ORCID Profile
0000-0003-1156-7056
Current Organisation
Monash University
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Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology) | Psychology |
Expanding Knowledge in Psychology and Cognitive Sciences | Expanding Knowledge in the Biological Sciences | Behaviour and Health | Workplace Safety | Expanding Knowledge in Technology
Publisher: Springer Science and Business Media LLC
Date: 27-03-2017
DOI: 10.1038/SREP45158
Abstract: Why do we go to sleep late and struggle to wake up on time? Historically, light-dark cycles were dictated by the solar day, but now humans can extend light exposure by switching on artificial lights. We use a mathematical model incorporating effects of light, circadian rhythmicity and sleep homeostasis to provide a quantitative theoretical framework to understand effects of modern patterns of light consumption on the human circadian system. The model shows that without artificial light humans wakeup at dawn. Artificial light delays circadian rhythmicity and preferred sleep timing and compromises synchronisation to the solar day when wake-times are not enforced. When wake-times are enforced by social constraints, such as work or school, artificial light induces a mismatch between sleep timing and circadian rhythmicity (‘social jet-lag’). The model implies that developmental changes in sleep homeostasis and circadian litude make adolescents particularly sensitive to effects of light consumption. The model predicts that ameliorating social jet-lag is more effectively achieved by reducing evening light consumption than by delaying social constraints, particularly in in iduals with slow circadian clocks or when imposed wake-times occur after sunrise. These theory-informed predictions may aid design of interventions to prevent and treat circadian rhythm-sleep disorders and social jet-lag.
Publisher: Wiley
Date: 25-10-2018
DOI: 10.1113/JP275917
Publisher: Elsevier BV
Date: 12-2012
DOI: 10.1016/J.JTBI.2012.08.031
Abstract: Unihemispheric sleep has been observed in numerous species, including birds and aquatic mammals. While knowledge of its functional role has been improved in recent years, the physiological mechanisms that generate this behavior remain poorly understood. Here, unihemispheric sleep is simulated using a physiologically based quantitative model of the mammalian ascending arousal system. The model includes mutual inhibition between wake-promoting monoaminergic nuclei (MA) and sleep-promoting ventrolateral preoptic nuclei (VLPO), driven by circadian and homeostatic drives as well as cholinergic and orexinergic input to MA. The model is extended here to incorporate two distinct hemispheres and their interconnections. It is postulated that inhibitory connections between VLPO nuclei in opposite hemispheres are responsible for unihemispheric sleep, and it is shown that contralateral inhibitory connections promote unihemispheric sleep while ipsilateral inhibitory connections promote bihemispheric sleep. The frequency of alternating unihemispheric sleep bouts is chiefly determined by sleep homeostasis and its corresponding time constant. It is shown that the model reproduces dolphin sleep, and that the sleep regimes of humans, cetaceans, and fur seals, the latter both terrestrially and in a marine environment, require only modest changes in contralateral connection strength and homeostatic time constant. It is further demonstrated that fur seals can potentially switch between their terrestrial bihemispheric and aquatic unihemispheric sleep patterns by varying just the contralateral connection strength. These results provide experimentally testable predictions regarding the differences between species that sleep bihemispherically and unihemispherically.
Publisher: Oxford University Press (OUP)
Date: 31-08-2023
Publisher: Cold Spring Harbor Laboratory
Date: 02-2023
DOI: 10.1101/2023.01.30.526303
Abstract: In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a convenient biomarker for this neural pathway. Recent work has found that in iduals differ substantially in their melatonin-suppressive response to light, with the most sensitive in iduals being up to 60 times more sensitive than the least sensitive in iduals. Planning experiments with melatonin suppression as an outcome needs to incorporate these in idual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is expensive and not feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (s le size, in idual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability relevant for practical applications.
Publisher: SAGE Publications
Date: 25-10-2017
Abstract: This article is part of a Journal of Biological Rhythms series exploring analysis and statistical topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article, we address issues related to the collection of multiple data points from the same organism or system at different times, since such longitudinal data collection is fundamental to the assessment of biological rhythms. Rhythmic longitudinal data require additional specific statistical considerations, ranging from curve fitting to threshold definitions to accounting for correlation structure. We discuss statistical analyses of longitudinal data including issues of correlational structure and stationarity, markers of biological rhythms, demasking of biological rhythms, and determining phase, waveform, and litude of biological rhythms.
Publisher: Public Library of Science (PLoS)
Date: 24-06-2010
Publisher: SAGE Publications
Date: 25-10-2017
Abstract: The Journal of Biological Rhythms will be publishing articles exploring analysis and statistical topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. By using case ex les and highlighting the pearls and pitfalls of statistical inference, the authors will identify and explain ways in which experimental scientists can avoid common analytical and statistical mistakes and use appropriate analytical and statistical methods in their research. In this first article, we address the first steps in analysis of data: understanding the underlying statistical distribution of the data and establishing associative versus causal relationships. These ideas are then applied to s le size, power calculations, correlation testing, differences between description and prediction, and the narrative fallacy.
Publisher: Elsevier BV
Date: 03-2011
DOI: 10.1016/J.JTBI.2010.12.018
Abstract: A recent physiologically based model of human sleep is extended to incorporate the effects of caffeine on sleep-wake timing and fatigue. The model includes the sleep-active neurons of the hypothalamic ventrolateral preoptic area (VLPO), the wake-active monoaminergic brainstem populations (MA), their interactions with cholinergic/orexinergic (ACh/Orx) input to MA, and circadian and homeostatic drives. We model two effects of caffeine on the brain due to competitive antagonism of adenosine (Ad): (i) a reduction in the homeostatic drive and (ii) an increase in cholinergic activity. By comparing the model output to experimental data, constraints are determined on the parameters that describe the action of caffeine on the brain. In accord with experiment, the ranges of these parameters imply significant variability in caffeine sensitivity between in iduals, with caffeine's effectiveness in reducing fatigue being highly dependent on an in idual's tolerance, and past caffeine and sleep history. Although there are wide in idual differences in caffeine sensitivity and thus in parameter values, once the model is calibrated for an in idual it can be used to make quantitative predictions for that in idual. A number of applications of the model are examined, using exemplar parameter values, including: (i) quantitative estimation of the sleep loss and the delay to sleep onset after taking caffeine for various doses and times (ii) an analysis of the system's stable states showing that the wake state during sleep deprivation is stabilized after taking caffeine and (iii) comparing model output successfully to experimental values of subjective fatigue reported in a total sleep deprivation study examining the reduction of fatigue with caffeine. This model provides a framework for quantitatively assessing optimal strategies for using caffeine, on an in idual basis, to maintain performance during sleep deprivation.
Publisher: Oxford University Press (OUP)
Date: 20-05-2019
DOI: 10.1093/SLEEP/ZSZ110
Abstract: We compared resident physician work hours and sleep in a multicenter clustered-randomized crossover clinical trial that randomized resident physicians to an Extended Duration Work Roster (EDWR) with extended-duration (≥24 hr) shifts or a Rapidly Cycling Work Roster (RCWR), in which scheduled shift lengths were limited to 16 or fewer consecutive hours. Three hundred two resident physicians were enrolled and completed 370 1 month pediatric intensive care unit rotations in six US academic medical centers. Sleep was objectively estimated with wrist-worn actigraphs. Work hours and subjective sleep data were collected via daily electronic diary. Resident physicians worked fewer total hours per week during the RCWR compared with the EDWR (61.9 ± 4.8 versus 68.4 ± 7.4, respectively p 0.0001). During the RCWR, 73% of work hours occurred within shifts of ≤16 consecutive hours. In contrast, during the EDWR, 38% of work hours occurred on shifts of ≤16 consecutive hours. Resident physicians obtained significantly more sleep per week on the RCWR (52.9 ± 6.0 hr) compared with the EDWR (49.1 ± 5.8 hr, p 0.0001). The percentage of 24 hr intervals with less than 4 hr of actigraphically measured sleep was 9% on the RCWR and 25% on the EDWR (p 0.0001). RCWRs were effective in reducing weekly work hours and the occurrence of consecutive hour shifts, and improving sleep duration of resident physicians. Although inclusion of the six operational healthcare sites increases the generalizability of these findings, there was heterogeneity in schedule implementation. Additional research is needed to optimize scheduling practices allowing for sufficient sleep prior to all work shifts. Clinical Trial: Multicenter Clinical Trial of Limiting Resident Work Hours on ICU Patient Safety (ROSTERS), t2/show/NCT02134847
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2022
DOI: 10.1101/2022.10.16.22280934
Abstract: Circadian rhythm disturbance is a common feature of many psychiatric disorders. Light is the primary input to the circadian clock, with daytime light strengthening rhythms and night light disrupting them. Therefore, habitual light exposure may represent an environmental risk factor for susceptibility to psychiatric disorders. We performed the largest to-date cross-sectional analysis of light, sleep, physical activity, and mental health ( n = 86,772 adults aged 62.4 ± 7.4 years 57% women). We examined the independent association of day and night light exposure with covariate-adjusted risk for psychiatric disorders and self-harm. Greater night light exposure was associated with increased risk for major depressive disorder, generalized anxiety disorder, PTSD, psychosis, bipolar disorder, and self-harm behavior. Independent of night light, greater day light exposure was associated with reduced risk for major depressive disorder, PTSD, psychosis, and self-harm behavior. These findings were robust to adjustment for sociodemographics, photoperiod, physical activity, and sleep quality. Avoiding light at night and seeking light during the day may be a simple and effective, non-pharmacological means of broadly improving mental health.
