Sleep timing and structure as a function of daily experiences

Sleep timing and structure as a function of daily experiences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sleep timing and structure as a function of daily experiences Péter Ujma, Robert Bodizs This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5290975/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sleep characteristics may be affected by daytime experiences, a fact that can be leveraged by non-pharmacological interventions to improve sleep. The effect of daily experiences on sleep has been only sporadically investigated in the previous literature, mainly with small interventional studies. In this study, we leverage BSETS, a large multiday observational study (N = 1901 nights in total) with extensive daily diaries and mobile EEG recordings conducted for at least 7 days per participant to investigate how naturally occurring daily experiences affect sleep during the subsequent night. The strongest influence was on the timing of sleep onset: even after controlling for day of the week, sleep onset occurred later after more intense days and pleasurable activities. After statistically accounting for this extended wakefulness, we found limited evidence that daily experiences influence sleep characteristics. Only four effects survived correction for multiple comparisons: sleep and N3 duration were longer after days with time at the workplace, REM latency was increased after social activity, and sleep onset latency was reduced after alcohol consumption. Our work shows that, aside from homeostatic effects resulting from extended wakefulness, sleep is relatively resilient to and only affected by a few distinct daytime experiences. Non-pharmacological interventions seeking to change sleep may need to utilize behavioral modifications outside the normally observed range. Health sciences/Biomarkers/Predictive markers Biological sciences/Neuroscience/Epigenetics in the nervous system/Epigenetics and plasticity Figures Figure 1 Figure 2 Figure 3 Introduction Sleep problems are common in industrialized nations, causing a significant burden on patients, health care providers and the economy [1]. Pharmacological means of improving sleep are available, but have less than perfect utility [2–5]. Non-pharmacological methods for improving sleep are preferable due to fewer side-effects, no risk of tolerance or dependence, the possibility of addressing root causes of sleep problems instead of symptomatic treatment, and comprehensive health benefits given that habits that improve sleep are likely to have a more general health-promoting effect [5]. Non-pharmacological methods for improving sleep have mainly been designed based on theoretical models of sleep regulation, especially the two-process model. The two-process model proposed by Borbély [6,7] is arguable the best accepted model of sleep regulation. In the two-process model, sleep timing, depth and duration are the function of the phase of the circadian rhythm and sleep pressure, the latter of which builds up during wakefulness in a phenomenon called process S. The synaptic homeostasis hypothesis proposed by Tononi and Cirelli [8,9] assigns a molecular mechanism to process S, suggesting that an increase in synaptic strength is an inevitable consequence of wakefulness which needs to be periodically downregulated during deep sleep episodes. Both the two-process model and the synaptic homeostasis hypothesis suggest that because sleep is homeostatically regulated, increasing the duration of wakefulness is a powerful mechanism for promoting sleep [10]. Sleep restriction protocols are widely used and efficacious non-pharmacological interventions used to improve sleep depth [11]. Some research, however, suggests [12] that sleep restriction comes with side-effects as despite its sleep depth-increasing effects it negatively affects vigilance and subjective sleepiness. A further prediction of the synaptic homeostasis hypothesis (although not necessarily the two-process model) is that more active and eventful wakefulness, even if its duration is unaltered, will similarly promote sleep depth without necessarily compromising its duration. In line with this prediction, some studies found increased sleep pressure and depth after a day of elevated mental or physical activity. For example, in early classic studies Horne and Minard [13] found increased EEG-based sleep depth after a full-day excursion, while Kobayashi et al [14] similarly found increased slow wave sleep after both a day with a 18 km jog (but not after a day with simple but repetitive mathematical tasks). Conversely, Hague et al [15] found that sleep depth is reduced in athletes after an inactive day. Findings about the effect of mental strain on subsequent sleep are more mixed. Recently, Cerasuolo et al [16] published a systematic review about the effect of mental activity on subsequent sleep characteristics with 85 studies meeting inclusion criteria. Sleep spindles, slow wave activity, NREM delta power, NREM sigma power, and subjective sleep quality were enhanced, while awakenings, arousals and state transitions were reduced in a relative majority of studies. These findings are tentatively in line with mental activity promoting sleep depth, but the studies were small and their results heterogeneous. Some literature exists about the effects of alcohol consumption and sexual activity as less conventional daily experiences on sleep. Experimental studies convincingly showed that alcohol consumption leads to increased sleep propensity in the first half of the night, but increased sleep fragmentation in the second [17]. Widespread belief exists that sexual intercourse induces somnolence [18], but empirical evidence for this is limited [19,20]. A larger literature exists about the effect of affective experiences on sleep [21], which, however, we considered beyond the scope of the current article which focuses on experiences during the day themselves rather than mood or emotions they may elicit. Despite the utility of designing non-pharmacological interventions that improve sleep without mere sleep restriction, the literature about daily experiences on sleep is inconclusive. First, most studies are small, and the mapping of daily experiences is unsystematic and heterogeneous. Second, most previous studies on the effect of enhanced wakefulness on sleep were experimental. We previously argued [22,23] that this approach is suboptimal, because it reduces the feasible sample size, only one intervention can be evaluated per study, and many interventions are exaggerated compared to typical daily experiences, impacting ecological validity and the potential of translation to clinical practice. We have instead argued for multiday observational studies [24] in which the natural day-to-day covariation of daily experiences and sleep is investigated. Because they study time-lagged, within-person associations between daily experiences and sleep (for example, whether the same person typically has deeper sleep after a mentally challenging day) causality can be clearly established, and the non-invasive protocol with extensive daily measurements enables both larger sample sizes and the study of more effects. While some multiday observational studies about the effect of daily experiences are available (e.g. [19,25]), these generally rely on self-reports of sleep rather than objective measurements (see [26–28] for some exceptions). In the current paper, we use BSETS, a large multiday observational study, to investigate the effects of a large array of self-reported experiences on subsequent objectively and subjectively measured sleep. Methods Participants We used data from the Budapest Sleep, Experiences and Traits Study (BSETS). The full BSETS protocol has been published separately [23]. In brief, BSETS recruited healthy volunteers to monitor their days and nights for a full week while performing their daily routine as normal. Each evening, participants filled out a questionnaire in which they indicated (yes/no) if a series of daily experiences happened to them, and subjectively rated their day (on a Likert scale) based on certain characteristics. The evening questionnaire also included a free-form diary of the events of the day. Each night, sleep was recorded with a Dreem2 mobile EEG headband. Each morning, participants filled out another questionnaire about their sleep, including subjectively rated sleep quality using the Groningen Sleep Quality Scale [29]. The Institutional Review Board (IRB) of Semmelweis University, as well as the Hungarian Medical Council (under 7040-7/2021/ EÜIG "Vonások és napi események hatása az alvási EEG-re" [The effect of traits and daily activities and experiences on the sleep EEG]), approved BSETS as compliant with the latest revision of the Declaration of Helsinki. All participants gave written informed consent on a form reviewed and approved by the IRB. 267 participants took part in BSETS, of which hypnogram data was available from 258 and quantitative EEG data from 249. The mean age was 28.86 years (SD=12.73 years, range: 18-76 years). 45% of participants were male and 55% female. Observation-level descriptive statistics, including detailed sample sizes are available in Table 1 . For all analyses we used all cases with available data for the relevant variables. Sleep characteristics Each night, participants slept with a Dreem2 mobile EEG headband, which recorded quantitative EEG using dry silicone electrodes with a sampling frequency of 250 Hz [23,30]. A validated [31] complimentary algorithm scored these signals to create a hypnogram, from which we extracted the objective sleep characteristics sleep efficiency (SE), total sleep time (TST), sleep onset latency (SOL), REM latency, wake after sleep onset (WASO), the duration and percentage of N1, N2, N3 and REM sleep, as well as the number of awakenings. The clock time at sleep onset (expressed as fractional hours relative to midnight) was also recorded. In quantitative EEG analyses, we used the channel F7-O1 to calculate power spectral density in the delta (0.5-4 Hz) and low sigma (10-13 Hz) frequency bands using the periodogram() MATLAB EEGLab function with 2-second nonoverlapping epochs and Hamming windows. These were to track slow wave and sleep spindle activity, respectively. We chose this channel, as in previous studies [10,22], based on preliminary analyses [23] showing a favorable tradeoff between data quality and availability. The low, as opposed to the high, sleep spindle range was chosen because it is better mapped with the frontal channel positions available with the Dreem2 device [23]. In addition to sleep macrostructure and quantitative EEG data, we also used scores on the Groningen Sleep Quality Scale (GSQS) as an indicator of subjective sleep quality. In total, we investigated effects on 18 sleep characteristics. Daily experiences In the evening diary, participants noted if any of the following 10 events happened to them: They spent time in the company of others (at least 30 minutes, excluding people they normally cohabit with) They were in school/university They were at a workplace They consumed alcohol (no data on quantity was collected) They watched movies or TV series for over an hour They spent at least one hour in front of a computer They had sexual contact with someone They drove a car or another vehicle for at least one hour They spent at least one hour travelling by public