Faculty Bibliography

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: The metabolic syndrome (MetS) and its components, which include hypertension, diabetes, dyslipidemia, and obesity, have been linked to obstructive sleep apnea (OSA). Blacks bear a greater burden of the metabolic syndrome risk factors and are generally at a greater risk for OSA than their white counterparts. This study aimed to determine whether Blacks with MetS are at a significantly increased risk for OSA than those without. It also ascertained which MetS component is the greatest predictor of OSA risk. Methods: A total of 1,035 participants with metabolic syndrome (ages 20-90, mean = 55.7 years; male/female ratio = 1:2.19) and 516 without metabolic syndrome (ages 20-91, mean = 62.4 years; male/female ratio = 1:2.57) were enrolled. They provided sociodemographic and anthropometric data. OSA risk was assessed using the Apnea Risk Evaluation System (ARESTM); patients with a score > 6 were considered at high OSA risk, based on validation studies. Multivariate-adjusted logistic regression was used to model OSA risk with MetS status as the primary outcome. The model also considered the contribution of hypertension, diabetes, dyslipidemia, and obesity, while controlling for differences in sex, birthplace, income, and education. Results: MetS was associated with an increased risk for OSA (OR = 3.85, 95% CI: 2.70-5.48). Diabetes (OR = 2.31, 95% CI: 1.77-3.01) and obesity (OR = 3.35, 95% CI: 2.12-5.28) were the strongest predictors of increased risk. Of note, the risk of OSA decreased slightly with increasing age (OR = 0.97, 95% CI: 0.96-0.98). Education, family income, birthplace and sex did not have significant effects on these associations (p > 0.05). Conclusion: These findings suggest that a diagnosis of MetS increases risks of OSA independent of the effects of MetS components (diabetes or obesity). Patients with metabolic syndrome, diabetes, or obesity should be routinely assessed for OSA risk and treated appropriately to improve overall health and quality of life

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

Introduction: This study ascertained whether inadequate sleep places unequal burden on blacks, relative to their white counterparts. Methods: Analysis was based on data obtained from adult Americans (age range: 18-85 years) who participated in the National Health Interview Survey, an important surveillance study of the health of the US population providing sociodemographic, health risk, and medical data from 1977-2009. Sleep duration was coded as either very short sleep [VSS] (< 5 hours), short sleep [SS] (5-6 hours), or long sleep [LS] (> 8 hours), referenced to 7-8 hour sleepers. Overweight was defined as BMI > 25.0 and < 29.9 kg/m² and obesity, BMI > 30 kg/m², referenced to normal weight (BMI = 18.5-24.9 kg/m²). Results: Multivariate-adjusted regression analyses indicated that among whites VSS was associated with a 10% increased likelihood of being overweight and 51% increased likelihood of being obese, relative to 7-8 hour sleepers. SS was associated with a 13%) increased likelihood of being overweight and 45% increased likelihood of being obese. LS was not a significant predictor of overweight, but it was associated with 21%o increased likelihood of being obese. Among blacks, VSS was associated with a 76% increased likelihood of being overweight and 81%o increased likelihood of being obese. SS was associated with a 16%o increased likelihood of being overweight and 32%o increased likelihood of being obese. As for whites, LS was not a significant predictor of overweight, but it was associated with a 25%o increased likelihood of being obese. Conclusion: Our investigation demonstrates strong linkages between inadequate sleep and overweight/obesity among both black and white Americans. While it cannot be said that insufficient sleep causes overweight or obesity, it is apparent that blacks sleeping 5 hours or less may be unequally burdened by sleep-related overweight/obesity

Introduction: Daily rhythmicity in locomotor activity has been studied in great detail in laboratory animals, but not in humans. We collected actigraphic data from individuals from five countries to explore the circadian organization of human physical activity. Methods: Physical activity was assessed using the Actical accelerometer in five countries differing in socioeconomic development as defined by the United Nations' Human Development Index (HDI): Ghana (HDI = 0.541) as a lower "medium" HDI country, South Africa (0.619) as "medium", Jamaica (0.727) and Seychelles (0.773) as "high", and the U.S.A. (0.910) as "very high". Participants (n = 2,213; ages 25-45 years; 50% were female) provided demographic and health data including body mass index. Individual activity records, lasting 7 days, were subjected to cosinor analysis to derive parameters of circadian activity rhythms: mesor (mean level), amplitude (half the range of excursion), acrophase (timing), and robustness (rhythm strength). Results: Activity records exhibited statistically significant 24-hour rhythmicity (p < 0.05). Averaged daily physical activity increased noticeably a few hours after sunrise and dropped off around sunset, peaking at 1:42 pm. Women and men did not differ significantly regarding the acrophase of the daily rhythm in each country, but the acrophase showed considerable between-country variation (~3 hours). In the United States, where there was substantial seasonal variation in photoperiod, acrophase followed local time, rather than the times of sunrise or sunset. Quantification of the socioeconomic stages of the five countries suggested that more developed countries have more obese residents (r = -0.83), who were less active (r = 0.69) and exhibited later activity timing (r = -0.69) than less developed countries (all p < 0.05). Conclusion: These results characterize human daily activity rhythm, revealing similarities and differences among five countries that differ in socioeconomic development. The results also document associations between individuals' activity rhythm, body mass index, and socioeconomic development of their respective country

