Journal of Biological Rhythms

Both active movement profiles and robust circadian rhythms are linked to improved health outcomes, yet the underlying mechanisms remain partially understood. We investigated this relationship in young adults (n = 169, aged 18-30 years) under real-world conditions using actigraphy data. We performed k-means clustering on 12 accelerometer-based features capturing magnitude, duration, frequency, and intensity distribution to derive movement behavior profiles. As a proxy of circadian rhythms integrity we computed the Circadian Function Index (CFI), which combines intradaily variability, interdaily stability, and relative amplitude. We also assessed circadian phase and sleep quality parameters. Additionally, we quantified light exposure and physical activity over 3-hour daily intervals. The unsupervised algorithm identified two non-overlapping profiles among participants, the More Active (MA) and the Less Active (LA) profiles. MA exhibited a higher CFI (0.81 {+/-} 0.06 vs. 0.69 {+/-} 0.06, p <0.001), which was also positively associated with early-evening physical activity, but not with light exposure. MA also showed an earlier circadian phase, estimated as the midpoint of the five least active hours (L5c, 04:30 {+/-} 01:03 vs. 04:59 {+/-} 01:15, p adj. = 0.04), which was inversely associated with early-morning physical activity and late-morning light exposure. We found no differences in sleep quality between MA and LA. Our results underscore the association between movement behavior and overall circadian rhythms integrity. Importantly, these findings reinforce actigraphy as a multidimensional tool for both health research and clinical applications.

We examined the overlap in the genes associated with daily rhythms and with behavioral plasticity in ants. We first investigated the daily rhythms of gene expression in the harvester ant, Pogonomyrmex barbatus, and how the rhythmic genes overlap with others previously shown to be associated with plasticity of foraging behavior. Then, to consider whether the overlap is conserved across ant species, we compared rhythms of gene expression in the diurnal, desert harvester ants with those previously reported for a distantly related nocturnal, subtropical carpenter ant, Camponotus floridanus. First, daily transcriptomes in P. barbatus showed that most genes were expressed in light-dark (LD) and constantly dark (DD) conditions at about the same levels; only 11 genes showed at least a two-fold change in expression. Network analysis identified eleven modules of P. barbatus genes under LD conditions. Of these 11 clusters, modules C1 and C2 seem to be central nodes of the gene expression network, because they are the most highly connected in LD, and show the strongest preservation in DD vs. LD, and contain core clock gene Period. Only one module, C2, showed significant overlap with P. barbatus genes that have 24h-rhythmic expression in both LD and DD. There was significant overlap between modules C1, C2, C10, C11, and P. barbatus genes found previously to be associated with plasticity in the regulation of foraging activity to manage water loss. A comparison of the daily transcriptome of P. barbatus with that of C. floridanus showed significant overlap of 24h-rhythmic genes in LD. Modules C1 and C2 of P. barbatus also overlap with C. floridanus genes previously shown to differ in expression rhythms in nurses and foragers. In combination, these results indicate that genes linking plasticity of the circadian clock and of behavior may be broadly conserved in ants.

Sex differences in sleep and wakefulness are well documented in humans but remain inconsistent in rodent studies, suggesting strong sensitivity to experimental context. In prior work, we observed no sex differences in sleep-wake architecture under relatively bright daytime light, raising the possibility that daytime illumination is a critical but underappreciated variable shaping sex-dependent sleep regulation. Here, we tested the hypothesis that daytime light intensity modulates sex differences in sleep-wake architecture and vulnerability to dim light at night (DLaN). Male and female C57BL/6J mice were exposed to acute (one night) or chronic (two weeks) DLaN (10 lux) under three daytime light intensities (50, 100, 300 lux). Sleep was assessed using electroencephalographic-based measures of vigilance states and slow wave activity (SWA). Dim daytime light (50 lux) unmasked robust sex differences in dark-phase sleep-wake architecture that were absent under brighter daytime light (300 lux). Acute DLaN reduced early-night wakefulness in both sexes under low daytime light but had minimal effect under bright daytime conditions. Following chronic DLaN, males exhibited reduced dim light-phase wakefulness and dampened rhythm amplitude, whereas females showed pronounced phase shifts, rhythm attenuation, and altered timing of SWA under 50 and 100 lux. These changes were largely prevented under bright daytime light. Together, these findings identify daytime light intensity as a critical contextual factor governing sex-specific regulation of sleep and vulnerability to nighttime light, providing a unifying framework to reconcile inconsistencies in the rodent sleep literature. HighlightsO_LIDaytime light intensity shapes sex differences in sleep-wake architecture C_LIO_LIAcute and chronic nighttime light elicit distinct sex-specific sleep responses C_LIO_LIFemales exhibit greater circadian and slow-wave vulnerability to nighttime light C_LIO_LIBrighter daytime light buffers sleep and circadian disruption C_LI

