Markov modeling of sleep stage transitions and ultradian REM sleep rhythm

Increased training load promotes sleep propensity and slow-wave sleep in endurance runners: Can a high-heat-capacity mattress topper modulate this effect?

The present study aimed to: (1) investigate sleep architecture in response to an overload training and taper periods among endurance runners; and (2) assess the sleep benefits of a high-heat-capacity mattress topper. Twenty-one trained male endurance runners performed a 2-week usual training regimen (baseline) followed by 2-week overload and taper periods. From overload to the end of the taper period, they were assigned into two groups based on the mattress topper used: high-heat-capacity mattress topper (n = 11) or low-heat-capacity mattress topper (n = 10). Training load was assessed daily using the session rating of perceived exertion. Following each period, sleep was monitored by polysomnography, and nocturnal core body temperature was recorded throughout the night. Irrespective of the group, awakening episodes > 5 min decreased following overload compared with baseline (-0.48, p = 0.05). Independently of mattress topper, each 100 A.U. increase in 7-day training load prior to polysomnographic recording was associated with higher slow-wave sleep proportion (β = +0.13%; p = 0.05), lower sleep-onset latency (β = -0.49 min; p = 0.05), and a reduction in the probability of transition from N1 sleep stage to wakefulness (β = -0.12%; p = 0.05). Sleeping on a high-heat-capacity mattress topper did not affect any sleep variable compared with a low-heat-capacity mattress topper. Increased training loads promote slow-wave sleep and sleep propensity, highlighting the adaptative nature of sleep to diurnal activity and the role of sleep in physiological recovery. Further studies are required on the potential benefits of high-heat-capacity mattress toppers on sleep architecture among athletes.

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.

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.

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.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

BACKGROUND

It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown.

OBJECTIVE

This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy.

METHODS

A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors.

RESULTS

Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.

CONCLUSIONS

Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.