Journal of Sleep Research

Sleep and nutrition are cornerstones of health, with likely reciprocal links between the two. The bidirectional link between sleep and food intake has been extensively studied, however, this literature relies almost completely on interventional and epidemiological studies. In the current study, we studied this link under naturalistic conditions, using data from a large sample of healthy participants. We investigated the bidirectional, time-lagged, within-person covariation between sleep, recorded by mobile EEGs and morning questionnaires; and food intake, recorded by retrospective evening questionnaires about the timing, content and satiation of previous-day meals. The findings supported links between sleep and food intake in both temporal directions. Later timing of the last meal was associated with later sleep onset, while a longer fasting period between the last meal and sleep was associated with reduced total sleep time, and reduced sleep efficiency. Sleep efficiency was higher following higher total daily energy intake. Later sleep was associated with a shift of next-day food intake to later hours. Overall, this naturalistic study suggests that the bidirectional relationship between sleep and food intake is primarily related to timing, not quality or composition. Contrary to some recommendations, we found no negative effect of reduced sleep quality after large, late, or satiating last meals.

Study Objectives. Sleep characteristics are often estimated using self-reports, which may differ from objective measurements, a phenomenon known as sleep discrepancy. However, the factors associated with the degree of sleep discrepancy remain poorly understood. Methods. In the current study, a large healthy participant sample of the Budapest Sleep, Experiences and Traits Study (total N=267, 1899 nights) completed a 7-day protocol including mobile EEG recordings and sleep diaries, and also provided questionnaire-based reports of habitual sleep. We compared analogous sleep metrics from these three modalities, and used cross-validated LASSO regression to investigate demographic, psychological and lifestyle-related factors associated with increased sleep discrepancy across all three modalities, at both between- and within-participant levels. Results. Daily diaries estimated EEG-based sleep timing accurately (mean r=0.83), but were less accurate for sleep onset latency and quality. In contrast, questionnaire reports of habitual sleep provided inaccurate measures of even sleep timing (mean r=0.49) and considerably misestimated sleep timing and duration. Insomnia and depressive symptoms, napping, co-sleeping and personality traits were associated with increased sleep discrepancy. Conclusion. In healthy adults, questionnaires about habitual sleep provide only moderately accurate and biased estimates of actual sleep. Daily diaries provide considerably more accurate estimates, but sleep onset latency and physiological sleep quality is estimated by all self-reports less accurately than sleep timing. Sleep discrepancy is also present in healthy participants, it is particularly and its degree is affected by non-pathological characteristics. Long-term monitoring by daily diaries or wearables should be preferred to self-report questionnaires to measure sleep.

Skin temperature and bed microclimate are established modulators of human sleep, but most prior work has relied on passive thermal materials. We used a smart bed capable of heating or cooling the microclimate to investigate how six temperature settings affect sleep architecture, autonomic activity, and electroencephalographic (EEG) activity. Eighteen adults (9 male, 9 female; mean age 44.5 years) completed two overnight polysomnography sessions with bed temperature assigned via Latin-square counterbalancing across three-hour segments of high cooling, medium cooling, low cooling, off, low heating, and medium heating. Mixed-effects regression models evaluated manipulation checks on induced thermal changes, the probability of sleep versus wake, and the probability of each sleep stage (N1, N2, N3, REM), while secondary analyses assessed sleep deepening, heart rate, and EEG spectral power. Temperature manipulations significantly affected sleep and physiological outcomes. All five active settings shifted sleep toward greater depth, increasing the odds of slow-wave sleep (odds ratios 1.21 to 3.65, largest for high cooling) and reducing the odds of light non-rapid eye movement sleep relative to off. Heating accelerated early sleep deepening relative to cooling without significantly altering sleep onset latency, whereas cooling produced a graded reduction in heart rate. EEG analyses revealed distinct cortical signatures for heating and cooling conditions. These findings demonstrate that active bed microclimate regulation modulates sleep architecture, autonomic activity, and cortical activity during sleep. The nonlinear pattern of effects suggests thermal "sweet spots" that may vary across the night and individuals, supporting the development of individualized programmable temperature-regulation strategies for sleep optimization.

