The incidence and temporal patterning of insomnia: a second study

Whether subjects with insomnia exhibit good sleep on some interval basis is unclear. Prior research suggests that patients with insomnia are highly variable with respect to night‐to‐night sleep continuity, that more than 40% of patients exhibit temporal patterning of good sleep, and that nearly 90% of patients exhibit better than average sleep following 1 to 3 nights of relatively poor sleep. The aim of the present study was to replicate and extend the above‐noted findings utilizing: (i) a large sample studied over an extended time interval (ii) absolute standards for ‘good’ and ‘poor’ sleep; and (iii) a formal statistical methodology to assess temporal patterning and the association of time in bed with bout duration of poor or average sleep. Thirty‐three subjects with insomnia and 33 good sleepers completed sleep diaries over the course of 110 days. It was found that subjects with insomnia (compared to good sleepers) had more poor nights (e.g. about 39 versus 7% of the assessed nights), a higher probability of a having a poor night on any given occasion (60% greater probability than good sleepers) and more consecutive nights of poor sleep between good sleep nights (median bout duration of approximately three versus one night). Lastly, it was found that (as would be predicted by both the Spielman model and the two‐process model) time in bed moderated bout duration in the insomnia group. That is, longer times in bed were associated with longer bouts of poor sleep.     

Sleep‐related attentional bias in poor versus good sleepers is independent of affective valence

Contradictory evidence exists relating to the presence of an attention bias to sleep‐related stimuli in poor sleepers/insomnia using the emotional Stroop task (EST). These inconsistencies may be due to methodological issues related to the affective valence of the sleep‐related stimuli. Thus, individuals may attend differentially to sleep‐related stimuli not because of their ‘sleep’ properties, but their negativity. The current study addresses this by controlling the affective valence of sleep‐related words. A total of 107 participants [mean age = 33.22 years, standard deviation (SD) = 12.31 years; 61.7% female] were recruited during an evening event at the Newcastle Science Festival. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and a computerized EST containing 20 non‐affective sleep‐related, 20 neutral and 20 negatively valenced threat words. Good and poor sleepers were categorized using the PSQI. There were no significant differences between groups on response latency to sleep‐related words (t₍₁₀₅₎ = –0.30, P = 0.76). However, the interaction between good versus poor sleepers and word‐type on response latency was significant (F_(2,210) = 3.06, P < 0.05). Poor sleepers took longer to respond to sleep‐related words (mean = 723.35, SD = 172.55) compared to threat words (mean = 694.63, SD = 162.17) than good sleepers (mean = 713.20, SD = 166.32; and mean = 716.65, SD = 181.14). The results demonstrate the presence of an attention bias towards sleep‐related stimuli compared to threat stimuli in poor sleepers. Accordingly, poor sleepers may be consumed by stimuli relevant to their specific difficulties, as well as being more highly attuned to negative cues that signal anxious states. Thus, the present research suggests that there are two opposing forces at play: one which facilitates performance (non‐specific threats) and one which hinders performance (personally relevant threats).     

EEG spectral analysis of NREM sleep in a large sample of patients with insomnia and good sleepers: effects of age,sex and part of the night

Previous studies of the differences between patients with insomnia and good sleepers with regard to quantitative electroencephalographic measures have mostly utilized small samples and consequently had limited ability to account for potentially important confounding factors of age, sex and part of the night. We conducted a power spectral analysis using a large database of sleep electroencephalographic recordings to evaluate differences between patients with insomnia (N = 803) and good sleepers (N = 811), while simultaneously accounting for these factors and their interaction. Comparisons of power as a function of age and part of the night were made between cohorts (patients with insomnia versus good sleepers) by sex. Absolute power in the delta, theta and sigma bands declined with age for both females and males. Females had significantly greater power than males at all ages, and for each band, cohort and part of the night. These sex differences were much greater than differences between patients with insomnia and good sleepers. Compared with good sleepers, patients with insomnia under age 40–45 years had reduced delta band power during Part 1 of the night. Females with insomnia over age 45 years had increased delta and theta band power in Parts 2 and 3 of the night, and males with insomnia under age 40 years had reduced theta power in Part 1. Females with insomnia had increased beta2 power in all parts of the night, and males with insomnia had reduced alpha power during all parts of the night. Relative power (the proportion that an individual frequency band contributes to the total power) decreased in the delta band and increased in all other bands with age for both cohorts, sexes and all parts of the night. This analysis provides a unique resource for quantitative information on the differences in power spectra between patients with insomnia and good sleepers accounting for age, sex and part of the night.     

