Hour Of Total Sleep Time Research Articles

Predicting stress in first-year college students using sleep data from wearable devices.

Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.

Impact on health outcomes associated with changing the clock 1 hour during fall and spring transitions in the Southern Hemisphere.

Changing the clocks seasonally is potentially harmful because it interferes with normal daytime activities. Studies aimed at quantifying this association are scant. The objective of this study was to determine the effects of 1 year's worth of changing the clocks (fall and spring transitions) on healthy young men located in the Southern Hemisphere in South America. We performed an observational prospective study. Thirty healthy male university students were evaluated from 2 weeks before to 2 weeks after both the fall and spring transitions. We administered an overall sleep questionnaire, assessed quality of life, recorded 7-day wrist actigraphy, and had participants perform a psychomotor vigilance task. We defined the 1-hour clock change as the primary exposure and the change in psychomotor vigilance task lapses of 500 milliseconds or more in response time as our primary outcome. Changes were evaluated by the Wilcoxon rank test (significance: P < .05). After the fall transition, we found a significant worsening in psychomotor vigilance task performance (median [interquartile range], 9.9 [6.0-14.3] lapses of ≥ 500 milliseconds in response time at baseline vs 16.8 [8.2-28.0] after transition; P < .002). Additionally, we found a median loss of about 1 hour of total sleep time and time in bed after the fall transition. Furthermore, participants presented with insomnia. Performance on the psychomotor vigilance task was also affected after the spring transition (16.7 [10-23] vs 23 [12.2-32.2]; P < .001). A decrease in performance in neurocognitive tests was found after both time transitions. The transition led to insomnia and a significant worsening of sleep variables. Labarca G, Henriquez-Beltrán M, Sanhueza R, etal. Impact on health outcomes associated with changing the clock 1 hour during fall and spring transitions in the Southern Hemisphere. J Clin Sleep Med. 2024;20(6):887-893.

Does sleep link child maltreatment to depressive symptoms among incoming first-year college students?

We examined whether sleep (i.e. quality, regularity, and duration) mediated associations between child maltreatment (CM) and depressive symptoms among emerging adults undergoing the major life transition of starting college. Students (N = 1400; 44% male; 48% non-Hispanic white, 20% non-Hispanic Asian, 15% Hispanic all races, 7% non-Hispanic black, and 10% non-Hispanic other races) completed daily sleep diaries for 9 weeks, followed by the Childhood Trauma Questionnaire-Short Form, Pittsburgh Sleep Quality Index, and the Center for Epidemiologic Studies Depression Scale (CES-D). DSD data were used to compute participants' Sleep Regularity Index and average 24-hour total sleep time. We used a nonparametric structural equation modeling bootstrap approach and full information maximum likelihood to account for missing data. In model 1, we controlled for sex and race and ethnicity. In model 2, we further adjusted for baseline CES-D scores. The prevalence of self-reported moderate-to-severe CM was 22%. Small but significant indirect effects of CM on greater depressive symptoms through worse sleep quality (β = 0.06, 95% CI = 0.04, 0.09) and lower sleep regularity (β = 0.02, 95% CI = 0.005, 0.03) were observed in model 1. In model 2, only the indirect effect of sleep quality remained significant (β = 0.03, 95% CI = 0.01, 0.06). Poorer sleep quality may partially account for associations between CM and depressive symptoms during the first semester of college. Including sleep as a target in student health interventions on college campuses may not only help buffer against poor mental health outcomes for students with CM, but also poor academic and socioeconomic outcomes long-term.

