Sleep Medicine

Major international sporting events frequently impose exogenous demands that challenge adult circadian rhythms, often leading to the misalignment of sleep-wake cycles and social schedules. This cross-sectional study investigated the impact of the FIFA 2022 World Cup on adult sleep patterns to assess the prevalence and determinants of tournament-associated circadian disruption. Through an online survey, we captured data on sleep duration, timing, and subjective quality from a diverse adult population using Pittsburgh Sleep Quality Index (PSQI) score. The results indicate that 81.3% had high problematic sleep according to PSQI scores, while only 9% perceived that their sleep pattern was impacted by watching matches during the tournament. While 83.7% of the participants had low or mild anxiety according to GAD-7 scores, we found that GAD-7 scores correlated significantly with PSQI scores. Married participants had significantly lower PSQI scores (RR 0.856, p = .005), while those who reported that their sleep hours had changed during the tournament had significantly higher PSQI scores (1.180, P-value <0.001). Males reported a significantly high impact of the tournament on their sleep (OR 2.622, P-value <0.001). In conclusion, our data demonstrate a discrepancy between self-perception of sleep quality and self-rated assessment by PSQI scores, as well as the substantial impact of major international sporting events on adult sleep hygiene. The results provide data-driven insights helpful in evaluating potential circadian risks and informing public health strategies for major sporting events such as the FIFA world cup.

Objectiveto evaluate the effectiveness of a sleep education program among young athletes in enhancing sleep quality and duration, as well as mood and academic performance. Design: prospective cohort study. MethodsWe included 639 players (11% female; mean age of 13.89{+/-}3.8 years) of 5 sports disciplines in a professional club were evaluated before and after a sport season, through 4 specific instruments: 1) sleep diaries to estimate nocturnal sleep duration, 2) the Childrens Sleep Disorder Score Scale (SDSC) to assess sleep quality, 3) the Sleep Vitality Scale (SVS) to examine mood, and 4) school records of academic performance. The sleep education program included staff, family and individual sessions. It focused on the promotion of healthy sleep habits. ResultsThe 16t-25 years-old (y-o) group exhibited an increase in nocturnal sleep duration (p=0.002), while the 12-15 y-o group showed a decrease (p=0.01). In contrast, the 7-11y-o group exhibited no change. For sleep quality, the 12-15y-o (p < 0.001) and 16-25y-o (p<0.001) groups, while the 7-11y-o group exhibited inferior sleep quality (p 0.001). Regarding mood, the 7-11y-o group showed a significant deterioration (p=0.008), while no changes were observed in the 12-15y-o and 16-25y-o groups. Academic performance exhibited a significant improvement in the 7-11y-o (p=0.001), 12-15y-o (p<0.001), and 16-25y-o (p=0.008) groups. ConclusionsAmong athletes aged 12-25y-o, participation in a sleep education program led to improvements in sleep quality and duration, accompanied by enhanced academic performance. However, this intervention did not yield positive effects for athletes between the ages of 7 and 11 years.

BackgroundGood quality sleep is essential for good health and well-being. Medical students are at no exception to this and are prone to greater risk for sleep deprivation. The major reason being challenges to maintain a high level of academic achievement and constant thirst to acquire new learning skills and knowledge. However, in this process they are circumstanced to various levels of stress that might cause potential damage to their cognitive functioning and mental exhaustion to a certain extent. ObjectivesThus, our study objectives were to evaluate the sleep pattern in first- and second-year medical students. To understand how the stress levels and academic performance are related to sleep pattern and to explore the copying strategies of stress in our study participants. MethodologyThis cross-sectional study was conducted using a self-reported, web-based, questionnaire that included questions on sleep quality and deprivation through Pittsburgh sleep quality index. All the eligible students of first and second year who were part of a premiere teaching hospital during February and March 2021 were included. Data was analysed using IBM SPSS version 24. ResultsOut of 180 participants, 91(50.55%) had their initiation of sleeping time from 12-2 am and also, majority of students 112 (62.22%) had a sleep duration of six to eight hours. However, 119 (66.1%) students had self-reported change in sleeping pattern which was found to be significantly associated with relatively greater number of academic factors as compared to social factors. Most of the students scored between 50-60% score in their four assessments amongst which their first assessment was significantly associated with change in sleep pattern (P 0.040). Also, these individual assessment score was found to significantly affect their duration of sleep. The common coping strategies adopted by students under study were talking to family members/ friends, music/ book reading (hobby). ConclusionMajority of students in our study had reported change in sleeping pattern. Also, association between stress factors and change in sleeping pattern were observed with academic stress factors proving to be more significantly associated than social stress factors. The academic performance of students was also found to be associated with change in sleeping pattern and duration of sleep.

