Journal of Sleep Research

Insomnia may play a causal role in type 2 diabetes (T2D). Addressing insomnia through cognitive behavioural therapy (CBTi) in people with non-diabetic hyperglycaemia could potentially reduce the risk of progression to T2D. To inform a future randomised trial, we performed a feasibility study of digital CBT (dCBTi) in individuals at increased risk of T2D. Participants were identified from ten primary care practices in the UK and given access to dCBTi. Outcomes were evaluated at baseline (week-0) and post-treatment (week-11). Primary feasibility outcomes were ability to recruit and treatment engagement. We also quantified within-group mean change (95%CI) in insomnia severity (Insomnia Severity Index), health-related quality of life (EQ-5D-3L), depression (Center for Epidemiologic Studies Depression Scale), chronotype (reduced Morningness-Eveningness Questionnaire), sleep (7-day actigraphy and diary), continuous glucose monitoring (7-days) and fasting blood metabolites (insulin, lipids, glucose, and C-reactive protein). The recruitment target was 20. Of 242 people completing screening, 36 were eligible, and 24 were enrolled (age 65.5{+/-}12.4 years, 70.8% female). Twenty-three (96%) completed post-intervention assessments. Treatment engagement was excellent (83.3% completed [≥]4 sessions). The intervention was associated with a large reduction in insomnia severity [-4.7(95%CI:-6.2 to -3.2), d=-1.4] and medium reduction in depressive symptoms [-2.7(95%CI:-5.1 to -0.2), d=-0.5]. Sleep diary parameters tended to show greater improvement following intervention relative to actigraphy. There was evidence of a reduction in serum lactate, glycerol and triglycerides but no clear change in glucose or insulin. Results suggest a full trial is likely feasible and that people with NDH find the intervention acceptable and beneficial. Trial RegistrationThis trial was prospectively registered on the UKs clinical study registry, the ISRCTN (ISRCTN19682964, https://doi.org/10.1186/ISRCTN19682964)

IntroductionThis study evaluated the performance of a wrist-worn wearable, Verily Study Watch (VSW), in detecting key sleep measures against polysomnography (PSG). MethodsWe collected data from 41 adults without obstructive sleep apnea or insomnia during a single overnight laboratory visit. We evaluated epoch-by-epoch performance for sleep versus wake classification, sleep stage classification and duration, total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings (NAWK). Performance metrics included sensitivity, specificity, Cohens kappa, and Bland-Altman analyses. ResultsSensitivity and specificity (95% CIs) of sleep versus wake classification were 0.97 (0.96, 0.98) and 0.70 (0.66, 0.74), respectively. Cohens kappa (95% CI) for 4-class stage detection was 0.64 (0.18, 0.82). Most VSW sleep measures had proportional bias. The mean bias values (95% CI) were 14.0 minutes (5.55, 23.20) for TST, - 13.1 minutes (-21.33, -6.21) for WASO, 2.97% (1.25, 4.84) for SE, -1.34 minutes (-7.29, 4.81) for SOL, 1.91 minutes (-8.28, 11.98) for light sleep duration, 5.24 minutes (-3.35, 14.13) for deep sleep duration, and 6.39 minutes (-0.68, 13.18) for REM sleep duration. Mean and median NAWK count differences (95% CI) were 0.05 (-0.42, 0.53) and 0.0 (0.0, 0.0), respectively. DiscussionResults support applying the VSW to track overnight sleep measures in free-living settings. Registered at clinicaltrials.gov (NCT05276362).

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.

