The Epidemiology of Sleep Quality and Sleep Patterns Among Thai College Students

Transcription

1 Any Use of Stimulants (%) Prevalence of poor sleep quality (%) The Epidemiology of Sleep and Sleep Patterns Among Thai College Students V Lohsoonthorn a, H Khidir b, G Casillas b, S Lertmaharit a, M Tadesse b,c, WC Pensukan d, Rattananupong T a, B Galaye b, MA Williams b a Faculty of Medicine, Chulalongkorn University, Bankgok, Thailand; b Department of Epidemiology, Multidisciplinary International Research Training Program, Harvard School of Public Health, Boston, MA, USA; c Department of Mathematics & Statistics, Georgetown University, Washington, DC; d School of Nursing, Walailak University, Nakhon Si Thammarat, Thailand BACKGROUND Sleep is physiologically essential for maintaining overall well-being. There are a number of negative health conditions that are associated with poor sleep, including type 2 diabetes, hypertension, obesity, and premature mortality as well as a higher incidence of mental health illnesses such as depression, anxiety, eating disorders, and dementia. Few studies have been conducted on the sleep patterns and sleep quality of older adolescent, young adults and college students. College students are of special interest because sleep patterns change due to biological factors such as circadian timing, social and educational demands, and dietary modifications such as increased consumption of caffeinated beverages. METHODS We conducted a cross-sectional study at seven colleges in Thailand. 2,854 students filled out self-administered questionnaires on socio-demographic characteristics, sleep and lifestyle behaviors. Sleep quality was assessed using the previously validated Pittsburgh Sleep Index (PSQI), a 9-item self-reported questionnaire that consists of seven sleep components related to sleep quality. Chi-square test and Student s t-test were used to determine bivariate differences for categorical and continuous variables, respectively. A quartile of ( 6 h) was used as a cutoff for defining short sleep duration. Adjusting for covariates of interest, we used multivariable logistic regression procedures to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between poor sleep quality and socio-demographic and behavioral factors. Figure - Prevalence of poor sleep quality by age and sex Table - Odds ratio (OR) and 95% confidence intervals (CI) for poor sleep quality Characteristic Sex Figure 2- Any Stimulant use by age and sex Each odds ratio is adjusted for all other covariates listed in the table Poor sleep quality: PSQI Global score>5 Table 2 - Odds ratios for sleep quality parameters in relation to selected lifestyle characteristics Sleep quality parameters Smoking status Short Sleep Duration (<6 hours) (n=,) Long Sleep Latency (>3 min) (n=753) Day Dysfunction due to sleep (n=724) Efficiency (<85%) (n=72) Sleep Medicine Use (n=56) Never.(Ref).(Ref).(Ref).(Ref).(Ref) Former.92(.52-.6).9 (.47-.7).4 ( ).27 ( ) 3.9 (.93-.6) Current.7(.5-.97).49 (.8-2.6).43 (.2-.98).25 ( ) 3.4 ( ) p-value for trend Any stimulant beverages.(ref).(ref).(ref).(ref).(ref).5(.9-.23).45 ( ).68 (.4-2.).9(.-.4).32 ( ) Alcohol consumption Unadjusted Age and sex adjusted Multivariate adjusted Male.(Ref).(Ref).(Ref) Female.8 ( ).9( ).7 ( ) Smoking status Never.(Ref).(Ref).(Ref) Former Current smoker Number of Alcohol drinks Age (years) >=22 Age (years).33 ( ).4 (.8-2.4).3 ( ).37 (.-.86).7(.65-2.).( ) < drink/month.(ref).(ref).(ref) -9 drinks/month.5 ( ).8 (.-.39).2 ( ) 2 drinks/month 2.2 (.-3.68) 2. ( ).4 ( ) One stimulant drink/week.(ref).(ref).(ref).6 ( ).6 ( ).5 ( ) Physical Activity.(Ref).(Ref).(Ref).88 (.76-.2).89 (.77-.3).97(.8-.7) < drink/month.(ref).(ref).(ref).(ref).(ref) -9 drinks/month.82 (.7-.96).5 ( ).39 (.6-.67).6 ( ).22 ( ) 2 drinks/month.32 ( ).59 ( ) 3. ( ).58 ( ) 5.75 ( ) p-value for trend.43.7 <... Male Female Males Females The prevalence of poor sleep quality was found to be 48.% (Figure ) A significant proportion of students used stimulant beverages (58.%) (Figure 2) Stimulant use (OR.5; 95%CI.3-.8) was found to be statistically significant and positively associated with poor sleep quality Alcohol consumption (OR 3.; 95% CI.7-5.6) and cigarette smoking (OR.4; 95% CI.-2.) also had statistically significant association with increased daytime dysfunction (Table ) Those who reported consuming at least one stimulant beverage per week had.5 times higher odds of having poor sleep quality [95% CI (.3-.8)] (Table ) Current smokers were less likely to report short sleep duration [OR.7, 95% CI(.5-.97)] but more likely to have long sleep latency [OR.49, 95% CI(.8-2.6)], daytime dysfunction due to sleep [OR.4, 95% CI(.- 2.)], and to use sleep medicines [OR 3., 95% CI(.4-6.5) (Table 2) Those who reported consuming more than 9 alcoholic beverages per month had more than 3 times higher odds of having daytime dysfunction due to sleep [OR 3., 95% CI(.7-5.6)] and more than 5 times higher odds of reporting use of sleep medicine [OR 5.8, 95% CI(2.-6.4)]. Poor sleep is prevalent among college students in Thailand. Cigarette smoking, alcohol consumption and stimulant use were all found to be risk factors for poor sleep quality. Young adults should be educated on the importance of sleep and to the risk factors associated with inadequate sleep. This research was supported by the Harvard School of Public Health Multidisciplinary International Research Training (MIRT) Program, National Institute for Minority Health and Health Disparities, National Institutes of Health (T37-MD449).