BackgroundNocturnal hypoglycaemia (NH) is a common and challenging complication in Type 1 Diabetes (T1D), disrupting blood glucose control and sleep physiology. Its real-world impact on sleep architecture remains poorly characterised. Consumer wearables offer a way to examine these associations under free-living conditions, providing detailed insight into behavioural and physiological responses to nocturnal blood glucose fluctuations. This study aims to assess how wearable-derived sleep metrics and physiological features could be used as indicators of NH, including the effects of how low blood glucose levels fall during hypoglycaemic events and the associated pre-event changes. MethodsWe conducted a comparative observational analysis of paired continuous glucose monitoring (CGM) and Garmin smartwatch data collected over 12 weeks from 17 adults with T1D. Nights were categorised as normoglycaemia, hyperglycaemia, or hypoglycaemia Level 1 ([&ge;]3.1 and <3.9 mmol/L), and hypoglycaemia Level 2 (<3.0 mmol/L). Thirteen sleep metrics, including total sleep time, wake after sleep onset (WASO), sleep-stage proportions, fragmentation indices, and physiological features such as heart rate, were compared using non-parametric tests. Pre-hypoglycaemic event analyses examined 60-minute and 15-minute windows preceding hypoglycaemia to identify early deviations in sleep and physiological metrics. ResultsAcross 573 nights, 17.5% involved Level 1 and 7.3% Level 2 hypoglycaemia. Level 2 hypoglycaemia was associated with 31 minutes less wakefulness, 17-25 minutes more REM, and up to 74% more deep sleep compared with normo-glycaemic nights. Sleep efficiency increased during hypoglycaemic events despite greater fragmentation. Pre-hypoglycaemic episode analyses revealed shorter awake and light-sleep bouts, as well as a 9.8% higher heart rate, preceding Level 2 episodes. ConclusionsWearable-derived sleep and physiological signals reveal clear intraindividual changes both before and during NH. Our findings indicate that Level 2 episodes are associated with deeper sleep and reduced behavioural arousal, suggesting that CGM alarms may be less effective at waking individuals during level2 NH. By characterising pre-hypoglycaemic changes that differ based on hypoglycaemia level, this work provides preliminary evidence for personalised, wearable-based early-warning systems. Such approaches could help distinguish nocturnal hypoglycaemic events and support more effective alerting, particularly in settings with limited or no access to CGM. Author SummaryO_ST_ABSWhy was this study done?C_ST_ABSPeople with Type 1 Diabetes (T1D) frequently experience nocturnal hypoglycaemia (low blood glucose at night), a dangerous event that often goes unnoticed because individuals are less able to recognise symptoms or wake up during sleep. These events also disrupt sleep in ways that are not well characterised under real-world conditions. Limited access to continuous glucose monitoring (CGM), especially in low- and middle-income countries, highlights the need for affordable alternatives to ensure nighttime safety. What did we do and find?Using more than 500 nights of paired smartwatch and CGM data, we investigated how sleep features change when blood glucose levels fall overnight. We found that hypoglycaemic nights show distinct alterations in sleep architecture, including increased REM and deep sleep, and greater micro-fragmentation. A key finding was that Level 2 hypoglycaemia was associated with deeper sleep and reduced wakefulness. This pattern indicates that individuals may be less likely to awaken during more severe events, even when alarms are present. Pre-hypoglycaemic episode analysis revealed additional early-warning signals, such as shorter awake and light-sleep bouts and elevated heart rate, before level 2 hypoglycaemia occurred. What do these findings mean?Smartwatches can capture sleep-based changes that appear before and during nocturnal hypoglycaemia. Because deeper sleep during Level 2 episodes may reduce responsiveness to CGM alerts, these results suggest that current alarm approaches could be improved by incorporating sleep features alongside glucose data. Such sleep-informed detection may enhance the reliability of hypoglycaemia alerts, reduce missed events during deep sleep, and provide a foundation for low-cost early-warning systems in settings where CGM is unavailable or unaffordable. Further research is needed in larger and more diverse populations, but this work provides early evidence that wearable-derived sleep features can meaningfully strengthen nocturnal hypoglycaemia detection.