transportation They had a serious conflict or fight with someone Beside these binary-coded variables, participants also noted how much time they spent outside (including walking and transportation except closed vehicles) and how much time they spent reading or playing on cell phones or other smart devices. In total 12 daily experiences were investigated. We exclude naps and exercise, also part of the evening diary, from the list of daily experiences investigated here, as these will be the subject of separate studies. We excluded binary-coded smart device use as this was better captured by self-reported times. We also excluded medication intake, as only low quality data (the rare intake of highly heterogeneous medications) was available. Otherwise, we report findings on all daily experiences available in BSETS, as listed in the protocol [23]. Day ratings In addition to specific experiences, participants marked down on a 10-level Likert scale whether their day was “Physically exhausting”, “Mentally exhausting”, “Interesting and eventful” or “Happy” (as opposed to “Sad”). We used these daily ratings as further predictors of sleep characteristics, for a total of 16. We excluded specific daily emotions, rated using the Positive and Negative Affect Scale, as these will be part of a separate investigation. Statistical analysis Statistical analysis followed a protocol identical to previous BSETS papers [10,22]. In brief, we performed multilevel models implemented with the MATLAB fitlme() function to estimate the effects of daily experiences on sleep characteristics. A separate model for each sleep experience and each sleep characteristic was run (272 models in total). Each model estimated Level 1 (within-individual) and Level 2 (between-individual) effects simultaneously. Within-individual effects indicate that within the same person, sleep was different after a daily experience. Due to the time-lagged, within-participant nature of these associations they strongly imply causality. Between-individual effects indicate that the frequency of daily experiences across the days of observations is associated with the means of sleep characteristics. These effects show correlations between typical sleep and daily experiences and do not imply causality. All models were corrected for day of the week (weekend/weekday), age, sex and lagged outcomes (the sleep metric used as the dependent variable from the previous night). Because of the well-known effect of process S on sleep characteristics, established also in BSETS [10], in order to separate the direct effects of daily experiences from indirect effects via extending wakefulness we also controlled all models for the duration of previous wakefulness. This was estimated by calculating the time between the last sleep epoch of the previous day’s EEG recording and the current day’s first sleep epoch [10]. This correction was not applied for models with sleep timing as the dependent variable as late sleep timing is a cause, not a consequence, of wakefulness duration. For a simpler analysis, we deviated from previous protocol [22] by using a transformation instead of winsorizing and generalized models for the skewed variables sleep onset latency, wake after sleep onset, and sleep efficiency. For the first two variables, a log10 transformation was used. For sleep efficiency, we used reflecting and log10 transforming so that the transformed value of SE was equal to SE t =log10(100-SE). Due to the large number of models tested and the need to balance power and replicability we employed a two-step procedure of significance testing. We report all findings which pass a relatively lax (given the number of models) significance threshold of p<0.01. However, we also performed a formal correction for multiple comparisons by performing the Benjamini-Hochberg correction for false discovery rate [32] across all sleep metrics for each daily experience. We focus our discussion on findings on those that pass this more conservative threshold. Data availability Raw data to replicate our analyses is available at https://osf.io/92agb/. Results Descriptive statistics Key descriptive statistics are reported in Table 1 . Daily experiences tended to show low to intermediate within-participant clustering, the lowest for “interesting” day ratings (ICC = 0.25), and the highest for driving (pseudo-ICC = 0.66) and time with smart devices (pseudo-ICC = 0.61). We created a correlation matrix of all sleep metrics including wakefulness duration, binary-coded daily experiences, and day ratings ( Supplementary data ). The co-occurrence of binary-coded experiences (based on tetrachoric correlations) and the point-biserial correlations between binary-coded experiences and day ratings were low to moderate, but in the expected direction. For example, days with car driving tended to preclude public transportation (r=-0.481), and days with time in the company of others were rated as more interesting (r = 0.248), happier (r = 0.138), and more physically exhausting (r = 0.121). Days with work were rated as more mentally exhausting (r = 0.274), while days with conflicts were rated as less happy (r=-0.108). Days with movie watching, computer use, and more time with smart devices had negative-signed correlations with other daily experiences and daily ratings, except for movie watching being slightly positively associated with sex on the same day (r = 0.104) and computer use being associated higher ratings of mental exhaustion (r = 0.165), suggesting that these are activities which crowd out most others. Day of the week (either as a 7-day categorical variable or coded as weekend/weekday) accounted for little variance in either sleep parameters or daily experiences. School attendance, work, public transportation use and ratings of days as mentally exhausting had the largest weekly variability, in line with social conventions scheduling free time for the weekend. Still, late sleep and interesting or pleasurable experiences tended to occur either on Friday or Saturday while work and chores were most frequently reported during the middle of the week. Within-participant effects Within-participant coefficients reflect the difference in sleep characteristics within the same person after having had a certain daily experience. These effects are in line with a causal interpretation. The sleep characteristic most frequently affected by daily experiences was sleep onset timing, which tended to be later after pleasurable or interesting activities. Significantly (after multiple comparisons) later sleep onset was observed after spending time in the company of others (B = 0.403 hours, p = 10 − 4 ), alcohol consumption (B = 0.937 hours, p = 10 − 17 ), and days which were self-rated as more interesting (B = 0.127 hours per Likert point, p = 10 − 11 ) or happier (B = 0.097 hours per Likert point, p = 10 − 5 ). Later sleep onset was also observed after sex (B = 0.369 hours, p = 0.009) and days self-rated as more physically exhausting (B = 0.052 hours per Likert point, p = 0.007) but due to the large number of sleep parameters tested these did not pass correction for multiple comparisons. Conversely, significantly earlier sleep onset was observed after desktop computer use (B=-0.401 hours, p = 10 − 4 ). Beyond changes in sleep timing four other alterations in sleep structure were observed. Spending time in the company of others was followed by increased REM latency (B = 9.317 minutes, p = 10 − 4 ), and alcohol consumption was followed by significantly reduced sleep latency (B=-0.06 log units, p = 0.005) Time at work was followed by increased total sleep time (B = 21.983 minutes, p = 0.003) and N3 duration (B = 5.708 minutes, p = 0.005). These effects all survived correction for multiple comparisons. Figure 1 presents an overview of within-participant effects. Between-participant effects Between-participant coefficients reflect correlations between typical sleep characteristics and the overall frequency of daily experiences or mean daily ratings. These effects do not necessarily imply causality. Only two between-participant effects were observed, and none passed a formal correction for multiple comparisons. In participants with more days at work lower mean N2 duration was observed (B=-24.223 minutes, p = 0.003), and in participants spending more time outside mean N1 percentage was lower (B=-0.001 per hour outside, p = 0.006). An interesting trend was that participants rating their mean day as happier tended to report better sleep on the Groningen Sleep Quality Scale (B=-0.219 GSQS points per Likert point, p = 0.013), in line with our previous findings about the correlation of subjective sleep ratings and hedonic tone [22]. Figure 2 presents an overview of between-participant effects. All between- and within-participant effects are available in table form in the Supplementary data . Associations uncorrected for sleep homeostasis Our models were corrected for sleep pressure, indexed by the duration of wakefulness prior to sleep. We chose this model specification because we have shown that even in the naturalistic setting of BSETS, sleep propensity is increased after extended wakefulness [10]. Because longer wakefulness was observed after a large number of daily experiences, especially pleasant ones ( Supplementary data ), deeper sleep after these would be expected due to homeostatic effects alone, rendering a statistical control for these necessary. To show the importance of controlling for homeostatic effects, we re-ran our analyses without the wakefulness duration covariate. While still no significant between-participant effects were seen, more interesting days now were also followed by significantly reduced sleep onset latency and WASO as well as increased delta power, while alcohol consumption and mentally exhausting days were both followed by increased N3 percentage ( Supplementary figures S1 -S2 , the full model outputs are available in the Supplementary data ). These effects could naively be interpreted as the experiences themselves leading to increased sleep propensity. However, their correlation with wakefulness duration ( Supplementary data ) and the disappearance of their effects after controlling for the latter indicates that they merely index homeostatic effects and evidence for any sleep-enhancing effects of the experiences themselves remains scarce. Discussion Sleep complaints are common, and non-pharmacological means of reducing them are preferred due to a more holistic effect and a more favorable side effect profile. Sleep restriction therapy is a widely used non-pharmacological intervention, but its relative difficulty, patient adherence and daytime side-effects are a concern [12]. Under prevailing theories of sleep regulation [7,9], it is plausible that enhancing wakefulness without changing its duration creates additional sleep pressure which improves subsequent sleep. Such effects, if well-supported scientifically, could be the basis of simple non-pharmacological interventions alleviating the disease burden caused by sleep complaints in the population. These interventions would be especially promising if instead of the drastic manipulations seen in some experimental studies [13] their content would be simple and within the observable range of the normal behavior of patients, such as an increase in time spent outside or a reduction in time spent with smart devices. In the current study we tested if such interventions are feasible by investigating