Study ObjectivesCannabidiol (CBD) is increasingly used as a health supplement, though few human studies have demonstrated benefits. The primary objective of this study was to evaluate the effects of an oral CBD-terpene formulation on sleep physiology in insomniacs. MethodsIn this double-blind, placebo-controlled, randomized clinical trial, 125 insomniacs received an oral administration of CBD (300 mg) and terpenes (1 mg each of linalool, myrcene, phytol, limonene, -terpinene, -terpineol, -pinene, and {beta}-caryophyllene) for [≥] four days/week over four weeks using a crossover design. The study medication was devoid of {Delta}9-Tetrahydrocannabinol ({Delta}9-THC). The primary outcome measure was the percentage of time participants spent in the combination of slow wave sleep (SWS) and rapid eye movement (REM) sleep stages, as measured by a wrist-worn sleep-tracking device. ResultsThis CBD-terpene regimen significantly increased the mean nightly percentage of time participants spent in SWS + REM sleep compared to the placebo [mean (SEM), 1.28% (0.60%), 95% C.I. 0.09 to 2.46, P = 0.03]. More robust increases were observed in participants with low baseline SWS + REM sleep, as well as in day-sleepers. For select participants, the increase in SWS + REM sleep averaged as much as 48 min/night over a four-week treatment period. This treatment had no effect on total sleep time (TST), resting heart rate or heart rate variability, and no adverse events were reported. ConclusionsSelect CBD-terpene ratios may increase SWS + REM sleep, and have the potential to provide a safe and efficacious alternative to over-the-counter (OTC) sleep aids and commonly prescribed sleep medications. BRIEF SUMMARYO_ST_ABSCurrent Knowledge/Study RationaleC_ST_ABSPhysicians are increasingly asked by their patients regarding the merits of using CBD for insomnia and other ailments, but lack any rigorous clinical research to support recommending its use. The current study represents the first double-blind, placebo-controlled and randomized crossover clinical trial to investigate how an oral formulation of cannabidiol (CBD) and terpenes influences sleep physiology in insomniacs. Study ImpactIn contrast to many OTC sleep aids and commonly prescribed sleep medicines, the CBD-terpene formulation increased SWS and REM sleep, which are critical for the immune system, tissue regeneration, cognition and memory. These results, if confirmed in larger clinical trials, suggests that CBD might offer a promising alternative to other prescription sleep medications and OTC sleep aids.

BackgroundThe locus coeruleus noradrenergic (LC-NE) system may provide a potential new target for pharmacological insomnia treatment, particularly in patients suffering from elevated stress. The selective 2 noradrenergic agonist dexmedetomidine (DEX) attenuates LC-NE activity in sub{-} anesthetic doses, yet no adequate non-parental delivery systems of DEX are currently available. To examine the feasibility of oro-mucosal DEX administration, we developed two distinct - one sublingual and one buccal - oro-mucosal, fast-disintegrating DEX formulas tailored for self{-} administration. Here we established their pharmacokinetic and pharmacodynamic (PK-PD) profiles. MethodsIn two separate studies in 8 male good sleepers and 17 men with subclinical insomnia, we administered sub-anesthetic doses (20 & 40 {micro}g) of the two formulas following a randomized, double-blind, placebo-controlled, cross-over design. We complemented the PK assessments with all{-} night polysomnography, nocturnal cortisol and melatonin measurements, assessments of cardiovascular functions during and after sleep, cortisol awakening response, and post-awakening examination of subjective state and vigilance. ResultsParticularly buccal DEX was rapidly absorbed and exhibited excellent dose-proportionality with minimal between-subject variation in exposure. In poor sleepers, 40 {micro}g of buccal DEX shortened the sleep latency by 11 min, increased the time spent in non-rapid-eye-movement sleep by 38 min, and elevated electroencephalographic slow wave energy (0.75-4.0 Hz) in the first half of the night by 23 % (Pall < 0.05). Rapid-eye-movement sleep latency was dose-dependently prolonged (20 {micro}g: 48 min; 40 {micro}g: 117 min; Pall < 0.01). Nocturnal cortisol, melatonin and heart rate, and morning cortisol were not significantly affected by DEX, nor did post-awakening orthostatic regulation, subjective sleepiness and mood, and psychomotor vigilance differ among the conditions. ConclusionsThe favorable PK-PD profile of oro-mucosal DEX delivery warrants further dose-finding and clinical studies, to establish the exact roles of 2 receptor agonism in pharmacological sleep enhancement and as possible novel mechanism to alleviate stress-related insomnia.