Sleep is a crucial factor in recovery and must be integrated into athletes training plans for optimal performance and well-being. Although professional athletes are advised to sleep for at least 8 hours, many experience shorter sleep durations or poor sleep quality. Sleep interventions have been recently proposed to improve sleep, but their effects remain unclear. This ecological study aimed to evaluate the sleep of a rugby team and to assess the effects of sleep interventions, including sleep hygiene education and relaxation techniques. Thirty-six male professional rugby players were evaluated during two pre- and post-intervention match weeks using objective (actimetric) and subjective (questionnaires) assessments. At baseline, 34 on 36 athletes slept less than 8h per night. Combining both sleep dimensions, 61.1 % of players were considered poor sleepers. After sleep interventions, subjective sleep quality improved (p = 0.001, 2 = 0.22), and athletes went to bed earlier (23:28 {+/-} 00:42 vs. 23:43 {+/-} 00:45 during pre-intervention; p = 0.01, 2 = 0.15). Positive effects of sleep interventions were especially observed among poor sleepers as their objective sleep quantity increased (405.2 min pre-intervention vs. 425.9 min post-intervention p = 0.004, 2 = 0.28). Sleep interventions, composed of theoretical and practical sessions, improved sleep characteristics and might be implemented in athletes daily routine. This study offers a simple and accessible method to assess athletes sleep, while providing adapted recommendations to optimize or enhance sleep quality and quantity. Key pointsO_LI61.1% of the rugby players exhibited both poor sleep quality and quantity during the week and weekends. C_LIO_LITheoretical and practical sleep interventions elicited objective and subjective sleep improvement, especially among poor sleepers. C_LIO_LISleep assessment and interventions represent an efficient, and feasible method among team-sport players for improving sleep quality. C_LI

Study ObjectivesExcessive daytime sleepiness (EDS) is common in participants with sleep disorders, particularly obstructive sleep apnea (OSA), and can be assessed using the Epworth Sleepiness Scale (ESS) and the Psychomotor Vigilance Test (PVT). However, the relationship between these measures of sleepiness/attention, and their relationships to OSA severity and treatment, remains understudied. This study examined these associations in a sleep center population. MethodsA total of 167 participants, primarily diagnosed or suspected of OSA (n=128 [76.6%]), completed the ESS and PVT during their clinical visit. Associations among ESS, PVT, OSA severity and CPAP adherence were examined using Pearsons correlations, unadjusted and controlling for age, sex and body mass index. ResultsResults showed no significant correlations between ESS and PVT measures of attention/vigilance. While higher ESS scores correlated with more severe apnea-hypopnea index (AHI) in participants with OSA, no association was found with PVT measures. Among participants using continuous positive airway pressure (CPAP), greater hours/night of usage was associated with lower ESS scores, but not with better PVT performance. ConclusionsOur data indicate that ESS scores track more closely than PVT to OSA severity and treatment. The findings suggest that the tendency to fall asleep as measured by the ESS and attention deficits on PVT may capture different aspects of "sleepiness". While the ESS is commonly used in sleep clinics, further research is needed to determine if PVT should also be used routinely in clinical practice. Brief summaryExcessive daytime sleepiness (EDS) is a prevalent symptom among individuals with obstructive sleep apnea (OSA), but the relationship between a subjective measure (Epworth Sleepiness Scale) and an objective measure (Psychomotor Vigilance Test) of sleepiness or attention, as well as how each relates to OSA severity and treatment, is not well understood. This study found no association between the ESS and measures from a 3-minute PVT, suggesting that these assessments are not evaluating the same aspects of "sleepiness" reported by participants. Higher ESS scores, but not worse PVT performance, was related to more severe OSA and less adherence to CPAP, indicating that the ESS tracks more closely than the PVT to OSA severity and treatment use.

