Actigraphy‐assessed sleep during school and vacation periods: a naturalistic study of restricted and extended sleep opportunities in adolescents

School‐related sleep restriction in adolescents has been identified by studies comparing weekday and weekend sleep. This study compared weekday and vacation sleep to assess restricted and extended sleep opportunities. One‐hundred and forty‐six adolescents (47.3% male) aged 16.2 ± 1.0 years (M ± SD) from the general community wore an actigraph continuously for 4 weeks: the last week of a school term (Time‐E), the following 2‐week vacation, and the first week of the next term. Self‐reported sleep was assessed for each of the three time intervals, and chronotype was assessed using the Morningness–Eveningness Questionnaire at Time‐E. Daily actigraphy bedtime, rise‐time, time‐in‐bed, total sleep time, sleep onset latency, sleep efficiency, and % wake after sleep onset were analysed using latent growth curve modelling. The removal of school‐related sleep restriction was associated with an abrupt delay in sleep timing and increase in sleep duration. Subsequently, bedtime and rise‐time showed further linear delays throughout the vacation, while changes in time‐in‐bed were non‐significant. Sleep onset latency increased linearly, peaking in the middle of the second vacation week. Across the first vacation week, total sleep time and sleep efficiency linearly decreased, while % wake after sleep onset increased. These changes stabilized during the second vacation week. Older age and eveningness were associated with later bedtime and rise‐time, whilst females had longer time‐in‐bed, total sleep time and sleep onset latency. Compared with school days, sleep during the vacation was characterized by later timing, longer duration, lower quality and greater variability. Recovery from school‐related sleep restriction appeared to be completed within the 2 weeks of naturalistic extended sleep.     

Sleep patterns and insomnia among adolescents: a population‐based study

The aim of the current study was to examine sleep patterns and rates of insomnia in a population‐based study of adolescents aged 16–19 years. Gender differences in sleep patterns and insomnia, as well as a comparison of insomnia rates according to DSM‐IV, DSM‐V and quantitative criteria for insomnia (Behav. Res. Ther., 41 , 2003, 427), were explored. We used a large population‐based study in Hordaland county in Norway, conducted in 2012. The sample included 10 220 adolescents aged 16–18 years (54% girls). Self‐reported sleep measurements included bedtime, rise time, time in bed, sleep duration, sleep efficiency, sleep onset latency, wake after sleep onset, rate and frequency and duration of difficulties initiating and maintaining sleep and rate and frequency of tiredness and sleepiness. The adolescents reported short sleep duration on weekdays (mean 6:25 hours), resulting in a sleep deficiency of about 2 h. A majority of the adolescents (65%) reported sleep onset latency exceeding 30 min. Girls reported longer sleep onset latency and a higher rate of insomnia than boys, while boys reported later bedtimes and a larger weekday–weekend discrepancy on several sleep parameters. Insomnia prevalence rates ranged from a total prevalence of 23.8 (DSM‐IV criteria), 18.5 (DSM‐V criteria) and 13.6% (quantitative criteria for insomnia). We conclude that short sleep duration, long sleep onset latency and insomnia were prevalent in adolescents. This warrants attention as a public health concern in this age group.     

Sleep quality is negatively related to video gaming volume in adults

Most literature on the relationship between video gaming and sleep disturbances has looked at children and adolescents. There is little research on such a relationship in adult samples. The aim of the current study was to investigate the association of video game volume with sleep quality in adults via face‐to‐face interviews using standardized questionnaires. Adults (n = 844, 56.2% women), aged 18–94 years old, participated in the study. Sleep quality was measured using the Pittsburgh Sleep Quality Index, and gaming volume was assessed by asking the hours of gaming on a regular weekday (Mon–Thurs), Friday and weekend day (Sat–Sun). Adjusting for gender, age, educational level, exercise and perceived stress, results of hierarchical regression analyses indicated that video gaming volume was a significant predictor of sleep quality (β = 0.145), fatigue (β = 0.109), insomnia (β = 0.120), bedtime (β = 0.100) and rise time (β = 0.168). Each additional hour of video gaming per day delayed bedtime by 6.9 min (95% confidence interval 2.0–11.9 min) and rise time by 13.8 min (95% confidence interval 7.8–19.7 min). Attributable risk for having poor sleep quality (Pittsburgh Sleep Quality Index > 5) due to gaming >1 h day was 30%. When examining the components of the Pittsburgh Sleep Quality Index using multinomial regression analysis (odds ratios with 95% confidence intervals), gaming volume significantly predicted sleep latency, sleep efficiency and use of sleep medication. In general, findings support the conclusion that gaming volume is negatively related to the overall sleep quality of adults, which might be due to underlying mechanisms of screen exposure and arousal.     