0548 Position-dependent obstructive sleep apnea and the cardiovascular risks

Abstract Introduction Position dependent obstructive sleep apnea (PdOSA) is highly prevalent, but the implication of PdOSA in cardiovascular (CV) outcomes has not been studied. We hypothesized that PdOSA is associated with lower risk of future CV events than those without PdOSA. Methods We included patients who underwent diagnostic polysomnography (PSG) at a single academic center. The study included patients with ≥2 hours of total sleep time, 30 minutes in both supine and non-supine sleep, and OSA (apnea hypopnea index [AHI]≥5/hr). We defined PdOSA as either POSA (overall AHI≥5/hr accompanied by supine AHI (sAHI) at least twice as high as non-supine AHI (ns-AHI)), or ePOSA (POSA criteria + ns-AHI &amp;lt; 5/hr (i.e., normalized)). Blood pressure (BP) was derived by averaging multiple measures of clinic BP within 1 year of PSG. All patients were followed up to 11 years to identify incident CV events (including acute coronary syndrome, stroke, and new heart failure). We examined the association of PdOSA with CV events with a logistic regression, adjusting for age, sex, race, BMI, systolic BP, and OSA severity (mild [AHI 5-15] vs. moderate to severe [≥15]). Results A total of 1750 patients (mean age: 54 years, female 60%) were included. POSA and ePOSA were present in 47% and 23% of the cohort. Patients with either POSA or ePOSA were less obese and had less severe OSA compared with those without POSA and ePOSA. Patients with ePOSA had lower BP (systolic BP 128.8 vs 131.2 mmHg, p=0.003) than those without ePOSA. Over a median 8.3 years of follow-up, a total of 340 CV events occurred. In multivariable analysis, both POSA and ePOSA were associated with lower odds of future CV events (OR 0.8, 95% CI [0.64-0.99], p=0.044; OR 0.69 [0.52-0.89], p=0.0061, respectively). Analysis stratified by OSA severity showed that the ePOSA association was present only in those with mild OSA (OR 0.67 [0.48-0.94], p=0.021). Conclusion Among patients with OSA, PdOSA is associated with more favorable CV outcomes than those without PdOSA. The benefit appears to be limited to those with mild OSA. Future studies should compare the CV risks between PdOSA patients and those without OSA. Support (if any)

Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study

ObjectivesThe concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor. MethodsWrist actigraphy measures of participants (N = 289, Mage = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment). ResultsExploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (β [95% confidence interval] = 0.63 [0.19, 1.08]). ConclusionsActigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.

Effects of Low-Sodium Oxybate on 24-Hour Total Sleep Time: Data From a Phase 3 Clinical Study in Adults With Idiopathic Hypersomnia (P5-13.001)

<h3>Objective:</h3> Evaluate the effects of lower-sodium oxybate (LXB; Xywav^®) on total sleep time (TST) in idiopathic hypersomnia. <h3>Background:</h3> Idiopathic hypersomnia is a debilitating central hypersomnolence disorder. Efficacy and safety of LXB for idiopathic hypersomnia in adults were established in a double-blind randomized withdrawal study (NCT03533114). <h3>Design/Methods:</h3> Participants began LXB treatment in an open-label titration and optimization period (10–14 weeks), followed by a 2-week, open-label, stable-dose period (SDP), then were randomized to placebo or continued LXB during a 2-week, double-blind, randomized withdrawal period (DBRWP). Participant-recorded daily sleep diaries from week 2 of the screening and SDP periods were analyzed. Results are reported by baseline TST in groups with &lt;8, ≥8 to &lt;9, or ≥9 hours of sleep per 24 hours and by treatment at study entry (treatment naive or taking alerting agents [AAs; stimulants or wake-promoting agents]). <h3>Results:</h3> There were 154 participants (mean±SD age, 40±14 years; 68% female). From baseline to end of SDP, estimated median reductions were observed in participants with ≥8 hours of reported baseline sleep in 24-hour TST (≥8 to &lt;9 hours=42 minutes; ≥9 hours=115 minutes), nocturnal TST (≥8 to &lt;9 hours=15 minutes; ≥9 hours=81 minutes), and nap duration (≥8 to &lt;9 hours=2.5 minutes; ≥9 hours=45.2 minutes). Minimal changes were observed in 24-hour TST, nocturnal TST, and nap duration in participants with &lt;8 hours of TST at baseline (1, 0, and 1.0 minutes, respectively). Reductions in TST were observed regardless of taking concomitant AAs in 24-hour TST (without AAs=61 minutes; with AAs=28 minutes), nocturnal TST (without AAs=32 minutes; with AAs=23 minutes), and nap duration (without AAs=11.3 minutes; with AAs=8.4 minutes). Common treatment-emergent adverse events (excluding placebo data) were nausea, headache, dizziness, anxiety, and vomiting. <h3>Conclusions:</h3> Open-label LXB treatment reduced TST and nap duration in participants with ≥8 hours of TST at baseline, and regardless of taking concomitant AAs. <b>Disclosure:</b> The institution of Dr. Morse has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Jazz Pharmaceuticals, Harmony Biosciences, Avadel Pharmaceuticals, Takeda Pharmaceuticals, Alkermes, National Institute for Health, and Geisinger Health Plan. The institution of Dr. Morse has received research support from Harmony Biosciences, Avadel Pharmaceuticals, Takeda Pharmaceuticals, Alkermes, National Institute for Health, and Geisinger Health Plan. Ms. Chen has received personal compensation for serving as an employee of Jazz Pharmaceuticals. Ms. Chen has received stock or an ownership interest from Jazz Pharmaceuticals. Teresa Steininger has received personal compensation for serving as an employee of Jazz Pharmaceuticals. Teresa Steininger has stock in Jazz Pharmaceuticals. Dr. Macfadden has received personal compensation for serving as an employee of Jazz pharma. Dr. Macfadden has stock in Jazz pharma.

