Cardio-metabolic health effects of CPAP treatment for sleep apnoea during weight loss: A randomised controlled pilot trial.

BACKGROUND AND AIMS: This study assessed whether the addition of continuous positive airway pressure (CPAP) during weight loss would enhance cardiometabolic health improvements in patients with obesity and Obstructive Sleep Apnoea (OSA). METHODS AND RESULTS: Patients with overweight or obesity, pre-diabetes and moderatesevere OSA were randomised to receive CPAP therapy with a weight loss programme (CPAP+WL) or a weight loss programme alone (WL alone). PRIMARY OUTCOME: 2-hour glucose assessed by an oral glucose tolerance test. SECONDARY OUTCOMES: 24 hr blood pressure, body composition (DEXA) and fasting blood markers. 17 patients completed 3-month follow-up assessments (8 CPAP+WL and 9 WL alone). Overall, participants in both groups lost ∼12 kg which reduced polysomnography determined OSA severity by ∼45 %. In the CPAP+WL group, CPAP use (compliance 5.29 hrs/night) did not improve any outcome above WL alone. There was no improvement in 2-hour glucose in either group. However, in the pooled (n = 17) analysis there were overall improvements in most outcomes including insulin sensitivity (.000965 units, p = .008), sleep systolic BP (- 16.2 mmHg, p = .0003), sleep diastolic BP (-9.8 mmHg, p = 0.02), wake diastolic BP (- 4.3 mmHg, p = .03) and sleepiness (Epworth Sleepiness Score -3.2, p = .0003). In addition, there were reductions in glucose area under the curve (-230 units, p = .009), total (-0.86 mmol/L, p = 0.006) and LDL cholesterol (-0.58 mmol/L, p = 0.007), triglycerides (-0.75 mmol/L, p = 0.004), fat mass (-7.6 kg, p < .0001) and abdominal fat (-310 cm3, p < .0001). CONCLUSION: Weight loss reduced OSA and improved sleepiness and cardiometabolic health. These improvements were not further enhanced by using CPAP. Results suggest weight loss should be the primary focus of treatment for patients with OSA and obesity.

SleepSync: Early Testing of a Personalised Sleep-Wake Management Smartphone Application for Improving Sleep and Cognitive Fitness in Defence Shift Workers.

Shift work, long work hours, and operational tasks contribute to sleep and circadian disruption in defence personnel, with profound impacts on cognition. To address this, a digital technology, the SleepSync app, was designed for use in defence. A pre-post design study was undertaken to examine whether four weeks app use improved sleep and cognitive fitness (high performance neurocognition) in a cohort of shift workers from the Royal Australian Air Force. In total, 13 of approximately 20 shift-working personnel from one base volunteered for the study. Sleep outcomes were assessed using the Insomnia Severity Index (ISI), the Patient-Reported Outcomes Measurement Information System (PROMIS), Sleep Disturbance and Sleep-Related Impairment Scales, the Glasgow Sleep Effort Scale, the Sleep Hygiene Index, and mental health was assessed using the Depression, Anxiety, and Stress Scale-21. Sustained attention was measured using the 3-min Psychomotor Vigilance Task (PVT) and controlled response using the NBack. Results showed significant improvements in insomnia (ISI scores 10.31 at baseline and 7.50 after app use), sleep-related impairments (SRI T-scores 53.03 at baseline to 46.75 post-app use), and healthy sleep practices (SHI scores 21.61 at baseline to 18.83 post-app use; all p < 0.001). Trends for improvement were recorded for depression. NBack incorrect responses reduced significantly (9.36 at baseline; reduced by -3.87 at last week of app use, p < 0.001), but no other objective measures improved. These findings suggest that SleepSync may improve sleep and positively enhance cognitive fitness but warrants further investigation in large samples. Randomised control trials with other cohorts of defence personnel are needed to confirm the utility of this intervention in defence settings.

Timing of Moderate to Vigorous Physical Activity, Mortality, Cardiovascular Disease, and Microvascular Disease in Adults With Obesity.

