Faculty Bibliography

Introduction: Short and long sleep duration are an important public health burden in the United States. They are influenced by race/ethnicity and socioeconomic status. Little is known about the relationships between psychological health and short and long sleep across race/ethnicity. Our study examined the racial/ethnic influence on the relationship between psychological distress and sleep duration. Methods: Data from the 2009 National Health Interview Survey (NHIS), N = 27,731 participants ages > 18 years, were analyzed to investigate the associations of psychological distress with inadequate sleep duration, adjusting for socio-demographic factors, health risk behaviors, and chronic diseases. Short sleep was coded as 8 hours. Psychological distress (PD), based on Kessler's 6 scale, assessed the frequency of feeling sad, nervous, restless, hopeless, worthless, and burdened over a 30-day period. Scores range from 0 to 24 and scores > 13 likely indicate serious mental illness. Results: Of the sample, 51.6% were female; 76.2%, White and 15.6%, Black/African-American, with a mean age of 35.79 +/- 22.4 yrs. Of the sample, 8.1%) of Blacks vs. 7.1% of Whites reported PD. Logistic regression analysis indicated that blacks and whites with PD have similar odds of reporting short sleep (Blacks: OR = 2.33, p < 0.05 and Whites: OR = 2.36, p < 0.05). However, different odds for long sleep were observed (Blacks: OR = 1.23, p < 0.05 and Whites: OR = 1.63, p < 0.05). These analyses adjusted for demographic, health risk behaviors, and chronic diseases. We also found discrepancies in the predictive model. For Whites, gender, marital status, family income, body mass index, arthritis, diabetes, hypertension, heart condition and PD predicted short sleep. For Blacks, only age, family income, hypertension and PD predicted short sleep. Conclusion: Psychological distress was the strongest predictor of short and long sleep for both groups. PD and short and long sleep were more prevalent among blacks than among whites. Our study underscored the significant role of PD in racial/ethnic sleep disparities

Introduction: Big data and complex system analyses provide unique opportunities to quantify dynamic omnidirectional interactions among multiple factors that impact diseases and health outcomes. Applying this type of analysis to sleep data is crucial, as sleep is linked to a host of chronic medical conditions. Methods: Analysis was based on the 2004-2013 National Health Interview Survey (N = 231,111). We employed a machine-learning Bayesian Belief Network (BBN) to model the probabilistic relationships (independent and additive) of sleep duration and physical activity to stroke risk. Factors considered included demographic, behavioral, health/medical, and psychosocial as well as sleep duration (short, average, and long), and physical activity (leisurely walking/bicycling, slow swimming/dancing, and simple gardening activities). Results: Of the sample, 48.1% were < 45 years; 77.4% were White; 15.9%), Black/African American; and 45.1% reported less than $35K annually. Overall, the model had a precision index of 95.84%. Average sleepers (7-8 hours) were 25% (2.3% to 3%) less likely to experience a stroke. Respectively, long sleepers (> 8 hours) were 146% (3% to 75%) and short sleepers (< 7 hours) were 25% (3% to 3.74%) more likely to report a stroke. A model-based adaptive method evidenced that combined effect of health status, hypertension, heart condition, income, and alcohol consumption increased the likelihood from 3% to 90%. Healthy sleep (7-8 hours) and vigorous exercise (30-60 minutes) three to six times per week significantly decreased stroke risk. Using the observational inference technique, we developed idiosyncratic profiles of protective behaviors (i.e. minutes and frequency of moderate or vigorous exercise per week and short, average or long sleep) that reduced stroke risk. Conclusion: Utilization of BBN analysis is important, as it provides a more dynamic risk stratification system. Our findings revealed healthy sleep and exercise routines reduced stroke risk based on systematic iterations using multiple demographic, behavioral, health/medical, and psychosocial factors

