Sleep Advances

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.

BackgroundDespite broad interest in the health implications of sleep duration, traditional measurements via polysomnography or actigraphy are often limited to one or a few nights per person. Given the potential variability of sleep duration over time, inferential uncertainty remains an important issue for relatively short observation windows. MethodsWe describe potential limitations of shorter duration sleep tracking by sub-sampling from longer-term observation windows, using a combined approach of simulated data from known distributions, in addition to real-world data (30-365 nights) from over 35,000 participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data to the study. ResultsSimulations demonstrate that the magnitude of deviation from truth, defined using all available observations per individual, as well as the presence and direction of bias, depended on the sub-sample size, the type of simulated distribution (Gaussian versus skewed), and the summary statistics of interest, such as central tendency (mean, median) and dispersion (standard deviation (SD), interquartile range). For example, the SD computed from n=7 observations from a simulated normal distribution (7+1 hours) showed a median 6.7% under-estimation bias (IQR 24% under- to 14.7% over-estimation). Real-world sleep duration data, when under-sampled and compared to longer observations within-participant, showed similar SD bias at 7 nights, and similar convergence rates approaching the true value (based on 90 nights) as longitdunal sample number increases. Shapiro-Wilk tests for normality and log-normality show that 64% of simulated log-normal (skew) distributions fail to reject normality at n=7 samples, while real-world sleep duration data most commonly failed both normality and log-normality tests. Finally, simulated cohorts with sleep durations of 7+1 hours mixed with a subset of 6+1 hours sleepers showed that a random single-night observation of "short sleep" (6 hours) is more likely from random variation of a 7-hour sleeper, than from an actual 6-hour sleeper. Extending the observation to n=7 nights mitigates this mis-classification risk. ConclusionThe results of simulations and empiric data patterns suggests that longer duration tracking provides important and tangible benefits to reduce bias and uncertainty in sleep health research that historically relies on small observation windows.

ObjectiveDetermine the optimal combination of digital health intervention component settings that increase average sleep duration by [&ge;]30 minutes per weeknight. MethodsOptimization trial using a 25 factorial design. The trial included 2 week run-in, 7 week intervention, and 2 week follow-up periods. Typically developing children aged 9-12y, with weeknight sleep duration <8.5 hours were enrolled (N=97). All received sleep monitoring and performance feedback. The five candidate intervention components (with their settings to which participants were randomized) were: 1) sleep goal (guideline-based or personalized); 2) screen time reduction messaging (inactive or active); 3) daily routine establishing messaging (inactive or active); 4) child-directed loss-framed financial incentive (inactive or active); and 5) caregiver-directed loss-framed financial incentive (inactive or active). The primary outcome was weeknight sleep duration (hours per night). The optimization criterion was: [&ge;]30 minutes average increase in sleep duration on weeknights. ResultsAverage baseline sleep duration was 7.7 hours per night. The highest ranked combination included the core intervention plus the following intervention components: sleep goal (either setting was effective), caregiver-directed loss-framed incentive, messaging to reduce screen time, and messaging to establish daily routines. This combination increased weeknight sleep duration by an average of 39.6 (95% CI: 36.0, 43.1) minutes during the intervention period and by 33.2 (95% CI: 28.9, 37.4) minutes during the follow-up period. ConclusionsOptimal combinations of digital health intervention component settings were identified that effectively increased weeknight sleep duration. This could be a valuable remote patient monitoring approach to treat insufficient sleep in the pediatric setting.

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 [&ge;]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.

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.

