Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

Association between longitudinal change of sleep patterns and the risk of cardiovascular diseases

STUDY OBJECTIVES

To investigate the role of longitudinal change of sleep patterns in the incidence of cardiovascular diseases (CVD).

METHODS

Based on UK Biobank, a total of 18 172 participants were enrolled. Five dimensions of healthy sleep including early chronotype, sleep 7-8 hours/day, free of insomnia, no snoring, and no frequent excessive daytime sleepiness were used to generate a healthy sleep score (HSS) ranging from 0 to 5. Corresponding to the HSS of 0-1, 2-3, and 4-5, the poor, intermediate, and healthy sleep patterns were defined. Based on changes in HSS across assessments 1 and 2, we calculated the absolute difference of HSS. For the change in sleep patterns, we categorized five profiles (stable healthy, worsening, stable intermediate, optimizing, and stable poor sleep patterns). The outcomes were incidence of CVD including coronary heart disease (CHD) and stroke. We assessed the adjusted hazard ratios and 95% confidence intervals (CIs) by Cox hazard models.

RESULTS

Compared with participants with stable poor patterns, those who improved their sleep patterns or maintained healthy sleep patterns had a 26% and 32% lower risk of CVD, respectively. Stable healthy sleep pattern was associated with a 29% and 44% reduced risk of CHD and stroke. Per unit, longitudinal increment of the HSS was related to an 8% lower risk of CVD and CHD. Compared with individuals with constant HSS, those with decreased HSS had a 13% higher risk of developing CVD.

CONCLUSIONS

Optimizing sleep patterns and maintaining a healthy sleep pattern may reduce the risk of CVD.

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.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

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.

Multivariate genome-wide association study of sleep health demonstrates unity and diversity

There has been a recent push to focus sleep research less on disordered sleep and more on the dimensional sleep health. Sleep health incorporates several dimensions of sleep: chronotype, efficiency, daytime alertness, duration, regularity, and satisfaction with sleep. A previous study demonstrated sleep health domains correlate only moderately with each other at the genomic level (|rGs| = 0.11-0.51) and show unique relationships with psychiatric domains (controlling for shared variances, duration, alertness, and non-insomnia independently related to a factor for internalizing psychopathology). Of the domains assessed, circadian preference was the least genetically correlated with all other facets of sleep health. This pattern is important because it suggests sleep health should be considered a multifaceted construct rather than a unitary construct. Prior genome-wide association studies (GWASs) have vastly increased our knowledge of the biological underpinnings of specific sleep traits but have only focused on univariate analyses. We present the first multivariate GWAS of sleep and circadian health (multivariate circadian preference, efficiency, and alertness factors, and three single-indicator factors of insomnia, duration, and regularity) using genomic structural equation modeling. We replicated loci found in prior sleep GWASs, but also discovered "novel" loci for each factor and found little evidence for genomic heterogeneity. While we saw overlapping genomic enrichment in subcortical brain regions and shared associations with external traits, much of the genetic architecture (loci, mapped genes, and enriched pathways) was diverse among sleep domains. These results confirm sleep health as a family of correlated but genetically distinct domains, which has important health implications.

Opposing brain signatures of sleep in task-based and resting-state conditions

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.

Sleep Traits Causally Affect the Brain Cortical Structure: A Mendelian Randomization Study

Background: Brain imaging results in sleep deprived patients showed structural changes in the cerebral cortex; however, the reasons for this phenomenon need to be further explored. Methods: This MR study evaluated causal associations between morningness, ease of getting up, insomnia, long sleep, short sleep, and the cortex structure. Results: At the functional level, morningness increased the surface area (SA) of cuneus with global weighted (beta(b) (95% CI): 32.63 (10.35, 54.90), p = 0.004). Short sleep increased SA of the lateral occipital with global weighted (b (95% CI): 394.37(107.89, 680.85), p = 0.007. Short sleep reduced cortical thickness (TH) of paracentral with global weighted (OR (95% CI): -0.11 (-0.19, -0.03), p = 0.006). Short sleep reduced TH of parahippocampal with global weighted (b (95% CI): -0.25 (-0.42, -0.07), p = 0.006). No pleiotropy was detected. However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and the five types of sleep traits met the threshold. Conclusions: Our results potentially show evidence of a higher risk association between neuropsychiatric disorders and not only paracentral and parahippocampal brain areas atrophy, but also an increase in the middle temporal zone. Our findings shed light on the associations of cortical structure with the occurrence of five types of sleep traits.

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.