OBJECTIVE: To assess the association between timing of aerobic moderate to vigorous physical activity (MVPA) and risk of cardiovascular disease (CVD), microvascular disease (MVD), and all-cause mortality in adults with obesity and a subset with obesity and type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: Participants included adults with obesity (BMI ≥30 kg/m2) and a subset of those with T2D from the UK Biobank accelerometry substudy. Aerobic MVPA was defined as bouts of MVPA lasting ≥3 continuous minutes. Participants were categorized into morning, afternoon, or evening MVPA based on when they undertook the majority of their aerobic MVPA. The reference group included participants with an average of less than one aerobic MVPA bout per day. Analyses were adjusted for established and potential confounders. RESULTS: The core sample included 29,836 adults with obesity, with a mean age of 62.2 (SD 7.7) years. Over a mean follow-up period of 7.9 (SD 0.8) years, 1,425 deaths, 3,980 CVD events, and 2,162 MVD events occurred. Compared with activity in the reference group, evening MVPA was associated with the lowest risk of mortality (hazard ratio [HR] 0.39; 95% CI 0.27, 0.55), whereas afternoon (HR 0.60; 95% CI 0.51, 0.71) and morning MVPA (HR 0.67; 95% CI 0.56, 0.79) demonstrated significant but weaker associations. Similar patterns were observed for CVD and MVD incidence, with evening MVPA associated with the lowest risk of CVD (HR 0.64; 95% CI 0.54, 0.75) and MVD (HR 0.76; 95% CI 0.63, 0.92). Findings were similar in the T2D subset (n = 2,995). CONCLUSIONS: Aerobic MVPA bouts undertaken in the evening were associated with the lowest risk of mortality, CVD, and MVD. Timing of physical activity may play a role in the future of obesity and T2D management.

Pilot feasibility testing of biomathematical model recommendations for personalising sleep timing in shift workers.

Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts.

Mobile app for personalized sleep-wake management for shift workers: A user testing trial.

Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

The Effect of Light Therapy on Electroencephalographic Sleep in Sleep and Circadian Rhythm Disorders: A Scoping Review.

Light therapy is used to treat sleep and circadian rhythm disorders, yet there are limited studies on whether light therapy impacts electroencephalographic (EEG) activity during sleep. Therefore, we aimed to provide an overview of research studies that examined the effects of light therapy on sleep macro- and micro-architecture in populations with sleep and circadian rhythm disorders. We searched for randomized controlled trials that used light therapy and included EEG sleep measures using MEDLINE, PubMed, CINAHL, PsycINFO and Cochrane Central Register of Controlled Trials databases. Five articles met the inclusion criteria of patients with either insomnia or delayed sleep−wake phase disorder (DSWPD). These trials reported sleep macro-architecture outcomes using EEG or polysomnography. Three insomnia trials showed no effect of the timing or intensity of light therapy on total sleep time, wake after sleep onset, sleep efficiency and sleep stage duration compared to controls. Only one insomnia trial reported significantly higher sleep efficiency after evening light therapy (>4000 lx between 21:00−23:00 h) compared with afternoon light therapy (>4000 lx between 15:00−17:00 h). In the only DSWPD trial, six multiple sleep latency tests were conducted across the day (09:00 and 19:00 h) and bright light (2500 lx) significantly lengthened sleep latency in the morning (09:00 and 11:00 h) compared to control light (300 lx). None of the five trials reported any sleep micro-architecture measures. Overall, there was limited research about the effect of light therapy on EEG sleep measures, and studies were confined to patients with insomnia and DSWPD only. More research is needed to better understand whether lighting interventions in clinical populations affect sleep macro- and micro-architecture and objective sleep timing and quality.

Cortical waste clearance in normal and restricted sleep with potential runaway tau buildup in Alzheimer's disease.