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.     

Patterns of sleep quality during and after postacute rehabilitation in older adults: a latent class analysis approach

Sleep quality is related to emotional, physical, psychological and cognitive functioning and functional independence in later life. After acute health events, older adults are likely to utilize postacute rehabilitation services to improve functioning and facilitate return to independent living. Patterns of how sleep changes with postacute rehabilitation, and predictors of such patterns, are unknown. The current investigation employed latent class analysis (LCA) methods to classify older adults (n = 233) into groups based on patterns of self‐reported sleep quality pre‐illness, during postacute rehabilitation and up to 1 year following postacute rehabilitation. Using LCA, older adults were grouped into (1) consistently good sleepers (46%), (2) good sleepers who transitioned into poor sleepers (34%), (3) consistently poor sleepers (14%) and (4) poor sleepers who transitioned into good sleepers (6%). In three planned analyses, pain was an independent predictor of membership in classes 1 or 2 (good pre‐illness sleep quality) versus classes 3 or 4 (poor pre‐illness sleep quality), and of membership in class 1 (consistently good sleep) versus class 2 (good sleep that transitioned to poor sleep). A lower Mini‐Mental State Examination score was a predictor of membership in class 1 versus class 2. There were no statistically significant predictors of membership in class 3 versus class 4. Demographics, comorbidities and depressive symptoms were not significant predictors of class membership. These findings have implications for identification of older adults at risk for developing poor sleep associated with changes in health and postacute rehabilitation. The findings also suggest that pain symptoms should be targeted to improve sleep during postacute rehabilitation.     

Subjective–objective sleep discrepancy among older adults: associations with insomnia diagnosis and insomnia treatment

Discrepancy between subjective and objective measures of sleep is associated with insomnia and increasing age. Cognitive behavioural therapy for insomnia improves sleep quality and decreases subjective–objective sleep discrepancy. This study describes differences between older adults with insomnia and controls in sleep discrepancy, and tests the hypothesis that reduced sleep discrepancy following cognitive behavioural therapy for insomnia correlates with the magnitude of symptom improvement reported by older adults with insomnia. Participants were 63 adults >60 years of age with insomnia, and 51 controls. At baseline, participants completed sleep diaries for 7 days while wearing wrist actigraphs. After receiving cognitive behavioural therapy for insomnia, insomnia patients repeated this sleep assessment. Sleep discrepancy variables were calculated by subtracting actigraphic sleep onset latency and wake after sleep onset from respective self‐reported estimates, pre‐ and post‐treatment. Mean level and night‐to‐night variability in sleep discrepancy were investigated. Baseline sleep discrepancies were compared between groups. Pre–post‐treatment changes in Insomnia Severity Index score and sleep discrepancy variables were investigated within older adults with insomnia. Sleep discrepancy was significantly greater and more variable across nights in older adults with insomnia than controls, P ≤ 0.001 for all. Treatment with cognitive behavioural therapy for insomnia was associated with significant reduction in the Insomnia Severity Index score that correlated with changes in mean level and night‐to‐night variability in wake after sleep onset discrepancy, P < 0.001 for all. Study of sleep discrepancy patterns may guide more targeted treatments for late‐life insomnia.     