This study compared the effects of a 25-min nap opportunity and a 10-min non-sleep deep rest (NSDR) condition on perceptual, cognitive, and physical performance in physically active young adults. Sixty participants (26 female, 34 male; 22 {+/-} 4 years) were randomly assigned to one of three groups (nap, NSDR, control; n = 20 each). All groups completed identical assessments immediately, 20 min, and 40 min post-intervention. Mixed-effects models, adjusted for sex, prior-night sleep, and weekly physical activity, revealed a significant Group x Time interaction for sleepiness, fatigue, readiness to perform, and handgrip strength (p < 0.05). At 40 min post-intervention, the nap group reported lower fatigue than control and higher readiness to perform than both control and NSDR (p < 0.05). No significant effects were observed for the NSDR condition on perceptual, cognitive, or physical outcomes (p > 0.05). These findings indicate that a short nap can enhance perceived readiness and reduce fatigue after a brief latency period, whereas NSDR did not elicit significant effects under the present conditions.

The COVID-19 lockdown brought disruptions to daily life and social schedules which influenced sleep and stress. Young adults, already vulnerable to irregular sleep patterns and heightened psychological stress, may have experienced notable shifts in sleep quality, duration, and circadian alignment during this period. Seventy-nine urban young adults (18-25 years) provided matched pre-lockdown and during-lockdown data via an online survey. Measures included the Pittsburgh Sleep Quality Index (PSQI), Berlin Questionnaire for OSA risk, the National Stressful Events Survey Acute Stress Disorder Short Scale (NSESS-S), and self-reported height and weight for BMI. Social jet lag was derived from sleep timing. Within-participant changes were tested using Wilcoxon signed-rank tests; univariate and multivariate logistic regressions identified predictors of OSA risk. No significant changes were observed in PSQI scores, subjective sleep quality, sleep latency, acute stress, or social jet lag during lockdown. Sleep duration increased slightly, and BMI and social jet lag showed small numerical rises without statistical significance. OSA risk was positively associated with acute stress (p < 0.001), higher BMI (p < 0.05), and poor sleep quality (p < 0.001). In multivariate models, poor sleep quality was the strongest independent predictor of OSA risk, followed by acute stress and BMI. Lifestyle factors, including physical activity, screen time, and ambient noise, showed no significant associations. Flexible schedules during lockdown may have offset expected negative impacts on sleep and stress in this demographic. The strong links between OSA risk, poor sleep quality, and acute stress highlight the need for integrated behavioural and physiological approaches to sleep health in young adults.

BackgroundEmerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. HypothesisKey predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study DesignCross-sectional study. Level of EvidenceLevel 3. Methods121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). ResultsPhone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. ConclusionsOur findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical RelevanceEducation and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