Study Objectives: Glucagon-like peptide-1 (GLP-1) receptor agonists raise resting heart rate. We asked whether, after semaglutide initiation, the increase is larger during rapid eye movement (REM) sleep than during non-rapid eye movement (NREM) sleep. Methods: In the Human Phenotype Project, an Israeli cohort of generally healthy adults aged 40 to 70 years, 96 of 494 self-reported semaglutide initiators had a home sleep study (WatchPAT; sleep stages estimated from peripheral arterial tone and actigraphy) before and at least three months after starting. Each was propensity-matched 1:1 to a never-user of GLP-1 drugs measured at the same visits. We estimated the initiators' change minus their controls' change (difference-in-differences). The REM minus NREM contrast was designated primary after an exploratory analysis. Results: Relative to controls (90 to 94 pairs), heart rate rose during nocturnal wake (+3.89 beats per minute [bpm], 95% confidence interval 2.35 to 5.44), NREM sleep (+4.82 bpm, 3.12 to 6.52) and REM sleep (+6.25 bpm, 4.42 to 7.91); the REM increase exceeded the NREM increase by 1.42 bpm (0.98 to 1.89). The excess persisted across matching designs and on a relative scale; the proportion of REM sleep did not change. Never-users with similar weight loss showed no REM sleep heart-rate rise (+0.40 bpm, -0.63 to 1.50). Heart-rate variability decreases disappeared after adjustment for heart rate. Conclusions: Semaglutide initiation was associated with higher heart rate throughout sleep, most during REM sleep, unexplained by weight loss. Clinic measurements may understate this effect. Confirmation with polysomnographic staging is needed.

Background: Sleep-wake-circadian disorders are prevalent and contribute substantially to morbidity, mortality and reduced quality of life. Evaluation in real-world settings is essential to capture their complexity and association with long-term outcomes. Most sleep-wake registries are restricted to selected populations, lack systematic integration of objective sleep-wake-circadian assessments with the clinical data and provide limited follow-up. To address these limitations, we established the Bern Sleep-Wake Registry based on real-world data from a tertiary University Hospital sleep center. Methods: The registry includes data from patients referred to the multidisciplinary Sleep- Center at Inselspital, University Hospital Bern (funded in 1990), from the year 2000 onwards who underwent at least one objective assessment such as polysomnography, daytime sleep-wake recordings, vigilance tests, and actigraphy. The registry integrates clinical information, questionnaire scores, outputs of objective assessments and raw signal data, and is regularly updated to incorporate new patients and longitudinal follow-up information. Results: As of September 2025, records from over 11000 patients were evaluated (median age: 50 years (interquantile range 36, 61); 65% male). The most common sleep-wake-circadian disorders were sleep-related breathing disorders (67%), insomnia disorders (15%) and central disorders of hypersomnolence (9%). Available assessments included more than 12000 polysomnographies, 2400 multiple sleep latency tests, 2100 maintenance of wakefulness tests, 1800 vigilance tests and 4500 actigraphies. Conclusions: By longitudinally integrating multimodal deep clinical phenotyping with objective assessments and raw physiological signals, the Bern Sleep-Wake Registry provides a foundation for investigating disease trajectories, identifying novel biomarkers and developing data-driven approaches to precise diagnosis, prognosis and individualized treatment.

Targeted memory reactivation (TMR) during sleep can benefit recently acquired memories, but whether it can also influence memories after an initial period of consolidation remains unclear. Here, we tested whether auditory reactivation during non-rapid eye movement (NREM) sleep could strengthen declarative memories learned 24 h earlier. Twenty-six healthy young adults learned 30 sound word associations and returned the following day for a 90 min nap. During NREM sleep, participants in the Reactivation group received incomplete reminders consisting of the learned sound followed by the first syllable of the associated word, whereas the No-Reactivation group slept under the same conditions without memory related cues. Participants who received reminders showed significantly less forgetting, despite comparable training performance and sleep macroarchitecture. Across NREM sleep, reactivation was associated with greater slow oscillation and delta power, more slow oscillations and fast spindles, and greater slow oscillation spindle cooccurrence. The memory benefit remained significant after adjusting for NREM physiological measures and in sensitivity analyses restricted to overlapping physiological ranges between groups. Cue locked analyses revealed significant responses in the slow oscillation, delta, theta, and fast-spindle ranges, but the magnitude of these responses was not associated with memory change. These findings show that TMR during NREM sleep can benefit declarative memories after a 24 h consolidation interval and suggest that its effects extend beyond the immediate post-learning sleep period.