Integrating nap and night‐time sleep into sleep patterns reveals differential links to health‐relevant outcomes

Both night‐time sleep and nap behaviour have been linked consistently to health outcomes. Although reasons for napping are usually tied to night‐time sleep, the majority of studies assess their effects independently. The current study thus aimed to examine the health relevance of patterns of sleep behaviour that take into account both night‐time and daytime sleep habits. Night‐time sleep, recorded during 7 days via actigraphy from 313 participants (aged 34–82 years) of the Midlife in the United States II Biomarker study, was assessed. Blood and urine specimens were assayed for noradrenaline, interleukin‐6 and C‐reactive protein. Participants self‐reported nap behaviour, depressive symptoms, perceived chronic stress and the presence of medical symptoms and conditions. Overall, nappers (n = 208) showed elevated waist–hip ratios, C‐reactive protein and interleukin‐6 levels compared to non‐nappers and reported more physiological symptoms and conditions (all P ≤ 0.019). Within nappers, cluster analysis revealed three patterns of sleep behaviour—infrequent nappers with good night‐time sleep, frequent nappers with good night‐time sleep and nappers with poor night‐time sleep. Nappers with poor night‐time sleep thereby exhibited elevated noradrenaline levels, depressive symptoms and perceived stress scores compared to other groups (all P ≤ 0.041). These findings support the idea that nap–health relationships are complex, in that frequency of napping and accumulation of nap sleep is not related linearly to health consequences. Assessing nap behaviour in conjunction with night‐time sleep behaviour appeared crucial to elucidate further the health relevance of napping, particularly in terms of psychological health outcomes, including chronic stress and depressive symptoms.     

Unique sleep‐stage transitions determined by obstructive sleep apnea severity,age and gender

In obstructive sleep apnea, patients’ sleep is fragmented leading to excessive daytime sleepiness and co‐morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two‐step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two‐step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep‐states for power‐laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake‐state durations followed a power‐law distribution, while sleep‐state durations were characterized by an exponential distribution. Sleep‐stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea‐related clinical outcomes like arterial hypertension and daytime sleepiness.     

Development of sleep–wake rhythms during the first year of age

Circadian rhythms refer to biological rhythms that have an endogenous period length of approximately 24 hr. However, not much is known about the variance in the development of the sleep–wake rhythm. The study objectives were (a) to describe the normative variation in the development of a sleep–wake rhythm in infancy, (b) to assess whether slower development is related to sleep quality and (c) to evaluate factors that are related to the slower development of a sleep–wake rhythm. The study is based on a representative birth cohort. Questionnaires at the ages of 3 (n = 1,427) and 8 months (n = 1,302) and actigraph measurement at 8 months (n = 372) were available. Infants with significant developmental delays (n = 11) were excluded. The results are based on statistical testing and multivariate modelling. We found that the average percentage of daytime sleep was 36.3% (standard deviation [SD], 8.5%) at 3 months and 25.6% (SD, 6.6%) at 8 months. At both time‐points, infants with slower sleep–wake rhythm development slept more hours per day, had a later sleep–wake rhythm, more difficulties in settling to sleep and longer sleep‐onset latency; they also spent a longer time awake during the night. According to actigraph registrations, we found that the infants with slow development of a sleep–wake rhythm slept less and had a later start and end to night‐time sleep than the other infants. Infants’ sleep–wake rhythm development is highly variable and is related to parent‐reported and objectively measured sleep quality and quantity. Interventions to improve the sleep–wake rhythm might improve sleep quality in these infants.     

The effect of daytime napping and full‐night sleep on the consolidation of declarative and procedural information

Many studies investigating sleep and memory consolidation have evaluated full‐night sleep rather than alternative sleep periods such as daytime naps. This multi‐centre study followed up on, and was compared with, an earlier full‐night study (Schabus et al., 2004) investigating the relevance of daytime naps for the consolidation of declarative and procedural memory. Seventy‐six participants were randomly assigned to a nap or wake group, and performed a declarative word‐pair association or procedural mirror‐tracing task. Performance changes from before to after a 90‐min retention interval filled with sleep or quiet wakefulness were evaluated between groups. Associations between performance changes, sleep architecture, spindles, and slow oscillations were investigated. For the declarative task we observed a trend towards stronger forgetting across a wake period compared with a nap period, and a trend towards memory increase over the full‐night. For the procedural task, accuracy was significantly decreased following daytime wakefulness, showed a trend to increase with a daytime nap, and significantly increased across full‐night sleep. For the nap protocol, neither sleep stages, spindles, nor slow oscillations predicted performance changes. A direct comparison of day and nighttime sleep revealed that daytime naps are characterized by significantly lower spindle density, but higher spindle activity and amplitude compared with full‐night sleep. In summary, data indicate that daytime naps protect procedural memories from deterioration, whereas full‐night sleep improves performance. Given behavioural and physiological differences between day and nighttime sleep, future studies should try to characterize potential differential effects of full‐night and daytime sleep with regard to sleep‐dependent memory consolidation.     

