Importance:Women receiving chemotherapy for breast cancer experience insomnia and fatigue, which impair quality of life. To date, no randomized clinical trial (RCT) has evaluated the use of cognitive behavior therapy for insomnia (CBT-I) and bright light therapy (BLT) both alone and in combination, and few have evaluated the use of either during chemotherapy. Objective:To determine the main effects of CBT-I and BLT on insomnia and fatigue symptoms in women undergoing chemotherapy for breast cancer. Design, Setting, and Participants:Sleep, Cancer, Rest (SleepCARE) was a 6-week, 2 × 2 factorial, superiority, parallel RCT conducted at 5 metropolitan and regional hospitals in Australia from January 22, 2021, to August 21, 2024. Participants were women (aged ≥18 years) receiving chemotherapy for early or metastatic breast cancer. Interventions:Two brief interventions were administered: CBT-I and BLT alone (using light glasses at 1500 lux) and in combination, creating 4 groups (CBT-I alone, BLT alone, CBT-I plus BLT, and sleep hygiene education [SHE]). All interventions included SHE. The interventions lasted 6 weeks and included a 1:1 consultation session, emails sent once or twice per week, and a midpoint call for all groups, as well as light glasses for the BLT groups. Main Outcomes and Measures:Dual primary outcomes were Insomnia Severity Index (ISI) scores and Patient-Reported Outcomes Measurement Information System (PROMIS)-Fatigue T scores. Both are patient-reported outcome measures and measure insomnia and fatigue symptoms, respectively. Assessments occurred via surveys administered at baseline and at the midpoint (3 weeks), postintervention (6 weeks), and follow-up (3 months and 6 months) periods. Modified intention-to-treat analyses used latent growth models. Results:Of the 219 women enrolled (mean [SD] age, 50.7 [10.8] years; 54 [26.9%] with metastatic cancer), 55 were randomized to CBT-I, 55 to BLT, 52 to CBT-I plus BLT, and 57 to SHE. A total of 208 women (95.0%) with any data at any time point were analyzed. Insomnia symptoms (ISI score mean difference [MD], -2.19 [95% CI, -3.33 to -1.05] points; P = .002) but not fatigue symptoms (PROMIS-Fatigue score MD, -0.90 [95% CI, -3.08 to 1.28] points; P = .52) improved more in the CBT-I groups compared with the non-CBT-I groups. The BLT groups (compared with the non-BLT groups) did not differ in insomnia symptoms (ISI score MD, -0.88 [95% CI, -2.02 to 0.26] points; P = .26) or fatigue symptoms (PROMIS-Fatigue score MD, -0.71 [95% CI, -2.89 to 1.47] points; P = .52). Comparable results emerged in the high-adherence subgroup and in the subgroups with high initial insomnia and fatigue symptoms. However, exploratory subgroup analyses in women with metastatic breast cancer showed that BLT (vs non-BLT) improved insomnia symptoms (ISI score MD, -2.87 [95% CI, -5.06 to -0.67] points; P = .01) and fatigue symptoms (PROMIS-Fatigue score MD, -5.16 [95% CI, -9.57 to -0.76] points; P = .02). Conclusions and Relevance:In the SleepCARE RCT of CBT-I and BLT administered for 6 weeks to women receiving chemotherapy for breast cancer, CBT-I improved insomnia but not fatigue compared with SHE or BLT. BLT did not produce greater improvements in fatigue or insomnia symptoms compared with non-BLT treatment. The study findings indicate that brief CBT-I, but not BLT, may reduce insomnia symptoms among women receiving chemotherapy for breast cancer. Trial Registration:ANZCTR Identifier: ACTRN12620001133921.