Introduction: The prevalence of short sleep (< 7 hours) has gradually increased over the last four decades, warranting an in-depth analysis of its influence on health outcomes. This study explored influences of body mass index (BMI), physical activity, and emotional distress on associations between short sleep and chronic diseases. Methods: Data for the present analysis came from the National Health Interview Survey, an ongoing nationally representative cross-sectional study of non-institutionalized US adults (> 18 years) from 2004-2013 (N = 911,773). They provided health data including physician-diagnosed hypertension, coronary heart disease, diabetes, stroke, kidney disease, cancer, and history of heart attack. We used the recommended criterion of > 150 minutes/week of moderate physical activity or > 75 minutes/week of vigorous to define physical activity and Kessler's 6 scales to measure emotional distress. Structural equation modeling was used to assess effects of physical activity, BMI, and emotional distress on relationships between short sleep and chronic diseases. Results: Analysis showed 51.6% of the sample was female; 76.2%, white; and 15.6%), black with a mean age of 35.79 +/- 22.4 yrs. Physical activity negatively mediated relationships between short sleep and cancer, stroke, coronary heart disease, heart attack, hypertension, and diabetes (path coefficient estimate = -0.053), (p < 0.001). BMI positively mediated relationships between short sleep and smoking, hypertension, and diabetes (point coefficient estimate = 0.828), (p < 0.001). Emotional distress also positively mediated relationships between short sleep and hypertension, diabetes and coronary heart disease (point coefficient estimate = 0.743), (p < 0.743). Adjusted covariates included age, race, gender, marital status, and income. Conclusion: Results are consistent with previous reports regarding associations between short sleep and chronic diseases. Of interest, emotional distress and increased BMI had significant effects on relationships between short sleep and chronic diseases. Increased physical activity however, was not associated with short sleep

Introduction: Short and long sleep durations are linked to stroke and cardiovascular disease. However, results have relied primarily on regression analysis, which may not be optimal to model associations between sleep and medical outcomes. Big-data and complex-system analyses provide unique opportunities to quantify dynamic interactions between sleep and medical outcomes, adjusting for multiple risk factors. Methods: The current study utilized two types of analyses: logistic regression and Bayesian Belief Network, a form of complex system analysis, to assess sleep-related stroke risk. We used data from the 2004-2013 National Health Interview Survey, yielding 231,111 cases, to investigate how short (8 hrs) sleep durations impact stroke risk. In both analyses, we assessed the contribution of 34 demographic, medical, behavioral, and psychosocial factors. We used SPSS 20 to conduct regression analyses and Bayesia Lab's Tree Augmented Naive Bayes learning algorithm for complex system analysis. We compared results of both analytic models to determine their ecological and clinical utility. Results: Forty-eight percent of volunteers were < 45 yrs; 77.40% were White; 15.96%, Black/African American; and 45.1% made < $35K annually; 29.55%) reported short sleep and 8.9%, long sleep; 61.55%) were average sleepers (7-8 hrs.). Adjusted regression models indicated that relationships between short sleep and stroke were not significant (OR = 0.97, 95% CI = 0.92-1.02, p = 0.21); however, long sleep was associated with stroke (OR = 1.43, 95% CI = 1.32-1.52, p < 0.001). Results from Bayesian analysis indicated both short and long sleep were associated with stroke, but that long sleep doubled stroke risk (7.48%) relative to short sleep (3.74%). Regression model had a R2 of 0.24 for short sleep and long sleep, while the R2 for Bayesia was 0.73. Conclusion: Bayesia Belief Network analysis is superior to regression modeling because it provides ecologically and clinically valid findings. Although both short and long sleep durations are associated with stroke risk, long sleep seems a stronger predictor

Introduction: Studies have shown a significant association between long sleep (defined as > 8 hours) and various chronic conditions such as cardiovascular disease, obesity, cancer, and stroke. Moreover, research suggests that long sleep may be more detrimental to one's health than is short sleep. The purpose of this study was to determine the mediating role of body mass index (BMI), physical activity, and emotional distress on the relationship between long sleep and chronic conditions. Methods: The study utilized data from the National Health Interview Survey, an ongoing, nationally representative, cross-sectional study of noninstitutionalized U.S. adults (> 18 years) between 2003 and 2014 (n = 911,773). Structural equation modeling was employed to assess mediation of observed and latent factors using Path Coefficient Estimate (PCE). Physical activity was defined as 10+ minutes of either moderate or vigorous activity. We measured emotional distress with Kessler's 6 scale. Chronic diseases included hypertension, coronary heart disease, diabetes, stroke, kidney disease, cancer and history of heart attack. Results: Of the sample, 51.6% were female; 76.2%, White; and 15.6%, Black/African-American with a mean age of 35.79 +/- 22.4 yrs. Physical activity significantly mediated relationships between long sleep and presence of all chronic diseases (p < 0.001), while emotional distress only mediated relationships between long sleep and kidney disease, diabetes, stroke, and heart attack. Physical activity showed a negative relationship with long sleep (PCE = -0.012, p < 0.001), while emotional distress was positively associated with long sleep (PCE = 0.134, p < 0.01). Of interest, BMI did not significantly mediate relationships between long sleep and chronic conditions (PCE = -0.008, p = 0.322). Covariates included age, sex, income, race, and marital status. Conclusion: Our analysis revealed that the dynamic relationships between long sleep and chronic conditions were mediated by physical activity and emotional distress. Future research should explore the relationships between long sleep and BMI, which in our analyses did not prove significant

