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This investigation aimed to clarify the influence of circadian change and travel distance on National Basketball Association (NBA) team performance using a dataset from the 2014-2018 seasons. Data from 9,840 games were acquired from an open-access source. Game point differential and team free-throw percentage served as outcome variables. Time zone change (TZΔ) captured raw circadian delay/advance based on travel for a game and adjusted TZΔ (AdjTZΔ) evolved TZΔ by allowing acclimation to a novel TZ. We also further categorized AdjTZΔ into AdjTZΔ_A, which assumed travel the day before each game and AdjTZΔ_B, which assumed teams spent as many days in their home city as possible. Travel distance for each game was calculated. Linear mixed-effects modeling estimated associations, with games nested within team and year. Adjusted associations accounted for differences in team ability, whether the game was home or away, and whether the game occurred on the second half of a back-to-back game sequence. Greater circadian misalignment, regardless of delay or advance, and increasing travel distance negatively influenced NBA game performance. Yet, results suggest that performance outcomes may be more influenced by travel distance than circadian misalignment. Moreover, circadian misalignment and travel distance interacted to significantly influence game point differential. Furthermore, differences in results across analyses were observed between AdjTZΔ_A and AdjTZΔ_B, which suggests that subtle differences in constructed travel schedules can have notable impact on NBA performance outcomes. Lastly, playing on the second half of a back-to-back sequence emerged as a robust predictor of performance disadvantage, which corroborates the existing literature and provides further support for NBA schedule changes purposed to enhance competitive equity by reducing the number of back-to-back games across a season. These findings can help guide NBA teams on key strategies for reducing travel-related disadvantages and inform schedule makers on critical factors to prioritize across future schedules to attenuate competitive inequity from travel. Furthermore, they can help direct teams towards scenarios that are best to target for load management purposes due to the cumulative disadvantage arising from travel-related factors, opponent quality, game location, and game sequence.

Many studies have investigated the role that travel plays in athletic performance. However, these studies lacked a holistic representation of travel. For instance, they do not consider travel distance and uniquely focuses on travel direction.An open source (www.evolving-hockey.com) provided NHL (2013-2020) game data. In total, this resulted in 17,088 regular season games.Linear and quadratic versions of time zone change (TZΔ) and adjusted jet lag (AJL) were formulated. TZΔ captured circadian delay/advance based on travel for a game, with each TZ going eastward and westward reflected by -1 and +1, respectively. AJL advances TZΔ by allowing TZ acclimation, with each day resulting in a 1-unit change towards circadian neutral. AJL is a season-long rolling summation, which was computed using two different travel approaches: Approach A (AJL_A) assumes travel the day before each game, whereas Approach B (AJL_B) was designed to prioritize being home. A standardized flight tracker determined travel distance for each game. Team ability differences, characterized as difference in total season points, served as an analytic covariate. Outcome variables included goal differential, difference between actual and expected Fenwick save percentage (dFSv%), and goals saved above average (GSAA).GameDistance (β = -0.14, p = 0.0007), AJL_BResults suggest that circadian change, in both direction, and greater traveled distance can negatively impact NHL athletes.

STUDY OBJECTIVES: The multiple sleep latency test (MSLT) has limitations when evaluating disorders of hypersomnolence with unknown etiology. Alternative measures of hypersomnolence may objectively identify pathology in patients with complaints of daytime sleepiness that may not be captured by the MSLT alone. This study evaluated the impact of a multimodal hypersomnolence assessment relative to MSLT in patients with unexplained hypersomnolence. METHODS: Seventy-five patients with unexplained hypersomnolence were included in the analyzed sample. Polysomnography was performed without prescribed wake time, and the psychomotor vigilance task and pupillographic sleepiness test were completed between MSLT nap opportunities. Presence or absence of hypersomnolence for each assessment was defined using a priori cutpoints. Proportions of patients identified as hypersomnolent using the multimodal assessment relative to MSLT alone were evaluated, as well as the sensitivity and specificity of ancillary hypersomnolence measures relative to MSLT as a gold standard. RESULTS: The multimodal assessment more than doubled the proportion of patients identified as having objective deficits relative to MSLT ≤ 8 minutes alone. The combination of excessive sleep duration, lapses on the psychomotor vigilance task, and impairments on the pupillographic sleepiness test also had perfect sensitivity in identifying all patients identified as sleepy by the MSLT across 3 different MSLT cutpoints (5, 8, and 10 minutes). CONCLUSIONS: These data demonstrate the insufficiency of the MSLT as a singular tool to identify objective pathology in persons with unexplained hypersomnolence. Further efforts to refine and standardize multimodal assessments will likely improve diagnostic acumen and research into the causes of these disorders. CITATION: Plante DT, Cook JD, Prairie ML. Multimodal assessment increases objective identification of hypersomnolence in patients referred for multiple sleep latency testing. J Clin Sleep Med. 2020;16(8):1241–1248.