BackgroundThe maintenance of wakefulness test (MWT) measures the ability of an individual to maintain wakefulness in soporific conditions and despite limitations remains the mainstay of vigilance testing. Wake and sleep states are traditionally characterised by oscillatory activity on electroencephalogram (EEG) but there is a less utilised non-oscillatory component of background neural aperiodic activity which can be derived from EEG raw data and represented as an exponent or a gradient (1/f slope). There is emerging evidence that aperiodic activity can be used to predict cortical activity. The aim of this study is to assess the use of aperiodic activity as a novel neurobiological marker of vigilance. MethodsEEGs (4 x 50) from 50 patients who underwent the MWT between 2009 and 2023 at a single centre were analysed to determine their aperiodic activity. Statistical analyses utilising linear mixed models and linear regression were performed to assess the relationship between aperiodic exponent, aperiodic intercept and mean sleep latency. ResultsLinear mixed-effects modelling revealed that more negative aperiodic exponents represented by steeper 1/f slopes were associated with longer sleep onset latencies ({beta}=-8.08, p=0.002). Similarly, higher aperiodic intercept scores were associated with longer sleep onset latencies ({beta}=2.36, p=0.01). ConclusionThis study provides proof-of-concept that aperiodic activity may be predictive of vigilance. Given its practicality, cost-effectiveness and lower demand on health staff and patient time, it is suggested that aperiodic activity as a test of vigilance testing offers not only greater diagnostic objectivity but benefits to already resource-limited healthcare systems. Brief SummaryThe current gold standard for vigilance testing is the maintenance of wakefulness test (MWT) which measures the ability of an individual to maintain wakefulness in soporific conditions. Despite its advantages, the MWT is resource intensive and time consuming which has led to the exploration of alternative markers of wakefulness. Our study provides proof of concept that neural aperiodic activity may be a viable alternative or adjunct to current vigilance tests. Further, we describe the ease at which analysis of the aperiodic activity is performed and the potential advantages of its utility in clinical practice.

Background and purposeA new version of the Sleep Hygiene Index (SHI-10) has recently been introduced, and the psychometric performance in other populations is unknown. This study aimed to determine the dimensionality, internal consistency, and nomological validity of the SHI-10 among medical students at a Colombian university. MethodsA psychometric study was designed to determine indicators of validity (construct and nomological) and reliability (internal consistency) in which 309 medical students between 18 and 39 years (M=20.83, SD=2.68), and 54.69% were female. Construct validity (dimensionality) was tested through confirmatory factor analysis, internal consistency with Cronbachs alpha and McDonalds omega coefficients, and nomological validity through correlations with the Athens Insomnia Scale, Epworth Somnolence Scale, Generalized Anxiety Disorder (GAD)-7) and Patient Health Questionnaire (PHQ-9). ResultsThe four-dimensional structure of the SHI-10 showed adequate indicators of goodness of fit (Satorra-Bentlers chi-square of 43.30 [df of 29, p=.04], chi-square/df of 1.49, RMSEA of .04 [90%CI .01-.06], CFI of .97, TLI of .96 and SRMR .04). The four dimensions of the SHI-10 showed values less than .70 and limited nomological validity (most Pearson correlations were less than .30). ConclusionsThe SHI-10 shows a four-dimensional structure of SHI-10; however, the four dimensions of the SHI-10 present low internal consistency and limited nomological validity. More studies are needed to show the psychometric performance of the SHI-10.

While the SATED/Ru-SATED sleep health framework is well-recognized, there is no consensus for how many or which domains should be included in the construct of a multidimensional sleep health (MDSH) composite score or how the domains are defined. Therefore, the purpose of this scoping review is determine what domains are included in MDSH composite scores and how are those domains assessed. The Preferred Reporting Items for Systematic Reviews and Meta- Analyses extension for scoping reviews (PRISMA - ScR) and the Joanna Briggs Institutes (JBI) updated methodology for scoping reviews were used. Two authors independently reviewed titles/abstracts, full-texts, and performed data extraction, and one author resolved any discrepancies with discussion as needed. The search strategy generated 1,481 references, 107 underwent a full-text screening, and 39 were eligible for inclusion in this scoping review. Five and six domains were the most common number of domains included in the MDSH composite scores (n=17 for both). Seventeen articles included only self-report measures and n=19 articles included a mix of self-report and objective measures. Thirteen unique domains were identified with Duration being the most common (n=38), followed by Alertness/Sleepiness (n=35), Satisfaction/Quality (n=32), and Timing (n=31). In conclusion, while the domains most often included in the MDSH composite scores followed the SATED/Ru-SATED framework, there was variability in the domains included as well as variability in how the domains were assessed. Consensus is needed on the definition of sleep health domains or, at a minimum, clear reporting on the definitions used. Further research is needed to determine which MDSH domains are most associated with health outcomes.

