Journal of Sleep Research

Sleep-onset insomnia is widespread in modern society, and many individuals turn to music to improve their sleep. While clinical studies have shown that music can positively affect sleep quality, the impact on sleep initiation remains unclear. Furthermore, there is limited knowledge on the mechanisms by which music may facilitate sleep. Here, we investigated whether music can facilitate sleep onset and if the effect is related to brain waves synchronizing to the slow beat of the sleep music. We recorded participants with sleep-onset insomnia (N=53) during a 30-minute afternoon rest using electroencephalography (EEG). Participants were randomly divided into two groups. Twenty-four participants listened to music chosen from a sleep music playlist while resting, and 29 rested in silence. We evaluated the transition from wakefulness towards sleep with the delta-alpha ratio of the EEG. To assess neural synchronization to the beat of the music, we used an EEG frequency tagging approach. We found a higher degree of transition towards sleep in the music group compared to silence over the 30-minutes resting period. Furthermore, higher beat stability in the music was reflected in stronger neural frequency tagging at the musical beat. However, the analyses showed no relationship between sleep initiation and neural synchronization to the beat. In sum, our results revealed that music has a positive effect on sleep initiation and that there is neural synchronization to naturalistic sleep music with a steady beat, but we found no indication that this neural synchronization is the central mechanism driving enhanced sleep initiation with music.

BackgroundSocial jet lag (SJL), the discrepancy timing between work nights and free nights, reflects schedule-related circadian misalignment. Time-stamped CPAP adherence records may provide objective, longitudinal estimates of sleep timing and could augment conventional CPAP reports by adding information on sleep regularity and weekday-weekend misalignment. ObjectivesTo quantify CPAP-derived SJL in two independent clinical cohorts, characterize its behavioral correlates and age-related patterns, and assess cross-site reproducibility. MethodsWe analyzed CPAP-derived sleep timing in patients from Rutgers-RWJ Health (RWJ, N = 1,437) and Hackensack Meridian Health (HMH, N = 1,510) with at least 31 valid nights and at least one valid work night and free night. Mid-sleep on work nights (MSW) and free nights (MSF) was estimated using circular statistics. SJL was defined as the absolute circular difference between MSF and MSW and categorized as none (<1 h), moderate (1-2 h), or severe ([&ge;]2 h). Sleep duration, free-night rebound, age-stratified prevalence, and cross-site differences were evaluated using nonparametric and categorical tests. ResultsSJL was right-skewed at both sites, with median values below 0.5 h at RWJ and HMH. SJL >1 h was present in 21.2% and 16.4% of patients, respectively; severe SJL occurred in 4.0% and 2.8%. Moderate and severe SJL were associated with shorter work-night sleep and greater free-night rebound, consistent with weekday restriction and weekend compensation. SJL prevalence and variability were highest in younger and middle-aged adults, particularly those aged 26-50 years, and declined markedly after age 65. Core timing phenotypes, including MSW, MSF, and free-night rebound, were highly reproducible across sites despite modest differences in absolute sleep duration and overall SJL prevalence. ConclusionsIn CPAP-treated cohorts, SJL is common but usually modest, is associated with weekday sleep restriction and free-night rebound, and declines substantially with age. These findings support the use of routinely collected CPAP data as a scalable, low-burden source of device-anchored circadian screening phenotypes. CPAP-derived SJL may augment standard adherence reports by helping identify patients who warrant further behavioral, circadian, or activity-based assessment.

