Faculty Bibliography

STUDY OBJECTIVES/OBJECTIVE:To determine the effect of self-reported clinical diagnosis of Obstructive Sleep Apnea (OSA) on longitudinal changes in brain amyloid-PET and CSF-biomarkers (Aβ42, T-tau and P-tau) in cognitively normal (NL), mild cognitive impairment (MCI) and Alzheimer's Disease (AD) elderly. METHODS:Longitudinal study with mean follow-up time of 2.52±0.51 years. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants included 516 NL, 798 MCI and 325 AD elderly. Main Outcomes were annual rate-of-change in brain amyloid-burden (i.e. longitudinal increases in florbetapir-PET uptake or decreases in CSF-Aβ42 levels); and tau-protein aggregation (i.e. longitudinal increases in CSF total-tau (T-tau) and phosphorylated-tau (P-tau)). Adjusted multi-level mixed effects linear regression models with randomly varying intercepts and slopes was used to test whether the rate-of-biomarker-change differed between participants with and without OSA. RESULTS:In NL and MCI groups, OSA+ subjects experienced faster annual increase in florbetapir uptake (B=.06, 95% CI .02, .11 and B=.08, 95% CI .05, .12 respectively) and decrease in CSF-Aβ42 levels (B=-2.71, 95% CI -3.11, -2.35 and B=-2.62, 95% CI -3.23, -2.03, respectively); as well as increases in CSF T-tau (B=3.68, 95% CI 3.31, 4.07 and B=2.21, 95% CI 1.58, 2.86, respectively) and P-tau (B=1.221, 95% CI, 1.02, 1.42 and, B=1.74, 95% CI 1.22, 2.27, respectively); compared to OSA- participants. No significant variations in the biomarker changes over time were seen in the AD group. CONCLUSIONS:In both NL and MCI, elderly, clinical interventions aimed to treat OSA are needed to test if OSA treatment may affect the progression of cognitive impairment due to AD.

Blacks are at greater risk for lower sleep quality and higher risk for obstructive sleep apnea (OSA) than other racial groups. In this study, we summarize the development of a tailored website including visuals, key messages, and video narratives, to promote awareness about sleep apnea among community-dwelling blacks. We utilized mixed methods, including in-depth interviews, usability-testing procedures, and brief surveys (n = 9, 55% female, 100% black, average age 38.5 years). Themes from the qualitative analysis illuminated varied knowledge regarding OSA symptoms and prevalent self-reported experience with sleep disturbance and OSA symptoms (e.g., snoring). On a scale from 1 (not at all) to 5 (very high), participants provided favorable ratings of website usefulness (mean = 4.9), user friendliness (mean = 4.9) and attractiveness (mean = 4.3). Our findings suggest although tailored health communication has potential for serving as a tool for advancing health equity, usability-testing of health materials is critical to ensure that culturally and linguistically tailored messages are acceptable and actionable in the intended population.

Mobile technology has been designed to serve a number of functions relating to health, but we know little about individuals who use these tools to track sleep. This study utilized data from a cross-sectional, geographically diverse survey of adults in the USA (N = 934). Among the sample, 28.2% (n = 263) report current use of a mobile phone for sleep tracking. Income and gender were significant correlates of sleep tracking (p < 0.05). Compared to a poor diet, a reported "excellent" diet was associated with sleep tracking (p < 0.05). Interestingly, compared to individuals who never smoke, report of smoking "everyday" was associated with sleep tracking (p < 0.05). Finally, individuals who reported current use of their mobile device for other health functions (e.g., chat with their doctor or log symptoms) were more likely to report sleep tracking on their mobile device (p < 0.05). Results appear to suggest sleep tracking is common among individuals with good general health.

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.

