behavioral hallmarks of dementia warrant further studies on the therapeutic value of sleep-modulating compounds for tauopathies.SIGNIFICANCE STATEMENTDementia and associated behavioral symptoms such as memory loss and sleep disturbance are debilitating. Identifying treatments that alleviate symptoms and concurrently target cellular pathways contributing to disease progression is paramount for the patients and their caregivers. Here we show that a chronic treatment with trazodone, an antidepressant with positive effects on sleep, has beneficial effects on several cellular pathways contributing to neuroinflammation and tau pathology, in tauopathy-like rTg4510 mice. Trazodone also improved rapid eye movement (REM) sleep, the slowing of brain oscillations, and olfactory memory disturbances, which are all early symptoms observed in Alzheimer's disease. Thus, trazodone and compounds with REM sleep-promoting properties may represent a promising treatment approach to reduce the early symptoms of tauopathy and slow down disease progression.

Circadian clocks drive cyclic variations in many aspects of physiology, but some daily variations are evoked by periodic changes in the environment or sleep-wake state and associated behaviors, such as changes in posture, light levels, fasting or eating, rest or activity and social interactions; thus, it is often important to quantify the relative contributions of these factors. Yet, circadian rhythms and these evoked effects cannot be separated under typical 24-h day conditions, because circadian phase and the length of time awake or asleep co-vary. Nathaniel Kleitman's forced desynchrony (FD) protocol was designed to assess endogenous circadian rhythmicity and to separate circadian from evoked components of daily rhythms in multiple parameters. Under FD protocol conditions, light intensity is kept low to minimize its impact on the circadian pacemaker, and participants have sleep-wake state and associated behaviors scheduled to an imposed non-24-h cycle. The period of this imposed cycle, Τ, is chosen so that the circadian pacemaker cannot entrain to it and therefore continues to oscillate at its intrinsic period (τ, ~24.15 h), ensuring circadian components are separated from evoked components of daily rhythms. Here we provide detailed instructions and troubleshooting techniques on how to design, implement and analyze the data from an FD protocol. We provide two procedures: one with general guidance for designing an FD study and another with more precise instructions for replicating one of our previous FD studies. We discuss estimating circadian parameters and quantifying the separate contributions of circadian rhythmicity and the sleep-wake cycle, including statistical analysis procedures and an R package for conducting the non-orthogonal spectral analysis method that enables an accurate estimation of period, amplitude and phase.

BACKGROUNDTwenty-four-hour rhythmicity in transcriptomes of tissues and organs is driven by local circadian oscillators, systemic factors, the central circadian pacemaker, and light-dark cycles. This rhythmicity is to some extent organ- and tissue-specific such that the sets of rhythmic transcripts or their timing are different across tissues/organs. Monitoring rhythmicity of tissues and organs holds promise for circadian medicine, but in humans most tissues and organs are not easily accessible. To investigate the extent to which rhythmicity in the human blood transcriptome reflects rhythmicity in tissues and organs, we compared the overlap and timing of rhythmic transcripts in human blood and rhythmic transcripts in 64 tissues/organs of the baboon.METHODSRhythmicity in the transcriptomes of humans and baboons were compared using set logic, circular cross-correlation, circular clustering, functional enrichment analyses and partial least squares regression.RESULTSOf the 759 orthologous genes that were rhythmic in human blood, 652 (86%) were also rhythmic in at least one baboon tissue. Most of these genes were associated with basic processes such as transcription and protein homeostasis. 109 (17%) of the 652 overlapping rhythmic genes were reported as rhythmic in only one baboon tissue or organ and several of these genes have tissue/organ-specific functions. Analysis of the alignment between baboon and human transcriptomes showed that in these diurnal species, rhythmicity is aligned with the onset, rather than midpoint or end of light period. In both species, the timing of rhythmic transcripts displayed prominent ‘night’ and ‘day’ clusters, with genes in the dark cluster associated with translation. The timing of human and baboon transcriptomes was significantly correlated in 25 tissue/organs with an average earlier timing of 3.21 h (SD 2.47 h) in human blood.CONCLUSIONSThe human blood transcriptome contains sets of rhythmic genes that overlap with rhythmic genes of tissues/organs, some of which are tissue/organ-specific, in the baboon. The rhythmic sets vary across tissues/organs but the timing of most rhythmic genes is similar across human blood and baboon tissues/organs. These results have implications for our understanding of the regulation of rhythmicity across tissues/organs and species and development of blood transcriptome-based biomarkers for rhythmicity in tissues and organs.

Summary Circadian dysregulation causes sleep disturbance and impacts quality of life and functioning. Some interventions target circadian entrainment through modifying light exposure, but existing reviews of light interventions for sleep improvement include few studies in psychiatric populations. We examined effect of light interventions on sleep quality, duration and timing, and effect moderators. We included controlled studies in intrinsic circadian rhythm disorders (such as advanced or delayed sleep) and in neuropsychiatric disorders with assumed high prevalence of circadian dysregulation (such as affective and psychotic disorders). Articles were identified through database searching: 40 studies reporting 49 relevant intervention comparisons met inclusion criteria. Meta-analysis showed improvements in sleep continuity (ES = −0.23, p = 0.000), self-reported sleep disturbance (ES = −0.32, p = 0.014), and advancement of delayed sleep timing (ES = −0.34, p = 0.010). Although the small number of studies limited meta-regression, evening light avoidance was associated with greater increase in total sleep time. Effects of light on sleep and circadian outcomes have received limited attention in studies in psychiatric disorders, but results were promising in these groups. These findings invite further refinement and testing of light interventions to improve sleep in psychiatric disorders, with improved assessment and specification of problems, and the development and implementation of light schedule interventions for delayed sleep.

The high prevalence of sleep disturbance in neurodegenerative and psychiatric conditions is often interpreted as evidence for both sleep's sensitivity to and causal involvement in brain pathology. Nevertheless, how and which aspects of sleep contribute to brain function remains largely unknown. This review provides a critical evaluation of clinical and animal literature describing sleep and circadian disturbances in two distinct conditions and animal models thereof: Alzheimer's disease (AD) and schizophrenia. Its goal is to identify commonalities and distinctiveness of specific aspects of sleep disturbance and their relationship to symptoms across conditions. Despite limited standardisation, data imply that reductions in sleep continuity and alterations in sleep timing are common to AD and schizophrenia, whereas reductions in REM sleep and sleep spindle activity appear more specific to AD and schizophrenia, respectively. Putative mechanisms underlying these alterations are discussed. A standardised neuroscience based quantification of sleep and disease-independent assessment of symptoms in patients and animal models holds promise for furthering the understanding of mechanistic links between sleep and brain function in health and disease.

