Journal of Sleep Research

Sleep regularity is an important but under-measured dimension of sleep health. Objective indices from actigraphy or wearables are robust but resource-intensive. The Sleep Regularity Questionnaire (SRQ) offers a brief subjective tool, but its validity against objective and diary-based indices in healthy adults is unclear. In Part 1, 31 adults wore a smart ring continuously for 21 nights. Device-derived regularity metrics included the Sleep Regularity Index (SRI), interdaily stability (IS), social jetlag (SJL), composite phase deviation (CPD), and the standard deviation of sleep onset and wake time. In Part 2, 52 adults completed a one-week sleep diary, from which variability in sleep timing, total sleep time (TST), SJL and nightly perceived sleep quality were derived. All participants completed the SRQ and Brief Pittsburgh Sleep Quality Index (B-PSQI). In Part 1, associations between SRQ scores and device-derived SRI, IS, SJL, CPD and timing variability were small (absolute r [≤] 0.36). Higher SRQ Global and Sleep Continuity scores were moderately associated with better B-PSQI global scores (r -0.37 to -0.44). In Part 2, SRQ Global and Circadian Regularity showed small-to-moderate associations with higher diary-rated sleep quality and lower bedtime variability (r {approx} 0.40 and -0.32 to -0.34), while correlations with other diary metrics and B-PSQI were weak (absolute r [≤] 0.25). The SRQ shows modest convergent validity with diary-based timing variability and perceived sleep quality, but only weak correspondence with smart ring-based sleep regularity indices. It is likely to complement, rather than replace, objective monitoring in healthy adults with relatively regular sleep-wake patterns.

PuhrposeTo evaluate the short- and long-term cross-sectional associations between COVID-19 infection and multidimensional sleep health. MethodsData from the COVID-19 Outbreak Public Evaluation (COPE) initiative were used to examine the association between a novel multidimensional sleep health measure (COPE Multidimensional Sleep Health Scale, CMSHS) modeled from the RuSATED instrument and (1) COVID-19 infection and (2) post-acute sequelae of SARS-CoV-2 infection (PASC). ResultsData from 11,326 respondents were used for this study. The cohort was comprised of 51% women, 61% non-Hispanic White, and 17% Hispanic adults. COVID-19 infection was more prevalent among participants who had not received a booster vaccination (55.4% vs. 30.2%, p<0.001); the number of comorbid conditions was higher among those who had been infected (2.2% vs. 1.7%, p<0.001). Participants with COVID-19 infection had significantly lower CMSHS scores indicative of worse sleep health compared with uninfected participants (3.52 {+/-} 1.37 vs. 3.78 {+/-} 1.30; p < 0.001). Participants with PASC had lower CMSHS scores in comparison to those without PASC (2.72 {+/-} 1.30 vs. 3.82 {+/-} 1.28, p<0.001). In adjusted models, a progressive decline in CMSHS scores was observed over 12 months following infection (3.52 {+/-} 0.05 vs. 2.98 {+/-} 0.04; p < 0.001 for <1 month vs. 6-12 months). ConclusionCompared with uninfected individuals, multidimensional sleep health was worse among persons who had a COVID-19 infection. Individuals with PASC had greater and persistent reductions in sleep health for up to 12 months post-infection. Brief summaryO_LISeveral studies have examined the negative effects of COVID-19 on sleep, however the effects of COVID-19 infection on multidimensional sleep health remain poorly understood as do these associations over time. Using a large, population-based cohort, this study evaluates short- and long-term effects of Covid-19 infection on overall sleep health. C_LIO_LIThe study provides evidence that COVID-19 infection is associated with impairments in overall sleep health, with effects persisting up to 12 months post-infection. The findings in this study demonstrate that poor sleep health is an important long-term consequence of COVID-19 infection and emphasizes the need for sleep assessment among patients affected by COVID-19. C_LI

