[Detection and Application of Allostatic Load through Internet of Things Measurements].

Allostatic load refers to a vulnerable state of the nervous system caused by chronic or repeated exposure to challenges of daily life (e.g., psychological stressors) and considered to indicate risk of a transition to pathological state. In this paper, we first introduce a traditional method of assessing allostatic load that utilizes multiple biomarkers. Next, we demonstrate the potential of the Internet of Things (IoT) data sampling method to detect and control the vulnerable state in daily life.

Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults.

Introduction: Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology. Methods: We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00-10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure - specifically sleep stage transitions and sleep cycle characteristics - and intraclass correlation coefficients across nights. Results: NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium. Discussion: Our findings are consistent with a model for the underlying mechanisms that involves three subsystems - characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions - with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.

The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System.

BACKGROUND: Sleep is beneficial for physical and mental health. Several mobile and wearable sleep-tracking devices have been developed, and personalized sleep feedback is the most common functionality among these devices. To date, no study has implemented an objective push-type feedback message and investigated the characteristics of habitual sleep behavior and diurnal symptoms when receiving sleep feedback. OBJECTIVE: We conducted a mobile health intervention trial to examine whether sending objective push-type sleep feedback changes the self-reported mood, physical symptoms, and sleep behavior of Japanese office workers. METHODS: In total, 31 office workers (mean age 42.3, SD 7.9 years; male-to-female ratio 21:10) participated in a 2-arm intervention trial from November 30 to December 19, 2020. The participants were instructed to indicate their momentary mood and physical symptoms (depressive mood, anxiety, stress, sleepiness, fatigue, and neck and shoulder stiffness) 5 times a day using a smartphone app. In addition, daily work performance was rated once a day after work. They were randomly assigned to either a feedback or control group, wherein they did or did not receive messages about their sleep status on the app every morning, respectively. All participants wore activity monitors on their nondominant wrists, through which objective sleep data were registered on the web on a server. On the basis of the estimated sleep data on the server, personalized sleep feedback messages were generated and sent to the participants in the feedback group using the app. These processes were fully automated. RESULTS: Using hierarchical statistical models, we examined the differences in the statistical properties of sleep variables (sleep duration and midpoint of sleep) and daily work performance over the trial period. Group differences in the diurnal slopes for mood and physical symptoms were examined using a linear mixed effect model. We found a significant group difference among within-individual residuals at the midpoint of sleep (expected a posteriori for the difference: -15, 95% credible interval -26 to -4 min), suggesting more stable sleep timing in the feedback group. However, there were no significant group differences in daily work performance. We also found significant group differences in the diurnal slopes for sleepiness (P<.001), fatigue (P=.002), and neck and shoulder stiffness (P<.001), which was largely due to better scores in the feedback group at wake-up time relative to those in the control group. CONCLUSIONS: This is the first mobile health study to demonstrate that objective push-type sleep feedback improves sleep timing of and physical symptoms in healthy office workers. Future research should incorporate specific behavioral instructions intended to improve sleep habits and examine the effectiveness of these instructions.

Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach.

The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.

Discrepancies in the Time Course of Sleep Stage Dynamics, Electroencephalographic Activity and Heart Rate Variability Over Sleep Cycles in the Adaptation Night in Healthy Young Adults.