A recent elegant study published in this journal (Zerbini, Winnebeck & Merrow, J Pineal Res, e12723, 2021) reported data on weekly and seasonal changes in circadian timing, as assessed by the melatonin rhythm in dim light in a population that was exposed to a change from standard time to day light saving time. The authors highlight a one hour earlier timing of melatonin onset in summer compared with winter and a 20 minutes delay on work-free days compared with work days. The variations in the timing of the melatonin rhythm are reported in standard time and the authors imply that the data are consistent with synchronization to midday and that "we know that humans entrain to sun time." Here, we show that their extensive data are most parsimoniously explained by entrainment to local clock time and associated light exposure rather than "sun time," indexed by either dawn or midday.

Study Objectives Assess the validity of a subjective measure of sleepiness as an indicator of sleep drive by quantifying associations between intraindividual variation in evening sleepiness and bedtime, sleep duration, and next morning and subsequent evening sleepiness, in young adults. Methods Sleep timing and sleepiness were assessed in 19 students in late autumn and late spring on a total of 771 days. Karolinska Sleepiness Scales (KSS) were completed at half-hourly intervals at fixed clock times starting 4 h prior to participants’ habitual bedtime, and in the morning. Associations between sleepiness and sleep timing were evaluated by mixed model and nonparametric approaches and simulated with a mathematical model for the homeostatic and circadian regulation of sleepiness. Results Intraindividual variation in evening sleepiness was very large, covering four or five points on the 9-point KSS scale, and was significantly associated with subsequent sleep timing. On average, a one point higher KSS value was followed by 20 min earlier bedtime, which led to 11 min longer sleep, which correlated with lower sleepiness next morning and the following evening. Associations between sleepiness and sleep timing were stronger in early compared to late sleepers. Model simulations indicated that the directions of associations between sleepiness and sleep timing are in accordance with their homeostatic and circadian regulation, even though much of the variance in evening sleepiness and details of its time course remain unexplained by the model. Conclusion Subjective sleepiness is a valid indicator of the drive for sleep which, if acted upon, can reduce insufficient sleep.

"Fondation Recherche Alzheimer (SAO-FRA 2019/0025)"was missing from the acknowledgements. This has been corrected online.

The effects of orexinergic peptides are diverse and are mediated by orexin-1 and orexin-2 receptors. Antagonists that target both receptors have been shown to promote sleep initiation and maintenance. Here, we investigated the role of the orexin-2 receptor in sleep regulation in a randomised, double-blind, placebo-controlled, three-period crossover clinical trial using two doses (20 and 50 mg) of a highly selective orexin-2 receptor antagonist (2-SORA) (JNJ-48816274). We used a phase advance model of sleep disruption where sleep initiation is scheduled in the circadian wake maintenance zone. We assessed objective and subjective sleep parameters, pharmacokinetic profiles and residual effects on cognitive performance in 18 healthy male participants without sleep disorders. The phase advance model alone (placebo condition) resulted in disruption of sleep at the beginning of the sleep period compared to baseline sleep (scheduled at habitual time). Compared to placebo, both doses of JNJ-48816274 significantly increased total sleep time, REM sleep duration and sleep efficiency, and reduced latency to persistent sleep, sleep onset latency, and REM latency. All night EEG spectral power density for both NREM and REM sleep were unaffected by either dose. Participants reported significantly better quality of sleep and feeling more refreshed upon awakening following JNJ-48816274 compared to placebo. No significant residual effects on objective performance measures were observed and the compound was well tolerated. In conclusion, the selective orexin-2 receptor antagonist JNJ-48816274 rapidly induced sleep when sleep was scheduled earlier in the circadian cycle and improved self-reported sleep quality without impact on waking performance.

Sleep and circadian rhythms are considered to be important determinants of mental and physical health. Epidemiological studies have established the contribution of self-reported sleep duration, sleep quality and chronotype to health outcomes. Mental health and sleep problems are more common in women and men are more likely to be evening types. Few studies have compared the relative strength of these contributions and few studies have assessed these contributions separately in men and women. Furthermore, sleep and circadian characteristics are typically assessed with a limited number of instruments and a narrow range of variables is considered, leaving the understanding of the relative contribution of different predictors somewhat fractionary. We compared sleep quality, sleep duration and chronotype as predictors for self-reported mental and physical health and psychological characteristics in 410 men and 261 women aged 18 to 30. To ascertain that results were not dependent on the use of specific instruments we used a multitude of validated instruments including the Morningness-Eveningness-Questionnaire, Munich-ChronoType-Questionnaire, Pittsburgh-Sleep-Quality-Index, British-Sleep-Survey, Karolinska-Sleep-Diary, Insomnia-Severity-Index, SF-36-Health Survey, General-Health-Questionnaire, Dutch-Eating-Behaviour-Questionnaire, Big-Five-Inventory, Behaviour-Inhibition-System-Behaviour-Activation-System, and the Positive-Affect-Negative-Affect-Schedule. Relative contributions of predictors were quantified as local effect sizes derived from multiple regression models. Across all questionnaires, sleep quality was the strongest independent predictor of health and in particular mental health and more so in women than in men. The effect of sleep duration and social jetlag was inconspicuous. A greater insight into the independent contributions of sleep quality and chronotype may aid the understanding of sleep-health interactions in women and men.