Actigraphy is a non-invasive and cost-effective method for monitoring behavioral rhythms under real-world conditions by collecting time-resolved measurements of locomotor activity, light exposure, and temperature. Although several open-source packages support specific aspects of actigraphy analysis, aspects such as preprocessing, metric calculation, and mathematical modeling are often distributed across separate software packages, limiting interoperability and increasing programming overhead. Here we introduce circStudio, a Python package that unifies actigraphy data processing and mathematical modeling of circadian rhythms within a single framework. Built from the pyActigraphy codebase and integrating circadian models from the Arcascope circadian package, circStudio provides flexible preprocessing tools, support for multiple actigraphy file formats through adaptor classes, standalone functions for computing commonly used actigraphy metrics, and implementations of several mathematical models of circadian rhythms. The package enables users to move efficiently from raw wearable data to physiologically interpretable circadian outputs. Ultimately, circStudio aims to facilitate reproducible workflows and to provide a flexible foundation for research applications across circadian biology, sleep science, and digital health.

Circadian rhythm disruption is increasingly recognized as a systemic stressor that promotes immune dysregulation and gut microbial imbalance, processes implicated in a wide range of inflammatory and neurodegenerative diseases. However, therapeutic strategies targeting the gut-immune interface under conditions of circadian misalignment remain limited. Here, we investigated whether curcumin, a plant-derived polyphenol with known anti-inflammatory properties, mitigates inflammation and gut dysbiosis induced by severe circadian disruption in a rodent model of chronic jet lag Rats were subjected to repeated 12-hour inversions of the light-dark cycle and treated daily with curcumin (40 mg/kg/day) or vehicle delivered orally in almond butter. Circadian disruption significantly increased circulating proinflammatory cytokines and altered gut microbial composition. Curcumin treatment markedly reduced plasma levels of IFN-{gamma}, TNF-, IL-6, and CXCL1, decreased Peyers patch size, and partially restored circadian-regulated activity patterns. Shotgun metagenomic analysis revealed that curcumin shifted the gut microbiome toward a more eubiotic profile, characterized by increased species richness, reduced dominance of inflammatory taxa, decreased relative abundance of Proteobacteria, and increased Firmicutes, with a trend toward enrichment of Actinobacteria. Collectively, these findings demonstrate that curcumin attenuates systemic and intestinal inflammation associated with circadian rhythm disruption, likely through combined suppression of proinflammatory signaling and modulation of the gut microbiome. Despite its limited systemic bioavailability, curcumin exerted robust effects at the gut-immune interface, highlighting the microbiome as a critical therapeutic target for chronobiology-associated inflammatory disorders. These results support curcumin as a potentially promising chronoprotective intervention for conditions characterized by circadian misalignment, including shift work and jet lag.

The Drosophila adult central brain contains 240 circadian neurons, of which there are more than 25 different neuron subtypes based on connectomic data. Recent single cell RNA-seq (scRNAseq) characterization of these neurons "around the clock" also indicates a similar number of molecular subtypes of circadian neurons, but other conclusions from these transcriptomic studies warranted verifying and extending with other approaches. To this end: 1) We used a genetic multiplexing strategy to profile the transcriptomes of circadian neurons from multiple time points in a single experiment, reducing confounding technical variation between timepoints; 2) Large numbers of single nuclei were sequenced (snRNA-seq), which was enabled because the new method EL-INTACT purifies nuclei from frozen heads; 3) We assayed 12 time points under both light-dark (LD) and constant darkness (DD) conditions. These approaches showed dramatic transcriptional differences between time points in many circadian neuron types and enhanced time-of-day gene expression analysis. The data indicate that most of this regulation is transcriptional and circadian. There were however a small number of light-dependent transcripts, including a few that correspond to mammalian immediate-early genes. They probably play a role in the light-regulation of gene expression and behavior in specific neurons, perhaps circadian entrainment or phase-shifting. The results taken together provide a more comprehensive picture of gene expression heterogeneity within adult Drosophila circadian neurons including how intrinsic clock mechanisms and light cues are integrated across circadian neuron subtypes.

