The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

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, and together may provide a more complete picture of sleep health, while also 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 to behavioral, psychological, and cardiometabolic traits.

The sleep-circadian interface: A window into mental disorders.

Sleep, circadian rhythms, and mental health are reciprocally interlinked. Disruption to the quality, continuity, and timing of sleep can precipitate or exacerbate psychiatric symptoms in susceptible individuals, while treatments that target sleep-circadian disturbances can alleviate psychopathology. Conversely, psychiatric symptoms can reciprocally exacerbate poor sleep and disrupt clock-controlled processes. Despite progress in elucidating underlying mechanisms, a cohesive approach that integrates the dynamic interactions between psychiatric disorder with both sleep and circadian processes is lacking. This review synthesizes recent evidence for sleep-circadian dysfunction as a transdiagnostic contributor to a range of psychiatric disorders, with an emphasis on biological mechanisms. We highlight observations from adolescent and young adults, who are at greatest risk of developing mental disorders, and for whom early detection and intervention promise the greatest benefit. In particular, we aim to a) integrate sleep and circadian factors implicated in the pathophysiology and treatment of mood, anxiety, and psychosis spectrum disorders, with a transdiagnostic perspective; b) highlight the need to reframe existing knowledge and adopt an integrated approach which recognizes the interaction between sleep and circadian factors; and c) identify important gaps and opportunities for further research.

The effect of single-component sleep restriction therapy on depressive symptoms: A systematic review and meta-analysis.

Sleep restriction therapy is a behavioural component within cognitive behavioural therapy for insomnia and is an effective standalone treatment for insomnia, but its effect on depressive symptoms remains unclear. This review aimed to synthesise and evaluate the impact of single-component sleep restriction therapy on depressive symptoms relative to a control intervention. We searched electronic databases and sleep-related journals for randomised controlled trials and uncontrolled clinical trials, published from 1 January 1986 until 19 August 2023, that delivered sleep restriction therapy to adults with insomnia. Random-effects meta-analysis of standardised mean differences and Cochrane risk of bias assessment were performed on randomised controlled trials, while uncontrolled clinical trials were discussed narratively. The meta-analysis was pre-registered on PROSPERO (ID: CRD42020191803). We identified seven randomised controlled trials (N = 1102) and two uncontrolled clinical trials (N = 22). Findings suggest that sleep restriction therapy is associated with a medium effect for improvement in depressive symptoms at post-treatment (Nc = 6, g = -0.45 [95% confidence interval = -0.70 to -0.21], p < 0.001) and a small effect at follow-up (Nc = 4, g = -0.31 [95% confidence interval = -0.45 to -0.16], p < 0.001). Five of the seven included randomised controlled trials were judged to have a high risk of bias. Standalone sleep restriction therapy appears to be efficacious for improving depressive symptoms at post-treatment and follow-up. However, conclusions are tentative due to the small number of trials and because none of the trials was performed in a population with clinically defined depression. Large-scale trials are needed to test the effect of sleep restriction therapy in patients experiencing depression and insomnia. Findings also highlight the need to improve the standardisation and reporting of sleep restriction therapy procedures, and to design studies with more rigorous control arms to reduce potential bias.

A systematic review of the performance of actigraphy in measuring sleep stages.

The accuracy of actigraphy for sleep staging is assumed to be poor, but examination is limited. This systematic review aimed to assess the performance of actigraphy in sleep stage classification of adults. A systematic search was performed using MEDLINE, Web of Science, Google Scholar, and Embase databases. We identified eight studies that compared sleep architecture estimates between wrist-worn actigraphy and polysomnography. Large heterogeneity was found with respect to how sleep stages were grouped, and the choice of metrics used to evaluate performance. Quantitative synthesis was not possible, so we performed a narrative synthesis of the literature. From the limited number of studies, we found that actigraphy-based sleep staging had some ability to classify different sleep stages compared with polysomnography.

Clinical and cost-effectiveness of DREAMS START (Dementia RElAted Manual for Sleep; STrAtegies for RelaTives) for people living with dementia and their carers: a study protocol for a parallel multicentre randomised controlled trial.