the link between day-to-day variation of various experiences and subsequent sleep. Our main finding is that daily experiences principally affect the timing rather the structure of sleep. After a wide array of pleasurable experiences such as sexual intercourse, social activity and a day generally rated as more interesting or happy, sleep tends to occur later. Although pleasurable experiences are most likely to occur on the weekend ( Table 1 ), these effects are significant even if day of the week is controlled for. A likely explanation of this finding is that pleasurable experiences are timed for the evening hours and function as zeitgebers to extend wakefulness. In addition, the emotional processes inherent to pleasurable experiences could serve as arousal inputs and override both the circadian and the homeostatic mechanisms involved in sleep initiation [33]. Slightly longer wakefulness was indeed observed on more experience-rich days ( Supplementary data ). Models not accounting for wakefulness duration ( Supplementary figure S1-S2 ) found some evidence in favor of the sleep-promoting effects of experiences. We considered it essential to separate the effects of homeostatic effects resulting from increased wakefulness duration from the effects of wakeful experiences themselves. Therefore, in our main models we used the duration of previous wakefulness as a covariate. After this control, almost all apparent effects of daily experiences on the structure of subsequent sleep disappeared. In contrast to pioneering studies like those of Horne & Minard [13] or Kobayashi et al [14] we found no evidence that enhanced wakefulness (based on participants’ self-reports) affected the structure of subsequent sleep. While sleep occurred later after days rated as happier or more interesting, no effect on sleep structure was significant after controlling for homeostatic effects. A possibility of this discrepancy is either the well-known inflation of effect sizes (“winner’s curse”) in small early studies [34], or differences in the intensity of the experiences intended to promote sleep. For example, the nine participants of Horne & Minard participated in a full-day excursion lasting over 12 hours including visits to two separate outside venues, while in BSETS the intensity of wakefulness varied within the range naturally experienced by participants over the course of the 1-week study. In addition, most of the experiences reported in our present study were probably self-selected and self-paced in contrast to the Horne and Minard study, which was indeed interventional, involving the exposure of the subjects to multiple unexpected acquintances. Our findings do not rule out that very intensive enhancements of wakefulness promote sleep, however, these are less feasible as the basis of regularly administered non-pharmacological interventions. Our results provide little evidence that moderate enhancements or fluctuations in the intensity of wakeful experiences influence sleep structure. We found significantly longer sleep and N3 duration after work, but other than this no effect of school attendance, computer use, or self-reported mental exhaustion on sleep structure. This finding coheres with the broader literature, recently reviewed by Cerasuolo et al [16], which found mixed evidence at best for the effects of mental exhaustion on sleep. This review uses a vote-counting method to summarize studies, tabulating studies with significant negative, significant positive, and non-significant effects which we visually replicate on Figure 3 . The vote-counting method is suboptimal because low power can lead to false negative, while publication bias can lead to false positive findings, but it is notable that for most sleep metrics negative findings predominate. Positive results were only more frequent than null findings for EEG metrics and the rarely mapped subjective sleep quality (k=4). In our current study based on a large ecologically valid sample we also found little evidence that mental exhaustion, at least within the range of normal weekly variation, substantially changes subsequent sleep. However, workplace attendance, arguably the most challenging and least self-selected and self-paced experience related to cognitive demand, was associated with increased sleep duration, specifically that of N3 sleep, suggesting that in a sufficiently high dosage mental exhaustion may have some effect on sleep duration and intensity. Ethanol, the active compound of alcoholic beverages was shown to display a GABAmimetic profile with a prominent depressing effect on central nervous system activity and complex interaction with ligand-gated ion channels [35,36]. Experimental studies are well-suited to study the acute effects of alcohol on sleep. This is because, unlike many other daily experiences which are more difficult to model experimentally, alcohol consumption is a simple quasi-pharmacological intervention. Previous experimental studies [17] have convincingly demonstrated that sleep propensity is increased after alcohol consumption, with increased sleep fragmentation in the second half of the night. Because of the uniquely high validity of experimental studies to investigate this effect, BSETS results are best interpreted as a replication of experimental findings which confirm the validity of the study setup. We could replicate reduced sleep onset latency after alcohol consumption, but we found no evidence for increased sleep fragmentation or worse sleep quality. While precise dosage was not recorded, an analysis of free-form evening diaries suggests that most alcohol consumption in BSETS occurred in social settings in relatively modest quantities. Low dosage can explain the mild hypnotic effect of alcohol consumption with no detriment to overall sleep quality. There is widespread belief that sexual intercourse causes somnolence, especially in men [18,37,38], leading to reduced sleep onset latency and potentially improved sleep quality. Despite the penetration of this idea into popular media[a], the effect has been little investigated empirically. A recent diary-based study [19] found that participants reported decreased sleep latency after sexual intercourse. However, these self-reports are likely not unbiased. Participants were interviewed about their views on the effects of intercourse on sleep, and there was widespread belief that sleep onset latency is reduced after sex. Because both sexual intercourse and sleep latency were reported the next morning, it possible that participants’ recall was biased by expectations: that is, in line with their beliefs they erroneously reported lower sleep latency when they had sexual intercourse the previous night. After a small early study [20] to our knowledge ours is the second to investigate the effects of sexual intercourse on sleep with objective sleep data which is immune to this type of recall bias. Contrary to popular belief, but in line with early EEG findings, we found that sleep is not substantially altered after sexual intercourse, aside from later sleep onset. A possible limitation of our research is that we did not explicitly ask when sexual intercourse took place. Sex preceding sleep by too much may mask its effects on sleep parameters. However, as both the preferred and the realized time of day for sexual intercourse tends to be in the evening, often immediately before sleep [39,40] (see also[b] [c] [d] for valuable data from non-peer reviewed opinion surveys), it can be assumed that most sexual intercourse recorded in BSETS also occurred within a time window in which sleep-influencing effects could operate. Our study found increased REM latency after social activity, an isolated finding which, however, had a very low p-value (p=0.0002) and remained significant after corrections for multiple testing. Substantially reduced REM latency values, up to the point of sleep onset REM periods (REM latency < 15 min) are known to constitute a diagnostic marker of narcolepsy [41], whereas more moderate reductions were sometimes shown to associate with primary depressive symptomatology [42,43]. Intraindividual (day-to-day) variability in the latency of REM sleep was much less frequently studied. Whereas recent evidence suggest that physical activity might prolong REM latency [44], here we reveal a similar effect of social activity. Available evidence only allows a highly speculative interpretation of this finding, which could be based on the social bonding theory of REM sleep [45], suggesting that the urge for REM sleep is reduced after social interactions. Given the recent increase in the interest in the social aspects of dreaming [46], our findings on the relationship between social interactions and delayed emergence of REM sleep during the subsequent night might fuel the renewed interest in REM latency within the context of behavioral regulatory processes. Our study has a number of limitations. The most important limitation concerns the way daily experiences were recorded in BSETS. Unlike in an experimental study, which administers a carefully planned intervention even if its ecological validity is questionable, BSETS relied on participants self-reporting daily experiences as they saw them. While this intentional design feature increases ecological validity, it also means that the dosage of daily experiences in BSETS is hard to quantify, their timing is not explicitly recorded and usually not recoverable from daily diaries. Null results would be expected if participants’ reports of daily experiences have little to do with actual events that potentially change sleep. However, given the within-participant design of the study (which uses day-to-day differences in the ratings of days as the source of variation in daily experiences), the observation of the expected correlations between daily experiences (for example, between conflicts and emotional valence of the day), and highly significant effects on sleep timing our view is that the subjective experiences reported in BSETS reflects a relatively accurate picture of the daily experiences participants would be able to modify to improve their sleep. A further limitation arguably is that while hypotheses are strong about the sleep-modifying effects of certain experiences (for example, sex), they are weaker or non-existent concerning for others (such as public transportation use). We included all experiences recorded in BSETS because our goal was to present a comprehensive picture of how daily experiences change sleep. Overall, in a novel and arguably superior research design using multiday observations with mobile EEG recordings, we found strong evidence that the timing of sleep is affected by daily experiences. Less evidence was found, however, to support that daily experiences affect the structure of sleep. This observation calls for realism about the possible sleep-promoting effects of simple non-pharmacological interventions consisting of limiting certain common daily experiences (such as work or smart device use) or increasing others (such as time spent outside, enriching time spent awake, or spending more time in the company of others). While non-pharmacological interventions can be effective in promoting sleep, they either need to rely on well-supported homeostatic effects by extending the duration of wakefulness, or they need to be more intensive than simply fine-tuning the normal daily routine of those with sleep problems. Declarations Competing interests The authors declare no competing interests. Acknowledgements This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. This research was supported by the National Research, Development and Innovation Office – NKFIH (grant number: 138935), the Ministry of Culture and Innovation in Hungary (TKP2021-EGA-25, TKP2021-NKTA-47), as well as the CELSA Research Fund (CELSA/24/019). References Grandner MA. Sleep, health, and society. Sleep Med Clin 2022; 17: 117–139. Ujma PP, Bódizs R. Sleep alterations as a function of 88 health indicators. medRxiv 2023. doi:10.1101/2023.11.20.23298781. Tan X, Uchida S, Matsuura M, Nishihara K, Kojima T. Long-, intermediate- and short-acting benzodiazepine effects on human sleep EEG spectra. Psychiatry Clin Neurosci 2003; 57: 97–104. Arbon EL, Knurowska M, Dijk D-J. 