Daily Light Exposure Patterns Reveal Phase and Period of the Human Circadian Clock

Mathematical Analysis of Light-sensitivity Related Challenges in Assessment of the Intrinsic Period of the Human Circadian Pacemaker

Accurate assessment of the intrinsic period of the human circadian pacemaker is essential for a quantitative understanding of how our circadian rhythms are synchronized to exposure to natural and man-made light-dark (LD) cycles. The gold standard method for assessing intrinsic period in humans is forced desynchrony (FD) which assumes that the confounding effect of lights-on assessment of intrinsic period is removed by scheduling sleep-wake and associated dim LD cycles to periods outside the range of entrainment of the circadian pacemaker. However, the observation that the mean period of free-running blind people is longer than the mean period of sighted people assessed by FD (24.50 ± 0.17 h vs 24.15 ± 0.20 h, p < 0.001) appears inconsistent with this assertion. Here, we present a mathematical analysis using a simple parametric model of the circadian pacemaker with a sinusoidal velocity response curve (VRC) describing the effect of light on the speed of the oscillator. The analysis shows that the shorter period in FD may be explained by exquisite sensitivity of the human circadian pacemaker to low light intensities and a VRC with a larger advance region than delay region. The main implication of this analysis, which generates new and testable predictions, is that current quantitative models for predicting how light exposure affects entrainment of the human circadian system may not accurately capture the effect of dim light. The mathematical analysis generates new predictions which can be tested in laboratory experiments. These findings have implications for managing healthy entrainment of human circadian clocks in societies with abundant access to light sources with powerful biological effects.

Uncovering personal circadian responses to light through particle swarm optimization

BACKGROUND AND OBJECTIVES

Kronauer's oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider inter-individual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer's oscillator model.

METHODS

A Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model.

RESULTS

We demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user.

CONCLUSIONS

The robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.

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.

Pediatric sleep: current knowledge, gaps, and opportunities for the future

This White Paper addresses the current gaps in knowledge, as well as opportunities for future studies in pediatric sleep. The Sleep Research Society's Pipeline Development Committee assembled a panel of experts tasked to provide information to those interested in learning more about the field of pediatric sleep, including trainees. We cover the scope of pediatric sleep, including epidemiological studies and the development of sleep and circadian rhythms in early childhood and adolescence. Additionally, we discuss current knowledge of insufficient sleep and circadian disruption, addressing the neuropsychological impact (affective functioning) and cardiometabolic consequences. A significant portion of this White Paper explores pediatric sleep disorders (including circadian rhythm disorders, insomnia, restless leg and periodic limb movement disorder, narcolepsy, and sleep apnea), as well as sleep and neurodevelopment disorders (e.g. autism and attention deficit hyperactivity disorder). Finally, we end with a discussion on sleep and public health policy. Although we have made strides in our knowledge of pediatric sleep, it is imperative that we address the gaps to the best of our knowledge and the pitfalls of our methodologies. For example, more work needs to be done to assess pediatric sleep using objective methodologies (i.e. actigraphy and polysomnography), to explore sleep disparities, to improve accessibility to evidence-based treatments, and to identify potential risks and protective markers of disorders in children. Expanding trainee exposure to pediatric sleep and elucidating future directions for study will significantly improve the future of the field.

Empirical mode decomposition of local field potential data from optogenetic experiments

Introduction

This study investigated the effects of cocaine administration and parvalbumin-type interneuron stimulation on local field potentials (LFPs) recorded in vivo from the medial prefrontal cortex (mPFC) of six mice using optogenetic tools.

Methods

The local network was subject to a brief 10 ms laser pulse, and the response was recorded for 2 s over 100 trials for each of the six subjects who showed stable coupling between the mPFC and the optrode. Due to the strong non-stationary and nonlinearity of the LFP, we used the adaptive, data-driven, Empirical Mode Decomposition (EMD) method to decompose the signal into orthogonal Intrinsic Mode Functions (IMFs).

Results

Through trial and error, we found that seven is the optimum number of orthogonal IMFs that overlaps with known frequency bands of brain activity. We found that the Index of Orthogonality (IO) of IMF amplitudes was close to zero. The Index of Energy Conservation (IEC) for each decomposition was close to unity, as expected for orthogonal decompositions. We found that the power density distribution vs. frequency follows a power law with an average scaling exponent of ~1.4 over the entire range of IMF frequencies 2-2,000 Hz.

Discussion

The scaling exponent is slightly smaller for cocaine than the control, suggesting that neural activity avalanches under cocaine have longer life spans and sizes.

Efficient Estimation of the Human Circadian Phase via Kalman Filtering

Circadian rhythms play a vital role in maintaining a person's well-being but remain difficult to quantify accurately. Numerous approaches exist to measure these rhythms, but they often suffer from performance issues on the individual level. This work implements a Steady-State Kalman Filter as a method for estimating the circadian phase shifts from biometric signals. Our framework can automatically fit the filter's parameters to biometric data obtained for each individual, and we were able to consistently estimate the phase shift within 1 hour of melatonin estimates on 100% of all subjects in this study. The estimation method opens up the possibility of real-time control and assessment of the circadian system, as well as chronotherapeutic intervention.Clinical relevance- This establishes a near real-time alternative to melatonin measurements for the estimation of circadian phase shifts, with potential applications in feedback circadian control and chronotherapeutics.

Machine learning estimation of human body time using metabolomic profiling

Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.