Alexithymia is the inability to identify and describe emotions, associated with an increased risk of somatic and psychiatric disease. Alexithymia is also characterized by reduced subjective sleep quality. However, it is not well-known if this reduction in sleep quality reflects physiological changes in sleep, a specific alteration in perceived sleep quality not captured by objective metrics, or a non-specific spillover of the negative affective style which characterizes alexithymia. In BSETS, a large (N=228) EEG study of naturalistic sleep, we found that alexithymia symptoms are associated with worse habitual (AIS) and current (GSQS) subjective sleep quality ({beta}AIS=0.207, pAIS=0.002, {beta}GSQS=0.203, pGSQS=0.002). However, we observed no significant association between alexithymia and EEG-based sleep efficiency ({beta}=- 0.017, p=0.791). The association with subjective sleep quality was almost completely attenuated when controlling for either depressive symptoms (PHQ-9, {beta}AIS=0.001, pAIS=0.982, {beta}GSQS=0.044, pGSQS=0.486) or trait neuroticism (ZKPQ, BFI-44, {beta}AIS=0.057, pAIS=0.442, {beta}GSQS=0.048, pGSQS=0.514). A possible exception is the Difficulty Identifying Feelings alexithymia dimension, reflecting complaints about the labeling of interoceptive experiences, which was associated with lower subjective sleep quality even after covariate control. This pattern of findings suggests that alexithymia is characterized only by alterations of subjective sleep quality, and even these reflect a non-specific increase in negative affect which influences self-reports of sleep quality, rather than a specific reduction in perceived sleep quality itself.

Temperature-controlled sleep surfaces are used nightly by hundreds of thousands of people. Despite mass adoption, subjective reports of sleep quality have not yet been substantiated with objective measurements; whether differences in individual baseline sleep contribute to overall efficacy remains unknown. Cohort means aggregate individuals with qualitatively different sleep deficits, and a benefit concentrated in the impaired can vanish when averaged against those already sleeping well. In an open-label study of 171 adults, we examined wearable-derived sleep and overnight cardiovascular endpoints during programmed change of a thermal-controlled sleep surface across seven baseline and seven thermally regulated nights. Participants were classified on baseline performance, separately for each endpoint, rather than averaged as a single group. Cohort-wide, total sleep time increased 7.3 minutes (95% CI 1.7 to 12.8, p = 0.010), average nocturnal heart rate fell 1.79 bpm and minimum heart rate 1.75 bpm (both p < 0.001), and heart rate variability rose 3.25 ms (p = 0.001). Stratification revealed a baseline-dependent pattern. The most-deficient quartile gained 9.6% in total sleep time, 19.4% in REM, 13.1% in deep sleep, and 15.6% in HRV, all p < 0.001, and wake after sleep onset fell 26% in the most fragmented quartile (-16.3 minutes, p < 0.001). Objective gains were largest where the baseline deficit was greatest. Controlled trials are needed to validate these findings.