Publisher: SAGE Publications
Date: 20-01-2021
Abstract: Posttraumatic stress disorder (PTSD) and insomnia are characterized by sleep disturbances and daytime functional impairments. Actigraphy metrics can quantify diurnal rhythms via interdaily stability, intradaily variability, relative litude, and sleep regularity. Here, we (a) compared diurnal rhythms in PTSD, insomnia, and healthy control s les using linear mixed modeling (b) compared inter-in idual variability of diurnal rhythms between groups using variance ratio tests and (c) examined correlations between diurnal rhythms and sleep measures within the clinical s les. Participants ( N = 98) wore wrist-activity monitors for one week and completed the Insomnia Severity Index and Pittsburgh Sleep Quality Index. Both clinical s les displayed significantly lower interdaily stability, relative litude, and sleep regularity compared with controls. In iduals with PTSD and insomnia did not differ on mean diurnal rhythm metrics. Both clinical s les showed more inter-in idual variability in relative litude compared with controls, and the in iduals with PTSD were distinguished from those with insomnia by greater inter-in idual variability in interdaily stability and relative litude. Relative litude in the clinical s les was positively correlated with objective sleep efficiency and total sleep time. This is the first study to compare in iduals with PTSD and insomnia on measures of diurnal rhythms, revealing those with PTSD and insomnia to have less robust and more variable diurnal rhythms compared with controls. In iduals with PTSD differed from those with insomnia in inter-in idual variability of diurnal rest-activity stability and litude, highlighting this population as particularly heterogenous. Diurnal rhythm robustness might be considered an intervention target in insomnia and PTSD populations.
Publisher: SAGE Publications
Date: 19-05-2010
Abstract: The physiological mechanisms underlying interin idual differences in chronotype have yet to be established, although evidence suggests both circadian and homeostatic processes are involved. A physiologically based model is developed by combining models of the sleep-wake switch and circadian pacemaker, providing a means of examining how interactions between these systems affect chronotype. Specifically, chronotype is shown to depend on the relative influences of homeostatic and circadian drives, with a stronger homeostatic drive causing morningness. Changes to intrinsic circadian and homeostatic properties, including homeostatic clearance and production rates, and circadian period and litude, are also shown to affect chronotype. These results provide a framework for explaining several experimentally observed phenomena, including age-related morningness, adolescent eveningness, and familial advanced and delayed sleep-phase disorders. Additionally, experimental studies have shown that healthy adults on the extremes of the morningness-eveningness spectrum fall into two subtypes: those whose circadian phase markers are unaffected by chronotype, and those whose circadian phase markers track their chronotype. The model demonstrates that this spectrum likely results from interin idual differences in homeostatic kinetics in the first group, and differences in circadian period in the second group. Physiologically based modeling can thus guide diagnosis of sleep pathologies.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 2017
Publisher: MDPI AG
Date: 11-03-2019
DOI: 10.3390/NU11030587
Abstract: The timing of caloric intake is a risk factor for excess weight and disease. Growing evidence suggests, however, that the impact of caloric consumption on metabolic health depends on its circadian phase, not clock hour. The objective of the current study was to identify how in iduals consume calories and macronutrients relative to circadian phase in real-world settings. Young adults (n = 106 aged 19 ± 1 years 45 females) photographically recorded the timing and content of all calories for seven consecutive days using a smartphone application during a 30-day study. Circadian phase was determined from in-laboratory assessment of dim-light melatonin onset (DLMO). Meals were assigned a circadian phase relative to each participant’s DLMO (0°, ~23:17 h) and binned into 60° bins. Lean (n = 68 15 females) and non-lean (n = 38, 30 females) body composition was determined via bioelectrical impedance. The DLMO time range was ~10 h, allowing separation of clock time and circadian phase. Eating occurred at all circadian phases, with significant circadian rhythmicity (p 0.0001) and highest caloric intake at ~300° (~1900 h). The non-lean group ate 8% more of their daily calories at an evening circadian phase (300°) than the lean group (p = 0.007). Consumption of carbohydrates and proteins followed circadian patterns (p 0.0001) and non-lean participants ate 13% more carbohydrates at 240° (~1500 h) than the lean group (p = 0.004). There were no significant differences when caloric intake was referenced to local clock time or sleep onset time (p 0.05). Interventions targeting the circadian timing of calories and macronutrients for weight management should be tested.
Publisher: Springer Science and Business Media LLC
Date: 05-11-2020
DOI: 10.1038/S41598-020-75622-4
Abstract: The regular rise and fall of the sun resulted in the development of 24-h rhythms in virtually all organisms. In an evolutionary heartbeat, humans have taken control of their light environment with electric light. Humans are highly sensitive to light, yet most people now use light until bedtime. We evaluated the impact of modern home lighting environments in relation to sleep and in idual-level light sensitivity using a new wearable spectrophotometer. We found that nearly half of homes had bright enough light to suppress melatonin by 50%, but with a wide range of in idual responses (0–87% suppression for the average home). Greater evening light relative to an in idual’s average was associated with increased wakefulness after bedtime. Homes with energy-efficient lights had nearly double the melanopic illuminance of homes with incandescent lighting. These findings demonstrate that home lighting significantly affects sleep and the circadian system, but the impact of lighting for a specific in idual in their home is highly unpredictable.
Publisher: Informa UK Limited
Date: 15-01-2021
Publisher: Elsevier BV
Date: 05-2022
Publisher: Oxford University Press (OUP)
Date: 09-12-2016
DOI: 10.1093/SLEEP/ZSW009
Publisher: American Physical Society (APS)
Date: 31-05-2006
Publisher: Elsevier BV
Date: 08-2020
Publisher: SAGE Publications
Date: 2023
DOI: 10.1177/20552076231165972
Abstract: Development of personalized sleep–wake management tools is critical to improving sleep and functional outcomes for shift workers. The objective of the current study was to test the performance, engagement and usability of a mobile app ( SleepSync) for personalized sleep–wake management in shift workers that aid behavioural change and provide practical advice by providing personalized sleep scheduling recommendations and education. Shift workers ( n = 27 20 healthcare and 7 from other industries) trialled the mobile app for two weeks to determine performance, engagement and usability. Primary outcomes were self-reported total sleep time, ability to fall asleep, sleep quality and perception of overall recovery on days off. Secondary performance outcomes included sleep disturbances (insomnia and sleep hygiene symptoms, and sleep-related impairments) and mood (anxiety, stress and depression) pre- and post-app use. Satisfaction with schedule management, integration into daily routine and influence on behaviour were used to determine engagement, while the usability was assessed for functionality and ease of use of features. Total sleep time ( P = .04), ability to fall asleep ( P .001), quality of sleep ( P = .001), insomnia ( P = .02), sleep hygiene ( P = .01), sleep-related impairments ( P = .001), anxiety ( P = .001), and stress ( P = .006) were all improved, with non-significant improvements in recovery on days off ( P = .19) and depression ( P = .07). All measures of engagement and usability were scored positively by the majority of users. This pilot trial provides preliminary evidence of the positive impact of the SleepSync app in improving sleep and mood outcomes in shift workers, and warrants confirmation in a larger controlled trial.