OBJECTIVE: The aim of the present study was to characterize the cyclic sleep processes of sleep-stage dynamics, cortical activity, and heart rate variability during sleep in the adaptation night in healthy young adults. METHODS: Seventy-four healthy adults participated in polysomnographic recordings on two consecutive nights. Conventional sleep variables were assessed according to standard criteria. Sleep-stage continuity and dynamics were evaluated by sleep runs and transitions, respectively. These variables were compared between the two nights. Electroencephalographic and cardiac activities were subjected to frequency domain analyses. Cycle-by-cycle analysis was performed for the above variables in 34 subjects with four sleep cycles and compared between the two nights. RESULTS: Conventional sleep variables reflected lower sleep quality in the adaptation night than in the experimental night. Bouts of stage N1 and stage N2 were shorter, and bouts of stage Wake were longer in the adaptation night than in the experimental night, but there was no difference in stage N3 or stage REM. The normalized transition probability from stage N2 to stage N1 was higher and that from stage N2 to N3 was lower in the adaptation night, whereas that from stage N3 to other stages did not differ between the nights. Cycle-by-cycle analysis revealed that sleep-stage distribution and cortical beta EEG power differed between the two nights in the first sleep cycle. However, the HF amplitude of the heart rate variability was lower over the four sleep cycles in the adaptation night than in the experimental night. CONCLUSION: The results suggest the distinct vulnerability of the autonomic adaptation processes within the central nervous system in young healthy subjects while sleeping in a sleep laboratory for the first time.

Association of depressive symptoms with habitual sleep duration and sleep timing in junior high school students.

This study aims to investigate independent associations of habitual sleep durations and sleep timings on weekdays and weekends with depressive symptoms in adolescents who have classes in the morning. We studied grade 7-9 students (942 males and 940 females, aged 12-15 years), who had classes in the morning, at public junior high schools in Japan in a cross-sectional design. The students answered a self-report questionnaire, which covers habitual sleep durations, bedtimes and wake-up times on weekdays and weekends, and depressive symptoms. The Short Mood and Feelings Questionnaire (SMFQ) was used to determine the level of depressive symptoms. The relationship between the variables on sleep habits and the SMFQ score were studied using multivariate linear regression and generalized additive models (GAM), controlling for sex, age and school. Multivariate linear regression analysis revealed that sleep duration on weekdays and relative mid-sleep time on weekdays (i.e. mid-sleep time on weekdays - mid-sleep time on weekends) were independently significantly (p < .001) associated with the SMFQ score. GAM analysis also revealed that sleep duration on weekdays (a reverse J-shaped relationship) and the relative mid-sleep time on weekdays (a negative monotonic/linear relationship) were independently significantly (p < .001) associated with the SMFQ score. These associations were confirmed in both males and females when they were analyzed separately. These results suggest that sleep duration on weekdays and the relative mid-sleep time on weekdays may be independently associated with the level of depressive symptoms in junior high school students who have classes in the morning. These findings may have important implications for the development of novel strategies for preventing mental health problems in adolescents.

Sleep stage dynamics in young patients with sleep bruxism.

STUDY OBJECTIVES: We hypothesized that sleep stage dynamics are different in patients with sleep bruxism (SB) and that these changes are associated with the occurrence of rhythmic masticatory muscle activity (RMMA). METHODS: Fifteen healthy controls and 15 patients with SB underwent overnight polysomnography. Sleep variables and survival curves of continuous runs of each sleep stage were compared between the groups. Stage transition dynamics and the probability of stage fragmentation were analyzed for three epochs before and after the epoch with RMMA. Survival curves of continuous runs of each sleep stage, terminated with or without RMMA, were also compared. RESULTS: There were no significant differences in sleep variables between the groups, except for shorter sleep latency, shorter rapid eye movement (REM) latency, and longer total N1 duration in SB patients than in controls. REM sleep and N2 were significantly less continuous in SB patients than in controls. In the SB group, stage fragmentation probability was significantly increased for the epoch with RMMA compared with the baseline for all stages. Meanwhile, the occurrence of RMMA did not affect the continuity of N2 or REM; however, the occurrence of RMMA was preceded by more continuous N3 runs. CONCLUSIONS: Sleep stage dynamics differed between SB patients and controls. RMMA does not result in sleep disruption but is likely associated with dissipation of sleep pressure. Less continuity of REM sleep in SB may provide insights into the underlying pathophysiological mechanisms of SB, which may be related to REM sleep processes such as cortical desynchronized states or brainstem activation.

Markov modeling of sleep stage transitions and ultradian REM sleep rhythm.