An approach to understanding sleep and depressed mood in adolescents: person‐centred sleep classification

Depressive mood in youth has been associated with distinct sleep dimensions, such as timing, duration and quality. To identify discrete sleep phenotypes, we applied person‐centred analysis (latent class mixture models) based on self‐reported sleep patterns and quality, and examined associations between phenotypes and mood in high‐school seniors. Students (n = 1451; mean age = 18.4 ± 0.3 years; 648 M) completed a survey near the end of high‐school. Indicators used for classification included school night bed‐ and rise‐times, differences between non‐school night and school night bed‐ and rise‐times, sleep‐onset latency, number of awakenings, naps, and sleep quality and disturbance. Mood was measured using the total score on the Center for Epidemiologic Studies‐Depression Scale. One‐way anova tested differences between phenotype for mood. Fit indexes were split between 3‐, 4‐ and 5‐phenotype solutions. For all solutions, between phenotype differences were shown for all indicators: bedtime showed the largest difference; thus, classes were labelled from earliest to latest bedtime as ‘A’ (n = 751), ‘B’ (n = 428) and ‘C’ (n = 272) in the 3‐class solution. Class B showed the lowest sleep disturbances and remained stable, whereas classes C and A each split in the 4‐ and 5‐class solutions, respectively. Associations with mood were consistent, albeit small, with class B showing the lowest scores. Person‐centred analysis identified sleep phenotypes that differed in mood, such that those with the fewest depressive symptoms had moderate sleep timing, shorter sleep‐onset latencies and fewer arousals. Sleep characteristics in these groups may add to our understanding of how sleep and depressed mood associate in teens.     

Sleep and academic performance in later adolescence: results from a large population‐based study

The aim of the current study was to assess the association between sleep duration and sleep patterns and academic performance in 16–19 year‐old adolescents using registry‐based academic grades. A large population‐based study from Norway conducted in 2012, the youth@hordaland‐survey, surveyed 7798 adolescents aged 16–19 years (53.5% girls). The survey was linked with objective outcome data on school performance. Self‐reported sleep measures provided information on sleep duration, sleep efficiency, sleep deficit and bedtime differences between weekday and weekend. School performance [grade point average (GPA)] was obtained from official administrative registries. Most sleep parameters were associated with increased risk for poor school performance. After adjusting for sociodemographic information, short sleep duration and sleep deficit were the sleep measures with the highest odds of poor GPA (lowest quartile). Weekday bedtime was associated significantly with GPA, with adolescents going to bed between 22:00 and 23:00 hours having the best GPA. Also, delayed sleep schedule during weekends was associated with poor academic performance. The associations were somewhat reduced after additional adjustment for non‐attendance at school, but remained significant in the fully adjusted models. In conclusion, the demonstrated relationship between sleep problems and poor academic performance suggests that careful assessment of sleep is warranted when adolescents are underperforming at school. Future studies are needed on the association between impaired sleep in adolescence and later functioning in adulthood.     

Brief school‐based interventions to assist adolescents’ sleep‐onset latency: Comparing mindfulness and constructive worry versus controls

Difficulties falling asleep are common among adolescents, especially during times of stress. Adolescents may thus benefit from brief techniques (15 min) that decrease pre‐sleep cognitive‐emotional arousal and sleep‐onset latency. The present study used a 3 (intervention: mindfulness bodyscan mp3, constructive worry, control) by 3 (time: baseline, week 1, week 2) mixed‐model design on a school‐based sample of adolescents (N = 232; M_age = 15.9 ± 0.8 years, range = 14–18 years; 19% male), and a sub‐sample of adolescents with prolonged sleep‐onset latency (i.e. ≥30 min; N = 119; M_age = 16.9 ± 0.9 years; 21% male). It was expected that the 15‐min pre‐recorded breath‐based mindfulness bodyscan, and constructive worry, would decrease sleep‐onset latency and pre‐sleep arousal similarly over time, relative to the control condition. A significant interaction was observed among adolescents with prolonged sleep‐onset latency, who completed ≥3 days for at least 1 week (p = .001), where mindfulness decreased sleep‐onset latency relative to constructive worry and the control. Neither technique changed pre‐sleep worry or cognitive‐emotional arousal, or associated daytime functioning (both the whole sample and sub‐sample). A pre‐recorded mp3 breath‐based mindfulness bodyscan technique is a promising means by which adolescents with prolonged sleep‐onset latency can decrease sleep‐onset latency. This simple tool has potential for scalable dissemination by stakeholders (e.g. teachers), unqualified to treat adolescent sleep difficulties. Future studies are needed to determine whether benefits may extend to academic performance and mental health, if performed for a longer time period with increased compliance.     