Sleep habits of high school student-athletes and nonathletes during a semester.

Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between US high school student-athletes and nonathletes during a semester of school and competition. Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years, and 38 nonathletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time, sleep efficiency, bedtimes, wake times, and steps counted. Student-athletes and nonathletes did not differ in total sleep time (440.4 ± 46.4 vs 438.1 ± 41.7 min, P = .82) and sleep efficiency (93.6 ± 2.3 vs 92.9 ± 2.3%, P = .20). Fitbit data revealed that 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs 8,418 ± 2,489, P < .01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased total sleep time (P < .01). Earlier wake times were significantly correlated to increased steps per day (P < .01). Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health. Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med. 2022;18(9):2189-2196.

0420 Characterization of Brain Age in Patients with Prolonged Sleep Duration

Abstract Introduction We identified 35 consecutive extended sleep studies for patients with 10 or more hours of total sleep time, and applied the BAI model to these studies. The BAI model was trained using sleep studies from relatively healthy participants. The sleep EEG features were extracted from both the spectral domain and the waveform. For each sleep stage, we extracted 96 features, and each of the features was averaged across the sleep stages. The resulting features were concatenated to form 480 features to represent the entire recording of sleep. These features are fed into a linear regression model and then adjusted to reduce age-dependent bias. Methods We identified 35 consecutive extended sleep studies for patients with 10 or more hours of total sleep time, and applied the BAI model to these studies. The BAI model was trained using sleep studies from relatively healthy participants. The sleep EEG features were extracted from both the spectral domain and the waveform. For each sleep stage, we extracted 96 features, and each of the features was averaged across the sleep stages. The resulting features were concatenated to form 480 features to represent the entire recording of sleep. These features are fed into a linear regression model and then adjusted to reduce age-dependent bias Results 35 extended polysomnograms were reviewed for patients undergoing evaluation for hypersomnia, with a median age of 27 years (range 16-76), and female predominance (28/35, 80%). This hypersomnia cohort forms two clusters by Gaussian mixture modeling. Patients in the first cluster exhibit a brain age that correlates well to chronological age (mean BAI = 4.4yr), while patients in the second cluster exhibit a brain age 12.4 years younger than chronological age (mean BAI = -12.4yr). The EEG spectrogram in the low BAI cluster shows high spindle band power, high delta band power, and high peak frequency of posterior dominant rhythm when compared to their age norms. Conclusion A subset of hypersomnia patients demonstrates a combination of EEG features associated with lower chronological age. We anticipate that the findings here will lead to further insights in IH physiology and phenotypes. Support (If Any) NIH R01NS102190, R01NS102574, R01NS107291, RF1AG064312, R01AG062989, R01AG073410