Objectives Daily temperature variability is projected to increase in some world regions up to 100% by 2100. This study investigated associations between night-to-night temperature variability with sleep duration timing and regularity. Methods Data were collected from global users of the Withings Sleep Analyzer (n = 125,295) and ScanWatch (n = 238,550) between January 2020-September 2023. Sleep regularity was assessed using intra-monthly variation in sleep duration and timing (onset, midpoint and offset). Ambient temperature data were extracted from established climate models to calculate night-to-night temperature variability. Fixed-effects models were used to assess the association between night-to-night temperature variability and sleep regularity (duration and timing). Results Participants (n = 313,822) were middle-aged (50 ± 15 years), 70% male, with an average 17 months of data. During months with high vs. low night-to-night temperature variability (99th percentile: 4.3°C vs. 50th percentile: 1.9°C), sleep onset variability was higher in southern (mean [95%CI]; 17.9 [16.1, 19.6]min), central (6.5°C vs. 1.0°C; 32.2 [29.8, 34.6]min) and northern locations (5.5°C vs. 2.1°C; 11.4 [11.2, 11.7]min). There was greater variability in sleep midpoint and offset with higher night-to-night temperature variability, with similar effect sizes. High night-to-night temperature variability was associated with <10 minutes difference in sleep duration irregularity. Conclusions Our findings highlight that high night-to-night temperature variability is associated with a 20%-50% increase in sleep timing irregularity, with central and southern regions being more affected. Given that sleep irregularity is associated with adverse health outcomes, targeted interventions to mitigate the impact of temperature variability on sleep health is an important public health priority.

OBJECTIVES: This study aimed to investigate how alertness, sleepiness, and fatigue change across consecutive night compared to morning shifts among Arctic shift workers and whether these effects differ between seasons of midnight sun and polar night. METHODS: We conducted an observational crossover study of 118 shift workers from an industrial plant at a high latitude (71°N) in northern Norway. Eighty-one individuals participated in both the light (near 24-hour daylight) and dark (minimal natural light) seasons. Work schedules included blocks of seven consecutive morning shifts and seven consecutive night shifts, separated by four rest days. Alertness (psychomotor vigilance test), subjective sleepiness (Karolinska Sleepiness Scale), and subjective fatigue were measured at the end of shifts on days 1, 3, and 6 of each shift block. We analyzed data using multilevel mixed-effects regression models with season, shift type (morning/night), and consecutive workday number as fixed effects. RESULTS: Night shifts were linked to lower alertness and higher sleepiness and fatigue in both seasons, with the largest impairments on the first night. Across six consecutive night shifts, alertness improved and sleepiness and fatigue decreased, with similar trajectories in both seasons. There was no evidence that season significantly affected alertness, sleepiness, or fatigue. CONCLUSIONS: Night shifts generally impair alertness and increase sleepiness and fatigue, yet outcomes improved across consecutive nights. Despite the well-established effects of natural light on circadian rhythms, the seasonal photoperiod altered neither the shift-related impairments in alertness, sleepiness or fatigue nor the subsequent improvements across consecutive nights; workers showed similar adaptation in both seasons.

Background Delayed Sleep-Wake Phase Disorder (DSWPD) is a circadian rhythm disorder marked by a consistent and distressing delay in sleep timing relative to societal norms. While traditionally viewed as a circadian phase disorder, growing evidence shows psychological, behavioural, and physical health factors interact with circadian biology to influence onset, maintenance, and outcomes. Purpose of review: This review synthesises recent literature on DSWPD's multifactorial nature, focusing on aetiology, nosology, comorbidities, and treatment. It highlights emerging evidence supporting a multidimensional diagnostic approach and personalised, multimodal management. Recent findings Some individuals with DSWPD exhibit a significantly delayed circadian phase, while others show normal circadian timing but persistently delayed sleep behaviour. A spectrum approach or subtyping into circadian and behavioural variants has been proposed. Comorbidities with psychiatric conditions including depression, anxiety, ADHD and autism are common and may affect treatment response. Chronobiotic treatments remain core, but cognitive-behavioural and psychotherapeutic interventions are increasingly essential, especially in non-circadian or comorbid cases. Advances in wearable technology and circadian modelling offer promising tools for diagnosis, monitoring, intervention. Summary DSWPD is heterogeneous and requires an integrative, individualised approach considering circadian biology, behaviour and psychiatric comorbidities. A multidimensional diagnostic and treatment model could improve outcomes and functioning.