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Protocol of the Budapest sleep, experiences, and traits study: An accessible resource for understanding associations between daily experiences, individual differences, and objectively measured sleep. PLoS ONE 2023; 18: e0288909. Konjarski M, Murray G, Lee VV, Jackson ML. Reciprocal relationships between daily sleep and mood: A systematic review of naturalistic prospective studies. Sleep Med Rev 2018; 42: 47–58. Sin NL, Almeida DM, Crain TL, Kossek EE, Berkman LF, Buxton OM. Bidirectional, Temporal Associations of Sleep with Positive Events, Affect, and Stressors in Daily Life Across a Week. Ann Behav Med 2017; 51: 402–415. Messman BA, Slavish DC, Dietch JR, Jenkins BN, Ten Brink M, Taylor DJ. Associations between daily affect and sleep vary by sleep assessment type: What can ambulatory EEG add to the picture? Sleep Health 2021; 7: 219–228. Boon ME, Esfahani MJ, Vink JM, Geurts SAE, van Hooff MLM. The daily reciprocal associations between electroencephalography measured sleep and affect. J Sleep Res 2024; : e14258. Yap Y, Tung NYC, Collins J, Phillips A, Bei B, Wiley JF. Daily Relations Between Stress and Electroencephalography-Assessed Sleep: A 15-Day Intensive Longitudinal Design With Ecological Momentary Assessments. Ann Behav Med 2022. doi:10.1093/abm/kaac017. Simor P, Köteles F, Bódizs R, Bárdos G. A questionnaire based study of subjective sleep quality: The psychometric evaluation of the Hungarian version of the Groningen Sleep Quality Scale. Mentálhigiéné és Pszichoszomatika 2009; 10: 249–261. Dreem Inc. Dreem Whitepaper. Dreem Inc, 2017. Arnal PJ, Thorey V, Debellemaniere E, Ballard ME, Bou Hernandez A, Guillot A et al. The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging. Sleep 2020; 43. doi:10.1093/sleep/zsaa097. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological) 1995; 57: 289–300. Saper CB, Cano G, Scammell TE. Homeostatic, circadian, and emotional regulation of sleep. J Comp Neurol 2005; 493: 92–98. Ioannidis JPA. Why most discovered true associations are inflated. Epidemiology 2008; 19: 640–648. Criswell HE, Breese GR. A conceptualization of integrated actions of ethanol contributing to its GABAmimetic profile: a commentary. Neuropsychopharmacology 2005; 30: 1407–1425. Förstera B, Castro PA, Moraga-Cid G, Aguayo LG. Potentiation of gamma aminobutyric acid receptors (GABAAR) by ethanol: how are inhibitory receptors affected? Front Cell Neurosci 2016; 10: 114. Gallup GG, Platek SM, Ampel BC, Towne JP. Sex differences in the sedative properties of heterosexual intercourse. Evolutionary Behavioral Sciences 2020. doi:10.1037/ebs0000196. Pallesen S, Waage S, Thun E, Andreassen CS, Bjorvatn B. A national survey on how sexual activity is perceived to be associated with sleep. Sleep Biol Rhythms 2020; 18: 65–72. Fortenberry JD, Katz BP, Blythe MJ, Juliar BE, Tu W, Orr DP. Factors associated with time of day of sexual activity among adolescent women. J Adolesc Health 2006; 38: 275–281. Jocz P, Stolarski M, Jankowski KS. Similarity in chronotype and preferred time for sex and its role in relationship quality and sexual satisfaction. Front Psychol 2018; 9: 443. Golden EC, Lipford MC. Narcolepsy: Diagnosis and management. Cleve Clin J Med 2018; 85: 959–969. Kupfer DJ. REM latency: a psychobiologic marker for primary depressive disease. Biol Psychiatry 1976; 11: 159–174. Omichi C, Kadotani H, Sumi Y, Ubara A, Nishikawa K, Matsuda A et al. Prolonged Sleep Latency and Reduced REM Latency Are Associated with Depressive Symptoms in a Japanese Working Population. Int J Environ Res Public Health 2022; 19. doi:10.3390/ijerph19042112. Zapalac K, Miller M, Champagne FA, Schnyer DM, Baird B. The effects of physical activity on sleep architecture and mood in naturalistic environments. Sci Rep 2024; 14: 5637. McNamara P. REM sleep: A social bonding mechanism. New Ideas Psychol 1996; 14: 35–46. Tuominen J, Stenberg T, Revonsuo A, Valli K. Social contents in dreams: An empirical test of the Social Simulation Theory. Conscious Cogn 2019; 69: 133–145. Footnotes https://tvtropes.org/pmwiki/pmwiki.php/Main/PostCoitalCollapse https://rubenarslan.github.io/posts/2019-04-08-sex-by-day-and-by-night/ https://www.hana.co.uk/blog/hanas-big-sex-survey-data-results/ https://www.huffingtonpost.co.uk/entry/the-most-popular-time-and-day-to-have-sex-revealed_uk_645cc201e4b03e16f1a14403 Table Table 1 . Descriptive statistics for the variables in the study. Medians and SDs are omitted for binary variables. Means of binary variables can be interpreted as the proportion of days with this experience. ICC is calculated as adjusted R 2 from either a linear regression (continuous variables) or logistic regression (binary variables, Nagelkerke R 2 ) using participant ID as the single categorical predictor and the target variable as the dependent variable. R 2 weekend and R 2 day originate from analogous models using binary weekday/weekend or the specific day of the week, respectively, as the single categorical predictor. R 2 weekend and R 2 day are expressed in percentages, not proportions, to enhance readability. The last column shows the day of the week on which the highest values (continuous variables) or the most common observations (binary variable) were observed. For sleep variables, these indicate the previous day. For “Self-rated sleep”, the highest value indicates the worst self-rated sleep due to the coding scheme of the GSQS. Valid N Mean Median SD ICC R 2 weekend R 2 day Day with highest value Total sleep time 1718 396,77 402,75 91,21 0,24 -0,01 1,83 Friday Sleep onset latency 1718 14,84 10,50 14,70 0,31 0,01 0,10 Wednesday REM latency 1712 79,38 70,50 36,79 0,23 0,23 0,16 Wednesday Sleep onset time 1718 0,44 0,18 1,79 0,49 0,39 1,71 Saturday WASO 1718 21,01 15,00 21,57 0,38 -0,02 -0,07 Tuesday Sleep efficiency 1718 91,07 92,82 6,70 0,37 -0,01 0,10 Thursday N1 % 1718 6,07 5,58 2,49 0,53 -0,05 0,07 Tuesday N2 % 1718 46,30 46,40 9,17 0,46 -0,03 -0,27 Sunday N3 % 1718 21,80 21,08 9,49 0,48 0,06 0,63 Wednesday REM % 1718 25,83 25,44 7,48 0,30 -0,02 0,99 Friday N1 duration 1718 24,02 22,00 10,91 0,53 -0,01 0,59 Friday N2 duration 1718 186,20 185,50 60,75 0,35 0,03 0,69 Friday N3 duration 1718 83,02 84,00 31,90 0,58 0,02 -0,23 Thursday REM duration 1718 103,53 101,50 38,49 0,25 0,01 2,80 Friday Awakenings 1718 19,56 18,00 9,47 0,51 -0,03 0,44 Friday Self-rated sleep 1745 4,13 4,00 3,41 0,20 0,27 2,05 Wednesday Delta power 1448 1,12 1,11 0,19 0,56 -0,01 -0,25 Wednesday Sigma power 1448 -0,16 -0,16 0,14 0,72 -0,07 -0,33 Saturday Social interaction 1790 0,81 0,39 4,04 5,18 Tuesday School 1788 0,29 0,56 18,16 22,26 Thursday Work 1790 0,24 0,65 8,10 8,75 Thursday Alcohol consumption 1791 0,18 0,43 0,30 3,03 Saturday Watching films 1787 0,40 0,51 1,82 2,88 Saturday Computer use 1793 0,64 0,58 3,25 4,87 Wednesday Time with smart devices 1404 72,12 45,00 89,39 0,61 0,01 -0,20 Saturday Sexual activity 1771 0,13 0,50 0,79 2,59 Friday Driving 1794 0,17 0,66 0,00 0,74 Friday Public transportation 1795 0,37 0,53 6,96 8,56 Wednesday Conflict 1789 0,08 0,43 0,65 1,43 Friday Time outside 1743 75,58 50,00 87,49 0,39 0,00 -0,16 Saturday Physically exhausting day 1791 4,45 4,00 2,44 0,28 -0,06 0,31 Thursday Mentally exhausting day 1789 5,71 6,00 2,38 0,29 6,55 7,59 Thursday Interesting day 1789 5,86 6,00 2,44 0,25 0,06 1,24 Friday Happy day 1787 6,38 7,00 2,16 0,27 0,90 0,94 Saturday Wakefulness duration 1420 17,05 16,88 2,03 0,08 1,94 4,64 Thursday Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files CorrelationmatrixXmultilevelmodels.xlsx Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Pharmacological means of improving sleep are available, but have less than perfect utility [2\u0026ndash;5]. Non-pharmacological methods for improving sleep are preferable due to fewer side-effects, no risk of tolerance or dependence, the possibility of addressing root causes of sleep problems instead of symptomatic treatment, and comprehensive health benefits given that habits that improve sleep are likely to have a more general health-promoting effect [5]. Non-pharmacological methods for improving sleep have mainly been designed based on theoretical models of sleep regulation, especially the two-process model.\u003c/p\u003e \u003cp\u003eThe two-process model proposed by Borb\u0026eacute;ly [6,7] is arguable the best accepted model of sleep regulation. In the two-process model, sleep timing, depth and duration are the function of the phase of the circadian rhythm and sleep pressure, the latter of which builds up during wakefulness in a phenomenon called process S. The synaptic homeostasis hypothesis proposed by Tononi and Cirelli [8,9] assigns a molecular mechanism to process S, suggesting that an increase in synaptic strength is an inevitable consequence of wakefulness which needs to be periodically downregulated during deep sleep episodes.\u003c/p\u003e \u003cp\u003eBoth the two-process model and the synaptic homeostasis hypothesis suggest that because sleep is homeostatically regulated, increasing the duration of wakefulness is a powerful mechanism for promoting sleep [10]. Sleep restriction protocols are widely used and efficacious non-pharmacological interventions used to improve sleep depth [11]. Some research, however, suggests [12] that sleep restriction comes with side-effects as despite its sleep depth-increasing effects it negatively affects vigilance and subjective sleepiness.\u003c/p\u003e \u003cp\u003eA further prediction of the synaptic homeostasis hypothesis (although not necessarily the two-process model) is that more active and eventful wakefulness, even if its duration is unaltered, will similarly promote sleep depth without necessarily compromising its duration. In line with this prediction, some studies found increased sleep pressure and depth after a day of elevated mental or physical activity. For example, in early classic studies Horne and Minard [13] found increased EEG-based sleep depth after a full-day excursion, while Kobayashi et al [14] similarly found increased slow wave sleep after both a day with a 18 km jog (but not after a day with simple but repetitive mathematical tasks). Conversely, Hague et al [15] found that sleep depth is reduced in athletes after an inactive day.\u003c/p\u003e \u003cp\u003eFindings about the effect of mental strain on subsequent sleep are more mixed. Recently, Cerasuolo et al [16] published a systematic review about the effect of mental activity on subsequent sleep characteristics with 85 studies meeting inclusion criteria. Sleep spindles, slow wave activity, NREM delta power, NREM sigma power, and subjective sleep quality were enhanced, while awakenings, arousals and state transitions were reduced in a relative majority of studies. These findings are tentatively in line with mental activity promoting sleep depth, but the studies were small and their results heterogeneous.