Faculty Bibliography

Introduction: Sleep health encompasses a number of concepts, including "satisfaction," timing, efficiency, duration, and absence of disorders, and is related to mental/physical health. This analysis aims to explore the idea of "sleep satisfaction" and how it relates to these concepts.
Method(s): Data were from N=1,003 working-age adults age 22-60 in the Philadelphia area. Participants were asked to rate their satisfaction with sleep on a scale of 0-100 (100=max). Participants also completed the Insomnia Severity Index, Epworth Sleepiness Scale, Fatigue Severity Scale, Brief Inventory of Sleep Control, sleep duration (categorized as <=4h, 5-6h, 7-8h[ref], and >=9h), habitual bedtime, Patient Health Questionnaire (for depression), GAD7 anxiety scale, Perceived Stress Scale, and an overall indicator of health (Excellent, Very Good, Good, Fair, or Poor). Linear regression analyses examined whether each of these individually and/or uniquely contributed variance to sleep satisfaction after adjustment for age, sex, race/ethnicity, education, and income.
Result(s): The following significantly predicted worse sleep satisfaction (in order of decreasing magnitude): insomnia symptoms (B=- 2.99, p<0.0005), sleep duration in the <=4h (B=-38.57, p<0.0005), 5-6h (B=-19.03, p<0.0005), and >=9h (B=-8.15, p=0.032) ranges vs 7-8h, lack of sleep control (B=3.42, p<0.0005), later bedtime (B=-2.83, p<0.0005), sleepiness (-1.82, p<0.0005), fatigue (B=-0.86, p<0.0005), depression (B=-2.38, p<0.0005), anxiety (B=-2.16, p<0.0005), stress (B=-1.32, p<0.0005), and overall health rated as good (B=-15.29, p<0.0005), fair (B=-27.22, p<0.0005), and poor (B=-38.79, p<0.0005), vs excellent. In a model with all variables combined, unique variance was contributed only by insomnia, sleep duration, sleep control, and depression; other variables were non-significant.
Conclusion(s): Sleep satisfaction, as a concept, is related to nighttime sleep experiences, daytime impairment experiences, and overall mental and physical health. However, some of these relationships overlap and a combined model suggests that sleep satisfaction mostly represents a combination of nighttime sleep duration/ quality, perceived control, and daytime mood

Introduction: We determined whether the co-occurrence of OSA and hypertension interact synergistically to promote beta-Amyloid burden and cognitive decline in clinically normal older adults Methods: Prospective longitudinal study utilizing NYU cohort of community-dwelling cognitively-normal elderly, with baseline and at least one follow-up of CSF-Abeta42 (measured using ELISA), and neuropsychological visits. OSA was defined using AHI4%. Hypertension diagnosis was according to AHA-guidelines. Cognitive variables assessed included Logic-2, Animal-Fluency [AF], Vegetable-Fluency [VF]), Boston-Naming-Test [BNT], Digit-Symbol-Substitution-Test [DSST], Trails Making Test-A and B [TMT-A and B]). Linear mixed-effects models with random intercept and slope were used to assess associations between OSA, hypertension, and longitudinal changes in CSF-Abeta and cognition, controlling for age-at-baseline, sex, APOE4-status, years-of-education, and their interactions with time.
Result(s): Of the 98 participants, 63 (64.3%) were women. The mean (SD) age was 69.6 (7.3) years and follow-up time was 2.46 (0.64) years. OSA and hypertension were each associated with faster rate-of-change in CSF-Abeta42 (beta = -3.11; 95%CI, -3.71, -2.51; and beta= -2.82, 95% CI -3.29, -2.35, P < .01 for both respectively). The interaction of OSA and hypertension with time was significant (beta= -1.28, 95% CI -1.78 to -0.78, P < .01) suggesting a synergistic effect. No significant associations were seen between annual-changes in CSF-Abeta42 and cognitive-decline. However, faster decline in VF, and DSST were associated with OSA (beta = -0.054; 95%CI, -0.094, -0.013; P = .02; beta = -0.058; 95%CI, -0.084, -0.033; P < .05 for both respectively), and with hypertension (beta = -0.048; 95%CI, -0.079, -0.017; P = .04; beta = -0.078; 95%CI, -0.098, -0.057; P = .002; respectively). The interaction of OSA and hypertension with time was significant for both VF and DSST (beta = -0.033, 95%CI, -0.048, -0.018; P < .001 and beta = -0.040, 95%CI, -0.064, -0.016; P < .001, respectively), suggesting a synergistic effect.
Conclusion(s): In cognitive-normal elderly OSA individuals, vascular risk may complement AD-biomarkers in assessing risk of prospective cognitive-decline in preclinical AD