Diagnosing patients with central disorders of hypersomnolence (CDH) can be challenging. The emergence of wearable technology, such as actigraphy and consumer sleep trackers (CSTs), allows for objective characterization of habitual sleep-wake behavior, which can greatly assist the CDH diagnostic process. This review considers the current role and utility of wearable technology as a tool to estimate sleep-wake behavior in CDH. Actigraphy is recommended by the American Academy of Sleep Medicine (AASM) as a diagnostic tool in CDH and has been widely employed in field-based investigations, yet insufficient guidelines have been provided to optimize data collection and analysis. Due to several factors, the AASM does not currently recognize CSTs as a viable diagnostic tool. However, CSTs have demonstrated promising capabilities that may lead to future clinical and research utility in CDH. Actigraphy has an important role for sleep-wake assessment in CDH, but analytic standardization is a key barrier to their use. At present, CSTs are considered experimental, but their unique capabilities suggest they may one day be developed into a powerful tool in the assessment of these disorders.

To determine the optimal Actiwatch 2 setting configuration for the estimation of total sleep time (TST) in persons with suspected idiopathic hypersomnia.Thirty-three patients with a diagnosis of idiopathic hypersomnia (28 female; mean age = 33.7 ± 10.5) underwent ad libitum polysomnography with concurrent use of the Actiwatch 2. Actiwatch 2 sleep-wake activity threshold (SWAT; Low, Medium, and High) and sleep immobility onset and offset (SIOO; 5, 10, 15, 20, 25, and 30 epoch) duration were modified during data processing. The resultant 18 unique setting combinations were subsequently evaluated using Bland-Altman and epoch comparison analyses to determine optimal settings relative to polysomnography.Low SWAT + 25 Epoch SIOO displayed the least divergence from polysomnography (mean difference 3.4 minutes). Higher SWAT and lower SIOO increased sensitivity and accuracy, but at the expense of reducing specificity and the ability to accurately estimate TST.These results demonstrate that actigraphic settings should be carefully considered when estimating sleep duration. The Low + 25 Epoch configuration is indicated as most optimal for estimating TST in persons with suspected idiopathic hypersomnia.A commentary on this article appears in this issue on page 539.

Study Objectives Patients with unexplained hypersomnolence have significant impairment related to daytime sleepiness and excessive sleep duration, the biological bases of which are poorly understood. This investigation sought to examine relationships between objectively measured hypersomnolence phenotypes and epigenetic modification of candidate hypersomnolence genes to advance this line of inquiry. Methods Twenty-eight unmedicated clinical patients with unexplained hypersomnolence were evaluated using overnight ad libitum polysomnography, multiple sleep latency testing, infrared pupillometry, and the psychomotor vigilance task. DNA methylation levels on CpG sites annotated to 11 a priori hypersomnolence candidate genes were assessed for statistical association with hypersomnolence measures using independent regression models with adjusted local index of significance (aLIS) P-value threshold of 0.05. Results Nine CpG sites exhibited significant associations between DNA methylation levels and total sleep time measured using ad libitum polysomnography (aLIS p-value < .05). All nine differentially methylated CpG sites were annotated to the paired box 8 (PAX8) gene and its related antisense gene (PAX8-AS1). Among these nine differentially methylated positions was a cluster of five CpG sites located in the body of the PAX8 gene and promoter of PAX8-AS1. Conclusions This study demonstrates that PAX8/PAX8-AS1 DNA methylation levels are associated with total sleep time in persons with unexplained hypersomnolence. Given prior investigations that have implicated single nucleotide polymorphisms in PAX8/PAX8-AS1 with habitual sleep duration, further research that clarifies the role of DNA methylation levels on these genes in the phenotypic expression of total sleep time is warranted.