OBJECTIVE: One of the highly characteristic features of sleep is the cyclic occurrence of non-rapid eye movement (NREM) and REM sleep, which is referred to as the ultradian rhythm of sleep. Even though REM sleep was discovered over half a century ago, surprisingly, the mechanism of the ultradian REM sleep rhythm has not yet been fully elucidated. In the present study, we aim to provide a mechanistic insight into the generation of the ultradian REM sleep rhythm. Approach and Main results: By simulating hypnograms with the dynamic features of sleep stage transitions, i.e. stage transition probabilities and stage-specific survival time functions, we show that the second-order Markov transition probabilities and the stage-specific survival time functions can reproduce the central position (∼90 min) of the REM-onset intervals (ROIs), but with a larger variance in distribution. In addition, we demonstrate the direct effect of the increased probability of the transitions from light to deep sleep within NREM sleep on the prolongation of the ROIs in a dose-response manner. SIGNIFICANCE: These results suggest that dynamic sleep stage transitions constitute the basis of the formation of the ultradian rhythm of sleep; however, further elaboration of the model would be required to reduce the variability in rhythmicity.

A Robust Method with High Time Resolution for Estimating the Cortico-Thalamo-Cortical Loop Strength and the Delay when Using a Scalp Electroencephalography Applied to the Wake-Sleep Transition.

OBJECTIVES: This study aimed to describe a robust method with high time resolution for estimating the cortico-thalamo-cortical (CTC) loop strength and the delay when using a scalp electroencephalography (EEG) and to illustrate its applicability for analyzing the wake-sleep transition. METHODS: The basic framework for the proposed method is the parallel use of a physiological model and a parametric phenomenological model: a neural field theory (NFT) of the corticothalamic system and an autoregressive (AR) model. The AR model is a "stochastic" model that shortens the time taken to extract spectral features and is also a "linear" model that is free from the local-minimum problem. From the relationship between the transfer function of the AR model and the transfer function of the NFT in the low frequency limit, we successfully derived a direct expression of CTC loop strength and the loop delay using AR coefficients. RESULTS: Using this method to analyze sleep-EEG data, we were able to clearly track the wake-to-sleep transition, as the estimated CTC loop strength (c2) decreased to almost zero. We also found that the c2-distribution during nocturnal sleep is clearly bimodal in nature, which can be well approximated by the superposition of two Gaussian distributions that correspond to sleep and wake states, respectively. The estimated loop delay distributed ∼0.08 s, which agrees well with the previously reported value estimated by other methods, confirming the validity of our method. CONCLUSIONS: A robust method with high time resolution was developed for estimating the cortico-thalamo-cortical loop strength and the delay when using a scalp electroencephalography. This method can contribute not only to detecting the wake-sleep transition, but also to further understanding of the transition, where the cortico-thalamo-cortical loop is thought to play an important role.

Sleep continuity is positively correlated with sleep duration in laboratory nighttime sleep recordings.

Sleep duration varies widely across individuals and appears to be trait-like. Differences in the stability of underlying sleep processes may underlie this phenomenon. To investigate underlying mechanisms, we examined the relationship between sleep duration and sleep continuity in baseline polysomnography (PSG) recordings from three independently collected datasets: 1) 134 healthy controls (ages 37 ± 13 years) from the São Paulo Epidemiologic Sleep Study, who spent one night in a sleep laboratory, 2) 21 obstructive sleep apnea (OSA) patients who were treated with continuous positive airway pressure for at least 2 months (45 ± 12 years, respiratory disturbance index <15), who spent one night in a sleep laboratory with previous experience of multiple PSG studies, and 3) 62 healthy controls (28 ± 6 years) who, as part of larger experiments, spent 2 consecutive nights in a sleep laboratory. For each dataset, we used total sleep time (TST) to separate subjects into those with shorter sleep (S-TST) and those with longer sleep (L-TST). In all three datasets, survival curves of continuous sleep segments showed greater sleep continuity in L-TST than in S-TST. Correlation analyses with TST as a continuous variable corroborated the results; and the results also held true after controlling for age. There were no significant differences in baseline waking performance and sleepiness between S-TST and L-TST. In conclusion, in both healthy controls and treated OSA patients, sleep continuity was positively correlated with sleep duration. These findings suggest that S-TST may differ from L-TST in processes underlying sleep continuity, shedding new light on mechanisms underlying individual differences in sleep duration.