Introduction: Population estimates for sleep duration and quality are inconsistent because they rely on self-report and smaller samples using objective data. Tracking and wearable devices may provide more accurate estimates of sleep duration and quality. In this study, we investigated estimates of sleep duration and quality among 2 million users of a mobile sleep application in an urban city in the United States (U.S.).
Method(s): We examined sleep duration and quality from 2,194,897 users of SleepCycle, a popular sleep tracking app. over a four-year period (2015 to 2018). In this analysis, we specifically explored differences in sleep duration and quality by demographic factors, including age and sex. We utilized graphical matrix representations of data (heat maps) and geospatial analyses to compare sleep duration (in hours) and sleep quality (based on a composite score amalgamating time in bed, deep-sleep time, sleep consistency, and amount of times fully awake), considering potential effects of day of the week and seasonality.
Result(s): Among SleepCycle users, 45.6% (n=1,001,335) were female. The average age of the sample was 31.0 years. The mean sleep duration of the total sample was 7.11 hours; women slept longer than did men (M=7.27 hours vs. M=7.00 hours, p<.001). Increasing age tended to be associated with longer sleep duration and better sleep quality. Results also showed sleep duration was longer on weekends (M=7.19 hours), compared to weeknights (M=7.09 hours). Sleep duration was longest (M=7.18 hours) during the winter, but shortest during the summer (M=7.11 hours). Sleep quality was highest (M=72.75) during the winter, but lowest during the summer (M=71.99).
Conclusion(s): Our findings from big data are consistent with previously reported estimates of sleep duration and quality. Sleep duration varied by age, sex, day of the week, and season. Future studies should determine whether estimates of sleep duration and quality are affected by environmental factors such as geographic location

Introduction: Using the National Health and Aging Trends Study (NHATS), we examined use of sleeping medication, difficulty falling asleep, and trouble falling back asleep among individuals with and without cognitive impairment.
Method(s): Binomial logistic regression examined sleep medication use and insomnia symptoms (difficulty falling asleep or falling back asleep after awakening) by cognitive impairment (no dementia and possible or probable dementia). Sleep-related variables were collected on frequency scales ranging from 1 (every day) to 5 (never). Of the sample, 71.1% were White (n=3,369), 20.7% were Black (n=982), 5.0% were Hispanic (n=235), and 2.4% other (n=113); 60.4% were female (n=2,662) and 39.6% were male (n=1,875).
Result(s): Respondents were classified as having no dementia (63.7%), possible dementia (8.5%), or probable dementia (12.9%). Of the sample, 10.7% reported medication use every night, 2.5% 5-6 nights/week, 5.7% 2-4 nights/week, 6.6% once/week and 59.4% reported no use. Of the respondents, 8.3% reported difficulty sleeping every night, 8.0% reported 5-6 nights/week, 21.4% reported 2-4 nights/week, 22.9% reported rarely, and 23.5% reported never experiencing difficulty sleeping. Regarding difficulty falling back asleep, 4.9% reported difficulty every night, 7.4% reported 5-6 nights/week, 26.0% reported 2-4 nights/week, 20.4% reported rarely, and 24.3% reported never. Compared to individuals who reported never using sleep medications, those reporting nightly use were significantly more likely to be cognitively impaired (OR=1.44,95%CI: 1.14-1.82). Compared to individuals reporting never having difficulty falling asleep, those reporting difficulty falling asleep nightly were not more likely to have cognitive impairment (OR=0.74 95%CI: 0.67 to 1.19). Compared to individuals reporting never having difficulty falling back asleep after awakening, those frequently reporting difficulty falling back asleep were less likely to be cognitively impaired (OR=0.44,95%CI:0.22 to 0.64).
Conclusion(s): Cognitive impairment was positively associated with sleep medication use in adjusted models, but not with trouble falling asleep or difficulty falling back asleep after awakening. Our findings are consistent with the literature on deleterious consequences of sleep medications