Abstract Background: While total physical activity is a recognized modifier of cancer risk, accelerometer-derived digital phenotyping enables high-resolution mapping of circadian behavior. Whether these multidimensional patterns comprising step counts, sleep, physical activity, circadian rhythmicity, and light exposure independently influence the risk of incident colorectal cancer (CRC) has not been comprehensively evaluated Methods: We performed an exposure-wide association study (ExWAS) of 224 accelerometer-derived metrics among 95,050 UK Biobank participants who were free of CRC at accelerometry. To comprehensively define circadian rhythm patterns, we systematically categorized these metrics into five core behavioral domains: step counts, sleep architecture, physical activity bouts, circadian rhythmicity, and light exposure. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazards models with age as the underlying timescale. Results: During a median follow-up of 8.5 years, 775 participants developed CRC (503 colon; 269 rectal). In minimally adjusted models, 121 metrics showed nominal significance (31 for overall CRC, 89 for colon, and 1 for rectal cancer). Protective associations were predominantly observed for metrics characterizing activity intensity and bout structure; notably, higher mean acceleration during 5-10 minute bouts of moderate-to vigorous physical activity was associated with reduced CRC risk (HR 0.88 per SD). In contrast, no metrics within the defined sleep or light exposure domains reached nominal significance. These associations attenuated substantially following progressive adjustment for lifestyle and metabolic covariates, suggesting potential confounding or shared biological pathways. Conclusions: Our findings identified specific behavioral phenotypes within a multidimensional framework of circadian rhythm, including step counts, physical activity intensity, and bout structure, as being associated with CRC risk. However, the marked attenuation of signals after multivariable adjustment suggests these markers may not serve as independent predictors. These results underscore the complexity of multidimensional circadian digital biomarkers and necessitate independent replication to clarify their utility in cancer risk stratification.

BackgroundLong-term home-cage monitoring is essential to quantify spontaneous locomotor and social behaviors in group-housed mice, but analysis of high-density RFID tracking data remains a barrier to reproducibility. New methodsWe developed IntelliProfiler 2.0, a fully R-based pipeline tailored to the eeeHive 2D floor-mounted RFID array. The workflow performs data import from text logs, preprocessing, coordinate reconstruction, missing-value handling, feature extraction, statistical testing, and visualization in a single environment. Behavioral metrics include travel distance, close contact ratio (CCR), and a newly implemented inter-individual distance metric. ResultsIn four-day recordings of group-housed C57BL/6J mice (8 males and 8 females), IntelliProfiler 2.0 captured circadian phase-dependent locomotion and proximity patterns and reproduced sex-dependent differences consistent with prior analyses while incorporating updated hardware specifications. Radar-chart summaries enabled intuitive comparison of multidimensional behavioral profiles and inter-individual variability across light/dark phases. Comparison with existing methodsCompared with IntelliProfiler 1.0 and multi-tool workflows, IntelliProfiler 2.0 consolidates analysis into a single, script-based R pipeline, reducing operational complexity and improving reproducibility. The updated implementation supports recent manufacturer-driven changes, including antenna renumbering and multi-USB data export. ConclusionsIntelliProfiler 2.0 provides a reproducible, extensible framework for high-throughput behavioral phenotyping of group-housed mice and is scalable across hardware configurations, including simplified single-board recordings. HighlightsO_LIEnd-to-end R pipeline for eeeHive 2D floor-based RFID tracking analysis C_LIO_LIStandardized setup with comprehensive manuals and protocols C_LIO_LIInter-individual distance metric to quantify group spatial structure C_LIO_LICircadian- and sex-dependent behavioral profiling in group-housed mice C_LIO_LIRadar-charts summarize multidimensional behavioral profiles and variability C_LI