Night-to-night associations between light exposure and sleep health

Previous research has demonstrated that exposure to light preceding and during sleep is associated with poor sleep, but most research to date has utilized either experimental or cross-sectional designs. The current study expands upon prior studies by using a microlongitudinal design that examines the night-to-night associations between light and sleep health in a diverse sample of adults (pre-registered at osf.io/k5zgv). US adults aged 18-87 years from two parent studies (N = 124) wore an actiwatch for up to 10 nights. Light variables estimated from actigraphy include both average exposure and time above light threshold of 10 (TALT10 ) and 40 (TALT40 ) lux both during sleep and for the 1-hr preceding sleep. Actigraphy-based sleep variables included sleep offset, duration, percentage and fragmentation index. Higher average light exposure during sleep was associated with a later sleep-offset time, lower sleep percentage and higher fragmentation index (all p < 0.01). More minutes of TALT10 during sleep was associated with later sleep timing, lower sleep percentage and higher fragmentation index (all p < 0.01), and greater TALT40 during sleep was associated with lower sleep percentage. Light exposure was not related to sleep duration. In summary, greater light exposure during sleep was related to poorer sleep continuity and later wake time. The lack of association between light and sleep duration may be the result of compensating for sleep disruption by delaying wake time. Multi-level interventions to consistently reduce light levels during sleep should be considered.

Validation of the Entrainment Signal Regularity Index and associations with children's changes in BMI

OBJECTIVE

This study examined the validity of a novel metric of circadian health, the Entrainment Signal Regularity Index (ESRI), and its relationship to changes in BMI during the school year and summer.

METHODS

In a longitudinal observational data set, this study examined the relationship between ESRI score and children's (n = 119, 5- to 8-year-olds) sleep and physical activity levels during the school year and summer, differences in ESRI score during the school year and summer, and the association of ESRI score during the school year and summer with changes in BMI across those time periods.

RESULTS

The ESRI score was higher during the school year (0.70 ± 0.10) compared with summer (0.63 ± 0.11); t(111) = 5.484, p < 0.001. Whereas the ESRI score at the beginning of the school year did not significantly predict BMI change during the school year (β = 0.05 ± 0.09 SE, p = 0.57), having a higher ESRI score during summer predicted smaller increases in BMI during summer (β = -0.22 ± 0.10 SE, p = 0.03).

CONCLUSIONS

Overall, children demonstrated higher entrainment regularity during the school year compared with the summer. During summer, having a higher entrainment signal was associated with smaller changes in summertime BMI. This effect was independent of the effects of children's sleep midpoint, sleep regularity, and physical activity on children's BMI.

Quasi-causal associations between chronotype and post-traumatic stress disorder symptoms: A twin study

OBJECTIVE

The evening ("night owl") chronotype is associated with greater severity and lifetime prevalence of post-traumatic stress disorder (PTSD) symptoms compared to morning or intermediate chronotypes. This twin study investigated the gene-environment relationships between chronotype, recent PTSD symptoms, and lifetime intrusive symptoms.

METHODS

We used the reduced Horne-Östberg Morningness-Eveningness Questionnaire (rMEQ) to assess chronotype in a sample of 3777 same-sex adult twin pairs raised together (70.4% monozygotic, 29.6% dizygotic) in the community-based Washington State Twin Registry. PTSD symptoms were reported on the Impact of Events Scale (IES) and a single item for lifetime experience of intrusive symptoms after a stressful or traumatic event.

RESULTS

Genetic influences accounted for 50% of chronotype variance, 30% of IES score variance, and 14% of lifetime intrusive symptom variance. Bivariate twin models showed a phenotypic association (b_p) between evening chronotype and more severe PTSD symptoms (b_p = -0.16, SE = 0.02, p < .001) that remained significant even after adjusting for shared genetic and environmental influences (b_p = -0.10, SE = 0.04, p = .009), as well as age, sex, and self-reported sleep duration (b_p = -0.11, SE = 0.04, p = .004). An association was found between evening chronotype and lifetime intrusive symptoms (b_p = -0.11, SE = 0.03, p < .001) that was no longer significant after adjusting for shared genetic and environmental influences (b_p = 0.04, SE = 0.06, p = .558).

CONCLUSIONS

Our results suggest a "quasi-causal" relationship between evening chronotype and PTSD symptoms that is not purely attributable to genetic or shared environmental factors. Evening chronotype may increase vulnerability to pathologic stress responses in the setting of circadian misalignment, providing potential avenues of prevention and treatment using chronobiological strategies.

Usability and Acceptability of a Corneal-Plane α-Opic Light Logger in a 24-h Field Trial

Introduction

Exposure to light fundamentally influences human physiology and behaviour by synchronising our biological clock to the external light-dark cycle and controlling melatonin production. In addition to well-controlled laboratory studies, more naturalistic approaches to examining these "non-visual" effects of light have been developed in recent years. As naturalistic light exposure is quite unlike well-controlled stimulus conditions in the laboratory, it is critical to measure light exposure in a person-referenced way, the "spectral diet." To this end, light loggers have been developed to capture personalised light exposure. As an alternative to light sensors integrated into wrist-worn actimeters, pendants, or brooch-based light loggers, a recently developed wearable light logger laterally attached to spectacle frames enables the measurement of biologically relevant quantities in the corneal plane.

Methods

Here, we examine the usability and acceptability of using the light logger in an undergraduate student sample (n = 18, mean±1SD: 20.1 ± 1.7 years; 9 female; Oxford, UK) in real-world conditions during a 24-h measurement period. We probed the acceptability of the light logger using rating questionnaires and open-ended questions.

Results

Our quantitative results show a modest acceptability of the light logger. A thematic analysis of the open-ended questions reveals that the form factor of the device, in particular, size, weight, and stability, and reactions from other people to the wearer of the light logger, were commonly mentioned aspects.

Conclusion

In sum, the results indicate the miniaturisation of light loggers and "invisible" integration into extant everyday objects as key areas for future technological development, facilitating the availability of light exposure data for developing personalised intervention strategies in both research, clinical and consumer contexts.

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers

Background

Light exposure is an important driver and modulator of human physiology, behavior and overall health, including the biological clock, sleep-wake cycles, mood and alertness. Light can also be used as a directed intervention, e.g., in the form of light therapy in seasonal affective disorder (SAD), jetlag prevention and treatment, or to treat circadian disorders. Recently, a system of quantities and units related to the physiological effects of light was standardized by the International Commission on Illumination (CIE S 026/E:2018). At the same time, biometric monitoring technologies (BioMeTs) to capture personalized light exposure were developed. However, because there are currently no standard approaches to evaluate the digital dosimeters, the need to provide a firm framework for the characterization, calibration, and reporting for these digital sensors is urgent.