Disturbed sleep in attention‐deficit hyperactivity disorder (ADHD) is not a question of psychiatric comorbidity or ADHD presentation

Attention‐deficit hyperactivity disorder (ADHD) is a heterogeneous psychiatric disorder with three different presentations and high levels of psychiatric comorbidity. Serious sleep complaints are also common, but the role of the presentations and comorbidity in sleep is under‐investigated in ADHD. Consequently, the goal of the study was to investigate sleep problems in medicine‐naive school‐aged children (mean age = 9.6 years) with ADHD compared to controls using objective methods and to examine the role of comorbidity and presentations. Ambulatory polysomnography results suggested that children with ADHD (n = 76) had significantly more sleep disturbances than controls (n = 25), including a larger percentage of rapid eye movement (REM) sleep and more sleep cycles, as well as lower mean sleep efficiency, mean non‐REM (NREM) sleep stage 1 and mean NREM sleep stage 3. No significant between‐group differences were found on the multiple sleep latency test. Stratifying for comorbidity in the ADHD group did not reveal major differences between groups, but mean sleep latency was significantly longer in children with ADHD and no comorbidity compared to controls (36.1 min; SD = 30.1 versus 22.6 min; SD = 15.2). No differences were found between ADHD presentations. Our results support the presence of night‐time sleep disturbances in children with ADHD. Poor sleep does not appear to be attributable to comorbidity alone, nor do sleep disturbances differ within ADHD presentations.     

Objectively measured night‐to‐night sleep variations are associated with body composition in very elderly women

This cross‐sectional study examined the association between objectively measured sleep patterns and body composition in very elderly community‐dwelling women. Participants included 191 community‐dwelling adults aged ≥ 80 years (mean age: 83.4 ± 2.6 years; age range: 80–92 years). Sleep and physical activity were monitored via accelerometer (ActiGraph GT3X+) during at least five consecutive 24‐h periods. Night‐to‐night sleep pattern variability across all nights of recording was assessed using standard deviations (SDs). Body composition was assessed using dual‐energy X‐ray absorptiometry. Simple and multivariable linear regression analyses were performed. The mean number of nights with usable actigraphy data was 7.3 ± 1.3. On average, participants went to bed at 22:57 hours (SD: 1.11 h) and rose from bed at 6:27 hours (SD: 1.01 h). Night‐to‐night bedtime, sleep duration and sleep timing mid‐point variations correlated slightly with the percentage body fat and percentage lean mass (P < 0.05). Multiple linear regression analysis revealed significant associations of night‐to‐night bedtime variations and inconsistent sleep–wake patterns with all body composition indices after adjusting for potential confounding factors, including mean nightly sleep duration, self‐reported nap duration and daily physical activity. After further adjusting for night‐to‐night wake time, sleep timing mid‐point and sleep duration variations, greater bedtime variability remained associated significantly with all body composition indices except lean/fat mass ratio. Inconsistent sleep–wake patterns were associated independently with an increased fat mass and decreased lean mass among very elderly women. These findings suggest that in most elderly individuals, sleep patterns might be an important modifiable factor associated with obesity and sarcopenia development.     

A pilot study of a novel smartphone application for the estimation of sleep onset

The aim of the study was to investigate the accuracy of Sleep On Cue: a novel iPhone application that uses behavioural responses to auditory stimuli to estimate sleep onset. Twelve young adults underwent polysomnography recording while simultaneously using Sleep On Cue. Participants completed as many sleep‐onset trials as possible within a 2‐h period following their normal bedtime. On each trial, participants were awoken by the app following behavioural sleep onset. Then, after a short break of wakefulness, commenced the next trial. There was a high degree of correspondence between polysomnography‐determined sleep onset and Sleep On Cue behavioural sleep onset, r = 0.79, P < 0.001. On average, Sleep On Cue overestimated sleep‐onset latency by 3.17 min (SD = 3.04). When polysomnography sleep onset was defined as the beginning of N2 sleep, the discrepancy was reduced considerably (M = 0.81, SD = 1.96). The discrepancy between polysomnography and Sleep On Cue varied between individuals, which was potentially due to variations in auditory stimulus intensity. Further research is required to determine whether modifications to the stimulus intensity and behavioural response could improve the accuracy of the app. Nonetheless, Sleep On Cue is a viable option for estimating sleep onset and may be used to administer Intensive Sleep Retraining or facilitate power naps in the home environment.     