Light assists in regulating mood, and selective serotonin reuptake inhibitors (SSRIs) increase non-visual light sensitivity. It remains unclear whether light exposure patterns differ between individuals taking SSRIs compared to unmedicated individuals with no psychiatric history, or how everyday light exposure relates to mood and chronotype. This study examined objective light exposure (melanopic EDI) in relation to medication status (SSRI vs. control), mood symptoms, and chronotype. Participants (n = 76; 38 SSRIs, 38 controls) completed at least one week of field light monitoring using a wearable sensor and questionnaires assessing mood (DASS-21) and chronotype (MEQ). Overall light exposure did not differ between groups. However, when accounting for group, greater morning light exposure was associated with lower depressive and stress symptoms, and more time spent above 50 melanopic EDI was associated with fewer depressive symptoms. Greater morningness was linked to higher morning and daytime melanopic EDI, more time in bright light (>50 and >250 melanopic EDI), and differences in light regularity. These findings show that light exposure, particularly its timing and amount, relates to mood and chronotype, regardless of SSRI use. Future research targeting light behaviour may offer an accessible, cost-effective strategy for improving mood in both clinical and non-clinical populations.

ABSTRACT It was long believed early mammals were nocturnal to avoid interactions with day-active dinosaurs. However, recent evidence indicates many dinosaurs were likely nocturnal, suggesting more complex coevolutionary dynamics prevailed. We simulated coevolution of sleep in a general predator/prey system, using a physiological model. We discovered ‘temporal niche pursuit’ cycles across evolutionary timescales: prey repeatedly escaping into a novel temporal niche, with predators subsequently invading that niche. We characterized multiple oscillatory patterns for pursuit, involving distinct genetic and phenotypic mechanisms. A low-dimensional model recapitulated the dynamics of the physiological model. These findings reveal rich dynamical processes underlying selection of temporal niche.

Sleep regularity, the consistency of sleep-wake timing from one day to the next, is more strongly associated with longevity than adequate sleep duration. Whether this relationship persists across common diseases is unknown. We compared sleep regularity vs. sleep duration as risk factors for 199 diseases and disorders, using ten million hours of objective sleep-wake data (N=60,998, age[mean±SD]=62.8±7.8, 55% female). Multivariable-adjusted risks of incident diseases/disorders for regular/irregular and short/adequate sleepers were compared across 9.5 years of follow-up. Irregular sleep predicted risks for 131 diseases/disorders, more than double the number predicted by short sleep duration (63). Irregular sleep was a superior predictor than short sleep duration for 90 diseases/disorders, including circulatory, metabolic, digestive, renal, infectious, neurological, and musculoskeletal conditions, and mental disorders, whereas short sleep duration was the superior predictor for only 9 diseases/disorders. For models where short sleep duration explained disease risks, 83% were improved by adding sleep regularity. Sleep regularity was a stronger predictor of diseases/disorders than sleep duration in this cohort and should be considered an essential dimension of sleep health. ### Competing Interest Statement B. L. has received research grants from Withings, OURA, Medical Research Future Fund and NHMRC. A.C.R. received research funding from the Australian Research Council (Discovery Early Career Researcher Award - DE250101060). M.K.R. has consulted for Eli Lilly and has modest stock ownership in GSK. M.K.R. disclosures are not related to the current work. F.A.J.L.S. served on the Board of Directors for the Sleep Research Society and has consulted for the University of Alabama at Birmingham and Morehouse School of Medicine. F.A.J.L.S. interests were reviewed and managed by Brigham and Women's Hospital and Partners HealthCare under their conflict-of-interest policies. F.A.J.L.S. consultancies are not related to the current work. R.J.A. has research funding/support from the National Health and Medical Research Council, the Medical Research Future Fund, the Hospital Research Foundation, Flinders Foundation, Sydney Trains, Safework SA, Withings, ResMed Foundation, Neuroflex, Philips and the Lifetime Support Authority for research related to sleep. S.W.C. has consulted for Dyson. A.J.K.P. and S.W.C. are co-founders and co-directors of Circadian Health Innovations PTY LTD. A.J.K.P. and S.W.C. have received research funding from Versalux and Delos. S.W.C. has received research funding from Beacon Lighting. D.P.W., A.C.B., H.S., D.S., K.S., and R.S. have no conflicts of interest to disclose. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This research was conducted using UK Biobank data (Project ID: 6818), and ethical approval was granted by the North West Multi-centre Research Ethics Committee. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online via application to the UK Biobank.

