Beneficial Effects of an Online Mindfulness-Based Intervention on Sleep Quality in Italian Poor Sleepers during the COVID-19 Pandemic: A Randomized Trial.

Sleep of inadequate quantity and quality is increasing in the present 24 h society, with a negative impact on physical and mental health. Mindfulness-based interventions (MBIs) generate a state of calm behavior that can reduce hyperactivity and improve sleep. We hypothesized that our specific MBI, administered online, may improve sleep quality and foster emotion regulation and mindfulness. The Pittsburgh Sleep Quality Index (PSQI), Sleep Condition Indicator (SCI), Arousal Predisposition Scale (APS), Ford Insomnia Response to Stress Test (FIRST), Sleep Hygiene Index (SHI) and Insomnia Severity Index (ISI) were used to measure sleep quality and stability. Emotion regulation and mindfulness were measured via the Emotion Regulation Questionnaire (ERQ) and Five Facet Mindfulness Questionnaire (FFMQ). Our MBI included 12 biweekly integral meditation (IM) classes, recorded IM training for individual practice, and dietary advice to promote sleep regulation. Fifty-six voluntary poor sleepers with a PSQI score of >5 were randomly allocated to treated (n = 28) and control (n = 28) groups. Linear mixed models were used to estimate the effectiveness of the intervention. Statistically significant results were observed in the FFMQ sub-domain non-reactivity to inner experience (ß = 0.29 [0.06; -0.52], p = 0.01), PSQI (ß = -1.93 [-3.43; -0.43], p = 0.01), SCI (ß = 3.39 [0.66; 6.13], p = 0.02) and ISI (ß = -3.50 [-5.86; -1.14], p = 0.004). These results confirm our hypothesis regarding the beneficial effects of our intervention on sleep quality.

Sleep Continuity, Stability and Cyclic Organization Are Impaired in Insomniacs: A Case-Control Study.

The possibility of distinguishing insomniacs from good sleepers based on polysomnography (PSG) remains an open question. While these groups show modest differences in traditional PSG parameters, some studies suggest that finer measures may be more useful. Here we assess differences between good sleepers (GS), poor sleepers (PS) and insomniacs (IN) in classical PSG measures as well as in sleep continuity, stability and cyclic organization. PSG-monitored sleep (two nights) of 17 IN (diagnosed through a standard clinical interview; Pittsburgh Sleep Quality Index (PSQI) ≥ 5, Insomnia Severity Index (ISI) > 14) was compared to that of 33 GS (PSQI < 5) and 20 PS (PSQI ≥ 5, ISI ≤ 14). Compared to GS, IN were impaired in sleep macrostructure (sleep latency, sleep efficiency, WASO%) and in continuity, stability and organization, whereas PS only showed disrupted continuity and stability. Spindle parameters were comparable between IN and GS, but the former displayed enhanced power in fast frequency bands. Our findings support the hypothesis of a continuum between individuals with self-reported poor sleep and insomniacs. Further, they add to extant data on impaired sleep continuity, stability and organization in poor sleepers and elderly individuals, underlining the utility of including these measures in standard sleep assessments.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern.

STUDY OBJECTIVES: Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep. METHODS: Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate. RESULTS: Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm. CONCLUSIONS: Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

Sleep stability in isolated rapid eye movement sleep behavior disorder, Parkinson's disease, and dementia with Lewy bodies.