Reduced Slow-Wave Sleep Is Associated with High Cerebrospinal Fluid Aß42 Levels in Cognitively Normal Elderly.

STUDY OBJECTIVES: Emerging evidence suggests a role for sleep in contributing to the progression of Alzheimer disease (AD). Slow wave sleep (SWS) is the stage during which synaptic activity is minimal and clearance of neuronal metabolites is high, making it an ideal state to regulate levels of amyloid beta (Aß). We thus aimed to examine relationships between concentrations of Aß42 in the cerebrospinal fluid (CSF) and measures of SWS in cognitively normal elderly subjects. METHODS: Thirty-six subjects underwent a clinical and cognitive assessment, a structural MRI, a morning to early afternoon lumbar puncture, and nocturnal polysomnography. Correlations and linear regression analyses were used to assess for associations between CSF Aß42 levels and measures of SWS controlling for potential confounders. Resulting models were compared to each other using ordinary least squared linear regression analysis. Additionally, the participant sample was dichotomized into "high" and "low" Aß42 groups to compare SWS bout length using survival analyses. RESULTS: A significant inverse correlation was found between CSF Aß42 levels, SWS duration and other SWS characteristics. Collectively, total SWA in the frontal lead was the best predictor of reduced CSF Aß42 levels when controlling for age and ApoE status. Total sleep time, time spent in NREM1, NREM2, or REM sleep were not correlated with CSF Aß42. CONCLUSIONS: In cognitively normal elderly, reduced and fragmented SWS is associated with increases in CSF Aß42, suggesting that disturbed sleep might drive an increase in soluble brain Aß levels prior to amyloid deposition.

Effects of aging on slow-wave sleep dynamics and human spatial navigational memory consolidation.

The consolidation of spatial navigational memory during sleep is supported by electrophysiological and behavioral evidence. The features of sleep that mediate this ability may change with aging, as percentage of slow-wave sleep is canonically thought to decrease with age, and slow waves are thought to help orchestrate hippocampal-neocortical dialog that supports systems level consolidation. In this study, groups of younger and older subjects performed timed trials before and after polysomnographically recorded sleep on a 3D spatial maze navigational task. Although younger subjects performed better than older subjects at baseline, both groups showed similar improvement across presleep trials. However, younger subjects experienced significant improvement in maze performance during sleep that was not observed in older subjects, without differences in morning psychomotor vigilance between groups. Older subjects had sleep quality marked by decreased amount of slow-wave sleep and increased fragmentation of slow-wave sleep, resulting in decreased slow-wave activity. Across all subjects, frontal slow-wave activity was positively correlated with both overnight change in maze performance and medial prefrontal cortical volume, illuminating a potential neuroanatomical substrate for slow-wave activity changes with aging and underscoring the importance of slow-wave activity in sleep-dependent spatial navigational memory consolidation.

Apnea-induced rapid eye movement sleep disruption impairs human spatial navigational memory.

Hippocampal electrophysiology and behavioral evidence support a role for sleep in spatial navigational memory, but the role of particular sleep stages is less clear. Although rodent models suggest the importance of rapid eye movement (REM) sleep in spatial navigational memory, a similar role for REM sleep has never been examined in humans. We recruited subjects with severe obstructive sleep apnea (OSA) who were well treated and adherent with continuous positive airway pressure (CPAP). Restricting CPAP withdrawal to REM through real-time monitoring of the polysomnogram provides a novel way of addressing the role of REM sleep in spatial navigational memory with a physiologically relevant stimulus. Individuals spent two different nights in the laboratory, during which subjects performed timed trials before and after sleep on one of two unique 3D spatial mazes. One night of sleep was normally consolidated with use of therapeutic CPAP throughout, whereas on the other night, CPAP was reduced only in REM sleep, allowing REM OSA to recur. REM disruption via this method caused REM sleep reduction and significantly fragmented any remaining REM sleep without affecting total sleep time, sleep efficiency, or slow-wave sleep. We observed improvements in maze performance after a night of normal sleep that were significantly attenuated after a night of REM disruption without changes in psychomotor vigilance. Furthermore, the improvement in maze completion time significantly positively correlated with the mean REM run duration across both sleep conditions. In conclusion, we demonstrate a novel role for REM sleep in human memory formation and highlight a significant cognitive consequence of OSA.