\u003c/p\u003e \u003cp\u003eSome literature exists about the effects of alcohol consumption and sexual activity as less conventional daily experiences on sleep. Experimental studies convincingly showed that alcohol consumption leads to increased sleep propensity in the first half of the night, but increased sleep fragmentation in the second [17]. Widespread belief exists that sexual intercourse induces somnolence [18], but empirical evidence for this is limited [19,20]. A larger literature exists about the effect of affective experiences on sleep [21], which, however, we considered beyond the scope of the current article which focuses on experiences during the day themselves rather than mood or emotions they may elicit.\u003c/p\u003e \u003cp\u003eDespite the utility of designing non-pharmacological interventions that improve sleep without mere sleep restriction, the literature about daily experiences on sleep is inconclusive. First, most studies are small, and the mapping of daily experiences is unsystematic and heterogeneous. Second, most previous studies on the effect of enhanced wakefulness on sleep were experimental. We previously argued [22,23] that this approach is suboptimal, because it reduces the feasible sample size, only one intervention can be evaluated per study, and many interventions are exaggerated compared to typical daily experiences, impacting ecological validity and the potential of translation to clinical practice. We have instead argued for multiday observational studies [24] in which the natural day-to-day covariation of daily experiences and sleep is investigated. Because they study time-lagged, within-person associations between daily experiences and sleep (for example, whether the same person typically has deeper sleep after a mentally challenging day) causality can be clearly established, and the non-invasive protocol with extensive daily measurements enables both larger sample sizes and the study of more effects. While some multiday observational studies about the effect of daily experiences are available (e.g. [19,25]), these generally rely on self-reports of sleep rather than objective measurements (see [26\u0026ndash;28] for some exceptions).\u003c/p\u003e \u003cp\u003eIn the current paper, we use BSETS, a large multiday observational study, to investigate the effects of a large array of self-reported experiences on subsequent objectively and subjectively measured sleep.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used data from the Budapest Sleep, Experiences and Traits Study (BSETS). The full BSETS protocol has been published separately [23]. In brief, BSETS recruited healthy volunteers to monitor their days and nights for a full week while performing their daily routine as normal. Each evening, participants filled out a questionnaire in which they indicated (yes/no) if a series of daily experiences happened to them, and subjectively rated their day (on a Likert scale) based on certain characteristics. The evening questionnaire also included a free-form diary of the events of the day. Each night, sleep was recorded with a Dreem2 mobile EEG headband. Each morning, participants filled out another questionnaire about their sleep, including subjectively rated sleep quality using the Groningen Sleep Quality Scale [29].\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board (IRB) of Semmelweis University, as well as the Hungarian Medical Council (under 7040-7/2021/ EÜIG \"Vonások és napi események hatása az alvási EEG-re\" [The effect of traits and daily activities and experiences on the sleep EEG]), approved BSETS as compliant with the latest revision of the Declaration of Helsinki. All participants gave written informed consent on a form reviewed and approved by the IRB.\u003c/p\u003e\n\u003cp\u003e267 participants took part in BSETS, of which hypnogram data was available from 258 and quantitative EEG data from 249. The mean age was 28.86 years (SD=12.73 years, range: 18-76 years). 45% of participants were male and 55% female. Observation-level descriptive statistics, including detailed sample sizes are available in \u003cstrong\u003eTable 1\u003c/strong\u003e. For all analyses we used all cases with available data for the relevant variables.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSleep characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach night, participants slept with a Dreem2 mobile EEG headband, which recorded quantitative EEG using dry silicone electrodes with a sampling frequency of 250 Hz [23,30]. A validated [31] complimentary algorithm scored these signals to create a hypnogram, from which we extracted the objective sleep characteristics sleep efficiency (SE), total sleep time (TST), sleep onset latency (SOL), REM latency, wake after sleep onset (WASO), the duration and percentage of N1, N2, N3 and REM sleep, as well as the number of awakenings. The clock time at sleep onset (expressed as fractional hours relative to midnight) was also recorded.\u003c/p\u003e\n\u003cp\u003eIn quantitative EEG analyses, we used the channel F7-O1 to calculate power spectral density in the delta (0.5-4 Hz) and low sigma (10-13 Hz) frequency bands using the periodogram() MATLAB EEGLab function with 2-second nonoverlapping epochs and Hamming windows. These were to track slow wave and sleep spindle activity, respectively. We chose this channel, as in previous studies [10,22], based on preliminary analyses [23] showing a favorable tradeoff between data quality and availability. The low, as opposed to the high, sleep spindle range was chosen because it is better mapped with the frontal channel positions available with the Dreem2 device [23].\u003c/p\u003e\n\u003cp\u003eIn addition to sleep macrostructure and quantitative EEG data, we also used scores on the Groningen Sleep Quality Scale (GSQS) as an indicator of subjective sleep quality. In total, we investigated effects on 18 sleep characteristics. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDaily experiences\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the evening diary, participants noted if any of the following 10 events happened to them:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThey spent time in the company of others (at least 30 minutes, excluding people they normally cohabit with)\u003c/li\u003e\n\u003cli\u003eThey were in school/university\u003c/li\u003e\n\u003cli\u003eThey were at a workplace\u003c/li\u003e\n\u003cli\u003eThey consumed alcohol (no data on quantity was collected)\u003c/li\u003e\n\u003cli\u003eThey watched movies or TV series for over an hour\u003c/li\u003e\n\u003cli\u003eThey spent at least one hour in front of a computer\u003c/li\u003e\n\u003cli\u003eThey had sexual contact with someone\u003c/li\u003e\n\u003cli\u003eThey drove a car or another vehicle for at least one hour\u003c/li\u003e\n\u003cli\u003eThey spent at least one hour travelling by public transportation\u003c/li\u003e\n\u003cli\u003eThey had a serious conflict or fight with someone\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBeside these binary-coded variables, participants also noted how much time they spent outside (including walking and transportation except closed vehicles) and how much time they spent reading or playing on cell phones or other smart devices. In total 12 daily experiences were investigated.\u003c/p\u003e\n\u003cp\u003eWe exclude naps and exercise, also part of the evening diary, from the list of daily experiences investigated here, as these will be the subject of separate studies. We excluded binary-coded smart device use as this was better captured by self-reported times. We also excluded medication intake, as only low quality data (the rare intake of highly heterogeneous medications) was available. Otherwise, we report findings on all daily experiences available in BSETS, as listed in the protocol [23].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDay ratings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to specific experiences, participants marked down on a 10-level Likert scale whether their day was “Physically exhausting”, “Mentally exhausting”, “Interesting and eventful” or “Happy” (as opposed to “Sad”). We used these daily ratings as further predictors of sleep characteristics, for a total of 16.\u003c/p\u003e\n\u003cp\u003eWe excluded specific daily emotions, rated using the Positive and Negative Affect Scale, as these will be part of a separate investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis followed a protocol identical to previous BSETS papers [10,22]. In brief, we performed multilevel models implemented with the MATLAB fitlme() function to estimate the effects of daily experiences on sleep characteristics. A separate model for each sleep experience and each sleep characteristic was run (272 models in total). Each model estimated Level 1 (within-individual) and Level 2 (between-individual) effects simultaneously. Within-individual effects indicate that within the same person, sleep was different after a daily experience. Due to the time-lagged, within-participant nature of these associations they strongly imply causality. Between-individual effects indicate that the frequency of daily experiences across the days of observations is associated with the means of sleep characteristics. These effects show correlations between typical sleep and daily experiences and do not imply causality.\u003c/p\u003e\n\u003cp\u003eAll models were corrected for day of the week (weekend/weekday), age, sex and lagged outcomes (the sleep metric used as the dependent variable from the previous night). Because of the well-known effect of process S on sleep characteristics, established also in BSETS [10], in order to separate the direct effects of daily experiences from indirect effects via extending wakefulness we also controlled all models for the duration of previous wakefulness. This was estimated by calculating the time between the last sleep epoch of the previous day’s EEG recording and the current day’s first sleep epoch [10]. This correction was not applied for models with sleep timing as the dependent variable as late sleep timing is a cause, not a consequence, of wakefulness duration.\u003c/p\u003e\n\u003cp\u003eFor a simpler analysis, we deviated from previous protocol [22] by using a transformation instead of winsorizing and generalized models for the skewed variables sleep onset latency, wake after sleep onset, and sleep efficiency. For the first two variables, a log10 transformation was used. For sleep efficiency, we used reflecting and log10 transforming so that the transformed value of SE was equal to SE\u003csub\u003et\u003c/sub\u003e=log10(100-SE).\u003c/p\u003e\n\u003cp\u003eDue to the large number of models tested and the need to balance power and replicability we employed a two-step procedure of significance testing. We report all findings which pass a relatively lax (given the number of models) significance threshold of p\u0026lt;0.01. However, we also performed a formal correction for multiple comparisons by performing the Benjamini-Hochberg correction for false discovery rate [32] across all sleep metrics for each daily experience. We focus our discussion on findings on those that pass this more conservative threshold.