BACKGROUND: Non-REM sleep symptoms remain poorly understood in alpha-synucleinopathies. AIMS: The aims of the study were to compare sleep stability and transitions, arousals, and sleep cycle structure between isolated rapid eye movement (REM) sleep behavior disorder (iRBD), Parkinson's disease (PD), and dementia with Lewy Bodies (DLB). MATERIALS AND METHODS: Sleep transition and stability measures were assessed in one-night video-polysomnography records. Transition measures were the number of shifts between Wake and REM, Wake and NREM, and REM and NREM. Stability measures were the number of passages within the same sleep stage. We assessed arousals, the number/duration of sleep cycles (defined as a sequence of any NREM stage to REM), and the duration of N3 and REM sleep in each cycle. These variables were compared between two sets of groups (PD vs. DLB vs. iRBD and RDB+ vs. RBD-). RESULTS: We assessed 54 PD, 24 DLB, and 21 iRBD patients (54 RBD+, 22 RBD-). There were no significant differences regarding sleep stability measures. Arousal indices in N1 and N2 stages were significantly higher in PD compared with iRBD. 24% of the sample did not have any sleep cycle. PD had significantly fewer cycles than iRBD. Differences became non-significant when adjusting for medication. There was no effect of group or time of night in REM or N3 duration. There were no significant differences between RBD+ and RBD-. DISCUSSION: There were no significant differences in stability/transition measures. Arousals and disturbance in sleep cycling were higher in PD, but the difference was no longer significant after adjusting for medication. CONCLUSION: Different alpha-synucleinopathies have a similar degree of non-REM sleep instability, but medication could worsen symptoms in PD.

Cognitive deficit is correlated with sleep stability in insomnia: A cardiopulmonary coupling study.

OBJECTIVES: To assess the correlation of cognitive function with sleep stability and depressive-anxious symptoms in insomnia patients. METHODS: Twenty-two insomnia patients with cognitive impairment (insomnia-CI), 21 insomnia patients with normal cognition (insomnia-CN), and 15 matched healthy control subjects (HCs) were enrolled and completed neuropsychological tests, the Hamilton Depression and Anxiety Scales (HAMD and HAMA), the Epworth Sleepiness Scale, the Pittsburgh Sleep Quality Index (PSQI),the Insomnia Severity Index (ISI), and the cardiopulmonary coupling (CPC) examination. Ratios of high-frequency coupling (HFC), low-frequency coupling (LFC), and very low-frequency coupling (VLFC) measured by CPC analysis represent stable sleep, unstable sleep, and wake/rapid eye movement (REM) sleep, respectively. RESULTS: The HAMD, HAMA, PSQI, and ISI scores were higher in the insomnia-CN patients than in the HCs (all p < .01). However, no differences were found in the HFC, LFC, and VLFC ratio between the HCs and insomnia-CN groups. Compared with the insomnia-CN patients, insomnia-CI patients exhibited higher scores on the HAMD, HAMA (all p < .01), and PSQI (p < .05), performed worse on the Auditory Verbal Learning Test, Trial Making Test B, and Stroop Test B (all p < .01), had a lower HFC ratio, and had a higher LFC ratio in the CPC analysis (all p < .01). Furthermore, in the insomnia patients, poorer cognition was correlated with a decreased HFC ratio and an increased VLFC ratio (r = .356, p = .019; r = -.339, p =.026, respectively) and increased HAMD and HAMA scores (r = -.507, p < .001; r = -.561, p < .001, respectively); a higher VLFC ratio was correlated with an increased ISI score (r = .346, p = .023). CONCLUSIONS: Cognitive deterioration in insomnia patients was associated with a decreased stable sleep ratio, an increased wake/REM sleep ratio and more severe symptoms of depression and anxiety. CPC analysis can reflect the severity of insomnia.

Measuring sleep regularity: theoretical properties and practical usage of existing metrics.

STUDY OBJECTIVES: Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual's average; these traditional metrics include intra-individual standard deviation (StDev), interdaily stability (IS), and social jet lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: composite phase deviation (CPD) and sleep regularity index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. METHODS: Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect the measurement of sleep regularity: "scrambling" the order of days; daily vs. weekly variation; naps; awakenings; "all-nighters"; and length of study. RESULTS: SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. CONCLUSIONS: Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence.

STUDY OBJECTIVES: We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls. METHODS: Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan-Meier survival curves for wake and sleep stages, and median bout durations. RESULTS: Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02). CONCLUSIONS: NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence.

Learning Monologues at Bedtime Improves Sleep Quality in Actors and Non-Actors.