Executive summaryThis report describes the principal outcomes of an Impact Acceleration Account project (grant number EP/I000992/1) between the University of Surrey and Transport for London carried out between Oct. 2019 and Mar. 2020.The aim of the project was to compare the Health and Safety Executive (HSE) Fatigue Risk tool with SAFTE and other more recent models of fatigue, where fatigue here primarily means a reduced ability to function effectively and efficiently as a result of inadequate sleep.We have not sought to discuss the useability of the HSE Fatigue Risk tool or SAFTE since this has been discussed comprehensively elsewhere (e.g. [1, 2]). We have instead focussed on the fundamental principles underlying the models.All current biomathematical models have limitations and make asumptions that are not always evident from the accompanying documentation. Since full details of the HSE Fatigue Risk tool and the SAFTE model are not publicly available, Sections 1 and 2 give a mathematical description of the equations that we believe underlie each of these models.A comparison of predictions made by our versions of the HSE and SAFTE equations for one particular shift schedule of relevance to the UK and global tunnelling and construction industries is shown in Section 3. In this comparison, we use data collected durings TfL’s Crossrail project by Dragados1. Essentially, both models give broadly the same message for the schedule we looked at, but the ability to display fatigue as it develops within a shift is a strength of SAFTE.A summary of the strengths and limitations of the use of these kind of scheduling tools is given in the final Section 4. Limitations include:Models do not describe fatigue during times when people are not in shift (e.g. driving home). However, they could readily be extended to do so.Models assume people start well-rested. This is not always a good assumption and can lead to an under-estimate of fatigue.Most models are currently based on population averages, but there are large individual different. It would be possible to further develop models to include uncertainty in fatigue predictions associated with individual differences.Few mdels include the light environment, which is important both to promote short-term alertness and facilitate circadian alignment.Models are not transparent, which makes them hard to independently validate.It is hard to relate the outputs of current models to measureable outcomes in the field.We also discuss briefly recent developments in mathematical modelling of fatigue and possible future directions. These includeGuidance on scheduling and education on sleep and fatigue should be considered at least as important as current biomathematical models.Only by analysing and integrating high quality individual data on sleep, fatigue, performance, near misses, accidents, actual shift patterns with models can we develop better models and management systems to reduce fatigue and associated risks. Wearables combined with apps present a great opportunity to collect data at scale but need to be used appropriately.The importance of making time for sleep is not always recognised. Education, early diagnosis of sleep disorders such as sleep apnea, and self-monitoring all have a role to play in reducing fatigue-related risk in the work-place.Section 3 and Section 4 may be understood without reading the intermediate more mathematical sections.

BackgroundSleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse.MethodsWe used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model.ResultsPoorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1–8 and 1–12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration.ConclusionsReduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1–2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.

Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body posture. In this paper, we present the design and implementation of a non-contact sleep monitoring system that analyses body posture and movement. Supervised machine learning strategies applied to noncontact vision-based infrared camera data using a transfer learning approach, successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8–10 hour overnight sleep episodes representing a realistic domestic sleep situation. The methodology was evaluated against manually scored sleep poses and poses estimated using clinical polysomnography measurement technology. In a cohort of 12 healthy participants, we find that a ResNet-152 pre-trained network achieved the best performance compared with the standard de novo CNN network and other pre-trained networks. The performance of our approach was better than other video-based methods for sleep pose estimation and produced higher performance compared to the clinical standard for pose estimation using a polysomnography position sensor. It can be concluded that infrared video capture coupled with deep learning AI can be successfully used to quantify sleep poses as well as the transitions between poses in realistic nocturnal conditions, and that this non-contact approach provides superior pose estimation compared to currently accepted clinical methods.

Objective/Background The aim of this study was to examine the relationship between overnight consolidation of implicit statistical learning with spindle frequency EEG activity and slow frequency delta power during non-rapid eye movement (NREM) sleep in obstructive sleep apnea (OSA). Patients/Methods Forty-seven OSA participants completed the experiment. Prior to sleep, participants performed a reaction time cover task containing hidden patterns of pictures, about which participants were not informed. After the familiarisation phase, participants underwent overnight polysomnography. 24 h after the familiarisation phase, participants performed a test phase to assess their learning of the hidden patterns, expressed as a percentage of the number of correctly identified patterns. Spindle frequency activity (SFA) and delta power (0.5–4.5 Hz), were quantified from NREM electroencephalography. Associations between statistical learning and sleep EEG, and OSA severity measures were examined. Results SFA in NREM sleep in frontal and central brain regions was positively correlated with statistical learning scores (r = 0.41 to 0.31, p = 0.006 to 0.044). In multiple regression, greater SFA and longer sleep onset latency were significant predictors of better statistical learning performance. Delta power and OSA severity were not significantly correlated with statistical learning. Conclusions These findings suggest spindle activity may serve as a marker of statistical learning capability in OSA. This work provides novel insight into how altered sleep physiology relates to consolidation of implicitly learnt information in patients with moderate to severe OSA.

Study Objectives Sleep restriction (SR) leads to performance decrements across cognitive domains but underlying mechanisms remain largely unknown. The impact of SR on performance in rodents is often assessed using tasks in which food is the reward. Investigating how the drives of hunger and sleep interact to modulate performance may provide insights into mechanisms underlying sleep loss-related performance decrements. Methods Three experiments were conducted in male adult Wistar rats to assess: (1) effects of food restriction on performance in the simple response latency task (SRLT) across the diurnal cycle (n = 30); (2) interaction of food restriction and SR (11 h) on SRLT performance, sleep electroencephalogram, and event-related potentials (ERP) (n = 10–13); and (3) effects of food restriction and SR on progressive ratio (PR) task performance to probe the reward value of food reinforcement (n = 19). Results Food restriction increased premature responding on the SRLT at the end of the light period of the diurnal cycle. SR led to marked impairments in SRLT performance in the ad libitum-fed group, which were absent in the food-restricted group. After SR, food-restricted rats displayed a higher amplitude of cue-evoked ERP components during the SRLT compared with the ad libitum group. SR did not affect PR performance, while food restriction improved performance. Conclusions Hunger may induce a functional resilience to negative effects of sleep loss during subsequent task performance, possibly by maintaining attention to food-related cues.

Study Objectives Sleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease (AD). Our goal was to assess whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset. Methods We computed whole-genome PRS for AD and extensively phenotyped sleep under different sleep conditions, including baseline sleep, recovery sleep following sleep deprivation, and extended sleep opportunity, in a carefully selected homogenous sample of 363 healthy young men (22.1 years ± 2.7) devoid of sleep and cognitive disorders. Results AD PRS was associated with more slow-wave energy, that is, the cumulated power in the 0.5–4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss, and potentially with larger slow-wave sleep rebound following sleep deprivation. Furthermore, higher AD PRS was correlated with higher habitual daytime sleepiness. Conclusions These results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and support the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.