Time Estimation in Good and Poor Sleepers

Time estimation was examined in 148 older good and poor sleepers in analogue and naturalistic sleep settings. On analogue tasks, both “empty” time and time listening to an audiobook were overestimated by both good and poor sleepers. There were no differences between groups. “Empty” time was experienced as “dragging.” In the sleep setting, most poor sleepers underestimated nocturnal sleep and overestimated awake times related to their own sleep problem: sleep onset vs. sleep maintenance insomnia. Good sleepers did the opposite. Severity of sleep problem and size of time estimation errors were unrelated. Greater night-to-night wake time variability was experienced by poor than by good sleepers. Psychological adjustment was unrelated to time estimations and to magnification or minimization of sleep problems. The results suggest that for poor sleepers who magnify their sleep problem, self-monitoring can be of benefit by demonstrating that the sleep problem is not as severe as believed.     

Validity,potential clinical utility and comparison of a consumer activity tracker and a research‐grade activity tracker in insomnia disorder II: Outside the laboratory

Accurate assessment of sleep can be fundamental for monitoring, managing and evaluating treatment outcomes within diseases. A proliferation of consumer activity trackers gives easy access to objective sleep. We evaluated the performance of a commercial device (Fitbit Alta HR) relative to a research‐grade actigraph (Actiwatch Spectrum Pro) in measuring sleep before and after a cognitive behavioural intervention in insomnia disorder. Twenty‐five individuals with DSM‐5 insomnia disorder (M = 50.6 ± 15.9 years) wore Fitbit and Actiwatch and completed a sleep diary during an in‐laboratory polysomnogram, and for 1 week preceding and following seven weekly sessions of cognitive‐behavioural intervention for insomnia. Device performance was compared for sleep outcomes (total sleep time, sleep latency, sleep efficiency and wake after sleep onset). The analyses assessed (a) agreement between devices across days and pre‐ to post‐treatment, and (b) whether pre‐ to post‐treatment changes in sleep assessed by devices correlated with clinical measures of change. Devices generally did not significantly differ from each other on sleep variable estimates, either night to night, in response to sleep manipulation (pre‐ to post‐treatment) or in response to changes in environment (in the laboratory versus at home). Change in sleep measures across time from each device showed some correlation with common clinical measures of change in insomnia, but not insomnia diagnosis as a categorical variable. Overall, the Fitbit provides similar estimates of sleep outside the laboratory to a research grade actigraph. Despite the similarity between Fitbit and Actiwatch performance, the use of consumer technology is still in its infancy and caution should be taken in its interpretation.     

‘This is the last episode’: the association between problematic binge-watching and loneliness,emotion regulation,and sleep-related factors in poor sleepers

Evidence on the relation between binge-watching and sleep quality is still scarce and inconsistent and none has taken into account both the healthy and pathological dimensions of the phenomenon. This study aimed at filling this gap by investigating both aspects in healthy participants with high and low sleep quality. Further, we aimed at identifying sociodemographic, psychological and sleep-related determinants of problematic binge-watching in poor sleepers. We first conducted independent comparisons between good (n = 253) and poor sleepers (n = 209) on different binge-watching symptoms and motives, assessed through ‘Binge-watching Engagement and Symptoms’ and ‘Watching TV Series Motives’ questionnaires, respectively. Then, we focused on the problematic aspects of binge-watching in poor sleepers, investigating the role of emotion regulation, loneliness, and sleep-related factors using hierarchical multiple regressions. Comparisons between the two groups revealed a greater extent of binge-watching behaviour (t = −2.80, p = 0.005) and greater use of this practise to cope with negative emotions (t = −4.17, p < 0.001) in poor sleepers. In addition, hierarchical multiple regressions showed that gender (β = −0.166, p = 0.008), alcohol consumption (β = −0.135, p = 0.035), emotional dysregulation (β = 0.260, p = 0.001; β = 0.298, p < 0.001), feelings of loneliness (β = 0.159, p = 0.029; β = 0.199, p = 0.003), and daytime sleepiness (β = 0.149, p = 0.016) are significant determinants of problematic binge-watching in this population. In addition to showing for the first time the relationship between sleep quality and different aspects of binge-watching, our findings indicate that emotional dysregulation, feelings of loneliness, and daytime sleepiness play a key role in determining problematic binge-watching in poor sleepers, possibly due to the existence of a pathological vicious circle between these factors in poor sleepers.     