Objectives Research has associated both school night sleep duration (SNSD) and circadian preference (CP) with middle school-aged students’ attendance and tardiness, but the interaction between these characteristics remains unexplored within this context, along with the impact of SNSD and CP on school suspension likelihood. Thus, this study investigated associations between SNSD, CP, and their interaction with middle school-aged student attendance, tardiness, and suspension, while accounting for sociodemographic characteristics. Participants About 4175 students from 12 middle schools in the Madison, Wisconsin, Metropolitan School District. Methods Students completed a computer-based, sleep-related survey during class. Students reporting SNSD between 4 and 12 hours were included in the final analytical sample (N = 3860; mean age ± standard deviation = 12.0 ± 0.86 years; range = 10-16 years). CP was characterized using the single-item self-morningness/eveningness question. Linear mixed-effects regression estimated associations of SNSD and CP with tardiness and attendance. Logistic mixed-effects regression assessed associations with school suspension. Fully adjusted multilevel models included sociodemographic covariates, nesting students within schools. Results About 3860 student responses comprised the final sample, which had a SNSD of 8.15 ± 1.37 hours and general eveningness preference. Short-and-long SNSD associated with lower attendance and increased out-of-school suspension. Greater eveningness preference was negatively associated with attendance and tardiness. No SNSD and CP interactions emerged. Associations between sociodemographic characteristics and attendance, tardiness, and suspension were observed. Conclusions Results support need for efforts to improve middle school-aged student sleep health and account for eveningness preference. Research clarifying the interactive influence of sleep, circadian, and sociodemographic characteristics on attendance and achievement is warranted.

Hypersomnolence is a common and debilitating symptom in mood disorders. However, objective differentiation of excessive daytime sleepiness (EDS) from non-EDS in depression has not yet been achieved. This study compared performance on the Compensatory Tracking Task (CTT) and concurrently recorded high-density (hd)EEG theta power in 22 patients with major depressive disorder (MDD) and co-occurring EDS against 22 age- and sex-matched patients with MDD but no EDS, as well as 22 age- and sex-matched healthy controls. Though depressed hypersomnolent participants endorsed feeling sleepier than depressed non-hypersomnolent and healthy control participants prior to starting the CTT, no group differences in CTT performance were observed. Average hdEEG theta power was higher during periods of high error on the CTT compared to periods of low error, but did not differ between the groups. Though the CTT still holds promise as an objective neurobehavioral measure, these results do not indicate a capability to differentiate EDS from non-EDS in mood disorders.

Objective/Background: Dysfunctional sleep-related cognitions (SRCs) have been demonstrated in both insomnia and depression, but have not been evaluated in patients experiencing depression with co-occurring hypersomnolence. Given the prominence of maladaptive thinking in depression with comorbid insomnia, dysfunctional SRCs may also exist in depressed persons experiencing hypersomnolence. Identifying potentially maladaptive SRCs may assist development of cognitive-behavioral strategies to alleviate hypersomnolence and its related impairment, particularly when comorbid with depression. Participants: Twenty-two unmedicated persons with major depressive disorder (MDD) with comorbid hypersomnolence (MDD+/HYP+), as well as age- and sex-matched persons with MDD without hypersomnolence (MDD+/HYP-) and healthy controls (HC). Methods: Participants completed the Dysfunctional Beliefs and Attitudes About Sleep-16-item (DBAS-16) and underwent overnight polysomnography. Groups were compared across clinical and sleep domains, as well as DBAS-16 global, subscale, and individual item scores. Additional analyses evaluated DBAS-16 components while controlling for depression severity. Results: Groups significantly differed across all collected sleep and mood metrics consistent with diagnostic classification. MDD+/HYP+ DBAS-16 global score was significantly elevated, relative to HC, and was comparable to MDD+/HYP-. A DBAS-16 global score significant group effect was maintained while controlling for depression symptom severity, however only individual DBAS-16 items related to quantity and quality of sleep demonstrated particular relevance to MDD+/HYP+ compared to other groups. Conclusions: Results suggest potentially maladaptive SRCs in MDD+/HYP+. Further efforts are needed to clarify whether these beliefs and attitudes about sleep in persons with hypersomnolence are in fact dysfunctional, as well as identify relevant content for development of a novel hypersomnolence-related SRC metric.