0419 Characterization of Sleep Quality in Patients with Idiopathic Hypersomnia using Cardiopulmonary Coupling

Abstract Introduction Idiopathic Hypersomnia (IH) is characterized clinically by excessive daytime sleepiness, prolonged or unrefreshing sleep, and sleep inertia. The ICSD-3 classifies IH as having expected proportions of NREM and REM sleep, normal REM latency and relatively high sleep efficiency. However, there is limited granular data on sleep quality in these patients and whether this disease state comprises of patients with a homogenous spectrum of sleep quality. The aim of this study is to describe the sleep quality in patients with IH using the ECG derived sleep spectrogram. Methods This is a single center cross sectional study where we identified 37 consecutive extended sleep studies for patients with 10 or more hours of total sleep time (TST). The sleep spectrogram is an ECG or plethysmography based system which evaluates sleep stability based on the principle of autonomic and respiratory oscillations (cardiopulmonary coupling). High frequency coupling (HFC) is associated with stable NREM sleep. Sleep quality index (SQI) is strongly weighted by HFC. We a priori decided to divide this population into 2 groups based on SQI ≥55 (there is published supportive data for using this threshold in adults) and compare them using T-test. Multivariable logistic regression was performed adjusting for age, sex, TST and apnea-hypopnea index-3% (AHI) &amp;lt;10 to assess predictors of high or low SQI which was discovered. Results The mean age for the cohort was 35.4 years (SD 16.2), of which 30 (81%) were females. Mean TST was 660 minutes (SD 16.2) with mean sleep efficiency of 86% (SD 9) and mean SQI of 59% (SD 19.1). Mean AHI was 10.4 (SD 12.3). Stratifying the cohort based on SQI gave us 2 distinct groups with mean SQI of 71.5 (SD 9.3) in group with SQI≥55 and mean SQI of 38.5 (SD 11.6) in group with SQI &amp;lt;55 [p-value &amp;lt;0.001]. In a logistic regression analysis, the SQI was not predicted by AHI &amp;lt;10, age, gender or total sleep time. Conclusion Based on our cohort, at least two clusters were identified in IH. We anticipate that the findings here will lead to further insights in IH physiology and phenotypes. Support (If Any)

The Relationship Between Sleep Disturbance and Apolipoprotein E ε4 in Adults With Mild Cognitive Impairment and Alzheimer's Disease Dementia: An Integrative Review.

Apolipoprotein E ε4 (APOE ε4) was shown to be a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia. When coupled with sleep disturbance, APOE ε4 posed additional risks to cognitive impairment. But the literature on the association between sleep disturbance and the APOE ε4 status of persons who are cognitively impaired has not yet been systematically examined. To explore and synthesize the relationship between sleep disturbance and APOE ε4 status of adults with MCI and AD. An integrative review was guided by Whittemore and Knafl's methodology. Systematic searches identified studies with multiple sources published before May 20, 2021. A matrix and narrative synthesis was employed to organize and synthesize the findings. Joanna Briggs Institute (JBI) Critical Appraisal tools (2020) were used to evaluate the quality of the selected studies. A total of 7 studies were included. APOE ε4 was associated with poor sleep quality in terms of the deterioration of nighttime total sleep time, 24-hour total sleep time, rapid eye movement, sleep efficiency, sleep latency, and wake after sleep onset in a population with MCI or AD. The interacted and adjusted relationship between sleep disturbance and APOE ε4 on the progression of cognitive decline was inconsistent. There is evidence to support an association between sleep disturbance and APOE ε4 in individuals with cognitive impairment, but a further examination of the relationship between sleep parameters and APOE ε4 is warranted, especially as the causal or dose-response relationship remains unclear.

Sleeping through a pandemic: impact of COVID-19-related restrictions on narcolepsy and idiopathic hypersomnia.

