Journal of Sleep Research

Background: Sleep fragmentation and reduced sleep efficiency are markers of disrupted sleep architecture linked to cognitive and age-related decline. Current assessments rely on subjective reports prone to recall bias, limiting their effectiveness for longitudinal monitoring. Data-driven analysis of sleep using physiological signals such as EEG and EMG remains underutilised, particularly in mid-to-older adults. Objective: We present a deep learning pipeline for automated sleep staging and label-free abnormality scoring, with the primary objective of quantifying deviations in sleep architecture to capture progressive sleep disruption and longitudinal change. Methods: Temporal and attention-based models were benchmarked using datasets from the National Sleep Research Resource and PhysioBank. To improve class-specific performance, we introduce a stacking-based ensemble of sleep stage classifiers, each trained to specialise in a different stage. For longitudinal scoring, we develop a reconstruction loss-based abnormality metric using a temporal convolutional autoencoder trained on hypnograms generated by the sleep staging models. Results: Attention-based models, particularly AttnSleep, achieved the highest performance in both multimodal and single-channel settings (accuracy: 0.85 and 0.83; F1: 0.79 and 0.74, respectively). The encoder-decoder ensemble model improved overall classification accuracy by 3% compared to the best-performing biased base classifier, with a modest gain in N1-stage F1 score (0.444). The proposed abnormality score correlated with Pittsburgh Sleep Quality Index components and showed sensitivity to synthetic hypnogram degradation, highlighting its potential as a label-free indicator of sleep disruption. Conclusion: Automated classification and annotation-free scoring enable an end-to-end multimodal pipeline that supports scalable, objective sleep health monitoring, with relevance for future clinical deployment.

Background Insomnia symptoms are common in older adults. While observational studies suggest physical activity (PA) timing affects health outcomes, its effect on sleep remains unclear. We compared morning versus evening PA effects on insomnia severity and sleep quality in older adults with insomnia symptoms. Methods Eligible participants were aged 60 to 80 years with (sub)clinical insomnia (Insomnia Severity Index [ISI] score [≥]10). In a randomized cross-over trial, participants engaged in coached PA in the morning (10:00 - 11:00) or evening (19:30 - 20:30) for 14 days each. ISI scores were assessed post-intervention. Objective sleep parameters; duration, latency, efficiency, and timing, were assessed with a Withings Sleep Analyzer under the mattress. Subjective sleep quality was reported daily via smartphone app. Salivary dim light melatonin onset (DLMO) was measured on the final day of each intervention. Results Of 37 participants (mean ISI 14.3 {+/-} 3.3), 27 completed the study (mean age 69.8 {+/-} 5; 63% women). ISI scores improved after both morning ({Delta} - 2.5; 95% CI: - 1.14, - 3.83) and evening ({Delta} - 2.0; 95% CI: - 0.63, - 3.38) activity relative to baseline, but were not different between interventions. Compared to evening activity, sleep midpoint occurred earlier with morning activity (03:40 vs 04:00; {Delta} - 20 min; 95% CI: - 31, - 8). No differences in subjective sleep quality or DLMO were found. Exploratory analyses suggested insomnia scores improved specifically in late chronotypes following morning activity. Conclusions While morning vs. evening PA timing did not impact most sleep quality measures, it influenced sleep timing. Larger studies are needed to define optimal and personalized PA timing for improving sleep.

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.