Sleep characteristics as predictor variables of stress systems markers in insomnia disorder

This study investigates the extent to which sleep characteristics serve as predictor variables for inflammatory, hypothalamic–pituitary–adrenal and autonomic systems markers. Twenty‐nine participants with a diagnosis of insomnia disorder based on the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (age 25.3 ± 1.6 years, insomnia duration 6.6 ± 0.8 years) and 19 healthy control sleepers (age 25.4 ± 1.4 years) underwent a 2‐week at‐home evaluation keeping a sleep diary and wearing an actigraph, followed by a visit to the Research Center to measure blood pressure, and collect blood and urine samples. The actigraphy‐ and diary‐based variables of sleep duration, sleep‐onset latency, wake after sleep onset and sleep fragmentation/number of night‐time awakenings were averaged and entered as dependent variables in regression analyses. Composite scores were calculated for the autonomic (blood pressure, norepinephrine), inflammatory (monocyte counts, interleukin‐6, C‐reactive protein) and hypothalamic–pituitary–adrenal systems (cortisol), and used as predictor variables in regression models. Compared with controls, individuals with insomnia had a shorter sleep duration (P < 0.05), and a higher hypothalamic–pituitary–adrenal and inflammatory composite score (P < 0.05). The higher inflammatory score was mainly due to higher circulating monocytes (P < 0.05), rather than differences in interleukin‐6 or C‐reactive protein. In persistent insomnia disorder, cortisol is upregulated and associated with actigraphy‐ and diary‐based wake after sleep onset, suggesting that wake after sleep onset may serve as a marker to identify individuals at increased risks for disorders associated with a hyperactive hypothalamic–pituitary–adrenal system. The absence of autonomic and pro‐inflammatory changes (interleukin‐6, C‐reactive protein), despite a substantial decrease in actigraphic sleep duration, may relate to a higher resilience to the adverse biological consequences of insomnia in this young age group.     

A two-dimensional approach to assessing affective states in good and poor sleepers

This study examined a two-dimensional approach to assessing affective states among good and poor sleepers using the self-assessment manikin (SAM), a brief non-verbal self-report measure of affective states with separate ratings of valence and arousal. A sample of 286 undergraduate students completed the Pittsburgh Sleep Quality Index (PSQI) and the SAM. Participants were classified post hoc as either good (PSQI ≤ 5) or poor sleepers (PSQI > 5) using the PSQI and used the SAM to rate their current affective states (day) and their affective state at bedtime (night) the previous night. Compared to good sleepers, poor sleepers reported more negative affect and arousal at night and more negative affect during the day. Among poor sleepers, lower sleep quality and shorter sleep duration on the components of the PSQI were associated with more negative daytime valence. Among good sleepers, higher scores on the sleep medication and daytime dysfunction components of the PSQI were associated with more negative daytime valence. These findings indicate that the SAM appears to detect differences between good and poor sleepers on both valence and arousal of current daytime and retrospective night-time emotional states. This approach could be useful for the assessment of affective states related to sleep disturbance.     