Several studies show that pre-sleep learning determines changes in subsequent sleep, including improvements of sleep quality. Our aims were to confirm this finding using a more ecological task (learning a theatrical monologue) and to investigate whether the effect is modulated by expertise. Using a mixed design, we compared polysomnographic recordings of baseline sleep (BL, 9-h TIB) to those of post-training sleep (TR, with the same TIB but preceded by the training session), in one group of actors (N = 11) and one of non-actors (N = 11). In both groups, TR appears reorganized and re-compacted by the learning session, as shown, among others, by a significant decrease of WASO%, awakenings, arousals, and state transitions and by a trend towards an increased number of complete cycles and total cycle time. Concerning memory performance, the number of synonyms produced was significantly higher in the morning relative to immediate recall. No between-groups differences emerged either for sleep or memory variables. Our data confirm pre-sleep learning's beneficial effect on sleep quality in an ecological context. While expertise appears not to influence memory-related sleep mechanisms, results on morning recall support the recent view that sleep's role in memory processes consists in trace "transformation" for adaptive purposes, rather than rote consolidation.

Sleep continuity, stability and organization in good and bad sleepers.

The relationship between objective and subjective sleep quality is still debated. Here, we investigate differences in objective sleep parameters in habitual subjective good sleepers and bad sleepers with the aim of evaluating sleep continuity, stability and organization as possible determinants of subjective sleep quality. In total, 38 subjects (good sleepers, N = 18; bad sleepers, N = 20) underwent two nights of sleep recording. Traditional sleep parameters displayed no between-groups differences. Conversely, bad sleepers showed lower sleep continuity (awakenings frequency), stability (e.g. arousals and state transitions frequency) and organization (e.g. number of sleep cycles and time spent in cycles). Our findings point to the involvement of these measures in determining habitual sleep quality perception and suggest the possibility to include them in standard sleep assessments.

Variation in Sleep Stability with Differences in Severity of Sleep-Disordered Breathing in Children.

OBJECTIVES: The aim of this study was to analyze the association between obstructive sleep apnea (OSA) severity and various cardiopulmonary coupling (CPC) parameters in children with OSA. STUDY DESIGN: Retrospective cross-sectional study. METHODS: A cross-sectional study was conducted among 117 children (aged 7.96 ± 3.54 years, 86 male) who underwent both full-night polysomnography (PSG) and CPC for suspicion of sleep-disordered breathing (SDB). We analyzed the association between various CPC and PSG findings. RESULTS: The apnea-hypopnea index (AHI) was negatively correlated with high frequency coupling (HFC, r = -0.374, P < .001) and very low frequency coupling (VLFC, r = -0.192, P = .038) and positively correlated with low frequency coupling (LFC, r = 0.503, P < .001), elevated low frequency coupling (e-LFC, r = 0.475, P < .001), and narrow and broad band e-LFC (e-LFCNB and e-LFCBB ; r = 0.221, P = .016 and r = 0.468, P < .001, respectively). The arousal index was negatively correlated with HFC (r = - 0.466, P < .001) and positively correlated with LFC, e-LFC, e-LFCNB , and e-LFCBB (r = 0.543, r = 0.460, r = 0.239, and r = 0.445, respectively; all P < .001). In addition, we also found a significant difference in various CPC values according to OSA severity. CONCLUSION: CPC parameters accurately reflect sleep fragmentation and OSA severity in children. Thus, we can verify objective sleep quality using CPC analysis, which is a simple method of analyzing sleep stability in children with SDB. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:435-439, 2021.

Ultradian modulation of cortical arousals during sleep: effects of age and exposure to nighttime transportation noise.