Biological processes ranging from gene expression to sleep-wake cycles display oscillations with an approximately 24-hour period, or circadian rhythms. A challenge in analyzing circadian rhythms is that these rhythms vary across individuals and are based on an individuals internal circadian time (ICT), which is uniquely offset relative to the 24-hour day-night cycle time (zeitgeber time, or ZT). Many model-based methods have been proposed to predict ICT given an individuals biomarker measurements. However, the prediction accuracy of these methods is rarely validated using known ICT. In this article, we evaluate this accuracy for three state-of-the-art model-based methods: COFE, partial least squares regression, and TimeSignature. We find that if a single sample is obtained from each individual and a model is fit using only biomarker measurements as predictors, then ZT predicts ICT more accurately than any of the model-based ICT predictions. However, we also find that TimeSignature can outperform ZT when the model incorporates sine and cosine transforms of sample collection ZT as two additional predictors. These findings are based on analysis of three circadian transcriptome datasets as well as simulation studies, and highlight the importance of accounting for individual-level differences in biomarker oscillations to improve ICT prediction.

The light-dark cycle shaped by Earths rotation provided the evolutionary conditions under which circadian rhythms emerged. Consistent with this, previous studies indicate that less than 40% of total blind individuals, who lack photic input, entrain to the 24-h cycle, further evidencing the critical role of light as the dominant zeitgeber for circadian alignment. However, this assumption has been tested almost exclusively in temperate, high-latitude regions, where environmental cues vary seasonally. Near the equator, by contrast, photoperiod and temperature cycles remain exceptionally stable. This highlights a fundamental gap: can circadian rhythms in humans remain synchronised without light when environmental temporal cues are highly regular? We addressed this question in 58 blind adults (21-77 years; 43.1% female) living near the equator in Rio Grande do Norte, Brazil ([~]5{degrees}S), who wore wrist actigraphy continuously for four weeks. Light sensitivity was assessed through the pupillary light reflex (PLR; 22 PLR-reactive, 36 non-reactive). Applying a semi-supervised machine learning approach to uncover multidimensional patterns without prior categorisation, we identified two distinct phenotypes: a Higher Circadian Stability (HCS; 72%, n = 42) and a Lower Circadian Stability group (LCS; 28%, n = 16). Notably, 64% of PLR-non-reactive individuals (23 of 36) were classified within the HCS group, a proportion approximately 1.6 times higher than previously reported for blind cohorts. These findings demonstrate that, under exceptionally regular equatorial conditions, non-photic cues can sustain a robust circadian entrainment even in the absence of photic input. We propose that environmental regularity promotes the synergy of non-photic timing signals, underscoring ecological context as a key determinant of human circadian temporal organisation.

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.

BackgroundLight plays a critical role in mental health, as the primary input to the circadian system, which regulates mood, energy, and the sleep-wake cycle. Altered light sensitivity is a potential mechanism in circadian-associated mental disorders. MethodsActigraphy-derived sleep, physical activity, and circadian rhythm correlates of the pupillary light reflex were explored in young people with emerging mental disorders. Participants were 27 healthy controls (Mean age=25.67 {+/-} 2.83, 52% female) and 155 young people from the Neurobiology Youth Follow-up Study (Mean age=25.48 {+/-} 5.65; 60% female), recruited from an early intervention mental health service. 32% of the latter group were re-assessed over 12 months. Pupil constriction, average and maximal constriction velocity, and constriction latency were recorded by the PLR-3000 monocular pupillometer in response to dim (~10 lux) and bright (~1500 lux) pulses. ResultsCompared to healthy controls, young people with emerging mental disorders had a smaller change in pupil diameter (p=0.037) and a slower maximal constriction velocity (p=0.018) in response to dim light. In the full sample, decreased dim light sensitivity was correlated with later timing of actigraphy-derived sleep midpoint. Within the clinical cases, increased genetic risk for bipolar disorder was correlated with increased dim light sensitivity, and higher insomnia clinical scores were correlated with decreased dim light sensitivity. Pupillometry measures were stable across time and seasons. ConclusionAltered light sensitivity may be associated with the emergence of mood disorder in young people and with altered sleep-wake timing.