The effects of exercise on dynamic sleep morphology in healthy controls and patients with chronic fatigue syndrome.

Effects of exercise on dynamic aspects of sleep have not been studied. We hypothesized exercise altered dynamic sleep morphology differently for healthy controls relative to chronic fatigue syndrome (CFS) patients. Sixteen controls (38 ± 9 years) and 17 CFS patients (41 ± 8 years) underwent polysomnography on baseline nights and nights after maximal exercise testing. We calculated transition probabilities and rates (as a measure of relative and temporal transition frequency, respectively) between sleep stages and cumulative duration distributions (as a measure of continuity) of each sleep stage and sleep as a whole. After exercise, controls showed a significantly greater probability of transition from N1 to N2 and a lower rate of transition from N1 to wake than at baseline; CFS showed a significantly greater probability of transition from N2 to N3 and a lower rate of transition from N2 to N1. These findings suggest improved quality of sleep after exercise. After exercise, controls had improved sleep continuity, whereas CFS had less continuous N1 and more continuous rapid eye movement (REM) sleep. However, CFS had a significantly greater probability and rate of transition from REM to wake than controls. Probability of transition from REM to wake correlated significantly with increases in subjective fatigue, pain, and sleepiness overnight in CFS - suggesting these transitions may relate to patient complaints of unrefreshing sleep. Thus, exercise promoted transitions to deeper sleep stages and inhibited transitions to lighter sleep stages for controls and CFS, but CFS also reported increased fatigue and continued to have REM sleep disruption. This dissociation suggests possible mechanistic pathways for the underlying pathology of CFS.

Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia.

STUDY OBJECTIVES: Chronic fatigue syndrome (CFS) and fibromyalgia (FM) are medically unexplained conditions that often have overlapping symptoms, including sleep-related complaints. However, differences between the 2 conditions have been reported, and we hypothesized that dynamic aspects of sleep would be different in the 2 groups of patients. PARTICIPANTS: Subjects were 26 healthy control subjects, 14 patients with CFS but without FM (CFS alone), and 12 patients with CFS and FM (CFS+FM)-all women. MEASUREMENTS AND RESULTS: We studied transition probabilities and rates between sleep stages (waking, rapid eye movement [REM] sleep, stage 1 [S1], stage 2 [S2], and slow-wave sleep [SWS]) and duration distributions of each sleep stage. We found that the probability of transition from REM sleep to waking was significantly greater in subjects with CFS alone than in control subjects, which may be the specific sleep problem for people with CFS alone. Probabilities of (a) transitions from waking, REM sleep, and S1 to S2 and (b) those from SWS to waking and S1 were significantly greater in subjects with CFS+FM than in control subjects; in addition, rates of these transitions were also significantly increased in subjects with CFS+FM. Result (a) might indicate increased sleep pressure in subjects with CFS+FM whereas result (b) may be the specific sleep problem of subjects with CFS+FM. We also found that shorter durations of S2 sleep are specific to patients with CFS+FM, not to CFS alone. CONCLUSIONS: These results suggest that CFS and FM may be different illnesses associated with different problems of sleep regulation.

NREM sleep stage transitions control ultradian REM sleep rhythm.