Sleep Health at the Genomic Level: Six Distinct Factors and Their Relationships With Psychopathology

Background

Poor sleep is associated with many negative health outcomes, including multiple dimensions of psychopathology. In the past decade, sleep researchers have advocated for focusing on the concept of sleep health as a modifiable health behavior to mitigate or prevent these outcomes. Sleep health dimensions often include sleep efficiency, duration, satisfaction, regularity, timing, and daytime alertness. However, there is no consensus on how to best operationalize sleep health at the phenotypic and genetic levels. In some studies, specific sleep health domains were examined individually, while in others, sleep health domains were examined together (e.g., with an aggregate sleep health score).

Methods

Here, we compared alternative sleep health factor models using genomic structural equation modeling on summary statistics from previously published genome-wide association studies of self-reported and actigraphic sleep measures with effective sample sizes up to 452,633.

Results

Our best-fitting sleep health model had 6 correlated genetic factors pertaining to 6 sleep health domains: circadian preference, efficiency, alertness, duration, noninsomnia, and regularity. All sleep health factors were significantly correlated (|rgs| = 0.11-0.51), except for the circadian preference factor with duration and noninsomnia. Better sleep health was generally significantly associated with lower genetic liability for psychopathology (|rgs| = 0.05-0.48), yet the 6 sleep health factors showed divergent patterns of associations with different psychopathology factors, especially when controlling for covariance among the sleep health factors.

Conclusions

These results provide evidence for genetic separability of sleep health constructs and their differentiation with respect to associations with mental health.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank

BACKGROUND

Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes.

METHODS

Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC).

RESULTS

For MD vs. controls (n_(MD) = 24,229; n_(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n_(atypical) = 958; n_(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes.

LIMITATIONS

Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary.

DISCUSSION

Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

The Potential of the Remote Monitoring Digital Solutions to Sustain the Mental and Emotional Health of the Elderly during and Post COVID-19 Crisis in Romania

The COVID-19 pandemic amplified the elderly's aging-related dysfunctionalities and vulnerabilities. Research surveys, aimed at evaluating the socio-physical-emotional state of the elderly and obtaining data on their access to medical services and information media services during the pandemic, were carried out on Romanian respondents aged 65+. Identification and mitigation of the risk of emotional and mental long-term decline of the elderly after SARS-CoV-2 infection, based on the implementation of a specific procedure, can be performed through Remote Monitoring Digital Solutions (RMDSs). The aim of this paper is to propose a procedure for the identification and mitigation of the risk of emotional and mental long-term decline of the elderly after SARS-CoV-2 infection that comprises RMDS. The importance of using the knowledge obtained by COVID-19-related surveys corroborating the necessity of including personalized RMDS in the procedure is highlighted. The Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing) is an RMDS designed to address the improved preventative and proactive support for diminishing this risk and to provide suitable assistance for the elderly through a safe and efficient smart environment. Its comprehensive functionalities targeted supporting primary healthcare assistance, specific medical conditions-as the mental and emotional disorders post-SARS-CoV-2 infection-and enlarged access to aging-related information, together with customizable features, illustrated the match with the requirements included in the proposed procedure.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

Association analyses of the autosomal genome and mitochondrial DNA with accelerometry-derived sleep parameters in depressed UK biobank subjects

BACKGROUND

The bidirectional relationship between sleep disturbances and depression is well documented, yet the biology of sleep is not fully understood. Mitochondria have become of interest not only because of the connection between sleep and metabolism but also because of mitochondria's involvement in the production of reactive oxygen species, which are largely scavenged during sleep.

METHODS

Genome-wide association studies (GWAS) of eight accelerometry-derived sleep measures were performed across both the autosomal and mitochondrial DNA (mtDNA) among two severity levels of depression in UK Biobank participants. We calculated SNP heritability for each of the sleep measures. Linear regression was performed to test associations and mitochondrial haplogroups.

RESULTS

Variants included in the GWAS accounted for moderate heritability of bedtime (19.6%, p = 4.75 × 10^-7), sleep duration (16.6%, p = 8.58 × 10^-6) and duration of longest sleep bout (22.6%, p = 4.64 × 10^-4). No variants passed genome-wide significance in the autosomal genome. The top hit in the severe depression sample was rs145019802, near GOLGA8B, for sleep efficiency (p = 1.17 × 10^-7), and the top hit in the broad depression sample was rs7100859, an intergenic SNP, and nap duration (p = 1.25 × 10^-7). Top mtDNA loci were m.12633C > A of MT-ND5 with bedtime (p = 0.002) in the severe sample and m.16186C > T of the control region with nap duration (p = 0.002) in the broad sample.

CONCLUSION

SNP heritability estimates support the involvement of common SNPs in specific sleep measures among depressed individuals. This is the first study to analyze mtDNA variance in sleep measures in depressed individuals. Our mtDNA findings, although nominally significant, provide preliminary suggestion that mitochondria are involved in sleep.