RationaleObstructive sleep apnea (OSA) is a common, treatable chronic disease with significant health and societal consequences. Many patients face barriers to care due to systemic inequality, poverty, and other contributors to social vulnerability, leading to delayed diagnosis and more severe disease at presentation. Several studies have examined the impacts of social vulnerability on OSA severity using individual-level factors. However, there is comparatively limited work examining how neighbourhood-level indicators may influence OSA severity. This study aimed to determine whether social vulnerability, measured using a neighbourhood-level multidimensional index, is associated with OSA severity at referral to a tertiary sleep centre. MethodsWe conducted a retrospective observational study of adult patients referred to an academic hospital in Calgary, Canada for evaluation of OSA between November 2016 and November 2019. Patient data were linked using residential postal codes to the Canadian Index of Multiple Deprivation (CIMD), a census-based tool designed to reflect dimensions of social vulnerability in Canadian populations. CIMD divides social vulnerability into four dimensions including residential instability, ethnocultural composition, economic dependency, and situational vulnerability. We employed both linear and logistic mixed-effects models to assess the impact of neighbourhood-level social vulnerability on sleep apnea severity, using postal code as the grouping variable. OSA severity was based on home sleep apnea test (HSAT) derived oxygen desaturation index (ODI). Secondary outcomes included severe OSA (ODI [&ge;] 30), sleepiness based on Epworth Sleepiness Scale (ESS), and severe sleepiness (ESS > 15). ResultsThe study included 2,232 patients, 80% of whom had at least mild OSA. ODI was positively associated with situational vulnerability (p < 0.01) and inversely associated with ethnocultural composition (p < 0.01), though both associations lost significance after adjusting for BMI. ESS was independently associated with situational vulnerability (p < 0.01) and inversely with ethnocultural composition (p = 0.01), independent of BMI and ODI. Severe sleepiness was associated with situational vulnerability (p < 0.01) and residential instability (p = 0.02). ConclusionLiving in a socially deprived area was associated with OSA severity at time of referral, though this relationship appeared to be mediated by BMI. Deprivation dimensions were independently associated with sleepiness, highlighting the broader impact of social-related factors on sleepiness. These findings demonstrate the complex interplay between social vulnerability and sleep disorders and suggest that composite indices like the CIMD can enhance our understanding of these relationships.

Background Racial and ethnic and sex differences in sleep may exist, but there are limited data directly comparing objective estimates of sleep-disordered breathing (SDB), particularly in rapid eye movement (REM) versus non-rapid eye movement (NREM) sleep, among Black, Mexican American (MA) and non-Hispanic White (NHW) men and women. Our goal is to investigate health disparities in SDB in a new, diverse cohort of older adults. Research Question Do SDB parameters during REM and NREM sleep differ by race and ethnicity or sex in community-dwelling older adults?. Methods The Dormir Study conducted a comprehensive sleep examination among eligible participants enrolled in the ongoing community-based Health and Aging Brain Study-Health Disparities (HABS-HD) cohort (2020-4), among Black, MA, and NHW adults aged 50 years and older. Here we characterize racial and ethnic and sex differences in SDB indices assessed by an FDA-approved Peripheral Arterial Tonometry (PAT)-based home sleep testing system. Results We examined 821 participants, including 543 (66.1%) women, and 284 (34.6%) MA and 174 (21.2%) Black individuals, with a mean age of 66.6{+/-}8.5 years. Around half (50.5%) of the participants had moderate to severe SDB as defined by the respiratory event index (REI based on 3% desaturations) of [&ge;]15/ hour, 72.7% with moderate to severe REM SDB (REM-REI [&ge;] 15/hour) and 39.5% with moderate to severe NREM SDB (NREM-REI [&ge;]15/hour). The prevalence of SDB did not differ by race or sex. However, significant racial and ethnic and sex differences were observed for REM-specific SDB metrics. Overall, Black women had the highest REM REI, and NHW men had the lowest REM REI and REM ODI. After controlling for age, sex, education, income, employment status, cognitive status, BMI, history of hypertension, diabetes, stroke, and coronary heart disease, and sleep medication use, Black participants had a REM-REI that was 3 events per hour higher than that of NHW adults, while NREM-REI were similar. MA individuals had similar REM or NREM SDB parameters compared to NHW adults but exhibited higher average blood oxygen levels. Conclusions In this community-based cohort of middle- to older-aged NHW, MA, and Black adults, PAT-based measures of in-home sleep indicate a higher prevalence of REM SDB in Black adults, particularly Black women, compared to their NHW counterparts; this contrasts with the similar NREM parameters observed across racial and ethnic groups. Given the close link between REM SDB and adverse health outcomes, clinicians should pay more attention to this sleep apnea phenotype, especially in minoritized populations.