Objective

This article provides such a framework by applying the principles of verification, analytic validation and clinical validation (V3) as a state-of-the-art approach for tools and standards in digital medicine to light dosimetry.

Results

This article describes opportunities for the use of digital dosimeters for basic research, for monitoring light exposure, and for measuring adherence in both clinical and non-clinical populations to light-based interventions in clinical trials.

Fast tuning of observer-based circadian phase estimator using biometric data

Circadian rhythms play a vital role in maintaining an individual's well-being, and they have been shown to be the product of the master oscillator in the suprachiasmatic nuclei (SCN) located in the brain. The SCN however, is inaccessible for assessment, so existing standards for circadian phase estimation often focus on the use of indirect measurements as proxies for the circadian state. These methods often suffer from severe delays due to invasive methods of sample collection, making online estimation impossible. In this paper, we propose a linear state observer as an elegant solution for continuous phase estimation. This observer-based filter is used in isolating the frequency components of input biometric signals, which are then taken to be the circadian state. We start the design process by fixing the observer's oscillatory frequency at 24 hours, and then we tune its gains using an evolutionary optimization algorithm to extract the target components from individuals' data. The resulting filter was able to provide phase estimates with an average absolute error within 1.5 hours on all test subjects, given their minute-to-minute actigraphy data collected in ambulatory conditions.

Chronic jet lag-like conditions dysregulate molecular profiles of neurological disorders in nucleus accumbens and prefrontal cortex

Background

Patients with neurological disorders often display altered circadian rhythms. The disrupted circadian rhythms through chronic jetlag or shiftwork are thought to increase the risk and severity of human disease including, cancer, psychiatric, and related brain diseases.

Results

In this study, we investigated the impact of shiftwork or chronic jetlag (CJL) like conditions on mice's brain. Transcriptome profiling based on RNA sequencing revealed that genes associated with serious neurological disorders were differentially expressed in the nucleus accumbens (NAc) and prefrontal cortex (PFC). According to the quantitative PCR (qPCR) analysis, several key regulatory genes associated with neurological disorders were significantly altered in the NAc, PFC, hypothalamus, hippocampus, and striatum. Serotonin levels and the expression levels of serotonin transporters and receptors were significantly altered in mice treated with CJL.

Conclusion

Overall, these results indicate that CJL may increase the risk of neurological disorders by disrupting the key regulatory genes, biological functions, serotonin, and corticosterone. These molecular linkages can further be studied to investigate the mechanism underlying CJL or shiftwork-mediated neurological disorders in order to develop treatment strategies.

The circadian stimulus-oscillator model: Improvements to Kronauer’s model of the human circadian pacemaker

Modeling how patterns of light and dark affect circadian phase is important clinically and organizationally (e.g., the military) because circadian disruption can compromise health and performance. Limit-cycle oscillator models in various forms have been used to characterize phase changes to a limited set of light interventions. We approached the analysis of the van der Pol oscillator-based model proposed by Kronauer and colleagues in 1999 and 2000 (Kronauer99) using a well-established framework from experimental psychology whereby the stimulus (S) acts on the organism (O) to produce a response (R). Within that framework, using four independent data sets utilizing calibrated personal light measurements, we conducted a serial analysis of the factors in the Kronauer99 model that could affect prediction accuracy characterized by changes in dim-light melatonin onset. Prediction uncertainty was slightly greater than 1 h for the new data sets using the original Kronauer99 model. The revised model described here reduced prediction uncertainty for these same data sets by roughly half.

Circadian rhythms and disorders of the timing of sleep

The daily alternation between sleep and wakefulness is one of the most dominant features of our lives and is a manifestation of the intrinsic 24 h rhythmicity underlying almost every aspect of our physiology. Circadian rhythms are generated by networks of molecular oscillators in the brain and peripheral tissues that interact with environmental and behavioural cycles to promote the occurrence of sleep during the environmental night. This alignment is often disturbed, however, by contemporary changes to our living environments, work or social schedules, patterns of light exposure, and biological factors, with consequences not only for sleep timing but also for our physical and mental health. Characterised by undesirable or irregular timing of sleep and wakefulness, in this Series paper we critically examine the existing categories of circadian rhythm sleep-wake disorders and the role of the circadian system in their development. We emphasise how not all disruption to daily rhythms is driven solely by an underlying circadian disturbance, and take a broader, dimensional approach to explore how circadian rhythms and sleep homoeostasis interact with behavioural and environmental factors. Very few high-quality epidemiological and intervention studies exist, and wider recognition and treatment of sleep timing disorders are currently hindered by a scarcity of accessible and objective tools for quantifying sleep and circadian physiology and environmental variables. We therefore assess emerging wearable technology, transcriptomics, and mathematical modelling approaches that promise to accelerate the integration of our knowledge in sleep and circadian science into improved human health.

Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices

STUDY OBJECTIVES

Examine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and wake time).

METHODS

As part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4 ± .97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lx). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared.

RESULTS

DLMO predictions using the Hannay model outperformed DLMO predictions based on children's sleep/wake parameters with a Lin's Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41-0.59 for sleep/wake parameters. The mean absolute error was 31 min for the Hannay model compared to 35-38 min for the sleep/wake variables.

CONCLUSION

Our findings suggest that sleep/wake behaviors were weak proxies of DLMO phase in children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children.

CLINICAL TRIAL

The i Heart Rhythm Project: Healthy Sleep and Behavioral Rhythms for Obesity Prevention https://clinicaltrials.gov/ct2/show/NCT04445740.