Sleep and Rest-Activity Rhythms in Recovering Patients with Severe Concurrent Mental and Substance Use Disorder: A Pilot Study

Objective: Mental health and substance use disorders are commonly associated with disrupted sleep and circadian rest-activity rhythms. How these disorders in combination relate to sleep and circadian organization is not well studied. We provide here the first quantitative assessment of sleep and rest-activity rhythms in inpatients with complex concurrent disorders, taking into account categories of substance use (stimulant vs. stimulant and opioid use) and psychiatric diagnosis (psychotic disorder and mood disorder). We also explore how sleep and rest-activity rhythms relate to psychiatric functioning. Methods: A total of 44 participants (10 female) between the age of 20-60 years (median = 29 years) wore wrist accelerometers over 5-70 days and completed standardized questionnaires assessing chronotype and psychiatric functioning (fatigue, psychiatric symptom severity, and impulsiveness). To examine potential influences from treatment, we computed (1) length of stay; (2) days of abstinence from stimulants and opioids as a measure of withdrawal; and (3) a sedative load based on prescribed medications. Results: Participants exhibited a sustained excessive sleep duration, frequent nighttime awakenings, and advanced rest-activity phase related to sedative load. Sleep disruptions were elevated in participants with a history of opioid use. Patients with a psychotic disorder showed the longest sleep and most fragmented and irregular rest-activity patterns. Non-parametric circadian rhythm analysis revealed a high rhythm amplitude by comparison with population norms, and this was associated with greater psychiatric symptom severity. Psychiatric symptom severity was also associated with greater fatigue and later MCTQ chronotype. Conclusions: This pilot study provides initial information on the prevalence and severity of sleep and circadian rhythm disturbances in individuals with severe concurrent disorders. The results underline the need for further studies to start to understand the role of sleep in the disease and recovery process in this understudied population.

Association of daytime napping frequency and schizophrenia: a bidirectional two-sample Mendelian randomization study

BACKGROUND

The bidirectional causal association between daytime napping frequency and schizophrenia is unclear.

METHODS

A bidirectional two-sample Mendelian randomization (MR) analysis was conducted with summary statistics of top genetic variants associated with daytime napping frequency and schizophrenia from genome-wide association studies (GWAS). The single nucleotide polymorphisms (SNPs) data of daytime napping frequency GWAS came from the UK Biobank (n = 452,633) and 23andMe study cohort (n = 541,333), while the schizophrenia GWAS came from the Psychiatric Genomics Consortium (PGC, 36,989 cases and 113,075 controls). The inverse variance weighted (IVW) analysis was the primary method, with the weighted median, MR-Robust Adjusted Profile Score (RAPS), Radial MR and MR-Pleiotropy Residual Sum Outlier (PRESSO) as sensitivity analysis.

RESULTS

The MR analysis showed a bidirectional causal relationship between more frequent daytime napping and the occurrence of schizophrenia, with the odds ratio (OR) for one-unit increase in napping category (never, sometimes, usually) on schizophrenia was 3.38 (95% confidence interval [CI]: 2.02-5.65, P = 3.58 × 10^-6), and the beta for the occurrence of schizophrenia on daytime napping frequency was 0.0112 (95%CI: 0.0060-0.0163, P = 2.04 × 10^-5). The sensitivity analysis obtained the same conclusions.

CONCLUSION

Our findings support the bidirectional causal association between more daytime napping frequency and schizophrenia, implying that daytime napping frequency is a potential intervention for the progression and treatment of schizophrenia.

Shared brain and genetic architectures between mental health and physical activity

Physical activity is correlated with, and effectively treats various forms of psychopathology. However, whether biological correlates of physical activity and psychopathology are shared remains unclear. Here, we examined the extent to which the neural and genetic architecture of physical activity and mental health are shared. Using data from the UK Biobank (N = 6389), we applied canonical correlation analysis to estimate associations between the amplitude and connectivity strength of subnetworks of three major neurocognitive networks (default mode, DMN; salience, SN; central executive networks, CEN) with accelerometer-derived measures of physical activity and self-reported mental health measures (primarily of depression, anxiety disorders, neuroticism, subjective well-being, and risk-taking behaviors). We estimated the genetic correlation between mental health and physical activity measures, as well as putative causal relationships by applying linkage disequilibrium score regression, genomic structural equational modeling, and latent causal variable analysis to genome-wide association summary statistics (GWAS N = 91,105-500,199). Physical activity and mental health were associated with connectivity strength and amplitude of the DMN, SN, and CEN (r's ≥ 0.12, p's < 0.048). These neural correlates exhibited highly similar loading patterns across mental health and physical activity models even when accounting for their shared variance. This suggests a largely shared brain network architecture between mental health and physical activity. Mental health and physical activity (including sleep) were also genetically correlated (|rg| = 0.085-0.121), but we found no evidence for causal relationships between them. Collectively, our findings provide empirical evidence that mental health and physical activity have shared brain and genetic architectures and suggest potential candidate subnetworks for future studies on brain mechanisms underlying beneficial effects of physical activity on mental health.