2 The Epidemiology of Sleep, Sleep Patterns and Consumption of Energy Drinks and Other Caffeinated Beverages among Peruvian College Students Sixto E. Sanchez, 2, Claudia Martinez 3, Raphaelle A. Oriol 3, David Yanez 4, Benjamín Castañeda, Elena Sanchez 2, Bizu Gelaye 3, Michelle A. Williams 3 Universidad de San Martin de Porres, Lima, Peru; 2 Asociacion Civil Proyectos en Salud (PROESA), Peru; 3 Department of Epidemiology, Harvard School of Public Health, Multidisciplinary International Research Training Program, Boston, MA, USA; 4 Department of Biostatistics, University of Washington, Seattle, WA, USA BACKGROUND Sleep problems either due to poor sleep quality, insufficient sleep, or an untreated disorder have been associated with deficits in attention, lower academic performance, impaired social relationships, risk-taking behavior, and poorer health. College students tend to keep later sleep schedules and have irregular sleep patterns as they attempt to compensate for sleep debt from "all-nighters," factors which are linked to decreased academic achievement and increased symptoms of depression. To our knowledge, no investigations have examined sleep patterns among Peruvian college students or identified lifestyle correlates of sleep quality in this population. OBJECTIVES To estimate the prevalence of poor sleep quality among Peruvian college students and to examine the extent to which poor sleep quality and altered sleep patterns are associated with consumption of caffeinated beverages including energy drinks, and other caffeinated beverages. MATERIALS AND METHODS We conducted a cross-sectional study of 2,458 students attending Universidad de San Martin de Porres and Universidad de San Marcos in Lima, Peru Students filled out a self-administered questionnaire about sleep habits, sociodemographic and lifestyle characteristics The Pittsburgh Sleep Index (PSQI) was used to assess sleep quality Logistic regression procedures were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for poor sleep quality and sleep parameters (i.e. sleep duration, sleep latency, sleep efficiency, sleep medicine use, daytime dysfunction due to sleepiness) in relation to lifestyle Table - Characteristics of the study population N=,84 Good Sleep N=,374 Table presents bivariate associations between sleep quality and students demographic and lifestyle characteristics N=2,458 Age (Mean± SD) 2.9±2.6 2.± ± Age (years) 8 48 (9.6) 249 (8.) 232 (2.4) (6.) 226 (6.5) 69 (5.6) 2 39 (2.9) 7 (2.5) 48 (3.6) (6.) 22 (6.) 77 (6.3) (35.2) 58 (36.9) 358 (33.) Sex Male 965 (39.3) 52 (36.5) 463 (42.7) <. Female,493 (6.7) 872 (63.5) 62 (57.3) Cigarette Smoking Status Never,83 (74.4),3 (73.) 828 (76.4).86 Former 24 (8.8) 2 (8.7) 94 (8.7) Current 43 (6.8) 25 (8.3) 62 (4.9) Alcohol Consumption < drink/month 493 (2.) 263 (9.) 23 (2.2).3-9 drinks/month 979 (39.8) 579 (42.) 4 (36.9) 2 drinks/month 986 (4.) 532 (38.7) 454 (4.9) Body Mass Index (kg/m 2 ) Underweight (<8.5) 83 (4.5) 39(3.8) 44 (5.3).294 rmal ( ),227 (66.6) 675(66.5) 552 (66.7) Overweight ( ) 454 (24.6) 26 (25.7) 93 (23.3) Obese ( 3.) 78 (4.2) 4 (3.9) 38 (4.6) Any Physical Activity 828 (34.) 457 (33.6) 37 (34.6).63,63 (65.9) 92 (66.4) 7 (65.4) As shown in Table 2, approximately 39% of students reported short sleep duration ( 5 hours) Long sleep latency (> 3 minutes) was reported by 24.4% of participants; and frequent ( per week) complaints of daytime dysfunction due to sleep loss were also common in this cohort (32.%) Table 2 - PSQI sleep quality subscale by sex N=2,458 Male N=965 Female N=,493 Sleep Duration (hours) (39.2) 373 (38.7) 59 (39.6) < (3.9) 34 (35.3) 444 (29.7) (5.9) 57 (6.3) 232 (5.5) (3.) 94 (9.7) 226 (5.) Sleep Latency (minutes) (35.9) 375 (38.9) 57 (33.9) < (39.7) 328 (34.) 647 (43.3) (2.6) 245 (25.4) 286 (9.2) 6 7 (2.8) 7 (.8) 53 (3.6) Day Dysfunction Due to Sleep Loss Never 38 (2.9) 36 (4.) 82 (2.2).3 < once a week,35 (55.) 