INTRODUCTION: Many people living with dementia experience sleep disturbance and there are no known effective treatments. Non-pharmacological treatment options should be the first-line sleep management. For family carers, relatives' sleep disturbance leads to interruption of their sleep, low mood and breakdown of care. Our team developed and delivered DREAMS START (Dementia RElAted Manual for Sleep; STrAtegies for RelaTives), a multimodal non-pharmacological intervention, showing it to be feasible and acceptable. The aim of this randomised controlled trial is to establish whether DREAMS START is clinically cost-effective in reducing sleep disturbances in people living with dementia living at home compared with usual care. METHODS AND ANALYSIS: We will recruit 370 participant dyads (people living with dementia and family carers) from memory services, community mental health teams and the Join Dementia Research Website in England. Those meeting inclusion criteria will be randomised (1:1) either to DREAMS START or to usual treatment. DREAMS START is a six-session (1 hour/session), manualised intervention delivered every 1-2 weeks by supervised, non-clinically trained graduates. Outcomes will be collected at baseline, 4 months and 8 months with the primary outcome being the Sleep Disorders Inventory score at 8 months. Secondary outcomes for the person with dementia (all proxy) include quality of life, daytime sleepiness, neuropsychiatric symptoms and cost-effectiveness. Secondary outcomes for the family carer include quality of life, sleep disturbance, mood, burden and service use and caring/work activity. Analyses will be intention-to-treat and we will conduct a process evaluation. ETHICS AND DISSEMINATION: London-Camden & Kings Cross Ethics Committee (20/LO/0894) approved the study. We will disseminate our findings in high-impact peer-reviewed journals and at national and international conferences. This research has the potential to improve sleep and quality of life for people living with dementia and their carers, in a feasible and scalable intervention. TRIAL REGISTRATION NUMBER: ISRCTN13072268.

Improving sleep after stroke: A randomised controlled trial of digital cognitive behavioural therapy for insomnia.

Stroke is frequently accompanied by long-term sleep disruption. We therefore aimed to assess the efficacy of digital cognitive behavioural therapy for insomnia to improve sleep after stroke. A parallel group randomised controlled trial was conducted remotely in participant's homes/online. Randomisation was online with minimisation of between-group differences in age and baseline Sleep Condition Indicator-8 score. In total, 86 community-dwelling stroke survivors consented, of whom 84 completed baseline assessments (39 female, mean 5.5 years post-stroke, mean 59 years old), and were randomised to digital cognitive behavioural therapy or control (sleep hygiene information). Follow-up was at post-intervention (mean 75 days after baseline) and 8 weeks later. The primary outcome was self-reported insomnia symptoms, as per the Sleep Condition Indicator-8 (range 0-32, lower numbers indicate more severe insomnia, reliable change 7 points) at post-intervention. There were significant improvements in Sleep Condition Indicator-8 for digital cognitive behavioural therapy compared with control (intention-to-treat, digital cognitive behavioural therapy n = 48, control n = 36, 5 imputed datasets, effect of group p ≤ 0.02, η p 2 = 0.07-0.12 [medium size effect], pooled mean difference = -3.35). Additionally, secondary outcomes showed shorter self-reported sleep-onset latencies and better mood for the digital cognitive behavioural therapy group, but no significant differences for self-efficacy, quality of life or actigraphy-derived sleep parameters. Cost-effectiveness analysis found that digital cognitive behavioural therapy dominates over control (non-significant cost savings and higher quality-adjusted life years). No related serious adverse events were reported to the researchers. Overall, digital cognitive behavioural therapy for insomnia effectively improves sleep after stroke. Future research is needed to assess earlier stages post-stroke, with a longer follow-up period to determine whether it should be included as part of routine post-stroke care. Clinicaltrials.gov NCT04272892.

The effect of sleep restriction therapy for insomnia on REM sleep fragmentation: A secondary analysis of a randomised controlled trial.

Rapid eye movement sleep fragmentation is hypothesised to be a reliable feature of insomnia, which may contribute to emotion dysregulation. Sleep restriction therapy, an effective intervention for insomnia, has the potential to reduce rapid eye movement sleep fragmentation through its manipulation of basic sleep-wake processes. We performed secondary data analysis of a randomised controlled trial to examine whether sleep restriction therapy reduces rapid eye movement sleep fragmentation in comparison to a matched control arm. Participants (n = 56; 39 female, mean age = 40.78 ± 9.08 years) were randomly allocated to 4 weeks of sleep restriction therapy or 4 weeks of time in bed regularisation. Ambulatory polysomnographic recordings were performed at baseline, week 1 and week 4. Arousals during rapid eye movement and non-rapid eye movement sleep were scored blind to group allocation. The following rapid eye movement sleep fragmentation index was the primary outcome: index 1 = (rapid eye movement arousals + rapid eye movement awakenings + non-rapid eye movement intrusions)/rapid eye movement duration in hours. Secondary outcomes were two further indices of rapid eye movement sleep fragmentation: index 2 = (rapid eye movement arousals + rapid eye movement awakenings)/rapid eye movement duration in hours; and index 3 = rapid eye movement arousals/rapid eye movement duration in hours. A non-rapid eye movement fragmentation index was also calculated (non-rapid eye movement arousals/non-rapid eye movement duration in hours). Linear-mixed models were fitted to assess between-group differences. There was no significant group difference for the primary rapid eye movement fragmentation index at week 1 (p = 0.097, d = -0.31) or week 4 (p = 0.741, d = -0.06). There was some indication that secondary indices of rapid eye movement fragmentation decreased more in the sleep restriction therapy group relative to control at week 1 (index 2: p = 0.023, d = -0.46; index 3: p = 0.051, d = -0.39), but not at week 4 (d ≤ 0.13). No group effects were found for arousals during non-rapid eye movement sleep. We did not find clear evidence that sleep restriction therapy modifies rapid eye movement sleep fragmentation. Small-to-medium effect sizes in the hypothesised direction, across several indices of rapid eye movement fragmentation during early treatment, demand further investigation in future studies.