Background: Shiftwork disrupts natural sleep-wake cycles, alters light exposure patterns, and contributes to circadian misalignment. Detrimental health consequences associated with shift work include elevated risk for metabolic disorders, cardiovascular disease, cancer and all-cause mortality. Healthcare workers have one of the highest rates of shift work exposure, yet there are relatively few non-pharmacological interventions (with good evidence) developed to improve sleep outcomes in this population. Objective: A pre-post pilot interventional study assessed the acceptability and perceived effectiveness of commercial noise-masking earbuds on improving subjective sleep characteristics among National Health Service (NHS) healthcare staff working fast rotating shifts. Methods: Noise-masking sleep earbuds (Kokoon NightBuds) were worn for a pilot six-week intervention by twenty-seven NHS nurses (aged 26-43 years, 88.9% female) working fast rotating shifts from the EClocker Study. Sensors inside the earbuds were paired with a smartphone app to monitor sleep. An audio library in the smartphone app delivered personalised relaxation exercises and sleep techniques drawn from cognitive behavioural therapy for insomnia (CBT-I). A pre-post two-week monitoring period with daily smartphone-based Experience Sampling Methods (ESM) captured perceived daily sleep patterns. Acceptability and perceived effectiveness of the earbuds in promoting better sleep outcomes was assessed. Results: Use of the noise-masking sleep earbuds over a six-week period was associated with positive sleep improvement trends and elicited promising acceptability. Almost two thirds of NHS fast rotating shift nurses (63%) subjectively reported reductions in general sleep disturbance symptoms (PSQI Global), one in four experienced perceived sleep quality improvements (SQ; 25.9%), one in five reported sleeping longer (TST; 22.2%), and a third perceived falling asleep faster (SOL; 33.3%), had better sleep efficiency (SE; 33.3%) and improved daytime dysfunction (33.3%) (PSQI subcomponent scores). Sleep diaries (CSD) collected daily using smartphone-based ESM also demonstrated small improvements post-sleep earbud use; nurses reported sleeping an average 18 minutes longer (TST) and fell asleep more easily, on average 11 minutes faster (SOL). Sleep earbuds were generally well tolerated; 56% of nurses reported the earbuds as (somewhat to very) helpful, 52% reported (somewhat to strongly) falling asleep more easily (SOL), 44% felt (somewhat to strongly) their sleep quality was improved (SQ) and 30% agreed (somewhat to strongly) they slept longer (TST) and had less disturbed sleep. Conclusions: To our knowledge, this is the first study in Europe to pilot noise-masking earbuds as a potential non-pharmacological aid to improve sleep-wake behaviours or mitigate fatigue for healthcare staff. Preliminary results showed promising acceptability and (small) perceived sleep improvement trends following a targeted six-week earbud intervention in NHS fast rotating shift nurses.

Background: Sleep architecture fragmentation is common in Long COVID and dysautonomia, yet the relationship between block-level deep sleep consolidation and next-day functional wellbeing has not been characterized longitudinally in this population. We tested the hypothesis that block-level deep sleep architecture predicts next-day wellness better than aggregate stage duration. Methods: A prospective N-of-1 longitudinal study was conducted across 78 nights between January 1 and March 25, 2026 using a consumer wearable (Garmin Index Sleep Monitor). Next-day wellbeing was assessed using the Overall Feeling Score (OFS; 0-10 scale). A five-tier Architecture Group (AG) classification operationalized consolidation quality. A custom predictive model (Pendulum v5.2) quantified consolidation through block-level weighting and contextual penalties. Space weather metrics (Kp index, daily electron fluence, Load Score) were correlated with sleep architecture and OFS across same-day and lagged alignments. A secondary hierarchical regression examined illness as an independent covariate across the full dataset. Results: Architecture Group explained 56.7% of next-day OFS variance (r = 0.753, p < 0.001), compared with 17.0% for total deep sleep duration (r = 0.412) and 4.8% for the Garmin Sleep Score (r = 0.220). Pendulum v5.2 explained 43.0% of variance (r = 0.656). The deep sleep fragmentation phenotype occurred on 25.6% of nights despite adequate total deep sleep. Space weather explained 14.2% of OFS variance through architecture degradation; Load Score at 1-day lag was the strongest space weather predictor (r = -0.328, p = 0.004). A binary illness indicator explained an additional 5.7% of OFS variance beyond architecture and space weather (full-model R{superscript 2} = 55.2%), with a mean illness-night residual of -0.56 (SD = 0.36; 95% CI [-0.72, -0.40]), consistent with immune/viral flares reducing OFS through pathways independent of sleep architecture. Conclusions: Block-level deep sleep consolidation quality is a substantially stronger predictor of next-day wellbeing than total stage duration in Long COVID with dysautonomia. Space weather constitutes a measurable environmental modifier operating through architecture degradation. Immune and viral flares constitute a significant architecture-independent confound consistent with direct neuroinflammatory effects on functional capacity. This framework may be applicable across conditions where sleep architecture fragmentation plays a pathophysiological role.