Smartphone use among medical students has become pervasive. While existing literature links excessive smartphone use to poor sleep quality, the specific behavioral pattern most strongly associated with sleep disruption remains insufficiently characterized. This study investigated whether the timing of smartphone cessation relative to sleep onset is more strongly associated with poor sleep quality than total daily screen time among medical students in Punjab, Pakistan, and examined the moderating role of exam period status. A cross-sectional anonymous online survey was conducted among medical students across Punjab, Pakistan (May 2026). Sleep quality was assessed using items informed by Pittsburgh Sleep Quality Index (PSQI) response formats. Descriptive statistics, chi-square tests, and binary logistic regression were applied to 369 eligible responses, reported in accordance with STROBE guidelines. Of 369 respondents (49.9% female, 48.2% male), 74.8% reported using smartphones 6 or more hours daily and 61.2% used their smartphone until falling asleep. Overall, 75.7% reported poor sleep quality. Students using smartphones until sleep onset had 95.1% poor sleep quality compared to 44.8% in those who ceased use before sleeping (p<0.001). In logistic regression with both variables entered simultaneously, bedtime use until sleep onset remained independently associated with poor sleep quality (OR 15.3, 95% CI 5.7-41.2, p<0.001), while total daily screen time lost significance (OR 1.8, 95% CI 0.7-4.7, p=0.228). Outside exam periods, 99.0% of students using smartphones until sleep onset reported poor sleep quality versus 24.2% of those who stopped before sleeping, a difference of 74.8 percentage points (p<0.001). During exam periods, no significant association was observed (p=0.075), suggesting exam-related stress may attenuate the bedtime behavior effect. Hostel-dwelling students showed the highest prevalence of bedtime smartphone use, with 79.0% using smartphones until sleep onset compared to 23.2% of family-living students (p<0.001). Bedtime smartphone use until sleep onset is more strongly associated with poor sleep quality than total daily screen time among Pakistani medical students. Medical institutions should consider integrating targeted digital wellness education specifically addressing bedtime cessation timing into student health programs, with particular attention to hostel-dwelling students.

Objective: We examined the link between cohabitation with a partner and nighttime smartphone use through the social control of health behavior theory. Background: Nighttime smartphone use is a behavioral risk factor for sleep problems. While previous research has predominantly focused on individual-level risks of sleep disturbances, the role of social context remains underexplored. Theoretical frameworks, specifically the Social Control of Health Behavior, suggest that social relationships regulate health-related behaviors; however, it is unclear how far this regulation extends to modern digital behaviors among couples. Method: We analyzed survey data from three waves of the SmartSleep Study (2018, 2020, and 2023; total N = 25,028), including a longitudinal follow-up subset (N = 1,003). We tested multivariate associations between living with a partner, changes in cohabitation status and frequent nighttime smartphone use by fitting generalized linear mixed-effects models. Additionally, we mapped the complex interplay between indicators of social integration, social support, smartphone use, and sleep quality using hierarchical clustering of non-linear correlations. Results: Cohabiting participants had lower odds of frequent nighttime smartphone use compared to those living alone (OR = 0.66; 95% CI: 0.61, 0.72). This lower risk was driven primarily by cohabitation with a partner (OR = 0.49; 95% CI: 0.36, 0.66). Longitudinal analysis supported these findings, showing that sustained cohabitation was associated with less frequent nighttime use (OR = 0.56; 95% CI: 0.38, 0.82). Clustering analysis revealed that indicators of social integration and support clustered with favorable sleep quality. Conclusion: Our findings suggest that the health-protective effects of cohabitation with a partner extend to digital behaviors. Consistent with social control of health behavior theory, the presence of a partner appears to reduce frequent nighttime smartphone use, highlighting the critical importance of considering social context when addressing digital health hygiene and promoting sleep.

Objectives: Self-applied, low-density EEG offers opportunities to examine sleep in the home environment, yet its feasibility during behavioural sleep interventions remains unexplored. This pilot study aimed to evaluate the feasibility and acceptability of a self-applied, low-density EEG device during sleep restriction therapy (SRT) and explore effects on sleep and affect. Methods: Seventeen adults with insomnia and depressive symptoms completed a 2-week baseline and 4 weeks of SRT. The primary outcome was the proportion of expected EEG recordings completed and scoreable. Secondary outcomes included clinical measures, sleep continuity (sleep diary, actigraphy), sleep architecture (low-density EEG for 9 nights), power spectral density, and affect. Data were analysed with linear mixed models. Cohen's d and 95% confidence intervals were reported. Results: Feasibility was demonstrated (92% of expected EEG nights completed). SRT was associated with reductions in insomnia severity, depressive symptoms, negative affect, and increases in positive affect. Robust improvements were observed across treatment in sleep continuity (SOL, WASO, SE) from diary, which were paralleled by actigraphy. EEG revealed reduced TIB, TST, N1, N2, REM sleep, and REM latency during week one. Reductions in EEG-derived TIB and N1 sleep were maintained at night 28. There were no reliable differences for spectral or spindle measures. Conclusions: These findings suggest that self-applied, low-density EEG during SRT is feasible, acceptable, and may capture sleep changes during treatment. They highlight the potential for multi-night monitoring of sleep interventions at home and elucidating mechanisms underlying therapeutic change.