STUDY OBJECTIVES: To evaluate the ability of a multisensory fitness tracker, the Jawbone UP3 (JB3), to quantify and classify sleep in patients with suspected central disorders of hypersomnolence. METHODS: This study included 43 patients who completed polysomnography (PSG) and a Multiple Sleep Latency Test (MSLT) with concurrent wrist-worn JB3 and Actiwatch 2 (AW2) recordings for comparison. Mean differences in nocturnal sleep architecture variables were compared using Bland-Altman analysis. Sensitivity, specificity, and accuracy were derived for both devices relative to PSG. Ability of the JB3 to detect sleep onset rapid eye movement periods (SOREMPs) during MSLT naps was also quantified. RESULTS: JB3 demonstrated a significant overestimation of total sleep time (39.6 min, P < .0001) relative to PSG, but performed comparably to AW2. Although the ability of the JB3 to detect epochs of sleep was relatively good (sensitivity = 0.97), its ability to distinguish light, deep, and REM sleep was poor. Similarly, the JB3 did not correctly identify a single SOREMP during any MSLT nap opportunity. CONCLUSIONS: The JB3 did not accurately quantify or classify sleep in patients with suspected central disorders of hypersomnolence, and was particularly poor at identifying REM sleep. Thus, this device cannot be used as a surrogate for PSG or MSLT in the assessment of patients with suspected central disorders of hypersomnolence. CITATION: Cook JD, Prairie ML, Plante DT. Ability of the multisensory Jawbone UP3 to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography and actigraphy. J Clin Sleep Med. 2018;14(5):841–848.

Introduction Excessive daytime sleepiness (EDS) frequently presents as a secondary symptom, but some experience EDS in the absence of an explanatory medical condition, psychiatric illness, sleep disorder, or medication/substance use. The neurobiology underlying unexplained EDS (uEDS) is poorly understood, which contributes to the existing limitations in uEDS classification, assessment, and treatment. This investigation was designed to identify potential neuroanatomical and neurofunctional correlates of uEDS. Methods Data were acquired from the open-access Stockholm Sleepy Brain (SSB) Project, which included either young (20–30 years old) or older (65–75 years old) adults. SSB criteria ruled out common EDS explanations. A uEDS sample (N = 18; Percentage Female = 33%; Percentage Young = 33%) was established using Epworth Sleepiness Scale (ESS) ≥ 11. Age-and-sex matched controls without EDS (noEDS) were identified. T1-weighted MRI and resting-state fMRI (rs-fMRI) data were compiled for each subject, as well as depression, anxiety, and global health self-ratings. Processing pipelines were performed on T1-weighted and rs-fMRI data. Neuroanatomical analyses compared groups using voxel-based morphometry and across gray matter (GM), white matter (WM), and cerebral spinal fluid volume. Threshold free cluster enhancement was used across all neuroanatomical comparisons. For neurofunctional analyses, seed-based connectivity analysis was performed with a seed placed in the left hemisphere of the medial prefrontal cortex (MNI coordinates: 2 -46 12). Fischer Z-transformed functional connectivity maps were compared across groups. Depression, anxiety, and global health scores were included as covariates and corrections were applied for multiple comparisons, across all analyses. Results Group characteristics were comparable, except for ESS. Significantly increased GM volume (middle occipital gyrus and precuneus) was associated with uEDS, relative to noEDS. Robust, bilateral increases in WM matter (thalamus, cerebellum, and middle frontal gyrus) were observed for uEDS, relative to noEDS. No significant group differences were observed in rs-fMRI. Conclusion Significant neuroanatomical alterations were associated with uEDS that included increases in both GM and WM. These findings converge on previous research associating anatomical differences within the default mode network with uEDS. Future research using more sensitive quantitative measures of WM is warranted. Support (if any) This project was supported by a National Institute Nursing Research grant (NR018288).