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.

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 [&le;] 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 [&le;] 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.

Predictors of Health-Related Quality of Life in Indonesian Women with Systemic Lupus Erythematosus: A Cross-Sectional Within-Cohort Analysis ObjectiveThis study aims to determine the effects of sleep quality along with age, marital status, socioeconomic status, depression, anxiety, disease activity, pain scale, and dose of corticosteroids on quality of life in women with SLE. MethodsVariables were assessed in 75 women with SLE using the Pittsburgh Sleep Quality Index (PSQI), Lupus Quality of Life (Lupus QoL), Depression, Anxiety, and Stress Scale-21 (DASS-21), and Mexican SLE Disease Activity Index (MEX-SLEDAI). Bivariate and multivariate analyses were performed to determine contributors to quality of life. ResultsOf 75 subjects, 35 (46.7%) patients had poor sleep quality. The mean QoL score for patients is 84.27. Poor sleepers had impaired QoL in physical health (p = 0.003), emotional health (p = 0.007), pain (p = 0.003), and planning (p = 0.006), with fatigue (p < 0.0001) as the most significantly impaired. Younger age (Mean {+/-} SD = 81.1 {+/-} 12.67; p = 0.014) and anxiety or depression (Mean {+/-} SD = 56.66 {+/-} 8.17; p = 0.006) were significantly associated with lower quality-of-life scores. The linear regression results showed an R-squared of 0.361, with anxiety ({beta} = 21.402), sleep quality ({beta} = 8.392), and age ({beta} = 5.526) as the most significant variables. Marital status, socioeconomic status, disease activity, pain scale, and corticosteroid dose did not correlate with QoL. ConclusionPoor sleep quality, anxiety, and younger age were significant independent predictors of lower QoL in women with SLE, explaining 36.1% of the variance. These findings suggest that psychosocial and sleep interventions are crucial for improving well-being in this population, potentially more so than focusing solely on disease activity. Summary Box: Key MessagesO_ST_ABSWhat is already known on this topic?C_ST_ABSO_LISystemic Lupus Erythematosus (SLE) significantly diminishes health-related quality of life (HRQoL), particularly in Asian populations where severe organ involvement is more prevalent. C_LIO_LISleep disturbances affect a large majority of SLE patients globally (approximately 62%) and are closely linked to psychological distress and fatigue. C_LIO_LITraditional management focuses heavily on controlling systemic disease activity and organ damage, yet these clinical markers often correlate poorly with patient-reported well-being3. C_LI What does this study add?O_LIIn this cohort of Indonesian women with SLE, nearly half (46.7%) reported poor sleep quality. C_LIO_LIMultivariate analysis identified anxiety/depression ({beta} = -21.402), poor sleep quality ({beta} = - 8.392), and younger age ({beta} = 5.526) as the most significant independent predictors of lower HRQoL, collectively explaining 36.1% of the variance. C_LIO_LIFatigue was identified as the HRQoL domain most severely impaired by poor sleep quality. C_LIO_LINotably, in patients with predominantly low disease activity, markers like the MEX-SLEDAI and corticosteroid dose did not significantly impact HRQoL, highlighting a "disconnect" between clinical control and patient-perceived health. C_LI How might this study affect clinical practice?O_LIThe findings advocate for a shift in the SLE management paradigm from purely inflammatory control toward a multidisciplinary approach that includes routine screening for sleep quality and psychological health. C_LIO_LIClinicians should prioritize interventions such as Cognitive-Behavioral Therapy for Insomnia (CBT-I) and anxiety management, especially for younger patients who may experience greater disruption to life milestones. C_LI

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.

Wearable devices can collect changes in human behaviors related to mental health including depression and anxiety. Here, we examined whether and how digital metrics from a consumer-grade wearable smart ring (Oura Ring) differed by severity of depression and anxiety symptoms using data from a large-scale population-based sample of young adults (n=1,290, age range: 33-35). Participants wore the ring for two weeks, assessing sleep architecture, nocturnal heart rate (HR), heart rate variability (HRV), and movement intensity. Mental health symptoms were assessed using the Generalized Anxiety Disorder 7-item and Hopkins Symptom Checklist-25 scales. On average, participants with higher depression and/or anxiety symptoms had lower levels of rapid eye movement and had higher levels of deep and light sleep, elevated nocturnal HR, reduced HRV, and lower daytime movement compared to non-symptom individuals. Findings suggest that symptoms of depression and anxiety may manifest in physiological and behavioral metrics collected by consumer-grade wearable devices.