To assess the impact of coronavirus disease 2019 (COVID-19)-related restrictions on narcolepsy type 1 (NT2), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH). Participants with NT1, NT2, and IH followed in a university hospital completed an online 78-question survey assessing demographic, clinical, and occupational features of the population during the first COVID-19-related lockdown. A total of 219 of 851 (25.7%) respondents of the survey reported a mean increase of 1.2 ± 1.9 hours (P < .001) in night sleep time and a mean decrease of 1.0 ± 3.4 points (P < .001) on the Epworth Sleepiness Scale during lockdown. Bedtime was delayed by 46.1% of participants and wakeup time was delayed by 59.6%, driven primarily by participants with IH. Teleworkers (but not in-person workers) reported a mean increase of 0.9 ± 1.2 hours in night sleep (P < .001) and a mean decrease in sleepiness score of 1.6 ± 3.1 (P < .001). Cataplexy improved in 54.1% of participants with NT1. Sleepiness correlated with psychological wellness (r = .3, P < .001). As many as 42.5% enjoyed the lockdown, thanks to reallocation of time usually spent commuting toward longer sleep time, hobbies, and family time, and appreciated a freer napping schedule. Conversely, 13.2% disliked the lockdown, feeling isolation and psychological distress. Extended sleep time, circadian delay (in patients with IH), and teleworking resulted in decreased symptoms of central hypersomnias. These findings suggest that people with IH, NT1, and NT2 may benefit from a decrease in social and professional constraints on sleep-wake habits, and support advocacy efforts aimed at facilitating workplace and schedule accommodations for this population. Nigam M, Hippolyte A, Dodet P, etal. Sleeping through a pandemic: impact of COVID-19-related restrictions on narcolepsy and idiopathic hypersomnia. J Clin Sleep Med. 2022;18(1):255-263.

511 Pathological sleep prolongation and sleep onset REM period

Abstract Introduction ICSD-3 employs two definitions of pathological sleepiness: sleep prolongation with 24-hour total sleep time (24hrPSG TST) ≥660 and high sleep propensity with mean sleep latency(mSL) ≤8 min on MSLT. Multiple SOREMPs on MSLT reflects the pathophysiology (sleep instability) of narcolepsy, but it is not clear whether the lack of SOREMPs is associated with the pathophysiology of idiopathic hypersomnia. We performed 24hr PSG and PSG-MSLT sequentially to understand the meaning of SOREMP in those with pathological sleep prolongation. Methods Fifty-six consecutive patients visiting Seiwa Hospital or Koishikawa Tokyo Hospital affiliated to Institute of Neuropsychiatry with suspected idiopathic hypersomnia with habitually long sleep time were evaluated by 3-day sleep studies: unattended 24hr PSG followed by PSG and MSLT from January 2017 to November 2020. After excluding inadequate recordings or other sleep pathologies, we analyzed 52 data and found that 39 patients (23 females, 21.8 ± 7.9 years old, BMI 20.4 ± 2.5 kg/m2) exhibited pathological sleep prolongation. Results We divided 39 patients with pathological sleep prolongation into those with at least one SOREMP(n=9) and those without SOREMP(n=30) and explored differences in clinical symptoms and PSG variables. There were no differences in conventional sleep variables except for PSG sleep latency, shorter in those with SOREMP (20.8min vs 43.8min, p=0.032). Also 24hr PSG sleep variables showed no differences except for the number of NREM-REM cycles, more in those with SOREMP (10.4 vs 8.1, p=0.037). Frequency of clinical symptoms such as REM related phenomena and various symptoms characteristic for idiopathic hypersomnia did not differ between groups except for less frequency of general malaise p=0.003 and orthostatic hypotension p=0.049 in those with SOREMP. We had similar results when we compared 5 patients with multiple SOREMPs and 30 patients without SOREMP. Conclusion Our results indicated that sleep variables and clinical characteristics of idiopathic hypersomnia in those with pathological sleep prolongation were mostly the same regardless of the status of SOREMP on MSLT, suggesting that the absence of SOREMPs on MSLT were not fundamentally related to the pathophysiology of those with pathological sleep prolongation (idiopathic hypersomnia with long sleep time). Support (if any):

Sleep time and efficiency in patients undergoing laboratory-based polysomnography.

Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591-1598.