Sleep architecture is essential for metabolic and cardiovascular health, yet the impact of day-to-day dietary variation on objective sleep physiology remains unclear. Using 4.8 thousand person-nights with real-time dietary logs and multi-stage wearable sleep recordings, we examined how prior-day nutrition relates to next-night sleep under free-living conditions. Higher fiber density was associated with increased restorative sleep, including +0.59 pp deep sleep, +0.76 pp REM sleep, -1.35 pp light sleep, and -1.14 bpm lower mean nocturnal heart rate. Greater plant diversity and higher whole-plant food intake were similarly associated with lower nocturnal heart rate (-0.72 to -0.94 bpm). Meal-timing behaviors primarily influenced sleep duration, sleep-onset latency, and autonomic tone: heavier evening meals were associated with +7.7 min longer total sleep time and +0.73 bpm higher nocturnal heart rate. In contrast, short-term variation in macronutrient energy distribution and micronutrient consumption showed no robust associations with sleep outcomes. When analyses were restricted to more extreme dietary contrasts, effect magnitudes increased while remaining directionally consistent. These findings indicate that routine daily dietary choices, particularly plant-forward composition and meal timing, have immediate and measurable effects on objective sleep architecture.

Background/ObjectivesElectromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for Rapid eye movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM-sleep motor activity. MethodsSeventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3-second mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. ResultsIn participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. Across 12,941 3-second mini epochs, EMG, actigraphy, and video detected 1,703, 1,613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG-actigraphy ({kappa} = 0.27 {+/-} 0.10) to actigraphy-video ({kappa} = 0.41 {+/-} 0.12) and EMG-video ({kappa} = 0.45 {+/-} 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. ConclusionActigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patient, with actigraphy showing the highest sensitivity and manually scored video the lowest.

RationaleObstructive sleep apnea (OSA) is linked to cardiovascular, metabolic, and cognitive morbidity. Although COVID-19 has been associated with long-term respiratory and neurological sequelae, its role in precipitating new-onset OSA remains unclear. ObjectivesTo evaluate whether SARS-CoV-2 infection increases risk of developing OSA up to 4.5 years post-infection and how risk varies by hospitalization status, demographics, comorbidities, and vaccination status. MethodsThis retrospective cohort study used electronic health records from the Montefiore Health System in the Bronx. Adults tested for SARS-CoV-2 between March 1, 2020, and August 17, 2024, were classified as hospitalized COVID+, non-hospitalized COVID+, or COVID-. Patients with prior OSA or inadequate follow-up were excluded. Inverse probability weighting adjusted for demographic, clinical, socioeconomic, and vaccination covariates. New-onset OSA was assessed using weighted Cox proportional hazards models. Secondary outcomes including hypertension, myocardial infarction, heart failure, stroke, arrhythmia, pulmonary hypertension, type 2 diabetes, and obesity were evaluated with Poisson regression. Sensitivity analysis used a pre-pandemic control cohort. ResultsAmong 910,393 eligible patients, hospitalized [HR 1.41 (95% CI 1.14-1.73)] and non-hospitalized [HR 1.33 (95% CI 1.22-1.46)] COVID+ patients had higher adjusted risk of new-onset OSA versus COVID- controls. Similar findings were observed using historical controls (n=621046). After OSA onset, hospitalized COVID+ patients had higher risks of heart failure and pulmonary hypertension, while non-hospitalized COVID+ patients had higher risk of obesity vs COVID- patients. ConclusionsSARS-CoV-2 infection is independently associated with increased risk of new-onset OSA. These findings support targeted screening in post-COVID populations.

BackgroundObstructive sleep apnea (OSA), as measured by the Apnea Hypopnea Index (AHI), is associated with adverse outcomes. Measures that characterize the temporal variability in events may provide information over and beyond a simple summary of event frequency as measured by the AHI. Research QuestionTo assess whether temporal variability in the occurrence of obstructive apnea/hypopneas during the night is associated with all-cause mortality or incident cardiovascular disease (CVD). Study Design and MethodsData from the Sleep Heart Health Study (SHHS), a prospective multi-site community-based cohort were analyzed. For each person, the intervals between apnea/hypopnea events (inter-event interval; IEI) were used to calculate a coefficient of variation for their IEIs (IEI_CV). Risk for mortality (n=5,701) and incident CVD (n=4,373) were estimated by adjusted Cox proportional hazard models. Sensitivity analyses were conducted to test potential explanatory variables such as hypoxic burden and duration of uninterrupted sleep. ResultsIn 11.8 years of follow-up (median, IQR 10.6-12.2), 1,287 deaths occurred. After adjusting for potential confounders, including OSA severity, participants in the lowest quartile of IEI_CV (Q1) had a 40% higher risk of all-cause mortality compared with those in the highest quartile (Q4) (hazard ratio [HR] = 1.40; 95% confidence interval [CI], 1.20-1.64). In 11.5 years of follow-up (IQR 7.9-12.7), 867 CVD events occurred. The adjusted hazard rate for CVD was 29% higher (HR=1.29 [1.06-1.56]) for those with less variable IEI. Minimal reductions in effects sizes were observed after additional adjustment for hypoxic burden and additional novel and traditional covariates. In sensitivity analyses, adjusting for the longest bout of uninterrupted sleep without respiratory events attenuated the association for CVD incidence (HR=1.15 [0.89-1.50]). InterpretationThe temporal distribution of respiratory events - specifically, less variability in inter-event intervals (more regular event occurrences) - is associated with higher mortality and incident CVD.