OBJECTIVE: The objective of the study was to examine the independent association of emotional distress with unhealthy sleep duration (defined as <7 or >8 hours). METHODS: Data from the 2009 National Health Interview Survey (NHIS), a cross-sectional household survey, were analyzed to investigate the associations of emotional distress with unhealthy sleep durations, adjusting for sociodemographic factors, health risks, and chronic diseases through hierarchical multiple logistic regression analysis. PARTICIPANTS: A total of 27,731 participants (age range 18-85 years) from the NHIS 2009 dataset were interviewed. MEASURES: Unhealthy sleep duration is defined as sleep duration <7 or >8 hours, whereas healthy sleep is defined as sleep duration lasting for 7-8 hours. Emotional distress is based on the Kessler 6 Non-Specific Distress Battery, which assesses the frequency of feeling sad, nervous, restless, hopeless, worthless, and burdened, over a 30-day period. RESULTS: Of the sample, 51.7% were female; 83.1% were white and 16.9% were black. Eleven percent experienced emotional distress and 37.6% reported unhealthy sleep. Adjusted logistic regression analysis revealed that individuals with emotional distress had 55% greater odds of reporting unhealthy sleep (odds ratio [OR] =1.55, 95% confidence interval [CI] =1.42, 1.68, P<0.001). CONCLUSION: Emotional distress, an important proxy for poor psychological health, was a significant predictor of unhealthy sleep, independent of the influences of several factors including demographic (age, education, sex, race/ethnicity, and family income), health risks (alcohol consumption and smoking status), and chronic diseases/conditions (diabetes, obesity, hypertension, heart disease, cancer, and arthritis).

INTRODUCTION: This study assessed associations of depression and anxiety with risk of obstructive sleep apnea (OSA) among non-Hispanic blacks in the Metabolic Syndrome Outcome (MetSO) study. METHOD: A total of 1,035 patients participated. ARESTM score >/=6 defined high OSA risk. Moderate depression was defined by a CES-D score >/=16. Moderate anxiety was measured by a BAI score >/=16. RESULTS: The mean age was 62 +/- 14 years; 70% were female. A total of 93% were diagnosed with hypertension; 61%, diabetes; and 72%, dyslipidemia; 90% were overweight/obese; 33% had a history of heart disease; and 10% had a stroke. Logistic regression analysis, adjusting for age and gender, showed that patients with depression had nearly two-fold increased odds of being at risk for OSA (OR 1.75, 95% CI 1.02-2.98, p < 0.05). Patients with anxiety had three-fold increased odds of being at risk for OSA (OR 3.30, 95% CI 2.11-5.15, p < 0.01). After adjusting for marital status and income, patients with anxiety had a 6% increase in OSA risk (OR 1.06, 95% CI 1.04-1.09, p < 0.05), but depression was no longer significant. CONCLUSION: Our results suggest that non-Hispanic blacks with metabolic syndrome who experience anxiety and/or depression should be screened for OSA. Key messages This study assessed associations of moderate to severe depression and anxiety with risk of obstructive sleep apnea (OSA) among non-Hispanic blacks with metabolic syndrome. Patients with depression had nearly two-fold increased odds of being at risk for OSA. Patients with anxiety had three-fold increased odds of being at risk for OSA.

