Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness

Enhanced Circadian Phase Tracking: A 5-h DLMO Sampling Protocol Using Wearable Data

Circadian medicine aims to leverage the body's internal clock to develop safer and more effective therapeutics. Traditionally, biological time has been estimated using dim light melatonin onset (DLMO), a method that requires collecting saliva samples over a long period under controlled conditions, to ensure the observation of DLMO, making it time-consuming and labor-intensive. While some studies have mitigated this by reducing the length of the sampling window, they significantly failed to identify the DLMO for shift workers. In this study, we present a framework that reduces the DLMO experiment time for shift workers to just 5 h. This approach combines sleep-wake pattern data from wearable devices with a mathematical model to predict DLMO prospectively. Based on this prediction, we define a targeted 5-h sampling window, from 3 h before to 2 h after the estimated DLMO. Testing this framework with 19 shift workers, we successfully identified the DLMO for all participants, whereas traditional methods failed for more than 40% of participants. This approach significantly reduces the experiment time required for measuring the DLMO of shift workers from 24 h to 5 h, simplifying the circadian phase measurements for shift workers.

Effect of lithium on circadian activity level and flexibility in patients with bipolar disorder: results from the Oxford Lithium Trial

BACKGROUND

Disruption of circadian rest-activity is prevalent in patients with bipolar disorder (BD). Lithium's impact on circadian rhythms has been documented in cell lines, animal models, and pharmacogenomics studies in patients with BD. However, the causal relationship between such disruption and BD remains unclear.

METHODS

We investigated the early effects of lithium on circadian rest-activity in an exploratory analysis of a randomised, placebo-controlled, double-blind six-week study on patients with BD. Participants were assigned to receive either lithium or a placebo in a 1:1 ratio. Circadian activity was monitored using actigraphy, and daily affect was assessed through ecological momentary assessment. A computational model was used to quantify different types of activity variability, and the impact of lithium on activity level, activity onset time and their variability were analysed using linear mixed models.

FINDINGS

Of the thirty-five participants who began treatment, 19 received lithium and 16 received a placebo. Lithium significantly altered circadian rest-activity patterns, including reducing daytime activity levels (after 4 weeks, below as well: Cohen's d = -0.19, p = 0.002, linear mixed model, ibid.), advancing the onset of daytime activity (Cohen's d = -0.14, p = 0.018), and increasing the volatility of both daytime activity level (Cohen's d = 0.10, p = 0.002) and its onset time (Cohen's d = 0.13, p < 0.001), independent of affective symptoms changes.

INTERPRETATION

This study establishes a causal link between lithium treatment and reduced circadian activity with advanced circadian phase, potentially via temporarily increasing their volatility (flexibility). Significant circadian changes were detected within one week of starting lithium, highlighting their potential as an early biomarker for treatment response.

FUNDING

This research was supported by the Wellcome Trust Strategic Award (CONBRIO: Collaborative Oxford Network for Bipolar Research to Improve Outcomes, reference No. 102,616/Z), NIHR Oxford Health Biomedical Research Centre and the NIHR Oxford cognitive health Clinical Research Facility.

Dynamical mechanism for the interplay of circadian, homeostatic, and ultradian rhythm in normal human sleep