Bright Light Increases Alertness and Not Cortisol in Healthy Men: A Forced Desynchrony Study Under Dim and Bright Light (I)

Light-induced improvements in alertness are more prominent during nighttime than during the day, suggesting that alerting effects of light may depend on internal clock time or wake duration. Relative contributions of both factors can be quantified using a forced desynchrony (FD) designs. FD designs have only been conducted under dim light conditions (<10 lux) since light above this amount can induce non-uniform phase progression of the circadian pacemaker (also called relative coordination). This complicates the mathematical separation of circadian clock phase from homeostatic sleep pressure effects. Here we investigate alerting effects of light in a novel 4 × 18 h FD protocol (5 h sleep, 13 h wake) under dim (6 lux) and bright light (1300 lux) conditions. Hourly saliva samples (melatonin and cortisol assessment) and 2-hourly test sessions were used to assess effects of bright light on subjective and objective alertness (electroencephalography and performance). Results reveal (1) stable free-running cortisol rhythms with uniform phase progression under both light conditions, suggesting that FD designs can be conducted under bright light conditions (1300 lux), (2) subjective alerting effects of light depend on elapsed time awake but not circadian clock phase, while (3) light consistently improves objective alertness independent of time awake or circadian clock phase. Reconstructing the daily time course by combining circadian clock phase and wake duration effects indicates that performance is improved during daytime, while subjective alertness remains unchanged. This suggests that high-intensity indoor lighting during the regular day might be beneficial for mental performance, even though this may not be perceived as such.

The effects of a real-life lifestyle program on physical activity and objective and subjective sleep in adults aged 55+ years

Study objectives

Age related changes in sleep result in an increasing prevalence of poor sleep in mid-aged and older adults. Although physical activity has shown to benefit sleep in studies in controlled settings, this has not yet been examined in a real-life lifestyle program. The aims of this study were to: 1) examine the effects of a lifestyle program on moderate-to-vigorous physical activity and objective and subjective sleep in adults aged 55+ years; and 2) examine if the effects differed between good and poor sleepers.

Methods

This controlled pretest-posttest trial examined the effects of the 12-week group-based real-life lifestyle program ‘Lekker Actief’ on moderate-to-vigorous physical activity (measured using accelerometers) and sleep (measured using accelerometers and the Pittsburgh Sleep quality Index, PSQI). The main component of the program was a 12-week progressive walking program, complemented by an optional muscle strengthening program and one educational session on healthy nutrition. Of the 451 participants who were tested pre-intervention, 357 participants completed the posttest assessment (200 in the intervention group and 157 in the control group). Effects on moderate-to-vigorous physical activity and on objective sleep (sleep efficiency, total sleep time, wake time after sleep onset (WASO) and number of awakenings) as well as subjective sleep (sleep quality) were examined in crude and in adjusted multiple regression models. An interaction term between program (control versus intervention) and sleep category (good and poor) was included in all models.

Results

Moderate-to-vigorous physical activity levels significantly increased in the intervention group compared with the control group (43,02 min per day; 95%CI: 12.83–73.22; fully adjusted model). The interaction terms revealed no differences between good and poor sleepers regarding the effect of the intervention on moderate-to-vigorous physical activity. There were no significant effects on sleep, except for good sleepers who showed an increase in number of awakenings/night by 1.44 (CI 95% 0.49; 2.24).

Conclusions

Although this program was effective in increasing physical activity, it did not improve sleep. Lifestyle programs should be promoted to increase physical activity, but more is needed to improve sleep as well.

This trial was registered at ClinicalTrials.gov (Trial registration NCT03576209).

Weekly and seasonal variation in the circadian melatonin rhythm in humans: A response

We read with interest the commentary by Skeldon and Dijk about our article "Weekly, seasonal and chronotype-dependent variation of dim light melatonin onset." The discussion points raised by Skeldon and Dijk are currently among the most hotly debated in human circadian science. What external factors determine human phase of entrainment? How great is the contribution of natural versus artificial light and sun time versus social time? Our intra-individual data add to the still limited evidence from field studies in this matter. In their commentary, Skeldon and Dijk formulate two either-or hypotheses, postulating that humans entrain either solely to the natural light-dark cycle (sun time referenced by midday) (H₁ ) or solely to the light selected by local clock time and social constraints (H₂ ). Neither hypothesis accounts for the effect of season on human light exposure. We interpreted our findings along more complex lines, speculating that the 1-h earlier melatonin rise in summer found in our sample is likely the combined result of daylight saving time (DST)-induced behavioral advances and a stronger natural zeitgeber in summer (light exposure determined by social and seasonal factors, H_original ). Here, we show how the criticism by Skeldon and Dijk is based on two sentences quoted out of context (misrepresenting our hypothesis as H₁ ) and that their hypothesis H₂  leaves out important seasonal components in light exposure.

Distinct Circadian Assessments From Wearable Data Reveal Social Distancing Promoted Internal Desynchrony Between Circadian Markers

Mobile measures of human circadian rhythms (CR) are needed in the age of chronotherapy. Two wearable measures of CR have recently been validated: one that uses heart rate to extract circadian rhythms that originate in the sinoatrial node of the heart, and another that uses activity to predict the laboratory gold standard and central circadian pacemaker marker, dim light melatonin onset (DLMO). We first find that the heart rate markers of normal real-world individuals align with laboratory DLMO measurements when we account for heart rate phase error. Next, we expand upon previous work that has examined sleep patterns or chronotypes during the COVID-19 lockdown by studying the effects of social distancing on circadian rhythms. In particular, using data collected from the Social Rhythms app, a mobile application where individuals upload their wearable data and receive reports on their circadian rhythms, we compared the two circadian phase estimates before and after social distancing. Interestingly, we found that the lockdown had different effects on the two ambulatory measurements. Before the lockdown, the two measures aligned, as predicted by laboratory data. After the lockdown, when circadian timekeeping signals were blunted, these measures diverged in 70% of subjects (with circadian rhythms in heart rate, or CRHR, becoming delayed). Thus, while either approach can measure circadian rhythms, both are needed to understand internal desynchrony. We also argue that interventions may be needed in future lockdowns to better align separate circadian rhythms in the body.

A method for characterizing daily physiology from widely used wearables

Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the "Social Rhythms" iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method.