Background: Sleep spindles are fundamental for plasticity and memory consolidation. Here we sought to target different spindle states in sleeping healthy participants with real-time EEG-burst repetitive transcranial magnetic stimulation (rTMS) at hippocampal ripple frequency, and test the spindle state-dependent induction of corticospinal and sensorimotor cortical plasticity. We hypothesized that the spindle-trough is a particularly critical state for plasticity induction because hippocampal ripples are naturally nested in the spindle-trough, reflecting replay of memory traces and facilitating memory consolidation. Methods: Fourteen participants underwent four experimental nights, in which rTMS was applied either at the spindle-trough, spindle-peak, spindle random phase or during spindle-free epochs. Readouts of plasticity were changes in resting-state EEG (rsEEG) power, motor evoked potential (MEP) amplitude, local mean field amplitude (LMFA, for the N45 and P60 potential components), immediate response slope (IRS) and TMS-related time frequency response (TFR), tested 10 and 30 min after the end of the rTMS interventions upon awakening, and compared to pre-sleep baseline. Results: Spindle-trough rTMS resulted in pre- to post-sleep decreases of rsEEG beta-band power, MEP amplitude, P60-LMFA, IRS and TFR in the alpha-band, and an increase in the N45-LMFA. None of the other spindle state-dependent rTMS interventions resulted in consistent plastic changes. Conclusions: Targeting the spindle-trough with ripple-burst rTMS stands out in consistently leading to long-term depression-like changes across a broad array of corticospinal and sensorimotor cortical excitability readouts. This opens the intriguing opportunity of targeted manipulation of human sleep physiology for improving specific behavioral processes, such as memory consolidation.

Understanding how sleep changes across development requires methods that are both efficient and sensitive to maturational differences in sleep physiology. Spectral sleep scoring is a promising approach, but has primarily been validated in adult populations. This study presents and validates a spectral scoring approach for pediatric sleep. Participants (n = 192; 9 to 18y) completed overnight in-lab sleep studies. Visual scoring was determined by registered polysomnographic technologists and confirmed by board certified sleep physicians following AASM guidelines. Visual stages included: wake, non-rapid eye movement (NREM) stage 1 (N1), NREM stage 2 (N2), NREM stage 3 (N3), and rapid-eye movement (REM) sleep. Spectral activity within each epoch was assessed for the following spectral stages: Wake (40 to 95 Hz), Light (11 to 15.5 Hz), Hi Deep (1 to 3 Hz), Lo Deep (0.1 to 1 Hz), and REM (17 to 26 Hz). Concordance rates were checked across agreement matrices between visual and spectral stages, with expected high agreement between: 1) visual Wake and spectral Wake; 2) visual N2 and spectral Light; 3) visual N3 and spectral HiDeep/LoDeep; and 4) visual REM and spectral REM. Cohens {kappa} was calculated from concordance matrices. There was high agreement between visual and spectral stages (71-89%, {kappa} =.76). Our results provide evidence that spectral scoring is a promising method for efficiently scoring pediatric sleep. Moreover, this method provides additional, clinically relevant information that is not readily captured through traditional visual scoring. This may be particularly valuable for assessing sleep physiology in pediatric clinical populations, where developmental differences in EEG patterns may complicate visual scoring.

Background Sleep, mood, and affective states are mutually connected. There is a paucity of studies, however, that have considered bidirectional relationships between daily sleep-affective dyads in naturalistic settings, particularly for shift workers. Objective To evaluate the dynamic and temporal interplay of daily smartphone-based self-reported sleep measurements, dimensions of affective experience and cognitive processing in UK shift working nurses. Methods The EClocker Study prospectively monitored 102 National Health Service (NHS) nurses (aged 25-61 years, 83.3% female) working standard (day shift) and non-standard (fast rotating shifts) schedules over a two-week period. Smartphone-based Experience Sampling Methodology (ESM) recorded daily sleep, mood, momentary affect and cognitive attentional functioning. Self-reported burnout, emotional dysregulation, emotion reactivity and affective dimensions (positive and negative) were also collected. Findings Overall, NHS nurses reported a high prevalence of depressive symptoms, stress, burnout and sleep-circadian rhythm disturbances. Generalised Additive Modelling (GAMs) revealed that NHS nurses higher perceived sleep quality predicted better next-day mood state, while better daytime mood was associated with reduced sleep onset latency, such that participants reported falling asleep faster. In contrast, daytime mood or affect (positive and negative) had no substantial, direct impact on nurses subjective sleep parameters (sleep quality, sleep duration, sleep efficiency). Exposure to fast rotating night shifts across the two-week study was associated with more frequent response errors on a Choice Reaction Time (CRT) cognitive task, while daytime somnolence did not adversely influence nurses momentary reaction time speeds or attentional function. Conclusions Clinically relevant sleep impairments, insomnia-related symptoms, elevated stress, and poor mood were pervasive in a sample of UK NHS nurses, regardless of shift type. Sleep quality impacted next-day mood and daytime mood impacted sleep latency, while rotating shifts led to an increase in cognitive errors. Recognising the impact of shiftwork and designing interventions to promote better sleep quality offer potential to enhance mood and performance in healthcare professionals. Clinical implications We need to implement and evaluate interventions that regularise sleep patterns and promote sleep quality to alleviate mood symptoms among frontline NHS shift workers.

