Faculty Bibliography

Increasing evidence links cognitive-decline and Alzheimer's disease (AD) to various sleep disorders, including obstructive sleep apnea (OSA). With increasing age, there are substantial differences in OSA's prevalence, associated comorbidities and phenotypic presentation. An important question for sleep and AD researchers is whether OSA's heterogeneity results in varying cognitive-outcomes in older-adults compared to middle-aged adults. In this review, we systematically integrated research examining OSA and cognition, mild cognitive-impairment (MCI) and AD/AD biomarkers; including the effects of continuous positive airway pressure (CPAP) treatment, particularly focusing on characterizing the heterogeneity of OSA and its cognitive-outcomes. Broadly, in middle-aged adults, OSA is often associated with mild impairment in attention, memory and executive function. In older-adults, OSA is not associated with any particular pattern of cognitive-impairment at cross-section; however, OSA is associated with the development of MCI or AD with symptomatic patients who have a higher likelihood of associated disturbed sleep/cognitive-impairment driving these findings. CPAP treatment may be effective in improving cognition in OSA patients with AD. Recent trends demonstrate links between OSA and AD-biomarkers of neurodegeneration across all age-groups. These distinct patterns provide the foundation for envisioning better characterization of OSA and the need for more sensitive/novel sleep-dependent cognitive assessments to assess OSA-related cognitive-impairment.

OBJECTIVE:Sleep, which plays an important role in health and well-being, is socially patterned such that certain demographic groups have worse sleep health than others. One possible mechanism driving sleep disparities is social capital. The current study examines the association between social capital and self-reported sleep variables (eg, duration, insomnia symptoms, and daytime sleepiness) among a sample of 1007 participants from the Sleep Health and Activity, Diet and Environment Study (SHADES). METHODS:Logistic regressions were used to estimate whether the sleep variables were associated with social capital measures. All models control for age, sex, race/ethnicity (Non-Hispanic White, Black/African-American, Hispanic/Latino, Asian, and multicultural/other), income, and education (less than high school, high school graduate, some college, and college graduate). RESULTS:Lower likelihood of membership in groups was seen for long sleepers (>9hrs, p-value<0.05) and beliefs that neighbors rarely/never help each other was more likely among short sleepers (5-6hrs, p-value<0.05), relative to 7-8 h sleepers. A decreased sense of belonging was seen among short sleepers (5-6hrs, p-value<0.05). Decreased likelihood of trust was reported by those with moderate-severe insomnia (p-value<0.05). Similarly, neighborhood improvement efforts were less likely among individuals with moderate-to-severe insomnia (p-value<0.05). CONCLUSIONS:Results of our study show that short and long sleep duration, as well as insomnia, were inversely related to measures of social capital, such as group memberships and a sense of neighborhood belonging. Future research may explore the directionality of the relationship between social capital and sleep and perhaps consider future interventions to improve low social capital and/or poor sleep in community samples.

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.

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.

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.

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.

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: Sleep health is associated with many domains of functioning. Yet, changing behaviors linked to improved sleep health is difficult. Beliefs about the health impact of sleep may motivate behavior change. This analysis examined which beliefs about sleep might motivate sleep behavior change.
Method(s): Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study, consisting of N=1007 community-dwelling adults age 22-60. Participants were asked, regarding "the single most important thing you personally could do to improve your sleep," whether participants were in the stage of precontemplation (not considered change), contemplation (considered but not decided), preparation (decided but not acting), and action stages of change from the transtheoretical model. They were also asked items from the Sleep Practices and Attitudes Questionnaire (SPAQ) regarding the degree to which they agree with whether "not getting enough sleep" can cause sleepiness, drowsy driving, weight gain, heart disease, high cholesterol, hypertension, moodiness, lower energy, decreased sex drive, missed days at work, decreased performance, memory/concentration problems, diabetes, and/or tiredness. Ordinal logistic regressions evaluated increased likelihood of stage of change, based on degree of agreement with those statements, adjusted for age, sex, race/ethnicity, and education. Post-hoc analyses also examined sleep duration as an additional covariate.
Result(s): In adjusted analyses, stage of change was associated with degree of agreement that insufficient sleep can cause sleepiness (OR=1.17, p=0.035), weight gain (OR=1.20, p<0.0005), heart disease (OR=1.21, p=0.001), cholesterol (OR=1.13, p=0.047), hypertension (OR=1.16, p=0.014), moodiness (OR=1.42, p<0.0005), decreased energy (OR=1.30, p=0.002), absenteeism (OR=1.13, p=0.007), decreased performance (OR=1.20, p=0.003), concentration/ memory problems (OR=1.23, p=0.004), diabetes (OR=1.14, p=0.042), and feeling tired (OR=1.39, p<0.0005). When sleep duration was added to the model, significant relationships remained for weight, heart, hypertension, moodiness, energy, absenteeism, performance, memory, diabetes, and tiredness.
Conclusion(s): Degree of belief that insufficient sleep can cause outcomes such as moodiness, occupational problems, and health problems may impact whether an individual is contemplating/ attempting to improve their sleep. This may guide education/outreach efforts

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