Accumulation of waste in cortical tissue and glymphatic waste clearance via extracellular voids partly drives the sleep-wake cycle and modeling has reproduced much of its dynamics. Here, new modeling incorporates higher void volume and clearance in sleep, multiple waste compounds, and clearance obstruction by waste. This model reproduces normal sleep-wake cycles, sleep deprivation effects, and performance decreases under chronic sleep restriction (CSR). Once fitted to calibration data, it successfully predicts dynamics in further experiments on sleep deprivation, intermittent CSR, and recovery after restricted sleep. The results imply a central role for waste products with lifetimes similar to tau protein. Strong tau buildup is predicted if pathologically enhanced production or impaired clearance occur, with runaway buildup above a critical threshold. Predicted tau accumulation has timescales consistent with the development of Alzheimer's disease. The model unifies a wide sweep of phenomena, clarifying the role of glymphatic clearance and targets for interventions against waste buildup.

Prediction of shiftworker alertness, sleep, and circadian phase using a model of arousal dynamics constrained by shift schedules and light exposure.

STUDY OBJECTIVES: The study aimed to, for the first time, (1) compare sleep, circadian phase, and alertness of intensive care unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (2) investigate how different environmental constraints affect predictions and agreement with data. METHODS: The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison. RESULTS: All input constraints produced similar prediction for KSS, with 56%-60% of KSS scores predicted within ±1 on a day and 48%-52% on a night shift. Accurate prediction of an individual's circadian phase required individualized light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35%-47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81 ± 6% and 87 ± 5% depending on choice of input constraint. CONCLUSIONS: The use of individualized environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase, and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.

Progress in modelling of brain dynamics during anaesthesia and the role of sleep-wake circuitry.

General anaesthesia is used widely in surgery and during interventional medical procedures, but little is known about the exact neural mechanisms for how unconsciousness arises from administering an anaesthetic drug. Computational modelling of brain dynamics has already provided valuable insights into the neural circuitry involved in generating this state. Current theories for the origin of electroencephalographic (EEG) features in brain activity under GABAergic anaesthetic drugs have been proposed through modelling results. While much attention has been paid to describing alpha and delta oscillations, burst suppression, paradoxical excitation and the possibility of hysteresis during transitions to and from unconscious state, these models have focused only on the role of the thalamocortical system. Recent empirical findings suggest that anaesthetic drugs may act directly on the neural circuitry regulating sleep and wake states and circadian rhythms in the hypothalamus. Coupled with the common behavioural features found in physiological sleep and general anaesthesia, this evidence serves as a foundation for the 'shared circuits hypothesis' which proposes that anaesthetic-induced unconsciousness arises predominantly through modulation of the hypothalamic sleep-wake switch. This paper reviews the key findings from computational models describing brain states during the administration of anaesthetic drugs, with a focus on those enhancing GABAergic inhibition given their widespread use in practice and that almost all models of anaesthesia have focused on these drugs. We draw physiological and behavioural links between brain states during sleep and anaesthesia, and aim to highlight the importance of computational modelling in advancing our understanding of anaesthesia by considering sleep and circadian mechanisms in generating unconsciousness in future work.

How do travelers manage jetlag and travel fatigue? A survey of passengers on long-haul flights.

Jetlag and travel fatigue can impair functioning, but it is unknown what strategies are used by travelers to minimize these consequences. Passengers on Qantas Airways flights were invited to take part in online surveys. Long-haul flights of ≥8 h into and out of Australia were targeted, which involved time differences of 1 to 18 h between the origin and destination. Passengers were queried about the use of travel booking choices before the flight, and the use of behavioral strategies before, during, and after flight for reducing jetlag and travel fatigue. Surveys were completed by N = 460 passengers aged 18 to 78 (43% male; mean age 50 y). Selecting a seat location (59%) and choosing a direct flight (52%) were the most common booking strategies. Almost all (99%) employed specific behavioral strategies during flight, with fewer implementing strategies before flight (73%) and after flight (89%). During the journey, 81% consumed or avoided caffeine and alcohol, 68% altered food intake, 68% used comfort/relaxation strategies, 53% light exposure, 35% physical activity, 31% compression stockings, 15% pharmaceutical sleep aids, and 8% melatonin. Surprisingly, only 1 of 460 passengers reported using a jetlag app. Younger travelers were more likely to adopt any strategy before the flight than older travelers (χ 22 = 14.90, p =.01), while female travelers appeared more likely than male travelers to use strategies before (77% vs. 68%) and after flight (91% vs. 85%). Reason for travel, flight cabin, leg of journey, and country of residence were not significantly associated with the use of behavioral strategies. Nearly all passengers took measures to improve the experience and consequences of long-haul flying. The results suggest that interventions around food/drink and physical activity may be highly acceptable to passengers for mitigating travel fatigue and that greater public education on evidence-based strategies may be helpful for reducing travel fatigue and jetlag.