For sleep scoring in rodents, the use of electromyography (EMG) is considered the gold standard to assess muscle tone; however, EMG electrodes require invasive surgical implantation, are sensitive to muscular placement, can have extensive noise due to non-muscular signal contamination, and are at risk of failure due to mechanical stress. The use of an accelerometer (Acc) integrated on the recording headstage offers an alternative for movement detection. However, the degree of agreement between these two approaches has never been systematically evaluated. To understand if these tools allow similar scoring outcomes, seven trained scorers manually classified mouse sleep recordings using hippocampal local field potential (LFP) together with EMG or Acc. When comparing scoring between EMG and Acc, overall epoch-by-epoch accuracy exceeded 90% with a kappa score above 0.8, indicating excellent agreement. However, Acc was found to under classify Wake by 4.92% while overclassifying NREM sleep by 3.29%, possibly reflecting a decreased sensitivity of Acc to subtle changes in muscle tone used in distinguishing Wake from NREM sleep. Comparing EMG and Acc signal for agreed epochs vs disagreed epochs revealed a weaker correlation between the two signals for disagreed epochs, especially for Wake and NREM, suggesting that disagreements reflected genuine signal differences rather than scorer subjectivity. These findings support LFP/Acc as a valid approach to sleep scoring, however, it is noted that Acc based scoring slightly underestimated Wake epochs, favoring NREM sleep, relative to traditional EMG approach.

Study ObjectivesVigilance-state transitions are continuous biological processes, yet conventional rodent sleep scoring relies on discrete epochs that obscure intermediate states. As no standardized framework exists for characterizing these intermediate states in rodents, this study aimed to characterize the temporal dynamics of transitions in mice and validate a machine-learning approach for objective detection. MethodsChronic EEG and EMG recordings were obtained from male C57BL/6N mice. We extracted 56-second windows containing stable transitions between Wakefulness (WAKE), Non-Rapid Eye Movement Sleep (NREMS), and Rapid Eye Movement Sleep (REMS). Eight trained experts manually annotated the onset and duration of transitions to establish ground truth and assess inter-rater reliability. Using quantitative EEG/EMG features (e.g., spectral power, complexity, EMG variance) derived from stable states, Support Vector Machine (SVM) classifiers were trained to predict transition midpoints in independent test animals. ResultsInter-rater agreement among experts was moderate to low, particularly for WAKE to NREMS and NREMS to REMS transitions, reflecting inherent ambiguity in manual scoring. Temporal analysis revealed distinct dynamics across transition types; NREMS to REMS transitions were significantly longer than all others, while REMS to NREMS transitions were the most abrupt. Despite the variability in human scoring, SVM models trained only on stable-state features successfully predicted expert-defined transition midpoints. ConclusionsOur approach not only characterized the recognizable dynamics across transition types in mice, but also provides a reproducible framework for quantifying sleep-wake transitions, which is crucial for studying arousal stability and related impairments in disease. Statement of SignificanceTraditional sleep scoring enforces discrete boundaries between vigilance states, overlooking transitional dynamics that may be critical for understanding arousal regulation. We developed a novel hypnodensity-based framework to systematically identify and characterize intermediate vigilance states in mice using EEG-EMG recordings. By combining expert annotations with machine learning, we revealed that transitions between sleep and wake involve continuous processes with mixed state features, rather than instantaneous switches. This approach provides the first standardized method for quantifying transitional vigilance states in rodents, enabling deeper investigation of arousal instability in neurological disorders. Our framework advances automated sleep analysis beyond classical three-state classification