Microsleep as a marker of sleepiness in obstructive sleep apnea patients

We hypothesized that: (a) the presence of microsleep (MS) during a Maintenance Wakefulness Test (MWT) trial may represent a reliable marker of sleepiness in obstructive sleep apnea (OSA) patients; (b) the number of MSs will be higher in sleepy versus non‐sleepy patients with a borderline MWT mean sleep latency; and (c) scoring MS during MWT analysis may help physicians to recognize patients with a higher degree of sleepiness. We analysed the MWT data of 112 treatment‐naïve OSA patients: 20 with short sleep latency (SL, sleep latency <12.8 min), 43 with borderline latency (BL, sleep latency between 12.8 and 32.6 min) and 49 with normal latency (NL, sleep latency >32.6 min). Microsleep was identified in all SL, in 42 BL and in 18 NL patients, with a median latency of 5.6 min. Accordingly, patients were classified into two subgroups: group A (n = 43) with microsleep latency <5.6 min and group B (n = 69) with microsleep latency >5.6 min when present. The mean sleep latency in the MWT was 14.5 ± 7.5 min in group A and 34.6 ± 7.4 min in group B (p < 0.0001). The number of microsleep episodes during each MWT trial was higher in group A than in group B. Sleep latency survival curves demonstrated different patterns of sleep latency in these groups (log‐rank test <0.0001). This finding was confirmed in a Cox proportional hazard analysis: the presence of a mean MS latency <5.6 min is associated with an increasing risk of falling asleep during the MWT (RR, 1.93; 95 CI 1.04–3.6; p = 0.03). We conclude that the detection of microsleep may help in discriminating OSA patients with and without daytime vigilance impairment.     

Changes in sleep pattern,sense of time and digital media use during COVID‐19 lockdown in Italy

Italy is one of the major COVID‐19 hotspots. To reduce the spread of the infections and the pressure on Italian healthcare systems, since March 10, 2020, Italy has been under a total lockdown, forcing people into home confinement. Here we present data from 1,310 people living in the Italian territory (M_age = 23.91 ± 3.60 years, 880 females, 501 workers, 809 university students), who completed an online survey from March 24 to March 28, 2020. In the survey, we asked participants to think about their use of digital media before going to bed, their sleep pattern and their subjective experience of time in the previous week (March 17–23, which was the second week of the lockdown) and up to the first week of February (February 3–10, before any restriction in any Italian area). During the lockdown, people increased the usage of digital media near bedtime, but this change did not affect sleep habits. Nevertheless, during home confinement, sleep timing markedly changed, with people going to bed and waking up later, and spending more time in bed, but, paradoxically, also reporting a lower sleep quality. The increase in sleep difficulties was stronger for people with a higher level of depression, anxiety and stress symptomatology, and associated with the feeling of elongation of time. Considering that the lockdown is likely to continue for weeks, research data are urgently needed to support decision making, to build public awareness and to provide timely and supportive psychosocial interventions.     

Chronic stress undermines the compensatory sleep efficiency increase in response to sleep restriction in adolescents

To investigate the effects of real‐life stress on the sleep of adolescents, we performed a repeated‐measures study on actigraphic sleep estimates and subjective measures during one regular school week, two stressful examination weeks and a week's holiday. Twenty‐four adolescents aged 17.63 ± 0.10 years (mean ± standard error of the mean) wore actigraphs and completed diaries on subjective stress, fatigue, sleep quality, number of examinations and consumption of caffeine and alcohol for 4 weeks during their final year of secondary school. The resulting almost 500 assessments were analysed using mixed‐effect models to estimate the effects of mere school attendance and additional examination stress on sleep estimates and subjective ratings. Total sleep time decreased from 7:38 h ± 12 min during holidays to 6:40 h ± 12 min during a regular school week. This 13% decrease elicited a partial compensation, as indicated by a 3% increase in sleep efficiency and a 6% decrease in the duration of nocturnal awakenings. During examination weeks total sleep time decreased to 6:23 h ± 8 min, but it was now accompanied by a decrease in sleep efficiency and subjective sleep quality and an increase in wake bout duration. In conclusion, school examination stress affects the sleep of adolescents. The compensatory mechanism of more consolidated sleep, as elicited by the sleep restriction associated with mere school attendance, collapsed during 2 weeks of sustained examination stress.     