The subjective–objective mismatch in sleep perception among those with insomnia and sleep apnea

The diagnosis and management of insomnia relies primarily on clinical history. However, patient self‐report of sleep–wake times may not agree with objective measurements. We hypothesized that those with shallow or fragmented sleep would under‐report sleep quantity, and that this might account for some of the mismatch. We compared objective and subjective sleep–wake times for 277 patients who underwent diagnostic polysomnography. The group included those with insomnia symptoms (n = 92), obstructive sleep apnea (n = 66) or both (n = 119). Mismatch of wake duration was context dependent: all three groups overestimated sleep latency but underestimated wakefulness after sleep onset. The insomnia group underestimated total sleep time by a median of 81 min. However, contrary to our hypothesis, measures of fragmentation (N1, arousal index, sleep efficiency, etc.) did not correlate with the subjective sleep duration estimates. To unmask a potential relationship between sleep architecture and subjective duration, we tested three hypotheses: N1 is perceived as wake; sleep bouts under 10 min are perceived as wake; or N1 and N2 are perceived in a weighted fashion. None of these hypotheses exposed a match between subjective and objective sleep duration. We show only modest performance of a Naïve Bayes Classifier algorithm for predicting mismatch using clinical and polysomnographic variables. Subjective–objective mismatch is common in patients reporting insomnia symptoms. We conclude that mismatch was not attributable to commonly measured polysomnographic measures of fragmentation. Further insight is needed into the complex relationships between subjective perception of sleep and conventional, objective measurements.     

Short persistent sleep duration is associated with poor receptive vocabulary performance in middle childhood

The aim of this study was to examine whether short sleep duration is associated with poor receptive vocabulary at age 10 years. In the Quebec Longitudinal Study of Child Development, parents reported their children's nocturnal sleep duration annually from ages 2.5 to 10 years, and children were assessed for receptive vocabulary using the Peabody Picture Vocabulary Test—Revised (PPVT‐R) at ages 4 and 10 years. Groups with distinct nocturnal sleep duration trajectories were identified and the relationships between sleep trajectories and poor PPVT‐R performance were characterized. In all, 1192 children with available sleep duration and PPVT‐R data participated in this epidemiological study. We identified four longitudinal nocturnal sleep trajectories: short persistent sleepers (n = 72, 6.0%), short increasing sleepers (n = 47, 3.9%), 10‐h sleepers (n = 628, 52.7%) and 11‐h sleepers (n = 445, 37.3%). In all, 14.8% of the children showed poor PPVT‐R performance at age 10 years. Nocturnal sleep trajectories and poor PPVT‐R performance at age 10 were associated significantly (P = 0.003). After adjusting for baseline receptive vocabulary performance at age 4 and other potential confounding variables, logistic regression analyses suggest that, compared to 11‐h sleepers, the odds ratio of presenting poor receptive vocabulary at age 10 was 2.67 [95% confidence interval (CI): 1.24–5.74, P = 0.012] for short persistent sleepers and 1.66 (95% CI: 1.06–2.59, P = 0.026) for 10‐h sleepers. These results corroborate previous findings in early childhood, and indicate that short sleep duration is associated with poor receptive vocabulary during middle childhood.     

Alpha‐wave frequency characteristics in health and insomnia during sleep

Appearances of alpha waves in the sleep electrencephalogram indicate physiological, brief states of awakening that lie in between wakefulness and sleep. These microstates may also cause the loss in sleep quality experienced by individuals suffering from insomnia. To distinguish such pathological awakenings from physiological ones, differences in alpha‐wave characteristics between transient awakening and wakefulness observed before the onset of sleep were studied. In polysomnographic datasets of sleep‐healthy participants (n = 18) and patients with insomnia (n = 10), alpha waves were extracted from the relaxed, wake state before sleep onset, wake after sleep‐onset periods and arousals of sleep. In these, alpha frequency and variability were determined as the median and standard deviation of inverse peak‐to‐peak intervals. Before sleep onset, patients with insomnia showed a decreased alpha variability compared with healthy participants (P < 0.05). After sleep onset, both groups showed patterns of decreased alpha frequency that was lower for wake after sleep‐onset periods of shorter duration. For patients with insomnia, alpha variability increased for short wake after sleep‐onset periods. Major differences between the two groups were encountered during arousal. In particular, the alpha frequency in patients with insomnia rebounded to wake levels, while the frequency in healthy participants remained at the reduced level of short wake after sleep‐onset periods. Reductions in alpha frequency during wake after sleep‐onset periods may be related to the microstate between sleep and wakefulness that was described for such brief awakenings. Reduced alpha variability before sleep may indicate a dysfunction of the alpha generation mechanism in insomnia. Alpha characteristics may also prove valuable in the study of other sleep and attention disorders.     