Sleep expression in wild animals is regulated by their ecological context. Yet, how populations of a species differ in their sleep in response to environmental challenges such as urbanization, remains to be fully understood. Robust quantification of sleep in small animals, such as lizards, has so far been methodologically difficult, limiting population-level assessments. Using recently developed miniature loggers, we recorded sleep in 19 wild-caught individuals of the Peninsular rock agama lizard (Psammophilus dorsalis). We compared individuals from urban and rural environments and measured electro-oculogram (EOG)-derived sleep parameters: sleep duration, bout frequency, bout duration, and interval between bouts. We then subjected all individuals to an acute stressor (handling-restraint) and examined changes in sleep expression. We found that this species slept mostly at night, with an average total sleep time of 11h 10min. EOG-derived sleep characteristics did not differ substantially between populations, although daytime sleep bouts were more consolidated (shorter interval between bouts) in urban lizards. Contrary to evidence from mammals, acute restraint stress did not impact sleep, overall or for either population. To complement these measures, we also measured the latency to arousal after exposure to vibration stimuli in a separate set of urban and rural lizards (n = 12 each), and found that urban individuals responded marginally quicker at night compared rural individuals, demonstrating greater vigilance and/or more fragmented sleep. Overall, we show that sleep characteristics of wild animals in a common garden condition can be conserved within a species, with limited context-dependent variation between populations.

Sleep is critical for memory consolidation and emotional regulation, yet the respective roles of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep remain unclear. Here, using a within-subject crossover design, we recorded high-density electroencephalography (EEG) across two experimental nights while participants viewed neutral or aversive film clips in a counterbalanced order. Combining with daytime functional localizers establishing neural patterns of aversive vs. neutral emotional processing, multivariate pattern analysis revealed that the reactivation of aversive vs. neutral memory during nocturnal sleep was both stage-dependent and event-specific. In NREM sleep, valence-specific reactivation was time-locked to slow oscillation (SO)-spindle complexes but not to either event alone; in REM sleep, reactivation occurred selectively during phasic REM periods marked by rapid eye movements. Critically, NREM SO-spindle coupling percentage was associated with consolidation of temporal memories; whereas phasic REM reactivation strength was linked to overnight dissipation of negative affect. Our findings provide direct evidence that sleep reprocesses emotional experiences through dissociable stage- and event-specific mechanisms, laying out a framework for future targeted sleep-based interventions.

The prevalence of shift work-centric industries and the rise of flexible working render it crucial to investigate the health risks posed by unnatural patterns of work and sleep. Shift working in healthcare has been linked to emotional dysregulation, reduced alertness, and cognitive impacts, with Shift Work Disorder (SWD) classified as a circadian rhythm sleep-wake disorder in the DSM-5. These impacts have historically been attributed to schedule-related sleep disturbances; however certain individuals appear more acutely affected. One measure of individual inflexibility to altered routines is chronotype distinctness, an amplitude dimension of the Caen Chronotype Questionnaire (CCQ). Here, we use Structural Equation Modelling (SEM) to unravel the mechanisms by which sleep and chronotype distinctness govern the neuropsychiatric symptom profiles of a cohort of National Health Service (NHS) shift workers (n=102). We constructed a measurement model, extracting latent constructs from questionnaires for sleep disturbances (PSQI), chronotype distinctness (CCQ), mood disorder (MDQ), depression (PHQ8) and emotional reactivity (ERS), and cognitive assessments. Correlations were identified between constructs, then translated into two SEMs - day and rotating shifts respectively - with sleep and distinctness as predictors of detrimental effects. Models were tested for direct effects, significant paths, and overall model fit. We found that, whilst sleep governed fatigue-based symptoms in day-shift workers, chronotype distinctness determined the severity of adverse effects in rotating-shift workers, including mood disorders and cognitive impairments. We surmise that high chronotype distinctness should be considered a risk factor for adverse effects surrounding night and rotating-shift work, and that interventions should incorporate chronotype-specific remediation.