Abstract P204: Stroke Topography Impacts Sleep Architecture in Post-Acute Stroke

Introduction/Objective: Sleep-wake disturbances (SWDs) are common amongst stroke patients; however, little work has been done on the effect of stroke location on sleep architecture. The aim of this study was to investigate the impact of stroke topography on prevalence and severity of SWDs in the post-acute phase of ischemic stroke recovery. Methods: Ischemic stroke patients (n=55) were assessed for SWDs via overnight attended polysomnography in a post-acute rehabilitation setting. The mean age was 55 ± 1.4 years and mean latency from injury was 106 ± 11 days. Sleep measures included total sleep time (TST), sleep and REM latency, sleep efficiency, percent time in sleep stages, apnea/hypopnea index (AHI), wake after sleep onset (WASO), and arousal index. Patients who did not have at least four hours of TST were excluded. Stroke patients were identified as having supratentorial or infratentorial injuries. Results: Results showed a significant difference between supratentorial and infratentorial stroke in regards to sleep efficiency, REM sleep, and sleep apnea. Patients with infratentorial stroke (n=15) displayed significantly poorer sleep efficiency (F(1,53)=12.386, p&lt;.001), decreased REM sleep (F(1,53)=5.944), p&lt;.05) and higher AHI (F(1,53)=4.698, p&lt;.05). In addition to displaying a decreased amount of REM, infratentorial stroke patients displayed significantly shorter bouts of REM (F(1,52)=7.482, p&lt;.01). Neither age nor AHI were significantly correlated with the amount or duration of REM (p&gt;.05). Conclusion: Infratentorial ischemic stroke patients display significant disruptions in sleep and may require close monitoring for sleep-wake disturbances in the post-acute period. REM sleep is particularly effected when compared to supratentorial ischemic stroke.

What Are the Factors Affecting Total Sleep Time During Video Polysomnography in Infants?

The aim of the study is to investigate factors affecting total sleep time (TST) during infant polysomnography (PSG) and assess if <4 hours of TST is sufficient for accurate interpretation. Overall, 242 PSGs performed in 194 infants <6 months of chronological age between March 2013 and December 2015 were reviewed to identify factors that affect TST, including age of infant, location and timing of study, presence of medical complexity, and presence of nasal tubes. A continuum of apnea-hypopnea index (AHI) in relation to TST was reviewed. Data were examined in infants who had TST <4 hours and low AHI. Greater TST (p < 0.001) was noted among infants during nocturnal PSGs, at older chronological and post-menstrual ages, and without medical complexity. The presence of nasogastric/impedance probes reduced TST (p = 0.002). Elevated AHIs were identified even in PSGs with TST <4 hours. Short TST may have affected interpretation and delayed initial management in one infant without any inadvertent complications. Clinical factors such as PMA and medical complexity, and potentially modifiable factors such as time of day and location of study appeared to affect TST during infant PSGs. TST < 4 hours can be sufficient to identify high AHI allowing physician interpretation. · Less than 4 hours of TST is enough for interpretation of infant polysomnography.. · Shorter TST appears related to infant age, medical complexity, and higher apnea-hypopnea index.. · Modifiable factors seen with higher TST were time of day, environment, and presence of nasal tubes..