Introduction: Previous studies have shown that racial/ethnic minorities are more likely to be short and/or long sleepers, which may increase risk for morbidity/mortality. This analysis provides a more recent update from a very large national dataset, including representation of additional groups and examination of the role of poverty.
Method(s): Data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS, collected by the CDC) were used. N=464,671 adults >18yrs from all US states/territories provided data on sleep, demographics, and socioeconomics. Sleep duration was categorized as very short (<=4h), short (5-6h), normal (7-8h as reference), and long (>=9h). Race/ethnicity was self-reported as Non-Hispanic White, Black/ African-American, Hispanic/Latino, American-Indian/Alaskan-Native (AIAN), Native Hawaiian/Pacific-Islander (NHPI), or Multiracial/Other. Covariates included age, sex, relationship status, education, employment, and home ownership. Interactions were explored with poverty (income<$20,000) were explored. Multinomial logistic regressions were weighted using BRFSS-specific weights.
Result(s): A significant race-by-poverty interaction was seen (p<0.0005). Compared to non-poor Non-Hispanic White, increased very short sleep was seen among those who were non-poor Black/African-American (RRR=2.1, p<0.0005), Asian (RRR=1.6, p=0.001), AIAN (RRR=1.4, p=0.001), NHPI (RRR=2.0, p=0.002), and Multiracial/Other (RRR=2.2, p<0.0005), and poor Non-Hispanic White (RRR=1.8, p<0.0005), Black/African-American (RRR=1.8, p<0.0005), AIAN (RRR=1.5, p=0.007), NHPI (RRR=2.4, p=0.005), and Multiracial/Other (RRR=3.4, p<0.0005). Compared to non-poor White, increased short sleep was seen among non-poor Black/African-American (RRR=1.7, p<0.0005), Asian (RRR=1.3, p<0.0005), AIAN (RRR=1.2, p=0.02), NHPI (RRR=1.3, p=0.02), Multiracial/Other (RRR=1.3, p<0.0005), and poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), Asian (RRR=1.3, p=0.04), and Multiracial/Other (RRR=2.2, p<0.0005). Compared to non-poor Non-Hispanic White, increased long sleep was seen for Non-Poor Black/African-American (RRR=1.4, p<0.0005), Poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), and AIAN (RRR=1.3, p<0.05).
Conclusion(s): Established racial/ethnic sleep disparities are supported in this large national sample, with additional information on understudied vulnerable groups including AI/AN and NH/PI. Further, the this study as the contribution of poverty status

Introduction: Several studies have demonstrated that insomnia symptoms negatively impact adherence to Continuous Positive Airway Pressure (CPAP). Yet, little is known about psychosocial factors that may buffer the associated negative effects. The present study explored the role of resilience, the ability to function in the face of or following adversity, on reducing the negative effects of insomnia on CPAP adherence.
Method(s): The study sample included volunteers from a large sleep center enrolling individuals newly diagnosed with Obstructive Sleep Apnea (OSA). For this analysis, we examined volunteers with complete data (n=45) on insomnia severity (based on the Insomnia Severity Index (ISI)), resilience (based on the Connor Davidson Resiliency Scale (CD-RISC)), and objective median hours of CPAP use over the first 30 days of treatment.
Result(s): The mean age was 55.4 years (SD=15.7); 62.2% male, and 33% black. The mean ISI score was 13.0 (SD=6.3), mean CD-RISC was 30.7 (SD=5.7) and mean CPA use over the first 30 days was 5.9 (SD=1.9). In the linear regression, ISI was positively correlated with increased hours of CPAP use (r=-0.305, p=.047). Resilience was not significantly correlated with CPAP use (r=0.216, p=.163), likely attributable to the sample size. ISI correlated with CPAP use among those with low resilience (r=-0.461, p=.027), but not among those with high resilience (r=-0.039, p=.870). There was a significant interaction (B(SE)=0.22 (0.08); p=.005) between ISI and resilience on median hours of CPAP use, indicating that resilience may moderate the association between ISI and hours of CPAP use.
Conclusion(s): Results of our study indicated that resilience is an important factor and may reduce the negative effects of insomnia on CPAP adherence. Notably, the high resilience score in this sample could signal an important target for tailoring CPAP adherence interventions to address unique characteristics of each subgroup