Background: Higher Body Mass Index (BMI) is an established risk factor of sleep disturbance. It is not known if the effect is homogeneous across the lifecourse or if there is a particular time point in life that might be best to target. Methods: Two-sample Mendelian randomization (MR) was used to investigated the effect of childhood adiposity (adjusting on adulthood adiposity and obstructive sleep apnea (OSA)) on insomnia, morning chronotype, sleep duration, daytime sleepiness and daytime napping. Similarly, total, and direct effect of adulthood adiposity on these outcomes was explored. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank for childhood and adulthood adiposity (n=453,169) and large-scale consortia of OSA (Million Veteran Program) (n=410,268), insomnia, and chronotype (23andMe) (n=1,978,022 and n=248,1000, respectively). Results: Two-sample univariable MR analysis provided no evidence of an effect of genetically predicted childhood adiposity on later life insomnia (Odds ratio (OR)= 0.94, 95% Confidence interval (CI)= 0.87, 1.03). Whereas, multivariable MR (adjusted for adulthood adiposity) analysis provide strong evidence of direct protective effect of genetically predicted childhood adiposity on later life insomnia (OR= 0.70, CI= 0.64, 0.77). Further, both in univariable and multivariable MR, a strong positive effect of increased childhood body size on morning chronotype was observed (OR= 1.16, CI= 1.01, 1.33 and OR= 1.36, CI= 1.15, 1.62, respectively) after accounting for adulthood body size. In both analysis the estimate did not change considerably after aditionally adjusting for OSA. However, childhood and adulthood adiposity found to be associated with OSA and OSA with insomnia. In both univariable and multivariable analysis, increased body size in adulthood increased the risk of having insomnia and a morning chronotype. Conclusions: The findings suggest that higher body size in childhood is not a risk factor for later life insomnia, whereas higher body size in adulthood was. Further, if healthy body size is maintained in adulthood, high childhood adiposity may decrease the risk of insomnia and increase the risk of being a morning person in later life. Keywords: childhood, adulthood, obesity, insomnia, morning chronotype, medelian randomization

Background: Although sleep disorders are generally associated with cognitive decline, definitions of sleep conditions and cognitive function vary greatly across studies. Therefore, we evaluated associations between consensus definitions of sleep and cognitive outcomes across multiple studies. Methods: Consensus sleep measures were ascertained at baseline visits in three cohort studies (HCHS/SOL, MESA and ARIC, N=11,563): short and long sleep ([&le;]6hours and [&ge;]9hours), insomnia (Women's Health Initiative Insomnia Rating Scale [&ge;]10 or [&ge;]6 for 3 questions), daytime sleepiness (Epworth Sleepiness [&ge;]11) and obstructive sleep apnea (OSA, Apnea-Hypopnea Index [&ge;]15). Cognitive measures were ascertained at baseline and follow-up visits in each study and used to calculate harmonized outcomes: global cognitive function (GCF) at baseline, change in GCF between baseline and follow-up, poor GCF at baseline ([&ge;]1SD below the median), and incident poor GCF at follow-up. Study-specific analyses were adjusted for age, sex, center, education, race/ethnicity, APOE4 and language of cognitive assessments as needed. Results were meta-analyzed across studies. Results: Long sleep was consistently associated with lower baseline GCF ({beta}=-0.19, SE=0.03, p=3.78x10-14, FDR p=7.56x10-13, p-heterogeneity=0.78) and increased odds of poor baseline GCF (OR=1.77, 95% CI=1.49-2.09, p=2.96x10-11, FDR p=2.96x10-10, p-heterogeneity=0.22) across cohorts. Insomnia was also suggestively associated with lower baseline GCF ({beta}=-0.051, SE=0.02, p=0.01, FDR p=0.07, p-heterogeneity=0.005) and increased odds of poor baseline GCF (OR=1.22, 95% CI=1.06-1.41, p=6.76x10-3, FDR p=0.05, p-heterogeneity=0.03). Sleep measures were not associated with change in cognitive function. Discussion: Associations of long sleep and insomnia with harmonized cross-sectional GCF across three large, multiethnic cohorts suggest that cognitive function may be associated with distinct sleep phenotypes.