Association between the visceral adiposity index and risks of all-cause and cause-specific mortalities in a large cohort: Findings from the UK biobank

BACKGROUND AND AIMS

The visceral adiposity index (VAI) has been recently established as a measure of visceral fat distribution and is shown to be associated with a wide range of adverse health events. However, the precise associations between the VAI score and all-cause and cause-specific mortalities in the general population remain undetermined.

METHODS AND RESULTS

In this large-scale prospective epidemiological study, 357,457 participants (aged 38-73 years) were selected from the UK Biobank. We used Cox competing risk regression models to estimate the association between the VAI score and all-cause, cardiovascular disease (CVD), cancer, and other mortalities. The VAI score was significantly correlated with an increased risk of all-cause mortality (hazard ratio [HR], 1.200; 95% confidence interval [CI], 1.148-1.255; P < 0.0001), cancer mortality (HR, 1.224; 95% CI, 1.150-1.303; P < 0.0001), CVD mortality (HR, 1.459; 95% CI, 1.148-1.255; P < 0.0001), and other mortalities (HR, 1.200; 95% CI, 1.148-1.255; P < 0.0001) after adjusting for a series of confounders. In addition, the subgroup analyses showed that HRs were significantly higher in participants who were male, aged below 65 years, and body mass index less than 25.

CONCLUSION

In summary, VAI was positively associated with an increased risk of all-cause and cause-specific mortalities in a nationwide, well-characterised population identified in a UK Biobank. The VAI score might be a complementary traditional predictive indicator for evaluating the risk of adverse health events in the population of Western adults aged 38 years and older.

Lockdown strictness and mental health effects among older populations in Europe

This paper investigates whether lockdown policies aggravated mental health problems of older populations (50 and over) in Europe during the first COVID-19 wave. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE COVID-19 questionnaire) and from the Oxford COVID-19 Government Response Tracker for 17 countries, we estimate the causal effect of lockdown policies on mental health by combining cross-country variability in the strictness of the policies with cross-individual variability in face-to-face contacts prior to the pandemic. We find that lockdown policies worsened insomnia, anxiety, and depression by 5, 7.2 and 5.1 percentage points, respectively. This effect was stronger for women and those aged between 50 and 65. Interestingly, lockdown policies notably damaged the mental health of healthy populations. We close with a discussion of lockdown policies targeted at individuals above 65 and/or with pre-existing conditions.

Sleep Quality Among Burn Survivors And The Importance Of Intervention: A Systematic Review And Meta-Analysis

Burn survivors undergo a plethora of physiologic disturbances which can greatly affect quality of life (QOL) and healing processes. This review aimed to systematically examine sleep quality among individuals with burns and to explore the effectiveness of interventions using a meta-analytic approach. A systematic review of the literature was conducted by searching for articles using various databases. Titles and abstracts were screened and full texts of retained articles were assessed based on eligibility criteria. Methodological quality was ascertained in all articles using various scales. Overall, 5,323 articles were screened according to titles and abstracts and 25 articles were retained following full-text screening. Of the twenty-five articles, 17 were assessed qualitatively while 8 were included in the meta-analysis. Based on the qualitative analysis, sleep was found to be negatively affected in burn patients. The subsample of 8 articles included in the meta-analysis showed an overall weighted mean effect size (Hedges's g) of 1.04 (SE = 0.4, 95% CI, z = 3.0; p < 0.01), indicating a large, positive effect of intervention on sleep quality for burn patients. This review was able to demonstrate the detrimental effects of burn injury on sleep quality. Several interventions have been examined throughout the literature and have shown to be beneficial for sleep quality. However, there is great heterogeneity between existing interventions. The results from this review suggest that further research is needed before recommendations can be made as to which intervention is most effective at improving sleep in patients suffering from burn injuries.

Actigraphy monitoring in anxiety disorders: A mini-review of the literature

Sleep disturbances and changes of activity patterns are not uncommon in anxiety disorders, but they are rarely the object of attention. Actigraphic monitoring of day and night activity patterns could provide useful data to detect symptom worsening, prevent risk periods, and evaluate treatment efficacy in those disorders. Thus, we have conducted a systematic search of the scientific literature to find any original study using actigraphic monitoring to investigate activity and sleep patterns in patients affected by any type of anxiety disorder according to the definition of the DSM-5. We found only six studies fulfilling these criteria. Three studies report significant findings in patients suffering from anxiety disorders. Overall, the samples and methods are heterogeneous. Although the authors support the interest of actigraphic monitoring in anxiety disorders, the evidence to date is very limited.