Preventing sleep deficit in adolescents: Long‐term effects of a quasi‐experimental school‐based intervention study

Adolescents are at risk of sleep deficit, which has serious consequences for their daytime functioning. However, school‐based interventions to improve sleep have shown limited success. This might be due to the content of the programmes (e.g., not targeting central factors such as daytime stress and technology use) or because changes have not been captured due to a lack of long‐term follow‐ups. Hence, the aim of this study was to evaluate the long‐term effects of a school‐based sleep education curriculum including time‐management training. The study used a quasi‐experimental design. Participants were 3,622 adolescents (mean age 13.7, 48% girls); 286 were in the intervention group and 3,336 were followed as a natural control group. Data were collected before the intervention and at a 1‐year follow‐up. We divided participants into three groups according to baseline sleep duration (calculated from self‐reported bed‐ and wake times, minus sleep onset latency): insufficient (<7 hr), borderline (7–8 hr) and adequate (>8 hr). Adolescents in the intervention group were ~2 times less likely to report insufficient sleep at follow‐up as compared to controls. Sleep knowledge improved significantly in the intervention group but there were no changes in emotional sleep hygiene (e.g., bedtime worry) and perceived stress. Surprisingly, technology use increased and behavioural sleep hygiene worsened in the intervention group. Although the mechanisms of change need further investigation, the results of this study point to potential long‐term benefits of school‐based sleep programmes.     

Slow oscillating transcranial direct current stimulation during sleep has a sleep‐stabilizing effect in chronic insomnia: a pilot study

Recent evidence suggests that lack of slow‐wave activity may play a fundamental role in the pathogenesis of insomnia. Pharmacological approaches and brain stimulation techniques have recently offered solutions for increasing slow‐wave activity during sleep. We used slow (0.75 Hz) oscillatory transcranial direct current stimulation during stage 2 of non‐rapid eye movement sleeping insomnia patients for resonating their brain waves to the frequency of sleep slow‐wave. Six patients diagnosed with either sleep maintenance or non‐restorative sleep insomnia entered the study. After 1 night of adaptation and 1 night of baseline polysomnography, patients randomly received sham or real stimulation on the third and fourth night of the experiment. Our preliminary results show that after termination of stimulations (sham or real), slow oscillatory transcranial direct current stimulation increased the duration of stage 3 of non‐rapid eye movement sleep by 33 ± 26 min (P = 0.026), and decreased stage 1 of non‐rapid eye movement sleep duration by 22 ± 17.7 min (P = 0.028), compared with sham. Slow oscillatory transcranial direct current stimulation decreased stage 1 of non‐rapid eye movement sleep and wake time after sleep‐onset durations, together, by 55.4 ± 51 min (P = 0.045). Slow oscillatory transcranial direct current stimulation also increased sleep efficiency by 9 ± 7% (P = 0.026), and probability of transition from stage 2 to stage 3 of non‐rapid eye movement sleep by 20 ± 17.8% (P = 0.04). Meanwhile, slow oscillatory transcranial direct current stimulation decreased transitions from stage 2 of non‐rapid eye movement sleep to wake by 12 ± 6.7% (P = 0.007). Our preliminary results suggest a sleep‐stabilizing role for the intervention, which may mimic the effect of sleep slow‐wave‐enhancing drugs.     