This review introduces a conceptual framework for understanding stakeholder management (ShM) in the clinical and community-based research environment. In recent years, an evolution in practice has occurred in many applicants for public and non-governmental funding of public health research in hospital settings. Community health research projects are inherently complex, have sought to involve patients and other stakeholders in the center of the research process. Substantial evidence has now been provided that stakeholder involvement is essential for management effectiveness in clinical research. Feedback from stakeholders has critical value for research managers inasmuch as it alerts them to the social, environmental, and ethical implications of research activities. Additionally, those who are directly affected by program development and clinical research, the patients, their families, and others, almost universally have a strong motivation to be involved in the planning and execution of new program changes. The current overview introduces a conceptual framework for ShM in the clinical research environment and offers practical suggestions for fostering meaningful stakeholder engagement. The fifth edition of PMBOK((R)) of the Project Management Institute, has served as basis for many of the suggested guidelines that are put forward in this article.

Insufficient sleep is associated with cardiometabolic disease and poor health. However, few studies have assessed its determinants in a nationally representative sample. Data from the 2009 behavioral risk factor surveillance system were used (N = 323,047 adults). Insufficient sleep was assessed as insufficient rest/sleep over 30 days. This was evaluated relative to sociodemographics (age, sex, race/ethnicity, marital status, region), socioeconomics (education, income, employment, insurance), health behaviors (diet, exercise, smoking, alcohol), and health/functioning (emotional support, BMI, mental/physical health). Overall, insufficient sleep was associated with being female, White or Black/African-American, unemployed, without health insurance, and not married; decreased age, income, education, physical activity; worse diet and overall health; and increased household size, alcohol, and smoking. These factors should be considered as risk factors for insufficient sleep.

BACKGROUND: Increased legalization of marijuana has resulted in renewed interest in its effects on body weight and cardiometabolic risk. Conflicting data exist regarding marijuana effects on body weight, waist circumference as well as lipid profiles, blood pressure and cardiovascular disease. Furthermore, there is a dearth of data available on this effect in the black population. OBJECTIVE: To assess the metabolic profile and cardiovascular risk factors as well as body weight and waist circumference among urban black marijuana users. METHODS: A cross sectional study design involving 100 patients seen in a Family Practice clinic at University hospital of Brooklyn, NY, USA, over a period of 3 months from January 2014 to March 2014. Participants were administered a questionnaire regarding marijuana use, and other associated behaviors. Socio-demographic, laboratory, and clinical data were collected. We report measures of central tendencies, and dispersion for continuous variables and the frequency of distribution for categorical variables. RESULTS: Of the 100 patients surveyed, 57% were females. The mean (+/-SEM) age of the entire cohort was 46.3 years+/-1.5; range, 19-78 years. The mean body mass index (BMI) was 29.6 kg/m2+/-0.73; SBP=128.0 mmHg+/-1.69; DBP=76.1 mmHg+/-1.17. Current marijuana users had the lowest waist circumference compared to former or never users respectively (32.9+/-0.66 vs. 35.9+/-0.88 vs. 33.4+/-0.74), p<0.01. Diastolic blood pressure in mmHg was significantly higher among former marijuana users compared to current or never users, (80.0+/-2.1 vs. 73.3+/-2.3 vs. 73.4+/-1.6), p<0.01. Current marijuana users showed a tendency (not statistically significant) towards lower total cholesterol, Triglycerides (TG), High Density Lipoprotein (HDL)-cholesterol, Low Density Lipoprotein (LDL)-cholesterol, body mass index (BMI) and systolic blood pressure, compared to former users or never users. CONCLUSION: Current marijuana use is associated with significantly lower waist circumference, compared to former users and never users. Except for diastolic BP that was significantly lower among current users, other metabolic parameters showed tendency towards favorable profile. Further studies are needed to characterize the metabolic effects and to elucidate mechanisms of actions of marijuana in view of its rapid rate of utilization in the USA and around the world.

BACKGROUND: Biological rhythmicity has been extensively studied in animals for many decades. Although temporal patterns of physical activity have been identified in humans, no large-scale, multi-national study has been published, and no comparison has been attempted of the ubiquity of activity rhythms at different time scales (such as daily, weekly, monthly, and annual). METHODS: Using individually worn actigraphy devices, physical activity of 2,328 individuals from five different countries (adults of African descent from Ghana, South Africa, Jamaica, Seychelles, and the United States) was measured for seven consecutive days at different times of the year. RESULTS: Analysis for rhythmic patterns identified daily rhythmicity of physical activity in all five of the represented nationalities. Weekly rhythmicity was found in some, but not all, of the nationalities. No significant evidence of lunar rhythmicity or seasonal rhythmicity was found in any of the groups. CONCLUSIONS: These findings extend previous small-scale observations of daily rhythmicity to a large cohort of individuals from around the world. The findings also confirm the existence of modest weekly rhythmicity but not lunar or seasonal rhythmicity in human activity. These differences in rhythm strength have implications for the management of health hazards of rhythm misalignment. Key messages Analysis of the pattern of physical activity of 2,328 individuals from five countries revealed strong daily rhythmicity in all five countries, moderate weekly rhythmicity in some countries, and no lunar rhythmicity or seasonal rhythmicity in any of the countries.

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).