Publisher: JMIR Publications Inc.
Date: 13-11-2017
Abstract: earable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. e developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. e designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the in idual to improve these measures. e identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. ew semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.
Publisher: Wiley
Date: 03-08-2021
DOI: 10.1111/JPI.12757
Abstract: During the COVID‐19 pandemic, schools around the world rapidly transitioned from in‐person to remote learning, providing an opportunity to examine the impact of in‐person vs remote learning on sleep, circadian timing, and mood. We assessed sleep‐wake timing using wrist actigraphy and sleep diaries over 1‐2 weeks during in‐person learning (n = 28) and remote learning (n = 58, where n = 27 were repeat assessments) in adolescents (age M ± SD = 12.79 ± 0.42 years). Circadian timing was measured under a single condition in each in idual using salivary melatonin (Dim Light Melatonin Onset DLMO). Online surveys assessed mood (PROMIS Pediatric Anxiety and Depressive Symptoms) and sleepiness (Epworth Sleepiness Scale – Child and Adolescent) in each condition. During remote (vs in‐person) learning: (i) on school days, students went to sleep 26 minutes later and woke 49 minutes later, resulting in 22 minutes longer sleep duration (all P .0001) (ii) DLMO time did not differ significantly between conditions, although participants woke at a later circadian phase (43 minutes, P = .03) during remote learning and (iii) participants reported significantly lower sleepiness ( P = .048) and lower anxiety symptoms ( P = .006). Depressive symptoms did not differ between conditions. Changes in mood symptoms were not mediated by sleep. Although remote learning continued to have fixed school start times, removing morning commutes likely enabled adolescents to sleep longer, wake later, and to wake at a later circadian phase. These results indicate that remote learning, or later school start times, may extend sleep and improve some subjective symptoms in adolescents.
Publisher: Wiley
Date: 10-03-2022
DOI: 10.1111/JPI.12791
Abstract: The daily rhythm of plasma melatonin concentrations is typically unimodal, with one broad peak during the circadian night and near‐undetectable levels during the circadian day. Light at night acutely suppresses melatonin secretion and phase shifts its endogenous circadian rhythm. In contrast, exposure to darkness during the circadian day has not generally been reported to increase circulating melatonin concentrations acutely. Here, in a highly‐controlled simulated night shift protocol with 12‐h inverted behavioral/environmental cycles, we unexpectedly found that circulating melatonin levels were significantly increased during daytime sleep ( p .0001). This resulted in a secondary melatonin peak during the circadian day in addition to the primary peak during the circadian night, when sleep occurred during the circadian day following an overnight shift. This distinctive diurnal melatonin rhythm with antiphasic peaks could not be readily anticipated from the behavioral/environmental factors in the protocol (e.g., light exposure, posture, diet, activity) or from current mathematical model simulations of circadian pacemaker output. The observation, therefore, challenges our current understanding of underlying physiological mechanisms that regulate melatonin secretion. Interestingly, the increase in melatonin concentration observed during daytime sleep was positively correlated with the change in timing of melatonin nighttime peak ( p = .002), but not with the degree of light‐induced melatonin suppression during nighttime wakefulness ( p = .92). Both the increase in daytime melatonin concentrations and the change in the timing of the nighttime peak became larger after repeated exposure to simulated night shifts ( p = .002 and p = .006, respectively). Furthermore, we found that melatonin secretion during daytime sleep was positively associated with an increase in 24‐h glucose and insulin levels during the night shift protocol ( p = .014 and p = .027, respectively). Future studies are needed to elucidate the key factor(s) driving the unexpected daytime melatonin secretion and the melatonin rhythm with antiphasic peaks during shifted sleep/wake schedules, the underlying mechanisms of their relationship with glucose metabolism, and the relevance for diabetes risk among shift workers.
Publisher: Oxford University Press (OUP)
Date: 08-11-2021
Publisher: Springer Science and Business Media LLC
Date: 29-07-2019
DOI: 10.1038/S41598-019-47311-4
Abstract: A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in in iduals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy in iduals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within ± 1 hour in 67% and ± 1.5 hours in 100% of participants, with mean absolute error of 41 ± 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within ± 1 hour in 66% and ± 2 hours in 87% of participants, with mean absolute error of 63 ± 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within ± 1 hour in 42% and ± 2 hours in 53% of participants, with mean absolute error of 143 ± 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within ± 2 hours for the vast majority of in iduals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.
Publisher: SAGE Publications
Date: 15-09-2011
Abstract: Early attempts to characterize free-running human circadian rhythms generated three notable results: 1) observed circadian periods of 25 hours (considerably longer than the now established 24.1- to 24.2-hour average intrinsic circadian period) with sleep delayed to later circadian phases than during entrainment 2) spontaneous internal desynchrony of circadian rhythms and sleep/wake cycles—the former with an approximately 24.9-hour period, and the latter with a longer (28-68 hour) or shorter (12-20 hour) period and 3) bicircadian (48-50 hour) sleep/wake cycles. All three results are reproduced by Kronauer et al.’s (1982) coupled oscillator model, but the physiological basis for that phenomenological model is unclear. We use a physiologically based model of hypothalamic and brain stem nuclei to investigate alternative physiological mechanisms that could underlie internal desynchrony. We demonstrate that experimental observations can be reproduced by changes in two pathways: promotion of orexinergic (Orx) wake signals, and attenuation of the circadian signal reaching hypothalamic nuclei. We reason that delayed sleep is indicative of an additional wake-promoting drive, which may be of behavioral origin, associated with removal of daily schedules and instructions given to participants. We model this by increasing Orx tone during wake, which reproduces the observed period lengthening and delayed sleep. Weakening circadian input to the ventrolateral preoptic nucleus (possibly mediated by the dorsomedial hypothalamus) causes desynchrony, with observed sleep/wake cycle period determined by degree of Orx up-regulation. During desynchrony, sleep/wake cycles are driven by sleep homeostasis, yet sleep bout length maintains circadian phase dependence. The model predicts sleep episodes are shortest when started near the temperature minimum, consistent with experimental findings. The model also correctly predicts that it is possible to transition to bicircadian rhythms from either a synchronized or desynchronized state. Our findings suggest that feedback from behavioral choices to physiology could play an important role in spontaneous internal desynchrony.
Publisher: Cold Spring Harbor Laboratory
Date: 30-06-2022
DOI: 10.1101/2022.06.29.22277078
Abstract: Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interin idual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interin idual differences. We performed a genome-wide gene-by-environment interaction study (GWIS) in 280,897 in iduals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (in idual time of day preference), acting as ‘light sensitivity’ variants for the impact of daylight on the circadian system. We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634 p 5×10 −8 ), where additional minor alleles were found to enhance the morningness effect of daytime light exposure ( β GxE = -.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and in Per2 + hypothalamic neurons. Linkage disequilibrium score regression identified significant genetic correlations of the light sensitivity GWIS with chronotype and sleep duration, such that greater light sensitivity was associated with later chronotype, greater insomnia symptoms and shorter sleep duration. Greater light sensitivity was also genetically correlated with greater risk for PTSD. This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental health.
Publisher: Oxford University Press (OUP)
Date: 03-2021
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 02-2013
Publisher: Proceedings of the National Academy of Sciences
Date: 02-05-2011
Abstract: The circadian rhythms of melatonin and body temperature are set to an earlier hour in women than in men, even when the women and men maintain nearly identical and consistent bedtimes and wake times. Moreover, women tend to wake up earlier than men and exhibit a greater preference for morning activities than men. Although the neurobiological mechanism underlying this sex difference in circadian alignment is unknown, multiple studies in nonhuman animals have demonstrated a sex difference in circadian period that could account for such a difference in circadian alignment between women and men. Whether a sex difference in intrinsic circadian period in humans underlies the difference in circadian alignment between men and women is unknown. We analyzed precise estimates of intrinsic circadian period collected from 157 in iduals (52 women, 105 men aged 18–74 y) studied in a month-long inpatient protocol designed to minimize confounding influences on circadian period estimation. Overall, the average intrinsic period of the melatonin and temperature rhythms in this population was very close to 24 h [24.15 ± 0.2 h (24 h 9 min ± 12 min)]. We further found that the intrinsic circadian period was significantly shorter in women [24.09 ± 0.2 h (24 h 5 min ± 12 min)] than in men [24.19 ± 0.2 h (24 h 11 min ± 12 min) P 0.01] and that a significantly greater proportion of women have intrinsic circadian periods shorter than 24.0 h (35% vs. 14% P 0.01). The shorter average intrinsic circadian period observed in women may have implications for understanding sex differences in habitual sleep duration and insomnia prevalence.