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRaw data to replicate our analyses is available at https://osf.io/92agb/.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eKey descriptive statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eDaily experiences tended to show low to intermediate within-participant clustering, the lowest for \u0026ldquo;interesting\u0026rdquo; day ratings (ICC\u0026thinsp;=\u0026thinsp;0.25), and the highest for driving (pseudo-ICC\u0026thinsp;=\u0026thinsp;0.66) and time with smart devices (pseudo-ICC\u0026thinsp;=\u0026thinsp;0.61). We created a correlation matrix of all sleep metrics including wakefulness duration, binary-coded daily experiences, and day ratings (\u003cb\u003eSupplementary data\u003c/b\u003e). The co-occurrence of binary-coded experiences (based on tetrachoric correlations) and the point-biserial correlations between binary-coded experiences and day ratings were low to moderate, but in the expected direction. For example, days with car driving tended to preclude public transportation (r=-0.481), and days with time in the company of others were rated as more interesting (r\u0026thinsp;=\u0026thinsp;0.248), happier (r\u0026thinsp;=\u0026thinsp;0.138), and more physically exhausting (r\u0026thinsp;=\u0026thinsp;0.121). Days with work were rated as more mentally exhausting (r\u0026thinsp;=\u0026thinsp;0.274), while days with conflicts were rated as less happy (r=-0.108). Days with movie watching, computer use, and more time with smart devices had negative-signed correlations with other daily experiences and daily ratings, except for movie watching being slightly positively associated with sex on the same day (r\u0026thinsp;=\u0026thinsp;0.104) and computer use being associated higher ratings of mental exhaustion (r\u0026thinsp;=\u0026thinsp;0.165), suggesting that these are activities which crowd out most others.\u003c/p\u003e \u003cp\u003eDay of the week (either as a 7-day categorical variable or coded as weekend/weekday) accounted for little variance in either sleep parameters or daily experiences. School attendance, work, public transportation use and ratings of days as mentally exhausting had the largest weekly variability, in line with social conventions scheduling free time for the weekend. Still, late sleep and interesting or pleasurable experiences tended to occur either on Friday or Saturday while work and chores were most frequently reported during the middle of the week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWithin-participant effects\u003c/h2\u003e \u003cp\u003eWithin-participant coefficients reflect the difference in sleep characteristics within the same person after having had a certain daily experience. These effects are in line with a causal interpretation.\u003c/p\u003e \u003cp\u003eThe sleep characteristic most frequently affected by daily experiences was sleep onset timing, which tended to be later after pleasurable or interesting activities.\u003c/p\u003e \u003cp\u003eSignificantly (after multiple comparisons) later sleep onset was observed after spending time in the company of others (B\u0026thinsp;=\u0026thinsp;0.403 hours, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), alcohol consumption (B\u0026thinsp;=\u0026thinsp;0.937 hours, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e), and days which were self-rated as more interesting (B\u0026thinsp;=\u0026thinsp;0.127 hours per Likert point, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) or happier (B\u0026thinsp;=\u0026thinsp;0.097 hours per Likert point, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Later sleep onset was also observed after sex (B\u0026thinsp;=\u0026thinsp;0.369 hours, p\u0026thinsp;=\u0026thinsp;0.009) and days self-rated as more physically exhausting (B\u0026thinsp;=\u0026thinsp;0.052 hours per Likert point, p\u0026thinsp;=\u0026thinsp;0.007) but due to the large number of sleep parameters tested these did not pass correction for multiple comparisons. Conversely, significantly earlier sleep onset was observed after desktop computer use (B=-0.401 hours, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eBeyond changes in sleep timing four other alterations in sleep structure were observed. Spending time in the company of others was followed by increased REM latency (B\u0026thinsp;=\u0026thinsp;9.317 minutes, p\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and alcohol consumption was followed by significantly reduced sleep latency (B=-0.06 log units, p\u0026thinsp;=\u0026thinsp;0.005) Time at work was followed by increased total sleep time (B\u0026thinsp;=\u0026thinsp;21.983 minutes, p\u0026thinsp;=\u0026thinsp;0.003) and N3 duration (B\u0026thinsp;=\u0026thinsp;5.708 minutes, p\u0026thinsp;=\u0026thinsp;0.005). These effects all survived correction for multiple comparisons.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e presents an overview of within-participant effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBetween-participant effects\u003c/h2\u003e \u003cp\u003eBetween-participant coefficients reflect correlations between typical sleep characteristics and the overall frequency of daily experiences or mean daily ratings. These effects do not necessarily imply causality.\u003c/p\u003e \u003cp\u003eOnly two between-participant effects were observed, and none passed a formal correction for multiple comparisons. In participants with more days at work lower mean N2 duration was observed (B=-24.223 minutes, p\u0026thinsp;=\u0026thinsp;0.003), and in participants spending more time outside mean N1 percentage was lower (B=-0.001 per hour outside, p\u0026thinsp;=\u0026thinsp;0.006). An interesting trend was that participants rating their mean day as happier tended to report better sleep on the Groningen Sleep Quality Scale (B=-0.219 GSQS points per Likert point, p\u0026thinsp;=\u0026thinsp;0.013), in line with our previous findings about the correlation of subjective sleep ratings and hedonic tone [22].\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e presents an overview of between-participant effects.\u003c/p\u003e \u003cp\u003eAll between- and within-participant effects are available in table form in the \u003cb\u003eSupplementary data\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations uncorrected for sleep homeostasis\u003c/h2\u003e \u003cp\u003eOur models were corrected for sleep pressure, indexed by the duration of wakefulness prior to sleep. We chose this model specification because we have shown that even in the naturalistic setting of BSETS, sleep propensity is increased after extended wakefulness [10]. Because longer wakefulness was observed after a large number of daily experiences, especially pleasant ones (\u003cb\u003eSupplementary data\u003c/b\u003e), deeper sleep after these would be expected due to homeostatic effects alone, rendering a statistical control for these necessary.\u003c/p\u003e \u003cp\u003eTo show the importance of controlling for homeostatic effects, we re-ran our analyses without the wakefulness duration covariate. While still no significant between-participant effects were seen, more interesting days now were also followed by significantly reduced sleep onset latency and WASO as well as increased delta power, while alcohol consumption and mentally exhausting days were both followed by increased N3 percentage (\u003cb\u003eSupplementary figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2\u003c/b\u003e, the full model outputs are available in the \u003cb\u003eSupplementary data\u003c/b\u003e). These effects could naively be interpreted as the experiences themselves leading to increased sleep propensity. However, their correlation with wakefulness duration (\u003cb\u003eSupplementary data\u003c/b\u003e) and the disappearance of their effects after controlling for the latter indicates that they merely index homeostatic effects and evidence for any sleep-enhancing effects of the experiences themselves remains scarce.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSleep complaints are common, and non-pharmacological means of reducing them are preferred due to a more holistic effect and a more favorable side effect profile. Sleep restriction therapy is a widely used non-pharmacological intervention, but its relative difficulty, patient adherence and daytime side-effects are a concern [12]. Under prevailing theories of sleep regulation [7,9], it is plausible that enhancing wakefulness without changing its duration creates additional sleep pressure which improves subsequent sleep. Such effects, if well-supported scientifically, could be the basis of simple non-pharmacological interventions alleviating the disease burden caused by sleep complaints in the population. These interventions would be especially promising if instead of the drastic manipulations seen in some experimental studies [13] their content would be simple and within the observable range of the normal behavior of patients, such as an increase in time spent outside or a reduction in time spent with smart devices. In the current study we tested if such interventions are feasible by investigating the link between day-to-day variation of various experiences and subsequent sleep.\u003c/p\u003e\n\u003cp\u003eOur main finding is that daily experiences principally affect the timing rather the structure of sleep. After a wide array of pleasurable experiences such as sexual intercourse, social activity and a day generally rated as more interesting or happy, sleep tends to occur later. Although pleasurable experiences are most likely to occur on the weekend (\u003cstrong\u003eTable 1\u003c/strong\u003e), these effects are significant even if day of the week is controlled for. A likely explanation of this finding is that pleasurable experiences are timed for the evening hours and function as zeitgebers to extend wakefulness. In addition, the emotional processes inherent to pleasurable experiences could serve as arousal inputs and override both the circadian and the homeostatic mechanisms involved in sleep initiation [33].