Poor Sleep Quality Increases Sedentary Time In A College Student Cohort

Quality sleep is essential for health and quality of life, and can impact academic performance. Prior research has shown a reduced sleep time in college students. A relationship has been shown between sleep quality and physical activity (PA), but has not been examined extensively in a college cohort, nor has sedentary behavior been factored in. PURPOSE: To examine the effect of sleep quality on sedentary and PA behavior in college students. METHODS: Eighty-one female (n = 53) and male (n = 28) college students (age = 20.2 ± 1.5 yr; BMI = 25.1 ± 4.7; % body fat = 31.8 ± 10.2) underwent 7-day objective PA and sleep assessment via ActiGraph accelerometer. Poor sleep quality was defined as total sleep time (TST) < 6 hours or sleep efficiency (SE; TST / Total time in bed) <85%. One-way ANOVA was utilized to assess mean differences in PA and sedentary behavior between poor sleep (PS) and normal sleep (NS). RESULTS: Based on weekly averages, 22 subjects had poor sleep quality by TST criteria, and 43 subjects had poor sleep quality by SE criteria. Based on TST, PS resulted in greater number of sedentary bouts per day (20.6 ± 2.8) vs. NS (18.5 ± 3.8, P = 0.02), fewer minutes per sedentary break per day (45.2 ± 9.1 and 53.5 ± 14.5, for PS and NS, respectively, P = 0.02), and greater average sedentary minutes per day (701.4 ± 79.4 and 645.1 ± 106.6 for PS and NS, respectively, P = 0.03). Average moderate-to-vigorous PA minutes (MVPA) did not differ between PS (65.0 ± 30.0) and NS (57.6 ± 23.9, P =0.25), nor did any other PA variable. Based on SE, the PS (65.3 ± 27.2) had greater MVPA minutes compared to NS (53.2 ± 22.7, P = 0.04). No other PA variable differed. There was no difference in body composition between groups when analyzing by TST or SE criteria. CONCLUSION: Poor sleep quality, defined as less than 6 hours of TST, appears to have a greater impact on sedentary behavior than PA behavior in a college student population. When sleep quality is poor, college students experience a greater amount of sedentary behavior, while PA variables did not change. This may be a result of the college lifestyle, where walking on campus to classes and other PA is needed regardless of daytime sleepiness as a result of poor sleep. This is one of the first studies to show a relationship between sleep quality and sedentary time in a college-age population.

0190 Impact of Menopause-Related Sleep Fragmentation on Daytime Sleepiness and Neurobehavioral Performance: Results of an Experimental Model

Abstract Introduction Cognitive performance may be adversely affected during the menopause transition from hot flash-induced sleep fragmentation even without changes in sleep duration. We examined the effects of experimentally-induced sleep fragmentation without shortened sleep duration on daytime sleepiness and neurobehavioral performance in women in a high and low estradiol (E2) state. Methods Seven pre-menopausal women (29.4 ± 3.8 years) participated in two 6-day inpatient studies repeated in a high-E2 (mid-to-late follicular phase) then low-E2 state (gonadotropin-releasing hormone agonist-induced E2 suppression - similar to levels during menopause) ~6 weeks apart. Sleep was uninterrupted on nights 1–2 [8-h time-in-bed (TIB)] and fragmented on nights 3–5 (9-h TIB) using an auditory stimulus delivered every 15 min that sustained wake for 2 minutes, producing 1-h total wake after sleep onset. Wakefulness was confirmed by event-markers during polysomnographically-recorded sleep episodes. Daytime subjective sleepiness (Karolinska Sleepiness Scale; KSS) and neurobehavioral performance (Psychomotor Vigilance Task; PVT) were assessed every 2–3 hours on study days 2–5. The effects of study day and E2 state on KSS scores and PVT measured reaction time (RT) and attentional failures (RT&amp;gt;500ms) were examined using linear mixed models. Results Participants reported feeling sleepier (+10%), had longer RTs (+22ms), and more attentional failures (+53%) after sleep fragmentation than after uninterrupted sleep (all p&amp;lt;0.001). While there was no main effect of E2 state, there was a differential effect of sleep fragmentation by E2 state on PVT, but not sleepiness, such that the increase in RT and attentional failures in response to sleep fragmentation was only observed in the high-E2 state (p&amp;lt;0.001). Conclusion Eight hours of total sleep time may not be sufficient to maintain subjective sleepiness and PVT performance levels when sleep is not consolidated. These findings have important implications for understanding the role of sleep and E2-modulated cognitive impairment during the menopause transition. Support This work was supported by the NIH: 5R01 AG053838-02 (HJ) and K24-HL105664 (EBK).

Observation and Interview-based Diurnal Sleepiness Inventory for measurement of sleepiness in patients referred for narcolepsy or idiopathic hypersomnia.