Lighting and Alzheimer's Disease and Related Dementias: Spotlight on Sleep and Depression

Alzheimer's disease and related dementias is the collective term for a progressive neurodegenerative disease for which there is presently no cure. This paper focuses on two symptoms of the disease, sleep disturbances and depression, and discusses how light can be used as a non-pharmacological intervention to mitigate their negative effects. Bright days and dark nights are needed for health and well-being, but the present components of the built environment, especially those places where older adults spend most of their days, are too dimly illuminated during the day and too bright at night. To be effective light needs to be correctly specified, implemented, and measured. Yet without the appropriate specification and measurement of the stimulus, researchers will not be able to successfully demonstrate positive results in the field, nor will lighting designers and specifiers have the confidence to implement lighting solutions for promoting better sleep and mood in this population.

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource-intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation-identifying the time at which the switch in classification occurs-to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy-based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.

Characterizing the modern light environment and its influence on circadian rhythms

Humans have largely supplanted natural light cycles with a variety of electric light sources and schedules misaligned with day-night cycles. Circadian disruption has been linked to a number of disease processes, but the extent of circadian disruption among the population is unknown. In this study, we measured light exposure and wrist temperature among residents of an urban area during each of the four seasons, as well as light illuminance in nearby outdoor locations. Daily light exposure was significantly lower for individuals, compared to outdoor light sensors, across all four seasons. There was also little seasonal variation in the realized photoperiod experienced by individuals, with the only significant difference occurring between winter and summer. We tested the hypothesis that differential light exposure impacts circadian phase timing, detected via the wrist temperature rhythm. To determine the influence of light exposure on circadian rhythms, we modelled the impact of morning and night-time light exposure on the timing of the maximum wrist temperature. We found that morning and night-time light exposure had significant but opposing impacts on maximum wrist temperature timing. Our results demonstrate that, within the range of exposure seen in everyday life, night-time light can delay the onset of the maximum wrist temperature, while morning light can lead to earlier onset. Our results demonstrate that humans are minimizing natural seasonal differences in light exposure, and that circadian shifts and disruptions may be a more regular occurrence in the general population than is currently recognized.

The interindividual variability of sleep timing and circadian phase in humans is influenced by daytime and evening light conditions

Human cognitive functioning shows circadian variations throughout the day. However, individuals largely differ in their timing during the day of when they are more capable of performing specific tasks and when they prefer to sleep. These interindividual differences in preferred temporal organization of sleep and daytime activities define the chronotype. Since a late chronotype is associated with adverse mental and physical consequences, it is of vital importance to study how lighting environments affect chronotype. Here, we use a mathematical model of the human circadian pacemaker to understand how light in the built environment changes the chronotype distribution in the population. In line with experimental findings, we show that when individuals spend their days in relatively dim light conditions, this not only results in a later phase of their biological clock but also increases interindividual differences in circadian phase angle of entrainment and preferred sleep timing. Increasing daytime illuminance results in a more narrow distribution of sleep timing and circadian phase, and this effect is more pronounced for longer photoperiods. The model results demonstrate that modern lifestyle changes the chronotype distribution towards more eveningness and more extreme differences in eveningness. Such model-based predictions can be used to design guidelines for workplace lighting that help limiting circadian phase differences, and craft new lighting strategies that support human performance, health and wellbeing.

Light-based methods for predicting circadian phase in delayed sleep-wake phase disorder

Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep-wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep-wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.

Predicting circadian phase across populations: a comparison of mathematical models and wearable devices

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase non-invasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 hour, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.

Workshop report. Circadian rhythm sleep-wake disorders: gaps and opportunities

This White Paper presents the results from a workshop cosponsored by the Sleep Research Society (SRS) and the Society for Research on Biological Rhythms (SRBR) whose goals were to bring together sleep clinicians and sleep and circadian rhythm researchers to identify existing gaps in diagnosis and treatment and areas of high-priority research in circadian rhythm sleep-wake disorders (CRSWD). CRSWD are a distinct class of sleep disorders caused by alterations of the circadian time-keeping system, its entrainment mechanisms, or a misalignment of the endogenous circadian rhythm and the external environment. In these disorders, the timing of the primary sleep episode is either earlier or later than desired, irregular from day-to-day, and/or sleep occurs at the wrong circadian time. While there are incomplete and insufficient prevalence data, CRSWD likely affect at least 800,000 and perhaps as many as 3 million individuals in the United States, and if Shift Work Disorder and Jet Lag are included, then many millions more are impacted. The SRS Advocacy Taskforce has identified CRSWD as a class of sleep disorders for which additional high-quality research could have a significant impact to improve patient care. Participants were selected for their expertise and were assigned to one of three working groups: Phase Disorders, Entrainment Disorders, and Other. Each working group presented a summary of the current state of the science for their specific CRSWD area, followed by discussion from all participants. The outcome of those presentations and discussions are presented here.

Circadian Phase Prediction From Non-Intrusive and Ambulatory Physiological Data

Chronotherapy aims to treat patients according to their endogenous biological rhythms and requires, therefore, knowing their circadian phase. Circadian phase is partially determined by genetics and, under natural conditions, is normally entrained by environmental signals (zeitgebers), predominantly by light. Physiological data such as melatonin concentration and core body temperature (CBT) have been used to estimate circadian phase. However, due to their expensive and intrusive obtention, other physiological variables that also present circadian rhythmicity, such as heart rate variability, skin temperature, activity, and body position, have recently been proposed in several studies to estimate circadian phase. This study aims to predict circadian phase using minimally intrusive ambulatory physiological data modeled with machine learning techniques. Two approaches were considered; first, time-series were used to train artificial neural networks (ANNs) that predict CBT and melatonin dynamics and, second, a novel approach that uses scalar variables to build regression models that predict the time of the minimum CBT and the dim light melatonin onset (DLMO). ANNs require less than 48 hours of minimally intrusive data collection to predict circadian phase with an accuracy of less than one hour. On the other hand, regression models that use only three variables (body mass index, activity, and heart rate) are simpler and show higher accuracy with less than one minute of error, although they require longer times of data collection. This is a promising approach that should be validated in further studies considering a broader population and a wider range of conditions, including circadian misalignment.

Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers

STUDY OBJECTIVES

A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers.

METHODS

A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available (www.predictDLMO.com).

RESULTS

Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively.

CONCLUSION

This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.

Circadian disruption in mice through chronic jet lag-like conditions modulates molecular profiles of cancer in nucleus accumbens and prefrontal cortex

Biological rhythms regulate physiological activities. Shiftwork disrupts normal circadian rhythms and may increase the risk of cancer through unknown mechanisms. To mimic environmental light/dark changes encountered by shift workers, a protocol called 'chronic jet lag (CJL)' induced by repeatedly shifting light-dark cycles has been used. Here, we subjected mice to CJL by advancing light-dark cycle by 6 h every 2 days, and conducted RNA sequencing to analyze the expression profile and molecular signature in the brain areas of prefrontal cortex and nucleus accumbens. We also performed positron emission tomography (PET) imaging to monitor changes related to glucose metabolism in brain. Our results reveal systematic reprogramming of gene expression associated with cancer-related pathways and metabolic pathways in prefrontal cortex and nucleus accumbens. PET imaging indicates that glucose uptake level was significantly reduced in whole brain as well as the individual brain regions. Moreover, qPCR analysis describes that the expression levels of cancer-related genes were altered in prefrontal cortex and nucleus accumbens. Overall, these results suggest a molecular and metabolic link with CJL-mediated cancer risk, and generate hypotheses on how CJL increases the susceptibility to cancer.

The Role of Light Sensitivity and Intrinsic Circadian Period in Predicting Individual Circadian Timing

There is large interindividual variability in circadian timing, which is underestimated by mathematical models of the circadian clock. Interindividual differences in timing have traditionally been modeled by changing the intrinsic circadian period, but recent findings reveal an additional potential source of variability: large interindividual differences in light sensitivity. Using an established model of the human circadian clock with real-world light recordings, we investigated whether changes in light sensitivity parameters or intrinsic circadian period could capture variability in circadian timing between and within individuals. Healthy participants (n = 12, aged 18-26 years) underwent continuous light monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim-light melatonin onset (DLMO) was measured each week. Using the recorded light patterns, a sensitivity analysis for predicted DLMO times was performed, varying 3 model parameters within physiological ranges: (1) a parameter determining the steepness of the dose-response curve to light (p), (2) a parameter determining the shape of the phase-response curve to light (K), and (3) the intrinsic circadian period (tau). These parameters were then fitted to obtain optimal predictions of the three DLMO times for each individual. The sensitivity analysis showed that the range of variation in the average predicted DLMO times across participants was 0.65 h for p, 4.28 h for K, and 3.26 h for tau. The default model predicted the DLMO times with a mean absolute error of 1.02 h, whereas fitting all 3 parameters reduced the mean absolute error to 0.28 h. Fitting the parameters independently, we found mean absolute errors of 0.83 h for p, 0.53 h for K, and 0.42 h for tau. Fitting p and K together reduced the mean absolute error to 0.44 h. Light sensitivity parameters captured similar variability in phase compared with intrinsic circadian period, indicating they are viable targets for individualizing circadian phase predictions. Future prospective work is needed that uses measures of light sensitivity to validate this approach.

Early chronotype with advanced activity rhythms and dim light melatonin onset in a rural population

Studying communities at different stages of urbanisation and industrialisation can teach us how timing and intensity of light affect the circadian clock under real-life conditions. We have previously described a strong tendency towards morningness in the Baependi Heart Study, located in a small rural town in Brazil. Here, we tested the hypothesis that this morningness tendency is associated with early circadian phase based on objective measurements (as determined by dim light melatonin onset, DLMO, and activity) and light exposure. We also analysed how well the previously collected chronotype questionnaire data were able to predict these DLMO values. The average DLMO observed in 73 participants (40 female) was 20:03 ± 01:21, SD, with an earlier average onset in men (19:38 ± 01:16) than in women (20:24 ± 01:21; P ≤ .01). However, men presented larger phase angle between DLMO and sleep onset time as measured by actigraphy (4.11 hours vs 3.16 hours; P ≤ .01). Correlational analysis indicated associations between light exposure, activity rhythms and DLMO, such that early DLMO was observed in participants with higher exposure to light, higher activity and earlier light exposure. The strongest significant predictor of DLMO was morningness-eveningness questionnaire (MEQ) (beta=-0.35, P ≤ .05), followed by age (beta = -0.47, P ≤ .01). Sex, light exposure and variables derived from the Munich chronotype questionnaire were not significant predictors. Our observations demonstrate that both early sleep patterns and earlier circadian phase have been retained in this small rural town in spite of availability of electrification, in contrast to metropolitan postindustrial areas.

Personalized Office Lighting for Circadian Health and Improved Sleep

In modern society, the average person spends more than 90% of their time indoors. However, despite the growing scientific understanding of the impact of light on biological mechanisms, the existing light in the built environment is designed predominantly to meet visual performance requirements only. Lighting can also be exploited as a means to improve occupant health and well-being through the circadian functions that regulate sleep, mood, and alertness. The benefits of well-lit spaces map across other regularly occupied building types, such as residences and schools, as well as patient rooms in healthcare and assisted-living facilities. Presently, Human Centric Lighting is being offered based on generic insights on population average experiences. In this paper, we suggest a personalized bio-adaptive office lighting system, controlled to emit a lighting recipe tailored to the individual employee. We introduce a new mathematical optimization for lighting schedules that align the 24-h circadian cycle. Our algorithm estimates and optimizes parameters in experimentally validated models of the human circadian pacemaker. Moreover, it constrains deviations from the light levels desired and needed to perform daily activities. We further translate these into general principles for circadian lighting. We use experimentally validated models of the human circadian pacemaker to introduce a new algorithm to mathematically optimize lighting schedules to achieve circadian alignment to the 24-h cycle, with constrained deviations from the light levels desired for daily activities. Our suggested optimization algorithm was able to translate our findings into general principles for circadian lighting. In particular, our simulation results reveal: (1) how energy constrains drive the shape of optimal lighting profiles by dimming the light levels in the time window that light is less biologically effective; (2) how inter-individual variations in the characteristic internal duration of the day shift the timing of optimal lighting exposure; (3) how user habits and, in particular, late-evening light exposure result in differentiation in late afternoon office lighting.