Introduction Sleep and circadian factors play an important role in school attendance, academic performance, and daytime behaviors among adolescents. This investigation assessed school night sleep duration (SNSD) and circadian preference (CP) association with first period tardies (FPT) using a middle-aged sample from the Madison (Wisconsin) Metropolitan School District (MMSD), prior to implementation of a planned district-wide delay in middle school start times. Methods 4,175 middle-school aged students from 12 MMSD schools completed a sleep survey, which included SNSD and a validated 4-level measure of CP. Self-reported SNSD between 4-and-12 hours served as final sample inclusion criterion. Mixed effects modeling was employed with students nested within school. Linear regression determined SNSD and CP effect on student tardiness. Individual, year-long FPT served as outcome variable. Inclusion of SNSD quadratic term was not statistically indicated. Full model covariates included age, sex, race, parent educational level, homelessness, free and reduced lunch, and special education status. Results Final sample included 3,860 students. Univariate regression determined a significant CP association with FPT [β=1.20, 95% CI (0.54, 1.86), F(1,10.41)=13.7, p=0.004), but not SNSD [β=-0.31, 95% CI (-0.70, -0.09), F(1,10.21)=2.5, p=0.14]. SNSD and CP interaction was not significant. CP significance was maintained in the full model [β=1.24, 95% CI (-0.70, -0.09), F(1,11.21)=13.7, p=0.004]. Evening preference associated with 3.72 more FPT, relative to morning preference. Conclusion Results suggest evening preference is associated with increased risk of tardiness among middle school students. Future research that examines the relationships between delayed school start times, circadian preference, and impact on school tardiness is indicated. Support This research was generously supported by a grant from the Madison Education Partnership (MEP).

Study objectives To clarify whether hypersomnolence disorder is associated with a specific sleep phenotype and altered neurophysiological function in persons with and without hypersomnolence disorder and major depressive disorder (MDD). Methods Eighty-three unmedicated persons with and without hypersomnolence disorder and/or MDD underwent ad libitum high-density EEG polysomnography. Clinical and sleep architecture variables were compared between groups. Topographic patterns of slow-wave activity (SWA) relative to healthy controls were compared, with correlations between topographic SWA and daytime sleepiness assessed. Reductions in SWA in hypersomnolence disorder were mapped to specific cortical areas using source localization. Results Regardless of the presence or absence of comorbid MDD, persons with hypersomnolence disorder had increased sleep duration relative to both controls and persons with MDD without hypersomnolence. Participants with hypersomnolence disorder also demonstrated reduced bilateral centroparietal low-frequency activity during nonrapid eye movement sleep relative to controls, a pattern not observed in persons with MDD but without hypersomnolence. SWA in these regions was negatively correlated with subjective measures of daytime sleepiness. Source localization demonstrated reductions in SWA in the supramarginal gyrus, somatosensory, and transverse temporal cortex in participants with hypersomnolence disorder. Conclusions Hypersomnolence disorder is characterized by increased sleep duration with normal sleep continuity, regardless of the presence or absence of comorbid depression. Reduced local SWA may be a specific neurophysiological finding in hypersomnolence disorder. Further research is warranted to elucidate the mechanisms through which these cortical changes are related to clinical complaints of daytime sleepiness.

Background Patient heterogeneity is problematic for the accurate assessment and effective treatment of Hypersomnolence Disorder. Clustering analysis is a preferred approach for establishing homogenous subclassifications. Thus, this investigation aimed to identify more homogeneous subclassifications of Hypersomnolence Disorder through clustering analysis. Methods Patients undergoing polysomnography (PSG) and multiple sleep latency test (MSLT) assessment for hypersomnolence were recruited as part of a larger investigation. A sample of patients with Hypersomnolence Disorder was determined based on a post hoc chart review protocol. After removing persons with missing data, 62 participants were included in the analyses. Self-report total sleep time, Epworth Sleepiness Scale (ESS) score, and Sleep Inertia Questionnaire (SIQ) score were chosen as clustering variables to mirror Hypersomnolence Disorder diagnostic traits. A statistically-driven clustering process produced two clusters using Ward's D hierarchical approach. Clusters were compared across characteristics, self-report measures, PSG/MSLT results, and additional objective measures. Results The resulting clusters differed across a variety of hypersomnolence-related subjective metrics and objective measurements. A more severe hypersomnolence phenotype was identified in a cluster that also had elevated depressive symptoms. This cluster endorsed significantly greater daytime sleepiness, sleep inertia, and functional impairment, while displaying longer sleep duration and worse vigilance. Conclusions These results provide growing support for a nosological reformulation of hypersomnolence associated with psychiatric disorders. Future research is necessary to solidify the conceptualization and characterization of unexplained hypersomnolence presenting with-and-without psychiatric illness.