An ever-increasing burden of non-communicable diseases, especially in the post pandemic times and an association of sleep problems with them highlighted a felt need to estimate the sleep problem in India. A meta-analysis of the studies conducted on Indian data was planned adhering to PRISMA guidelines. An electronic search of available literature was performed on databases including PubMed, Google Scholar, PsycNet, and Epistemonikos. 100 eligible articles were analysed. To assess the methodological quality 10-points Joanna Briggs Institute (JBI) checklist for prevalence studies was used. The pooled estimates for prevalence of Insomnia found were 25.7%, OSA 37.4%, and RLS 10.6%. An increased prevalence was seen in patients of diabetes, heart disease patients and in otherwise healthy population. Subgroup analysis showed a higher prevalence in patient population and in the otherwise healthy population too,; e.g. Insomnia 32.3% (95% CI: 18.6% to 49.9%, I2=99.4%) and 15.1% (95% CI: 8.0% to 26.6%, I2=99.1%); OSA 48.1% (95% CI: 36.1% to 60.3%, I2=97.4%) and 14.6% (95% CI: 9.2% to 22.5%, I2=97.4%) and RLS 13.1% (95% CI: 8.7% to 19.3%, I2=91.9%) and 6.6% (95% CI: 2.4% to 16.4%, I2=99.1%) respectively. Excessive daytime sleepiness remained prevalent (19.6%) (95 % CI: 8.4% to 39.1%, I2=99.8%) in the healthy, which was alarming. A multipronged approach for sleep management, evaluation and research is the need of the hour for managing non communicable disorders and for promoting sleep health in the healthy population.

Chronic partial and acute total sleep loss have a distinct impact on sleep architecture. Namely, acute sleep deprivation primarily leads to a strong rebound of slow wave sleep, while chronic sleep restriction results in an increased propensity of REM sleep. The aim of this work was to examine whether these different effects would translate into quantifiable changes in sleep state boundaries and dynamics using a model-based method. Besides conventional sleep stage scoring, we applied an EEG model (state space approach) for dynamic analysis of nocturnal EEG recordings in 14 healthy subjects under experimental chronic sleep restriction (last of 7 nights with 5 hours of time in bed) and after acute sleep deprivation (sleep following 40 hours of wakefulness), in comparison to baseline sleep. Subjects under chronic sleep restriction revealed increased similarities in the frequency composition of REM sleep and wakefulness and thus, a decreased differentiation of state boundaries between the two behavioral states. Contrarily, acute sleep deprivation affected the spectral composition of NREM sleep. Only acute sleep deprivation resulted in more stable slow wave sleep. Our explorative study confirmed that the distinct effects of increased REM sleep and slow wave sleep propensity following acute total and chronic partial sleep loss are reflected in differential changes of behavioral state boundaries and sleep dynamics. This suggests that these sleep structure characteristics are state dependent, which may allow using such measures in the future to track treatment effects in clinical populations characterized by sleep behavioral state dysregulation.