Measuring melatonin by immunoassay

The pineal gland hormone melatonin continues to be of considerable interest to biomedical researchers. Of particular interest is the pattern of secretion of melatonin in relation to sleep timing as well as its potential role in certain diseases. Measuring melatonin in biological fluids such as blood and saliva presents particular methodological challenges since the production and secretion of the hormone are known to be extremely low during the light phase in almost all situations. Active secretion only occurs around the time of lights out in a wide range of species. The challenge then is to develop practical high-throughput assays that are sufficiently sensitive and accurate enough to detect levels of melatonin less than 1 pg/mL in biological fluids. Mass spectrometry assays have been developed that achieve the required sensitivity, but are really not practical or even widely available to most researchers. Melatonin radioimmunoassays and ELISA have been developed and are commercially available. But the quality of the results that are being published is very variable, partly not only because of poor experimental designs, but also because of poor assays. In this review, I discuss issues around the design of studies involving melatonin measurement. I then provide a critical assessment of 21 immunoassay kits marketed by 11 different companies with respect to validation, specificity and sensitivity. Technical managers of the companies were contacted in an attempt to obtain information not available online or in kit inserts. A search of the literature was also conducted to uncover papers that have reported the use of these assays, and where possible, both daytime and night-time plasma or saliva melatonin concentrations were extracted and tabulated. The results of the evaluations are disturbing, with many kits lacking any validation studies or using inadequate validation methods. Few assays have been properly assessed for specificity, while others report cross-reaction profiles that can be expected to result in over estimation of the melatonin levels. Some assays are not fit for purpose because they are not sensitive enough to determine plasma or saliva DLMO of 10 and 3 pg/mL, respectively. Finally, some assays produce unrealistically high daytime melatonin levels in humans and laboratory animals in the order of hundreds of pg/mL. In summary, this review provides a comprehensive and unique assessment of the current commercial melatonin immunoassays and their use in publications. It provides researchers new to the field with the information they need to design valid melatonin studies from both the perspective of experimental/clinical trial design and the best assay methodologies. It will also hopefully help journal editors and reviewers who may not be fully aware of the pitfalls of melatonin measurement make better informed decisions on publication acceptability.

Integrating wearable data into circadian models

The emergence of wearable health sensors in the last decade has the potential to revolutionize the study of sleep and circadian rhythms. In particular, recent progress has been made in the use of mathematical models in the prediction of a patient's internal circadian state using data measured by wearable devices. This is a vital step in our ability to identify optimal circadian timing for health interventions. We review the available data for fitting circadian phase models with a focus on wearable data sets. Finally, we review the current modeling paradigms and explore avenues for developing personalized parameter sets in limit cycle oscillator models in order to further improve prediction accuracy.

Novel Approaches for Assessing Circadian Rhythmicity in Humans: A Review

The temporal organization of molecular and physiological processes is driven by environmental and behavioral cycles as well as by self-sustained molecular circadian oscillators. Quantification of phase, amplitude, period, and disruption of circadian oscillators is essential for understanding their contribution to sleep-wake disorders, social jet lag, interindividual differences in entrainment, and the development of chrono-therapeutics. Traditionally, assessment of the human circadian system, and the output of the SCN in particular, has required collection of long time series of univariate markers such as melatonin or core body temperature. Data were collected in specialized laboratory protocols designed to control for environmental and behavioral influences on rhythmicity. These protocols are time-consuming, expensive, and not practical for assessing circadian status in patients or in participants in epidemiologic studies. Novel approaches for assessment of circadian parameters of the SCN or peripheral oscillators have been developed. They are based on machine learning or mathematical model-informed analyses of features extracted from 1 or a few samples of high-dimensional data, such as transcriptomes, metabolomes, long-term simultaneous recording of activity, light exposure, skin temperature, and heart rate or in vitro approaches. Here, we review whether these approaches successfully quantify parameters of central and peripheral circadian oscillators as indexed by gold standard markers. Although several approaches perform well under entrained conditions when sleep occurs at night, the methods either perform worse in other conditions such as shift work or they have not been assessed under any conditions other than entrainment and thus we do not yet know how robust they are. Novel approaches for the assessment of circadian parameters hold promise for circadian medicine, chrono-therapeutics, and chrono-epidemiology. There remains a need to validate these approaches against gold standard markers, in individuals of all sexes and ages, in patient populations, and, in particular, under conditions in which behavioral cycles are displaced.

Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions

The "International Biomarkers Workshop on Wearables in Sleep and Circadian Science" was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: (1) characterize the term "wearable" for use in sleep and circadian science and identify relevant sleep and circadian metrics for wearables to measure; (2) assess the current use of wearables in sleep and circadian science; (3) identify current barriers for applying wearables to sleep and circadian science; and (4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms "wearables," "nearables," and "ingestibles." Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.