Depression and sleep problems go hand-in-hand, while clinical improvement often emerges along the normalization of sleep architecture and realignment of the circadian rhythm. Antidepressant effects of sleep deprivation and cognitive behavioral therapy targeted at insomnia further demonstrate the confluence of sleep and mood. Moreover, recent literature showing that ketamine influences many processes related to sleep-wake neurobiology, have led to novel hypotheses explaining rapid and sustained antidepressant effects. Surprisingly, studies addressing ketamines antidepressant effects have had a narrow focus on solely on pharmacological aspects and often ignore the role of physiology. To illustrate this discrepancy, we conducted a literature review on articles around rapid-acting antidepressants published between 2009-2019. A gross keyword check indicated overall ignorance of sleep in most studies. To investigate the topic closer, we focused on the most cited preclinical and clinical research papers. Circadian rhythm, timing of drug administration and behavioral tests relative to light cycles, sleep, and their potential association with experimental observations were mentioned only in a handful of the papers. Most importantly, in preclinical reports the treatments have been preferentially delivered during the inactive period, which is polar opposite to clinical practice and research. We hope this report serves as a wake-up call for sleep in the field and urges (re)examining rapid-acting antidepressant effects from the perspective of wake-sleep physiology.

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.

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.

Behavioural responses to COVID-19 lockdown will define the long-term impact of psychological stressors on sleep and brain health. Here we tease apart factors that help protect against sleep disturbance. We capitalise on the unique restrictions during COVID-19 to understand how time of day of daylight exposure and outside exercise interact with chronotype and sleep quality. 3474 people from the UK (median age 62, range 18-91) completed our online SleepQuest Study between 29th April and 13th May 2020 - a set of validated questionnaires probing sleep quality, depression, anxiety and attitudes to sleep alongside bespoke questions on the effect of COVID-19 lockdown on sleep, time spent outside and exercising and self-help sleep measures. Significantly more people (n=1252) reported worsened than improved sleep (n=562) during lockdown (p<0.0001). Factors significantly associated with worsened sleep included low mood (p<0.001), anxiety (p<0.001) and suspected, proven or at risk of COVID-19 symptoms (all p<0.03). Sleep improvement was related to the increased length of time spent outside (P<0.01). Older peoples sleep quality was less affected than younger people by COVID lockdown (p<0.001). Better sleep quality was associated with going outside and exercising earlier, rather than later, in the day. However, the benefit of being outside early is driven by improved sleep in owl (p=0.0002) and not lark (p=0.27) chronotype, whereas, the benefit of early exercise (inside or outside) did not depend on chronotype. Defining the interaction between chronotype, mental health and behaviour will be critical for targeted lifestyle adaptations to protect brain health through current and future crises.

BackgroundAccumulating evidences have shown that lifestyle interventions such as exercise and diet are associated with improved sleep quality. However, the underlying molecular mechanisms remain unclear. Assessing exercise and diet intervention associated changes in circulating metabolomics profile in people with insomnia symptoms may help to identify molecular biomarkers that may link lifestyle changes to improved sleep outcomes. MethodsThe present study is a part of a 6-month randomized lifestyle intervention on sleep disorder subjects. Seventy-two Finnish men (aged: 51.6 {+/-} 10.1 years; body mass index, BMI: 29.3 {+/-} 3.9 kg/m2) with chronic insomnia symptoms who were assigned into different intervention groups completed this study (exercise n = 24, diet n = 27 and control n = 21). The exercise group was assigned to a progressive aerobic exercise training with intensity of 60 - 75% of estimated maximum heart rate, 3 - 5 times a week. The diet group aimed to reduce their total energy intakes by 300 to 500 kcal per day for the first three months. The control group were advised to maintain their current lifestyle. Sleep was assessed by using a non-contact sleep monitoring devise (Beddit sleep tracker). Blood samples were collected in the morning between 7:00 and 9:00 a.m. after overnight fasting. Gas Chromatography Time-Of-Flight Mass Spectrometry (GC-TOF-MS) method was used to determine the serum metabolites. ResultsTwenty-one metabolites were significantly changed in the exercise group, thirty-three metabolites in the diet group and five metabolites in the control group after intervention, respectively. The differential metabolites after exercise intervention were mainly related to glycerolipids and carbohydrates metabolism, while dietary intervention altered mainly amino acids metabolism and fatty acids metabolism related metabolites. We subsequently assessed the change of those metabolites with the change of sleep parameters and found that decreased alpha-ketoisocaproic acid (r = -0.52, p = 0.026) was correlated with improved sleep efficiency (SE) in the exercise group. Change of 3-hydroxybutric acid (r = -0.47, p = 0.025) and D-glucopyranose (r = -0.54, p = 0.006) correlated negatively with SE in the diet group. On the other hand, oxalic acid (r = 0.49, p = 0.021), D-glucopyranose (r = 0.43, p = 0.048), 4-deoxyerythronic acid (r = 0.60, p = 0.004) and tagatose (r = 0.51, p = 0.016) correlated positively with change of SOL, and 2-keto-isovaleric acid (r = 0.45, p = 0.029) correlated with TST in the diet group. ConclusionIn conclusion, this study identified circulating metabolites that may represent a part of a biological mechanism through which lifestyle interventions are associated with improved sleep quality in people with insomnia.