Sleep irregularity, characterised by inconsistent sleep duration and timing, is increasingly linked to adverse health outcomes including hypertension. However, the persistence and long-term effects of irregular sleep remain unclear, largely due to short sleep monitoring periods in past studies. This study aimed to examine the chronic, cumulating impacts of irregular sleep on sleep health (sleep duration, efficiency, timing) and blood pressure. We analysed approximately 20 months of under-mattress sleep sensor data from 95,819 global participants. Each person-month of sleep data was classified as 'irregular' if sleep duration variability was ≥ 90 min and sleep midpoint variability was ≥ 60 min (intra-individual standard deviation): thresholds that were previously found to correlate with poor cardiovascular health. Approximately 20% of person-months were classified as irregular by these criteria. Participants with chronic irregular sleep (≥ 75% of months irregular) exhibited 8% lower sleep efficiency, 40 min more wake after sleep onset, and 46% higher odds of hypertension compared to those with regular sleep (0%-25% of months irregular). Irregular sleep patterns were persistent, with over 80% likelihood of continuation after six consecutive irregular months. These findings suggest that chronic sleep irregularity is common, persistent, and associated with poorer sleep quality and elevated blood pressure. This underscores the need for interventions to promote consistent sleep patterns to support cardiovascular health.

Abstract The sleep patterns of infants and young children differ from adult sleep patterns, with longer duration and multiple bouts per 24 hours. There is also considerable heterogeneity in infant sleep, both between individuals and within individuals across development. While mathematical models have been used to understand the mechanisms that regulate adult sleep, the development of sleep from infancy through early childhood has remained largely unexplored. Here we used an established mathematical model for adult sleep regulation to investigate how the underlying mechanisms of sleep mature from ages 1 month to 5 years, and identify a basis for inter-individual differences at each age. Using a Bayesian approach to estimate joint distributions of model parameters at different ages, we found that: (i) decreases in the rate of accumulation of sleep homeostatic pressure captured the reduction in sleep duration across development; while (ii) increases in the time scale of the sleep homeostatic drive captured the consolidation of sleep into fewer sleep bouts per day across development. In terms of inter-individual differences, we found larger spread in the parameters within the earlier stages of infancy (<1 year). The center and boundaries of the joint distributions evolved with age through the parameter space, converging towards previously described adult parameter values. A bifurcation analysis of the homeostatic time scale parameter revealed that progressive consolidation of sleep occurs through abrupt loss of bouts one at a time, punctuated by narrow intervals of non-entrained (>24-h) cycles. These results establish plausible, population-level trajectories in physiological parameters underpinning maturation of sleep regulation through infancy and early childhood. Author Summary The sleep patterns of infants and young children differ from adult sleep patterns, having both longer duration and being split into multiple bouts per 24 hours. Sleep patterns also vary greatly child to child and change considerably with development. While mathematical models have been used to understand adult sleep, the development of sleep from infancy through early childhood has remained largely unexplored. Here we used an established mathematical model for adult sleep regulation to investigate how the underlying mechanisms of sleep mature from ages 1 month to 5 years, and identify a basis for inter-individual differences at each age. Using our method, which inferred population level parameter distributions from population-level data summaries, we found that: (i) decreases in the rate of accumulation of sleep homeostatic pressure captured the reduction in sleep duration across development; while (ii) increases in the time scale of the sleep homeostatic drive captured the consolidation of sleep into fewer sleep bouts per day across development. In terms of inter-individual differences, we found larger spread in the parameters within the earlier stages of infancy (<1 year). These results establish plausible, population-level trajectories in physiological parameters underpinning maturation of sleep regulation through infancy and early childhood.