Introduction: Actigraphy sleep-wake classification methods increasingly seek to leverage raw acceleration data and machine-learning-based classification, but performance evaluation in pediatrics is limited. We trained machine-learning models using pediatric data and compared their sleep-wake classification performance with existing algorithms for children. Methods: Sixty-five children (46% female, ages 5.3 to 17.7 years) completed in-lab overnight polysomnography and wore a GENEActiv device on their non-dominant wrist. The acceleration data were converted into 30-second epochs and aligned with physician-scored sleep-wake data from electroencephalography. Seven machine-learning models were trained using leave-one-subject-out cross-validation. Epoch-by-epoch analyses generated performance metrics (e.g., balanced accuracy [BA]) and discrepancy analyses provided overall sleep duration bias estimates. The combination of highest performance and least bias was used to rank using Euclidean distance scores - where a lower score represents closer to perfect performance and zero bias. For benchmarking, we included GGIR sleep scoring algorithms and an adult trained random forest classifier. Results: Overall, 560.1 hours of polysomnography and actigraphy data were collected (74.4% of epochs were scored as sleep). The pediatric-trained local-global long-short term memory (LSTM) classifier had the most optimal epoch-by-epoch performance (e.g., BA=0.85, sensitivity=0.88, specificity=0.83, ROC-AUC=0.95, and Cohen kappa=0.67). These metrics exceeded that of an adult-trained random forest classifier and GGIR-based algorithms. Discrepancy analyses revealed that overall sleep duration was underestimated by an average of 25 minutes using the LSTM classifier with no proportional bias. Conclusion: We trained seven pediatric sleep-wake classifiers that had strong ability to detect sleep and wake, with the LSTM classifier being most optimal.

Background Wearable-based studies have largely examined activity and sleep using static summaries or single time windows, potentially missing how chronic patterns and recent behavioral changes jointly relate to depressive symptom severity. We evaluated whether combining long-term habitual behavior with short-term dynamics improves characterization of moderate-to-severe depressive symptoms. Methods We analyzed Fitbit data from All of Us participants with Patient Health Questionnaire-9 (PHQ-9) assessments, defining moderate-to-severe symptoms as PHQ-9 [&ge;] 10 (N=248). Logistic regression evaluated long-term measures (past-year step count and awake time after sleep onset) and short-term dynamics (30-day step decline and 30-day sleep duration variability), adjusting for demographics. Performance was assessed via repeated stratified 10-fold cross-validation. Results Thirty percent of participants (n = 74) had moderate-to-severe depressive symptoms. Higher long-term step count was associated with lower odds of elevated symptoms (OR = 0.75 per 1,000 steps/day), greater awake time after sleep onset with higher odds (OR = 1.27 per 1%), a 30-day step decline with higher odds (OR = 2.70), and greater 30-day sleep variability with higher odds (OR = 1.07 per percentage point). Short-term dynamics provided complementary information beyond long-term measures alone. The combined model achieved the highest discrimination (area under the curve [AUC] = 0.80 vs. 0.73 demographics-only), though findings should be interpreted as exploratory given the modest sample size. Limitations The sample was modest in size (N = 248), PHQ-9 reflects symptom severity rather than clinical diagnosis, causal inference is not possible given the cross-sectional outcome assessment, and Fitbit users may not represent broader populations. Conclusions Long-term behavioral patterns and short-term changes in activity and sleep were associated with depressive symptom severity, supporting wearable-derived measures as potential adjunctive markers in mental health research.