Slow waves are characteristic waveforms that occur during non-rapid eye movement (NREM) sleep that play an integral role in sleep quality and brain plasticity. Benzodiazepines are commonly used medications that alter slow waves, however, their effects may depend on the time of night and measure used to characterize slow waves. Prior investigations have utilized minimal scalp derivations to evaluate the effects of benzodiazepines on slow waves, and thus the topography of changes to slow waves induced by benzodiazepines has yet to be fully elucidated. This study used high-density electroencephalography (hdEEG) to evaluate the effects of oral temazepam on slow wave activity, incidence, and morphology during NREM sleep in 18 healthy adults relative to placebo. Temazepam was associated with significant decreases in slow wave activity and incidence, which were most prominent in the latter portions of the sleep period. However, temazepam was also associated with a decrease in the magnitude of high-amplitude slow waves and their slopes in the first NREM sleep episode, which was most prominent in frontal derivations. These findings suggest that benzodiazepines produce changes in slow waves throughout the night that vary depending on cortical topography and measures used to characterize slow waves. Further research that explores the relationships between benzodiazepine-induced changes to slow waves and the functional effects of these waveforms is indicated.

Introduction Sleep plays an important role in adolescent education and development. Sleep impacts student school attendance, academic performance, and daytime behaviors. There has been limited investigation into the impact on sleep duration (SD) on school suspension risk. Given the growing public health and policy focus on altering school start times to increase SD, this study assessed SD association with school suspension risk using a middle-school aged sample from the Madison (Wisconsin) Metropolitan School District (MMSD), prior to implementation of a planned district-wide delay in middle school start times. Methods 4,175 middle-school aged students from 12 MMSD schools completed a sleep survey, which included school-night SD (SNSD). Self-reported SNSD between 4-and-12 hours served as criterion for inclusion in final sample. Mixed effects modeling was employed with students nested within school. Logistic regression determined SNSD association with in-school (ISS) and out-of-school (OSS) suspensions. ISS and OSS were dichotomized (No ISS/OSS = 0; nonzero ISS/OSS = 1) to serve as outcome variables. Full model covariates included age, sex, race, circadian preference, parent educational level, homelessness, free and reduced lunch, and special education status. Results Final sample included 3,860 students. Shorter SNSD associated with greater likelihood of OSS [OR = 0.83, 95% CI (-0.28, -0.09), X2 = 16.1, p < 0.0001], but not ISS [OR = 0.97, 95% CI (-0.14, -0.070), X2 = 0.44, p = 0.51]. Significance between SNSD and OSS was maintained in the full model [OR = 0.84, 95% CI (-0.27, -0.08), X2 = 13.2, p = 0.0003]. Each additional hour of sleep associated with 16% lower risk of OSS. Conclusion These results suggest that students with shorter SD are at increased risk for OSS, which further highlights the potential deleterious impact of short SD on adolescent educational experience. Support This research was generously supported by a grant from the Madison Education Partnership (MEP).

Measuring sleep duration and early onset rapid eye movement sleep (REMS) is critical in the assessment of suspected central disorders of hypersomnolence (CDH). Current multi-sensor activity trackers that integrate accelerometry and heart rate are purported to accurately quantify sleep time and REMS; however, their utility in suspected CDH has not been established. This investigation aimed to determine the ability of a current, multi-sensor tracker, Fitbit Alta HR (FBA-HR), to quantify and classify sleep in patients with suspected CDH relative to polysomnography (PSG). Forty-nine patients (46 female; mean age, 30.3 ± 9.84 years) underwent ad libitum PSG with concurrent use of the FBA-HR. FBA-HR sleep variable quantification was assessed using Bland-Altman analysis. FBA-HR all sleep (AS), light sleep (LS; PSG N1 + N2), deep sleep (DS; PSG N3) and REMS classification was evaluated using epoch-by-epoch comparisons. FBA-HR-detected sleep-onset rapid eye movement periods (SOREMPs) were compared against PSG SOMREMPs. FBA-HR displayed significant overestimation of total sleep time (11.6 min), sleep efficiency (1.98%) and duration of deep sleep (18.2 min). FBA-HR sensitivity and specificity were as follows: AS, 0.96, 0.58; LS, 0.73, 0.72;DS, 0.67, 0.92; REMS, 0.74, 0.93. The device failed to detect any nocturnal SOREMPs. Device performance did not differ appreciably among diagnostic subgroups. These results suggest FBA-HR cannot replace EEG-based measurements of sleep and wake in the diagnostic assessment of suspected CDH, and that improvements in device performance are required prior to adoption in clinical or research settings.