Background Sleep and circadian rhythm disturbances in schizophrenia are common, but incompletely characterized. We aimed to describe and compare the magnitude and heterogeneity of sleep-circadian alterations in remitted schizophrenia and compare them with those in interepisode bipolar disorder. Methods EMBASE, Medline, and PsycINFO were searched for case–control studies reporting actigraphic parameters in remitted schizophrenia or bipolar disorder. Standardized and absolute mean differences between patients and controls were quantified using Hedges’ g, and patient–control differences in variability were quantified using the mean-scaled coefficient of variation ratio (CVR). A wald-type test compared effect sizes between disorders. Results Thirty studies reporting on 967 patients and 803 controls were included. Compared with controls, both schizophrenia and bipolar groups had significantly longer total sleep time (mean difference [minutes] [95% confidence interval {CI}] = 99.9 [66.8, 133.1] and 31.1 [19.3, 42.9], respectively), time in bed (mean difference = 77.8 [13.7, 142.0] and 50.3 [20.3, 80.3]), but also greater sleep latency (16.5 [6.1, 27.0] and 2.6 [0.5, 4.6]) and reduced motor activity (standardized mean difference [95% CI] = −0.86 [−1.22, −0.51] and −0.75 [−1.20, −0.29]). Effect sizes were significantly greater in schizophrenia compared with the bipolar disorder group for total sleep time, sleep latency, and wake after sleep onset. CVR was significantly elevated in both diagnoses for total sleep time, time in bed, and relative amplitude. Conclusions In both disorders, longer overall sleep duration, but also disturbed initiation, continuity, and reduced motor activity were found. Common, modifiable factors may be associated with these sleep-circadian phenotypes and advocate for further development of transdiagnostic interventions that target them.

Study ObjectivesSleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease. Our goal was to assess whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset.MethodsWe computed whole-genome Polygenic Risk Scores (PRS) for AD and extensively phenotyped sleep under different sleep conditions, including baseline sleep, recovery sleep following sleep deprivation and extended sleep opportunity, in a carefully selected homogenous sample of healthy 363 young men (22.1 y ± 2.7) devoid of sleep and cognitive disorders.ResultsAD PRS was associated with more slow wave energy, i.e. the cumulated power in the 0.5-4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss, and potentially with large slow wave sleep rebound following sleep deprivation. Furthermore, higher AD PRS was correlated with higher habitual daytime sleepiness.ConclusionsThese results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.Statement of SignificanceWe show that the genetic liability for developing for Alzheimer’s disease (AD), as grasped over the entire genome using polygenic risk scores (PRS), is associated with sleep intensity and daytime sleepiness in healthy individuals devoid of sleep disorders and aged < 30 y, i.e. 30 to 60 years before typical onset of AD cognitive symptoms. Sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative. The findings reinforce the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.

In this study, a novel sleep pose identification method has been proposed for classifying 12 different sleep postures using a two-step deep learning process. For this purpose, transfer learning as an initial stage retrains a well-known CNN network (VGG-19) to categorise the data into four main pose classes, namely: supine, left, right, and prone. According to the decision made by VGG-19, subsets of the image data are next passed to one of four dedicated sub-class CNNs. As a result, the pose estimation label is further refined from one of four sleep pose labels to one of 12 sleep pose labels. 10 participants contributed for recording infrared (IR) images of 12 pre-defined sleep positions. Participants were covered by a blanket to occlude the original pose and present a more realistic sleep situation. Finally, we have compared our results with (1) the traditional CNN learning from scratch and (2) retrained VGG-19 network in one stage. The average accuracy increased from 74.5% & 78.1% to 85.6% compared with (1) & (2) respectively.