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 12-2021
Publisher: SAGE Publications
Date: 02-2012
Abstract: The effects of permanent shift work on entrainment and sleepiness are examined using a mathematical model that combines a model of sleep-wake switch in the brain with a model of the human circadian pacemaker entrained by light and nonphotic inputs. The model is applied to 8-hour permanent shift schedules to understand the basic mechanisms underlying changes of entrainment and sleepiness. Average sleepiness is shown to increase during the first days on the night and evening schedules, that is, shift start times between 0000 to 0700 h and 1500 to 2200 h, respectively. After the initial increase, sleepiness decreases and stabilizes via circadian re-entrainment to the cues provided by the shifts. The increase in sleepiness until entrainment is achieved is strongly correlated with the phase difference between a circadian oscillator entrained to the ambient light and one entrained to the shift schedule. The higher this phase difference, the larger the initial increase in sleepiness. When entrainment is achieved, sleepiness stabilizes and is the same for different shift onsets within the night or evening schedules. The simulations reveal the presence of a critical shift onset around 2300 h that separates schedules, leading to phase advance (night shifts) and phase delay (evening shifts) of the circadian pacemaker. Shifts starting around this time take longest to entrain and are expected to be the worst for long-term sleepiness and well-being of the workers. Surprisingly, we have found that the circadian pacemaker entrains faster to night schedules than to evening ones. This is explained by the longer photoperiod on night schedules compared to evening. In practice, this phenomenon is difficult to see due to days off on which workers switch to free sleep-wake activity. With weekends, the model predicts that entrainment is never achieved on evening and night schedules unless the workers follow the same sleep routine during weekends as during work days. Overall, the model supports experimental observations, providing new insights into the mechanisms and allowing the examination of conditions that are not accessible experimentally.
Publisher: Wiley
Date: 29-06-2022
DOI: 10.1002/OBY.23451
Abstract: Later circadian timing of energy intake is associated with higher body fat percentage. Current methods for obtaining accurate circadian timing are labor‐ and cost‐intensive, limiting practical application of this relationship. This study investigated whether the timing of energy intake relative to a mathematically modeled circadian time, derived from easily collected ambulatory data, would differ between participants with a lean or overweight/obesity body fat percentage. Participants ( N = 87) wore a light‐ and activity‐measuring device (actigraph) throughout a cross‐sectional 30‐day study. For 7 consecutive days within these 30 days, participants used a time‐st ed‐picture phone application to record energy intake. Body fat percentage was recorded. Circadian time was defined using melatonin onset from in‐laboratory collected repeat saliva s ling or using light and activity or activity data alone entered into a mathematical model. Participants with overweight/obesity body fat percentages ate 50% of their daily calories significantly closer to model‐predicted melatonin onset from light and activity data (0.61 hours closer) or activity data alone (0.86 hours closer both log‐rank p 0.05). Use of mathematically modeled circadian timing resulted in similar relationships between the timing of energy intake and body composition as that observed using in‐laboratory collected metrics. These findings may facilitate use of circadian timing in time‐based interventions.
Publisher: Public Library of Science (PLoS)
Date: 26-10-2017
Publisher: Public Library of Science (PLoS)
Date: 05-09-2013
Publisher: The Royal Society
Date: 13-10-2011
Abstract: Arousal is largely controlled by the ascending arousal system of the hypothalamus and brainstem, which projects to the corticothalamic system responsible for electroencephalographic (EEG) signatures of sleep. Quantitative physiologically based modelling of brainstem dynamics theory is described here, using realistic parameters, and links to EEG are outlined. Verification against a wide range of experimental data is described, including arousal dynamics under normal conditions, sleep deprivation, stimuli, stimulants and jetlag, plus key features of wake and sleep EEGs.
Publisher: Oxford University Press
Date: 23-08-2018
DOI: 10.1093/OSO/9780198778240.003.0003
Abstract: The function of sleep was a longstanding mystery in neuroscience, but there is now compelling empirical evidence for several key functions of sleep. Elucidating these functions and their underlying pathways is a hot area for the field of sleep research today, and many open questions remain. What we have gleaned from recent data is that it is important to view sleep as a synthesis of processes that enable improved functioning during wakefulness. There is no single universal function of sleep, but rather a collection of synergistic functions that are each of varying importance to different species. In humans, sleep plays critical roles in consolidating memories, restoring energy stores in the brain, clearing wastes from the brain, immune function, metabolic function, and overall health.
Publisher: Elsevier BV
Date: 02-2021
Publisher: Optica Publishing Group
Date: 11-08-2021
DOI: 10.1364/OE.431373
Abstract: Light has many non-visual effects on human physiology, including alterations in sleep, mood, and alertness. These effects are mainly mediated by photoreceptors containing the photopigment melanopsin, which has a peak sensitivity to short wavelength (‘blue’) light. Commercially available light sensors are commonly wrist-worn and report photopic illuminance and are calibrated to perceive visual brightness and hence cannot be used to investigate the non-visual impacts of light. In this paper, we report the development of a wearable spectrophotometer designed to be worn as a pendant or affixed to clothing to capture spectral power density data close to eye level in the visible wavelength range 380-780 nm. From this, the relative impact of a given light stimulus can be determined for each photoreceptive input in the human eye by calculating effective illuminances. This device showed high accuracy for all effective illuminances while measuring a range of commonly encountered light sources by calibrating for directional response, dark noise, sensor saturation, non-linearity, stray-light and spectral response. Features of the device include IoT-integration, onboard data storage and processing, Bluetooth Low Energy (BLE) enabled data transfer, and cloud storage in one cohesive unit.
Publisher: Oxford University Press (OUP)
Date: 10-01-2023
Abstract: Light is the main time cue for the human circadian system. Sleep and light are intrinsically linked light exposure patterns can influence sleep patterns and sleep can influence light exposure patterns. However, metrics for quantifying light regularity are lacking, and the relationship between sleep and light regularity is underexplored. We developed new metrics for light regularity and demonstrated their utility in adolescents, across school term and vacation. Daily sleep/wake and light patterns were measured using wrist actigraphy in 75 adolescents (54% male, 17.17 ± 0.83 years) over 2 weeks of school term and a subsequent 2-week vacation. The Sleep Regularity Index (SRI) and social jetlag were computed for each 2-week block. Light regularity was assessed using (1) variation in mean daily light timing (MLiT) (2) variation in daily photoperiod and (3) the Light Regularity Index (LRI). Associations between SRI and each light regularity metric were examined, and within-in idual changes in metrics were examined between school and vacation. Higher SRI was significantly associated with more regular LRI scores during both school and vacation. There were no significant associations of SRI with variation in MLiT or daily photoperiod. Compared to school term, all three light regularity metrics were less variable during the vacation. Light regularity is a multidimensional construct, which until now has not been formally defined. Irregular sleep patterns are associated with lower LRI, indicating that irregular sleepers also have irregular light inputs to the circadian system, which likely contributes to circadian disruption.
Publisher: Springer Science and Business Media LLC
Date: 08-06-2021
DOI: 10.1038/S41380-021-01157-3
Abstract: Late diurnal preference has been linked to poorer mental health outcomes, but the understanding of the causal role of diurnal preference on mental health and wellbeing is currently limited. Late diurnal preference is often associated with circadian misalignment (a mismatch between the timing of the endogenous circadian system and behavioural rhythms), so that evening people live more frequently against their internal clock. This study aims to quantify the causal contribution of diurnal preference on mental health outcomes, including anxiety, depression and general wellbeing and test the hypothesis that more misaligned in iduals have poorer mental health and wellbeing using an actigraphy-based measure of circadian misalignment. Multiple Mendelian Randomisation (MR) approaches were used to test causal pathways between diurnal preference and seven well-validated mental health and wellbeing outcomes in up to 451,025 in iduals. In addition, observational analyses tested the association between a novel, objective measure of behavioural misalignment (Composite Phase Deviation, CPD) and seven mental health and wellbeing outcomes. Using genetic instruments identified in the largest GWAS for diurnal preference, we provide robust evidence that early diurnal preference is protective for depression and improves wellbeing. For ex le, using one-s le MR, a twofold higher genetic liability of morningness was associated with lower odds of depressive symptoms (OR: 0.92, 95% CI: 0.88, 0.97). It is possible that behavioural factors including circadian misalignment may contribute in the chronotype depression relationship, but further work is needed to confirm these findings.
