Two-process Model Of Sleep Regulation Research Articles

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations – rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.

The Two-Process Model: Origin of Its Concepts and Their Implications

The two-process model of sleep regulation has served as a conceptual framework in the last four decades for understanding sleep physiology. In the 1970s, long-term recordings of sleep in rats were obtained thanks to EEG telemetry. NonREM sleep and REM sleep were found to differ in their time course and response to light-dark protocols. There were indications for their coupling to the circadian system, in particular the light-dark and the dark-light transitions. With the advent of quantitative EEG analysis, slow-wave activity in nonREM sleep was recognized as a sleep-wake-dependent variable. The term “sleep homeostasis” was coined to specify the regulated balance between sleep and waking. The regulatory homeostatic process was designated as “Process S”. In the two-process model, its interaction with the circadian pacemaker “Process C” can account for sleep duration under various experimental protocols. Local, use-dependent slow-wave activity changes were demonstrated in both humans and rats by the selective, unilateral activation of a cortical region prior to sleep. Finding that rest in invertebrates has sleep-like regulatory properties opened a new realm of animal studies. Comparative sleep studies in a broad variety of animal species confirmed the validity of the basic concepts of the two-process model. Recent studies have addressed sleep-related changes of brain temperature as an indicator of brain metabolism; the application of the model to Drosophila; the divergence of cortical and subcortical states; and sleep in an increasing number of species and taxa.

0179 Mathematical Models to Predict Sleep Latency and Sleep Duration

Abstract Introduction The demands of a 24/7 society prevent millions of shift workers around the world from obtaining regular nightly sleep, negatively affecting their health, safety, and productivity. To mitigate this problem, workers should maximize the benefits of their atypical sleep opportunities. This requires assessing the ability of an individual to initiate and maintain sleep at the intended time, given their recent sleep history. Here, we present two validated mathematical models, one for predicting sleep latency and a second for predicting sleep duration, as decision aids for planning effective sleep schedules that maximize restorative sleep. Methods As an extension of the two-process model of sleep regulation, the proposed models predict sleep latency and sleep duration by accounting for the nonlinear interactions between homeostatic sleep pressure and the circadian rhythm. We estimated the models’ parameter values for a population of young, healthy adults using experimental data from two studies and validated the models by comparing their predictions with experimental measurements from 22 different studies, involving a variety of sleep conditions spanning the entire circadian cycle and a total of 317 unique subjects. Results Comparison of the sleep-latency and sleep-duration models’ predictions with the experimental data showed that the models yielded acceptable average prediction errors: 4.0 minutes for sleep latency and 0.8 hours for sleep duration. Importantly, we identified conditions under which small shifts in bedtime may result in disproportionately large differences in sleep duration--knowledge that may be applied to improve productivity, safety, and sustainability in civilian and military operations. Conclusion By extending the capabilities of existing predictive fatigue-management tools, the mathematical models described here allow users to determine the most opportune times to sleep. This information can be used to optimize sleep efficiency, allowing shift workers to maximize the benefits of restorative sleep. Support (if any)

0386 Brief cognitive-behavioral therapy for insomnia among Japanese university students with sleep disturbances

Abstract Introduction Sleep disturbances among Japanese university students negatively affect their academic performance and mental health. Student habits, such as naps and using smartphones in bed, may lead to sleep problems. We hypothesized the Sleep Health through University Student Habits (SHUSH) program, a four-week cognitive-behavioral therapy for insomnia intervention, would improve sleep disturbances in university students. Methods We used a pretest-posttest, single arm pilot study design and evaluated the feasibility and preliminary efficacy of SHUSH. Occupational therapy and physical therapy students (N = 18) in a university with self-reported sleep disturbances (Insomnia Severity Index; ISI score ≥ 9) were offered 90-minutes of sleep education (e.g., two-process model of sleep regulation, stimulus control, and sleep hygiene) then prescribed personalized sleep restriction therapy (SRT) and stimulus control therapy (SCT) goals based upon 10-days of sleep diary data. This was followed by weekly 15-minute individualized meetings for three consecutive weeks; SRT and SCT goals were titrated based upon sleep diaries, and occupational therapists supported treatment adherence. Assessments included validated Japanese versions of sleep-related (e.g., sleep disturbance), mental health-related (e.g., depression) and sleep diary (e.g., sleep efficiency) variables. Dependent t-tests (d) and Wilcoxon rank sum tests (r) were used to test differences between baseline and posttest assessments. Results There were 15 students who completed SHUSH with a mean age of 19.7 years; 9 were women. Improvements were found in sleep disturbance (r = 0.843), sleep quality (r = 0.851), daytime sleepiness (d = 0.762), sleep hygiene practice (d = 1.216) and change from an eveningness towards a morningness circadian preference (d = 0.990). Anxiety (d = 0.604) and depression (d = 0.729) improved. Sleep onset latency (r = 0.851), nighttime awakening (r = 0.514), and sleep efficiency (r = 0.880) also improved. Participants were satisfied with the SHUSH group-based education, one-on-one therapist meetings, and program brevity; there were no adverse events. Conclusion SHUSH is a feasible and preliminarily effective sleep improvement program for reducing sleep disturbances among Japanese university students. Improved sleep was associated with improved mental health and sleep-promoting behaviors. A randomized controlled trial of SHUSH is recommended. Support (if any) Fulbright U.S. Scholar Award to Japan