\u003c/p\u003e\n\u003cp\u003eSlightly longer wakefulness was indeed observed on more experience-rich days (\u003cstrong\u003eSupplementary data\u003c/strong\u003e). Models not accounting for wakefulness duration (\u003cstrong\u003eSupplementary figure S1-S2\u003c/strong\u003e) found some evidence in favor of the sleep-promoting effects of experiences. We considered it essential to separate the effects of homeostatic effects resulting from increased wakefulness duration from the effects of wakeful experiences themselves. Therefore, in our main models we used the duration of previous wakefulness as a covariate. After this control, almost all apparent effects of daily experiences on the structure of subsequent sleep disappeared. \u003c/p\u003e\n\u003cp\u003eIn contrast to pioneering studies like those of Horne \u0026amp; Minard [13]\u003csup\u003e \u003c/sup\u003eor Kobayashi et al\u003csup\u003e \u003c/sup\u003e[14]\u003csup\u003e \u003c/sup\u003ewe found no evidence that enhanced wakefulness (based on participants\u0026rsquo; self-reports) affected the structure of subsequent sleep. While sleep occurred later after days rated as happier or more interesting, no effect on sleep structure was significant after controlling for homeostatic effects. A possibility of this discrepancy is either the well-known inflation of effect sizes (\u0026ldquo;winner\u0026rsquo;s curse\u0026rdquo;) in small early studies\u003csup\u003e \u003c/sup\u003e[34], or differences in the intensity of the experiences intended to promote sleep. For example, the nine participants of Horne \u0026amp; Minard participated in a full-day excursion lasting over 12 hours including visits to two separate outside venues, while in BSETS the intensity of wakefulness varied within the range naturally experienced by participants over the course of the 1-week study. In addition, most of the experiences reported in our present study were probably self-selected and self-paced in contrast to the Horne and Minard study, which was indeed interventional, involving the exposure of the subjects to multiple unexpected acquintances. Our findings do not rule out that very intensive enhancements of wakefulness promote sleep, however, these are less feasible as the basis of regularly administered non-pharmacological interventions. Our results provide little evidence that moderate enhancements or fluctuations in the intensity of wakeful experiences influence sleep structure. \u003c/p\u003e\n\u003cp\u003eWe found significantly longer sleep and N3 duration after work, but other than this no effect of school attendance, computer use, or self-reported mental exhaustion on sleep structure. This finding coheres with the broader literature, recently reviewed by Cerasuolo et al [16], which found mixed evidence at best for the effects of mental exhaustion on sleep. This review uses a vote-counting method to summarize studies, tabulating studies with significant negative, significant positive, and non-significant effects which we visually replicate on \u003cstrong\u003eFigure 3\u003c/strong\u003e. The vote-counting method is suboptimal because low power can lead to false negative, while publication bias can lead to false positive findings, but it is notable that for most sleep metrics negative findings predominate. Positive results were only more frequent than null findings for EEG metrics and the rarely mapped subjective sleep quality (k=4). \u003c/p\u003e\n\u003cp\u003eIn our current study based on a large ecologically valid sample we also found little evidence that mental exhaustion, at least within the range of normal weekly variation, substantially changes subsequent sleep. However, workplace attendance, arguably the most challenging and least self-selected and self-paced experience related to cognitive demand, was associated with increased sleep duration, specifically that of N3 sleep, suggesting that in a sufficiently high dosage mental exhaustion may have some effect on sleep duration and intensity.\u003c/p\u003e\n\u003cp\u003eEthanol, the active compound of alcoholic beverages was shown to display a GABAmimetic profile with a prominent depressing effect on central nervous system activity and complex interaction with ligand-gated ion channels [35,36]. Experimental studies are well-suited to study the acute effects of alcohol on sleep. This is because, unlike many other daily experiences which are more difficult to model experimentally, alcohol consumption is a simple quasi-pharmacological intervention. Previous experimental studies [17] have convincingly demonstrated that sleep propensity is increased after alcohol consumption, with increased sleep fragmentation in the second half of the night. Because of the uniquely high validity of experimental studies to investigate this effect, BSETS results are best interpreted as a replication of experimental findings which confirm the validity of the study setup. We could replicate reduced sleep onset latency after alcohol consumption, but we found no evidence for increased sleep fragmentation or worse sleep quality. While precise dosage was not recorded, an analysis of free-form evening diaries suggests that most alcohol consumption in BSETS occurred in social settings in relatively modest quantities. Low dosage can explain the mild hypnotic effect of alcohol consumption with no detriment to overall sleep quality.\u003c/p\u003e\n\u003cp\u003eThere is widespread belief that sexual intercourse causes somnolence, especially in men [18,37,38], leading to reduced sleep onset latency and potentially improved sleep quality. Despite the penetration of this idea into popular media[a], the effect has been little investigated empirically. A recent diary-based study [19] found that participants reported decreased sleep latency after sexual intercourse. However, these self-reports are likely not unbiased. Participants were interviewed about their views on the effects of intercourse on sleep, and there was widespread belief that sleep onset latency is reduced after sex. Because both sexual intercourse and sleep latency were reported the next morning, it possible that participants\u0026rsquo; recall was biased by expectations: that is, in line with their beliefs they erroneously reported lower sleep latency when they had sexual intercourse the previous night. After a small early study [20] to our knowledge ours is the second to investigate the effects of sexual intercourse on sleep with objective sleep data which is immune to this type of recall bias. Contrary to popular belief, but in line with early EEG findings, we found that sleep is not substantially altered after sexual intercourse, aside from later sleep onset. A possible limitation of our research is that we did not explicitly ask when sexual intercourse took place. Sex preceding sleep by too much may mask its effects on sleep parameters. However, as both the preferred and the realized time of day for sexual intercourse tends to be in the evening, often immediately before sleep [39,40] (see also[b] [c] [d] for valuable data from non-peer reviewed opinion surveys), it can be assumed that most sexual intercourse recorded in BSETS also occurred within a time window in which sleep-influencing effects could operate.\u003c/p\u003e\n\u003cp\u003eOur study found increased REM latency after social activity, an isolated finding which, however, had a very low p-value (p=0.0002) and remained significant after corrections for multiple testing. Substantially reduced REM latency values, up to the point of sleep onset REM periods (REM latency \u0026lt; 15 min) are known to constitute a diagnostic marker of narcolepsy [41], whereas more moderate reductions were sometimes shown to associate with primary depressive symptomatology [42,43]. Intraindividual (day-to-day) variability in the latency of REM sleep was much less frequently studied. Whereas recent evidence suggest that physical activity might prolong REM latency [44], here we reveal a similar effect of social activity. Available evidence only allows a highly speculative interpretation of this finding, which could be based on the social bonding theory of REM sleep [45], suggesting that the urge for REM sleep is reduced after social interactions. Given the recent increase in the interest in the social aspects of dreaming [46], our findings on the relationship between social interactions and delayed emergence of REM sleep during the subsequent night might fuel the renewed interest in REM latency within the context of behavioral regulatory processes.\u003c/p\u003e\n\u003cp\u003eOur study has a number of limitations. The most important limitation concerns the way daily experiences were recorded in BSETS. Unlike in an experimental study, which administers a carefully planned intervention even if its ecological validity is questionable, BSETS relied on participants self-reporting daily experiences as they saw them. While this intentional design feature increases ecological validity, it also means that the dosage of daily experiences in BSETS is hard to quantify, their timing is not explicitly recorded and usually not recoverable from daily diaries. Null results would be expected if participants\u0026rsquo; reports of daily experiences have little to do with actual events that potentially change sleep. However, given the within-participant design of the study (which uses day-to-day differences in the ratings of days as the source of variation in daily experiences), the observation of the expected correlations between daily experiences (for example, between conflicts and emotional valence of the day), and highly significant effects on sleep timing our view is that the subjective experiences reported in BSETS reflects a relatively accurate picture of the daily experiences participants would be able to modify to improve their sleep. A further limitation arguably is that while hypotheses are strong about the sleep-modifying effects of certain experiences (for example, sex), they are weaker or non-existent concerning for others (such as public transportation use). We included all experiences recorded in BSETS because our goal was to present a comprehensive picture of how daily experiences change sleep.\u003c/p\u003e\n\u003cp\u003eOverall, in a novel and arguably superior research design using multiday observations with mobile EEG recordings, we found strong evidence that the timing of sleep is affected by daily experiences. Less evidence was found, however, to support that daily experiences affect the structure of sleep. This observation calls for realism about the possible sleep-promoting effects of simple non-pharmacological interventions consisting of limiting certain common daily experiences (such as work or smart device use) or increasing others (such as time spent outside, enriching time spent awake, or spending more time in the company of others). While non-pharmacological interventions can be effective in promoting sleep, they either need to rely on well-supported homeostatic effects by extending the duration of wakefulness, or they need to be more intensive than simply fine-tuning the normal daily routine of those with sleep problems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis paper was supported by the J\u0026aacute;nos Bolyai Research Scholarship of the Hungarian Academy of Sciences. This research was supported by the National Research, Development and Innovation Office \u0026ndash; NKFIH (grant number: 138935), the Ministry of Culture and Innovation in Hungary (TKP2021-EGA-25, TKP2021-NKTA-47), as well as the CELSA Research Fund (CELSA/24/019).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrandner MA. Sleep, health, and society. \u003cem\u003eSleep Med Clin\u003c/em\u003e 2022; 17: 117\u0026ndash;139.\u003c/li\u003e\n\u003cli\u003eUjma PP, B\u0026oacute;dizs R. Sleep alterations as a function of 88 health indicators. \u003cem\u003emedRxiv\u003c/em\u003e 2023. doi:10.1101/2023.11.20.23298781.\u003c/li\u003e\n\u003cli\u003eTan X, Uchida S, Matsuura M, Nishihara K, Kojima T. Long-, intermediate- and short-acting benzodiazepine effects on human sleep EEG spectra. \u003cem\u003ePsychiatry Clin Neurosci\u003c/em\u003e 2003; 57: 97\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eArbon EL, Knurowska M, Dijk D-J. 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Similarity in chronotype and preferred time for sex and its role in relationship quality and sexual satisfaction. \u003cem\u003eFront Psychol\u003c/em\u003e 2018; 9: 443.\u003c/li\u003e\n\u003cli\u003eGolden EC, Lipford MC. Narcolepsy: Diagnosis and management. \u003cem\u003eCleve Clin J Med\u003c/em\u003e 2018; 85: 959\u0026ndash;969.\u003c/li\u003e\n\u003cli\u003eKupfer DJ. REM latency: a psychobiologic marker for primary depressive disease. \u003cem\u003eBiol Psychiatry\u003c/em\u003e 1976; 11: 159\u0026ndash;174.\u003c/li\u003e\n\u003cli\u003eOmichi C, Kadotani H, Sumi Y, Ubara A, Nishikawa K, Matsuda A \u003cem\u003eet al.