555 (57.5) 796 (53.3) -2 times per week 637 (25.9) 225 (23.3) 42 (27.6) 3 times per week 52 (6.2) 49 (5.) 3 (6.9) Sleep Efficiency (%) 85 2,7 (8.7) 792 (82.),25 (8.4) < (2.9) 4 (4.6) 76 (.8) (3.4) 9 (.9) 65 (4.3) < 65 5 (2.) 3 (.4) 37 (2.5) Sleep Medicine Use Never 2,298 (93.5) 97 (94.9),38 (92.5).2 < once a week 8 (4.4) 28 (3.) 8 (5.4) -2 times per week 4 (.7) 8 (.8) 23 (.5) 3 times per week (.4) 2 (.6) 9 (.6) Sleep,84 (44.) 463 (48.) 62 (4.6).2,374 (55.9) 52 (52.) 872 (58.4) Female students were more likely to have short sleep duration (p.), long sleep latency (p.), poor sleep efficiency (p.), and more day time dysfunction due to sleep loss (p =.3) than their male counterparts Current smokers were also more than twice as likely to use sleep medications (OR = 2.; 95% CI ) as compared with never smokers (Table 3) Alcohol consumption was significantly associated with short sleep duration and sleep medication use Consumption of 3 stimulant beverages per week was associated with a:.49-fold higher odds of short sleep duration (< 5 hours) (95% CI.4-.94), a.93- fold higher odds of longer sleep latency (95% CI ) and 2.-fold higher odds of using sleep medications (95% CI ) Table 3 -Odds ratios for sleep quality parameters in relation to lifestyle characteristics Sleep Parameters (n=2,458) Short Sleep Duration ( 5 hrs) (n=964) Long Sleep Latency (>3 min) (n=6) Day Dysfunction due to Sleep Loss (n=789) Efficiency (<85%) (n=45) Our study extends the existing literature by documenting associations of the consumption of different types of energy drinks with overall sleep quality. Research that elucidates independent and joint effects of such wellness programs and policies will give public health professionals and educators the information and impetus needing for investing in and promoting campaigns to address poor sleep quality in young adults. This research was supported by the Harvard School of Public Health Multidisciplinary International Research Training (MIRT) Program, National Institute for Minority Health and Health Disparities, National Institutes of Health (T37-MD449). Sleep Medicine Use (n=6) n Smoking Status Never,83. (Reference). (Reference). (Reference). (Reference). (Reference) Former (.55-.).42 (.4-.95).98 ( ).72 ( ).45 ( ) Current 43. (.8-.25).33 (.4-.69).83 (.65-.5).3 ( ) 2. ( ) p-value for trend <. Alcohol Consumption < drink/month 493. (Reference). (Reference). (Reference). (Reference). (Reference) -9 drinks/month ( ).9 ( ).94 (.74-.8).8 (.8-.44) 2.8 ( ) 2 drinks/month (.2-.59).26( ).8 (.64-.2).7 (.8-.42).34 ( ) p-value for trend Number of Stimulant Beverages/week,46. (Reference). (Reference). (Reference). (Reference). (Reference) (.77-.5).32 (.6-.64).6 (86-.3).59 ( ).89 ( ) ( ).6 (.4-.3).8 ( ).46 ( ).8 ( ) (.4-.94).93 ( ).45 (.-.9). (.8-.52) 2. ( ) p-value for trend.3 < Physical Activity 828. (Reference). (Reference). (Reference). (Reference). (Reference),63.97 (.8-.5).34 (.9-.64).99 (.83-.9).83 (.69-.3).7 (.76-.5)

3 The Epidemiology of Sleep, Sleep Patterns and the Consumption of Caffeinated Beverages and Khat use among Ethiopian College Students Seblewengel Lemma, Sheila Patel 2,, Yared Tarekegn2, Mahlet Tadesse 2,3,Yemane Berhane, Bizu Gelaye 2, & Michelle A. Williams 2 Addis Continental Institute of Public Health, Addis Ababa, Ethiopia 2Department of Epidemiology, Multidisciplinary International Research Training Program, Harvard School of Public Health, Boston, Massachusetts, USA 3Department of Mathematics & Statistics, Georgetown University, Washington, DC BACKGROUND & OBJECTIVE Short sleep duration (generally defined as less than 7 hours) increases rates of mortality and has been reported as an important risk factor for adverse cardiovascular, endocrine, immune and nervous system outcomes, mood and anxiety disorders, and substance abuse Of 2,23 students,,75 (52.7%) were classified as having poor sleep quality. Of those who experience poor sleep quality, 82.3% consume some type of coffee containing beverage and.9% reported using Khat (Table ) The prevalence of poor sleep quality is higher among females across all age groups, except at age 2 study has been conducted on sleep quality and sleep patterns in relation with consumption of caffeinated beverages and other stimulants among Ethiopians Students who reported consuming any caffeinated beverages a week were.48-times as likely to report long sleep latency (OR=.48; 95% CI:.9-.83) as compared to non-users MATERIALS & METHODS A total of 2,23 Ethiopian college students completed self-administered questionnaires which ascertained demographic information and included questions regarding behavioral risk factors of poor sleep quality. Chi-square test was used to determine bivariate differences for categorical and student s t-test were used to determine bivariate differences for continuous variables. Multivariable logistic regression procedures were used to estimate odds ratios and 95% confidence intervals for the associations between poor sleep quality and socio-demographic and behavioral factors. Sleep quality was assessed using the Pittsburgh Sleep Index and all analyses were conducted using IBM SPSS statistics software The consumption of 2 or more alcoholic drinks per month was found to increase the odds of sleep medicine use (OR=9.25; 95% CI: ) (Table 2) Table Consumption of caffeinated beverages and Khat use according to sleep quality Exposure N=,55 n (%) Any coffee containing beverages 27 (7.7) 965 (82.3) Khat consumption 94 (88.)) 22 (.9) Khat consumption /week (89.9) 53 (5.2) 5 (4.9) Good Sleep N=,75 n (%) 229 (2.8) 824 (78.2) 847 (9.7) 87 (9.3) Figure -Prevalence of poor sleep quality in relation to age and sex Male 5 Female (92.6) 36 (3.9) 33 (3.5) Table 2 Prevalence and odds ratios for sleep quality parameters in relation to stimulant drinks and lifestyle characteristics Sleep quality parameters Smoking status Never Long Sleep Latency (>3 min) Short Sleep (n=,82) Duration ( 6 hrs) (n=979) (n=2,23) n Day Dysfunction due to Sleep (n=673) Efficiency (<85%) (n=675) Sleep Medicine Use (n=74) 2, (.43-.5).68 ( ).79 (.46-.3).74 (.-2.73) 2.84 ( ) Alcohol consumption < drink/mon,9-9 drinks/mon (.64-.5).2 ( ).6 (.8-.39).25 ( ).9 ( ) 2 drinks/mon 27.8 (.37-.8).24 ( ).56 ( ).47 ( ) 9.25 ( ) (.84-2.).72 (.9-2.7)..93 ( ).28 ( ) ( ).9 (.22-3.) ( ) 4.42 ( ) <..48 (.9-.83).2 ( ).5 (.9-.45).5 ( ).29 (.7-.57). (.8-.23).27 (.3-.58).6 ( ) Ever p-value for trend.2 Khat use/week 2, (.49-.2) ( ) p-value for trend.777 Any caffeine containing beverages consumption 436,789. (.8-.23) Physical activity (.78-.5),495 Adjusted for age and gender 7 Prevalence of poor sleep (%) The objective of this study was to examine the demographic and lifestyle correlates of poor sleep quality and the associations of poor sleep quality and altered sleep patterns with the consumption of caffeinated beverages and other stimulants Cigarette smoking and Khat use were found to increase the odds of long sleep latency (smoking OR=.68; 95% CI: ; Khat OR=.57; 95% CI.6-2.), poor sleep efficiency (smoking OR=.75; 95% CI: ; Khat OR=.54; 95% CI:.4-2.9) and sleep medicine use (smoking OR=2.84; 95% CI: ; Khat OR=2.93; 95% CI: ) (Table 2) College students experience sleep problems and this significantly affects their health, academic performance, and mood. Sleep problems are associated with a number of adverse outcomes including; deficit in academic performance and attention, impaired social relationships, poor health, depression and other risk taking behaviors. Our observation among Ethiopian college students suggest consuming coffee containing beverage and use of Khat can contribute to poor sleep quality. Increased educational awareness may avoid the build-up of a chronic sleep debt during early adulthood and effective management of sleep disorders This research was supported by the Multidisciplinary International Research Training (MIRT) Program, National Institute for Minority Health and Health Disparities, National Institutes of Health (T37MD449).