Background: Improvements in health technology offer opportunities for remote disease screening, diagnosis and monitoring. The Withings Sleep Analyzer (WSA), an under mattress ballistocardiograph sensor able to detect body movement, breathing, and cardiac ejection is a promising technology for the non-invasive detection and monitoring of neurodegenerative diseases. InSleep46 aims to evaluate whether the WSA is able to detect preclinical Alzheimer's disease in members of the 1946 British Birth cohort, now in their late 70s. Objectives: To assess feasibility of deployment of a remote sleep, circadian and physiological monitoring device in a population of older adults. Participants: 356 participants from the Insight 46 neuroimaging sub-study (1946 British Birth Cohort), all born in one week in March 1946. Methods: We describe remote recruitment, device installation, and troubleshooting protocols. Feasibility analysis examined participant characteristics associated with recruitment and successful device set-up using logistic regression. Troubleshooting events for device installation and maintenance were recorded over a mean 14-month follow-up period. Results: During the feasibility analysis period, 263 (74%) participants, mean (SD) age 77 years (0.47) agreed to take part, of whom 245 (93%) successfully set up the WSA. Recruitment and successful set up of the WSA were not dependent on cognitive ability, socioeconomic position, or educational attainment. 162 (62%) of recruited individuals required [&ge;]1 troubleshooting call (mean 2.3 per participant, range 0-16). 603 calls were required in total. Conclusion: Deployment of a remote sleep and physiological monitoring device in an older adult population is feasible. Most participants required individualised assistance to set up the device. For the technology to be widely implemented, the set up must be accessible, with dedicated support available.

Background: In-laboratory polysomnography (PSG) remains the diagnostic reference standard for sleep disorders but is resource-intensive and capacity-constrained. Limited-channel home sleep apnea testing (HSAT) improves access and reduces costs compared to in-laboratory polysomnography, but underestimates disease severity due to its inability to measure true sleep time and cannot identify non-respiratory sleep disorders including periodic limb movement disorder and parasomnias.1-5 Comprehensive home polysomnography (hPSG) may preserve diagnostic fidelity while reducing system costs, improving access for patients unable to attend laboratory-based studies, and shortening time to diagnosis and therapy initiation. Objective: To estimate the short-term budget impact to a U.S. commercial health plan of substituting an appropriately selected proportion of in-laboratory PSG with comprehensive hPSG using the Onera Sleep Test System (STS). Methods: We developed a transparent budget impact model following ISPOR good practice guidelines for a hypothetical 1-million-member commercial plan. The model estimates the annual diagnostic population (top-of-funnel) using age- and sex-stratified prevalence, an undiagnosed fraction of 85%, symptom prevalence among undiagnosed individuals (30%), and an annual testing rate (12%).2-3 Baseline costs reflect current diagnostic pathways using HSAT (50% first-line) and in-laboratory PSG (50% first-line), including HSAT-to-PSG escalations (20%) and PSG repeats (4%). The intervention scenario substitutes a defined share of in-laboratory PSG and selected HSAT with Onera hPSG. Scenario and sensitivity analyses explore parameter uncertainty. Results: In the base case, approximately 4,364 individuals entered the OSA diagnostic workflow annually. Baseline diagnostic costs were estimated at $6.23 PMPM, comprising $5.45 million in PSG costs and $0.79 million in HSAT costs. Introducing Onera hPSG (30% PSG replacement, 5% HSAT replacement in Year 1) reduced per member costs to $5.66 PMPM, yielding net savings of $0.57 PMPM ($567,262 annually). In Year 3 scenarios (60% PSG, 10% HSAT replacement), savings increased to $1.64 PMPM (approximately $1.64 million annually). Sensitivity analyses demonstrated net savings ranging from $0.03 to $8.05 PMPM, depending on adoption levels. Conclusions: Partial substitution of in-laboratory PSG with Onera hPSG may yield incremental budget savings for U.S. commercial payers while maintaining access to full polysomnographic assessment. Results support further payer-specific analyses incorporating real-world utilization and downstream outcomes. Keywords: obstructive sleep apnea; polysomnography; home sleep testing; budget impact analysis; health economics