Nurse-delivered sleep restriction therapy in primary care for adults with insomnia disorder: a mixed-methods process evaluation.

BACKGROUND: Sleep restriction therapy (SRT) is a behavioural therapy for insomnia. AIM: To conduct a process evaluation of a randomised controlled trial comparing SRT delivered by primary care nurses plus a sleep hygiene booklet with the sleep hygiene booklet only for adults with insomnia disorder. DESIGN AND SETTING: A mixed-methods process evaluation in a general practice setting. METHOD: Semi-structured interviews were conducted in a purposive sample of patients receiving SRT, the practice nurses who delivered the therapy, and also GPs or practice managers at the participating practices. Qualitative data were explored using framework analysis, and integrated with nurse comments and quantitative data, including baseline Insomnia Severity Index score and serial sleep efficiency outcomes to investigate the relationships between these. RESULTS: In total, 16 patients, 13 nurses, six practice managers, and one GP were interviewed. Patients had no previous experience of behavioural therapy, needed flexible appointment times, and preferred face-to-face consultations; nurses felt prepared to deliver SRT, accommodating patient concerns, tailoring therapy, and negotiating sleep timings despite treatment complexity and delays between training and intervention delivery. How the intervention produced change was explored, including patient and nurse interactions and patient responses to SRT. Difficulties maintaining SRT, negative attitudes towards treatment, and low self-efficacy were highlighted. Contextual factors, including freeing GP time, time constraints, and conflicting priorities for nurses, with suggestions for alternative delivery options, were raised. Participants who found SRT a positive process showed improvements in sleep efficiency, whereas those who struggled did not. CONCLUSION: SRT was successfully delivered by practice nurses and was generally well received by patients, despite some difficulties delivering and applying the intervention in practice.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

Associations between self-reported sleep, overnight memory consolidation, and emotion perception: A large-scale online study in the general population.

Experimental studies suggest that short or disrupted sleep impairs memory consolidation, mood, and perception of emotional stimuli. However, studies have chiefly relied on laboratory-based study designs and small sample sizes. The aim of this fully online and pre-registered study was to investigate the association between sleep and overnight memory consolidation, emotion perception, and affect in a large, self-selected UK sample. A total of 1646 participants (473 completed) took part in an online study, where they completed a declarative (word-pairs) memory task, emotion perception task (valence ratings of images), and rated their affect within 2 h of bed-time. The following morning, participants reported on their state affect, sleep for the previous night, completed a cued recall task for the previously presented word-pairs, rated the valence of previously viewed images, and completed a surprise recognition task. Demographic data and habitual sleep quality and duration (sleep traits) were also recorded. Habitual sleep traits were associated with immediate recall for the word-pairs task, while self-reported sleep parameters for the specific night were not associated with overnight memory consolidation. Neither habitual sleep traits, nor nightly sleep parameters were associated with unpleasantness ratings to negative stimuli or overnight habituation. Habitual poor sleep was associated with less positive and more negative affect, and morning affect was predicted by the specific night's sleep. This study suggests that overnight emotional processing and declarative memory may not be associated with self-reported sleep across individuals. More work is needed to understand how findings from laboratory-based studies extrapolate to real-world samples and contexts.

The European Insomnia Guideline: An update on the diagnosis and treatment of insomnia 2023.

Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential-diagnostic purposes (A). Polysomnography should be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders, etc.), treatment-resistant insomnia (A) and for other indications (B). Cognitive-behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (including patients with comorbidities), either applied in-person or digitally (A). When cognitive-behavioural therapy for insomnia is not sufficiently effective, a pharmacological intervention can be offered (A). Benzodiazepines (A), benzodiazepine receptor agonists (A), daridorexant (A) and low-dose sedating antidepressants (B) can be used for the short-term treatment of insomnia (≤ 4 weeks). Longer-term treatment with these substances may be initiated in some cases, considering advantages and disadvantages (B). Orexin receptor antagonists can be used for periods of up to 3 months or longer in some cases (A). Prolonged-release melatonin can be used for up to 3 months in patients ≥ 55 years (B). Antihistaminergic drugs, antipsychotics, fast-release melatonin, ramelteon and phytotherapeutics are not recommended for insomnia treatment (A). Light therapy and exercise interventions may be useful as adjunct therapies to cognitive-behavioural therapy for insomnia (B).

Clinical and cost-effectiveness of nurse-delivered sleep restriction therapy for insomnia in primary care (HABIT): a pragmatic, superiority, open-label, randomised controlled trial.

BACKGROUND: Insomnia is prevalent and distressing but access to the first-line treatment, cognitive behavioural therapy (CBT), is extremely limited. We aimed to assess the clinical and cost-effectiveness of sleep restriction therapy, a key component of CBT, which has the potential to be widely implemented. METHODS: We did a pragmatic, superiority, open-label, randomised controlled trial of sleep restriction therapy versus sleep hygiene. Adults with insomnia disorder were recruited from 35 general practices across England and randomly assigned (1:1) using a web-based randomisation programme to either four sessions of nurse-delivered sleep restriction therapy plus a sleep hygiene booklet or a sleep hygiene booklet only. There was no restriction on usual care for either group. Outcomes were assessed at 3 months, 6 months, and 12 months. The primary endpoint was self-reported insomnia severity at 6 months measured with the insomnia severity index (ISI). The primary analysis included participants according to their allocated group and who contributed at least one outcome measurement. Cost-effectiveness was evaluated from the UK National Health Service and personal social services perspective and expressed in terms of incremental cost per quality-adjusted life year (QALY) gained. The trial was prospectively registered (ISRCTN42499563). FINDINGS: Between Aug 29, 2018, and March 23, 2020 we randomly assigned 642 participants to sleep restriction therapy (n=321) or sleep hygiene (n=321). Mean age was 55·4 years (range 19-88), with 489 (76·2%) participants being female and 153 (23·8%) being male. 580 (90·3%) participants provided data for at least one outcome measurement. At 6 months, mean ISI score was 10·9 (SD 5·5) for sleep restriction therapy and 13·9 (5·2) for sleep hygiene (adjusted mean difference -3·05, 95% CI -3·83 to -2·28; p<0·0001; Cohen's d -0·74), indicating that participants in the sleep restriction therapy group reported lower insomnia severity than the sleep hygiene group. The incremental cost per QALY gained was £2076, giving a 95·3% probability that treatment was cost-effective at a cost-effectiveness threshold of £20 000. Eight participants in each group had serious adverse events, none of which were judged to be related to intervention. INTERPRETATION: Brief nurse-delivered sleep restriction therapy in primary care reduces insomnia symptoms, is likely to be cost-effective, and has the potential to be widely implemented as a first-line treatment for insomnia disorder. FUNDING: The National Institute for Health and Care Research Health Technology Assessment Programme.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Background: Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. Methods: We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality. Findings: After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77). Interpretation: Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Associations between sleep health and grey matter volume in the UK Biobank cohort (n = 33 356).

As suggested by previous research, sleep health is assumed to be a key determinant of future morbidity and mortality. In line with this, recent studies have found that poor sleep is associated with impaired cognitive function. However, to date, little is known about brain structural abnormalities underlying this association. Although recent findings link sleep health deficits to specific alterations in grey matter volume, evidence remains inconsistent and reliant on small sample sizes. Addressing this problem, the current preregistered study investigated associations between sleep health and grey matter volume (139 imaging-derived phenotypes) in the UK Biobank cohort (33 356 participants). Drawing on a large sample size and consistent data acquisition, sleep duration, insomnia symptoms, daytime sleepiness, chronotype, sleep medication and sleep apnoea were examined. Our main analyses revealed that long sleep duration was systematically associated with larger grey matter volume of basal ganglia substructures. Insomnia symptoms, sleep medication and sleep apnoea were not associated with any of the 139 imaging-derived phenotypes. Short sleep duration, daytime sleepiness as well as late and early chronotype were associated with solitary imaging-derived phenotypes (no recognizable pattern, small effect sizes). To our knowledge, this is the largest study to test associations between sleep health and grey matter volume. Clinical implications of the association between long sleep duration and larger grey matter volume of basal ganglia are discussed. Insomnia symptoms as operationalized in the UK Biobank do not translate into grey matter volume findings.