Co-sleeping with pets or children is common, yet its effects on sleep, cardiorespiratory physiology, and behavioral outcomes are understudied. We examined within-person associations between co-sleeping with a dog, cat, or child and sleep characteristics, measures of cardiorespiratory physiology, and next-day physical activity in 1,649,083 person-days from 11,733 adults wearing the WHOOP wearable device. Participants reported nightly co-sleeping via a daily journal in the devices companion smartphone application, and linear mixed-effects models compared nights with and without co-sleeping within the same individual. Co-sleeping was associated with modest improvements in cardiorespiratory physiology, including lower resting heart rate (0.8-1.1 beats/min), lower respiratory rate (0.04-0.07 breaths/min), and higher heart rate variability (1.41-1.95 ms). Sleep outcomes were mixed, with longer sleep duration (5.5-9.6 min) but more disturbances (0.36-0.40 instances) and slightly less restorative sleep (0.23-1.17%). Associations were generally consistent across groups, although child co-sleeping showed greater sleep disruption. Next-day physical activity was higher following dog and cat co-sleeping (7.6 and 7.4 intensity-weighted min, respectively) but lower following child co-sleeping (2.6 intensity-weighted min). Although effect sizes were small ({beta} range: 0.008-0.045), findings suggest that co-sleeping is associated with a trade-off between modest cardiorespiratory benefits and mild sleep disruption, indicating that co-sleeping decisions may be driven more by personal and contextual factors than by concerns about physiologic impact.

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.

This study aimed to explore night-to-night variability of multiscale sleep patterns by analyzing subcutaneous electroencephalography (sqEEG) from 20 healthy participants over one year (205-388 nights per participant, 6,429 nights in total). We utilized the time series of aperiodic slopes, sigma and slow-wave power as a new whole-night unit of sleep macrostructure. Using dynamic time warping, we calculated the distances (differences) between those time series to assess night-to-night sleep macrostructure dissimilarity. We found that the overall sleep macrostructural patterns were relatively similar across nights (20% dissimilarity), while their temporal alignment was quite variable (time series warped by ~60% for the best alignment). Lower variation in macrostructure dissimilarity was associated with better subjective sleep quality (r=-0.25). Then, we qualitatively compared yearlong variation in macroscale, microscale (sleep stage proportions, mean spectral power) and mesoscale (sleep cycle duration) metrics. We found that intra-individual night-to-night variation was '"low (coefficients of variation < 20%) for spectral power, sleep duration, N2 and REM sleep; ''medium'' (20-40%) - for N3 and macrostructure dissimilarity; and "high" (>40%) - for sleep cycle duration, wake and N1. In summary, different sleep metrics showed differential night-to-night variability, which was more metric-specific than scale-dependent. This might reflect a distinction between more trait-like versus more dynamically varying features of sleep, although this assumption needs further clarification.

Evidence suggests that insomnia disorder is associated with pathophysiological alterations that may contribute to long-term physical, mental and inflammatory-related health risks. Cognitive behavioral therapy for insomnia (CBTi) is the first-line treatment for insomnia disorder, yet its effects on physiological outcomes remain unclear. This randomized-controlled trial examined the effects of CBTi on cardiovascular and immunological biomarkers. Sixty-two participants with insomnia disorder were randomized to group-CBTi (N = 33, 75.8% female, Mage = 48.8 + 17.1 years) or Waitlist (WL) control (N = 29, 75.9% female, Mage = 52.2 + 15.6 years). Cardiovascular parameters included systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), and nocturnal heart rate variability (HRV). Inflammatory markers from blood samples included C-reactive protein (CRP), tumor necrosis factor-alpha (TNF- ), interleukin-6 (IL-6) and brain-derived neurotrophic factor (BDNF). All outcomes were assessed at baseline (T1), post-treatment assessment (T2, following completion of CBTi or WL period), and 6-months for the WL group (T3, after CBTi for the WL participants). No significant Group-by-time effects were observed for SBP, DBP, HR, HRV and any inflammatory markers (ps > .05) from T1 to T2. When pooling treatment effects following CBTi exposure across both groups (T1 to T2 in CBTi group and T1 to T3 in WL group), no significant biomarker changes were observed. Overall, results indicate that CBTi did not produce detectable changes in cardiovascular or inflammatory markers among healthy individuals with insomnia disorder. These findings suggest physiological responses to CBTi are complex and may reflect dynamic and context-dependent processes (https://www.isrctn.com/ISRCTN13983243).

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.