4 Percentage of Participants (%) The Epidemiology of Sleep and Consumption of Stimulant Beverages among Patagonian Chilean College Students JC Velez a, A Souza b,s Traslaviña b, C Barbosa, A Wosu b, A Andrade a, M Frye a, AL Fitzpatrick c, B Gelaye a, MA Williams a, a El Centro de Rehabilitación Club de Leones Cruz del Sur, Punta Arenas, Chile; b Multidisciplinary International Research Training Program, Harvard School of Public Health, Boston, MA; and c Departments of Epidemiology and Global Health, University of Washington, Seattle, WA Introduction & Objectives Consumption of energy drinks has increased in recent years. These drinks, typically high in caffeine and sugar content, are targeted toward youth and advertised as performance enhancing beverages for athletics, school, and social situations. Studies have shown that stimulant use among healthy adolescents may be associated with feelings of jitteriness and nervousness, difficulty in sleeping, loss of appetite, and stomach discomfort. However, few studies have examined the relationship between energy drink consumption and sleep quality. In this study, we assessed sleep patterns and sleep quality among Patagonian, Chilean college students. We also evaluated the extent to which stimulant beverage use and other lifestyle characteristics are associated with poor sleep quality. Materials and Methods The study was a cross-sectional survey of 832 students attending four universities in Punta Arenas, Chile. Participants completed self-administered, anonymous questionnaires about their demographics and lifestyle behaviors including sleep habits, stimulant beverage, smoking and alcohol consumption. The Pittsburgh Sleep Index (PSQI) was used to measure parameters of sleep. Data were analyzed by logistic regression to determine the associations between use of specific stimulant beverages and poor sleep quality. Results Of the 832 students who participated, 7.% were female with mean age of 2.9 ± 3.4 years A total of (45.6%) males and 32 (54.4%) females exhibited poor sleep quality as evaluated by the PSQI Approximately 55% of them reported consuming at least one type of stimulant beverage per week There was a positive relationship between variety of energy drink consumption and short sleep duration (OR:.84, 95 % CI: , p=.) adjusted for age and sex Approximately 8% of participants reported using energy drinks as a consequence of sleep deprivation, and an additional 27% cited energy drink consumption to offset a general need for energy and 29% in order to study (Figure ) 6.8% (8) of those who reported drinking energy drinks had poor overall sleep quality (p=.2). This percentage was higher than those of cola, coffee and yerba mate drinkers, and the only statistically significant association found (Figure 2) Current smoking was positively associated with sleep medicine use (OR: 2.55, 95 % CI: , p=.2), and with day dysfunction due to sleepiness (OR: % CI: , p =.2) (Table ) Stimulant beverage consumers (OR.8; CI ; p=.4) had more than an 8% increased risk of reporting poor sleep quality than non-stimulant consumers Figure : Motivations for consuming energy drinks Figure 2: Sleep and Stimulant Beverage Consumption p= Energy Drinks p= p= Coffee Yerba Mate Cola Good Sleep p= Results Table : Odds of Parameters, according to Lifestyle Characteristics Sleep quality parameters Smoking Status Short Sleep Duration (<6 hours) (N=457) Long Sleep Latency (>3 min) (N=345) Discussion High energy drink consumption was positively associated with poor sleep quality. This could be due to the high caffeine and sugar content of these drinks, or the quantity of drinks students consumed. Additional studies could ascertain how much caffeine and sugar students ingest on average through various stimulant beverages. Day Dysfunction due to Sleep Loss (N=85) % % % Never (Reference) (Reference) 6.. (Reference) Former (.7.97) ( ) ( ) Current ( ) (.97.8) ( ) p-value for trend Alcohol Consumption Low Moderate High (Reference) (Reference) (Reference) ( ) 4.5. (.7.74) ( ) (.9 2.2) (.77.85) ( ) p-value for trend Any Stimulant Beverages (Reference) (Reference) 7.4. (Reference) (.53.96) (.9.62) (.8-2.8) Physical Activity 59.. (Reference) (Reference) 2.. (Reference) (.5.96) 4..2 (.82.52) (.74.55) Adjusted for age and gender In the process of analyzing the data, we found it difficult to find complete nutritional information on many of the energy drinks students reported consuming. It is possible that many participants are not aware of how much caffeine and sugar these beverages contain. Energy drink producers should be more forthcoming with ingredient information, and efforts to inform students about the potential effects and risks of high energy drink consumption on sleep patterns should be addressed on university campuses. This research was supported by the Multidisciplinary International Research Training (MIRT) Program, National Institute for Minority Health and Health Disparities, National Institutes of Health (T37-MD449).