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.

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

ObjectiveTo examine the effect of COVID-19 on treatment adherence and self-reported sleep duration among patients with Obstructive Sleep Apnea (OSA) treated with positive airway pressure (PAP) therapy. MethodsRetrospective review of medical records of patients seen in Sleep and Circadian Clinic at Brigham Health during the immediate period of one month after the national lockdown was announced on March 15, 2020. Patients with OSA were included only if PAP adherence data was available in the 12-months prior and in the month after the lockdown. Patients with other sleep disorders and OSA patients without the adherence data were excluded. ResultsMean age was 63.5{+/-} 13.9 years, 55% of the participants were men, and mean BMI was 31.8 {+/-} 7.9 kg/m2. Severe OSA was noted among 59.5% compared to 29.3% moderate, and 11.2% mild OSA. Increased number of patients reported insomnia after the lockdown (41% vs 48%, p= 0.02). Gender stratification noted worsening insomnia only among women. There was no significant difference in PAP adherence as measured by the hours of use, self-reported sleep duration or in the use of sleep medications. ConclusionPost COVID-19 lockdown had a negative impact on sleep as evidenced by increased reporting of insomnia particularly among women, but no impact on PAP adherence or self-reported sleep duration.

BackgroundSleep timing reflects daily routines and lifestyle patterns, which influence cardiovascular health through circadian mechanisms that regulate cardiovascular processes. Wearable devices enable sensor-based assessment of sleep timing, offering insights into daily behavior. This study examined how the regularity of wearable device-determined sleep timing (bedtime, wake-up time, and sleep midpoint) predicts incident major adverse cardiac event (MACE) and cardiovascular disease (CVD) mortality over a 10-year follow-up in midlife. MethodsThe study included 3,231 participants (39.5% men) from the Northern Finland Birth Cohort 1966 who attended the 46-year follow-up in 2012-2014. Participants were followed until December 31, 2023, or until a MACE (acute myocardial infarction, unstable angina, stroke, heart failure hospitalization, or CVD death) or were censored due to moving abroad or dying from a non-cardiovascular cause. Sleep timing regularity was assessed via 7-day standard deviation for bedtime, wake-up time, and sleep midpoint, categorized into tertiles: regular, fairly regular, and irregular. Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (CIs), adjusting for gender, employment status, body mass index, systolic blood pressure, glycated hemoglobin, low-density lipoprotein cholesterol, and total physical activity. Analyses were stratified by sleep duration below or above the group median (7 h 56 min). ResultsIn total, 128 participants (4.0%) experienced MACEs during the follow-up period. Irregular sleep timing was associated with an elevated risk, but this association was observed only among participants whose sleep period was shorter than the group median. Individuals with irregular bedtimes had a 2.01-fold higher risk of MACEs compared to those with regular bedtimes (HR = 2.01, 95% CI: 1.00-4.01, p = 0.049), and those with irregular sleep midpoints had a 2.00-fold higher risk compared to those with regular midpoints (HR = 2.00, 95% CI: 1.01-3.98, p = 0.048). ConclusionsAmong the participants with sleep durations under eight hours, irregular sleep timing was a significant risk factor for MACEs. Specifically, variability in bedtime and sleep midpoint, but not in wake-up time, was associated with increased risk. These findings highlight the importance of consistent sleep behavior, particularly regular bedtimes, as a potential target for health promotion.