Structured AbstractO_ST_ABSBackgroundC_ST_ABSHair cortisol analysis allows assessment of long-term cortisol exposure and may provide insight into chronic hypothalamic-pituitary-adrenal activation in medical residents and residency on-call responsibilities. ObjectiveTo determine the hair cortisol concentration(HCC) representing 3 months of medical residency and secondarily, its association with various on-call models (in-hospital, night float, home call and no call). DesignCross-sectional study of 66 medical residents who were recruited to provide hair samples collected after a three-month block in medical residency. SettingAcademic, tertiary health care system. ParticipantsVolunteer sample of first through third year medical and primary care residents. Exposure3 cm of hair was divided into 3 segments of 1 cm each; each segment represented 1 month of cumulative cortisol production. Main Outcome MeasureHCC results were compared to a published, cortisol assay-specific normative population reference interval. HCC results were interpreted according to a priori categorizations of moderate (+1.5SD), considerable (+2SD) or extreme (> +3SD) HCC elevations. Associations with various on-call models were an exploratory secondary outcome. ResultsThe median age was 28 (26-30) years with median sleep duration of 2 hours on in-hospital call. 40% of trainees had at least one HCC segment above the threshold deemed marked elevation. Median HCC was significantly higher for in-hospital and night float vs. no call (285 ng/g and 335 ng/g vs 78 ng/g p<0.05) and approached significance compared to home call (190 ng/g, p= 0.06). Conclusions and RelevanceWe have described chronic exposure to endogenous cortisol in medical residency. Nearly half of trainees experienced at least one month of severe hypothalamic-pituitary-adrenal axis activation in a 3-month timeframe; many had marked chronic cortisol elevations across the entire 3 month observation frame. HCC was higher in months where in-hospital on-call was required. This may have implications for long-term health of trainees and raises questions about the structure of duty hours and sequence of care acuity blocks within residency training programs.

BackgroundAutistic individuals experience higher rates of sleep problems throughout their lives, and there is considerable heterogeneity in manifestations of these issues that remains unexplained. Here, we examine associations over time of heterogenous sleep trajectories with autism diagnosis, and behavioural and genetic factors related to autism. MethodWe used data from the Avon Longitudinal Study of Parents and Children (N=13,886, autistic n=150). The primary outcome was parent and self-reported night-time sleep duration, measured on 10 occasions (between 0.5y and 15.5y). The independent variables were autism diagnosis, autism polygenic score (PGS) and four parent-reported autistic traits: repetitive behaviour, social communication, speech coherence, and sociability. Latent class growth analysis was conducted to identify heterogenous classes of sleep trajectories, and these trajectory classes were regressed onto the independent variables. ResultsFour night-time sleep duration trajectory subclasses were identified; shorter (n=512, 4.1%), longer (n=1654, 13.1%), intermediate-shorter (n=3630, 28.8%), and intermediate-longer (used as the reference class; n=6825, 54.1%). An autism diagnosis was associated with a shorter or intermediate-shorter sleep duration trajectory, compared to the reference class. Similarly, higher scores in domains of repetitive behaviour, speech coherence and social communication were associated with shorter sleep duration trajectories. The autism PGS and sociability were not associated with any sleep trajectories compared to the intermediate-longer sleep trajectory (reference group). ConclusionAn autism diagnosis and specific autistic traits were associated with poorer long-term sleep outcomes across childhood and adolescence, highlighting the need for early, sustained sleep interventions, and the potential of trait-specific mechanisms for sleep problems. HighlightsO_LIFour distinct night-time sleep duration trajectories were identified across development C_LIO_LIAutism diagnosis predicted shorter and intermediate-shorter sleep trajectories C_LIO_LISpecific (but not all) autistic traits were linked to shorter sleep trajectories C_LIO_LIAutism PGS did not predict sleep duration trajectories C_LI