Introduction: While there is epidemiologic evidence that racial/ ethnic minorities report shorter sleep duration and poorer sleep quality than whites, few studies have assessed sleep continuity (SC), variable by variable (e.g., SL, NWAK, WASO, EMA, & TST). The present analysis assesses in a quantitative way whether insomnia symptom severity varies by race/ethnicity.
Method(s): An archival analysis was conducted on an existing database of 4,206 individuals who completed a screening survey on-line at https://urldefense.proofpoint.com/v2/url?u=http- 3A__www.sleeplessinphilly.com&d=DwIBAg&c=j5oPpO0eBH1iio48DtsedeElZfc04rx3ExJHeIIZuCs&r=CY_ mkeBghQnUPnp2mckgsNSbUXISJaiBQUhM-Uz9W58&m=_icVcFoc7ulJmPF3ojT4VQ- keh3a2N4OhtHGRLx7AN4&s=GRc5DD1Hlq9WkqeVHjBH7X9hXNa8mcKsHyVAl9iK8QI&e=. Variables collected included estimates for: sleep latency (SL), number of awakenings (NWAK), wake after sleep onset (WASO), early morning awakenings (EMA) and total sleep time (TST).
Result(s): The sample for the present analysis was comprised of 2,049 whites (63.4%), 1,007 blacks (31.2%), and 175 Hispanics (5.4%). The overall mean age was 39.0+/-14.7, 60.4% of the sample was female, and the average BMI was 28.0+/-7.1. For all SC variables, blacks significantly differed from whites: SL (49.2+/-38.3 vs. 42.8+/-30.5; p<.001); NWAK (2.64+/-1.7 vs. 2.50+/-1.6; p<.001); WASO (47.3+/-43.4 vs. 29.9+/-30.5; p<.000); EMA (63.4+/-41.8 vs. 57.2+/-33.0; p<.000); Hispanics did not significantly differ from whites with respect to the above measures. For self-reported TST, blacks and Hispanics significantly differed from whites (316.4+/-85.1; 356.2+/-73.7; 365.8+/-80.6, respectively; p<0.000).
Conclusion(s): Our results suggest that blacks exhibit marginally worse sleep continuity (statistically significant owing to the large sample sizes) and shorter TSTs. Analysis is ongoing to evaluate Time in Bed [TIB], calculated TST, SE%, sleep period, sleep schedule differences, and percentage of group with Insufficient Sleep Disorder by race, in matched samples

Introduction: Being awake at night is associated with cognitive/affective dysregulation. Recently, it was found that nocturnal wakefulness is also a risk factor for completed suicide (self-harm). The present analysis examines whether nocturnal wakefulness is also a risk factor for violent crime (harm to others).
Method(s): Data were obtained from the National Archive of Criminal Justice Data and included rates of murder, violent sexual assault, robbery, aggravated assault, and simple assault across each hour of the day in 2016. These data were aggregated from law enforcement agencies in 38 states and Washington DC. Data were examined separately for adults (>18) and juveniles (<18). Standardized Incidence Ratios (SIRs) were computed to evaluate the proportion of violent crimes committed at each hour, relative to what would be expected given the proportion of the population awake at each hour (determined from normative values obtained from the American Time Use Survey).
Result(s): Without adjustment for likelihood of being awake, violent crime peaks at 7-10pm in juveniles and 2-4pm among adults. This pattern changed after adjustment, revealing increased likelihood at night. For adults, more violent crime than would be expected by chance was observed at 23:00 (SIR=1.56), 0:00 (SIR=2.44), 1:00 (SIR=2.97), and 2:00 (SIR=2.86), and also 15:00 (SIR=1.43). For juveniles, more violent crime than would be expected by chance was observed at 22:00 (SIR=1.84), 23:00 (SIR=3.14), 0:00 (SIR=5.71), 1:00 (SIR=8.69), 2:00 (SIR=10.33), 3:00 (SIR=8.04), 4:00 (SIR=2.87).
Conclusion(s): For adults, more violent crimes than expected occurred at night, peaking around 1-2am. For juveniles, there was also an elevated likelihood of crimes at night, peaking slightly later (2-3am). This is in contrast to unweighted crime statistics, which show peaks in the early afternoon in adults and early evening for juveniles. These data lend further credibility to the concept that there may be a biological vulnerability to cognitive/affective dysfunction when awake at night