The association between stressful childhood experiences (SCE) and psychotic symptoms is still not clearly understood, and different causal pathways have been proposed. Generalized estimating equation modeling was used to test the dose-response relationship between SCE and delusions and hallucinations at baseline and follow-up periods and the possible confounding effects of dissociation on this relationship. The prevalence of SCE in individuals with psychotic disorders was high, with more co-occurring SCE categories being positively associated with more types of delusions and hallucinations. Each additional SCE was associated with a 1.20 increase in the incidence rate ratio (95% confidence interval [CI; 1.09, 1.32]) for hallucinations and a 1.19 increase (CI [1.09, 1.29]) for delusions, supporting a dose-response association. After we controlled for the mediating effects of dissociative symptoms at follow-up, SCE remained independently associated with delusions. We propose that cumulative SCE can result in complex trauma reactions that present with a broad range of symptomatology, including dissociative, posttraumatic stress disorder, and psychotic symptoms.

Introduction: Inadequate sleep duration is an important public health burden in the United States. However, there is a paucity of information on the relationships between psychological health and inadequate sleep. Our study examined the relationships between emotional distress and inadequate sleep. Methods: Data from the 2009 National Health Interview Survey (NHIS), N = 27,731 participants 18 years and older, were analyzed to investigate the associations of emotional distress with inadequate sleep duration, adjusting for socio-demographic factors, health risks, and chronic diseases. We deine inadequate sleep as less than 7 or greater than 8 hours sleep durations; compared to healthy sleep (7-8 hours). We measured emotional distress, based on Kessler's 6 scale, which assesses the frequency of feeling sad, nervous, restless, hopeless, worthless, and burdened over a 30-day period. Responses were used to generate a score ranging from 0 to 24. Scores of > 13 are considered likely to indicate serious mental illness. Results: Of the sample, 52% were female, 80% were white, 12% black, and 8% other; 30% hold a HS diploma and 31% reported a family income below 31K. Analysis also showed that 10.9% reported emotional distress and 37.2% reported inadequate sleep. Results of our logistic regression analysis revealed that individuals with emotional distress had 57% greater odd of reporting inadequate sleep (OR = 1.5795% C.I. 1.54, 1.61, p < 0.001); the model adjusted for age, sex, race/ethnicity, marital status, education, combined family income, body mass index, history of alcohol consumption, smoking status, and chronic diseases including, arthritis, hypertension, diabetes, cancer, coronary heart disease and heart conditions. Conclusion: Our indings showed that emotional distress, an important proxy for psychological health, was the strongest predictor of inadequate sleep. Other factors associated with inadequate sleep include demographic (age, education, sex, race/ethnicity, combined family income), health!

Introduction: Many risk factors have been implicated in the pathophysiology of obstructive sleep apnea (OSA). Recent evidence suggests that medical risk factors, such as uncontrolled/high blood pressure (BP), high cholesterol, triglycerides, high body mass index, diabetes, and dyslipidemia (all indicators of metabolic syndrome) are highly comorbid with OSA. However, data on the relationships between these risk factors and OSA among blacks with metabolic syndrome are lacking. Methods: Data for the present study were collected from 340 participants from the Metabolic Syndrome Outcome (MetSO) study, a NIHfunded cohort study of 1,035 blacks with metabolic syndrome (mean age = 62 + 13 years, 69% female, and 43% with annual family income < $10K). During initial interviews, patients provided sociodemographic, health risks, and history of chronic diseases. Patients with a score > 6 on the Apnea Risk Evaluation System (ARES) were considered at high OSA risk. Logistic regression analyses were employed to investigate whether metabolic syndrome indicators, particularly uncontrolled blood pressure, increased the odds of OSA. Results: Of the sample, 77.1% was at risk for OSA and 16.8% had uncontrolled BP. Analysis also showed 60.4% were diabetic, 8.9% had a stroke history, 74.3% had dyslipidemia, 91.1% were either overweight or obese and 30.9% had heart disease. Mean systolic BP was 134.8 + 18.4; diastolic BP was 75.6 + 11.9; LDL cholesterol was 105.6 + 36.9; HDL cholesterol was 48.0 + 17.3; triglycerides was 135.8 + 81.2; glucose was 138.4 + 68.3; and HbA1c was 7.93 + 1.63. Logistic regression analysis showed that uncontrolled BP independently increased the odds of OSA risk (OR = 1.94, 95% CI = 1.12-3.32, p < 0.01). Conclusion: Our indings suggest that uncontrolled BP was associated with a twofold greater risk of OSA in blacks. The clinical implication of this inding is that blacks with metabolic syndrome and who have uncontrolled BP should be screened for the presence of OSA