Publisher: Wiley
Date: 20-06-2021
DOI: 10.1111/JPI.12745
Abstract: The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of s les 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 in iduals 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 in idual'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.
Publisher: JMIR Publications Inc.
Date: 08-06-2018
DOI: 10.2196/JMIR.9410
Publisher: Oxford University Press (OUP)
Date: 17-04-2021
Abstract: Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an in idual’s average these traditional metrics include intra-in idual standard deviation (StDev), interdaily stability (IS), and social jet lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: composite phase deviation (CPD) and sleep regularity index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Multiple sleep–wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect the measurement of sleep regularity: “scrambling” the order of days daily vs. weekly variation naps awakenings “all-nighters” and length of study. SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep–wake data for unbiased estimates, whereas CPD and SRI required larger s le sizes to detect group differences. Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and s le size, and which aspects of sleep regularity are most pertinent to the research question.
Publisher: BMJ
Date: 05-2022
DOI: 10.1136/BMJOPEN-2021-055716
Abstract: During adolescence, sleep and circadian timing shift later, contributing to restricted sleep duration and irregular sleep-wake patterns. The association of these developmental changes in sleep and circadian timing with cognitive functioning, and consequently academic outcomes, has not been examined prospectively. The role of ambient light exposure in these developmental changes is also not well understood. Here, we describe the protocol for the Circadian Light in Adolescence, Sleep and School (CLASS) Study that will use a longitudinal design to examine the associations of sleep-wake timing, circadian timing and light exposure with academic performance and sleepiness during a critical stage of development. We also describe protocol adaptations to enable remote data collection when required during the COVID-19 pandemic. Approximately 220 healthy adolescents aged 12–13 years (school Year 7) will be recruited from the general community in Melbourne, Australia. Participants will be monitored at five 6 monthly time points over 2 years. Sleep and light exposure will be assessed for 2 weeks during the school term, every 6 months, along with self-report questionnaires of daytime sleepiness. Circadian phase will be measured via dim light melatonin onset once each year. Academic performance will be measured via national standardised testing (National Assessment Program-Literacy and Numeracy) and the Wechsler In idual Achievement Test—Australian and New Zealand Standardised Third Edition in school Years 7 and 9. Secondary outcomes, including symptoms of depression, anxiety and sleep disorders, will be measured via questionnaires. The CLASS Study will enable a comprehensive longitudinal assessment of changes in sleep-wake timing, circadian phase, light exposure and academic performance across a key developmental stage in adolescence. Findings may inform policies and intervention strategies for secondary school-aged adolescents. Ethical approval was obtained by the Monash University Human Research Ethics Committee and the Victorian Department of Education. Dissemination plans include scientific publications, scientific conferences, via stakeholders including schools and media. Recruitment occurred between October 2019 and September 2021, data collection from 2019 to 2023.
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.SLEEP.2019.03.009
Abstract: In healthy populations, irregular sleep patterns are associated with delayed sleep and poor functional/mood outcomes. Currently, it is unknown whether irregular sleep contributes to poor functional/mood outcomes in in iduals with Delayed Sleep-Wake Phase Disorder (DSWPD). In 170 patients with DSWPD, we collected sleep-wake patterns, dim light melatonin onset (DLMO), and functional/mood outcomes. The Sleep Regularity Index (SRI) and other sleep timing metrics were computed. Correlations of SRI were computed with phase angle (difference between DLMO and desired bedtime), sleep timing and quality variables, daytime function, sleep-related daytime impairment, mood, and insomnia symptom severity. Path analyses assessed whether SRI or total sleep time mediated the associations between sleep onset time and phase angle with daytime functioning, sleep-related impairment, and mood outcomes. Higher SRI was associated with earlier sleep and longer total sleep time, but did not relate to sleep quality, daytime function, or mood outcomes. Path analysis showed that phase angle was directly associated with all outcome variables, whereas sleep onset time was not directly associated with any. SRI mediated the effects of sleep onset time and phase angle on daytime function. Total sleep time mediated the effects of sleep onset time and phase angle on sleep-related impairment. In iduals with DSWPD who have more delayed sleep and a greater phase angle also have more irregular sleep. This suggests that it is not delayed sleep timing per se that drives poor functional outcomes in DSWPD, but rather the timing of sleep relative to circadian phase and resultant irregular sleep patterns.
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.JAD.2019.05.076
Abstract: Misalignment of circadian timing in patients with depression has commonly been reported, but the underlying mechanisms are not known. In idual differences in the sensitivity of the circadian system to light affect how the biological clock synchronizes with the external environment and can lead to misalignment of rhythms. We investigated the sensitivity of the circadian system to light in unmedicated (for >3 months) women with a current or previous diagnosis of major depression, and healthy controls. Baseline melatonin levels in dim light (<1 lux) were assessed, followed by melatonin levels in normal indoor lighting of 100 lux in order to determine melatonin suppression. Patients currently experiencing a depressive episode showed significantly lower levels of melatonin suppression to light compared to remitted patients and controls, with large effect sizes. Remitted patients and controls showed similar suppression. The relatively small s le, and lack of long-term, within subject assessments, make it difficult to determine the potential causal role of reduced light sensitivity in the development of circadian disruption. We conclude that hyposensitivity of the circadian system to light may contribute to circadian misalignment in patients with depression. Interventions that increase sensitivity to light or provide stronger light cues may assist in normalizing circadian clock function.
Publisher: Walter de Gruyter GmbH
Date: 29-01-2014
Abstract: Subjective ratings of the best drivers in the history of Formula One are common, but objective analyses are h ered by the difficulties involved in comparing drivers who raced for different teams and in different eras. Here, we present a new method for comparing performances within and between eras. Using a statistical model, we estimate driver and team contributions to performance, as well as the effects of competition with other drivers. By adjusting for team and competition effects, underlying driver performances are revealed. Using this method, we compute adjusted scoring rates for 1950–2013. Driver performances are then compared using: (i) peak performances for 1-year, 3-year, and 5-year intervals and (ii) number of ch ionships. Overall, these comparisons rank Clark, Stewart, Fangio, Alonso, and Schumacher as the five greatest drivers. We confirm the model’s accuracy by comparing its performance predictions to 2010–2013 lap-time data. The results of the analysis are generally in good agreement with expert opinions regarding driver performances. However, the model also identifies several undervalued and overvalued driver performances, which are discussed. This is the first objective method for comparing Formula One drivers that has yielded sensible results. The model adds a valuable perspective to previous subjective analyses.
Publisher: SAGE Publications
Date: 16-10-2020
Abstract: There is large interin idual variability in circadian timing, which is underestimated by mathematical models of the circadian clock. Interin idual differences in timing have traditionally been modeled by changing the intrinsic circadian period, but recent findings reveal an additional potential source of variability: large interin idual 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 in iduals. 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 in idual. 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 in idualizing circadian phase predictions. Future prospective work is needed that uses measures of light sensitivity to validate this approach.
Publisher: Oxford University Press (OUP)
Date: 15-05-2022
DOI: 10.1093/ABM/KAAC017
Abstract: Recent studies have found bi-directional relations between stress and sleep. However, few studies have examined the daily associations between stress and electroencephalography (EEG) measured sleep. This study examined the temporal associations between repeated ecological momentary assessments of stress and EEG-estimated sleep. Ninety-eight international or interstate undergraduate students (Mage = 20.54 ± 1.64, 76.5% female, 84.7% Asian) reported their stress levels four times daily at morning awakening, afternoon, evening, and pre-bedtime across 15 consecutive days (& ,000 total observations). Next-day stress was coded as an average of morning, afternoon, and evening stress. Z-Machine Insight+ recorded over 1,000 nights EEG total sleep time (TST), sleep onset latency, wake after sleep onset, sleep efficiency (SE), slow-wave sleep (SWS), and rapid eye movement (REM) sleep duration. Multilevel models, adjusted for covariates (i.e., sociodemographic, health factors, and daily covariates) and lagged outcomes, tested the daily within- and between-level stress-sleep associations. After adjusting for covariates, within-person shorter TST (b = −0.11 [−0.21, −0.01], p = .04), lower SE (b = −0.02 [−0.03, 0.00], p = .04), less SWS (b = −0.38 [−0.66, −0.10], p = .008), and less REM sleep (b = −0.32 [−0.53, −0.10], p = .004) predicted higher next-day stress. Pre-bedtime stress did not predict same-night sleep. No significant results emerged at the between-person level. These findings demonstrate that poor or short sleep, measured by EEG, is predictive of higher next-day stress. Results for sleep architecture support the role of SWS and REM sleep in regulating the perception of stress. Given that only within-person effects were significant, these findings highlight the importance of examining night-to-night fluctuations in sleep affecting next-day stress and its impact on daytime functioning.
Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.CCT.2022.106877
Abstract: Insomnia and fatigue symptoms are common in breast cancer. Active cancer treatment, such as chemotherapy, appears to be particularly disruptive to sleep. Yet, sleep complaints often go unrecognised and under treated within routine cancer care. The abbreviated delivery of cognitive behavioral therapy for Insomnia (CBTI) and bright light therapy (BLT) may offer accessible and cost-effective sleep treatments in women receiving chemotherapy for breast cancer. The Sleep, Cancer and Rest (SleepCaRe) Trial is a 6-month multicentre, randomized, controlled, 2 × 2 factorial, superiority, parallel group trial. Women receiving cytotoxic chemotherapy for breast cancer at tertiary Australian hospitals will be randomly assigned 1:1:1:1 to one of four, non-pharmacological sleep interventions: (a) Sleep Hygiene and Education (SHE) (b) CBTI (c) BLT (d) CBT-I + BLT combined and simultaneously delivered. Each sleep intervention is delivered over 6 weeks, and will comprise an introductory session, a mid-point phone call, and regular emails. The primary (insomnia, fatigue) and secondary (health-related quality of life, rest activity rhythms, sleep-related impairment) outcomes will be assessed via online questionnaires at five time-points: baseline (t0, prior to intervention), mid-point intervention (t2, Week 4), post-intervention (t3, Week 7), 3-months (t4, Week 18), and 6-months follow-up (t5, Week 30). This study will report novel data concerning the comparative and combined efficacy of CBT-I and BLT during chemotherapy. Findings will contribute to the development of evidence-based early sleep and fatigue intervention during chemotherapy for breast cancer. Clinical trial information Registered with the Australian New Zealand Clinical Trials Registry (anzctr.org.au/), Registration Number: ACTRN12620001133921.
Publisher: Elsevier BV
Date: 02-2023
Publisher: SAGE Publications
Date: 12-2019
Publisher: MDPI AG
Date: 12-04-2020
DOI: 10.3390/CLOCKSSLEEP2020012
Abstract: Light is a variable of key interest in circadian rhythms research, commonly measured using wrist-worn sensors. The GENEActiv Original is a cost-effective and practical option for assessing light in ambulatory settings. With increasing research on health and well-being incorporating sleep and circadian factors, the validity of wearable devices for assessing light environments needs to be evaluated. In this study, we tested the accuracy of the GENEActiv Original devices (n = 10) for recording light under a range of ecologically relevant lighting conditions, including LED, fluorescent, infrared, and outdoor lighting. The GENEActiv output had a strong linear relationship with photopic illuminance. However, the devices consistently under-reported photopic illuminance, especially below 100 lux. Accuracy below 100 lux depended on the light source, with lower accuracy and higher variability under fluorescent lighting. The device’s accuracy was also tested using light sources of varying spectral composition, which indicated that the device tends to under-report photopic illuminance for green light sources and over-report for red light sources. Furthermore, measures of photopic illuminance were impacted by infrared light exposure. We conclude that the GENEActiv Original is suitable for mapping light patterns within an in idual context, and can reasonably differentiate indoor vs. outdoor lighting, though the accuracy is variable at low light conditions. Given the human circadian system’s high sensitivity to light levels below 100 lux, if using the GENEActiv Original, we recommend also collecting light source data to better understand the impact on the circadian system, especially where participants spend prolonged periods in dim lighting.
Publisher: Proceedings of the National Academy of Sciences
Date: 28-05-2019
Abstract: Electric lighting has fundamentally altered how the human circadian clock synchronizes to the day/night cycle. Exposure to light after dusk is pervasive in the modern world. We examined group-level sensitivity of the circadian system to evening light and the degree to which sensitivity varies between in iduals. We found that, on average, humans are highly sensitive to evening light. Specifically, 50% suppression of melatonin occurred at lux, which is comparable to or lower than typical indoor lighting used at night, as well as light produced by electronic devices. Significantly, there was a -fold difference in sensitivity to evening light across in iduals. Interin idual differences in light sensitivity may explain differential vulnerability to circadian disruption and subsequent impact on human health.
Publisher: Informa UK Limited
Date: 26-03-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer Science and Business Media LLC
Date: 12-06-2017
DOI: 10.1038/S41598-017-03171-4
Abstract: The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exposure were assessed using salivary dim-light melatonin onset (DLMO) and wrist-worn photometry, respectively. DLMO occurred later (00:08 ± 1:54 vs. 21:32 ± 1:48 p 0.003) the daily sleep propensity rhythm peaked later (06:33 ± 0:19 vs. 04:45 ± 0:11 p 0.005) and light rhythms had lower litude (102 ± 19 lux vs. 179 ± 29 lux p 0.005) in Irregular compared to Regular sleepers. A mathematical model of the circadian pacemaker and its response to light was used to demonstrate that Irregular vs. Regular group differences in circadian timing were likely primarily due to their different patterns of light exposure. A positive correlation (r = 0.37 p 0.004) between academic performance and SRI was observed. These findings show that irregular sleep and light exposure patterns in college students are associated with delayed circadian rhythms and lower academic performance. Moreover, the modeling results reveal that light-based interventions may be therapeutically effective in improving sleep regularity in this population.
Publisher: SAGE Publications
Date: 02-2013
Abstract: Melatonin is endogenously produced and released in humans during nighttime darkness and is suppressed by ocular light exposure. Exogenous melatonin is used to induce circadian phase shifts and sleep. The circadian phase-shifting ability of a stimulus (e.g., melatonin or light) relative to its timing may be displayed as a phase response curve (PRC). Published PRCs to exogenous melatonin show a transition from phase advances to delays approximately 1 h after dim light melatonin onset. A previously developed mathematical model simulates endogenous production and clearance of melatonin as a function of circadian phase, light-induced suppression, and resetting of circadian phase by light. We extend this model to include the pharmacokinetics of oral exogenous melatonin and phase-shifting effects via melatonin receptors in the suprachiasmatic nucleus of the mammalian hypothalamus. Model parameters are fit using 2 data sets: (1) blood melatonin concentration following a 0.3- or 5.0-mg dose, and (2) a PRC to a 3.0-mg dose of melatonin. After fitting to the 3.0-mg PRC, the model correctly predicts that, by comparison, the 0.5-mg PRC is slightly decreased in litude and shifted to a later circadian phase. This model also reproduces blood concentration profiles of various melatonin preparations that differ only in absorption rate and percentage degradation by first-pass hepatic metabolism. This model can simulate experimental protocols using oral melatonin, with potential application to guide dose size and timing to optimally shift and entrain circadian rhythms.
Publisher: SAGE Publications
Date: 04-2007
Abstract: A quantitative, physiology-based model of the ascending arousal system is developed, using continuum neuronal population modeling, which involves averaging properties such as firing rates across neurons in each population. The model includes the ventrolateral preoptic area (VLPO), where circadian and homeostatic drives enter the system, the monoaminergic and cholinergic nuclei of the ascending arousal system, and their interconnections. The human sleep-wake cycle is governed by the activities of these nuclei, which modulate the behavioral state of the brain via diffuse neuromodulatory projections. The model parameters are not free since they correspond to physiological observables. Approximate parameter bounds are obtained by requiring consistency with physiological and behavioral measures, and the model replicates the human sleep-wake cycle, with physiologically reasonable voltages and firing rates. Mutual inhibition between the wake-promoting monoaminergic group and sleep-promoting VLPO causes ``flip-flop'' behavior, with most time spent in 2 stable steady states corresponding to wake and sleep, with transitions between them on a timescale of a few minutes. The model predicts hysteresis in the sleep-wake cycle, with a region of bistability of the wake and sleep states. Reducing the monoaminergic-VLPO mutual inhibition results in a smaller hysteresis loop. This makes the model more prone to wake-sleep transitions in both directions and makes the states less distinguishable, as in narcolepsy. The model behavior is robust across the constrained parameter ranges, but with sufficient flexibility to describe a wide range of observed phenomena.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2019
DOI: 10.1038/S41598-019-47290-6
Abstract: 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 in iduals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-in idual 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.