Exploring the impact of experimental sleep restriction and sleep deprivation on subjectively perceived sleep parameters

We aimed to investigate the effect of increased sleep pressure and shortened sleep duration on subjective sleep perception in relation to electroencephalographic sleep measures. We analyzed the data from a study in which 14 healthy male volunteers had completed a baseline assessment with 8 hr time in bed, a sleep deprivation (40 hr of wakefulness) and a sleep restriction protocol with 5 hr time in bed during 7 nights. In this work, we assessed perception index, derived through dividing the subjectively perceived total sleep time, wake after sleep onset and sleep latency duration by the objectively measured one at each condition. We found that total sleep time was subjectively underestimated at baseline and shifted towards overestimation during sleep restriction and after deprivation. This change in accuracy of subjective estimates was not associated with any changes in sleep architecture or sleep depth. Wake after sleep onset was significantly underestimated only during sleep restriction. Sleep latency was always overestimated subjectively without any significant change in this misperception across conditions. When comparing accuracy of subjective and actimetry estimates, subjective estimates regarding total sleep time and wake after sleep onset deviated less from electroencephalography derived measures during sleep restriction and after deprivation. We conclude that self‐assessments and actimetry data of patients with chronic sleep restriction should be interpreted cautiously. The subjectively decreased perception of wake after sleep onset could lead to overestimated sleep efficiency in such individuals, whereas the underestimation of sleep time and overestimation of wake after sleep onset by actimetry could lead to further underestimated sleep duration.     

The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient‐ and device‐specific factors

Clinical actigraphy devices provide adequate estimates of some sleep measures across large groups. In practice, providers are asked to apply clinical or consumer wearable data to individual patient assessments. Inter‐individual variability in device performance will impact such patient‐specific interpretation. We assessed two devices, clinical and consumer, to determine the magnitude and predictors of this individual‐level variability. One hundred and two patients (55 [53.9%] female; 56.4 [±16.3] years old) undergoing polysomnography wore Jawbone UP3 and/or Actiwatch2. Device total sleep time, sleep efficiency, wake after sleep onset and sleep latency were compared with polysomnography. Demographics, sleep architecture and clinical measures were compared to device performance. Actiwatch overestimated total sleep time by 27.2 min (95% confidence limits [CL], 138.3 min over to 84.0 under), overestimated sleep efficiency by 6.8% (95% CL, 34.1% over to 20.5% under), overestimated sleep onset latency by 2.6 min (95% CL, 63.3 over to 58.2 under) and underestimated wake after sleep onset by 50.7 min (95% CL, 162.5 under to 61.2 over). Jawbone overestimated total sleep time by 59.1 min (95% CL, 208.6 min over to 90.5 under) and overestimated sleep efficiency by 14.9% (95% CL, 52.6% over to 22.7% under). In multivariate models, age, sleep onset latency, wake after sleep onset, % N1 and apnea–hypopnea index explained only some of the variance in device performance. Gender also affected performance. Actiwatch and Jawbone mis‐estimate sleep measures with very wide confidence limits and accuracy varies with multiple patient‐level characteristics. Given these large individual inaccuracies, data from these devices must be applied only with extreme caution in clinical practice.     

Exercise before bed does not impact sleep inertia in young healthy males

Sleep inertia is the transitional state marked by impaired cognitive performance and reduced vigilance upon waking. Exercising before bed may increase the amount of slow‐wave sleep within the sleep period, which has previously been associated with increased sleep inertia. Healthy males (n = 12) spent 3 nights in a sleep laboratory (1‐night washout period between each night) and completed one of the three conditions on each visit – no exercise, aerobic exercise (30 min cycling at 75% heart rate), and resistance exercise (six resistance exercises, three sets of 10 repetitions). The exercise conditions were completed 90 min prior to bed. Sleep was measured using polysomnography. Upon waking, participants completed five test batteries every 15 min, including the Karolinska Sleepiness Scale, a Psychomotor Vigilance Task, and the Spatial Configuration Task. Two separate linear mixed‐effects models were used to assess: (a) the impact of condition; and (b) the amount of slow‐wave sleep, on sleep inertia. There were no significant differences in sleep inertia between conditions, likely as a result of the similar sleep amount, sleep structure and time of awakening between conditions. The amount of slow‐wave sleep impacted fastest 10% reciprocal reaction time on the Psychomotor Vigilance Task only, whereby more slow‐wave sleep improved performance; however, the magnitude of this relationship was small. Results from this study suggest that exercise performed 90 min before bed does not negatively impact on sleep inertia. Future studies should investigate the impact of exercise intensity, duration and timing on sleep and subsequent sleep inertia.     