5 Is Obstructive Sleep Apnea Associated With Hypertension Among Asian Young Adults? Wipawan C. Pensuksan, Xiaoli Chen 2, Vitool Lohsoonthorn 3, Somrat Lertmaharit 3,4, Bizu Gelaye 2, Michelle A. Williams 2 School of Nursing, Walailak University, Nakhon Si Thammarat, Thailand 2 Department of Epidemiology, Harvard School of Public Health, Boston, MA 25, USA 3 Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 4 College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand BACKGROUND Obstructive sleep apnea (OSA) has been linked to hypertension in some but not all studies, most of which have been conducted among middleaged and older individuals in rth American and European countries. Few studies have focused on young adults, especially those in Southeast Asian countries undergoing epidemiologic transitions. Objectives To examine associations of OSA with elevated blood pressure (BP) and hypertension among young adults. To study whether weight status (obesity) may mediate the association between OSA and hypertension METHODS A total of 29 college students aged 8-28 years from seven universities in Thailand participated in this study. OSA was assessed using the Berlin Questionnaire. BP and anthropometric measurements were taken by trained research staff. Elevated BP and hypertension were defined as resting BP 2/8 mmhg and 4/9 mmhg, respectively. Linear and logistic regression models were fitted to estimate odds ratios (OR) and 95% confidence intervals (95% CI) of elevated BP and hypertension. Stratified analysis and mediation analysis were conducted to examine whether weight status could modify/mediate the associations. The prevalence of OSA was 6.3%. Overall, 2.9% of participants had elevated BP and 4.5% had hypertension. Participants with OSA had a >2-fold higher odds of having elevated BP (adjusted OR=2.38; 95%CI ) and hypertension (2.55; 95%CI: ), respectively. Associations of similar magnitudes were observed among men and women. However, the associations were only evident among overweight and obese individuals. When BMI (a covariate thought to be in the causal pathway of OSA and hypertension) entered into the model, associations were greatly attenuated (OR=.9; 95%CI: for elevated BP) and (OR=.2; 95%CI: for hypertension). Obesity evaluated by BMI accounted for 49% of the association between OSA and hypertension. Figure. Mediation model for the influence of obesity on the association between sleep apnea and hypertension The values of β are indicated for the associations between sleep apnea and obesity, obesity and hypertension (adjusted for sleep apnea), and sleep apnea and hypertension. β indicates the association after it is adjusted for the mediator (obesity). The proportion of the total effect that was mediated was about.49 in other words, obesity evaluated by BMI accounted for 49% of the association between sleep apnea and hypertension. S OSA among overweight and obese young adults is associated with elevated BP and hypertension. Obesity is a partial mediator for the association between OSA and hypertension. Enhanced efforts directed towards screening and diagnosing OSA among young adults could be one strategy for improving cardiovascular health. Financial Support: This study was supported by National Institutes of Health (NIH) and National Institute on Minority Health and Health Disparities (NIMHD) grants (T37-MD449) and NIH/NCRR/NCATS (8ULTR7).