Period3 (Per3) is one of the most robustly rhythmic genes in humans and animals. It plays a significant role in temporal organisation in peripheral tissues. The effects of PER3 variants on many phenotypes have been investigated in targeted and genome-wide studies. PER3 variants, especially the human variable number tandem repeat (VNTR), associate with diurnal preference, mental disorders, non-visual responses to light, brain and cognitive responses to sleep loss/circadian misalignment. Introducing the VNTR into mice alters responses to sleep loss and expression of sleep homeostasis-related genes. Several studies were limited in size and some findings were not replicated. Nevertheless, the data indicate a significant contribution of PER3 to sleep and circadian phenotypes and diseases, which may be connected by common pathways. Thus, PER3-dependent altered light sensitivity could relate to high retinal PER3 expression and may contribute to altered brain response to light, diurnal preference and seasonal mood. Altered cognitive responses during sleep loss/circadian misalignment and changes to slow wave sleep may relate to changes in wake/activity-dependent patterns of hypothalamic gene expression involved in sleep homeostasis and neural network plasticity. Comprehensive characterisation of effects of clock gene variants may provide new insights into the role of circadian processes in health and disease.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them. Fil: Hughes, Michael E.. Washington University School Of Medicine In St. Louis; Estados Unidos Fil: Abruzzi, Katherine C.. Brandeis University; Estados Unidos Fil: Allada, Ravi. Northwestern University; Estados Unidos Fil: Anafi, Ron. University of Pennsylvania; Estados Unidos Fil: Arpat, Alaaddin Bulak. Universite de Lausanne; Suiza Fil: Asher, Gad. Weizmann Institute Of Science Israel; Israel Fil: Baldi, Pierre. University of California at Irvine; Estados Unidos Fil: de Bekker, Charissa. University Of Central Florida; Estados Unidos Fil: Bell Pedersen, Deborah. Texas A&M University; Estados Unidos Fil: Blau, Justin. University of New York; Estados Unidos Fil: Brown, Steve. Universitat Zurich; Suiza Fil: Ceriani, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Chen, Zheng. University Of Texas Health Science Center At Houston; Estados Unidos Fil: Chiu, Joanna C.. University of California at Davis; Estados Unidos Fil: Cox, Juergen. Max Planck Institute Of Biochemistry; Alemania Fil: Crowell, Alexander M.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: DeBruyne, Jason P.. Morehouse School Of Medicine; Estados Unidos Fil: Dijk, Derk-Jan. University Of Surrey; Reino Unido Fil: DiTacchio, Luciano. University Of Kansas Medical Center; Estados Unidos Fil: Doyle, Francis J.. Harvard University; Estados Unidos Fil: Duffield, Giles E.. University of Notre Dame; Estados Unidos Fil: Dunlap, Jay C.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: Eckel Mahan, Kristin. University Of Texas Medical School At Houston; Estados Unidos Fil: Esser, Karyn A.. University Of Florida College Of Medicine; Estados Unidos Fil: FitzGerald, Garret A.. University of Pennsylvania; Estados Unidos Fil: Forger, Daniel B.. University Of Michigan; Estados Unidos Fil: Francey, Lauren J.. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Fu, Ying Hui. University of California; Estados Unidos Fil: Gachon, Frédéric. Nestlé Institute Of Health Sciences; Suiza Fil: Gatfield, David. Universite de Lausanne; Suiza Fil: de Goede, Paul. University Of Amsterdam; Países Bajos Fil: Golden, Susan S.. University of California at San Diego; Estados Unidos Fil: Green, Carla. University of Texas; Estados Unidos Fil: Harer, John. University of Duke; Estados Unidos Fil: Harmer, Stacey. University of California at Davis; Estados Unidos Fil: Haspel, Jeff. Washington University; Estados Unidos Fil: Hastings, Michael H.. The Medical Research Council Laboratory Of Molecular Biology; Reino Unido Fil: Herzel, Hanspeter. Charité Universitätsmedizin Berlin; Alemania Fil: Herzog, Erik D.. Washington University in St. Louis; Estados Unidos Fil: Hoffmann, Christy. Washington University in St. Louis; Estados Unidos Fil: Hong, Christian. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Hughey, Jacob J.. Vanderbilt University School Of Medicine; Estados Unidos Fil: Hurley, Jennifer M.. Rensselaer Polytechnic Institute; Estados Unidos Fil: de la Iglesia, Daniel Horacio. University Of Washington; Estados Unidos Fil: Johnson, Carl. Vanderbilt University; Estados Unidos Fil: Kay, Steve A.. University of California at San Diego; Estados Unidos Fil: Koike, Nobuya. Kyoto Prefectural University Of Medicine; Japón Fil: Kornacker, Karl. Ohio State University; Estados Unidos Fil: Kramer, Achim. Charité Universitätsmedizin Berlin; Alemania Fil: Lamia, Katja. The Scripps Research Institute; Estados Unidos Fil: Leise, Tanya. Amherst College; Estados Unidos Fil: Lewis, Scott A.. Washington University; Estados Unidos Fil: Li, Jiajia. Washington University; Estados Unidos Fil: Li, Xiaodong. Wuhan University; China Fil: Liu, Andrew C.. The University Of Memphis; Estados Unidos Fil: Loros, Jennifer J.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: Martino, Tami A.. University of Guelph; Canadá Fil: Menet, Jerome S.. Texas A&M University; Estados Unidos Fil: Merrow, Martha. Ludwig Maximilians Universitat; Alemania Fil: Millar, Andrew J.. University of Edinburgh; Reino Unido Fil: Mockler, Todd. Donald Danforth Plant Science Center; Estados Unidos Fil: Naef, Felix. Ecole Polytechnique Federale de Lausanne; Suiza Fil: Nagoshi, Emi. Universidad de Ginebra; Suiza Fil: Nitabach, Michael N.. University of Yale. School of Medicine; Estados Unidos Fil: Olmedo, Maria. Universidad de Sevilla; España Fil: Nusinow, Dmitri A.. Donald Danforth Plant Science Center; Estados Unidos Fil: Ptácek, Louis J.. University of California; Estados Unidos Fil: Rand, David. University of Warwick; Reino Unido Fil: Reddy, Akhilesh B.. The Francis Crick Institute; Reino Unido Fil: Robles, Maria S.. Ludwig Maximilians Universitat; Alemania Fil: Roenneberg, Till. Ludwig Maximilians Universitat; Alemania Fil: Rosbash, Michael. Brandeis University; Estados Unidos Fil: Ruben, Marc D.. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Rund, Samuel S.C.. University of Edinburgh; Reino Unido Fil: Sancar, Aziz. University of North Carolina; Estados Unidos Fil: Sassone Corsi, Paolo. University of California at Irvine; Estados Unidos Fil: Sehgal, Amita. University of Pennsylvania; Estados Unidos Fil: Sherrill Mix, Scott. University of Pennsylvania; Estados Unidos Fil: Skene, Debra J.. University Of Surrey; Reino Unido Fil: Storch, Kai Florian. Mcgill University, Douglas Mental Health University Institute; Canadá Fil: Takahashi, Joseph S.. Ut Southwestern Medical Center; Estados Unidos Fil: Ueda, Hiroki R.. The University of Tokyo; Japón Fil: Wang, Han. Soochow University; China Fil: Weitz, Charles. Harvard Medical School; Estados Unidos Fil: Westermark, P. O.. Leibniz Institute For Farm Animal Biology; Alemania Fil: Wijnen, Herman. University of Southampton; Reino Unido Fil: Xu, Ying. Soochow University; China Fil: Wu, Gang. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Yoo, Seung Hee. University of Texas; Estados Unidos Fil: Young, Michael. The Rockefeller University; Estados Unidos Fil: Zhang, Eric Erquan. National Institute Of Biological Sciences, Beijing; China Fil: Zielinski, Tomasz. University of Edinburgh; Reino Unido Fil: Hogenesch, John B.. Cincinnati Children's Hospital Medical Center; Estados Unidos

Common cold sufferers frequently report sleep disruption during the symptomatic period of infections. We examined the effects of treatment with a topical aromatic pharmaceutical ointment (Vicks VapoRub®), on associated sleep disturbances. The effects of Vicks VapoRub® versus placebo (petrolatum ointment) on subjective and objective measured sleep parameters were assessed in an exploratory study of 100 common cold patients, in a randomized, single blind, controlled, two-arm, parallel design study. The primary efficacy variable was subjective sleep quality measured with the SQSQ (Subjective Quality of Sleep Questionnaire). Additional measures included, ease of falling asleep and depth of sleep (measured with a post-sleep Visual Analog Scale), total sleep time, sleep onset latency, activity score, percentage of sleep, sleep efficiency (measured with actigraphy and SQSQ) and sleep quality index measured with a modified Karolinska Sleep Diary (KSD). The primary endpoint, “How was the quality of your sleep last night?” showed a statistically significant difference in change from baseline in favour of VapoRub treatment (p = 0.0392) versus placebo. Positive effects of VapoRub versus placebo were also observed for “How refreshed did you feel upon waking up?” (p = 0.0122) (SQSQ), “Did you get enough sleep?” (p = 0.0036) (KSD), “How was it to get up?” (p = 0.0120) (KSD) and “Do you feel well-rested?” (p = 0.0125) (KSD). No statistically significant changes from baseline versus placebo were detected in the Actiwatch endpoints. Vicks VapoRub® when applied before retiring to bed can reduce subjective sleep disturbances during a common cold. The results of this exploratory study support the belief among patients that the use of VapoRub improves subjective sleep quality during common cold which was associated with more refreshing sleep.