ObjectivesDrug induced sleep endoscopy (DISE) is a standardly used investigation for surgical planning in obstructive sleep apnoea management once conservative treatments have proven inadequate. There are a variety of anaesthetic agents used to obtain sedation necessary for DISE. These agents may have different effect on the upper airway and other parameters important in the diagnosis of the site of collapse during sleep. We aimed to review the commonly agents and evaluate the significance of their impact on the the diagnosis. MethodsA search was conducted through PubMed looking for studies on commonly used anaesthetic agents and their effect on the upper airway and cardiopulmonary parameters. Results: Of the 109 studies yielded by the search, 19 were deemed relevant to the review and met all inclusion criteria. The agents reviewed were: propofol, dexmedetomidine, remifentanil, isoflurane, sevoflurane, midazolam and topical lidocaine. A meta-analysis was not conducted due to the limited number of relevant studies and the heterogeneity of outcomes measured. All agents examined gave some element of airway collapse and impact on cardiopulmonary measures. Most of these effects were shown to be dose-dependent. Of the agents considered dexmedetomidine and propofol gave the most consistently reliable and physiologically safe representations of upper airway collapse seen in OSA patients. ConclusionThere is limited information and no industry standard for the sedative regimen used for DISE. Of the agents reviewed those that caused least cardiopulmonary instability, respiratory depression and exaggerated upper airway collapse were deemed the most appropriate for DISE. The agent that best meet these criteria is dexmedetomidine followed by propofol.

Wearable and nearable devices offer a novel opportunity to measure extensive behavioral and neurophysiological data directly from participants in their home environment. The Simons Sleep Project (SSP) was designed to accelerate research into sleep and daily behaviors in individuals with autism using such techniques. This open-science resource contains raw and processed data from Dreem3 EEG headbands, multi-sensor EmbracePlus smartwatches, and Withings Sleep mats, as well as parent questionnaires and daily sleep diaries. Data were collected successfully for >3600 days/nights from 102 adolescents (10-17 years old) with idiopathic autism and 98 of their non-autistic siblings. Whole-exome sequencing data is also available for all participants and their parents. To demonstrate the utility of this extensive dataset, we first present the breadth of synchronized high-resolution data available across multiple sensors/devices. We then demonstrate that objective sleep measures (e.g., total sleep time) from the three devices are more accurate and reliable than parent reported measures and reveal that sleep onset latency (SOL) was the only objectively defined sleep measure that differed significantly between autistic children and their siblings (of those examined in the study). Moreover, SOL was reliably associated with the severity of multiple behavioral difficulties in all children, regardless of autism diagnosis. These results highlight the importance of measuring sleep directly from participants using objective measures and demonstrate the extensive opportunities afforded by the SSP to further study autism and develop new digital phenotyping techniques for multiple research domains.

Excessive daytime sleepiness (EDS) is a common but inconsistently predicted symptom of obstructive sleep apnea (OSA). OSA is typically diagnosed with polysomnography (PSG), and the current standard for severity assessment is the apnea-hypopnea index (AHI). AHI has many limitations, including its inability to explain physiological mechanisms or reflect variability in patient symptoms, such as EDS. This retrospective study aims to find physiological and demographic parameters that better predict EDS in patients with OSA and to evaluate whether these parameters outperform AHI using PSG data from the Mount Sinai Integrative Sleep Center. Clinical variables used to predict EDS included arousal index (AI), average oxygen desaturation during sleep, average heart rate during sleep, and AHI, along with demographic variables including age, sex, and BMI. Hypothesis tests, logistic regression models, and decision tree classifier models were performed on the data to discriminate sleepy from nonsleepy patients as determined by an Epworth Sleepiness Scale (ESS) score [&ge;] 10. AI and oxygen desaturation were found to be the most predictive physiological variables, and sex and BMI were found to be the most predictive demographic variables. The final decision tree model with these four variables outperformed the AHI in predicting EDS. These findings suggest that daytime sleepiness in OSA can be better explained by measures of apnea burden, oxygenation impairment, and patient demographics than by AHI alone, although these remain only modestly predictive. Future studies should focus on investigating more comprehensive physiological markers, multi-night sleep data, and more objective assessments of sleepiness.