Modeling melanopsin-mediated effects of light on circadian phase, melatonin suppression, and subjective sleepiness.

A physiologically based model of arousal dynamics is improved to incorporate the effects of the light spectrum on circadian phase resetting, melatonin suppression, and subjective sleepiness. To account for these nonvisual effects of light, melanopic irradiance replaces photopic illuminance that was used previously in the model. The dynamic circadian oscillator is revised according to the melanopic irradiance definition and tested against experimental circadian phase resetting dose-response and phase response data. Melatonin suppression function is recalibrated against melatonin dose-response data for monochromatic and polychromatic light sources. A new light-dependent term is introduced into the homeostatic weight component of subjective sleepiness to represent the direct alerting effect of light; the new term responds to light change in a time-dependent manner and is calibrated against experimental data. The model predictions are compared to a total of 14 experimental studies containing 26 data sets for 14 different spectral light profiles. The revised melanopic model shows on average 1.4 times lower prediction error for circadian phase resetting compared to the photopic-based model, 3.2 times lower error for melatonin suppression, and 2.1 times lower error for subjective sleepiness. Overall, incorporating melanopic irradiance allowed simulation of wavelength-dependent responses to light and could explain the majority of the observations. Moving forward, models of circadian phase resetting and the direct effects of light on alertness and sleep need to use nonvisual photoreception-based measures of light, for example, melanopic irradiance, instead of the traditionally used illuminance based on the visual system.

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals 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 individuals 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 individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.

What works for jetlag? A systematic review of non-pharmacological interventions.

Jetlag is a combination of travel fatigue and circadian misalignment resulting from air travel across time zones. Routinely recommended interventions based on circadian science include timely exposure to light and darkness (scheduled sleep), but the real-world effectiveness of these and other non-circadian strategies is unknown. We systematically reviewed the evidence for non-pharmacological interventions for jetlag. PubMed, EMBASE, Scopus, and Web of Science were searched. Studies reviewed 1) involved human participants undergoing air travel with a corresponding shift in the external light-dark cycle; 2) administered a non-pharmacological intervention; 3) had a control or comparison group; and 4) examined outcomes such as jetlag symptoms, sleep, cognitive/physical performance, mood, fatigue, or circadian markers. Thirteen studies used light exposure, physical activity, diet, chiropractic treatment, or a multifaceted intervention to counteract jetlag. Nine studies found no significant change in the outcomes, three reported mixed findings, and one was positive. The null findings are likely due to poorly designed circadian interventions and neglect of contributors to travel fatigue. Higher quality studies that schedule darkness as well as light, in the periods before, during, and after flight are needed to reduce the circadian component of jetlag. Interventions should also address the stressors that contribute to travel fatigue.

Sleep Modelling across Physiological Levels.

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms-the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

Spiking patterns and synchronization of thalamic neurons along the sleep-wake cycle.