BackgroundAs sleep data from wearable devices are increasingly available in health research, there are new opportunities to understand sleep regulation behaviors as modifiable risk factors for disease. At such a large scale (tens of thousands of people over millions of day-level observations), prioritizing and interpreting sleep behaviors is challenging while maintaining biological relevance and modifiability. In this work, we aim to address this challenge by proposing a framework to interpret Fitbit data through a well-known neurobiological framing of sleep regulation, the two-process model. MethodsWe use data from the All of Us Research Program, a national biobank with passively collected Fitbit data for 32,292 people across 15,754,893 total days. We map Fitbit behaviors (b) to either circadian (C) or homeostatic (S) processes. Using iterative exploratory factor analysis to obtain weights, the Fitbit Cb and Sb are then weighted at the level of each day to create Cb and Sb scores. FindingsCb and Sb scores were found to align with expected real-world relationships with age, seasonality, shift work, and napping. Cb and Sb scores were interpreted with relation to depression, where it was found that Sb scores are highly associated with likelihood of diagnosis (OR = 1.5, p < 2e-16) while Cb and Sb scores are equally associated with severity (Sb score {beta} = 0.2, Cb score {beta} = 0.21, p < 2e-16). InterpretationCb and Sb scores support longitudinal interpretation (e.g., changes in Sb around treatment), aggregation (e.g., differences in Cb between two groups), and actionable modification (e.g., reduce naps to improve poor Sb). Overall, our behavior scores allow for interpretation of wearables sleep data and can be utilized across many disease contexts to better understand how sleep influences health. FundingThis work was supported by NIH training grant T32GM145734 and NIH R21HL172038.

IntroductionSleep spindles are electroencephalographic elements characteristic of non-rapid eye movement sleep generated by thalamo-cortical interactions. Spindles have been linked to some of the cognitive benefits afforded by sleep and high spindle activity is associated with increased arousal threshold (deeper sleep). Here, we demonstrate that targeting the thalamus with Transcranial Electrical Stimulation with Temporal Interference (TES-TI) can enhance spindle activity. Methods24 participants (25.5 {+/-} 9.5 years; 69.6% F) underwent thalamic TES-TI stimulation during daytime naps. Three stimulation protocols were tested during stage 2 of non-rapid eye movement sleep (N2): fixed difference frequency of 10 Hz (TES15kHz-TI10Hz), difference frequency matched to individual spindle peak (TES15kHz-TIPeak), and carrier frequency only (TES15kHz). Spectral power in the spindle (sigma) band and integrated spindle activity (ISA) were compared before and during the stimulation, and across stimulation protocols. ResultsTES15kHz-TI10Hz stimulation was associated with a significant increase in sigma band power ({Delta}[x]STIM-PRE = 0.49 log10{micro}V2, p = 0.021) and ISA ({Delta}[x]STIM-PRE = 7.48 {micro}V/s, p = 0.042). Cluster-based analysis localized the increase in sigma power over the frontal and centro-parietal areas (p = 0.022). Linear mixed effects models showed that both sigma band power and ISA during stimulation increased significantly in TES15kHz-TI10Hz compared to the TES15kHz protocol ({beta} = 0.67 log10{micro}V2, p = 0.018; {beta} = 14.70 {micro}V/s, p = 0.0077), while the TES15kHz-TIPeak did not show the same effect. ConclusionsThis study provides evidence supporting the successful use of TES-TI targeting the thalamus to enhance sleep spindle activity. Stimulation at a fixed difference frequency of 10 Hz increased sigma band power and ISA, whereas neither stimulation matched to individual sigma band peak nor TES alone produced comparable effects. These promising results warrant further investigations into the cognitive and clinical impact of TES-TI, a non-invasive neuromodulation tool that can reach deep brain regions. Statement of significanceThis study provides evidence that thalamo-cortical networks, which are central to many physiological and pathological brain activities, can be modulated non-invasively in humans. More specifically, the findings show that transcranial electrical stimulation with temporal interference targeting the thalamus can selectively enhance sleep spindle activity. This work introduces a new strategy for precisely targeting sleep-generating mechanisms regulated by deep brain circuits without surgery or medication. Key next steps include determining how this increase in spindle activity can positively impact cognition and assessing the translational potential of this approach for clinical populations.