Background Sleep problems are prevalent in schizophrenia spectrum disorders, and include psychophysiological insomnia, and sleep disturbances caused predominantly by circadian dysregulation. Studies using motion sensing wearables (including actigraphy) have shown a diverse range of rest activity patterns, including mis-timed sleep, irregular sleep, or non-24hr (free-running) sleep patterns. Sleep and circadian rhythm disturbances impact negatively on functioning and quality of life, and long term poor sleep is linked to weight gain, and other serious physical health conditions. Many of the factors supposed to cause or maintain poor sleep in this group are modifiable through behavioural and environmental alterations. Methods An expert consensus study (Delphi study) was conducted to examine and explore the views of relevant experts, regarding the appropriate contents and format of an occupational therapy intervention to improve sleep in schizophrenia spectrum disorders. Experts were recruited with expertise in: occupational therapy, sleep, insomnia, circadian rhythm, and schizophrenia spectrum disorders. Experts with clinical and/or research expertise were sought, as well as service users and carers. We conducted three rounds of online surveys, individual interviews, and a day of content presentation, comments and voting, and discussion groups with service users and carers. Quantitative data were analysed using descriptive statistics and presented graphically for comparisons. Qualitative data (survey comments and interviews) was analysed using thematic analysis. Analysis and data collection was iterative; with later rounds exploring views raised during earlier rounds. The results informed the development of the intervention now being tested in a single group feasibility study recruiting twenty participants. Using mixed methods, this study will evaluate acceptability, homework adherence and trial related procedures, informing adjustments prior to larger scale testing. Results The Delphi study recruited and retained participants well (n=82 of target=85, 66% uptake from professionals approached, 97% completion), suggesting a high level of interest in this topic. Participants reached consensus regarding relevant content within the initial assessment, increasing daytime natural light exposure (when possible), approaches to evening routine, and the approach to activity and occupation. Views varied regarding the relative importance of sleep restriction and building ‘sleep drive’, versus reducing arousal such as through relaxation, mindfulness, or cognitive approaches to anxiety. Often service users’ and carers’ views reflected those of professionals, however on some areas views between these groups differed, such as acceptable levels of intervention burden, and regarding wearables. Overall views highlighted the importance of a tailored intervention, with adjustable and selectable elements. The tailoring of recommendations will be achieved through a thorough assessment, including longitudinal use of light and movement sensing wearables, and a detailed initial interview. Many intervention components are optional based on needs, and the homework recommendations from ‘core’ intervention elements will vary depending upon individual needs, preferences, and baseline problems and situation. Discussion The diversity of sleep disturbance presentations within this group poses a unique challenge when developing a brief first-line intervention. Individually tailored recommendations can be derived through the combination of wearable technology and self-report, and a growing body of work informing models of sleep disturbance in this group.

Using quantitative EEG (qEEG) analysis, we investigated sleep EEG microstructure as correlates of neurobehavioural performance after 24 h of extended wakefulness in untreated OSA.Eight male OSA patients underwent overnight polysomnography (PSG) at baseline followed by 40 h awake with repeated performance testing (psychomotor vigilance task [PVT] and AusEd driving simulator). EEG slowing during REM and spindle density during NREM sleep were calculated using power spectral analysis and a spindle detection algorithm at frontal and central electrode sites. Correlations between sleep EEG microstructure measures and performance after 24-h awake were assessed.Greater EEG slowing during REM sleep was associated with slower PVT reaction times (rho = - 0.79, p = 0.02), more PVT lapses (rho = 0.87, p = 0.005) and more AusEd crashes (rho = 0.73, p = 0.04). Decreased spindle density in NREM sleep was also associated with slower PVT reaction times (rho = 0.89, p = 0.007). Traditional PSG measures of disease severity were not consistent correlates of neurobehavioural performance in OSA.Sleep EEG microstructure measures recorded during routine PSG are associated with impaired vigilance in OSA patients after sleep deprivation.Quantitative brain oscillatory (or EEG)-based measures of sleep may better reflect the deleterious effects of untreated OSA than traditional PSG metrics in at-risk individuals. Trial Registration ACTRN12606000066583.

Introduction Disturbances in sleep and circadian rhythm (SCR) are frequently reported prior to and during episodes of relapse in schizophrenia, and may serve as an early marker of deterioration. However, this has never been demonstrated objectively. Novel approaches using mobile technologies are enabling the longitudinal sampling of sleep-circadian rhythms in the real-world. In this preliminary descriptive analysis, we asked whether SCR disruption captured by a remote-monitoring system accompanies symptomatic deterioration in schizophrenia. Methods The Sleepsight study gathered light, geolocation, phone interaction and physical activity parameters from wearable and smartphone sensor-streams, passively, continuously, remotely and in real-time over 12 months, in 36 individuals with schizophrenia. Fluctuations in clinical status were also sampled via a daily smartphone sleep and symptom diary, and relapse events were determined through clinical record review. Results 15 episodes of relapse were identified over the study period, in 12 individuals. Reduction in mean sleep duration was observed to accompany deterioration in 10 episodes, and preceded the onset of significant disturbance of mental state in six of these cases. The longitudinal mean sleep duration for one participant who experienced three relapse episodes over the study period is illustrated in figure 1. Markedly disrupted circadian rhythms including free-running rhythms and relative coordination with weak entrainment were observed in three participants, and were associated with poorer outcomes. Discussion Sleep and circadian rhythm disturbances commonly accompany relapse in schizophrenia, and emerges prior to deterioration in over half of cases. Sleep-circadian dysregulation may therefore be implicated in the pathophysiology of relapse, and serve as a predictive marker of impending deterioration.

In this study, a novel hybrid tensor factorisation and deep learning approach has been proposed and implemented for sleep pose identification and classification of twelve different sleep postures. We have applied tensor factorisation to infrared (IR) images of 10 subjects to extract group-level data patterns, undertake dimensionality reduction and reduce occlusion for IR images. Pre-trained VGG-19 neural network has been used to predict the sleep poses under the blanket. Finally, we compared our results with those without the factorisation stage and with CNN network. Our new pose detection method outperformed the methods solely based on VGG-19 and 4-layer CNN network. The average accuracy for 10 volunteers increased from 78.1% and 75.4% to 86.0%.