Spiking patterns and synchronization dynamics of thalamic neurons along the sleep-wake cycle are studied in a minimal model of four coupled conductance-based neurons. The model simulates two thalamic neurons coupled via a gap junction and driven by a synaptic input from a two-neuron model of sleep regulation by the hypothalamus. In accord with experimental data, the model shows that during sleep, when hypothalamic wake-active neurons are silent, the thalamic neurons discharge bursts of spikes. During wake, the excitatory synaptic input from the hypothalamus drives the coupled thalamic neurons to a state of tonic firing (single spikes). In the deterministic case, the thalamic neurons synchronize in-phase in the bursting regime but demonstrate multi-stability of out-of-phase, in-phase, and asynchronous states in the tonic firing. However, along the sleep-wake cycle, once the neurons synchronize in-phase during sleep (bursting), they stay synchronized in wake (tonic firing). It is thus found that noise is needed to reproduce the experimentally observed transitions between synchronized bursting during sleep and asynchronous tonic firing during wake. Overall, synchronization of bursting is found to be more robust to noise than synchronization of tonic firing, where a small disturbance is sufficient to desynchronize the thalamic neurons. The model predicts that the transitions between sleep and wake happen via chaos because a single thalamic neuron exhibits chaos between regular bursting and tonic activity. The results of this study suggest that the sleep- and wake-related dynamics in the thalamus may be generated at a level of gap junction-coupled clusters of thalamic neurons driven from the hypothalamus which would then propagate throughout the thalamus and cortex via axonal long-range connections.

The effect of consecutive transmeridian flights on alertness, sleep-wake cycles and sleepiness: A case study.

Travel across time zones disrupts circadian rhythms causing increased daytime sleepiness, impaired alertness and sleep disturbance. However, the effect of repeated consecutive transmeridian travel on sleep-wake cycles and circadian dynamics is unknown. The aim of this study was to investigate changes in alertness, sleep-wake schedule and sleepiness and predict circadian and sleep dynamics of an individual undergoing demanding transmeridian travel. A 47-year-old healthy male flew 16 international flights over 12 consecutive days. He maintained a sleep-wake schedule based on Sydney, Australia time (GMT + 10 h). The participant completed a sleep diary and wore an Actiwatch before, during and after the flights. Subjective alertness, fatigue and sleepiness were rated 4 hourly (08:00-00:00), if awake during the flights. A validated physiologically based mathematical model of arousal dynamics was used to further explore the dynamics and compare sleep time predictions with observational data and to estimate circadian phase changes. The participant completed 191 h and 159 736 km of flying and traversed a total of 144 time-zones. Total sleep time during the flights decreased (357.5 min actigraphy; 292.4 min diary) compared to baseline (430.8 min actigraphy; 472.1 min diary), predominately due to restricted sleep opportunities. The daily range of alertness, sleepiness and fatigue increased compared to baseline, with heightened fatigue towards the end of the flight schedule. The arousal dynamics model predicted sleep/wake states during and post travel with 88% and 95% agreement with sleep diary data. The circadian phase predicted a delay of only 34 min over the 16 transmeridian flights. Despite repeated changes in transmeridian travel direction and flight duration, the participant was able to maintain a stable sleep schedule aligned with the Sydney night. Modelling revealed only minor circadian misalignment during the flying period. This was likely due to the transitory time spent in the overseas airports that did not allow for resynchronisation to the new time zone. The robustness of the arousal model in the real-world was demonstrated for the first time using unique transmeridian travel.

Prediction of Cognitive Performance and Subjective Sleepiness Using a Model of Arousal Dynamics.

A model of arousal dynamics is applied to predict objective performance and subjective sleepiness measures, including lapses and reaction time on a visual Performance Vigilance Test (vPVT), performance on a mathematical addition task (ADD), and the Karolinska Sleepiness Scale (KSS). The arousal dynamics model is comprised of a physiologically based flip-flop switch between the wake- and sleep-active neuronal populations and a dynamic circadian oscillator, thus allowing prediction of sleep propensity. Published group-level experimental constant routine (CR) and forced desynchrony (FD) data are used to calibrate the model to predict performance and sleepiness. Only the studies using dim light (<15 lux) during alertness measurements and controlling for sleep and entrainment before the start of the protocol are selected for modeling. This is done to avoid the direct alerting effects of light and effects of prior sleep debt and circadian misalignment on the data. The results show that linear combination of circadian and homeostatic drives is sufficient to predict dynamics of a variety of sleepiness and performance measures during CR and FD protocols, with sleep-wake cycles ranging from 20 to 42.85 h and a 2:1 wake-to-sleep ratio. New metrics relating model outputs to performance and sleepiness data are developed and tested against group average outcomes from 7 (vPVT lapses), 5 (ADD), and 8 (KSS) experimental protocols, showing good quantitative and qualitative agreement with the data (root mean squared error of 0.38, 0.19, and 0.35, respectively). The weights of the homeostatic and circadian effects are found to be different between the measures, with KSS having stronger homeostatic influence compared with the objective measures of performance. Using FD data in addition to CR data allows us to challenge the model in conditions of both acute sleep deprivation and structured circadian misalignment, ensuring that the role of the circadian and homeostatic drives in performance is properly captured.