STUDY OBJECTIVES:High ambient temperatures are associated with negative health outcomes, including heat stress, injuries and accidents, and poor mental health. Sleep loss may contribute to these adverse impacts. However, robust supporting evidence is lacking. METHODS:Data from a global sample of sleep tracker users (N = 317 758; Withings Sleep Analyzer [WSA]: n = 116 879; Withings ScanWatch: n = 200 879) between January 2020 and September 2023 (~165 million nights) were analyzed. Ambient temperatures (24 hour average) were extracted from a climate model for each nightly observation based on the users' location. We used the case-time-series design with participant-year-week intercept and spline functions to derive exposure-response curves between temperature and short sleep (<6 hours/night) prevalence, adjusting for other time-varying factors and meteorological variables. RESULTS:High temperatures (99th vs 50th percentile of the observed global distribution; 27.3°C vs 12.2°C) were associated with (mean [95%CI]) -15.2 [-15.6, -14.9] and -16.9 [-17.4, -16.4] minute sleep loss in the users of the smartwatch and under-mattress sensors, respectively. Similarly, high temperatures were associated with an approximately 40% relative increase in the probability of short sleep on the same night (WSA: Risk Ratio: RR [95%CI]; 1.40 [1.38, 1.41]; ScanWatch: 1.43 [1.41, 1.44]). Estimates ranged between 10% and 75% depending on the country. Sleep loss to high temperatures was higher in participants residing in Europe and countries with lower national gross domestic product per capita. CONCLUSIONS:High temperatures negatively impact sleep duration and increase the probability of short sleep globally. Our findings suggest that rising temperatures may increase the health impacts of short sleep. Statement of Significance High ambient temperatures are linked to negative health outcomes, with sleep loss potentially contributing to these effects. This study analyzed data from 317 758 global sleep tracker users to examine the relationship between temperature and short sleep. Results showed an approximately 40% relative increase in the probability of short sleep at high temperatures (99th vs 50th percentile; 27.3°C vs 12.2°C) globally. Sleep loss to high temperatures was higher in participants residing in countries with lower national gross domestic product per capita and older adults. These results suggest that sleep inadequacy from rising temperature due to climate change may further amplify global inequalities. Our findings also highlight the urgent need for targeted strategies to mitigate temperature-induced sleep loss.

Importance:Light at night causes circadian disruption, which is a known risk factor for adverse cardiovascular outcomes. However, it is not well understood of cardiovascular diseases. Objective:To assess whether day and night light exposure is associated with incidence of cardiovascular diseases, and whether associations of light with cardiovascular diseases differ according to genetic susceptibility, sex, and age. Design, Setting, and Participants:This prospective cohort study analyzed cardiovascular disease records across 9.5 years (June 2013 to November 2022) from UK Biobank participants who wore light sensors in a naturalistic setting. Data were analyzed from September 2024 to July 2025. Exposure:Approximately 13 million hours of light exposure data, tracked by wrist-worn light sensors (1 week each), categorized into the 0 to 50th, 51st to 70th, 71st to 90th, and 91st to 100th percentiles. Main Outcomes and Measures:Incidence of coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, and stroke after light tracking were derived from UK National Health Service records. Risks of cardiovascular diseases were assessed using Cox proportional hazards models (3 primary models adjusted at 3 levels) and reported as hazard ratios (HRs). Results:A total of 88 905 individuals were included (mean [SD] age, 62.4 [7.8] years; 50 577 female [56.9%]). Compared with individuals with dark nights (0-50th percentiles), those with the brightest nights (91st-100th percentiles) had significantly higher risks of developing coronary artery disease (adjusted HR [aHR], 1.32; 95% CI, 1.18-1.46), myocardial infarction (aHR, 1.47; 95% CI, 1.26-1.71), heart failure (aHR, 1.56; 95% CI, 1.34-1.81), atrial fibrillation (aHR, 1.32; 95% CI, 1.18-1.46), and stroke (aHR, 1.28; 95% CI, 1.06-1.55). These associations were robust after adjusting for established cardiovascular risk factors, including physical activity, smoking, alcohol, diet, sleep duration, socioeconomic status, and polygenic risk. Larger-magnitude associations of night light with risks of heart failure (P for interaction = .006) and coronary artery disease (P for interaction = .02) were observed for females, and larger-magnitude associations of night light with risks of heart failure (P for interaction = .04) and atrial fibrillation (P for interaction = .02) were observed for younger individuals in this cohort. Conclusions and Relevance:In this cohort study, night light exposure was a significant risk factor for developing cardiovascular diseases among adults older than 40 years. These findings suggest that, in addition to current preventive measures, avoiding light at night may be a useful strategy for reducing risks of cardiovascular diseases.