Home versus laboratory assessments of melatonin production and melatonin onset in young adults complaining of a delayed sleep schedule

Recent evidence points toward an association between higher non‐visual sensitivity to light and a later circadian phase in young adults complaining of a delayed sleep schedule. Light exposure in the evening may therefore induce a larger suppression of melatonin production in these individuals, which might: (a) bias home estimates of melatonin onset; and (b) decrease sleep propensity at bedtime. In this study, we compared home and laboratory melatonin onsets and production in sleep‐delayed and control participants, using saliva samples collected in the 3 hr preceding habitual bedtime. The mean light intensity measured during saliva sampling at home was ~10 lux in both groups. Melatonin suppression at home was significant, averaging 31% and 24% in sleep‐delayed and control individuals, respectively. Group difference in melatonin suppression was not significant. Estimates of melatonin onset were on average 27 min later at home than in laboratory conditions, with no group difference. Looking specifically at sleep‐delayed participants, there was no correlation between non‐visual sensitivity to light and home–laboratory differences in melatonin onsets. However, higher light sensitivity was associated with greater melatonin suppression in the hour before habitual bedtime. Greater melatonin suppression before bedtime was also associated with a later circadian phase. These results indicate that the validity of home estimates of melatonin onset is similar in sleep‐delayed and in control individuals. Results also suggest that increased non‐visual sensitivity to light could impact melatonin secretion in sleep‐delayed individuals and contribute to a late bedtime by delaying circadian phase and decreasing sleep propensity.     

Endothelial function and sleep: associations of flow‐mediated dilation with perceived sleep quality and rapid eye movement (REM) sleep

Endothelial function typically precedes clinical manifestations of cardiovascular disease and provides a potential mechanism for the associations observed between cardiovascular disease and sleep quality. This study examined how subjective and objective indicators of sleep quality relate to endothelial function, as measured by brachial artery flow‐mediated dilation (FMD). In a clinical research centre, 100 non‐shift working adults (mean age: 36 years) completed FMD testing and the Pittsburgh Sleep Quality Index, along with a polysomnography assessment to obtain the following measures: slow wave sleep, percentage rapid eye movement (REM) sleep, REM sleep latency, total arousal index, total sleep time, wake after sleep onset, sleep efficiency and apnea–hypopnea index. Bivariate correlations and follow‐up multiple regressions examined how FMD related to subjective (i.e. Pittsburgh Sleep Quality Index scores) and objective (i.e. polysomnography‐derived) indicators of sleep quality. After FMD showed bivariate correlations with Pittsburgh Sleep Quality Index scores, percentage REM sleep and REM latency, further examination with separate regression models indicated that these associations remained significant after adjustments for sex, age, race, hypertension, body mass index, apnea–hypopnea index, smoking and income (Ps < 0.05). Specifically, as FMD decreased, scores on the Pittsburgh Sleep Quality Index increased (indicating decreased subjective sleep quality) and percentage REM sleep decreased, while REM sleep latency increased (Ps < 0.05). Poorer subjective sleep quality and adverse changes in REM sleep were associated with diminished vasodilation, which could link sleep disturbances to cardiovascular disease.     

Sleep and morningness-eveningness in the ‘middle’ years of life (20–59y)

SUMMARY The following four issues were assessed in a group of 110 adults between the age of 20 and 59y: (1) the effect of age (regarded as a continuous variable) on polysomnographic sleep characteristics, habitual sleep-diary patterns, and subjective sleep quality; (2) the effects of age on morningness-eveningness; (3) the effects of morningness-eveningness on sleep, after controlling for the effects of age; and (4) the role of morningness-eveningness as a mediator of the age and sleep relationship. Increasing age was related to earlier habitual waketime, earlier bedtime, less time in bed and better mood and alertness at waketime. In the laboratory, increasing age was associated with less time asleep, increased number of awakenings, decreased sleep efficiency, lower percentages of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, higher percentages of Stage 1 and 2, shorter REM latency and reduced REM activity and density. Increasing age was also associated with higher morningness scores. After controlling for the effects of age, morningness was associated with earlier waketime, earlier bedtime, less time in bed, better alertness at waketime, less time spent asleep, more wake in the last 2 h of sleep, decreased REM activity, less stage REM (min and percentage), more Stage 1 (min and percentage) and fewer minutes of Stage 2. For one set of variables (night time in bed, waketime, total sleep time, wake in the last 2 h of sleep and minutes of REM and REM activity), morningness-eveningness accounted for about half of the relationship between age and sleep. For another set of variables (bedtime, alertness at waketime, percentages of REM and Stage 1), morningness-eveningness accounted for the entire relationship between age and sleep. In conclusion, age and morningness were both important predictors of the habitual sleep patterns and polysomnographic sleep characteristics of people in the middle years of life (20-59 y).