The Empatica E4 wristband provides continuous multi-modal physiological monitoring including blood volume pulse (BVP), electrodermal activity (EDA) and skin temperature (TEMP) but its validity for sleep-stage-specific autonomic and thermoregulatory monitoring has not been systematically evaluated against concurrent polysomnography (PSG). Using the Wearanize+ dataset which provides synchronised PSG, Empatica E4, and Zmax EEG recordings from 100 home-recorded participants; a systematic validation of Empatica E4 physiological signals against PSG ground truth across five sleep stages was conducted. Of 100 participants, 92 had Empatica data; 69 met Zmax EEG signal quality criteria and formed the analysis sample. Heart rate (HR) from the pre-computed Empatica HR channel showed valid stage-specific patterns (Wake: 70.9 bpm, N3: 61.2 bpm) and moderate inter-device MeanNN correspondence with PSG ECG (Spearman r=0.35-0.42 across stages). Skin temperature showed the expected thermoregulatory pattern (Wake: 33.92C, N3: 35.48C) and is recommended for downstream analyses. Tonic EDA showed an inverted stage pattern attributable to wrist sweat accumulation during deep sleep, representing a known confound for wrist-worn EDA during sleep. Phasic EDA showed plausible patterns and may be used with caution. These findings establish a validated feature set for Empatica E4 sleep research and directly inform multimodal psychiatric biomarker studies using the Wearanize+ dataset.

The slow oscillation (SO) of non-rapid eye movement (NREM) sleep has been implicated in the restorative properties of sleep. Slow oscillatory transcranial direct current stimulation (SO-tDCS), involving a positive oscillatory current applied to the scalp at a peak frequency of 0.75 Hz, has been used to enhance SO power during NREM sleep. We examined whether enhancing SO power with SO-tDCS during a restricted nighttime sleep opportunity would accelerate the restorative properties of sleep during an otherwise insufficient sleep period and help sustain performance during subsequent extended wakefulness. A total of twenty-six healthy young adults (ages 18-39, n=16 females) completed a 15-day study. After 7 baseline nights at home and 3 baseline nights in the laboratory, participants entered the laboratory for 5 consecutive days including a baseline day, a 2-hour nighttime sleep period with participants randomized to the SO-tDCS (n=11) or SHAM (n=15) condition, 46 hours of sleep deprivation, and two recovery nights. In the SO-tDCS condition, stimulation was administered for one hour starting exactly 60 minutes after sleep onset, with intervals of five minutes of continuous stimulation followed by one minute of no stimulation. Polysomnographic recordings were conducted during each sleep period. Performance was assessed using the Psychomotor Vigilance Test (PVT) approximately every 75 minutes across baseline, sleep deprivation, and recovery. Prior to the two-hour sleep opportunity, a Paired Words Associate Task was administered. Participants listened to 54-word pairs and were asked to recall 46 of the word pairs, with up to three attempts to successfully recall at least 60% of word pairs (T0). Recall was also assessed 20- (T20) and 120-minutes (T120) after awakening from the two-hour sleep period. Data were analyzed using mixed-effects ANOVA. PVT performance (defined as mean response time and number of response times greater than 1,000 ms) significantly declined across sleep deprivation with performance degradations peaking in the early morning hours. Participants in the STIM condition demonstrated significantly better performance during sleep deprivation relative to the SHAM condition. On the PWAT, participants in the SHAM condition recalled fewer word-pairs upon awakening relative to T0. In sharp contrast, performance of participants in the SO-tDCS condition did not deteriorate at T20 and was actually improved at T120 relative to T0. We conclude that SO-tDCS can robustly accelerate the restorative properties of sleep and can additionally enhance sleep related memory consolidation when sleep opportunity is restricted.

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, 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.