Study ObjectivesOur study introduced the 2023 UK Biobank sleep questionnaire and described variation in sleep health dimensions and prevalence of disordered sleep. MethodsA questionnaire comprising validated measures and bespoke items was developed to capture key self-reported domains of sleep health and symptoms of sleep disorders. We quantified cohort prevalence of operationally defined sleep disorders and assessed patterning of sleep health dimensions across key sociodemographic and clinically relevant variables. Results327,752 individuals were invited of whom 185,056 (56.5%) completed at least one module and were included in the analysis. Respondents were predominately from a White ethnic background (96.8%), had a mean age of 69.9 (SD, 7.5) years, 57.9% were female, and 25.5% were in employment. Compared to non-respondents, respondents were more likely to be female, tended to be better educated, healthier, and exhibit lower levels of socioeconomic deprivation, although baseline sleep variables were similar between respondents and non-respondents. Around 40% of respondents reported sleep duration less than 7 hours and 49% reported poor sleep quality (Pittsburgh Sleep Quality Index > 5). Approximately one-quarter (25.2%) met criteria for at least one operationally defined sleep disorder, with insomnia being the most common (14.4%) followed by obstructive sleep apnoea (8.0%), restless legs syndrome (4.1%), and frequent nightmares (3.7%). Sleep disorders were associated with higher levels of anxiety, depression, fatigue, and cognitive complaints. ConclusionsPoor sleep quality and operationally defined sleep disorders are common in the UK Biobank cohort. Sleep questionnaire data can now be integrated with a range of biomedical information to advance understanding of sleep. Statement of significanceA comprehensive sleep questionnaire was introduced to the UK Biobank, with over 185,000 participants providing data. Overall, respondents reported relatively poor sleep quality; 40% reported sleep duration less than 7 hours, and 25% met criteria for at least one sleep disorder. Enhanced assessment of sleep in UK Biobank now enables integration with extensive biomedical data, including genetic, wearable, imaging, lifestyle, biomarker, and electronic health record data, offering opportunities to investigate the biological and environmental factors that influence sleep and circadian systems, and their impact on health.

BackgroundResearch on sleep disparities across different sociodemographic groups is limited and often yields inconsistent findings. We aimed to examine differences in objective sleep measures by race and ethnicity, sex, and age within a diverse cohort of community-dwelling older adults. MethodsWe analyzed cross-sectional data from 838 participants aged [&ge;]50 years in the Dormir Study (2020-2024). Sleep metrics, including sleep duration, sleep efficiency, wake after sleep onset (WASO), and sleep fragmentation index (SFI), were derived from 7-day wrist actigraphy. Race and ethnicity (Black; Mexican American [MA]; Non-Hispanic White [NHW]), sex, and age (<65; [&ge;]65 years) were self-reported. We compared sleep metrics across sociodemographic groups and assessed their multivariable associations using linear, logistic, and multinomial regression models. ResultsWe studied 190 (22.7%) Black, 282 (33.6%) MA, and 366 (43.7%) NHW Dormir participants, with a mean age of 66.7 {+/-}8.4 years, and 64.8% women. Compared to NHW participants, Black and MA participants had shorter mean sleep duration (Black: 7.1 {+/-}1.2 hours; MA: 7.1 {+/-}1.1 hours; NHW: 7.5 {+/-}1.1 hours; p<0.0001), lower median sleep efficiency (Black: 87.2%; MA: 87.8%; NHW: 90.6%; p<0.0001), longer median WASO (Black: 61.2 minutes; MA: 56.7 minutes; NHW: 44.4 minutes; p<0.0001), and higher mean SFI (Black: 32.0 {+/-}11.0%; MA: 27.3 {+/-}9.7%; NHW: 24.0 {+/-}9.0%; p<0.0001). Compared to men, women had longer mean sleep duration (women: 7.4 {+/-}1.1 hours; men: 7.1 {+/-}1.2 hours; p=0.0004) and lower mean SFI (women: 25.9 {+/-}8.8%; men: 28.9 {+/-}12.1%; p=0.0001). Older participants had longer mean sleep duration (old: 7.4 {+/-}1.1 hours; young: 7.1 {+/-}1.1 hours; p<0.0001), higher median sleep efficiency (old: 89.8%; young: 87.7%; p<0.0001), shorter median WASO (old: 48.5 minutes; young: 56.8 minutes; p<0.0001), and lower mean SFI (old: 26.1 {+/-}10.2%; young: 28.1 {+/-}10.2%; p=0.007). After adjusting for socioeconomic and behavioral factors, comorbidities, and sleep medications, findings were consistent except for age group comparisons in which differences were no longer significant. ConclusionsOur findings demonstrate significant variations in objective sleep measures across sociodemographic groups, with non-White participants and men experiencing poorer sleep quality. These disparities may contribute to health inequalities, emphasizing the need for targeted interventions to support at-risk populations.