Study ObjectivesSleep plays a crucial role for mental health. This study examines sleep tracking in naturalistic settings for patients with major depressive episodes (MDE) using actigraphy, smartphone data, bed sensors, and the ecological momentary assessment (EMA) and assesses discrepancies between these modalities. MethodsWe measured sleep onset, offset, and total sleep time (TST) over two weeks for 172 participants, including healthy controls and three MDE subgroups (borderline personality disorder, major depressive disorder, and bipolar disorder). Agreement between measurement modalities was assessed using Bland-Altman plots and Pearson correlation. Predictors of sleep alignment were analyzed using mixed-effects models, accounting for demographics, daylight length, and participant subgroup. ResultsPatients showed greater sleep variability than healthy controls. Actigraphy overestimated TST compared to bed sensors (0.48 min) and smartphones (0.99 min), while the smartphone underestimated TST compared to other modalities. Older age improved alignment between actigraphy and bed sensors, as well as smartphone and bed sensor sleep offset. TST alignment (smartphone vs. bed sensor) was worse in females and bipolar/borderline patients. Longer daylight duration improved TST and sleep offset alignment across modalities. ConclusionsOur study highlights measurement biases, seasonal effects, and demographic factors associated with discrepancies in objective sleep measures. While these modalities show potential and offer several advantages in assessing sleep over longer periods, the discrepancies and factors associated with misalignment should be considered in future studies or clinical settings. Statement of SignificanceTracking sleep in psychiatric patients is challenging due to frequent sleep disturbances, making accurate assessment crucial for diagnosis and care. Traditional methods are limited to lab settings, restricting long-term monitoring. This study evaluates the alignment of naturalistic sleep tracking using actigraphy, bed sensors, smartphone data, and self-reports in both healthy individuals and patients with depressive disorders. Our findings demonstrate the feasibility of using these non-invasive methods to monitor sleep for patients with major depressive episodes. We uncover systematic biases in sleep estimates across modalities, reveal demographic and environmental factors that influence measurement agreement, and show that psychiatric populations exhibit more variability in sleep patterns. This work addresses a critical gap in validating consumer-grade sleep tracking technologies for psychiatric populations in naturalistic contexts.

Study Objectives Co morbid insomnia and sleep apnea (COMISA) is challenging clinically and difficult to treat. Our goal was to assess how much COMISA is the mere addition of two phenotypes or display features indicative of genuine statistical interactions. Methods A total of 152,487 patients from 240 sleep centers across 30 US states were included. Insomnia was defined as difficulty initiating/maintaining sleep with daytime fatigue/sleepiness occurring "often"/"always". OSA was defined as having an Apnea Hypopnea Index (AHI) more than 15 events/h. Modified Poisson regression was conducted to evaluate multiplicative interactions between insomnia and OSA on common comorbidities and sleep symptoms. Additive interactions were also examined. Linear regression models were used to evaluate additive interactions for PSG parameters. The false discovery rate was controlled using the Benjamini Hochberg procedure. Results After adjustment for confounders, insomnia and OSA demonstrated positive interactions for depression, chronic muscular pain, headache, subjective excessive daytime sleepiness (EDS), naps, and pre-sleep anxious and muscular tension (adjusted p < 0.05). Furthermore, insomnia and OSA demonstrated positive interactions for parameters related to respiratory disturbance, including AHI, oxygen desaturation index (ODI), respiratory disturbance index (RDI), total arousal index (AI) and respiratory AI, and negative interactions for minimum oxygen saturation and percentage of rapid eye movement stage (REM%) (adjusted p < 0.05). Furthermore, the adverse effects of insomnia and OSA on AHI, ODI, RDI and REM% were substantially amplified in males. Conclusions Our findings demonstrate that insomnia and OSA do not merely coexist but genuinely interact synergistically to amplify selected adverse clinical outcomes.