Objective: Several endotypes contribute to the development of Obstructive Sleep Apnea (OSA). However, efforts to measure these endotypes have been challenging. In this paper, we propose a new method that overcomes some of these challenges. Methods: To test the feasibility of this new method, data from the Sleep Heart Health Study (SHHS) were analyzed and two oxygen-based endotypes were identified and plotted on a graphical model: the steady-state SpO2 and the SpO2 arousal threshold. The first is the oxygen saturation that would occur during sleep if there were no arousals, and it is a measure of upper airway collapsibility (a more collapsible airway produces a lower SpO2). The latter is the oxygen saturation that triggers arousals. These endotypes were validated by assessing their ability to detect positional and state-related changes in airway collapsibility and arousal threshold. Results: The study showed that it was feasible to measure oxygen-based endotypes in 95% of SHHS participants. As expected, steady-state SpO2 was lower during supine vs. non-supine sleep, as well as during REM vs. NREM sleep. Also, the SpO2 arousal threshold was similar between supine and non-supine sleep. However, SpO2 arousal threshold was not lower in REM sleep vs. NREM sleep. Therefore, in 3 of the 4 conditions, the oxygen-based endotypes moved in the expected direction due to positional or sleep state changes. Conclusion: Although further validation experiments are required, this study indicates that OSA endotyping using the pulse oximetry signal is feasible. The oxygen-based endotypes could be used to aid therapeutic decision making.

Background Postoperative sleep disorder, a frequently observed complication, is associated with heightened pain sensitivity, exacerbated inflammatory reactions, and compromised tissue repair. Sufentanil, a highly selective -opioid receptor agonist, is widely used in patient-controlled intravenous analgesia (PCIA) and has been associated with reduced sleep efficiency. Oxycodone, as a /{kappa} dual receptor agonist, has shown a lower incidence of adverse effects in clinical practice. Despite these pharmacological differences, the comparative effects of oxycodone- versus sufentanil-based PCIA on postoperative sleep remain poorly characterized. Recent advances in wearable devices demonstrate strong agreement with polysomnography (PSG) in intergroup comparisons of sleep efficiency and total sleep time, enabling continuous, non-invasive, multi-night sleep monitoring and offering a viable alternative for clinical postoperative sleep research. Hence, we design this clinical trial to compare postoperative sleep efficiency between patients receiving oxycodone-based versus sufentanil-based PCIA under wearable sleep monitoring. Methods This study is a randomized, double-blind, placebo-controlled trial that was conducted at a single center. A sample size of 68 patients was determined through calculation, and these patients will be randomly assigned to either the oxycodone group or the sufentanil group. Sleep monitoring was initiated using a wristband device one day before surgery after recruitment. The sleep quality data at different setting time will be monitored. All patients will be followed up by blinded evaluators at baseline and 1, 2, and 30 days after the intervention. The follow-up included pain scores, postoperative complications and adverse events, etc. Discussion By integrating a modern photoelectric device with first-line analgesics, we hope the result of the study will inform perioperative sleep management, guide clinical analgesic selection, and improve patient recovery quality.