Objective: To characterise the symptoms of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Method: 1028 adults with ME/CFS completed The Index of ME Symptoms (TIMES) online. Raw ordinal data were Rasch transformed into interval data so parametric statistics were used. Results: Mean TIMES score was 57.2/100 (sd 5.4) indicating a severe symptom burden affecting multiple body systems. The correlations between symptom burden, age and duration were negligible, and moderate with ME/CFS severity. Women had a greater symptom burden than men. All participants experienced fatigue, neurological symptoms and dysautonomia. The mean Fatigue Scale score was severe (67.7 (sd 19.9)) and moderate for the Neurological Scale (mean 45.11 (sd 9.45)) and Dysautonomia Scale (43.98 (sd 8.42)). Over 90% experienced cognitive, pain, motor-sensory, sleep, cardio-respiratory, cranial nerve and gastro-intestinal symptoms to some degree. They were mild-moderately troublesome overall, except cognitive symptoms which were severe. Conclusions. ME/CFS causes a heavy multi-system symptom burden. Although most individual symptoms were mild-moderately troublesome, the cumulative effect was severe or very severe. Fatigue was the most common and troublesome problem followed by cognitive symptoms, sleep disturbance and pain. Women experienced a greater symptom burden than men, and there was a moderate relationship between symptom burden and disease severity.

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 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: Bidirectional next-day associations between sleep disturbances and affective symptoms have been shown in previous research, yet the consecutive day effects between these factors remains poorly understood. Methods: We analysed longitudinal ecological momentary assessment (EMA) data obtained from a subsample of young persons in the Brazilian High-Risk Cohort (BHRC) study collected in 2020-2021. Participants reported sleep quality each morning and rated affective symptoms relating to mood, anxiety, and energy four times daily for 28 days. We selected 88 individuals (17.83{+/-}1.74 years, 56 [63.6%] female gender) with at least one instance where individuals were observed three-days in a row. Within-person bidirectional next-day effects between sleep quality and affective symptoms were estimated using mixed-effects regression analysis adjusting. We then applied g-estimation approaches to estimate the effect that lagged sleep quality and consecutive improvements in sleep quality had on affective symptoms. Results: Sleep quality and affective symptoms had bidirectional next-day effects, with sleep quality tending to have greater influence on affective symptoms than the reverse. Improved lagged sleep quality had positive effects on affective symptoms incrementally above the prior night's sleep quality. Also, improvement of sleep quality across consecutive days had incremental and approximately equal effects on affective symptoms. Conclusions: Sleep quality and affective symptoms exhibit a feedback loop, whereby poor sleep quality influences affective symptoms over consecutive days. Breaking these feedback loops, by improving sleep quality across several consecutive nights should improve affective symptoms. This supports interventions that target sustained improvement in sleep and possibly circadian regulation to improve affective symptoms.

Sleep is thought to promote memory consolidation through the offline reactivation and reorganization of newly acquired information. However, most studies assess memory shortly after sleep, leaving unresolved whether an initial post-learning sleep episode produces enduring modifications that influence how memories respond to later reactivation. Importantly, the absence of behavioral differences after prolonged retention intervals does not necessarily imply that sleep failed to modify the original memory. Instead, sleep-dependent changes may persist in latent forms that are not readily captured by conventional memory assessments. Here, we investigated whether post-learning sleep produces lasting changes in declarative memories that influence their subsequent response to reactivation. In Study 1, participants learned a declarative memory task and were assigned to either a short nap, a wake condition, or an exploratory long-nap condition that included both NREM and REM sleep. Memory was assessed one week later. Despite substantial forgetting across the retention interval, no significant differences in memory performance were observed between groups. In Study 2, participants learned the same task and subsequently underwent either a short nap or wakefulness. Memory was reactivated six days after learning using an incomplete reminder previously shown to induce memory updating in human declarative memory, and memory was tested one day later. Under these conditions, participants who slept after learning showed better memory performance than wake controls. Moreover, sleep physiological measures predicted the magnitude of the post-reactivation memory benefit. These findings suggest that post-learning sleep induces enduring modifications in declarative memories that are not readily detectable through delayed memory testing alone. Instead, these sleep-dependent changes become evident when memories are challenged through subsequent reactivation. Our results indicate that sleep-dependent consolidation influences the future expression of memory, shaping how memories respond to later reactivation experiences and providing new insight into the relationship between consolidation and reconsolidation.