Gambling behaviour is a persistent and growing societal problem. An unexplored factor that may encourage gambling behaviour is the impact of circadian photoreception on cognitive processes underlying the behaviour. We investigated the influence of circadian photoreception on loss aversion in gambling by altering the blue content of light while maintaining the same visual brightness. Fifteen participants (age 18-27 years, M = 20.40, SD = 2.03) completed an economic decision-making task under blue-enriched and blue-depleted light, of equivalent visual brightness, on separate occasions in a randomised order. The task required participants to choose between taking a risky gamble of a positive and negative outcome, or a less risky guaranteed outcome. Hierarchical Bayesian Modelling was conducted to derive individual parameter estimates for loss aversion, and trial-by-trial performance was analysed using linear mixed models. The findings demonstrated that individuals were significantly less loss averse under blue-enriched light compared to blue-depleted light (β = - .43, 95% CI [- .82, - .04], p = .03). This study shows that exposure to light that preferentially targets circadian photoreception reduces loss aversion, which may encourage gambling behaviour.

Light is the primary circadian time cue, but there are large interindividual differences in how sensitive the circadian system is to light. Currently, it is not well understood how individual differences in light sensitivity interact with real-world light environments to determine sleep and circadian timing. We used a validated computational model to simulate sleep and circadian timing (predicted dim light melatonin onset) under realistic assumptions about light and work schedules. Simulations were repeated varying light sensitivity (translated to equivalent ED50 values for interpretability), as well as evening, morning, and daytime illuminances. Brighter evening light led to later predicted circadian and sleep timing, with this effect being amplified by high light sensitivity. Reducing evening light was particularly beneficial for those with high light sensitivity or a long circadian period. Brighter morning light was beneficial for individuals with a long circadian period, or those with both high light sensitivity and high evening light. However, bright morning light could be maladaptive in individuals with a short circadian period or those with low light sensitivity and low evening light. Brighter daytime light attenuated the delaying effects of evening artificial light across conditions, indicating that increasing daytime light was the most universally beneficial lighting intervention. Our results demonstrate how circadian light sensitivity can be used to tailor individual-level solutions that support optimal sleep and circadian timing.

STUDY OBJECTIVES:Irregular sleep is a major risk factor for adverse health. In a global sample with technology-enabled long-term objective sleep data spanning 3.5 years, we investigated variability in sleep duration and timing over weekdays, months, seasons, and years. METHODS:Registered users of an FDA-cleared under-mattress sleep sensor who had ≥28 nights of sleep recordings and averaged ≥4 nights per/week between January 2020 and September 2023 were included for analyses. Generalized nonlinear fixed effects models were used to assess associations between sleep duration and sleep timing with weekday, month, season, and year. Sub-group analyses were conducted by age, sex, and location. RESULTS:Data from 116 879 adults (90 333 males, 26 546 females) aged 49 ± 14 years were analyzed. Weekday variation was observed, with 20-35 minutes longer sleep duration on weekends versus weekdays. Time to bed and time out of bed were 30-40 minutes and 60-80 minutes later on weekends, respectively. Seasonal variation in sleep duration was also evident; sleep duration was 15-20 minutes longer during winter in the northern hemisphere, 15-20 minutes shorter during summer in the southern hemisphere, and variations reduced closer to the equator. Sleep duration decreased from 2020 to 2023 but the effect was small (2.5 minutes). CONCLUSIONS:These novel findings underscore the seasonal nature of human sleep, influenced by demographics and geography.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, so together may provide a more complete picture of sleep health, while illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p < 8.3e-9), along with 341 previously reported loci (p < 5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2 = 0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post-GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections (including potential causal links) to behavioral, psychological, and cardiometabolic traits. Data-driven composite sleep health scores, combining self-reported sleep duration, snoring, chronotype, insomnia, and sleepiness, provide heritable, interpretable phenotypes and novel GWAS discoveries elucidating regulatory pathways.