STUDY OBJECTIVES: The present study aimed at assessing the temporal non-rapid eye movement (NREM) EEG arousal distribution within and across sleep cycles and its modifications with aging and nighttime transportation noise exposure, factors that typically increase the incidence of EEG arousals. METHODS: Twenty-six young (19-33 years, 12 women) and 16 older (52-70 years, 8 women) healthy volunteers underwent a 6-day polysomnographic laboratory study. Participants spent two noise-free nights and four transportation noise exposure nights, two with continuous and two characterized by eventful noise (average sound levels of 45 dB, maximum sound levels between 50 and 62 dB for eventful noise). Generalized mixed models were used to model the time course of EEG arousal rates during NREM sleep and included cycle, age, and noise as independent variables. RESULTS: Arousal rate variation within NREM sleep cycles was best described by a u-shaped course with variations across cycles. Older participants had higher overall arousal rates than the younger individuals with differences for the first and the fourth cycle depending on the age group. During eventful noise nights, overall arousal rates were increased compared to noise-free nights. Additional analyses suggested that the arousal rate time course was partially mediated by slow wave sleep (SWS). CONCLUSIONS: The characteristic u-shaped arousal rate time course indicates phases of reduced physiological sleep stability both at the beginning and end of NREM cycles. Small effects on the overall arousal rate by eventful noise exposure suggest a preserved physiological within- and across-cycle arousal evolution with noise exposure, while aging affected the shape depending on the cycle.

The effect of complex cognitive training on subsequent night sleep.

Using a nap design, we have recently shown that training at a complex cognitive task at bedtime improves objective sleep quality by reducing sleep fragmentation. In order to extend our findings to nighttime sleep, here we assess the impact of a multi-componential cognitive task at bedtime on the subsequent sleep episode of subjects reporting habitual bad sleep, allegedly characterized by high sleep fragmentation. In a within-subjects design, 20 subjective bad sleepers underwent polysomnographic recording in three conditions: (a) baseline sleep (BL); (b) post-training sleep (TR), preceded by a complex ecological task, i.e. a modified version of the word game Ruzzle; (c) post-active control sleep (AC), preceded by a control task. Sleep in TR was more organized (higher number of cycles and longer time spent in cycles) and showed lower microarousal frequency than in AC and BL. As for sleep continuity (total and brief awakening frequency) and other stability measures (state transition and functional uncertainty period frequency, time in functional uncertainty), both TR and AC showed significant improvements compared with BL. Arousal frequency was also reduced in TR relative to BL. Our results show a clear impact of cognitive training on subsequent night sleep, basically consisting of an increase in sleep continuity, stability and organization. In our sample of bad sleepers, these post-training changes end up representing a notable sleep improvement, also consistently reflected in subjective sleep quality perception. Therefore, ecological pre-sleep cognitive training should be further studied as an easily accessible complementary approach in standard therapies for sleep-disordered populations.

[Relationship between systolic blood pressure load and sleep stability in patients with essential hypertension].

Objective: To explore the relationship between blood pressure load and sleep stability in hypertensive patients by cardiopulmonary coupling monitoring (CPC) and 24-hour ambulatory blood pressure monitoring. Methods: One hundred and fifty-five patients with essential hypertension were divided into high load group and low load group according to whether the blood pressure load was higher than 50%. The relationship between sleep stability and systolic blood pressure load was analyzed by logistic regression. Results: After adjusting for sex, age and body mass index (BMI), logistic regression analysis showed that stable sleep was the protective factor (OR=0.736,P=0.047) for the decrease of nocturnal blood pressure load, and unstable sleep was the risk factor for increasing day time blood pressure load (OR=1.336, P=0.037) in patients with hypertension. Conclusions: The effect of sleep stability on blood pressure load is different between day and night. Stable sleep is the protective factor of nocturnal systolic blood pressure load decrease, and unstable sleep is the harmful factor of daytime systolic blood pressure load increase. Increasing sleep stability helps to reduce systolic blood pressure load.

Automatic sleep classification using adaptive segmentation reveals an increased number of rapid eye movement sleep transitions.