Objective Endogenous neurosteroids that potentiate the gamma-aminobutyric acid type A (GABAA) receptor are thought to enhance the generation of sleep spindles. This study tested the hypothesis that the 5α-reductase inhibitor finasteride, an agent associated with reductions in neurosteroids, would be associated with reduced sleep spindles in men referred for polysomnography. Methods Spectral analysis and spindle waveform detection were performed on electroencephalographic (EEG) sleep data in the 11–16 Hz sigma band, as well as several subranges, from 27 men taking finasteride and 27 matched comparison patients (ages 18 to 81 years). Results No significant differences between groups were observed for spectral power or sleep spindle morphology measures, including spindle density, amplitude, duration, and integrated spindle activity. Conclusions Contrary to our hypothesis, these findings demonstrate that finasteride is not associated with alterations in sleep spindle range activity or spindle morphology parameters. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Hypersomnolence plays an important role in the presentation, treatment and course of mood disorders. However, there has been relatively little research that examines objective measures of sleep duration and continuity in patients with depression and hypersomnolence, despite the use of these factors in sleep medicine nosological systems. This study compared total sleep time and efficiency measured by naturalistic actigraphic recordings followed by ad libitum polysomnography (PSG; without prescribed wake time) in 22 patients with major depressive disorder and co-occurring hypersomnolence against age- and sex-matched healthy sleeper controls. The major depressive disorder and co-occurring hypersomnolence group demonstrated significantly longer sleep duration compared with healthy sleeper controls quantified by sleep diaries, actigraphy and ad libitum PSG. No between-group differences in sleep efficiency (SE), latency to sleep or wake after sleep onset were observed when assessed using objective measures. To further contextualize these findings within the broader scientific literature, a systematic review was performed to identify other comparable investigations. A meta-analysis of pooled data demonstrated patients with mood disorders and co-occurring hypersomnolence have significantly greater sleep duration and similar SE compared with healthy controls when assessed using ad libitum PSG. These results suggest current sleep medicine nosology that distinguishes hypersomnia associated with psychiatric disorders primarily as a construct characterized by low SE and increased time in bed may not be accurate. Future studies that establish the biological bases hypersomnolence in mood disorders, as well as clarify the accuracy of nosological thresholds to define excessive sleep duration, are needed to refine the diagnosis and treatment of these disorders.

Consumer sleep trackers : a new tool to fight the hidden epidemic of obstructive sleep apnoea?

Hypersomnia is common in psychiatric disorders, yet there are few self-report measures that adequately characterize this sleep disturbance. The objective of this study was to validate the Hypersomnia Severity Index (HSI), a tool designed to measure severity, distress and impairment of hypersomnia in psychiatric populations. Psychometric properties were evaluated in an undergraduate Scale Development sample (N = 381) and two psychiatric Scale Validation samples: euthymic bipolar participants with a range of sleep complaints (N = 89), and unmedicated unipolar depressed participants (N = 21) meeting operational criteria for hypersomnolence disorder. Exploratory factor analysis and confirmatory factor analysis in the Scale Development and Validation samples, respectively, suggested a two-factor structure representing Hypersomnia Symptoms and Distress/Impairment best fit the data. Convergent validity was established by significant associations with the Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), and the Sheehan Disability Scale in both samples. Construct validity was further supported by significant correlations between the Scale Validation sample and two weeks of diary- and actigraphy-determined total sleep time and time in bed. A cutoff score of 10 maximally discriminated between those with hypersomnia and those without. The HSI shows promise as a measure of hypersomnia that is commonly seen in psychiatric disorders, and may be of use to both researchers and clinicians. Support This work is supported by grants from the American Sleep Medicine Foundation ( 76-JF-12 ), the Brain and Behavior Research Foundation ( 19193 ), and NIMH K23MH099234 (DTP); National Science Foundation Graduate Research Fellowship Program and Stanford Child Health Research Institute (KAK); and R34MH080958 and R01MH105513 (AGH).