Consumer sleep trackers can provide useful insight into sleep and sleep patterns. However, large scale performance evaluation studies against direct sleep measures are needed to comprehensively understand sleep tracker accuracy. This study evaluated performance of an under-mattress sensor to estimate sleep and wake versus polysomnography, during multiple in-laboratory protocols in a large sample including individuals with and without sleep disorders and during day versus night sleep opportunities. 183 participants (51% male, mean[SD] age=45[18] years) attended the sleep laboratory for a research study that included simultaneous polysomnography and under-mattress sensor (Withings Sleep Analyzer [WSA]) recordings. Epoch-by-epoch analyses with confusion matrices were used to determine accuracy, sensitivity, and specificity of the WSA versus polysomnography. Bland-Altman plots examined bias in sleep duration, efficiency, onset-latency, and wake after sleep onset. Overall WSA sleep-wake classification accuracy was 83%, sensitivity 95%, and specificity 37%. The WSA significantly overestimated total sleep time (48[81]minutes), Sleep efficiency (9[15]%), sleep onset latency (6[26]), and underestimated wake after sleep onset (54[78]), p<0.05. Accuracy and specificity were higher for night versus daytime sleep opportunities in healthy individuals (89% and 47% versus 82% and 26% respectively, p<0.05). Accuracy and sensitivity were also higher for healthy individuals (89% and 97%) versus those with sleep disorders (81% and 91%, p<0.05). WSA performance is comparable to other consumer sleep trackers, with high sensitivity but poor specificity compared to polysomnography. Poorer accuracy and specificity during daytime versus night-time sleep opportunities is likely due to increased wake time and reduced sleep efficiency. Contactless, under-mattress sleep sensors show promise for accurate sleep monitoring, noting the tendency to over-estimate sleep particularly where wake time is high.

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.

IntroductionWearable accelerometers are a valuable tool for monitoring sleep, sedentary behaviour, and physical activity patterns within 24h time-use in free-living environments. While wrist-worn accelerometers are favoured for monitoring sleep, they do not accurately distinguish between sitting and lying positions (Narayanan et al., 2020). This study aims to determine whether back or thigh-mounted accelerometers yield sleep metrics comparable to wrist-worn devices using an open-source algorithm originally validated for the wrist. MethodsData from 20 healthy sleepers were collected using Axivity AX3 accelerometers. Participants wore accelerometers on their right thigh, low-back, and wrist for one night of sleep in their own bed. Sleep metrics were calculated using the van Hees algorithm through the GGIR package in R. The primary outcomes were: Total Sleep Time (TST), Wake After Sleep Onset (WASO), Awakenings (AWK), Sleep Efficiency (SE), Sleep Interval (SI) and Sleep Onset Timestamp (SOT). Within-subject ANOVA with Tukeys post hoc, Pearson correlation coefficients, Bland-Altman plots, and Cohens d were used to assess the comparability of sleep metrics between the body placements. ResultsData analysis included all 20 participants. Mid-thigh accelerometers demonstrated a strong linear relationship with wrist accelerometers across all metrics (r = 0.86-0.98). Bland-Altman plots demonstrated a narrow 95% confidence interval suggesting that wrist and mid-thigh metrics are in good agreement, except for AWK which is slightly underestimated by the mid-thigh device. Conversely, low-back accelerometers demonstrated moderate linear relationship with the wrist (r = 0.63-0.98) and the Bland-Altman results showed wide limits of agreement with significant overestimations of TST, SE, SI and underestimations of WASO, AWK, SOT. Cohens d demonstrated small differences between mid-thigh and wrist devices, except for AWK (d= 0.42). Low-back values for WASO, SE, and AWK showed moderate differences. ConclusionsThis analysis demonstrates that the mid-thigh accelerometer yields comparable sleep metrics to wrist-worn devices when processed with the van Hees algorithm.

Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM) often co-occur as medically unexplained conditions linked to disrupted physiological regulation, including altered sleep. Building on the work of Kishi et al. [7], who identified differences in sleep-stage transitions in CFS and CFS+FM females, we exploited the same strictly controlled clinical cohort using a Bayesian Network (BN) to quantify detailed patterns of sleep and its dynamics. Our BN confirmed that sleep transitions are best described as a second-order process [14], achieving a next-stage predictive accuracy of 70.6%, validated on two independent data sets with domain shifts (60.1-69.8% accuracy). Notably, we demonstrated that sleep dynamics can reveal the actual diagnoses. Our BN successfully differentiated healthy, CFS, and CFS+FM individuals, achieving an AUROC of 75.4%. Using interventions, we quantified sleep alterations attributable specifically to CFS and CFS+FM, identifying changes in stage prevalence, durations, and first- and second-order transitions. These findings reveal novel markers for CFS and CFS+FM in early-to-mid-adulthood females, offering insights into their physiological mechanisms and supporting their clinical differentiation.