Sleep health is essential for older adults. However, validity of wrist- and waist-worn devices for assessing sleep under free-living conditions remains unclear. This study evaluated the accuracy of a wrist-worn smartwatch (Silmee W22) and a waist-worn activity monitor (MTN-221) in measuring key sleep parameters, using portable electroencephalography (EEG; Insomnograf K2) as the reference. Healthy older adults wore all devices simultaneously for at least three nights. Total sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency were analyzed using Bland- Altman plots, multilevel models, and intraclass correlation coefficients (ICCs). Fifty-five participants completed the study, yielding valid EEG-paired data for 49 participants with Silmee W22 (238 nights) and 53 with MTN-221 (265 nights). Silmee W22 overestimated total sleep time by 35 min and sleep efficiency by 8.1%, whereas MTN-221 overestimated it by 3 min and sleep efficiency by 1.0%. Both devices underestimated sleep onset latency and wake after sleep onset, with greater discrepancies observed as the estimated values increased. ICCs for total sleep time were 0.60-0.75 for Silmee W22 and 0.66-0.79 for MTN-221, while agreement for sleep onset latency and wake after sleep onset remained lower. While Silmee W22 did not provide sufficiently accurate estimates of total sleep time, MTN-221 yielded estimates that may offer practical benefits for large-scale sleep monitoring in older adults. In both devices, estimates of sleep onset latency, wake after sleep onset, and sleep efficiency should be interpreted with caution due to misclassification of quiet wakefulness. Further algorithm refinement is warranted.

PurposeWe aimed to use a large dataset to compare self-reported and primary care measures of insomnia symptom prevalence in England and establish whether they identify participants with similar characteristics. MethodsWe analysed data from 163,748 UK Biobank participants in England (aged 38-71 at baseline) with linked primary care electronic health records. We compared the percentage of those self-reporting usually having insomnia symptoms at UK Biobank baseline assessment (2006-2010) to those with a Read code for insomnia symptoms in their primary care records prior to baseline. We stratified prevalence in both groups by sociodemographic, lifestyle, sleep and health characteristics. ResultsWe found that 29% of the sample self-reported having insomnia symptoms, whilst only 6% had a Read code for insomnia symptoms in their primary care records. Only 10% of self-reported cases had an insomnia symptom Read code, whilst 49% of primary care cases self-reported having insomnia symptoms. In both primary care and self-reported data, prevalence of insomnia symptom cases was highest in females, older participants and those with the lowest household incomes. However, whilst snorers and risk takers were more likely to be a primary care case, they were less likely to self-report insomnia symptoms than non-snorers and non-risk takers. ConclusionsOnly a small proportion of individuals experiencing insomnia symptoms present to primary care. However, the sociodemographic characteristics of people attending primary care with insomnia were consistent with those with self-reported insomnia, thus primary care records are a valuable data source for studying risk factors for insomnia. Key PointsO_LIAround a third of the general population is thought to suffer from insomnia symptoms, but estimates are based on small samples and rely on people self-reporting their symptoms. C_LIO_LIElectronic health records (EHRs) offer a more objective means of measuring insomnia prevalence, but small-scale studies suggest they only capture a small proportion of insomnia cases. It is therefore unclear how useful EHRs are in measuring the prevalence of insomnia. C_LIO_LIIn a sample of over 160,000 UK Biobank participants in England we found that 29% of participants self-reported having insomnia symptoms, whilst only 6% had a Read code for insomnia symptoms in their primary care records. C_LIO_LICharacteristics of people attending primary care with insomnia symptoms are similar to those self-reporting insomnia symptoms, suggesting EHRs offer a valuable data source for studying risk factors for insomnia. C_LI Plain Language SummaryAround a third of the general population is thought to suffer from insomnia symptoms, but estimates are based on the responses of a small number of people and rely on them reporting their own symptoms. Peoples medical records offer a more objective way of finding out how many people have insomnia, but only capture people who go to their doctor for help. In this study we compared 160,000 peoples answers to a question on insomnia symptoms to their primary care records. We found that 29% of people reported insomnia symptoms, whereas only 6% had insomnia symptoms recorded in their medical records. However, the characteristics of those reporting insomnia and those with insomnia in their medical records were similar. This means that although medical records only capture a small proportion of those suffering from insomnia, they do still provide useful information for researchers studying risk factors for insomnia.