Sleep has many faces: The interplay of sleep and work in predicting employees’ energetic state over the course of the day.

Sleep affects employees' functioning. In this study, we differentiate biological (chronotype), quantitative (daily sleep duration), and qualitative (daily sleep quality) sleep characteristics and examine their relationship with the trajectory of employees' vigor over the course of the day. Building on the two-process model of sleep regulation and the job demands-resources model, we examine whether sleep characteristics are differentially related to the trajectory of vigor as an energetic state. Furthermore, we expect that favorable sleep characteristics have a protective function during the workday in the interplay with daily job demands (workload) and job resources (autonomy). We conducted an experience-sampling study across ten workdays with three daily measurement occasions (171 employees, 1,631 days, 4,351 measurement occasions). Multilevel growth curve modeling showed that, on average, vigor followed a positive quadratic daily trajectory, mainly characterized by a decrease in vigor over the course of the day-after a slight increase early in the day. The decrease in vigor was particularly strong after nights with high sleep quality and for employees with an early chronotype. However, the relation between sleep quality and decrease in vigor occurred only on days with high workload. These results emphasize the importance of looking at the differential effects of sleep characteristics and on-the-job experiences on employees' energetic state during the day. These findings provide helpful suggestions on how to structure work and leisure time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The two-process model of sleep regulation: Beginnings and outlook.

SummaryThe two‐process model serves as a major conceptual framework in sleep science. Although dating back more than four decades, it has not lost its relevance for research today. Retracing its origins, I describe how animal experiments aimed at exploring the oscillators driving the circadian sleep–wake rhythm led to the recognition of gradients of sleep states within the daily sleep period. Advances in signal analysis revealed that the level of slow‐wave activity in non‐rapid eye movement sleep electroencephalogram is high at the beginning of the 12‐light period and then declines. After sleep deprivation, the level of slow‐wave activity is enhanced. By scheduling recovery sleep to the animal's activity period, the conflict between the sleep–wake‐dependent and the circadian influence resulted in a two‐stage recovery pattern. These experiments provided the basis for the first version of the two‐process model. Sleep deprivation experiments in humans showed that the decline of slow‐wave activity during sleep is exponential. The two‐process model posits that a sleep–wake‐dependent homeostatic process (Process S) interacts with a process controlled by the circadian pacemaker (Process C). At present, homeostatic and circadian facets of sleep regulation are being investigated at the synaptic level as well as in the transcriptome and proteome domains. The notion of sleep has been extended from a global phenomenon to local representations, while the master circadian pacemaker has been supplemented by multiple peripheral oscillators. The original interpretation that the emergence of sleep may be viewed as an escape from the rigid control imposed by the circadian pacemaker is still upheld.

Many faces of sleep regulation: beyond the time of day and prior wake time.

The two-process model of sleep regulation posits two main processes regulating sleep: the circadian process controlled by the circadian clock and the homeostatic process that depends on the history of sleep and wakefulness. The model has provided a dominant conceptual framework for sleep research since its publication ~ 40 years ago. The time of day and prior wake time are the primary factors affecting the circadian and homeostatic processes, respectively. However, it is critical to consider other factors influencing sleep. Since sleep is incompatible with other behaviors, it is affected by the need for essential behaviors such as eating, foraging, mating, caring for offspring, and avoiding predators. Sleep is also affected by sensory inputs, sickness, increased need for memory consolidation after learning, and other factors. Here, we review multiple factors influencing sleep and discuss recent insights into the mechanisms balancing competing needs.

Glymphatic Dysfunction: A Bridge Between Sleep Disturbance and Mood Disorders.