Isolated rapid eye movement sleep behavior disorder is a strong clinical marker of future alpha-synucleinopathy, but earlier stages of this risk pathway remain insufficiently characterized. Rapid eye movement sleep without atonia is the polysomnographic substrate of this disorder and may also be detected in individuals without clinical dream-enactment behavior. Whether isolated rapid eye movement sleep without atonia is a benign finding or an early risk state for future rapid eye movement sleep behavior disorder and neurodegeneration remains unknown. DREAMER is a multicenter, prospective, observational cohort protocol designed to identify adults without clinical rapid eye movement sleep behavior disorder who show isolated rapid eye movement sleep without atonia during full-night laboratory video-polysomnography. Four Italian sleep centers will use harmonized eligibility criteria, standardized clinical and sleep assessment, quantitative REM Atonia Index scoring, secure web-based data capture, and planned longitudinal follow-up. Adults aged 40 years or older undergoing video-polysomnography will be screened. Participants with prior rapid eye movement sleep behavior disorder or technically inadequate REM sleep/chin electromyographic data will be excluded. Isolated rapid eye movement sleep without atonia will be defined in participants without clinical rapid eye movement sleep behavior disorder using a REM Atonia Index threshold of <0.85. The target recruitment is more than 500 participants over 18 months, with an expected enriched subgroup of approximately 85 individuals with isolated rapid eye movement sleep without atonia. Ancillary neurophysiological assessments and blood sampling for future biomarker studies will be obtained when feasible. DREAMER is intended to create a harmonized, trial-ready cohort for evaluating isolated rapid eye movement sleep without atonia as a potential early risk marker for incident rapid eye movement sleep behavior disorder and subsequent neurodegenerative outcomes. The study is registered at ClinicalTrials.gov as DREAMER, ClinicalTrials.gov Identifier NCT06140511.

Purpose In deprescribing studies, a prescription-free gap is typically used to determine if patients discontinued their treatment. An appropriate gap depends on the typical time between prescriptions during continued use. This work aims to characterise the interval between prescriptions of chronic drugs using different methods for a cohort of older people in primary care in Ireland. Methods The empirical prescription interval was analysed for 38,154 patients for the twenty most common drug classes and the association between covariates and the interval was analysed using a multi-level model. Estimates were also compared to those obtained from the parametric waiting time distribution (pWTD) approach. Results Available covariates had consistent relationships with prescription intervals across drug classes. For example, each additional prescription issue was associated with an increase in the interval by 5.0 (NSAIDs) to 19.7 days ("Other antidepressants"). Full public health cover was associated with a -29.0 day (inhaled adrenergics) to -11.0 day (opioids) change relative to partial cover, while other/private cover had a -17.9 day (benzodiazepines and associated drugs) to -7.1 day (SSRI and SNRIs) change relative to partial cover. The pWTD also produced consistent estimates of the population interval for most drugs. Conclusions The interval varied substantially within drug classes, due to a mixture of patient, practice and unmodelled factors. Variation between practices was effectively explained, with residual variation between patients and within patients. The pWTD approach is useful for describing complex distributions of intervals, and may be more appropriate for inferring a gap than summarising truncated data.

Background: Circadian rest-activity rhythms weaken with age, but whether sleep disorders modify this trajectory is unknown. Methods: We analyzed wrist accelerometry data from 4,386 participants aged 6-80 years in the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Circadian features were extracted using cosinor analysis and nonparametric methods; a Circadian Disruption Index (CDI) was constructed from five standardized components. Survey-weighted regression with natural cubic splines and Wald F-tests tested age-by-sleep-disorder interactions using Taylor series linearization for variance estimation. Results: Doctor-diagnosed sleep disorder (N = 360, 8.2%) was associated with significantly different age-related trajectories of amplitude (F(2,17) = 11.24, p = 0.0008) and MESOR (F(2,17) = 8.22, p = 0.0032), both surviving Bonferroni correction (p < 0.006). CDI was higher in those with a sleep disorder (0.290 vs. 0.131, p < 0.001) and was independently associated with higher BMI (beta = 1.33 kg/m2, p < 0.001), higher HbA1c (beta = 0.089%, p = 0.004), greater diabetes prevalence (beta = 3.8 percentage points, p < 0.001), and worse depressive symptoms (beta = 0.43 PHQ-9 points, p = 0.020). Sensitivity analyses using a broader sleep problem exposure did not replicate these interactions. Conclusions: Doctor-diagnosed sleep disorders are associated with an altered age-related decline in circadian amplitude and mean activity level. CDI was independently linked to cardiometabolic and depressive outcomes, supporting a mechanistic connection between clinically significant sleep pathology and circadian disruption across the lifespan.