STUDY OBJECTIVES: The cyclic sequence of NREM and REM sleep, the so-called ultradian rhythm, is a highly characteristic feature of sleep. However, the mechanisms responsible for the ultradian REM sleep rhythm, particularly in humans, have not to date been fully elucidated. We hypothesize that a stage transition mechanism is involved in the determination of the ultradian REM sleep rhythm. PARTICIPANTS: Ten healthy young male volunteers (AGE: 22 ± 4 years, range 19-31 years) spent 3 nights in a sleep laboratory. The first was the adaptation night, and the second was the baseline night. On the third night, the subjects received risperidone (1 mg tablet), a central serotonergic and dopaminergic antagonist, 30 min before the polysomnography recording. MEASUREMENTS AND RESULTS: We measured and investigated transition probabilities between waking, REM, and NREM sleep stages (N1, N2, and N3) within the REM-onset intervals, defined as the intervals between the onset of one REM period and the beginning of the next, altered by risperidone. We also calculated the transition intensity (i.e., instantaneous transition rate) and examined the temporal pattern of transitions within the altered REM-onset intervals. We found that when the REM-onset interval was prolonged by risperidone, the probability of transitions from N2 to N3 was significantly increased within the same prolonged interval, with a significant delay and/or recurrences of the peak intensity of transitions from N2 to N3. CONCLUSIONS: These results suggest that the mechanism governing NREM sleep stage transitions (from light to deep sleep) plays an important role in determining ultradian REM sleep rhythms.

Sleep stage transitions in chronic fatigue syndrome patients with or without fibromyalgia.

Chronic fatigue syndrome (CFS) and fibromyalgia (FM) are medically unexplained conditions that share considerable overlapping symptoms, including sleep-related complaints. However, differences between the two conditions have been reported, and we hypothesized that dynamic aspects of sleep, recently attracting scientific interests, would be different in the two groups of patients. We thus study transition probabilities between sleep stages of CFS patients with or without FM. Subjects were 26 healthy controls, 14 CFS patients without FM (CFS alone) and 12 CFS patients with FM (CFS+FM) - all women. We studied transition probabilities between sleep stages (waking, REM sleep and Stage I, Stage II and slow-wave sleep (Stage III+IV)). We found that probabilities of transition from REM sleep to waking were significantly greater in CFS alone than in controls; we have reported previously this sleep disruption as the specific sleep problem for CFS alone [Kishi et al., 2008]. Probabilities of transitions from waking, REM sleep and Stage I to Stage II, and those from slow-wave sleep to Stage I, were significantly greater in CFS+FM than in controls; the former might indicate increased sleep pressure in CFS+FM and the latter may be the specific sleep problem of CFS+FM. These results suggest that CFS and FM are different illnesses associated with different problems of sleep regulation.

Dynamics of sleep stage transitions in healthy humans and patients with chronic fatigue syndrome.

Physiological and/or pathological implications of the dynamics of sleep stage transitions have not, to date, been investigated. We report detailed duration and transition statistics between sleep stages in healthy subjects and in others with chronic fatigue syndrome (CFS); in addition, we also compare our data with previously published results for rats. Twenty-two healthy females and 22 female patients with CFS, characterized by complaints of unrefreshing sleep, underwent one night of polysomnographic recording. We find that duration of deep sleep (stages III and IV) follows a power-law probability distribution function; in contrast, stage II sleep durations follow a stretched exponential and stage I, and REM sleep durations follow an exponential function. These stage duration distributions show a gradually increasing departure from the exponential form with increasing depth of sleep toward a power-law type distribution for deep sleep, suggesting increasing complexity of regulation of deeper sleep stages. We also find a substantial number of REM to non-REM sleep transitions in humans, while this transition is reported to be virtually nonexistent in rats. The relative frequency of this REM to non-REM sleep transition is significantly lower in CFS patients than in controls, resulting in a significantly greater relative transition frequency of moving from both REM and stage I sleep to awake. Such an alteration in the transition pattern suggests that the normal continuation of sleep in light or REM sleep is disrupted in CFS. We conclude that dynamic transition analysis of sleep stages is useful for elucidating yet-to-be-determined human sleep regulation mechanisms with pathophysiological implications.