Mounting evidence demonstrates a close relationship between sleep disturbance and mood disorders, including major depression disorder (MDD) and bipolar disorder (BD). According to the classical two-process model of sleep regulation, circadian rhythms driven by the light–dark cycle, and sleep homeostasis modulated by the sleep–wake cycle are disrupted in mood disorders. However, the exact mechanism of interaction between sleep and mood disorders remains unclear. Recent discovery of the glymphatic system and its dynamic fluctuation with sleep provide a plausible explanation. The diurnal variation of the glymphatic circulation is dependent on the astrocytic activity and polarization of water channel protein aquaporin-4 (AQP4). Both animal and human studies have reported suppressed glymphatic transport, abnormal astrocytes, and depolarized AQP4 in mood disorders. In this study, the “glymphatic dysfunction” hypothesis which suggests that the dysfunctional glymphatic pathway serves as a bridge between sleep disturbance and mood disorders is proposed.

Homeostatic response to sleep restriction in adolescents.

The high prevalence of chronic sleep restriction in adolescents underscores the importance of understanding how adolescent sleep is regulated under such conditions. One component of sleep regulation is a homeostatic process: if sleep is restricted, then sleep intensity increases. Our knowledge of this process is primarily informed by total sleep deprivation studies and has been incorporated in mathematical models of human sleep regulation. Several animal studies, however, suggest that adaptation occurs in chronic sleep restriction conditions, showing an attenuated or even decreased homeostatic response. We investigated the homeostatic response of adolescents to different sleep opportunities. Thirty-four participants were allocated to one of three groups with 5, 7.5, or 10 h of sleep opportunity per night for five nights. Each group underwent a protocol of nine nights designed to mimic a school week between two weekends: two baseline nights (10 h sleep opportunity), five condition nights (5, 7.5, or 10 h), and two recovery nights (10 h). Measures of sleep homeostasis (slow-wave activity and slow-wave energy) were calculated from frontal and central EEG derivations and compared to predictions derived from simulations of the homeostatic process of the two-process model of sleep regulation. Only minor differences were found between empirical data and model predictions, indicating that sleep homeostasis is preserved under chronic sleep restriction in adolescents. These findings improve our understanding of effects of repetitive short sleep in adolescents.

Circadian Activity Rhythm in Early Relapsing-Remitting Multiple Sclerosis.

While relapsing–remitting is the most prevalent course of multiple sclerosis, the prognostic/predictive markers of the worsening of symptomatology are still debated. With reference to other diseases, the study of the circadian activity rhythm, according to the theoretical framework of the two-process model of sleep regulation and applying functional linear modeling, proved to be useful to identify a possible marker. The usefulness of the study of circadian activity rhythm in multiple sclerosis is strengthened by recent findings indicating a potential involvement of circadian factors in the multifactorial etiopathology of the disorder. The aim of the present study was to verify whether circadian activity rhythm of early relapsing–remitting multiple sclerosis patients presents specific alterations, through functional linear modeling. Thirty-five relapsing–remitting multiple sclerosis patients (24 females; mean age ± SD = 31.51 ± 7.74) and 35 healthy controls (24 females; mean age ± SD = 31.29 ± 8.02) were enrolled. They wore an actigraph around the non-dominant wrist for one week. Relapsing–remitting multiple sclerosis patients showed a peak in motor activity around 5:00 a.m., higher than that of healthy controls. The timing of the peak in motor activity in the patients could be explained according to the hyperactive hypothalamus-pituitary-adrenal axis and higher cortisol awakening response reported in these patients.

Gating and the Need for Sleep: Dissociable Effects of Adenosine A1 and A2A Receptors.

Roughly one-third of the human lifetime is spent in sleep, yet the reason for sleep remains unclear. Understanding the physiologic function of sleep is crucial toward establishing optimal health. Several proposed concepts address different aspects of sleep physiology, including humoral and circuit-based theories of sleep-wake regulation, the homeostatic two-process model of sleep regulation, the theory of sleep as a state of adaptive inactivity, and observations that arousal state and sleep homeostasis can be dissociated in pathologic disorders. Currently, there is no model that places the regulation of arousal and sleep homeostasis in a unified conceptual framework. Adenosine is well known as a somnogenic substance that affects normal sleep-wake patterns through several mechanisms in various brain locations via A1 or A2A receptors (A1Rs or A2ARs). Many cells and processes appear to play a role in modulating the extracellular concentration of adenosine at neuronal A1R or A2AR sites. Emerging evidence suggests that A1Rs and A2ARs have different roles in the regulation of sleep. In this review, we propose a model in which A2ARs allow the brain to sleep, i.e., these receptors provide sleep gating, whereas A1Rs modulate the function of sleep, i.e., these receptors are essential for the expression and resolution of sleep need. In this model, sleep is considered a brain state established in the absence of arousing inputs.