Publisher: Oxford University Press (OUP)
Date: 15-12-2022
Abstract: Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interin idual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interin idual differences. We performed a genome-wide gene-by-environment interaction study (GWIS) in 280 897 in iduals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (in idual time of day preference), acting as “light sensitivity” variants for the impact of daylight on the circadian system. We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634 p & 5 × 10−8), where additional minor alleles were found to enhance the morningness effect of daytime light exposure (βGxE = −.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and genes expressed in Per2+ hypothalamic neurons. Linkage disequilibrium score regression identified Bonferroni significant genetic correlations of greater light sensitivity GWIS with later chronotype and shorter sleep duration. Greater light sensitivity was nominally genetically correlated with insomnia symptoms and risk for post-traumatic stress disorder (PTSD). This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental health.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Oxford University Press (OUP)
Date: 21-09-2023
Publisher: Public Library of Science (PLoS)
Date: 28-03-2016
Publisher: Elsevier BV
Date: 12-2008
DOI: 10.1016/J.JTBI.2008.08.022
Abstract: A physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep-wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. Calibration gives an improved parameter set, in which the form of the homeostatic drive is better constrained, and its weighting relative to the circadian drive is increased. Within the newly constrained parameter ranges, the model predicts repayment of sleep debt consistent with experiment in both quantity and distribution, asymptoting to a maximum repayment for very long deprivations. Recovery is found to depend on circadian phase, and the model predicts that it is most efficient to recover during normal sleeping phases of the circadian cycle, in terms of the amount of recovery sleep required. The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data.
Publisher: American Academy of Pediatrics (AAP)
Date: 03-2021
Abstract: Extended-duration work rosters (EDWRs) with shifts of 24+ hours impair performance compared with rapid cycling work rosters (RCWRs) that limit shifts to 16 hours in postgraduate year (PGY) 1 resident-physicians. We examined the impact of a RCWR on PGY 2 and PGY 3 resident-physicians. Data from 294 resident-physicians were analyzed from a multicenter clinical trial of 6 US PICUs. Resident-physicians worked 4-week EDWRs with shifts of 24+ hours every third or fourth shift, or an RCWR in which most shifts were ≤16 consecutive hours. Participants completed a daily sleep and work log and the 10-minute Psychomotor Vigilance Task and Karolinska Sleepiness Scale 2 to 5 times per shift approximately once per week as operational demands allowed. Overall, the mean (± SE) number of attentional failures was significantly higher (P =.01) on the EDWR (6.8 ± 1.0) compared with RCWR (2.9 ± 0.7). Reaction time and subjective alertness were also significantly higher, by ∼18% and ∼9%, respectively (both P & .0001). These differences were sustained across the 4-week rotation. Moreover, attentional failures were associated with resident-physician–related serious medical errors (SMEs) (P =.04). Although a higher rate of SMEs was observed under the RCWR, after adjusting for workload, RCWR had a protective effect on the rate of SMEs (rate ratio 0.48 [95% confidence interval: 0.30–0.77]). Performance impairment due to EDWR is improved by limiting shift duration. These data and their correlation with SME rates highlight the impairment of neurobehavioral performance due to extended-duration shifts and have important implications for patient safety.
Publisher: Public Library of Science (PLoS)
Date: 20-03-2014
Publisher: Informa UK Limited
Date: 08-2019
DOI: 10.1080/07420528.2019.1644652
Abstract: Studies on circadian timing in depression have produced variable results, with some investigations suggesting phase advances and others phase delays. This variability may be attributable to differences in participant diagnosis, medication use, and methodology between studies. This study examined circadian timing in a s le of unmedicated women with and without unipolar major depressive disorder. Participants were aged 18-28 years, had no comorbid medical conditions, and were not taking medications. Eight women were experiencing a major depressive episode, nine had previously experienced an episode, and 31 were control participants with no history of mental illness. Following at least one week of actigraphic sleep monitoring, timing of salivary dim light melatonin onset (DLMO) was assessed in light of <1 lux. In currently depressed participants, melatonin onset occurred significantly earlier relative to sleep than in controls, with a large effect size. Earlier melatonin onset relative to sleep was also correlated with poorer mood for all participants. Our results indicate that during a unipolar major depressive episode, endogenous circadian phase is advanced relative to sleep time. This is consistent with the early-morning awakenings often seen in depression. Circadian misalignment may represent a precipitating or perpetuating factor that could be targeted for personalized treatment of major depression.
Publisher: Springer Science and Business Media LLC
Date: 27-10-2017
DOI: 10.1038/S41598-017-14611-6
Abstract: Despite sleep disturbance being a common complaint in in iduals with autism, specific sleep phenotypes and their relationship to adaptive functioning have yet to be identified. This study used cluster analysis to find distinct sleep patterns and relate them to independent measures of adaptive functioning in in iduals with autism. Approximately 50,000 nights of care-giver sleep/wake logs were collected on school-days for 106 in iduals with low functioning autism (87 boys, 14.77 ± 3.11 years) for 0.5–6 years (2.2 ± 1.5 years) from two residential schools. Using hierarchical cluster analysis, performed on summary statistics of each in idual across their recording duration, two clusters of in iduals with clearly distinguishable sleep phenotypes were found. The groups were summarized as ‘unstable’ sleepers (cluster 1, n = 41) and ‘stable’ sleepers (cluster 2, n = 65), with the former exhibiting reduced sleep duration, earlier sleep offset, and less stability in sleep timing. The sleep clusters displayed significant differences in properties that were not used for clustering, such as intellectual functioning, communication, and socialization, demonstrating that sleep phenotypes are associated with symptom severity in in iduals with autism. This study provides foundational evidence for profiling and targeting sleep as a standard part of therapeutic intervention in in iduals with autism.
Publisher: Oxford University Press (OUP)
Date: 23-10-2020
Abstract: Sleep is an emergent, multi-dimensional risk factor for diabetes. Sleep duration, timing, quality, and insomnia have been associated with diabetes risk and glycemic biomarkers, but the role of sleep regularity in the development of metabolic disorders is less clear. We analyzed data from 2107 adults, aged 19–64 years, from the Sueño ancillary study of the Hispanic Community Health Study/Study of Latinos, followed over a mean of 5.7 years. Multivariable-adjusted complex survey regression methods were used to model cross-sectional and prospective associations between the sleep regularity index (SRI) in quartiles (Q1-least regular, Q4-most regular) and diabetes (either laboratory-confirmed or self-reported antidiabetic medication use), baseline levels of insulin resistance (HOMA-IR), beta-cell function (HOMA-β), hemoglobin A1c (HbA1c), and their changes over time. Cross-sectionally, lower SRI was associated with higher odds of diabetes (odds ratio [OR]Q1 vs. Q4 = 1.64, 95% CI: 0.98–2.74, ORQ2 vs. Q4 = 1.12, 95% CI: 0.70–1.81, ORQ3 vs. Q4 = 1.00, 95% CI: 0.62–1.62, ptrend = 0.023). The SRI effect was more pronounced in older (aged ≥ 45 years) adults (ORQ1 vs. Q4 = 1.88, 95% CI: 1.14–3.12, pinteraction = 0.060) compared to younger ones. No statistically significant associations were found between SRI and diabetes incidence, as well as baseline HOMA-IR, HOMA-β, and HbA1c values, or their changes over time among adults not taking antidiabetic medication. Our results suggest that sleep regularity represents another sleep dimension relevant for diabetes risk. Further research is needed to elucidate the relative contribution of sleep regularity to metabolic dysregulation and pathophysiology.
Publisher: Oxford University Press (OUP)
Date: 14-12-2019
DOI: 10.1093/SLEEP/ZSZ300
Abstract: Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric—the Composite Phase Deviation (CPD)—to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. CPD for sleep was a significant predictor of average well-being (e.g. Stressed–Calm: b = −6.3, p & 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed–Calm, b = 1.0, p & 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. NCT02846077.