Sleep disturbance is a common and important component of affective illness. Fitness activity trackers are emerging as alternative means to estimate sleep in psychiatric patients; however, their ability to quantify sleep in mood disorders has not been empirically evaluated. Thus, this study sought to evaluate the utility of the Fitbit Flex (FBF) to estimate sleep in patients with major depressive disorder (MDD) relative to gold standard polysomnography (PSG) and a widely-used actigraph (Actiwatch-2; AW-2).Twenty-one patients with unipolar MDD wore the FBF and AW-2 during in-laboratory PSG. Bland-Altman analysis compared sleep variables among devices. Epoch-by-epoch analysis further evaluated sensitivity, specificity, and accuracy for the FBF and AW-2 relative to PSG.The FBF demonstrated significant limitations in quantifying sleep and wake, relative to PSG. In the normal setting, the FBF significantly overestimated sleep time and efficiency, and displayed poor ability to correctly identify wake epochs (i.e. low specificity). In the sensitive setting, the FBF significantly underestimated sleep time and efficiency relative to PSG. Performance characteristics of the FBF were more similar to the AW-2 in the normal compared to sensitive setting.Participants were young to middle aged and predominantly female, which may limit generalizability of findings. Study design also precluded ability to assess longitudinal performance of FBF.The FBF is not an adequate substitute for PSG when quantifying sleep in MDD, and the settings of the device sizably impact its performance relative to PSG and other standard actigraphs. The limitations and capabilities of the FBF should be carefully considered prior to clinical and research implementation.

Benzodiazepines are commonly used medications that alter sleep spindles during non-rapid eye movement (NREM) sleep, however the topographic changes to these functionally significant waveforms have yet to be fully elucidated. This study utilized high-density electroencephalography (hdEEG) to investigate topographic changes in sleep spindles and spindle-range activity caused by temazepam during NREM sleep in 18 healthy adults. After an accommodation night, sleep for all participants was recorded on two separate nights after taking either placebo or oral temazepam 15 mg. Sleep was monitored using 256-channel hdEEG. Spectral analysis and spindle waveform detection of sleep EEG data were performed for each participant night. Global and topographic data were subsequently compared between temazepam and placebo conditions. Temazepam was associated with significant increases in spectral power from 10.33 to 13.83 Hz. Within this frequency band, temazepam broadly increased sleep spindle duration, and topographically increased spindle amplitude and density in frontal and central-posterior regions, respectively. Higher frequency sleep spindles demonstrated increased spindle amplitude and a paradoxical decrease in spindle density in frontal and centroparietal regions. Further analysis demonstrated temazepam both slowed the average frequency of spindle waveforms and increased the relative proportion of spindles at peak frequencies in frontal and centroparietal regions. These findings suggest that benzodiazepines have diverse effects on sleep spindles that vary by frequency and cortical topography. Further research that explores the relationships between topographic and frequency-dependent changes in pharmacologically-induced sleep spindles and the functional effects of these waveforms is indicated.

Objective Changes in slow waves during non-rapid eye movement (NREM) sleep in response to acute total sleep deprivation are well-established measures of sleep homeostasis. This investigation utilized high-density electroencephalography (hdEEG) to examine topographic changes in slow waves during repeated partial sleep deprivation. Methods Twenty-four participants underwent a 6-day sleep restriction protocol. Spectral and period-amplitude analyses of sleep hdEEG data were used to examine changes in slow wave energy, count, amplitude, and slope relative to baseline. Results Changes in slow wave energy were dependent on the quantity of NREM sleep utilized for analysis, with widespread increases during sleep restriction and recovery when comparing data from the first portion of the sleep period, but restricted to recovery sleep if the entire sleep episode was considered. Period-amplitude analysis was less dependent on the quantity of NREM sleep utilized, and demonstrated topographic changes in the count, amplitude, and distribution of slow waves, with frontal increases in slow wave amplitude, numbers of high-amplitude waves, and amplitude/slopes of low amplitude waves resulting from partial sleep deprivation. Conclusions Topographic changes in slow waves occur across the course of partial sleep restriction and recovery. Significance These results demonstrate a homeostatic response to partial sleep loss in humans.