Timing of the human sleep-wake cycle is determined by social constraints, biological processes (sleep homeostasis and circadian rhythmicity) and environmental factors, particularly natural and electrical light exposure. To what extent seasonal changes in the light-dark cycle affect sleep timing and how this varies between weekdays and weekends has not been firmly established. We examined sleep and activity patterns during weekdays and weekends in late autumn (standard time, ST) and late spring (daylight saving time, DST), and expressed their timing in relation to three environmental reference points: clock-time, solar noon (SN) which occurs one clock hour later during DST than ST, and the midpoint of accumulated light exposure (50% LE). Observed sleep timing data were compared to simulated data from a mathematical model for the effects of light on the circadian and homeostatic regulation of sleep. A total of 715 days of sleep timing and light exposure were recorded in 19 undergraduates in a repeated-measures observational study. During each three-week assessment, light and activity were monitored, and self-reported bed and wake times were collected. Light exposure was higher in spring than in autumn. 50% LE did not vary across season, but occurred later on weekends compared to weekdays. Relative to clock-time, bedtime, wake-time, mid-sleep, and midpoint of activity were later on weekends but did not differ across seasons. Relative to SN, sleep and activity measures were earlier in spring than in autumn. Relative to 50% LE, only wake-time and mid-sleep were later on weekends, with no seasonal differences. Individual differences in mid-sleep did not correlate with SN but correlated with 50% LE. Individuals with different habitual bedtimes responded similarly to seasonal changes. Model simulations showed that light exposure patterns are sufficient to explain sleep timing in spring but less so in autumn. The findings indicate that during autumn and spring, the timing of sleep associates with actual light exposure rather than sun time as indexed by SN.

Disturbances of the sleep-wake cycle are highly prevalent and diverse. The aetiology of some sleep disorders, such as circadian rhythm sleep-wake disorders, is understood at the conceptual level of the circadian and homeostatic regulation of sleep and in part at a mechanistic level. Other disorders such as insomnia are more difficult to relate to sleep regulatory mechanisms or sleep physiology. To further our understanding of sleep-wake disorders and the potential of novel therapeutics, we discuss recent findings on the neurobiology of sleep regulation and circadian rhythmicity and its relation with the subjective experience of sleep and the quality of wakefulness. Sleep continuity and to some extent REM sleep emerge as determinants of subjective sleep quality and waking performance. The effects of insufficient sleep primarily concern subjective and objective sleepiness as well as vigilant attention, whereas performance on higher cognitive functions appears to be better preserved albeit at the cost of increased effort. We discuss age-related, sex and other trait-like differences in sleep physiology and sleep need and compare the effects of existing pharmacological and non-pharmacological sleep- and wake-promoting treatments. Successful non-pharmacological approaches such as sleep restriction for insomnia and light and melatonin treatment for circadian rhythm sleep disorders target processes such as sleep homeostasis or circadian rhythmicity. Most pharmacological treatments of sleep disorders target specific signalling pathways with no well-established role in either sleep homeostasis or circadian rhythmicity. Pharmacological sleep therapeutics induce changes in sleep structure and the sleep EEG which are specific to the mechanism of action of the drug. Sleep- and wake-promoting therapeutics often induce residual effects on waking performance and sleep, respectively. The need for novel therapeutic approaches continues not at least because of the societal demand to sleep and be awake out of synchrony with the natural light-dark cycle, the high prevalence of sleep-wake disturbances in mental health disorders and in neurodegeneration. Novel approaches, which will provide a more comprehensive description of sleep and allow for large-scale sleep and circadian physiology studies in the home environment, hold promise for continued improvement of therapeutics for disturbances of sleep, circadian rhythms and waking performance.

Local and national governments around the world are currently considering the elimination of the annual switch to and from Daylight Saving Time (DST). As an international organization of scientists dedicated to studying circadian and other biological rhythms, the Society for Research on Biological Rhythms (SRBR) engaged experts in the field to write a Position Paper on the consequences of choosing to live on DST or Standard Time (ST). The authors take the position that, based on comparisons of large populations living in DST or ST or on western versus eastern edges of time zones, the advantages of permanent ST outweigh switching to DST annually or permanently. Four peer reviewers provided expert critiques of the initial submission, and the SRBR Executive Board approved the revised manuscript as a Position Paper to help educate the public in their evaluation of current legislative actions to end DST.

Adequate synchronisation of endogenous circadian rhythms to external time is beneficial for human health [1]. But how circadian time (biological time) and the numbers on the clock (clock time) are related is tricky to understand, as many of us experience when we change from standard time (ST) to daylight saving time (DST) and during jet-lag. How confused we can be is also exemplified by two bills currently making their way through the Californian state legislature. Senate Bill SB-328 Pupil Attendance: School Start Time [2] prohibits middle and high schools from starting earlier than 8:30 in the morning. Senate Bill AB-807 Daylight Saving Time [3] would result in a switch to permanent DST. Similar debates on school start times and DST are happening throughout North America and Europe. Here we explain why a switch to permanent DST could negate any beneficial effects of delaying school start times.

Highlights • A new method to quantify infra slow oscillations (ISO) in sleep EEG was developed. • Binary sleep spindling signal (on-off) shows ISO at peak frequency, Background It has previously been reported that EEG sigma (10–15 Hz) activity during sleep exhibits infraslow oscillations (ISO) with a period of 50 s. However, a detailed analysis of the ISO of individually identified sleep spindles is not available. New method We investigated basic properties of ISO during baseline sleep of 34 healthy young human participants using new and established methods. The analyses focused on fast sleep spindle and sigma activity (13–15 Hz) in NREM stage 2 and slow wave sleep (SWS). To describe ISO in sigma activity we analyzed power of power of the EEG signal. For the study of ISO in sleep spindle activity we applied a new method in which the EEG signal was reduced to a spindle on/off binary square signal. Its spectral properties were contrasted to that of a square signal wherein the same spindles and also the inter spindle intervals were permutated randomly. This approach was validated using surrogate data with imposed ISO modulation. Results We confirm the existence of ISO in sigma activity albeit with a frequency below the previously reported 0.02 Hz. These ISO are most prominent in the high sigma band and over the centro-parieto-occipital regions. A similar modulation is present in spindle activity. ISO in sleep spindles are most prominent in the centro-parieto-occipital regions, left hemisphere and second half of the night independent of the number of spindles. Conclusions The comparison of spectral properties of binary event signals and permutated event signals is effective in detecting slow oscillatory phenomena.