The reference standard for sleep classification uses manual scoring of polysomnography with fixed 30-s epochs. This limits the analysis of sleep pattern, structure and, consequently, detailed association with other physiologic processes. We aimed to improve the details of sleep evaluation by developing a data-driven method that objectively classifies sleep in smaller time intervals. Two adaptive segmentation methods using 3, 10 and 30-s windows were compared. One electroencephalographic (EEG) channel was used to segment into quasi-stationary segments and each segment was classified using a multinomial logistic regression model. Classification features described the power in the clinical frequency bands of three EEG channels and an electrooculographic (EOG) anticorrelation measure for each segment. The models were optimised using 19 healthy control subjects and validated on 18 healthy control subjects. The models obtained overall accuracies of 0.71 ± 0.09, 0.74 ± 0.09 and 0.76 ± 0.08 on the validation data. However, the models allowed a more dynamic sleep, which challenged a true validation against manually scored hypnograms with fixed epochs. The automated classifications indicated an increased number of stage transitions and shorter sleep bouts using models with smaller window size compared with the hypnograms. An increased number of transitions from rapid eye movement (REM) sleep was likewise expressed in the model using 30-s windows, indicating that REM sleep has more fluctuations than captured by today's standard. The models developed are generally applicable and may contribute to concise sleep structure evaluation, research in sleep control and improved understanding of sleep and sleep disorders. The models could also contribute to objective measuring of sleep stability.

Beyond Hypnograms: Assessing Sleep Stability Using Acoustic and Electrical Stimulation.

OBJECTIVE: Conventional polysomnographic recordings reflect brain dynamics associated with sleep architecture. We hypothesized that noninvasive tools like transcranial alternating current stimulation (tACS) and acoustic stimulation (for generating event related potentials [ERPs]) would help to predict sleep stability and provide a window to actively assess brain activity during sleep. MATERIALS AND METHODS: Twelve healthy male volunteers participated in the multiple whole-night polysomnography (PSG) recording protocol. Acoustic tones (100 msec duration) were presented throughout night to evaluate ERP during sleep. Furthermore, 30 sec tACS were presented during nonrapid eye movement (NREM) and rapid eye movement (REM) sleep on subsequent two nights without disturbing the subject's sleep. For ERP analysis, event-locked artifact-free epochs from each sleep stage were averaged separately. For tACS analysis, 30 sec prestimulus and poststimulus artifact-free EEG epochs were subjected to bootstrapping-based comparison of power spectral values. RESULTS: Acoustic stimulation generated sleep stage-dependent ERP components (N350, N550, and P900) in all participants. The tACS stimulation during NREM sleep (0.75 Hz) increased parietal delta power but decreased frontocentral theta and increased frontal gamma power when delivered during REM sleep (40Hz). These interventions provide details on sleep stability as larger N550-P900 ERP-complex correlated with lower NREM disruptions (Spearman's rho = -0.553; p = 0.049; n = 10) and tACS-related theta power perturbation with higher REM disruptions (Spearman's rho = 0.734; p = 0.030; n = 7). CONCLUSIONS: Noninvasive brain stimulation approaches such as sleep ERP and sleep tACS are reliable tools to evaluate sleep stability during NREM and REM sleep, respectively, but more large-sample studies are warranted.

[The association between blood pressure variability and sleep stability in essential hypertensive patients with sleep disorder].

Objective: To investigate the association of blood pressure variability and sleep stability in essential hypertensive patients with sleep disorder by cardiopulmonary coupling. Methods: Performed according to strict inclusion and exclusion criteria, 88 new cases of essential hypertension who came from the international department and the cardiology department of china-japan friendship hospital were enrolled. Sleep stability and 24 h ambulatory blood pressure data were collected by the portable sleep monitor based on cardiopulmonary coupling technique and 24 h ambulatory blood pressure monitor. Analysis the correlation of blood pressure variability and sleep stability. Results: In the nighttime, systolic blood pressure standard deviation, systolic blood pressure variation coefficient, the ratio of the systolic blood pressure minimum to the maximum, diastolic blood pressure standard deviation, diastolic blood pressure variation coefficient were positively correlated with unstable sleep duration (r=0.185, 0.24, 0.237, 0.43, 0.276, P<0.05). Conclusions: Blood pressure variability is associated with sleep stability, especially at night, the longer the unstable sleep duration, the greater the variability in night blood pressure.