ObjectiveExposure to light at night (LAN) may influence sleep timing and regularity. Here, we test whether greater light exposure during sleep (LEDS) associates with greater irregularity in sleep onset timing in a large cohort of older adults. MethodsLight exposure and activity patterns, measured via wrist-worn actigraphy (ActiWatch Spectrum), were analyzed in 1,933 participants with 6+ valid days of data in the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 Sleep Study. Summary measures of LEDS averaged across nights were evaluated in linear and logistic regression analyses to test the association with standard deviation (SD) in sleep onset timing (continuous variable) and irregular sleep onset timing (SD[&ge;]1.36 hours, binary). Night-to-night associations between LEDS and absolute differences in nightly sleep onset timing were also evaluated with distributed lag non-linear models and mixed models. ResultsIn between-individual linear and logistic models adjusted for demographic, health, and seasonal factors, every 5-lux unit increase in LEDS was associated with an increase of 7.8 minutes in sleep onset SD ({beta}=0.13 hours, 95%CI:0.09-0.17) and 40% greater odds (OR=1.40, 95%CI:1.24-1.60) of irregular sleep onset. In within-individual night-to-night mixed model analyses, every 5-lux unit increase in LEDS the night prior (lag0) was associated with a 2.2-minute greater deviation of sleep onset the next night ({beta}=0.036 hours, p<0.05). Conversely, every 1-hour increase in sleep deviation (lag0) was associated with a 0.35-lux increase in future LEDS ({beta}=0.347 lux, p<0.05). ConclusionLEDS was associated with greater irregularity in sleep onset in between-individual analyses and subsequent deviation in sleep timing in within-individual analyses, supporting a role for LEDS in exacerbating irregular sleep onset timing. Greater deviation in sleep onset was also associated with greater future LEDS, suggesting a bidirectional relationship. Maintaining a dark sleeping environment and preventing LEDS may promote sleep regularity and following a regular sleep schedule may limit LEDS.

Study ObjectivesIn older adults, self-reported sleep measures may be inaccurate, but polysomnography (PSG) is burdensome. We assessed the performance of an electroencephalography-measuring headband (HB) or actigraphy (ACT) compared with PSG in older adults with sleep disturbances. MethodsSixty-three adults aged [&ge;]60 years who reported symptoms of insomnia and/or daytime sleepiness [&ge;]once/week completed a week-long, home-based protocol during which they wore the HB for seven nights, an actigraph for seven days and nights, and completed a one-night level II unattended PSG. For the current analysis, we compared total sleep time (TST) and wake after sleep onset (WASO) from all three devices on the PSG night. We calculated absolute differences and intraclass correlation coefficients (ICCs) for TST and WASO between HB and ACT, respectively, vs. PSG. We also evaluated the performance of the HB among subgroups of the poorest sleepers according to the presence of sleep apnea, insomnia, poor sleep quality, and periodic limb movements of sleep. Feasibility of the HB was assessed by measures of adherence (i.e., ability to use the HB over seven nights) and usability (i.e., ratings of items from the WEarable Acceptability Range [WEAR] scale). ResultsThe average age was 72.8 [standard deviation 6.6] years, 63.5% were female, and 63.5% identified as non-Hispanic White. On PSG, averages for TST and WASO were 370.1 [93] and 88.9 [63] minutes, respectively. For the HB vs. PSG, mean differences and ICCs were -11.9 minutes and 0.83 [0.74, 0.89] for TST; and -15.5 minutes and 0.65 [0.48, 0.77] for WASO. For ACT vs. PSG, mean differences for TST and WASO were larger, and ICCs showed lower levels of agreement. The HB performed well among the poorest sleepers, with ICCs >0.65 for TST and WASO. On average, participants wore the HB for 6.5 [0.8] nights, and usability was rated highly. ConclusionsThe HB demonstrated good agreement with PSG, outperforming ACT, including among the poorest sleepers. Devices like the HB might provide feasible measures of sleep that are more accurate than ACT and enhance the management of sleep health in older adults with sleep disturbances. Future research should focus on further validation of these devices in habitual sleep environments.