Sleep arousals trigger rapid autonomic shifts, yet their specific sympathetic signatures remain poorly characterized due to the mixed sympathetic-parasympathetic nature of traditional cardiovascular markers. Electrodermal activity (EDA), driven exclusively by sympathetic sudomotor pathways, offers a more direct opportunity to characterize arousal-related autonomic responses during sleep. This study quantifies the evolution of EDA-based features associated with arousal events in 100 adults using polysomnography and high-resolution EDA recordings. We implemented a time-varying frequency decomposition framework to isolate sleep-specific sympathetic components, extracting statistical and peak-based features from arousal segments and matched stable-sleep controls. Compared to controls, arousal segments exhibited robust sympathetic modulation in EDA persisting 40 seconds post-arousal. While long arousals produced robust responses, short arousals showed negligible sudomotor responses. REM and NREM sleep showed consistent feature trajectories, with greater variability during REM. The observed activation is primarily driven by clustered sympathetic bursts and amplitude enhancement rather than shifts in peak frequency. These findings establish EDA as a highly sensitive marker of sleep-related autonomic activation and provide a quantitative baseline for characterizing sympathetic responses to sleep arousals.

Poor sleep is linked to consumption of sugary foods/beverages and high neural responsivity to palatable food cues. Yet, whether hedonic liking for sweet taste explains these associations remains unclear. We examined cross-sectional associations of five sleep traits (chronotype, sleep duration, insomnia frequency, snoring, daytime dozing) and a composite sleep score with sweet food liking, and total and free sugar intake in 76,734 UK Biobank participants (39-72 years, 56.3% female). Models adjusted for age, sex, ethnicity, socioeconomic deprivation, and body mass index (Bonferroni-corrected =0.0025). Evening chronotype, more frequent insomnia and daytime dozing, and lower composite sleep score were associated with higher sweet food liking. Associations with intake were stronger for free than total sugar. Evening chronotype was associated with higher free sugar intake (g/day: {beta}=1.523, 1.309-1.737; g/1000 kcal: {beta}=0.450, 0.361-0.538), and daytime dozing showed a dose-response (dozing often vs never/rarely: g/day {beta}=6.307, 4.631-7.983). Snoring was associated with higher absolute (but not energy-adjusted) free sugar intake. A healthier sleep score was associated with lower free sugar intake (g/day {beta}=-2.193 [-2.464 to -1.922]; g/1000 kcal {beta}=-0.691 [-0.804 to -0.579]) but higher energy-adjusted total sugar intake ({beta}=0.633 [0.485-0.781]). Mediation analyses indicated sweet liking accounted for 15%-91% of several sleep trait and free sugar intake associations (indirect effects p<0.001). Poorer sleep health, particularly evening chronotype and daytime sleepiness, was associated with greater sweet liking and higher free sugar intake, with sweet liking partially mediating associations between sleep traits and sugar consumption. Sweet-taste liking may represent an underexamined pathway linking sleep/circadian disruption to free sugar intake.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

BackgroundSleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Although actigraphy provides objective sleep measurement, it is limited by cost and monitoring duration. Passive smartphone-based mobile sensing offers a scalable alternative, but its validity in youths with BD is unclear. MethodsAnalyses included adolescents and young adults (ages 14-25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics (total sleep time [TST], sleep onset, sleep offset, midsleep, wake after sleep onset [WASO]) were compared with smartphone-derived proxies (total offline time [TOT], onset, offset, midsleep, phone use after sleep onset [PASO]). Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between WASO and PASO. Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence vs. absence of mood symptoms and anxiety, and weekend effects. ResultsMobile sensing showed strong convergence with actigraphy for sleep timing and duration (standardized {beta} = 0.54-0.75, all p < .0001). RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing. PASO underestimated WASO (RMSE = 48.8 minutes), but greater WASO significantly increased the odds of detecting any PASO (OR per 15 minutes = 1.35, p < .0001). Findings were robust across sensitivity analyses. ConclusionsPassive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

BackgroundEmerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. HypothesisKey predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study DesignCross-sectional study. Level of EvidenceLevel 3. Methods121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). ResultsPhone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. ConclusionsOur findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical RelevanceEducation and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Type 1 narcolepsy (NT1), a disorder caused by the loss of hypocretin/orexin transmission, is characterized by daytime sleepiness and symptoms where Rapid Eye Movement (REM) sleep, a state normally occurring from middle to late in the night, can intermingle with wakefulness. This results in cataplexy and sleep paralysis, episodes of muscle paralysis when awake, or in the generation of dream-like hallucinations and vivid dreaming, periods of visual imagery or sensory experiences that occur while awake, notably when falling asleep (hypnagogic hallucinations) or lingering dreams with over-realistic recall. Using deep learning of nocturnal sleep polysomnography (PSG) signals (EEG, EMG and EOG) applied to sleep stage scoring, we found that NT1 shows abnormally short wake to REM sleep transitions and occurrences of abnormal sleep stages probabilities of wake, REM sleep and N1 (very light NREM) sleep abnormally co-occurs (sleep stage mixing). Interestingly, although presence of these during sleep enables NT1 diagnosis with performances similar to gold standard diagnostic procedure, the multiple sleep latency test (MSLT), the cortical localization of these dissociations remains unclear. In this work, we used electrode specific predictions of sleep stages to explore if these are global or observed at the local cortical level. Surprisingly, although sleep stage mixing was preeminent between REM sleep, N1 and wake across all electrodes, it was found to fluctuate across locations, with stronger fluctuations found in frontal and central locations, notably in the dominant (left) hemisphere. The strongest single discriminator for NT1 was N1-REM stage mixing across central electrodes (C3-C4), showing 4.3-fold higher dissociation in NT1 patients (Cohens d = 0.61). Analysis of sleep stage dissociations across varying time scales revealed that windows lasting several minutes were most predictive of NT1 status, aligning with the duration of clinically reported symptoms of dissociated REM sleep in narcolepsy. Local N1-W-REM sleep dissociations correlated with CSF orexin/hypocretin levels and severity as measured using MSLT. The predominance of stage mixing in frontal and central regions, areas typically associated with executive and motor control, may contribute to the partial preservation of awareness during dissociated REM phenomena. Further, self-reports of hypnagogic hallucinations correlated best with dissociations involving occipital locations, in agreement with its usual visual content. Coherence analysis was also conducted but did not reveal additional insight. These results suggest that orexin deficiency destabilizes REM sleep organization across cortical projection area contributing both to REM sleep dissociation and to abnormal state transitions observed in NT1.

ObjectiveThis study explored the recovery experiences of individuals who report having (largely) recovered from long covid and who attributed their improvement to mind-body approaches. Design, setting and participantsWe conducted an explorative qualitative study using purposive recruitment through social media and snowball sampling. Eighteen adult women (aged 37-62 years), who self-identified as having had long covid and having substantially recovered through mind-body approaches participated in semi-structured interviews. Data were analysed using Saunders practical thematic analysis. ResultsDespite variation in personal narratives, a common trajectory emerged: participants moved away from a biomedical explanatory model towards one centred on nervous system dysregulation. This shift, sometimes following initial scepticism, was often described as a turning point, sparking hope and motivation to engage in self-directed strategies. Recovery was not linear but an iterative process, involving cycles of practice, reflection (especially when progress stagnated) and adaptation of mind-body techniques. Over time, participants gained insights into contributing factors and, in many cases, made intentional life changes to support ongoing recovery. These patterns echo findings from previous research on mind-body approaches in chronic pain and chronic fatigue, and align with neuroscientific perspectives on symptom generation. Most participants navigated this process without formal clinical support, relying instead on online communities and actively avoiding sources of (biomedical) information that conflicted with their new understanding. ConclusionsWhile causal inferences cannot be drawn from qualitative data, this study highlights potential mechanisms that may underpin recovery for people with long covid using mind-body approaches. Further research is needed to develop structured interventions, and to evaluate their efficacy and safety. Future research should also explore how prevailing narratives within healthcare and society influence treatment engagement and recovery trajectories. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIThis is the first study exploring experiences of recovery from long covid using mind-body approaches. C_LIO_LIIn-depth, real-world accounts capture the lived-experiences over time and allow in-depth exploration if the recovery process, while the semi-structured design facilitates the emergence of themes rarely captured in clinical research. C_LIO_LIGeneralisability is limited due to self-identified long covid status, lack of formal diagnostic verification, absence of strict definitions of mind-body approaches and recovery, and a relatively homogenous sample (mostly highly educated women). C_LI