6 Obstructive Sleep Apnea (OSA) Is Associated With Multiple Anthropometric Indices of General Obesity and Central Obesity Among Young Adults Xiaoli Chen, Wipawan C. Pensuksan 2, Vitool Lohsoonthorn 3, Somrat Lertmaharit 3,4, Bizu Gelaye, Michelle A. Williams Obstructive sleep apnea (OSA) is a common disorder characterized by repetitive episodes of upper airway obstruction that occur during sleep. OSA may predispose individuals to lead to obesity or excess weight through increased sympathetic activation, sleep fragmentation, ineffective sleep, and disrupted metabolism. A growing body of evidence suggests that OSA is independently associated with health outcomes and traffic accidents. There is a paucity of research evaluating OSA and its related obesity among young adults, especially among healthy young adults in Asian countries. OBJECTIVE Department of Epidemiology, Harvard School of Public Health, Boston, MA 25, USA 2 School of Nursing, Walailak University, Nakhon Si Thammarat, Thailand 3 Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 4 College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand BACKGROUND The prevalence of OSA was 6.3%; % of college students were overweight (BMI: 25-29kg/m 2 ) and 5% were obese (BMI 3); 2% had central obesity (men: WC 9cm; women: WC 8cm). OSA was significantly associated with overweight and obesity, independent of potential confounders. Students with OSA were more likely to have central obesity than those without OSA. OSA was significantly related to joint effects of general and central obesity. Associations between OSA and obesity were robust and evident for both genders, individuals with normal and elevated blood pressure, and those with and without psychological distress. Figure 2. Associations of OSA with the joint effects of general and central obesity among 29 college students in Thailand, according to gender distribution Adjusted for sociodemographic and lifestyle factors, psychological distress, and blood pressure To evaluate the association between OSA and obesity in college students To assess the joint effects of general and central obesity with OSA METHODS a Student t-test for continuous variables; Chi-square test for categorical variables; b Evaluated by General Health Questionnaire 2-item scale (GHQ-2); c Hypertension defined as: Systolic BP 4 mmhg or diastolic BP 9 mmhg; d Elevated BP: Systolic BP 2 mmhg or diastolic BP 8 mmhg. A total of 2,9 male and female college students aged 8-28 years in Thailand. Height, weight, waist circumference (WC), hip circumference, and blood pressure were measured by trained research staff. OSA was determined by the Berlin Questionnaire. Multinomial logistic regression analyses were conducted for overweight and obesity compared with normal weight group. Univariate and multivariable logistic regression models were fit; odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated. Stratified analyses were conducted to evaluate whether the associations varied by gender, blood pressure level, and psychological distress. b) Body mass index (BMI) and waist-to-hip ratio (WHR) Adjusted for sociodemographic & lifestyle factors, psychological distress, and blood pressure Adjusted for sociodemographic & lifestyle factors, psychological distress, and blood pressure This study shows significant and independent associations of OSA with general and central obesity among young adults, suggesting that OSA could be a risk factor for obesity and consequent cardiovascular morbidities. These findings have clinical and public health implications for OSA screening and treatment among young adults. ACKNOWLEDGMENTS This study was supported by National Institutes of Health (NIH) and National Institute on Minority Health and Health Disparities (NIMHD) grants (T37-MD449) and NIH/NCRR/NCATS (8ULTR7).

7 Sleep Problems in Relation to Kidney Disease among Sub Saharan Africans Bizu Gelaye, Yemane Berhane 2,Michelle A. Williams Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA 2 Addi Continental Institute of Public Health, Addis Ababa, Ethiopia BACKGROUND & OBJECTIVE Chronic kidney disease (CKD) has been linked to increased cardiometabolic disease risk, decreased health-related quality of life and mortality. Despite its increased prevalence and enormous public health implications of CKD, the factors responsible for its development and progression are poorly understood. Sleep problems, including short sleep duration and poor sleep quality, are common and under recognized problems in sub Saharan Africa. Given the increased burden of CKD risk in sub Saharan Africa and limited reports of associations between sleep quality and kidney diseases; we examined