BackgroundThe increasing prevalence of depression and functional disability in older adults highlights the need for targeted interventions, with sleep as a potentially modifiable factor, yet the longitudinal effects and mediating role of sleep remain poorly understood. MethodsThis review and conceptual framework aimed to examine the pairwise bidirectional associations between sleep, depression, and functional disability and identify the longitudinal mediating role of sleep in the bidirectional relationship between depression and functional disability in older adults. The academic databases PsycArticles, PubMed, MEDLINE, Science Citation Index, Social Sciences Citation Index, ProQuest Dissertations and Theses Global, Cochrane, and Scopus were searched for research published in English between January 2000 and June 2024. Systematic review and cohort study designs were eligible. All included studies were assessed for quality using the Critical Appraisal Skill Programme checklist (CASP 2024). Results397,289 citations were identified, and 82 studies meeting the inclusion criteria were included. Cohort studies and reviews provide evidence that there is a dynamic reciprocal correlation between sleep, depression, and functional disability in the older population. We propose that sleep may increase the risk of depression and functional disability in the follow-up years, with sleep acting as a potential mediating factor between depression and functional disability. There was a selection bias in the study samples, as most studies focused on specific populations or regions. Moreover, some of the cohort studies included lacked sufficient follow-up time to observe long-term effects. ConclusionsThis review and conceptual framework highlight that sleep health can provide crucial insights for mitigating the adverse effects experienced by older adults due to depression and functional disability. For healthcare professionals and policymakers, it provides evidence about prioritizing sleep health as an accessible step to foster a healthy lifestyle. PROSPERO registration numberCRD42024556536. What is already known on this topicWith the increasing aging population, improving the physical and mental health of older adults has become a key social issue. Substantial epidemiological studies have confirmed the existence of bidirectional relationships between depression, sleep disorders, and functional disability in older adults, with all three variables influencing each other. However, the complex interaction mechanisms among these three variables remain unclear, and further research is needed to explore whether sleep plays a longitudinal mediating role between depression and functional disability. What this study addsThis study significantly enhances our understanding by providing robust evidence of the dynamic, bidirectional relationships among sleep, depression, and functional disability in older adults. Unlike previous research that primarily examined pairwise relationships, our study delves deeper by proposing a comprehensive conceptual framework. This framework underscores the potential mediating role of sleep, suggesting that sleep disturbances are not merely consequences of depression and functional disability but also active contributors to their interaction and progression. By elucidating these underlying mechanisms and potential pathways, our study sheds light on the complex interplay among these three variables, ultimately enhancing the quality of life for older adults. How this study might affect research, practice or policyThis study paves the way for deeper investigation into the causal mechanisms connecting sleep, depression, and functional disability. It highlights the critical importance of prioritizing resources for sleep-related research and interventions, recognizing their significant potential to enhance the well-being of an aging population. This holistic approach aims to foster a more comprehensive understanding and effective strategies for promoting healthy aging.

While sleep is important, our understanding of its molecular mechanisms is limited. Over the last two decades, protein kinases have been implicated in sleep regulation, with a prominent role for Ca++/calmodulin-dependent kinase II (CaMKII) {beta}. Of all the known mouse genetic mutants, the biggest changes in sleep was reported to be observed in Camk2b gene knockout mice: sleep was reduced by approximately 120 minutes (mins) over 24 hours (hrs). We have reexamined the sleep phenotype in Camk2a and Camk2b knockout mice, and while we have observed sleep reduction in Camk2a knockout mice, we did not find sleep reduction in Camk2b mutants.We did find both Camk2a and Camk2b participated in homeostatic sleep rebound, though. Because CamKII and {beta} are widely known to be crucial kinases with interesting properties, it is worthwhile to keep our results in record for a general service to the field.