BackgroundDementia affects over 57 million people worldwide. UK and international policy position personalised, conversation-based care planning as central to post-diagnostic support. However, delivery in primary care is inconsistent, and many practitioners lack dementia-specific communication training. Existing evidence focuses on single roles or settings, leaving a gap in understanding how communication operates across the primary care workforce. AimsTo identify what helps and hinders effective communication for integrated dementia care planning and determine the support and training needs of the wider primary care workforce. MethodsO_LISemi-structured interviews - 11 people with dementia, 13 family carers, and 19 primary care practitioners from diverse roles, exploring experiences of care planning conversations C_LIO_LIReflexive thematic analysis C_LI ResultsThree themes were developed, progressing from micro-level communication practices (Theme 1: Beyond the tick-box), through triadic dynamics (Theme 2: Balancing voices in the conversation), to organisational influences (Theme 3: From silos to meaningful shared care planning). Time and Conversation as intervention cut across all themes, shaping trust and disclosure. Participants reported reliance on tick box approaches, inconsistent preparation, and uncertainty about care plan purpose and ownership. Non-clinical roles were commonly viewed as well placed to support meaningful conversations, but were often described as constrained by unclear remit and weak integration. ConclusionsA persistent gap remains between policy ambitions and everyday practice. Time-pressured, checklist-driven encounters and fragmented systems undermine shared decision-making. The expanded primary care workforce offers untapped potential to address these gaps, but this requires clearer roles, formal integration, and targeted investment in communicative skills.

BackgroundProlonged wakefulness, restricted sleep, and circadian factors can impact driving performance and road safety. Currently, there are no effective objective roadside tests to detect the state of drivers sleepiness during or prior to driving, or predict future driving impairment risk. This paper reports on an extended wakefulness protocol used to determine if a portable virtual reality device to administer vestibular-ocular motor function (VOM) tests can effectively detect 1) drivers state of sleepiness during or just prior to driving, and 2) predict trait sleepiness and future driving risk. MethodsFifty healthy adults with regular sleep within 9pm to 8am were recruited for an experimental laboratory procedure which involved two phases: an initial overnight sleep study, and a subsequent period of extended wakefulness lasting ~29 hours. During the wakefulness phase, participants undertook neurobehavioural testing, a simulated driving test, and repeat assessments of VOM to establish if ocular markers can predict sleepiness state and sleepiness-related performance impairments (Trial registry ACTRN12621001610820). DiscussionThis protocol outlined a study that aimed to establish the sensitivity of VOM test the effects of extended wakefulness and circadian phase on driver state and trait sleepiness and subsequent sleepiness-related driving impairment. Furthermore, the protocol aims to define the best VOM predictors to identify driver sleepiness state (road side testing and pre-drive assessments) and sleepiness trait (predicting future driving risk) to establish proof of concept for its potential application as a roadside, pre-drive and general sleepiness related fitness to drive test.

This study compared the effects of a 25-min nap opportunity and a 10-min non-sleep deep rest (NSDR) condition on perceptual, cognitive, and physical performance in physically active young adults. Sixty participants (26 female, 34 male; 22 {+/-} 4 years) were randomly assigned to one of three groups (nap, NSDR, control; n = 20 each). All groups completed identical assessments immediately, 20 min, and 40 min post-intervention. Mixed-effects models, adjusted for sex, prior-night sleep, and weekly physical activity, revealed a significant Group x Time interaction for sleepiness, fatigue, readiness to perform, and handgrip strength (p < 0.05). At 40 min post-intervention, the nap group reported lower fatigue than control and higher readiness to perform than both control and NSDR (p < 0.05). No significant effects were observed for the NSDR condition on perceptual, cognitive, or physical outcomes (p > 0.05). These findings indicate that a short nap can enhance perceived readiness and reduce fatigue after a brief latency period, whereas NSDR did not elicit significant effects under the present conditions.

Background: Nursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. Aims: To explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. Methods: A total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. Results: Poor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). Conclusions: Social support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.