Irregular sleep is increasingly related to poorer health, with stronger links to cardiovascular disease and mortality than sleep duration. Its impact on health-related quality of life, however, remains unclear, particularly in community-based populations. This study examined whether objectively measured sleep regularity is associated with physical and mental health-related quality of life. Sleep regularity was calculated using the Sleep Regularity Index from actigraphy data in 768 middle-aged to older adults from the Raine Study (median age [range] = 57 [53-61]; 58% female). Physical and mental health-related quality of life were assessed using the 12-item Short Form Health Survey. Quantile regression was used to examine associations at the 25th, 50th, and 75th percentiles, adjusting for age, sex, comorbidity count, sleep duration, and shift work. Median sleep regularity scores declined with self-rated health, from 77.17 (excellent) to 61.49 (poor). A 10-unit increase in sleep regularity was associated with higher mental health scores at the 25th (1.80; 95% CI: 0.90-2.60), 50th (1.20; 95% CI: 0.50-1.90), and 75th (0.50; 95% CI: 0.20-0.90) percentiles. For physical health, a 10-unit increase in sleep regularity was associated with a 1.20 (95% CI: 0.30-2.20) higher score at the 25th percentile, with no evidence of association at higher percentiles. These findings suggest that poorer sleep regularity is related to lower physical and mental health-related quality of life. Future research should explore whether improving sleep regularity can enhance quality of life in middle-aged to older adults.

Darkness is equated with sadness. This study explored how light that differentially impacts non-visual photoreception (blue-enriched vs. blue-depleted light) affects how we feel about ourselves. In a repeated-measured design, 35 participants (22 female participants, 13 male participants, Mage = 20.29, SD = 2.09) completed the self-referential encoding task (SRET) under both blue-enriched or blue-depleted light conditions, with light conditions randomized and counterbalanced between sessions. The SRET involved participants deciding whether positive (e.g. "good") and negative (e.g. "terrible") words were self-descriptive. Trial-by-trial performance analysis using logistic mixed effects models revealed that blue-enriched light significantly increased the likelihood of rejecting negative words as self-descriptive. A hierarchical drift-diffusion model (HDDM) further examined latent decision-making processes and found evidence accumulation to be faster under blue-enriched light when rejecting negative descriptors, suggesting rejecting negative self-descriptors was easier under blue-enriched light. We find light can acutely influence self-perception, with blue-enriched light decreasing negative self-thoughts.

OBJECTIVE:Circadian rhythms play a key role in metabolic health. Rest-activity rhythms, which are in part driven by circadian rhythms, may be associated with diabetes risk. There is a need for large prospective studies to comprehensively examine different rest-activity metrics to determine their relative strength in predicting risk of incident type 2 diabetes. RESEARCH DESIGN AND METHODS:In actigraphy data from 83,887 UK Biobank participants, we applied both parametric and nonparametric algorithms to derive 13 different metrics characterizing different aspects of rest-activity rhythm. Diabetes cases were identified using both self-reported data and health records. We used Cox proportional hazards models to assess associations between rest-activity parameters and type 2 diabetes risk and random forest models to determine the relative importance of these parameters in risk prediction. RESULTS:We found that multiple rest-activity characteristics were predictive of a higher risk of incident diabetes, including lower pseudo-F statistic (hazard ratio [HR] of quintile 1 [Q1] vs. Q5 1.27; 95% CI 1.09-1.46; Ptrend < 0.001), lower amplitude (HRQ1 vs. Q5 2.56; 95% CI 2.21-2.97; Ptrend < 0.001), lower midline estimating statistic of rhythm (HRQ1 vs. Q5 2.59; 95% CI 2.24-3.00; Ptrend < 0.001), lower relative amplitude (HRQ1 vs. Q5 4.64; 95% CI 3.74-5.76; Ptrend < 0.001), lower M10 (HRQ1 vs. Q5 3.82; 95% CI 3.20-4.55; Ptrend < 0.001), higher L5 (HRQ5 vs. Q1 1.88; 95% CI 1.62-2.19; Ptrend < 0.001), and later L5 start time (HRQ5 vs. Q1 1.20; 95% CI 1.04-1.38; Ptrend = 0.004). Random forest models ranked most of the rest-activity metrics as top predictors of diabetes incidence, when compared with traditional diabetes risk factors. The findings were consistent across subgroups of age, sex, BMI, and shift work status. CONCLUSIONS:Rest-activity rhythm characteristics measured from actigraphy data may serve as digital biomarkers for predicting type 2 diabetes risk.