Validity,potential clinical utility,and comparison of consumer and research‐grade activity trackers in Insomnia Disorder I: In‐lab validation against polysomnography

Consumer activity trackers claiming to measure sleep/wake patterns are ubiquitous within clinical and consumer settings. However, validation of these devices in sleep disorder populations are lacking. We examined 1 night of sleep in 42 individuals with insomnia (mean = 49.14 ± 17.54 years) using polysomnography, a wrist actigraph (Actiwatch Spectrum Pro: AWS) and a consumer activity tracker (Fitbit Alta HR: FBA). Epoch‐by‐epoch analysis and Bland?Altman methods evaluated each device against polysomnography for sleep/wake detection, total sleep time, sleep efficiency, wake after sleep onset and sleep latency. FBA sleep stage classification of light sleep (N1 + N2), deep sleep (N3) and rapid eye movement was also compared with polysomnography. Compared with polysomnography, both activity trackers displayed high accuracy (81.12% versus 82.80%, AWS and FBA respectively; ns) and sensitivity (sleep detection; 96.66% versus 96.04%, respectively; ns) but low specificity (wake detection; 39.09% versus 44.76%, respectively; p = .037). Both trackers overestimated total sleep time and sleep efficiency, and underestimated sleep latency and wake after sleep onset. FBA demonstrated sleep stage sensitivity and specificity, respectively, of 79.39% and 58.77% (light), 49.04% and 95.54% (deep), 65.97% and 91.53% (rapid eye movement). Both devices were more accurate in detecting sleep than wake, with equivalent sensitivity, but statistically different specificity. FBA provided equivalent estimates as AWS for all traditional actigraphy sleep parameters. FBA also showed high specificity when identifying N3, and rapid eye movement, though sensitivity was modest. Thus, it underestimates these sleep stages and overestimates light sleep, demonstrating more shallow sleep than actually obtained. Whether FBA could serve as a low‐cost substitute for actigraphy in insomnia requires further investigation.     

The Good Sleeper Scale-15 items: a questionnaire for the standardised assessment of good sleepers

Research with ‘good sleepers’ is ubiquitous, yet there are no standardised criteria to identify a ‘good sleeper’. The present study aimed to create and validate a questionnaire for identifying good sleepers for use in research studies known as the Good Sleeper Scale-15 items (GSS-15). Data were derived from a population-based survey of Australian adults (n = 2,044). A total of 23 items were chosen for possible inclusion. An exploratory factor analysis (EFA) was conducted on ~10% of the survey dataset (n = 191) for factor identification and item reduction. A confirmatory factor analysis (CFA) was conducted on the remaining data (n = 1,853) to test model fit. Receiver operating characteristic curves and correlations were conducted to derive cut-off scores and test associations with sleep, daytime functioning, health, and quality-of-life. The EFA identified six factors: ‘Sleep Difficulties’, ‘Timing’, ‘Duration’, ‘Regularity’, ‘Adequacy’, and ‘Perceived Sleep Problem’. The CFA showed that model fit was high and comparable to other sleep instruments, χ² (63) = 378.22, p < 0.001, root mean square error of approximation = 0.05, with acceptable internal consistency (α = 0.76). Strong correlations were consistently found between GSS-15 global scores and outcomes, including ‘a good night′s sleep’ (r = 0.7), ‘feeling un-refreshed’ (r = −0.59), and ‘experienced sleepiness’ (r = −0.51), p < 0.001. Cut-off scores were derived to categorise individuals likely to be a good sleeper (GSS-15 score ≥40) and those very likely to be a good sleeper (GSS-15 score ≥45). The GSS-15 is a freely available, robust questionnaire that will assist in identifying good sleepers for the purpose of sleep research. Future work will test relationships with other sleep measures in community and clinical samples.