Introduction: Sleep is related to socioeconomic status and impacts health. This study evaluated whether foregoing medical care due to cost impacts sleep and plays a role in sleep disparities and the sleep-obesity relationship.
Method(s): Data from the 2017 Behavioral Risk Factor Surveillance System (N=39,267 from 7 states). Sleep duration was assessed as hours/day. Participants were asked, "Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?" They were also asked for information about age, sex, race/ethnicity, education, income, employment, overall health, and access to health insurance. They were also asked for height/weight, which was used to compute body mass index (BMI).
Result(s): Access to health insurance was not associated with habitual sleep duration. However, foregoing medical care was associated with less sleep (B=-0.26, 95%CI[-0.35,-0.17], p<0.0005). There was an interaction with race/ethnicity; compared to non-Hispanic Whites, the effect was 115% larger among Blacks/African-Americans, 13% larger in Hispanics/Latinos, 101% larger and in the opposite direction for Asians, and non-significant for Multiracial. Race/ethnicity relationships to sleep duration were stratified by foregoing care. Among those who did not (90%), both short and long sleep duration were more likely among Blacks/African-Americans and other minority groups. Among those who did forego care (10%), these effects were dramatically reduced. Further, when sleep duration was evaluated as a predictor of obesity, this relationship was only seen among those who did not forego care.
Conclusion(s): Foregoing medical care due to cost is an independent risk factor for insufficient sleep, irrespective of income, employment, and access to insurance. It disproportionately affects Blacks/ African-Americans and may represent part of the reason why sleep disparities exist even after adjustment for most socioeconomic indices. Further, foregoing medical care may present such health risks that this subsumes the relationship between sleep and obesity

INTRODUCTION/BACKGROUND:False beliefs about sleep can persist despite contradicting scientific evidence, potentially impairing population health. Identifying commonly held false beliefs lacking an evidence base ("myths") can inform efforts to promote population sleep health. METHOD/METHODS:We compiled a list of potential myths using Internet searches of popular press and scientific literature. We used a Delphi process with sleep experts (n = 10) from the fields of sleep medicine and research. Selection and refinement of myths by sleep experts proceeded in 3 phases, including focus groups (Phase 1); email-based feedback to edit, add, or remove myths (Phase 2); and closed-ended questionnaires (Phase 3) where experts rated myths on 2 dimensions, falseness and public health significance, using 5-point Likert scale from 1 ("not at all") to 5 ("extremely false"). RESULTS:The current study identified 20 sleep myths. Mean expert ratings of falseness ranged from 5.00 (SD = 0.00) for the statement "during sleep the brain is not active" to 2.50 (SD = 1.07) for the statement "sleeping in during the weekends is a good way to ensure you get adequate sleep." Mean responses to public health significance ranged from 4.63 (SD = 0.74) for debunking the statement that "many adults need only 5 or less hours of sleep for general health" to 1.71 (SD = 0.49) for the statement that "remembering your dreams is a sign of a good night's sleep." CONCLUSION/CONCLUSIONS:The current study identified commonly held sleep myths that have a limited or questionable evidence base. Ratings provided by experts suggest areas that may benefit from public health education to correct myths and promote healthy sleep.

OBJECTIVE/UNASSIGNED:Workplace-based employee health promotion programs often target weight loss or physical activity, yet there is growing attention to sleep as it affects employee health and performance. The goal of this review is to systematically examine workplace-based employee health interventions that measure sleep duration as an outcome. DATA SOURCE/UNASSIGNED:We conducted systematic searches in PubMed, Web of Knowledge, EMBASE, Scopus, and PsycINFO (n = 6177 records). STUDY INCLUSION AND EXCLUSION CRITERIA/UNASSIGNED:To be included in this systematic review, studies must include (1) individuals aged >18 years, (2) a worker health-related intervention, (3) an employee population, and (4) sleep duration as a primary or secondary outcome. RESULTS/UNASSIGNED:Twenty studies met criteria. Mean health promotion program duration was 2.0 months (standard deviation [SD] = 1.3), and mean follow-up was 5.6 months (SD = 6.5). The mean sample size of 395 employees (SD = 700.8) had a mean age of 41.5 years (SD = 5.2). Measures of sleep duration included self-report from a general questionnaire (n = 12, 66.6%), self-report based on Pittsburgh Sleep Quality Index (n = 4, 22.2%), and self-report and actigraphy combined (n = 5, 27.7%). Studies most commonly included sleep hygiene (35.0%), yoga (25.0%), physical activity (10.0%), and cognitive-behavioral therapy for insomnia (10.0%) interventions. Across the interventions, 9 different behavior change techniques (BCTs) were utilized; the majority of interventions used 3 or fewer BCTs, while 1 intervention utilized 4 BCTs. Study quality, on average, was 68.9% (SD = 11.1). Half of the studies found workplace-based health promotion program exposure was associated with a desired increase in mean nightly sleep duration (n = 10, 50.0%). CONCLUSIONS/UNASSIGNED:Our study findings suggest health promotion programs may be helpful for increasing employee sleep duration and subsequent daytime performance.