\u003c/em\u003e Prolonged Sleep Latency and Reduced REM Latency Are Associated with Depressive Symptoms in a Japanese Working Population. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e 2022; 19. doi:10.3390/ijerph19042112.\u003c/li\u003e\n\u003cli\u003eZapalac K, Miller M, Champagne FA, Schnyer DM, Baird B. The effects of physical activity on sleep architecture and mood in naturalistic environments. \u003cem\u003eSci Rep\u003c/em\u003e 2024; 14: 5637.\u003c/li\u003e\n\u003cli\u003eMcNamara P. REM sleep: A social bonding mechanism. \u003cem\u003eNew Ideas Psychol\u003c/em\u003e 1996; 14: 35\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eTuominen J, Stenberg T, Revonsuo A, Valli K. Social contents in dreams: An empirical test of the Social Simulation Theory. \u003cem\u003eConscious Cogn\u003c/em\u003e 2019; 69: 133\u0026ndash;145. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tvtropes.org/pmwiki/pmwiki.php/Main/PostCoitalCollapse\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rubenarslan.github.io/posts/2019-04-08-sex-by-day-and-by-night/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hana.co.uk/blog/hanas-big-sex-survey-data-results/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.huffingtonpost.co.uk/entry/the-most-popular-time-and-day-to-have-sex-revealed_uk_645cc201e4b03e16f1a14403\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Descriptive statistics for the variables in the study. Medians and SDs are omitted for binary variables. Means of binary variables can be interpreted as the proportion of days with this experience. ICC is calculated as adjusted R\u003csup\u003e2\u003c/sup\u003e from either a linear regression (continuous variables) or logistic regression (binary variables, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e) using participant ID as the single categorical predictor and the target variable as the dependent variable. R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eweekend\u003c/sub\u003e and R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eday\u003c/sub\u003e originate from analogous models using binary weekday/weekend or the specific day of the week, respectively, as the single categorical predictor. R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eweekend\u003c/sub\u003e and R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eday\u003c/sub\u003e are expressed in percentages, not proportions, to enhance readability. The last column shows the day of the week on which the highest values (continuous variables) or the most common observations (binary variable) were observed. For sleep variables, these indicate the previous day. For \u0026ldquo;Self-rated sleep\u0026rdquo;, the highest value indicates the worst self-rated sleep due to the coding scheme of the GSQS.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValid N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eweekend\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eday\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay with highest value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eTotal sleep time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e396,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e402,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e91,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSleep onset latency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e14,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e10,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e14,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eREM latency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e79,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e70,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e36,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSleep onset time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eWASO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e21,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e15,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e21,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eTuesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSleep efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e91,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e92,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN1 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eTuesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN2 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e46,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e46,40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e9,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSunday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN3 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e21,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e21,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e9,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eREM %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e25,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e25,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e7,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN1 duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e24,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e22,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e10,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN2 duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e186,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e185,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e60,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eN3 duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e83,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e84,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e31,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eREM duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e103,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e101,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e38,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eAwakenings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e19,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e18,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e9,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSelf-rated sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e3,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eDelta power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSigma power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSocial interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eTuesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSchool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e18,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e22,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eWork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e8,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e8,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e3,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eWatching films\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eComputer use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e3,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eTime with smart devices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e72,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e45,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e89,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eSexual activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eDriving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003ePublic transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e8,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eWednesday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eTime outside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e75,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e50,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e87,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003ePhysically exhausting day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e-0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eMentally exhausting day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e7,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eInteresting day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFriday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eHappy day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e7,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eSaturday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.875%;\"\u003e\n \u003cp\u003eWakefulness duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e17,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e16,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e1,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eThursday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5290975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5290975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSleep characteristics may be affected by daytime experiences, a fact that can be leveraged by non-pharmacological interventions to improve sleep. The effect of daily experiences on sleep has been only sporadically investigated in the previous literature, mainly with small interventional studies. In this study, we leverage BSETS, a large multiday observational study (N\u0026thinsp;=\u0026thinsp;1901 nights in total) with extensive daily diaries and mobile EEG recordings conducted for at least 7 days per participant to investigate how naturally occurring daily experiences affect sleep during the subsequent night. The strongest influence was on the timing of sleep onset: even after controlling for day of the week, sleep onset occurred later after more intense days and pleasurable activities. After statistically accounting for this extended wakefulness, we found limited evidence that daily experiences influence sleep characteristics. Only four effects survived correction for multiple comparisons: sleep and N3 duration were longer after days with time at the workplace, REM latency was increased after social activity, and sleep onset latency was reduced after alcohol consumption. Our work shows that, aside from homeostatic effects resulting from extended wakefulness, sleep is relatively resilient to and only affected by a few distinct daytime experiences. Non-pharmacological interventions seeking to change sleep may need to utilize behavioral modifications outside the normally observed range.\u003c/p\u003e","manuscriptTitle":"Sleep timing and structure as a function of daily experiences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 23:58:39","doi":"10.21203/rs.3.rs-5290975/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b96cdfe-e2b0-4275-87b3-c304b9cf3e70","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39771124,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":39771125,"name":"Biological sciences/Neuroscience/Epigenetics in the nervous system/Epigenetics and plasticity"}],"tags":[],"updatedAt":"2024-12-09T16:51:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-03 23:58:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5290975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5290975","identity":"rs-5290975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}