Introduction: We determined whether Obstructive Sleep Apnea (OSA) and beta-Amyloid Burden (Abeta) act additively or synergistically to promote conversion from cognitive normal (CN) to mild cognitive impairment (MCI) and from MCI to AD.
Method(s): In this longitudinal observational study, we examined CN (n=298) and MCI (n=418) older adults from the ADNI database (adni.loni.usc.edu). OSA was self-reported during a clinical interview. Brain Abeta was assessed using Florbetapir-PET imaging. The primary outcome of the analysis was conversion from CN to MCI (CN participants) and from MCI to AD (MCI participants). Participants were required to have a baseline and at least one follow-up clinical visit that identified their cognitive status. Logistic mixed-effects models with random intercept and slope were used to assess associations between OSA, Abeta, and risk of conversion from CN to MCI, and MCI to AD. All models included age at baseline, sex, APOE4 status, years of education, and their interactions with time.
Result(s): Of the 716 participants, 329 (46%) were women. The overall mean (SD) age was 74.7 (5.0) years, and the overall mean (SD) follow-up time was 5.5 (1.7) years (Range: 2.7 - 10.9 years). In CN participants at baseline, conversion to MCI was associated with both OSA (beta = 0.418; 95% CI, 0.133 to 0.703; P < .001) and higher Abeta-burden (beta = 0.554; 95% CI, 0.215 to 0.892; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.169, 95% CI, 0.776 to 1.562; P < .001), suggesting a synergistic effect. In MCI participants at baseline, conversion to AD was associated with both OSA (beta = 0.637; 95% CI, 0.291 to 0.982; P < .001) and higher Abeta-burden (beta = 1.061; 95% CI, 0.625 to 1.497; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.312, 95% CI, 0.952 to 1.671; P < .001), suggesting a synergistic effect.
Conclusion(s): In both CN and MCI elderly, Abeta modified the risk of progression to AD in OSA participants. OSA patients maybe more physiologically susceptible as Abeta load becomes increasingly abnormal

Introduction: Based on principles of community-based participatory research methods (CBPR), a community-oriented framework was applied in three studies that focused on African- Americans/ Blacks (herein referred to as Blacks): The Metabolic Syndrome Outcome Study (MetSO), Tailored Approach to Sleep Health Education (TASHE), and Peer-Based Sleep Health Education and Social Support (PEERS-ED). We describe results of our application of this framework to enroll and study Blacks in these NIHfunded studies of obstructive sleep apnea (OSA).
Method(s): Our community-oriented framework includes strategic guidelines for effective intervention to engage communities in research and ensure cultural and linguistic appropriateness of sleep messages in behavioral interventions. Strategies included: 1) focus groups and in-depth interviews with key stakeholders; 2) establishing a community advisory board; 3) conducting Delphi surveys to identify high-priority diseases and conditions. Community barriers were identified through an iterative process using surveys and focus groups. Stakeholder groups were integral during the development, implementation and dissemination, reflecting a patient-oriented decision-making process with respect to key intervention components.
Result(s): MetSO, TASHE, and PEERS-ED reached nearly 3,000 Blacks at risk of OSA in New York City. Of those, 2,000 were screened for OSA. Sleep brochures were distributed to over 10,000 individuals. The mean age of community participants was 62+/-14 years; 69% were female; 43% had an annual income <$10,000; and 37% had 10); 10% reported an insomnia diagnosis and 12% used sleep medications. Based on WatchPAT data, 24% had moderate OSA and 18%, severe OSA. Compared to blacks receiving standard sleep messages, those exposed to tailored sleep messages in our interventions were nearly 4 times as likely to adhere to OSA care.
Conclusion(s): Community outreach may be an effective strategy in the reach and spread of sleep messages among low-income Blacks at-risk for OSA