the association of sleep problems with kidney disease among sub Saharan African adults. MATERIALS & METHODS The study was conducted among,9 adults attending an outpatient department in a major referral hospital in Ethiopia. Structured interviews were conducted to collect information about participants demographic and behavioral characteristics. The Pittsburgh Sleep Index (PSQI) was used to assess sleep habits and quality. CKD was defined by self-reported physician diagnosis Multivariable logistic regression was used to obtain adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for various sleep problems by CKD status A majority of participants were women (6%), and married (52%) Approximately 4% of participants reported that they were current cigarette smokers and 9.6% of participants reported consuming at least alcoholic beverage per week Khat consumption (a green plan with amphetamine-like effects) was reported by 5.8% of participants Table Characteristics of the study population (N=,9) N=,9 Women N=662 Men N=428 Sleep duration (hours) <5 7 (9.8) 58 (8.8) 49 (.5) (.9) 3 (2.6) 46 (.8) (2.) 83 (9.7) 99 (23.2) (57.2) 39 (59.) 232 (54.5) Sleep latency (minutes) 5 32 (27.9) 94 (25.3) 8 (4.5) (24.4) 44 (28.4) 2(28.4) (26.) (25.8) (25.8) >6 235 (2.7) 48 (22.5) 87 (2.4) Day dysfunction due to sleep Never 455 (4.9) 274 (4.5) 8 (42.5).653 < once a week 394 (36.2) 234(35.4) 6 (37.6) -2 times per week 92 (7.7) 23 (8.6) 69 (6.2) 3 times per week 46 (4.2) 3 (4.5) 6 (3.8) Sleep efficiency (%) (43.6) 292 (45.2) 73 (4.2) (3.2) 83 (2.9) 58 (3.8) (7.6) 52 (8.) 29 (6.9) < (35.5) 29 (33.9) 6 (38.) Sleep medicine during past month Never,49 (96.6) 638 (96.7) 4 (96.5).595 < once a week 7(.6) 3 (.5) 4 (.9) -2 times per week 3 (.2) 7 (.) 6 (.4) 3 times per week 7 (.6) 2 (.8) 5 (.2) Sleep Good 49 (39.8) 257 (4.4) 62 (38.9).627 Poor 635 (6.2) 38 (59.6) 255 (6.) Data presented as mean ±SD or number (%) Table 2-Pittsburgh Sleep Index components by sex N=,9 Women N=662 Men N=428 Sleep duration (hours) <5 7 (9.8) 58 (8.8) 49 (.5) (.9) 3 (2.6) 46 (.8) (2.) 83 (9.7) 99 (23.2) (57.2) 39 (59.) 232 (54.5) Sleep latency (minutes) 5 32 (27.9) 94 (25.3) 8 (4.5) (24.4) 44 (28.4) 2(28.4) (26.) (25.8) (25.8) >6 235 (2.7) 48 (22.5) 87 (2.4) Day dysfunction due to sleep Never 455 (4.9) 274 (4.5) 8 (42.5).653 < once a week 394 (36.2) 234(35.4) 6 (37.6) -2 times per week 92 (7.7) 23 (8.6) 69 (6.2) 3 times per week 46 (4.2) 3 (4.5) 6 (3.8) Sleep efficiency (%) (43.6) 292 (45.2) 73 (4.2) (3.2) 83 (2.9) 58 (3.8) (7.6) 52 (8.) 29 (6.9) < (35.5) 29 (33.9) 6 (38.) Sleep medicine during past month Never,49 (96.6) 638 (96.7) 4 (96.5).595 < once a week 7(.6) 3 (.5) 4 (.9) -2 times per week 3 (.2) 7 (.) 6 (.4) 3 times per week 7 (.6) 2 (.8) 5 (.2) Sleep Good 49 (39.8) 257 (4.4) 62 (38.9).627 Poor 635 (6.2) 38 (59.6) 255 (6.) Approximately 44% of participants reported having a fair or poor physical health status, and 34% of reported poor mental health status Sleep problems were common with approximately 6% of participants grouped as having poor sleep quality Approximately % of participants reported sleeping 5 hours per day while 47.7% of them reported longer sleep latency ( 3 minutes), and 2.9% reported having daytime dysfunction due sleepiness at least once per week A total of 56.4% were classified as having poor sleep efficiency (< 85%), and 2.8% reported using sleep medicine at least once per week After adjustment for potential confounders, compared to patients without CKD those with CKD were more likely to have poor sleep quality (AOR=2.22; 95%CI: ) Sleep subscales: short sleep duration (AOR,.77; 95% CI, ), long sleep latency (AOR=.78; 95% CI, ), daytime dysfunction due to sleep problems (AOR=.83; 95%CI: ), poor sleep efficiency (AOR=.59; 95%CI:.8-2.3), and sleep medication use (AOR= 3.9; 95% CI, ) were all positively associated with CKD The relationship between CKD and sleep problems was not explained by body mass index, history of diabetes and hypertension Figure Odds ratios (OR) and 95% confidence intervals (CI) of sleep in relation kidney disease Adjusted for age, sex, body mass index, alcohol consumption, Khat consumption, smoking status CKD is associated with a higher risk of sleep problems. Prospective studies are needed to confirm our findings and more thoroughly explore the potential mechanisms for sleep problems in CKD patients among sub Saharan Africans Identifying and treating sleep disorders may have a therapeutic benefit patients with CKD This research was supported by the Multidisciplinary International Research Training (MIRT) Program, National Institute for Minority Health and Health Disparities, National Institutes of Health (T37- MD449)