Sleep regularity and mortality: a prospective analysis in the UK Biobank

The Joint Association of Diet Quality and Sleep Regularity with Incident Cardiovascular Disease in the Multi-Ethnic Study of Atherosclerosis

BACKGROUND/OBJECTIVES

Diet quality and sleep regularity both influence cardiovascular disease (CVD) risk and may influence each other, but there is scarce evidence for their joint or interacting associations in relation to CVD. We assessed these associations in the Multi-Ethnic Study of Atherosclerosis (MESA).

METHODS

Participants free of CVD with valid diet and sleep measures in 2010-2013 were included and followed through 2020 for detection of incident CVD (188 events detected over 8.8 years among 1782 participants; 55% women). Sleep timing and duration regularity were assessed via the intra-individual SD of sleep onset time and duration across 5- to 7-day actigraphy. The Alternate Healthy Eating Index-2010 assessed diet quality. Sleep regularity and diet quality were dichotomized and cross-tabulated to estimate joint associations with CVD and to evaluate interaction via Cox proportional hazard models adjusting for potential confounders.

RESULTS

Participants with low diet quality and irregular sleep had higher CVD risk compared to those with high diet quality and regular sleep (adjusted HR [95% CI]: low diet quality + irregular sleep timing: 1.56 [1.03, 2.37]; low diet quality + irregular sleep duration: 1.70 [1.09, 2.67]). The joint associations were stronger than those for only one adverse behavior and similar to those for their combination. There was no evidence for additive or multiplicative interactions.

CONCLUSIONS

Having irregular sleep and low diet quality confers the highest CVD risk compared to having neither or only one of these behaviors. These results underscore the importance of interventions targeting these unhealthy lifestyle behaviors, especially when they co-occur.

Sleep Problems and Sleep Quality in the General Adult Population Living in South Tyrol (Italy): A Cross-Sectional Survey Study

It remains unclear how sleep health has developed in the general population after the COVID-19 pandemic. This study aimed to (1) investigate the prevalence of sleep problems and poor sleep quality and (2) explore the associated sociodemographic and health-related factors in South Tyrol, Italy. A cross-sectional, population-based survey was conducted with a stratified probabilistic sample of 4000 adults aged ≥ 18 years. Sleep quality was assessed using the brief version of the Pittsburgh Sleep Quality Index. Descriptive and logistic regression analyses were performed to analyze the data. A total of 2090 adults (53%) completed the survey. Poor sleep quality was reported by 17.8%, with 28.2% of participants reporting insufficient sleep duration (i.e., six hours or less), 12.7% having problems staying asleep (i.e., waking up to 3-4 times a week and unable to fall asleep again), and 8.7% having problems falling asleep (i.e., >30 min). Sleep problems and poor sleep quality were associated with sociodemographic and health-related factors, including gender, age, mother tongue, chronic disease, and sleep hygiene. Notably, Italian-speaking participants reported poorer sleep quality and greater difficulty staying asleep compared to German-speaking participants, highlighting potential sociocultural influences on sleep health. This study contributes to understanding the unique sleep health challenges in a multilingual region, highlighting the role of sociocultural factors in sleep quality differences between language groups.

Multidimensional Sleep Health: Definitions and Implications for Cardiometabolic Health: A Scientific Statement From the American Heart Association

Poor sleep health is associated with cardiometabolic disease and related risk factors, including heart disease, stroke, elevated blood pressure and lipid levels, inflammation, glucose intolerance, obesity, physical inactivity, poor diet, unhealthy substance use, poor mental health, and increased all-cause and cardiovascular mortality, and is associated with social determinants of cardiovascular health and health disparities. Therefore, sleep duration has been recognized by the American Heart Association as one of Life's Essential 8. Although chronic sleep duration is the sole metric used in Life's Essential 8, sleep health represents a multidimensional construct. This scientific statement outlines the concept of multidimensional sleep health (sleep duration, continuity, timing, regularity, sleep-related daytime functioning, architecture, and absence of sleep disorders) as it applies to cardiometabolic health. Considerations of how these dimensions are related to cardiometabolic health and patterned by sociodemographic status are explained, and knowledge gaps are highlighted. Additional data are needed to understand better how these various dimensions of sleep should be assessed and how interventions targeting sleep health in clinical and community settings can be leveraged to improve health.

Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4-8 hours before sleep onset

Wearable devices with sleep tracking functionalities can prompt behavioral changes to promote sleep, but proactively preventing poor sleep when it is likely to occur remains a challenge due to a lack of prediction models that can forecast sleep parameters prior to sleep onset. We developed models that forecast low sleep efficiency 4 and 8 hours prior to sleep onset using gradient boosting (CatBoost) and deep learning (Convolutional Neural Network Long Short-Term Memory, CNN-LSTM) algorithms trained exclusively on accelerometer data from 80,811 adults in the UK Biobank. Associations of various sleep and activity parameters with sleep efficiency were further examined. During repeated cross-validation, both CatBoost and CNN-LSTM exhibited excellent predictive performance (median AUCs > 0.90, median AUPRCs > 0.79). U-shaped relationships were observed between total activity within 4 and 8 hours of sleep onset and low sleep efficiency. Functional data analyses revealed higher activity 6 to 8 hours prior to sleep onset had negligible associations with sleep efficiency. Higher activity 4 to 6 hours prior had moderate beneficial associations, while higher activity within 4 hours had detrimental associations. Additional analyses showed that increased variability in sleep duration, efficiency, onset timing, and offset timing over the preceding four days was associated with lower sleep efficiency. Our study represents a first step towards wearable-based machine learning systems that proactively prevent poor sleep by demonstrating that sleep efficiency can be accurately forecasted prior to bedtime and by identifying pre-bed activity targets for subsequent intervention.

Sleep regularity and major adverse cardiovascular events: a device-based prospective study in 72 269 UK adults

BACKGROUND

This study examines the associations between device-measured sleep regularity and the risk of major adverse cardiovascular events (MACE), and aims to determine whether sufficient sleep duration attenuates or eliminates the effects of irregular sleep on MACE risk.

METHODS

A prospective cohort study of adults aged 40-79 years from the UK Biobank who wore wrist-attached accelerometers for 7 days was conducted. Sleep Regularity Index (SRI) scores were calculated for each participant using a validated algorithm, and categorised as irregular (SRI <71.6), moderately irregular (SRI between 71.6 and 87.3), and regular (SRI >87.3 (reference group)). Information on MACE and its subtypes (myocardial infarction, heart failure, stroke) was obtained from inpatient hospitalisation and death records.

RESULTS

We analysed data from 72 269 individuals followed for 8 years, without a previous history of MACE and without an event in the first year of follow-up. Irregular (HR 1.26, 95% CI 1.16 to 1.37) and moderately irregular sleepers (HR 1.08, 95% CI 1.01 to 1.70) were at higher risk of MACE compared with regular sleepers. Dose-response analyses treating SRI as a continuous measure showed that SRI was associated with MACE risk in a near-linear fashion, with a steeper MACE risk reduction at higher (better) SRI scores. Joint SRI and sleep duration analyses showed that meeting the age-specific sleep duration recommendation offsets MACE risk for moderately irregular sleepers (HR 1.07, 95% CI 0.96 to 1.18), but not for irregular sleepers (HR 1.19, 95% CI 1.06 to 1.35).

CONCLUSIONS

Irregular sleep was strongly associated with higher MACE risk. Adequate sleep duration was not sufficient to offset these adverse effects among irregular sleepers. This study supports the inclusion of sleep regularity in public health guidelines and clinical practice as a risk factor for cardiovascular disease.

The Past and Future of Sleep Medicine

The past of sleep medicine is rich with seminal discoveries, from the recognition of clinical syndromes to measurement of sleep itself to classic and novel therapeutics. Advances in neurobiology have mapped a number of sleep circuits, described the central and peripheral circadian system, and identified the cause of narcolepsy with cataplexy. Sleep apnea endotypes and phenotypes now have established clinical relevance, though treatment implications are a work in progress. Artificial intelligence will continue to change sleep medicine in a number of domains from aiding scoring to health outcome predictions. There is a large gap between the known science and clinical translational.

Stuck in time: The slow march of circadian medicine and how to speed it up

While there is considerable enthusiasm for the translational and clinical applications of chronobiology, their actual implementation is not progressing as rapidly as might be expected. Here we discuss the possibility that this may relate to a combination of conceptual, methodological, evidentiary and training challenges. These are compounded by the remaining, profound cultural differences between basic and applied chronobiologists. We argue that all these issues can be overcome by cross-faculty teaching, time, patience and goodwill, together with a set of more formal actions, such as the establishment of a collaborative framework for evidence generation, the engagement of relevant stakeholders and public health campaigns based on already available evidence. We hope that chronobiology, and in particular the transformative power of circadian medicine, will change health outcomes, increase safety and improve quality of life for patients worldwide. Now is the time to bring "internal time" into medicine.