Circadian activity rhythm in adult attention-deficit hyperactivity disorder

The aim of the present study was to analyze the features of circadian motor activity rhythm of adult attention-deficit hyperactivity disorder (ADHD) patients, by means of functional linear modeling, within the theoretical framework of the two-process model of sleep regulation. Thirty-two ADHD patients and 32 healthy controls (HCs) participated the study. Actiwatch AW64 actigraph was used to quantify motor activity data in 1-min epochs. Participants wore the actigraph on the non-dominant wrist for seven consecutive days. Results show that ADHD patients had significantly higher motor activity than HCs from 4:00 to 7:00, with a peak around 5:00, and from 12:00 to 18:00, with another peak around 14:00. According to the two-process model of sleep regulation, the circadian activity rhythm of ADHD patients may indicate a lower homeostatic sleep pressure, as reflected by the absence of post-lunch dip, which could be considered a potential trait marker of adult ADHD.

0285 CHARACTERIZATION OF SLEEP NEED DISSIPATION USING EEG BASED SLOW-WAVE ACTIVITY ANALYSIS IN TWO AGE GROUPS

Introduction: In the two-process model of sleep regulation, slow-wave activity (SWA, EEG power in the 0.5–4 Hz band) is a direct indicator of sleep need. SWA builds up during NREM sleep, declines before the onset of REM sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. The sleep regulation model of Borbely et al, 1999, states that the rate of decrease in sleep need S(t) is proportional to SWA, i.e. dS(t)/dt =-γSWA(t) where γ is the decay rate. The sleep need after T minutes of sleep is: S(T)=S0-γCSWA(T), where S0 is the sleep need at sleep-onset and CSWA(T) is the integral of SWA from sleep onset to time T. The goal in this research is to assess the effect of age on the dynamics of sleep need dissipation. Methods: Sleep EEG and EOG data were collected from 25 subjects (10M and 15F; 37.1 ± 6.5 years old) for 3 nights at home. The data was manually scored into sleep stages according to AASM rules. SWA was calculated for each 6-second epoch of NREM sleep. In this model, sleep need dissipation is completely determined by S0 and γ. To estimate these, two boundary conditions were used: 1) the final value of S(t) is 0, and 2) S(t*) coincides with SWA(t*) at time t* where SWA is maximum in the first sleep cycle. Results: Two age groups were defined using as threshold the median age (38) of subjects in the study. S0 did not significantly differ between groups while γ was significantly different (γ group1=2.82 and γ group2=2.12; p=0.006). These results suggest that the efficiency of sleep need dissipation significantly decreases in the older group by 25%. Conclusion: The rate of sleep need dissipation is proportional to slow wave activity. The proportionality coefficient γ (decay rate) can be estimated using a differential model. It was found that γ is significantly lower, by 25%, in the age group 38 to 47 as compared to the age group 22 to 38. The initial sleep need did not differ between groups.

Circadian pattern of motor activity in adults with attention-deficit/hyperactivity disorder

ABSTRACTThis study aims to describe the 24-hour activity rhythm in adults with attention-deficit/hyperactivity disorder (ADHD). A total of 18 ADHD patients and 37 healthy controls (HCs) wore an actigraph for 7 days. ADHD patients showed higher motor activity than HCs at 4:00, 6:00, 15:00 and 16:00 hour. Within the theoretical framework of the two-process model of sleep regulation, the observed data may be explained by lower homeostatic sleep pressure in ADHD. This could lead to an increase in motor activity in the second half of the night, when sleep need decreases more rapidly, and in the first half of the afternoon, when patients do not experience the typical post-lunch dip.

The two-process model of sleep regulation: a reappraisal.

In the last three decades the two-process model of sleep regulation has served as a major conceptual framework in sleep research. It has been applied widely in studies on fatigue and performance and to dissect individual differences in sleep regulation. The model posits that a homeostatic process (Process S) interacts with a process controlled by the circadian pacemaker (Process C), with time-courses derived from physiological and behavioural variables. The model simulates successfully the timing and intensity of sleep in diverse experimental protocols. Electrophysiological recordings from the suprachiasmatic nuclei (SCN) suggest that S and C interact continuously. Oscillators outside the SCN that are linked to energy metabolism are evident in SCN-lesioned arrhythmic animals subjected to restricted feeding or methamphetamine administration, as well as in human subjects during internal desynchronization. In intact animals these peripheral oscillators may dissociate from the central pacemaker rhythm. A sleep/fast and wake/feed phase segregate antagonistic anabolic and catabolic metabolic processes in peripheral tissues. A deficiency of Process S was proposed to account for both depressive sleep disturbances and the antidepressant effect of sleep deprivation. The model supported the development of novel non-pharmacological treatment paradigms in psychiatry, based on manipulating circadian phase, sleep and light exposure. In conclusion, the model remains conceptually useful for promoting the integration of sleep and circadian rhythm research. Sleep appears to have not only a short-term, use-dependent function; it also serves to enforce rest and fasting, thereby supporting the optimization of metabolic processes at the appropriate phase of the 24-h cycle.

Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

The Zurich 3-step concept for the management of behavioral sleep disorders in children: a before-and-after study.

Several strategies have been found to be effective for the treatment of childhood behavioral sleep disorders. One which has yet to be evaluated is the Zurich 3-step concept, which combines basic notions of the two-process model of sleep regulation (introducing a regular rhythm and adjusting bedtime to sleep need) with behavioral strategies. This uncontrolled before-and-after study describes our concept and its step-wise approach, assesses changes in sleep-wake variables and behavior problems, and also examines associations between changes in sleep-wake variables and behavior problems. A total of 79 children with sleep problems (age range 6-47 months, 42% females) were included. Sleep problems were assessed by the Infant Sleep Questionnaire, sleep-wake variables by diary and actigraphy, and behavior problems of children ≥ 18 months by the Child Behavior Checklist. A significant decrease in nocturnal wake duration (Cohen's d = -0.34) and a significant increase in the duration of the longest continuous nocturnal sleep period (Cohen's d = 0.19) were found from before to after intervention (on average 2.7 months, SD 1.5). The variability for sleep onset and end time decreased, and actigraphically measured circadian rest-activity cycle measures improved. Parent-reported internalizing and total behavior problems also decreased (Cohen's d = 0.66). The findings of both objective and subjective assessment techniques suggest that the Zurich 3-step concept is effective. Thus, the intervention concept may be useful in clinical practice with sleep-disordered children.

Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation

Caffeine is the most widely consumed stimulant to counter sleep-loss effects. While the pharmacokinetics of caffeine in the body is well-understood, its alertness-restoring effects are still not well characterized. In fact, mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available. In this paper, we describe a phenomenological model of the dose-dependent effects of caffeine on psychomotor vigilance task (PVT) performance of sleep-deprived subjects. We used the two-process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose-dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses. We developed and validated the model fits and predictions on PVT lapse (number of reaction times exceeding 500ms) data from two separate laboratory studies. At the population-average level, the model captured the effects of a range of caffeine doses (50–300mg), yielding up to a 90% improvement over the two-process model. Individual-specific caffeine models, on average, predicted the effects up to 23% better than population-average caffeine models. The proposed model serves as a useful tool for predicting the dose-dependent effects of caffeine on the PVT performance of sleep-deprived subjects and, therefore, can be used for determining caffeine doses that optimize the timing and duration of peak performance.

A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation

Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation (TSD). However, during sleep restriction (partial sleep loss) performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction (CSR) rather than by episodic TSD, the practical utility of this class of models has been limited.To better quantify performance during both CSR and TSD, we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individual's capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models, and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore, this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking.

Circadian amplitude and homeostatic buildup rate in postural control

Postural control during quiet stance is a common everyday physical activity. Sleepiness is increasingly prevalent in our 24-h society. Yet, little research exists that quantitatively links the fluctuations in sleepiness and postural control. This study quantifies the circadian amplitude and homeostatic buildup rate in postural control. With a force plate we assessed postural control in 12 participants (21–38 years) every 2h during 24h of sustained wakefulness. The sway area was 1.39±0.71mm2 at the circadian high around noon, and 4.02±0.67mm2 at the circadian low around 6 am (a 189% change, p=0.02). The circadian amplitude of the sway area was therefore 2.63mm2. The sway area was 1.92±0.64mm2 at the start of the 24-h period and 4.42±0.69mm2 at the end of the period (a 130% change, p<0.001). The homeostatic buildup rate of sway area was 0.04h−1. The circadian- and homeostatic effects on sway variability, sway velocity, sway frequency and fractal dimension were smaller but still significant. This study found that the circadian amplitude and homeostatic buildup rate are quantifiable from posturographic data, and that they have significant impact on postural control. This finding is important because it means that one could apply the framework of the famous two-process model of sleep regulation (published by Borbély in 1982) to explain the previously reported sleepiness-related changes in postural control.