The 90-minute ultradian rhythm is a hallmark of healthy human sleep, yet its governing mechanisms remain elusive. In this study, we develop a biologically grounded sleep model to unravel the complex dynamics underlying this rhythm. Our model integrates both circadian and ultradian drives, which collectively shape sleep architecture, along with bidirectional "flip-flop" switches that control transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Calibrated with empirically derived neurophysiological parameters, the model successfully reproduces core sleep features, including the 24-hour circadian rhythm and the 90-minute ultradian rhythm. To dissect state transition mechanisms, we employ potential landscape analysis to quantify how global stability is modulated by three key factors: circadian drive, homeostatic drive, and REM pressure. Our results reveal that the ultradian rhythm emerges from an interplay between a weak ultradian drive and REM pressure. In a reduced model focusing on NREM-REM interactions, we demonstrate that the periodic transitions between NREM and REM sleep arise from a saddle-node bifurcation on an invariant circle (SNIC) induced by REM sleep pressure. Additionally, the ultradian drive entrains the rhythmic NREM-REM system to exhibit the stable 90-minute ultradian rhythm, as characterized by the Arnold tongue. Our work provides the mechanistic explanation of the 90-minute ultradian rhythm, identifying REM pressure as its core regulator and highlighting the SNIC bifurcation together with the Arnold tongue as its dynamical mechanisms. This framework establishes testable neurophysiological requirements for experimental validation, thereby bridging theoretical models with empirical sleep neuroscience.

Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.

Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses

BACKGROUND

Shift work disrupts circadian rhythms and alters sleep patterns, resulting in various health problems. To quantitatively assess the impact of shift work on brain health, we evaluated the brain age index (BAI) derived from sleep electroencephalography (EEG) results in night-shift workers and compared it with that in daytime workers.

METHODS

We studied 45 female night shift nurses (mean age: 28.2 ± 3.3 years) and 44 female daytime workers (30.5 ± 4.7 years). Sleep EEG data were analyzed to calculate BAI. The BAI of night shift workers who were asleep during the daytime with those of daytime workers who were asleep at night were statistically compared to explore associations between BAI, duration of shift work, and sleep quality.

RESULTS

Night-shift workers exhibited significantly higher BAI (2.14 ± 6.04 vs. 0 ± 5.35), suggesting accelerated brain aging and altered sleep architecture, including reduced delta and sigma wave frequency activity during non-rapid eye movement sleep than daytime workers. Furthermore, poor deep sleep quality, indicated by a higher percentage of N1, lower percentage of N3, and higher arousal index, was associated with increased BAI among shift workers. Additionally, a longer duration of night-shift work was correlated with increased BAI, particularly in older shift workers.

CONCLUSION

Night-shift work, especially over extended periods, may be associated with accelerated brain aging, as indicated by higher BAI and alterations in sleep architecture. Interventions are necessary to mitigate the health impacts of shift work. Further research on the long-term effects and potential strategies for sleep improvement and mitigating brain aging in shift workers is warranted.

Biomathematical modeling of fatigue due to sleep inertia

Biomathematical models of fatigue capture the physiology of sleep/wake regulation and circadian rhythmicity to predict changes in neurobehavioral functioning over time. We used a biomathematical model of fatigue linked to the adenosinergic neuromodulator/receptor system in the brain as a framework to predict sleep inertia, that is, the transient neurobehavioral impairment experienced immediately after awakening. Based on evidence of an adenosinergic basis for sleep inertia, we expanded the biomathematical model with novel differential equations to predict the propensity for sleep inertia during sleep and its manifestation after awakening. Using datasets from large laboratory studies of sleep loss and circadian misalignment, we calibrated the model by fitting just two new parameters and then validated the model's predictions against independent data. The expanded model was found to predict the magnitude and time course of sleep inertia with generally high accuracy. Analysis of the model's dynamics revealed a bifurcation in the predicted manifestation of sleep inertia in sustained sleep restriction paradigms, which reflects the observed escalation of the magnitude of sleep inertia in scenarios with sleep restriction to less than ∼ 4 h per day. Another emergent property of the model involves a rapid increase in the predicted propensity for sleep inertia in the early part of sleep followed by a gradual decline in the later part of the sleep period, which matches what would be expected based on the adenosinergic regulation of non-rapid eye movement (NREM) sleep and its known influence on sleep inertia. These dynamic behaviors provide confidence in the validity of our approach and underscore the predictive potential of the model. The expanded model provides a useful tool for predicting sleep inertia and managing impairment in 24/7 settings where people may need to perform critical tasks immediately after awakening, such as on-demand operations in safety and security, emergency response, and health care.

Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study

BACKGROUND

The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults.

OBJECTIVE

We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults.

METHODS

We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease.

RESULTS

During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase.

CONCLUSIONS

Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data

BACKGROUND

Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown.

METHODS

We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method.

FINDINGS

The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms.

INTERPRETATION

Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively.

FUNDING

Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

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 β=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.

Method to determine whether sleep phenotypes are driven by endogenous circadian rhythms or environmental light by combining longitudinal data and personalised mathematical models

Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure along with social constraints and behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised interventions. The timing of the human sleep-wake cycle has been modelled as an interaction of a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate that individual sleep phenotypes in each of 34 older participants (65-83y) can be described by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights that different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.

A real-time, personalized sleep intervention using mathematical modeling and wearable devices

The prevalence of artificial light exposure has enabled us to be active any time of the day or night, leading to the need for high alertness outside of traditional daytime hours. To address this need, we developed a personalized sleep intervention framework that analyzes real-world sleep-wake patterns obtained from wearable devices to maximize alertness during specific target periods. Our framework utilizes a mathematical model that tracks the dynamic sleep pressure and circadian rhythm based on the user's sleep history. In this way, the model accurately predicts real-time alertness, even for shift workers with complex sleep and work schedules (N = 71, t = 13~21 days). This allowed us to discover a new sleep-wake pattern called the adaptive circadian split sleep, which incorporates a main sleep period and a late nap to enable high alertness during both work and non-work periods of shift workers. We further developed a mobile application that integrates this framework to recommend practical, personalized sleep schedules for individual users to maximize their alertness during a targeted activity time based on their desired sleep onset and available sleep duration. This can reduce the risk of errors for those who require high alertness during nontraditional activity times and improve the health and quality of life for those leading shift work-like lifestyles.

Chemotherapy delivery time affects treatment outcomes of female patients with diffuse large B cell lymphoma

BACKGROUNDChronotherapy is a drug intervention at specific times of the day to optimize efficacy and minimize adverse effects. Its value in hematologic malignancy remains to be explored, in particular in adult patients.METHODSWe performed chronotherapeutic analysis using 2 cohorts of patients with diffuse large B cell lymphoma (DLBCL) undergoing chemotherapy with a dichotomized schedule (morning or afternoon). The effect of a morning or afternoon schedule of rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) on survival and drug tolerability was evaluated in a survival cohort (n = 210) and an adverse event cohort (n = 129), respectively. Analysis of about 14,000 healthy individuals followed to identify the circadian variation in hematologic parameters.RESULTSBoth progression-free survival (PFS) and overall survival (OS) of female, but not male, patients were significantly shorter when patients received chemotherapy mostly in the morning (PFS HR 0.357, P = 0.033; and OS HR 0.141, P = 0.032). The dose intensity was reduced in female patients treated in the morning (cyclophosphamide 10%, P = 0.002; doxorubicin 8%, P = 0.002; and rituximab 7%, P = 0.003). This was mainly attributable to infection and neutropenic fever: female patients treated in the morning had a higher incidence of infections (16.7% vs. 2.4%) and febrile neutropenia (20.8% vs. 9.8%) as compared with those treated in the afternoon. The sex-specific chronotherapeutic effects can be explained by the larger daily fluctuation of circulating leukocytes and neutrophils in female than in male patients.CONCLUSIONIn female DLBCL patients, R-CHOP treatment in the afternoon can reduce toxicity while it improves efficacy and survival outcome.FUNDINGNational Research Foundation of Korea (NRF) grant funded by the Korean government (grant number NRF-2021R1A4A2001553), Institute for Basic Science IBS-R029-C3, and Human Frontiers Science Program Organization Grant RGY0063/2017.

A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration

Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.

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An algorithm for sleep-wake classification based on arm acceleration is presented

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The algorithm only uses a derivative of triaxial arm acceleration (jerk)

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The algorithm can accurately detect temporal awake during sleep