Wearables in Chronomedicine and Interpretation of Circadian Health

Wearable devices have gained increasing attention for use in multifunctional applications related to health monitoring, particularly in research of the circadian rhythms of cognitive functions and metabolic processes. In this comprehensive review, we encompass how wearables can be used to study circadian rhythms in health and disease. We highlight the importance of these rhythms as markers of health and well-being and as potential predictors for health outcomes. We focus on the use of wearable technologies in sleep research, circadian medicine, and chronomedicine beyond the circadian domain and emphasize actigraphy as a validated tool for monitoring sleep, activity, and light exposure. We discuss various mathematical methods currently used to analyze actigraphic data, such as parametric and non-parametric approaches, linear, non-linear, and neural network-based methods applied to quantify circadian and non-circadian variability. We also introduce novel actigraphy-derived markers, which can be used as personalized proxies of health status, assisting in discriminating between health and disease, offering insights into neurobehavioral and metabolic status. We discuss how lifestyle factors such as physical activity and light exposure can modulate brain functions and metabolic health. We emphasize the importance of establishing reference standards for actigraphic measures to further refine data interpretation and improve clinical and research outcomes. The review calls for further research to refine existing tools and methods, deepen our understanding of circadian health, and develop personalized healthcare strategies.

[Factors of depression according to actigraphy in the fall season]

OBJECTIVE

To evaluate the relationship between sleep parameters, physical activity, and illumination obtained by weekly actigraphy and depression symptoms measured by the Beck Depression Inventory (BDI-II) in young adults in the fall season.

MATERIAL AND METHODS

The crossover study, conducted during one fall month, included 122 adults (mean age 24.40 years, 76.6% females) from Tyumen (Russia). Participants were monitored for seven days using actigraphy and completed the Russian version of the BDI-II questionnaire to assess depressive symptoms. Actigraphy data were analyzed for quantitative and qualitative sleep parameters, dynamic illumination, and circadian rhythm of motor activity. Both mean values and regularity indicators were recorded. Statistical analysis was performed using linear and multiple linear regression methods, using the Benjamini-Hochberg procedure to control the incidence of false correlations associated with multiple testing.

RESULTS

The analysis showed significant correlations between various actigraphic indicators and the level of depression according to BDI-II. In particular, the BDI-II integral score was significantly associated with a decrease in the amplitude of the circadian rhythm of physical activity (PIM A: -0.258; p=0.005), a high instability of sleep efficiency (SL_EFF SD: -0.323; p=0.0003), a high standard deviation of the moment of awakening (WT SD: -0.258; p=0.005), a decrease in the inter-day stability of the activity rhythm (IS: -0.260; p=0.004), and a lower circadian light hygiene index (NA_bl: -0.193; p=0.036). After multiple comparison adjustments, low PIM A, reduced inter-day stability (IS), increased WT SD, and high SL_EFF SD remained significant predictors of depressive symptoms. In age-, sex-, and body mass index (BMI)-adjusted multiple regression, WT SD (β=-0.258; p=0.006), SL_EFF SD (β=-0.302; p=0.0006), and IS (β=-0.225; p=0.013) were significant factors.

CONCLUSION

The data obtained indicate a complex relationship between sleep dynamics and mental health in the context of BDI-II. They emphasize the critical importance of using parameters that assess sleep regularity as one of the markers of biological clock synchrony to increase informative value and improve the interpretation of data obtained using wearable devices.

Correlates of sleep variability in a mobile EEG-based volunteer study

Variable sleep patterns are a risk factor for disease, but the reasons some people express greater within-individual variability of sleep characteristics remains poorly understood. In our study, we leverage BSETS, a novel mobile EEG-based dataset in which 1901 nights in total were recorded from 267 extensively phenotyped participants to identify factors related to demographics, mental health, personality, chronotype and sleep characteristics which predict variability in sleep, including detailed sleep macrostructure metrics. Young age, late chronotype, and napping emerged as robust correlates of increased sleep variability. Correlations with other characteristics (such as student status, personality, mental health and co-sleeping) generally disappeared after controlling for age. We critically examine the utility of controlling the correlates of sleep variability for the means of sleep variables. Our research shows that age and sleep habits affecting the amount of sleep pressure at night are the most important factors underlying sleep variability, with a smaller role of other psychosocial variables. The avoidance of daytime naps emerges as the most promising modifiable behavior associated with increased sleep regularity.

Sleep, Well-Being, and Cognition in Medical Interns on a Float or Overnight Call Schedule

Importance

Extended work hours and night shifts are essential in health care, but negatively affect physician sleep, well-being, and patient care. Alternative schedules with shorter work hours and/or reduced irregularity might mitigate these issues.

Objective

To compare sleep, well-being, and cognition between interns working irregular, extended shifts (call schedule), and those working a more regular schedule with restricted hours (float schedule).