Significance Sleep disturbances are common in stress-related disorders but the nature of these sleep disturbances and how they relate to changes in the stress hormone corticosterone and changes in gene expression remained unknown. Here we demonstrate that in response to chronic mild stress, rapid–eye-movement sleep (REMS), a sleep state involved in emotion regulation and fear conditioning, changed first and more so than any other measured sleep characteristic. Transcriptomic profiles related to REMS continuity and theta oscillations overlapped with those for corticosterone, as well as with predictors for anhedonia, and were enriched for apoptotic pathways. These data highlight the central role of REMS in response to stress and warrant further investigation into REMS’s involvement in stress-related mental health disorders., One of sleep’s putative functions is mediation of adaptation to waking experiences. Chronic stress is a common waking experience; however, which specific aspect of sleep is most responsive, and how sleep changes relate to behavioral disturbances and molecular correlates remain unknown. We quantified sleep, physical, endocrine, and behavioral variables, as well as the brain and blood transcriptome in mice exposed to 9 weeks of unpredictable chronic mild stress (UCMS). Comparing 46 phenotypic variables revealed that rapid–eye-movement sleep (REMS), corticosterone regulation, and coat state were most responsive to UCMS. REMS theta oscillations were enhanced, whereas delta oscillations in non-REMS were unaffected. Transcripts affected by UCMS in the prefrontal cortex, hippocampus, hypothalamus, and blood were associated with inflammatory and immune responses. A machine-learning approach controlling for unspecific UCMS effects identified transcriptomic predictor sets for REMS parameters that were enriched in 193 pathways, including some involved in stem cells, immune response, and apoptosis and survival. Only three pathways were enriched in predictor sets for non-REMS. Transcriptomic predictor sets for variation in REMS continuity and theta activity shared many pathways with corticosterone regulation, in particular pathways implicated in apoptosis and survival, including mitochondrial apoptotic machinery. Predictor sets for REMS and anhedonia shared pathways involved in oxidative stress, cell proliferation, and apoptosis. These data identify REMS as a core and early element of the response to chronic stress, and identify apoptosis and survival pathways as a putative mechanism by which REMS may mediate the response to stressful waking experiences.

Abasolo, Daniel/0000-0002-4268-2885 WOS: 000455753100001 Symbolic dynamic analysis (SDA) methods have been applied to biomedical signals and have been proven efficient in characterising differences in the electroencephalogram (EEG) in various conditions (e.g., epilepsy, Alzheimer's, and Parkinson's diseases). In this study, we investigated the use of SDA on EEGs recorded during sleep. Lempel-Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel-Ziv complexity (PLZC), as well as power spectral analysis based on the fast Fourier transform (FFT), were applied to 8-h sleep EEG recordings in healthy men (n=31) and women (n=29), aged 20-74 years. The results of the SDA methods and FFT analysis were compared and the effects of age and sex were investigated. Surrogate data were used to determine whether the findings with SDA methods truly reflected changes in nonlinear dynamics of the EEG and not merely changes in the power spectrum. The surrogate data analysis showed that LZC merely reflected spectral changes in EEG activity, whereas PE and PLZC reflected genuine changes in the nonlinear dynamics of the EEG. All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub-stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. This study shows that SDA provides additional insights into the dynamics of sleep EEG and how it is affected by ageing. Ministry of Education of the Republic of TurkeyMinistry of National Education - Turkey; Royal Society Wolfson Research Merit AwardRoyal Society of London; Leverhulme TrustLeverhulme Trust [RPG-2014-267]; H. Lundbeck A/S (Valby, Denmark) The authors would like to thank the Ministry of Education of the Republic of Turkey for the higher education bursary scheme which provided funding for the Ph.D. of P.D. Tosun. D.-J. Dijk is supported by a Royal Society Wolfson Research Merit Award. R. Winsky-Sommerer is supported by a Leverhulme Trust research project grant (RPG-2014-267). The authors would also like to thank Mr. Giuseppe Atzori from the Clinical Research Centre for his advice and support with the dataset. The study for this dataset was funded by H. Lundbeck A/S (Valby, Denmark) and conducted at Surrey Sleep Research Centre (SSRC) and granted to Prof. Derk-Jan Dijk.

Disturbances of the sleep-wake cycle are highly prevalent and diverse. The aetiology of some sleep disorders, such as circadian rhythm sleep-wake disorders, is understood at the conceptual level of the circadian and homeostatic regulation of sleep and in part at a mechanistic level. Other disorders such as insomnia are more difficult to relate to sleep regulatory mechanisms or sleep physiology. To further our understanding of sleep-wake disorders and the potential of novel therapeutics, we discuss recent findings on the neurobiology of sleep regulation and circadian rhythmicity and its relation with the subjective experience of sleep and the quality of wakefulness. Sleep continuity and to some extent REM sleep emerge as determinants of subjective sleep quality and waking performance. The effects of insufficient sleep primarily concern subjective and objective sleepiness as well as vigilant attention, whereas performance on higher cognitive functions appears to be better preserved albeit at the cost of increased effort. We discuss age-related, sex and other trait-like differences in sleep physiology and sleep need and compare the effects of existing pharmacological and non-pharmacological sleep- and wake-promoting treatments. Successful non-pharmacological approaches such as sleep restriction for insomnia and light and melatonin treatment for circadian rhythm sleep disorders target processes such as sleep homeostasis or circadian rhythmicity. Most pharmacological treatments of sleep disorders target specific signalling pathways with no well-established role in either sleep homeostasis or circadian rhythmicity. Pharmacological sleep therapeutics induce changes in sleep structure and the sleep EEG which are specific to the mechanism of action of the drug. Sleep- and wake-promoting therapeutics often induce residual effects on waking performance and sleep, respectively. The need for novel therapeutic approaches continues not at least because of the societal demand to sleep and be awake out of synchrony with the natural light-dark cycle, the high prevalence of sleep-wake disturbances in mental health disorders and in neurodegeneration. Novel approaches, which will provide a more comprehensive description of sleep and allow for large-scale sleep and circadian physiology studies in the home environment, hold promise for continued improvement of therapeutics for disturbances of sleep, circadian rhythms and waking performance.

Daily variations in the environment have shaped life on Earth, with circadian cycles identified in most living organisms. Likewise, seasons correspond to annual environmental fluctuations to which organisms have adapted. However, little is known about seasonal variations in human brain physiology. We investigated annual rhythms of brain activity in a cross-sectional study of healthy young participants. They were maintained in an environment free of seasonal cues for 4.5 d, after which brain responses were assessed using functional magnetic resonance imaging (fMRI) while they performed two different cognitive tasks. Brain responses to both tasks varied significantly across seasons, but the phase of these annual rhythms was strikingly different, speaking for a complex impact of season on human brain function. For the sustained attention task, the maximum and minimum responses were located around summer and winter solstices, respectively, whereas for the working memory task, maximum and minimum responses were observed around autumn and spring equinoxes. These findings reveal previously unappreciated process-specific seasonality in human cognitive brain function that could contribute to intraindividual cognitive changes at specific times of year and changes in affective control in vulnerable populations.