First, to determine whether the 3-item Observation and Interview-based Diurnal Sleepiness Inventory (ODSI) measures the degree of excessive daytime sleepiness in patients with suspected narcolepsy or idiopathic hypersomnia (IH). Second, to assess the correlation between the ODSI and the Epworth Sleepiness Scale (ESS) as well as objective polysomnographic measurements. Third, to test the accuracy of the ODSI to detect narcolepsy or IH (narcolepsy/IH) compared with the ESS. A total of 181 patients complaining of excessive daytime sleepiness filled in the ESS and the ODSI and underwent measurements including actigraphy, full-night polysomnography, Multiple Sleep Latency Test, and 24-hour bedrest sleep recording. Narcolepsy or IH was diagnosed in 76 patients. The ODSI found excessive daytime sleepiness in 92.3% of all patients and in 98.7% of those diagnosed with narcolepsy/IH. In the whole population, the ODSI was significantly positively correlated with the ESS (R = .547; 95% confidence interval: .436, .642; P < .001) and weakly with 24-hour total sleep time on bedrest recording (R = .208; 95% confidence interval: .056, .350; P = .047) but not with the Multiple Sleep Latency Test. The ODSI offered a higher negative (92.9%) and positive (44.9%) predictive value to detect narcolepsy/IH than did the ESS (66.7% and 43.3%, respectively). In the IH group, the ODSI's third-item score (daily sleepiness duration) was significantly higher in patients with than without increased 24-hour total sleep time (P = .023). The ODSI is a brief, simple first-line questionnaire that explores both intensity and duration of daytime sleepiness and offers a high sensitivity to detect narcolepsy and IH.

Respiratory-Related Leg Movements of Sleep Are Associated With Serotonergic Antidepressants But Not Bupropion.

Respiratory-related leg movements (RRLMs) may contribute to the cardiovascular risk associated with obstructive sleep apnea (OSA). Selective serotonin reuptake inhibitors (SSRIs), but not bupropion, increase periodic leg movements in sleep. This study examines whether patients with OSA using SSRIs have more RRLMs than those taking bupropion or no antidepressant. Patients with an apnea-hypopnea index (AHI) of at least 10 events/h during a full-night diagnostic study or split-night study, who were taking bupropion (n = 32), an SSRI (n = 31), or no antidepressant (n = 31), were selected from a database of prestudy questionnaires. RRLMs were scored according to World Association of Sleep Medicine 2016 standards. Patients using SSRIs had significantly greater overall RRLM% (defined as the percentage of respiratory events associated with a leg movement, including apneas, hypopneas, and respiratory effort-related arousals), RRLM index, and periodic limb movement index relative to patients using bupropion and control patients. The difference between the RRLM% in the SSRI and bupropion groups was limited to patients undergoing split-night studies, and that of the SSRI and control groups was limited to patients undergoing full-night diagnostic studies. The greater number of RRLMs and PLMs in the SSRI group may contribute to treatment-emergent insomnia often seen with SSRI use. Fragmented sleep and elevated autonomic nervous system activation associated with increased RRLMs in patients with OSA taking SSRIs might also limit the tolerability of antidepressant treatment, as well as increase the risk for cardiovascular disease.

Normative values for sleep parameters in pre-schoolers using actigraphy

ObjectiveThere are currently no reference values for actigraphy-measured sleep length and fragmentation in preschool children. We created standardized parameters using a community sample. MethodsNinety-seven 2-to-6-year-old children (56 boys) wore an actigraph on their non-dominant wrist for seven days. The data was extracted and scored, calculating total sleep time, sleep latency, sleep efficiency, fragmentation index, circadian rhythm length, cosine peak and light/dark ratio. Subjects were divided into groups of 2–3-year-olds, 4–5-year-olds and 6-year-olds. Means and standard deviations were calculated, and reference values were created using the 2.5th and the 97.5th percentiles. ResultsReference intervals were 7 h 23 min–9 h 47 min for 24-hour total sleep time, 0.2–48.4 min for sleep latency, 69–87% for sleep efficiency, 23–53% for fragmentation index, 23 h 39 min–24 h 24 min for circadian rhythm length, 12:37–15:53 for the timing of the cosine peak, and 1.14–5.63 for the light-dark ratio. With increasing age, daily sleep time, sleep latency, sleep fragmentation, and napping decreased. ConclusionsWe were able to create previously non-established reference values, including trends with increasing age, on actigraphy-assessed sleep in preschool children. SignificanceSleep disorders in young children are easier to evaluate against normative data.