The effect of intranasal orexin-A (hypocretin-1) on sleep, wakefulness and attention in narcolepsy with cataplexy.

Narcolepsy with cataplexy is a sleep dysregulation disorder with alterations of REM sleep, i.e., sleep onset REM periods and REM sleep instability. Deficient orexin-A (hypocretin-1) signaling is assumed to be a major cause of narcolepsy with cataplexy. In this study we investigated fourteen subjects with narcolepsy with cataplexy in a within-subject, random-order crossover, placebo-controlled design. Patients received double-blinded intranasal orexin-A (435 nmol) or sterile water (placebo) in the morning. Administration was preceded by an adaptation night and followed by a modified maintenance of wakefulness test, attention testing and a second full night of polysomnographic recording. We found comparable sleep behavior during the adaptation nights between both conditions. After orexin-A administration patients had less wake-REM sleep transitions and a decreased REM sleep duration. In the subsequent night, patients showed an increased N2 duration. In the test of divided attention, patients had fewer false reactions after orexin-A administration. Our results support orexin-A to be a REM sleep stabilizing factor and provide functional signs for effects of orexin-A on sleep alterations and attention in narcolepsy with cataplexy.

The effects of pre-sleep learning on sleep continuity, stability, and organization in elderly individuals.

Several studies have consistently shown that pre-sleep learning is associated to changes of sleep structure. Whereas previous research has mainly focused on sleep states, namely REM and NREM amount, very little attention has been paid to the hypothesis that pre-sleep learning might improve sleep continuity, stability, and cyclic organization, which are often impaired in aging. Thus, aim of this research was to assess, in a sample of 18 healthy elderly subjects, whether a memory task administered at bedtime would determine changes in any sleep parameter, with special regard to sleep continuity, stability, and organization. To this purpose, a baseline sleep (BL), i.e., a normal sleep with 9-h time in bed (TIB), was compared to a post-training sleep (TR), with the same TIB but preceded by an intensive training session. For the latter, a verbal declarative task was used, consisting in learning paired-word lists, rehearsed, and recalled for three times in a row. To control for individual learning abilities, subjects were administered several sets of lists with increasing difficulty, until they reached an error rate ≥20% at third recall. Relative to BL, TR shows a significant reduction in the frequency of brief awakenings, arousals, state transitions, "functional uncertainty" (FU) periods, and in the percentage of time in FU over total sleep time (TST). A significant increase in the number of complete cycles, total cycle time (TCT), and TCT/TST proportion was also found. All these changes are evenly distributed over the sleep episode. No sleep stage measure display significant changes, apart from a slight reduction in the percentage of Stage 1. Scores at retest are negatively correlated with both the frequency of arousals and of state transitions. Our data suggest that pre-sleep learning can yield a beneficial re-organizing effect on elderlies' sleep quality. The inverse correlation between recall scores and the measures of sleep continuity and stability provides further support to the role of these features in memory processes.

Sleep state instabilities in major depressive disorder: Detection and quantification with electrocardiogram-based cardiopulmonary coupling analysis.

Sleep disruption is an important aspect of major depressive disorder but lacks an objective and inexpensive means of assessment. We evaluated the utility of electrocardiogram (ECG)-based cardiopulmonary coupling analysis to quantify physiologic sleep stability in patients with major depression. Relative to controls, unmedicated depressed patients had a reduction in high-frequency coupling, an index of stable sleep, an increase in low-frequency coupling, an index of unstable sleep, and an increase in very-low-frequency coupling, an index of wakefulness/REM sleep. The medicated depressed group showed a restoration of stable sleep to a level comparable with that of the control group. ECG-based cardiopulmonary coupling analysis may provide a simple, cost-efficient point-of-care method to quantify sleep quality/stability and to objectively evaluate the severity of insomnia in patients with major depression.