AIMS Cardiometabolic risk factors may impair health by altering the autonomic modulation of the cardiovascular system, a physiological process described by heart rate (HR) circadian oscillations. However, the impact of cardiometabolic health determinants on HR circadian oscillations remains scarcely characterized in real-world, population-based settings. To address this, we applied digital health technologies to investigate how cardiometabolic health determinants shape HR circadian oscillations in a real-world cohort of individuals free of cardiometabolic diseases. METHODS First, a 10-fold cross-validation of a model was performed, aiming at mitigating wearables measurement error caused by motion artifacts. This process was informed by 10,056 epochs of concurrent wearable-derived and polysomnographic HR assessment, yielding an average 1.3 bpm reduction in wearables measurement error. We subsequently applied this model to over 2 million 1-minute epochs of HR data, derived from 7-day continuous actigraphic recordings of 245 individuals free of cardiometabolic disorders. Functional-on-scalar regression modelling and both parametric and nonparametric analyses characterized HR circadian profiles and their relationships with demographics, lifestyle, chronotype, sleep health, and chronic insomnia diagnosis. A 6-dimension sleep health index was calculated. RESULTS Sex, chronotype, and sleep health predominantly shaped HR circadian oscillations. In detail, females consistently showed higher HR across the 24 hours. Moreover, chronotype was associated to a phase shift in HR circadian profiles, with later timings corresponding to eveningness. Notably, sleep health impacted HR circadian oscillations in a dose-dependent fashion: each additional impaired sleep dimension was associated with a 1.2 bpm HR increase during nighttime, alongside reduced circadian robustness and delayed oscillation timings. Finally, the earlier occurrence of morning HR peaks served as a digital biomarker of insomnia (80% specificity, 74% sensitivity). CONCLUSIONS This work provides a digital health framework to characterize HR circadian oscillations in free-living populations and supports its clinical utility in capturing the autonomic disruptions related to cardiometabolic health determinants.

Dream recall varies substantially both between individuals and from night to night within the same individual. Although nocturnal awakenings are thought to facilitate the encoding and later retrieval of dream experiences, it remains unclear whether dream recall is shaped primarily by awakening frequency or by more specific awakening characteristics, including duration, sleep stage, and timing within the night. Here, we analyzed two cohorts: cohort 1 consisted of 708 adults spanning the full range of dream recall frequency, assessed across three waves with home sleep recordings and questionnaire-based dream recall frequency measures; cohort 2 consisted of 124 adults with high dream recall frequency, assessed across multiple nights with home sleep recordings and daily dream reports. Using multilevel models with within-between decomposition, we examined trait-like and state-like associations between awakening measures and dream recall outcomes. At the trait level, both questionnaire-based dream recall frequency in cohort 1 and daily dream recall (i.e., a sense of having dreamed) in cohort 2 were associated with a specific nocturnal awakening profile: more habitual long REM awakenings and short NREM awakenings, with REM awakening effects remaining robust after adjustment for sleep duration. At the state level, in cohort 2, nights with more short and medium REM awakenings than usual increased the likelihood of morning dream recall, whereas nights with more long REM awakenings than usual increased the likelihood of morning dream content recall (i.e., remembering dream content). These findings support the arousal-retrieval and functional state-shift models, while highlighting important nuances in the associations between nocturnal awakenings and different dream recall outcomes.

ObjectiveK-complexes (KCs) are large-amplitude EEG events that represent N2 sleep stage and have been linked to sensory gating, sleep protection, and memory consolidation. Their detection remains limited by inter-rater variability in visual scoring and by the reliance of detectors on features that discard temporal information. We propose a two-stage detector that combines a rule-based candidate localization algorithm with a Support Vector Machine (SVM) classifier operating directly on the raw 2-seconds waveform, and we evaluate it against an adjudicated expert consensus of two different datasets. MethodsPolysomnographic recordings from 10 healthy adults (Dataset 1) were independently annotated by two human scorers; discordant events were adjudicated by a senior expert, yielding 240 consensus KCs. The automatic classifier was evaluated using subject-level 10-fold Group K-Fold cross-validation and compared directly against the two human scorers under identical conditions. Cross-dataset generalization was further assessed on the public DREAMS database (Dataset 2) under both external and internal training criteria. ResultsThe SVM classifier achieved the highest F1-score (79.4%) and accuracy (78.8%) among all scorers, with balanced recall (81.7%) and specificity (75.8%). Of the 58 false positives, 42 originated from events both experts had rejected yet displayed canonical KC morphology and received high classifier confidence (P(KC)>0.7 in 45.2% of cases). This pattern was replicated on Dataset 2. ConclusionA waveform-based classifier matches expert performance and systematically flags morphologically valid KCs that fall outside conventional visual-scoring criteria. SignificanceThese findings question the existence of an unambiguous ground truth for KC detection and support a data-driven redefinition of the event boundary, with implications for sleep staging and memory-consolidation research.