A unified model of melatonin, 6-sulfatoxymelatonin, and sleep dynamics.

A biophysical model of the key aspects of melatonin synthesis and excretion has been developed, which is able to predict experimental dynamics of melatonin in plasma and saliva, and of its urinary metabolite 6-sulfatoxymelatonin (aMT6s). This new model is coupled to an established model of arousal dynamics, which predicts sleep and circadian dynamics based on light exposure and times of wakefulness. The combined model thus predicts melatonin levels over the sleep-wake/dark-light cycle and enables prediction of melatonin-based circadian phase markers, such as dim light melatonin onset (DLMO) and aMT6s acrophase under conditions of normal sleep and circadian misalignment. The model is calibrated and tested against group average data from 10 published experimental studies and is found to reproduce quantitatively the key dynamics of melatonin and aMT6s, including the timing of release and amplitude, as well as response to controlled lighting and shift work.

Sleep Propensity under Forced Desynchrony in a Model of Arousal State Dynamics.

An improvement to our current quantitative model of arousal state dynamics is presented that more accurately predicts sleep propensity as measured with sleep dynamics depending on circadian phase and prior wakefulness. A nonlinear relationship between the circadian variables within the dynamic circadian oscillator model is introduced to account for the skewed shape of the circadian rhythm of alertness that peaks just prior to the onset of the biological night (the "wake maintenance zone") and has a minimum toward the end of the biological night. The revised circadian drive thus provides a strong inhibitory input to the sleep-active neuronal population in the evening, counteracting the excitatory effects of the increased homeostatic sleep drive as originally proposed in the opponent process model of sleep-wake regulation. The revised model successfully predicts the sleep propensity profile as reflected in the dynamics of the total sleep time, sleep onset latency, wake/sleep ratio, and sleep efficiency during a wide range of experimental protocols. Specifically, all of these sleep measures are predicted for forced desynchrony schedules with day lengths ranging from 1.5 to 42.85 h and scheduled time in bed from 0.5 to 14.27 h. The revised model is expected to facilitate more accurate predictions of sleep under normal conditions as well as during circadian misalignment, for example, during shiftwork and jetlag.

Physiological Markers of Arousal Change with Psychological Treatment for Insomnia: A Preliminary Investigation.

OBJECTIVES: The aim of this preliminary study was to evaluate if Sleep Restriction Therapy for insomnia is associated with modifications to physiological arousal, indexed through overnight measures of plasma cortisol concentrations and core body temperature. METHODS: In a pre-to-post open label study design, eleven patients with chronic and severe Psychophysiological Insomnia underwent 5 weeks of Sleep Restriction Therapy. RESULTS: Eight (73%) patients out of 11 consented completed therapy and showed a decrease in insomnia severity pre-to-post treatment (mean (SD): 18.1 (2.8) versus 8.4 (4.8); p = .001). Six patients were analyzed with pre-to-post overnight measures of temperature and cortisol. Contrary to our hypothesis, significantly higher levels of plasma cortisol concentrations were found during the early morning at post-treatment compared to baseline (p < .01), while no change was observed in the pre-sleep phase or early part of the night. Core body temperature during sleep was however reduced significantly (overall mean [95% CI]: 36.54 (°C) [36.3, 36.8] versus 36.45 [36.2, 36.7]; p < .05). CONCLUSIONS: Sleep Restriction Therapy therefore was associated with increased early morning cortisol concentrations and decreased core body temperature, supporting the premise of physiological changes in functioning after effective therapy. Future work should evaluate change in physiological variables associated with clinical treatment response. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ANZCTR 12612000049875.