Sleep is essential for health and light is an important environmental signal influencing its timing, quality, and regulation. Retinal light exposure reflects the interplay between environmental illumination and behavioral choices, yet it remains unclear which habitual light exposure-related behaviors meaningfully impact sleep outcomes. In this preregistered analysis, we examined associations between these behaviors, sleep timing, and sleep complaints in a large, international community sample (N = 774, Mage = 32.6 {+/-} 14.6 years). Participants completed the Light Exposure Behavior Assessment (LEBA), with four behavioral domains included in the analyses. Sleep timing, sleep disturbances and sleep-related daytime impairment were measured using established questionnaires. Bayesian analyses indicated that time spent outdoors and device use in bed were most strongly associated with sleep outcomes. Greater time outdoors was linked to earlier sleep timing and fewer sleep complaints, whereas more frequent device use in bed was associated with greater sleep disturbance and daytime impairment. Morning and daytime lighting practices and evening light control showed no conclusive evidence. Together, these findings highlight the relevance of everyday light exposure-related behaviors for sleep and support behavioral approaches to promoting healthy sleep in real-world contexts.

BackgroundPoor sleep quality in children can lead to physical and psychosocial problems. The FIFA World Cup has been shown to impact adult behaviors, but its effect on childrens sleep patterns is less understood. The study aimed to evaluate the impact of the FIFA World Cup 2022 (FWC-2022) on childrens sleep patterns. MethodsA cross-sectional survey was conducted between 27 November and 25 December 2022, targeting parents in Saudi Arabia (Arabia standard time) and countries with a +6-hour time difference. Participants completed the validated Childrens Sleep Habits Questionnaire (CSHQ), alongside demographics, time spent watching matches, and parental perceptions on sleep. ResultsA total of 848 parents participated, with 60.6% being mothers. The study found that children averaged 9.10 hours of sleep; 64.2% of parents observed no change, while 10.4% reported substantial changes. Parents aged [&ge;]45 and those noticing shifts in sleep habits reported higher problematic sleep scores. Larger families reported fewer sleep issues, with a negative correlation between family size and sleep problems. Childrens CSHQ scores indicate mild to moderate sleep difficulties across domains. No significant differences were observed between Saudi Arabia and countries with +6-hour time difference. However, one-third of children experienced delays in sleep onset exceeding one hour on weekdays during the World Cup. ConclusionSociodemographic factors, family dynamics, and major events like the FWC-2022 influence parental perceptions of child sleep issues. Older parents and smaller families reported more challenges, while higher socioeconomic status was linked to fewer bedtime difficulties. Our findings may be particularly relevant for FIFA 2026, where transcontinental hosting across North America will expose children globally to matches at even more variable times. Subtle impacts of prolonged event schedules highlight the need for interventions supporting healthy routines during such events, potentially through engaging, sleep-friendly technologies.

IntroductionSleep is essential for mental and physical well-being, yet a significant proportion of U.S. adults experience insufficient sleep (<7 hours per night). Short sleep duration has been associated with an increased risk of mental health disorders and poor physical health, but limited studies have quantified these associations. ObjectiveThis study examines the impact of short sleep duration on depression, self-reported poor mental health days, and poor physical health days. MethodologyData were obtained from the Behavioral Risk Factor Surveillance System (BRFSS) (2016-2023). Sleep duration was categorized as short sleep (<7 hours, coded as 1) or adequate sleep ([&ge;]7 hours, coded as 0). The primary outcomes were depression diagnosis, poor mental health days, and poor physical health days. Inverse Probability Weighting (IPW) was used to estimate the Average Treatment Effect (ATE), adjusting for demographic and socioeconomic factors. ResultsShort sleep duration was associated with a 5.6% increased risk of depression (ATE = 0.056, p < 0.001), 2.24 additional poor mental health days per month (ATE = 2.24, p < 0.001), and 1.8 more poor physical health days per month (ATE = 1.76, p < 0.001). ConclusionShort sleep duration significantly increases the risk of depression and worsens mental and physical health. Public health interventions promoting sleep hygiene are needed to mitigate these effects and improve overall well-being.

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.