IntroductionInvestigations of sleep homeostasis often involve tightly controlled experimental sleep deprivation in service of understanding mechanistic physiology. The extent to which the deep sleep response to recent sleep loss occurs in naturalistic settings remains under-studied. We tested the hypothesis that a homeostatic increase in deep sleep occurs on the night following occasional short duration nights that arise in naturalistic settings. MethodsWe analyzed sleep staging data in participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data. The analysis group included n=44,564 participants with at least 30 nights of sleep staging data from Apple Watch, from November 2022 to May 2023, totaling over 5.3 million nights. ResultsShort nights of sleep that were >=2 hours shorter than each participants median sleep duration occurred at least once in 92.9% of the cohort, most often in isolation (<7% of instances were consecutive short nights), and with a median duration of just over 4 hours. We observed that the amount of deep sleep increased on the subsequent night in proportion to the amount of sleep loss on the preceding short night, in a dose response manner for short night definitions ranging from 30 minutes to >=3 hours below the within-participant median sleep duration. Focusing on short nights that were at least 2 hours below the median duration, we found that 58.8% of participants showed any increase in subsequent deep sleep, with a median increase of 12% (absolute increase of 5 minutes). In addition, the variability in deep sleep after short nights markedly increased in a dose response manner. The deep sleep homeostatic response showed little correlation to sleep duration, timing, consistency, or sleep stages, but was inversely correlated with deep sleep latency (Spearman R = -0.28). ConclusionThe results provide evidence for homeostatic responses in a real-world setting. Although the deep sleep rebound amounts are modest, naturalistic short nights are a milder perturbation compared to experimental deprivation, and reactive behaviors potentially impacting sleep physiology are uncontrolled. The marked increase in variability of deep sleep amount after short nights may reflect unmeasured reactive behaviors such as caffeine or napping, which exert opposing pressures on deep sleep compared to the homeostat. The findings illustrate the utility of longitudinal sleep tracking to assess real-world correlates of sleep phenomenology established in controlled experimental settings.

ObjectiveInvestigate whether auditory closed-loop stimulation (CLAS) applied during sleep could be beneficial for sleep and declarative memory in individuals with chronic insomnia. MethodsWe performed a randomized crossover sham-controlled study on 27 individuals with chronic insomnia to assess changes in sleep and declarative memory between a night with CLAS (i.e., 2-ON-OFF blocks auditory tones locked to slow wave up-states during NREM) and a SHAM night. We conducted assessments of memory (word paired-associate learning task) and sleep (morning questionnaire, polysomnographic recordings) during both nights. ResultsWe found that applying CLAS in a population of individuals with chronic insomnia led to an acute increase in SO amplitude after auditory stimulation. However, we found no beneficial effect of a single night of CLAS on subjective and objective sleep or declarative overnight memory performance. There was an association between the increase in SO density during CLAS with fewer markers of sleep fragmentation (i.e., sleep fragmentation index, arousals), suggesting interindividual differences in response to CLAS in chronic insomnia. ConclusionsCLAS stimulation applied during NREM sleep in individuals with chronic insomnia is feasible but did not show consistent effects on EEG markers of sleep regulation. A subgroup of individuals with insomnia may be more responsive to the impact of CLAS on sleep maintenance.