Design, Setting, and Participants

In this observational longitudinal cohort study, interns in a Singapore-based teaching hospital were studied for 8 weeks from January 2022 to July 2023. Data were analyzed from July 2023 to July 2024.

Exposure

Participants worked either regular approximately 10-hour workdays, interspersed with 24 hour or more overnight calls 4 to 5 times a month, or a float schedule, which included regular approximately 10-hour workdays, and 5 to 7 consecutive approximately 12-hour night shifts every 2 months. Exposure was based on departmental training and operational needs.

Main Outcomes and Measures

Sleep was measured with wearable sleep trackers and an electronic diary. Day-to-day well-being and cognitive assessments were collected through a smartphone application. Assessments included the Sleep Regularity Index (SRI; determines the probability of an individual being in the same state [sleep or wake] at any 2 time points 24 hours apart, with 0 indicating highly random sleep patterns and 100 denoting perfect regularity) and Pittsburgh Sleep Quality Inventory (PSQI; scores ranges from 0 to 21, with higher scores indicating poorer sleep; a score greater than 5 suggests significant sleep difficulties).

Results

Participants (mean [SD] age, 24.7 [1.1] years; 57 female participants [59.4%]; 41 on call schedule [42.7%]; 55 on float schedule [57.3%]) provided 4808 nights of sleep (84.2%) and 3390 days (59.3%) of well-being and cognition assessments. Participants on a float schedule had higher SRI scores (mean [SD] score, 69.4 [6.16]) and had better quality sleep (PSQI mean [SD] score, 5.4 [2.3]), than participants on call schedules (SRI mean [SD] score, 56.1 [11.3]; t91 = 6.81; mean difference, 13.3; 95% CI, 9.40 to 17.22; P < .001; PSQI mean [SD] score, 6.5 [2.3]; t79 = 2.16; 95% CI, 0.09 to 2.15; P = .03). Overnight call shifts, but not night float shifts, were associated with poorer mood (-13%; β = -6.79; 95% CI, -9.32 to -4.27; P < .001), motivation (-21%; β = -10.09; 95% CI, -12.55 to -7.63; P < .001), and sleepiness ratings (29%; β = 15.96; 95% CI, 13.01 to 18.90; P < .001) and impaired vigilance (21 ms slower; β = 20.68; 95% CI, 15.89 to 25.47; P < .001) compared with regular day shifts. Night shifts with naps were associated with better vigilance (16 ms faster; β = -15.72; 95% CI, -28.27 to -3.17; P = .01) than nights without naps.

Conclusions and relevance

In this cohort study, 24-hour call schedules were associated with poorer sleep, well-being, and cognition outcomes than float schedules. Naps during night shifts benefited vigilance in both schedules.

Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.

Sleep duration irregularity and risk for incident cardiovascular disease in the UK Biobank

Background

Emerging evidence supports a link between circadian disruption as measured by higher night-to-night variation in sleep duration and increased risk of cardiovascular disease (CVD). It remains unclear whether this association varies by CVD types or may be modified by average sleep duration and genetic risk for CVD.

Methods

Our prospective analysis included 86,219 UK Biobank participants who were free from CVD when completing 7 days of accelerometer measurement in 2013-2016. Sleep irregularity was evaluated by the standard deviation (SD) of accelerometer-measured sleep duration over 7 days. Incident major CVD events, defined as fatal or nonfatal myocardial infarction (MI) and stroke, were identified through linkage to Hospital Episode Statistics data until May 31, 2022. Multivariable-adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs for associations of sleep duration SD with risk for major CVD events overall and for MI and stroke separately.

Results

We documented 2,310 incident cases of major CVD events (MI: 1,183, stroke: 1,175) over 636,258 person-years of follow-up. After adjusting for sociodemographic factors and family history of CVD, the HR (95% CI) associated with a 1-hour increase in sleep duration SD was 1.19 (1.10, 1.27) for CVD (p-trend<0.0001), 1.23 (1.11, 1.35) for MI (p-trend<0.0001), and 1.17 (1.05, 1.29) for stroke (p-trend=0.003). Additional adjustment for lifestyle factors, co-morbidities and sleep-related factors modestly attenuated these associations. Higher sleep irregularity was associated with higher CVD risk irrespective of genetic risk (p-interaction=0.43), but this association was stronger among individuals with longer average sleep duration >8 hours (p-interaction=0.006).

Conclusions

Higher night-to-night variation in accelerometer-measured sleep duration was associated with consistently higher risks for major CVD events. The association did not seem to be modified by genetic risk for CVD and was more pronounced in long sleepers.

Another benefit of regular sleep

A large observational study has found that irregular sleep-wake patterns are associated with a higher risk of overall mortality, and also mortality from cancers and cardiovascular disease.