Background In 2024, one-third of GP appointments in England were conducted by telephone. What happens during these consultations is largely unknown. Aim To test the feasibility of collecting recorded GP telephone consultations with linked data and consent for future research use. Design and setting Retrospective observational study in seven practices in South West England. Method Adults who had a telephone consultation at practices that routinely record calls were invited to consent to retrieval of call audio, a 4-month electronic health record (EHR) extract and a post-consultation patient questionnaire. Practice-level consent rates were analysed using regression models. Results Of 28 clinicians recruited, 19 GPs had consultations with patients whose recordings were retrievable, usable, and consented for future research. Of 2,053 invitations, 123 patients consented (6.0%). Consent was lower in more deprived practices (IMD 1-2 vs 9-10: OR=0.22, 95CI=0.09-0.54). Of 101 recordings retrieved, 96 were usable and 91 had consent for future research. 86/91 were linked to EHRs and 89/91 to post-consultation patient questionnaires. Mean consultation duration was 7 minutes 13 seconds; audible typing was heard in 69% (63/91). 161 problems were discussed (mean 1.77 per consultation). Most patients were happy their consultation was by telephone (96/117, 82%), although the majority reported usually preferring face-to-face appointments (68/115, 59%). Conclusion It is feasible to assemble a reusable archive of GP telephone consultations with linked data. However, recruitment was low using retrospective remote consent. Future work should test alternative recruitment approaches, particularly to improve patient engagement at practices serving deprived populations.

Background: Sleep debt and irregular sleep patterns are highly prevalent amongst adolescents. However, whether the absence of these sleep behaviours protects against subsequent depression remains unclear. Here, we examined the association of sleep debt, weekend catch-up sleep (WCS), and social jetlag (SJL) in adolescence with depression in young adulthood and identified underlying biopsychosocial mechanisms. Methods: Secondary data analyses were conducted using the Avon Longitudinal Study of Parents and Children. Bedtimes and wake-up times on school days and weekends (i.e., sleep duration) and sleep need were self-reported at 15 years. This was used to generate sleep debt (sleep need minus school day sleep duration), WCS (weekend sleep duration minus school day sleep duration), and SJL (absolute difference in the midpoint of sleep times between school days and weekends). Depression was assessed at 24 years with the Clinical Interview Schedule-Revised. Common mental health symptoms, biological, and school-related factors at 17 years were the mediators. Results: Logistic regression analyses revealed that greater WCS (adjusted odds ratio [AOR]=0.90; 95% CI=0.84-0.97; p=0.004) and lower sleep debt (AOR=1.10; 95% confidence interval [CI]=1.03-1.18; p=0.005) at age 15 reduced the likelihood of depression at 24 years. Irritability at 17 years partially mediated the relationship between sleep debt and depression (bias-corrected estimate=0.003; 95% CI=0.002-0.004; p<0.001). Conclusions: Adolescents who experience less sleep debt (i.e., less discrepancies between their actual sleep and their perceived sleep need) and those who extend their sleep duration on weekends are at reduced risk for depression in young adulthood. These findings underscore the need for greater opportunities for adolescents to obtain more hours of sleep to protect them against later poor mental health outcomes, such as depression. Keywords: Sleep; longitudinal studies; depression; ALSPAC