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

BackgroundAccurately detecting the Sleep Period Time (SPT) window in the daily life is essential for understanding habitual sleep and health. Although actigraphy devices (accelerometers) placement varies across studies, most SPT-detection algorithms are developed for wrist data. Open-source algorithms support reproducibility and transparency in estimating the SPT. AimsTo optimise and evaluate two open-source algorithms, HDCZA and HorAngle, for estimating the SPT window using hip-worn accelerometer data. MethodsA total of 109 children and 194 adults wore wrist and hip accelerometers for six nights and completed sleep diaries. An established algorithm combining wrist and diary data served as the reference. HDCZA and HorAngle parameters were optimised using Bayesian optimisation on 60% of the sample and evaluated in the remaining 40%. ResultsMean differences for sleep onset and wake-up were -3 and 4 minutes for HDCZA (limits of agreement [LoA]: -221,215 and -185,194; root-mean square error [RMSE]=111 and 97) and 0 and -4 minutes for HorAngle (LoA: -199,199 and -223,214; RMSE=111 and 112). For SPT duration, mean differences were 7 minutes (LoA: -252,266; RMSE=132) for HDCZA and -4 minutes (LoA: -254,246; RMSE=128). No significant differences in SPT duration were found (P=0.774; P=0.237). Both algorithms showed moderate agreement with the reference in ranking sleep duration ({kappa} {approx} 0.56-0.58). Differences were unrelated to age or sex but linked to non-wear time. ConclusionsBoth open-source algorithms demonstrated value for estimating the SPT window from hip data. While HDCZA requires no additional sensor-specific parameters, HorAngle depends on accurate axis identification. Statement of SignificanceAccurately estimating the sleep period time (SPT) window from hip-worn accelerometers is essential for studies assessing sleep in free-living conditions. However, most available algorithms were developed for wrist-worn data. This study optimised and validated two open-source algorithms, HDCZA and HorAngle, for hip-worn accelerometer data in children and adults. Both algorithms performed comparably to a wrist-based reference using sleep diaries, showing consistent agreement across age and sex. These methods enable researchers to estimate habitual sleep without additional sensors or diaries, improving reproducibility and scalability in observational research. The algorithms are openly implemented in the GGIR R package, offering accessible and standardised tools for analysing hip-based accelerometer data.

IntroductionSegmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accurately reflect the true temporal sleep dynamics. We aimed to develop a deep learning-based, high-temporal-resolution sleep-wake classifier leveraging temporally continuous manual reference scoring without fixed epoch boundaries and transfer learning techniques to facilitate progress toward a more physiologically consistent sleep assessment. MethodsThree independent datasets were utilized, of which two included sleep-wake scoring manually conducted in a temporally continuous manner. A U-Net based model was initially trained on a large dataset scored using 30-second epochs, with post hoc scoring modifications (n=2034). It was then fine-tuned via transfer learning using a subset of one of the datasets with temporally continuous scoring (n=39) and validated on both its holdout portion (n=40) and the other independent temporally continuous scoring dataset (n=20). Wakefulness and arousals were consolidated, acknowledging their shared physiological characteristics. Prediction confidence estimates were also generated. ResultsThe model achieved overall concordance of 88.96% ({kappa}=0.78) and 88.23% ({kappa}=0.76) in the holdout and second independent evaluation dataset, respectively, with temporally continuous scoring. Correlation between 1-second automatic predictions and temporally continuous manual scoring was r=0.93 (p<0.001) for total sleep time and r=0.67 (p<0.001) for sleep-to-wake transition index. ConclusionsThese findings support the utility of our model in addressing key limitations of 30-second epoch-based scoring and progressing toward more physiologically consistent sleep-wake assessment by providing a practical basis for subsequent analyses. Misclassifications generally showed lower confidences, indicating additional value for targeted review. Statement of SignificanceConventional sleep scoring remains constrained by fixed 30-second epochs, which may fail to capture the true temporal dynamics of the underlying changes between sleep and wakefulness. In this study, we used polysomnography data manually scored on a temporally continuous basis as the gold standard to develop and validate a deep learning model capable of classifying sleep and wakefulness-like states (consolidating wakefulness and arousal) at high temporal resolution without fixed 30-second epochs. The model demonstrated strong agreement with the gold standard, and as such, lays a practical foundation for deriving improved physiologically meaningful biomarkers of sleep fragmentation and continuity, with potential diagnostic and prognostic value and broad applicability toward a more precise and physiologically consistent sleep assessment.