Publisher: IEEE
Date: 08-2015
Publisher: American Physical Society (APS)
Date: 20-11-2008
Publisher: Frontiers Media SA
Date: 22-09-2021
DOI: 10.3389/FNINS.2021.700923
Abstract: Background: Cancer patients often describe poor sleep quality and sleep disruption as contributors to poor quality of life (QoL). In a cross-sectional study of post-treatment breast, endometrial, and melanoma cancer patients, we used actigraphy to quantify sleep regularity using the sleep regularity index (SRI), and examined relationships with reported sleep symptoms and QoL. Methods: Participants were recruited post-primary treatment (35 diagnosed with breast cancer, 24 endometrial cancer, and 29 melanoma) and wore an actigraphy device for up to 2 weeks and SRI was calculated. Self-report questionnaires for cancer-related QoL [European Organization for Research and Treatment of Cancer EORTC (QLQ-C30)] were completed. Data were compared using analysis of variance (ANOVA) or Chi-Square tests. Multivariate linear regression analysis was used to determine independent variable predictors for questionnaire-derived data. Results: Age distribution was similar between cohorts. Endometrial and breast cancer cohorts were predominantly female, as expected, and body mass index (BMI) was higher in the endometrial cancer cohort, followed by breast and melanoma. There were no differences between tumor groups in: total sleep time, sleep onset latency, bedtime, and SRI (breast 80.9 ± 8.0, endometrial 80.3 ± 12.2, and melanoma 81.4 ± 7.0) (all p & 0.05). A higher SRI was associated with both better functional and symptom scores, including increased global QoL, better physical functioning, less sleepiness and fatigue, better sleep quality, and associated with less nausea/vomiting, dyspnea, and diarrhea (all p & 0.05). Conclusion: In cancer patients post-treatment, greater sleep regularity is associated with increased global QoL, as well as better physical functioning and fewer cancer related symptoms. Improving sleep regularity may improve QoL for cancer patients.
Publisher: Springer International Publishing
Date: 2021
Publisher: Public Library of Science (PLoS)
Date: 16-06-2021
DOI: 10.1371/JOURNAL.PONE.0252350
Abstract: Light improves mood. The amygdala plays a critical role in regulating emotion, including fear-related responses. In rodents the amygdala receives direct light input from the retina, and light may play a role in fear-related learning. A direct effect of light on the amygdala represents a plausible mechanism of action for light’s mood-elevating effects in humans. However, the effect of light on activity in the amygdala in humans is not well understood. We examined the effect of passive dim-to-moderate white light exposure on activation of the amygdala in healthy young adults using the BOLD fMRI response (3T Siemens scanner n = 23). Participants were exposed to alternating 30s blocks of light (10 lux or 100 lux) and dark ( lux), with each light intensity being presented separately. Light, compared with dark, suppressed activity in the amygdala. Moderate light exposure resulted in greater suppression of amygdala activity than dim light. Furthermore, functional connectivity between the amygdala and ventro-medial prefrontal cortex was enhanced during light relative to dark. These effects may contribute to light’s mood-elevating effects, via a reduction in negative, fear-related affect and enhanced processing of negative emotion.
Publisher: Wiley
Date: 12-2017
DOI: 10.1002/AUR.1899
Abstract: Increased severity of problematic daytime behavior has been associated with poorer sleep quality in in iduals with autism spectrum disorder. In this work, we investigate whether this relationship holds in a real-time setting, such that an in idual's prior sleep can be used to predict their subsequent daytime behavior. We analyzed an extensive real-world dataset containing over 20,000 nightly sleep observations matched to subsequent challenging daytime behaviors (aggression, self-injury, tantrums, property destruction and a challenging behavior index) across 67 in iduals with low-functioning autism living in two U.S. residential facilities. Using support vector machine classifiers, a statistically significant predictive relationship was found in 81% of in iduals studied (P < 0.05). For all five behaviors examined, prediction accuracy increased up to approximately eight nights of prior sleep used to make the prediction, indicating that the behavioral effects of sleep may manifest on extended timescales. Accurate prediction was most strongly driven by sleep variability measures, highlighting the importance of regular sleep patterns. Our findings constitute an initial step towards the development of a real-time monitoring tool to pre-empt behavioral episodes and guide prophylactic treatment for in iduals with autism. Autism Res 2018, 11: 391-403. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. We analyzed over 20,000 nights of sleep from 67 in iduals with autism to investigate whether daytime behaviors can be predicted from prior sleep patterns. Better-than-chance accuracy was obtained for 81% of in iduals, with measures of night-to-night variation in sleep timing and duration most relevant for accurate prediction. Our results highlight the importance of regular sleep patterns for better daytime functioning and represent a step toward the development of 'smart sleep technologies' to pre-empt behavior in in iduals with autism.
Publisher: Public Library of Science (PLoS)
Date: 12-08-2020
Publisher: American Academy of Sleep Medicine (AASM)
Date: 15-09-2020
DOI: 10.5664/JCSM.8516
Publisher: SAGE Publications
Date: 04-2017
Abstract: Within the human population, there is large interin idual variability in the timing of sleep and circadian rhythms. This variability has been attributed to in idual differences in sleep physiology, circadian physiology, and/or light exposure. Recent experimental evidence suggests that the latter is necessary to evoke large interin idual differences in sleep and circadian timing. We used a validated model of human sleep and circadian physiology to test the hypothesis that intrinsic differences in sleep and circadian timing are lified by self-selected use of artificial light sources. We tested the model under 2 conditions motivated by an experimental study (Wright et al., 2013): (1) a “natural” light cycle, and (2) a “realistic” light cycle that included attenuation of light due to living indoors when natural light levels are high and use of electric light when natural light levels are low. Within these conditions, we determined the relationship between intrinsic circadian period (within the range of 23.7-24.6 h) and timing of sleep onset, sleep offset, and circadian rhythms. In addition, we simulated a work week, with fixed wake time for 5 days and free sleep times on weekends. Under both conditions, a longer intrinsic period resulted in later sleep and circadian timing. Compared to the natural condition, the realistic condition evoked more than double the variation in sleep timing across the physiological range of intrinsic circadian periods. Model predictions closely matched data from the experimental study. We found that if the intrinsic circadian period was long ( .2 h) under the realistic condition, there was significant mismatch in sleep timing between weekdays and weekends, which is known as social jetlag. These findings indicate that in idual tendencies to have very delayed schedules can be greatly lified by self-selected modifications to the natural light/dark cycle. This has important implications for therapeutic treatment of advanced or delayed sleep phase disorders.
Publisher: Elsevier BV
Date: 05-2010
DOI: 10.1016/J.JTBI.2010.02.028
Abstract: A quantitative physiologically based model of the sleep-wake switch is used to predict variations in subjective fatigue-related measures during total sleep deprivation. The model includes the mutual inhibition of the sleep-active neurons in the hypothalamic ventrolateral preoptic area (VLPO) and the wake-active monoaminergic brainstem populations (MA), as well as circadian and homeostatic drives. We simulate sleep deprivation by introducing a drive to the MA, which we call wake effort, to maintain the system in a wakeful state. Physiologically this drive is proposed to be afferent from the cortex or the orexin group of the lateral hypothalamus. It is hypothesized that the need to exert this effort to maintain wakefulness at high homeostatic sleep pressure correlates with subjective fatigue levels. The model's output indeed exhibits good agreement with existing clinical time series of subjective fatigue-related measures, supporting this hypothesis. Subjective fatigue, adrenaline, and body temperature variations during two 72h sleep deprivation protocols are reproduced by the model. By distinguishing a motivation-dependent orexinergic contribution to the wake-effort drive, the model can be extended to interpret variation in performance levels during sleep deprivation in a way that is qualitatively consistent with existing, clinically derived results. The ex le of sleep deprivation thus demonstrates the ability of physiologically based sleep modeling to predict psychological measures from the underlying physiological interactions that produce them.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Informa UK Limited
Date: 23-03-2023
Publisher: American Physical Society (APS)
Date: 24-02-2009
Start Date: 11-2021
End Date: 11-2024
Amount: $552,254.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2023
End Date: 03-2026
Amount: $430,986.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2022
End Date: 03-2025
Amount: $479,588.00
Funder: Australian Research Council
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