Circadian rhythm phase shifts caused by timed exercise vary with chronotype

BACKGROUNDThe circadian system entrains behavioral and physiological rhythms to environmental cycles, and modern lifestyles disrupt this entrainment. We investigated a timed exercise intervention to phase shift the internal circadian rhythm.METHODSIn 52 young, sedentary adults, dim light melatonin onset (DLMO) was measured before and after 5 days of morning (10 hours after DLMO; n = 26) or evening (20 hours after DLMO; n = 26) exercise. Phase shifts were calculated as the difference in DLMO before and after exercise.RESULTSMorning exercise induced phase advance shifts (0.62 ± 0.18 hours) that were significantly greater than phase shifts from evening exercise (-0.02 ± 0.18 hours; P = 0.01). Chronotype also influenced the effect of timed exercise. For later chronotypes, both morning and evening exercise induced phase advances (0.54 ± 0.29 hours and 0.46 ±0.25 hours, respectively). In contrast, earlier chronotypes had phase advances from morning exercise (0.49 ± 0.25 hours) but had phase delays from evening exercise (-0.41 ± 0.29 hours).CONCLUSIONLate chronotypes - those who experience the most severe circadian misalignment - may benefit from phase advances induced by exercise in the morning or evening, but evening exercise may exacerbate circadian misalignment in early chronotypes. Thus, personalized exercise timing prescription, based on chronotype, could alleviate circadian misalignment in young adults.TRIAL REGISTRATIONTrial registration can be found at www.clinicaltrials.gov (NCT04097886).FUNDINGFunding was supplied by NIH grants UL1TR001998 and TL1TR001997, the Barnstable Brown Diabetes and Obesity Center, the Pediatric Exercise Physiology Laboratory Endowment, the Arvle and Ellen Turner Thacker Research Fund, and the University of Kentucky.

Actigraphy for Assessing Light Effects on Sleep and Circadian Activity Rhythm in Alzheimer's Dementia: A Narrative Review

Background:

Alzheimer's Disease (AD) is often accompanied by severe sleep problems and circadian rhythm disturbances which may to some extent be attributed to a dysfunction in the biological clock. The 24-h light/dark cycle is the strongest Zeitgeber for the biological clock. People with AD, however, often live in environments with inappropriate photic Zeitgebers. Timed bright light exposure may help to consolidate sleep- and circadian rest/activity rhythm problems in AD, and may be a low-risk alternative to pharmacological treatment.

Objective & Method:

In the present review, experts from several research disciplines summarized the results of twenty-seven light intervention studies which used wrist actigraphy to measure sleep and circadian activity in AD patients.

Results:

Taken together, the findings remain inconclusive with regard to beneficial light effects. However, the considered studies varied substantially with respect to the utilized light intervention, study design, and usage of actigraphy. The paper provides a comprehensive critical discussion of these issues.

Conclusion:

Fusing knowledge across complementary research disciplines has the potential to critically advance our understanding of the biological input of light on health and may contribute to architectural lighting designs in hospitals, as well as our homes and work environments.

Compensating for Sensor Error in the Model Predictive Control of Circadian Clock Phase

The circadian oscillator regulates many critical biological functions; misalignment between the phase of this oscillator and the environment has been linked to adverse health outcomes. Thus, shifting the circadian phase of the oscillator to align with the environment using either light or small molecule pharmaceuticals as control inputs is desired. One challenge to controlling circadian phase is that the magnitude and direction of the phase shift caused by these inputs is dependent on the phase at which the input is delivered. Simulations show that model predictive control (MPC) can successfully shift the phase of the circadian clock using perfect knowledge of the current phase of the system. However, methods to assess circadian phase continuously in real time, as would be needed to implement MPC in vivo, are limited in their accuracy. Here, we explore the impact of imperfect sensing on our ability to control circadian phase. While some pathological patterns of sensor error can make control impossible, we show that by assuming errors in the phase sensor are bounded to be sufficiently small, we can bound the error of our MPC algorithm. We propose using the expected phase response curve to improve control when sensor error is present.

Sleep Timing in Late Autumn and Late Spring Associates With Light Exposure Rather Than Sun Time in College Students

Timing of the human sleep-wake cycle is determined by social constraints, biological processes (sleep homeostasis and circadian rhythmicity) and environmental factors, particularly natural and electrical light exposure. To what extent seasonal changes in the light-dark cycle affect sleep timing and how this varies between weekdays and weekends has not been firmly established. We examined sleep and activity patterns during weekdays and weekends in late autumn (standard time, ST) and late spring (daylight saving time, DST), and expressed their timing in relation to three environmental reference points: clock-time, solar noon (SN) which occurs one clock hour later during DST than ST, and the midpoint of accumulated light exposure (50% LE). Observed sleep timing data were compared to simulated data from a mathematical model for the effects of light on the circadian and homeostatic regulation of sleep. A total of 715 days of sleep timing and light exposure were recorded in 19 undergraduates in a repeated-measures observational study. During each three-week assessment, light and activity were monitored, and self-reported bed and wake times were collected. Light exposure was higher in spring than in autumn. 50% LE did not vary across season, but occurred later on weekends compared to weekdays. Relative to clock-time, bedtime, wake-time, mid-sleep, and midpoint of activity were later on weekends but did not differ across seasons. Relative to SN, sleep and activity measures were earlier in spring than in autumn. Relative to 50% LE, only wake-time and mid-sleep were later on weekends, with no seasonal differences. Individual differences in mid-sleep did not correlate with SN but correlated with 50% LE. Individuals with different habitual bedtimes responded similarly to seasonal changes. Model simulations showed that light exposure patterns are sufficient to explain sleep timing in spring but less so in autumn. The findings indicate that during autumn and spring, the timing of sleep associates with actual light exposure rather than sun time as indexed by SN.

What is the 'spectral diet' of humans?

Our visual perception of the world — seeing form and colour or navigating the environment — depends on the interaction of light and matter in the environment. Light also has a more fundamental role in regulating rhythms in physiology and behaviour, as well as in the acute secretion of hormones such as melatonin and changes in alertness, where light exposure at short-time, medium-time and long-time scales has different effects on these visual and non-visual functions. Yet patterns of light exposure in the real world are inherently messy: we move in and out of buildings and are therefore exposed to mixtures of artificial and natural light, and the physical makeup of our environment can also drastically alter the spectral composition and spatial distribution of the emitted light. In spatial vision, the examination of natural image statistics has proven to be an important driver in research. Here, we expand this concept to the spectral domain and develop the concept of the ‘spectral diet’ of humans.

Application of a Limit-Cycle Oscillator Model for Prediction of Circadian Phase in Rotating Night Shift Workers

Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3-5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers.